Radiation effect simulation test data credibility evaluation method

Abstract

The invention discloses a radiation effect simulation test data credibility evaluation method, and solves the technical problems that due to the lack of a quantitative data credibility evaluation method, a user can only judge the credibility of data by experience, the subjectivity is strong, systematicness and standards are lacked, and decision errors may be caused. A fuzzy comprehensive evaluation method and an analytic hierarchy process are combined, factors influencing the credibility of radiation effect simulation test data are sorted out from the side of a data production process, a judgment matrix is established, factors with unclear boundaries and difficult to quantify are quantified, and the membership level condition of the data credibility is comprehensively evaluated. The problem of quantitative evaluation of the reliability of radiation effect simulation test data is solved. The method is high in adaptive capacity, the factor set, the evaluation set, the judgment matrix and the membership matrix can be automatically adjusted according to the understanding degree of the data and the data purpose, meanwhile, the method provides constraint conditions for the judgment matrix, and it is guaranteed that the method can be correctly used.

Description

Technical Field

[0001] The invention relates to a radiation effect simulation test method, and in particular to a radiation effect simulation test data credibility evaluation method. Background Art

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

[0003] In the existing radiation effect simulation tests, due to the lack of quantitative data credibility evaluation methods, users can only judge the credibility of the data based on experience, which is highly subjective and lacks systematicity and standards. Not only will the accuracy and reliability of the judgment be affected, it may also lead to wrong decisions, and qualitative judgments may not meet subsequent data analysis and decision-making needs. Summary of the invention

[0004] The purpose of the present invention is to solve the technical problem that due to the lack of a quantitative method for evaluating the credibility of radiation effect simulation test data, users can only judge the credibility of the data based on experience, which is highly subjective, lacks systematicity and standards, and may lead to wrong decisions. A method for evaluating the credibility of radiation effect simulation test data is provided.

[0005] In order to achieve the above object, the present invention adopts the following technical solution:

[0006] A method for evaluating the credibility of radiation effect simulation test data is 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 element composition factor set U, and a proportional scale regarding the degree of importance is set; the various factors affecting the credibility of radiation effect simulation test data are selected and determined according to the specific radiation effect simulation test type conducted;

[0008] Step 2: Compare the importance of the elements in the factor set U in step 1 in pairs, and assign values ​​to the pairwise comparison results according to the proportional scale to form a judgment matrix A;

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

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

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

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

[0013] Step 7, multiply the feature vector W in step 3 by 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, the maximum membership principle or the weighted average principle is used to determine the evaluation result in the credibility membership evaluation set of the radiation effect simulation test data, and the credibility evaluation of the radiation effect simulation test data is completed.

[0015] Furthermore, the factor set U in step 1 is expressed as: U = {u1,u2,…,u m}, where u1,u2,…,u m Indicates the 1st, 2nd, ..., mth factors that affect the credibility of radiation efficacy test data, 3≤m≤11.

[0016] Furthermore, in step 2, the expression of the judgment matrix is:

[0017] A=(u ij ) m×m ;

[0018] in,

[0019] Among them, u ij It represents the result of assigning values ​​to the pairwise comparison results according to a certain proportional scale, m represents the total number of factors that affect the credibility of the radiation efficacy 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, the expression is:

[0022]

[0023] in,

[0024] RI represents the average random consistency index, and its specific values ​​are 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: Determine whether CR≤0.1 is satisfied. If so, the consistency of the judgment matrix is ​​good; proceed to step 5; otherwise, the consistency of the judgment matrix is ​​not good, return to step 2, and modify the importance of the elements in the factor set U.

[0027] Furthermore, in step 5, the evaluation set is expressed as:

[0028] V={v1,v2,…,v n};

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

[0030] Furthermore, the specific process of step 6 is as follows:

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

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

[0033] in,

[0034] The evaluation matrix of the membership relationship between the elements in the factor set U and the evaluation results in the evaluation set is obtained as follows:

[0035]

[0036] Furthermore, in step 8, the specific steps of using the maximum membership principle to determine the evaluation results in the credibility membership evaluation set of the radiation effect simulation test data are as follows:

[0037] The dimension of the fuzzy comprehensive evaluation vector M is n, and its n components are represented by M1, M2, ..., M n , let the largest component among n components be M k , where k represents the sequence number of the largest component in the component;

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

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

[0040] Furthermore, the specific steps of using the weighted average principle to determine the evaluation results of the credibility of the radiation effect simulation test data in the evaluation set in step 8 are as follows:

[0041] Take the level variable a=(a1,a2,...,a n ) as variables; where 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, the corresponding components in the fuzzy comprehensive evaluation vector M are used to weight the values ​​of each level and sum them to obtain the relative position D of the credibility of the evaluated radiation effect test data in the evaluation set:

[0043]

[0044] Among them, M i Represents the i-th component in the fuzzy comprehensive evaluation vector M.

[0045] Beneficial effects of the present invention:

[0046] 1. A method for evaluating the credibility of radiation effect simulation test data of the present invention combines the fuzzy comprehensive evaluation method with the hierarchical analysis method, sorts out the factors affecting the credibility of radiation effect simulation test data from the data production process, establishes a judgment matrix, quantifies some factors with unclear boundaries and difficult to quantify, conducts a comprehensive evaluation on the membership level of data credibility, and establishes a quantitative evaluation method for the credibility of radiation effect simulation test data, which solves the problem that when traditional users judge the credibility of data based on experience, the accuracy and reliability of the judgment will be interfered by human factors, which may cause decision-making errors.

[0047] 2. The method for evaluating the credibility of radiation effect simulation test data of the present invention has strong self-adaptability. The factor set, evaluation set, judgment matrix and membership matrix can be adjusted automatically according to the degree of understanding of the data and its use. At the same time, constraints are proposed for the judgment matrix to ensure that the method can be used correctly. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 It is a flow chart of an embodiment of a method for evaluating the credibility of radiation effect simulation test data of the present invention. DETAILED DESCRIPTION

[0049] The technical solution of the present invention will be clearly and completely described below in conjunction with the accompanying drawings and embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0050] The present embodiment provides a method for evaluating the credibility of radiation effect simulation test data, which combines the fuzzy comprehensive evaluation method and the hierarchical analysis method, sorts out the factors that affect the credibility of radiation effect simulation test data from the data production process, establishes a judgment matrix, quantifies some factors with unclear boundaries and difficult to quantify, and comprehensively evaluates the membership level of data credibility from multiple factors. In the use of the fuzzy comprehensive evaluation method, an important step is to determine the weight of each factor. In the present embodiment, the mathematical method and the qualitative method are also combined, and the weight of each factor is finally calculated through layer-by-layer sorting, and the credibility evaluation result is finally determined.

[0051] like Figure 1 As shown, the following steps are included:

[0052] Step 1: Various factors that affect the credibility of radiation effect test data are taken as the element composition factor set U; expressed as: U = {u1,u2,…,u m}, where u1,u2,…,u m Indicates the 1st, 2nd, ..., mth factors that affect the credibility of radiation effect simulation test data; 3≤m≤11, and sets a proportional scale regarding the degree of importance. The various factors that affect the credibility of radiation effect simulation test data are selected and determined based on the specific type of radiation effect simulation test conducted.

[0053] Step 2: Compare the importance of the elements in the factor set U in step 1 in pairs, 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 It represents the result of assigning values ​​to the pairwise comparison results according to a certain proportional scale, m represents the total number of factors that affect the credibility of the radiation efficacy 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 maximum eigenvalue λ of the judgment matrix A in step 2 max , determine the maximum eigenvalue λmax The corresponding eigenvector W;

[0058] Step 4: Use the maximum eigenvalue λ max Check the consistency of the judgment matrix A. If the consistency of the judgment matrix A reaches the preset value, execute step 5; otherwise, return to step 2 and modify the importance of the elements in the factor set U. The specific process is:

[0059] Step 4.1, define the consistency index CR, the expression is:

[0060]

[0061] in,

[0062] m represents the total number of factors that affect the credibility of radiation efficacy test data; RI represents the average random consistency index, and its specific values ​​are 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 CR is equal to 0, the judgment matrix is ​​completely consistent. In this embodiment, it is determined whether CR≤0.1 is satisfied. If so, the consistency of the judgment matrix is ​​good; execute step 5; otherwise, the consistency of the judgment matrix is ​​not good, return to step 2, and modify the importance of the elements in the factor set U.

[0065] Step 5: Construct an evaluation set based on the possible evaluation results of the credibility of the radiation effect simulation test data; the evaluation set is expressed as:

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

[0067] Among them, v j (j=1,2,...,n) represents the jth possible evaluation result of the credibility of the radiation efficacy test data, and n represents the total number of evaluation results.

[0068] Step 6: Evaluate the membership of the elements in the factor set U to each evaluation result in the evaluation set and construct a membership evaluation matrix. The specific process is as follows:

[0069] For the i-th element u in the factor set U i The membership of the jth possible evaluation result in the evaluation set is r ij , then the evaluation result of the i-th element is:

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

[0071] The evaluation matrix of the membership relationship between the elements in the factor set U and the evaluation results in the evaluation set is obtained as follows:

[0072]

[0073] Step 7, multiply the feature vector W in step 3 by the membership evaluation matrix to obtain the fuzzy comprehensive evaluation vector M;

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

[0075] The maximum membership principle is used to solve the fuzzy comprehensive evaluation vector:

[0076] The dimension of the fuzzy comprehensive evaluation vector M is n, and its n components are represented by M1, M2, ..., M n , let the largest component among n components be M k , where k represents the sequence number of the largest component in the component;

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

[0078] Then the credibility of the evaluated radiation effect test data belongs to the kth evaluation result of the evaluation set, that is, v k .

[0079] The weighted average principle is used to solve the fuzzy comprehensive evaluation vector:

[0080] Take the level 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] Taking the n components of the fuzzy comprehensive evaluation vector M as weights, the corresponding components in the fuzzy comprehensive evaluation vector M are used to weight the values ​​of each level and sum them to obtain the relative position D of the credibility of the evaluated radiation effect test data in the evaluation set:

[0082]

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

[0084] The following steps are performed using a method for evaluating the credibility of radiation effect simulation test data in this embodiment:

[0085] (1) Combining the understanding of radiation effect simulation test data and the survey results of 15 experts in the field of radiation effects from 15 units engaged in radiation effect related research across the country, the factors that affect the credibility of radiation effect simulation test data are determined to be four, namely 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 number of samples under exactly the same test conditions.

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

[0087] (2) Adopt the ratio scale value proposed by ALSatty as shown in Table 2 to establish the relative importance of each pair of factors.

[0088] Table 2

[0089] Proportional scale meaning 1 Two factors have the same importance 3 Compared with the two factors, the former is slightly more important than the latter. 5 Compared with the two factors, the former is obviously more important than the latter. 7 Compared with the two factors, the former is more important than the latter. 9 Compared with the two factors, the former is extremely more important than the latter. 2,4,6,8 Between the above adjacent values

[0090] Based on the analysis, the following judgment matrix is ​​established:

[0091]

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

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

[0094] (3) Establish the evaluation set V as {credible, basically credible, for reference}.

[0095] (4) Determine the membership matrix. For the following test data, "A certain unit's technicians conducted a total ionizing dose effect test on a certain type of electronic device on a cobalt source approved by experts, with a sample size of 2 and good repeatability, with an irradiation test outline but not reviewed by experts, and no test report issued by a third party", establish the following membership matrix:

[0096]

[0097] (5) Calculate the fuzzy comprehensive evaluation vector M.

[0098] M=W·R=[0.6200.17750.2025]

[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 level a={1,2,3} is selected, and after calculation, D=1.26 is obtained, 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 description is only a specific embodiment of the present invention, and a comparison of the effects of the specific embodiments and the related comparative examples, but the protection scope of the present invention is not limited thereto. Any changes or substitutions within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for evaluating the credibility of radiation effect simulation test data, characterized in that: The following steps are involved: Step 1: various factors affecting the credibility of radiation effect simulation test data are taken as element composition factor set U, and a proportional scale regarding the degree of importance is set; the various factors affecting the credibility of radiation effect simulation test data are selected and determined according to the specific radiation effect simulation test type conducted; Step 2: Compare the importance of the elements in the factor set U in step 1 in pairs, and assign values ​​to the pairwise comparison results according to the proportional scale to form a judgment matrix A; Step 3: Calculate the maximum eigenvalue λ of the judgment matrix A in step 2 max , determine the maximum eigenvalue λ max The corresponding eigenvector W; Step 4: Use the maximum eigenvalue λ max Check the consistency of the judgment matrix A. If the consistency of the judgment matrix A reaches the preset value, execute step 5; otherwise, return to step 2 and modify the importance of the elements in the factor set U; Step 5: construct an evaluation set based on possible evaluation results of the credibility of radiation effect simulation test data; Step 6: Evaluate the membership of the elements in the factor set U to each evaluation result in the evaluation set, and construct a membership evaluation matrix; Step 7, multiply the feature vector W in step 3 by 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, the maximum membership principle or the weighted average principle is used to determine the evaluation result in the credibility membership evaluation set of the radiation effect simulation test data, and the credibility evaluation of the radiation effect simulation test data is completed.

2. A method for evaluating the credibility of radiation effect simulation test data according to claim 1, characterized in that: The factor set U in step 1 is expressed as: U = {u1,u2,…,u m }, where u1,u2,…,u m Indicates the 1st, 2nd, ..., mth factors that affect the credibility of radiation efficacy test data, 3≤m≤11.

3. The method for evaluating the credibility of radiation effect simulation test data according to claim 1, characterized in that: In step 2, the expression of the judgment matrix A is: A=(u ij ) m×m ; in, Among them, u ij It represents the result of assigning values ​​to the pairwise comparison results according to a certain proportional scale, m represents the total number of factors that affect the credibility of the radiation efficacy test data; i represents the row coordinate of the judgment matrix, and j represents the column coordinate of the judgment matrix.

4. The method for evaluating the credibility of radiation effect simulation test data according to claim 1, characterized in that: The specific process of step 4 is as follows: Step 4.1, define the consistency index CR, the expression is: in, RI represents the average random consistency index, and its specific values ​​are as follows: When the values ​​of m are 3, 4, 5, 6, 7, 8, 9, 10, and 11, the values ​​of RI are 0.58, 0.90, 1.12, 1.24, 1.32, 1.41, 1.45, 1.49, and 1.51 respectively; Step 4.2: Determine whether CR≤0.1 is satisfied. If so, the consistency of the judgment matrix is ​​good; proceed to step 5; otherwise, the consistency of the judgment matrix is ​​not good, return to step 2, and modify the importance of the elements in the factor set U.

5. The method for evaluating the credibility of radiation effect simulation test data according to claim 1, characterized in that: In step 5, the evaluation set is expressed as: V={v1,v2,…,v n }; Among them, v j (j=1,2,…,n) represents the jth possible evaluation result of the credibility of radiation efficacy test data, and n represents the total number of evaluation results.

6. The method for evaluating the credibility of radiation effect simulation test data according to claim 1, characterized in that: The specific process of step 6 is as follows: For the i-th element u in the factor set U i To judge, the membership degree of the jth possible evaluation result in the evaluation set is r ij , requiring 0≤r ij ≤1, then the evaluation result of the i-th element is: R i ＝(r i1 ,r i2 ,…,r in )； in, The evaluation matrix of the membership relationship between the elements in the factor set U and the evaluation results in the evaluation set is obtained as follows:

7. The method for evaluating the credibility of radiation effect simulation test data according to claim 1, characterized in that: The specific steps of using the maximum membership principle in step 8 to determine the evaluation results of the credibility membership evaluation set of the radiation effect simulation test data are as follows: The dimension of the fuzzy comprehensive evaluation vector M is n, and its n components are represented by M1, M2, …, M n , let the largest component among n components be M k , where k represents the sequence number of the largest component in the component; M k =max{M1,M2,…,M n }; Then the credibility of the evaluated radiation effect test data belongs to the kth evaluation result in the evaluation set.

8. A method for evaluating the credibility of radiation effect simulation test data according to claim 7, characterized in that: The specific steps of using the weighted average principle in step 8 to determine the evaluation results of the credibility of the radiation effect simulation test data in the evaluation set are as follows: Take the level variable a=(a1,a2,…,a n ) as a variable; Among them, a1=1, a2=2,…,a n =n; n represents the total number of evaluation results; Taking the n components of the fuzzy comprehensive evaluation vector M as weights, the corresponding components in the fuzzy comprehensive evaluation vector M are used to weight the values ​​of each level and sum them to obtain the relative position D of the credibility of the evaluated radiation effect test data in the evaluation set: Among them, M i Represents the i-th component in the fuzzy comprehensive evaluation vector M.

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