Test and simulation data consistency discrimination method based on fuzzy mathematics

Abstract

The invention belongs to the technical field of structural strength tests, and particularly relates to a test and simulation data consistency judgment method based on fuzzy mathematics. The method comprises the following steps: firstly, constructing an evaluation index vector of test and simulation data consistency; then establishing an evaluation set for evaluating the consistency degree of test and simulation data; further determining an evaluation index weight of the consistency of the test and simulation data; then, test and simulation data consistency fuzzy comprehensive evaluation calculation is completed; and finally, determining a test and simulation data consistency fuzzy comprehensive evaluation result. According to the discrimination method provided by the invention, a fuzzy mathematics comprehensive evaluation theory is introduced in a test measurement and simulation analysis data consistency discrimination process. The method comprises the following steps of: firstly, comprehensively considering an objective fuzzy factor which influences test and simulation data consistency judgment, and carrying out quantitative characterization on the fuzzy factor through a statistical index; and then, combining the inherent technical characteristics of test measurement and the force transmission characteristics of the structure, considering the relative importance degree of each fuzzy factor and giving different weights for each fuzzy factor, thereby obtaining a test and simulation data consistency judgment result which is more in line with objective reality.

Description

Technical Field

[0001] The invention belongs to the technical field of structural strength test, and in particular relates to a method for judging consistency between test and simulation data based on fuzzy mathematics. Background Art

[0002] Static test is an important part of the aircraft development process. Static test can verify the rationality and standardization of the design and the accuracy of the finite element model. For large-scale static test, the finite element analysis results are generally used to guide and refer to the test design and implementation before the test. At the same time, during and after the test, the consistency of the finite element simulation analysis and the measured data is judged, so as to achieve the purpose of correcting the finite element model and guiding the subsequent finite element modeling and test design and implementation.

[0003] Usually, there are many measurement points in static tests (tens of thousands), and the designer has a large amount of numerical simulation analysis data. However, in the process of numerical simulation analysis, the simulation model established based on the design scheme simplifies the real structure to varying degrees of mechanics, and there are often certain differences between it and the actual structure. At the same time, the uncertainty of static test measurement itself is inevitable, making it particularly difficult to objectively and accurately judge the global consistency between test measurement and simulation data.

[0004] At present, the traditional method of judging the consistency between test measurement and simulation data generally directly uses statistical indicators for evaluation. By calculating the statistical indicators such as the correlation coefficient, mean square error and relative deviation between the test measurement data and the simulation analysis data, drawing the correlation curve of the simulation analysis and test measurement, and comparing the size of the indicator with the limit, the consistency between the test data and the simulation data can be judged. However, this method does not take into account the objective fuzzy factors, resulting in differences between the evaluation results and the actual situation. For example, the measurement direction of the strain gauge deviates from the actual principal stress direction of the structure, and the stress gradient at the location where the strain gauge is pasted is high, which will affect the accuracy of the consistency evaluation between the strain measurement data and the simulation data. At the same time, the consistency between the test measurement and the simulation analysis is affected by many factors, such as single strain gauge measurement data, flower strain gauge measurement data, displacement measurement data, force transmission path, etc., and the correlation between the factors is relatively complex, and the degree of influence of different factors is also relatively vague, which brings great difficulties to accurately and objectively judge the consistency of the measurement and simulation data.

[0005] The development of fuzzy comprehensive evaluation theory provides a solution to the above problems. The basic principle of fuzzy comprehensive evaluation is to focus on the many factors that affect the object being evaluated, determine the evaluation set of the object being evaluated and the weight of the evaluation index, make corresponding fuzzy evaluations for each factor, and then determine the membership function, establish a fuzzy judgment matrix, perform fuzzy operations, and finally obtain quantitative comprehensive evaluation results. The fuzzy comprehensive evaluation model is simple and can reflect the relative importance of each evaluation index. For complex problems with multiple factors and multiple levels, it can comprehensively consider various fuzzy and uncertain information, and the evaluation results are stable and reliable. Summary of the invention

[0006] The purpose of the present invention is to: Since the conventional method of using statistical indicators to judge the consistency of test and simulation data does not take into account the objective fuzzy factors, the evaluation results deviate from the objective reality. To solve this problem, the present invention proposes a method for judging the consistency of test and simulation data based on fuzzy mathematics, which combines the fuzzy mathematics comprehensive evaluation method with statistical indicators, takes into account the objective fuzzy factors in the evaluation process, and can truly and objectively judge the consistency of test and simulation data.

[0007] Technical solution of the present invention: The present invention provides a method for judging the consistency of test and simulation data combined with fuzzy mathematics comprehensive evaluation theory; through this method, a more objective and practical evaluation result can be obtained for the consistency of test measurement and simulation analysis data in static tests. To achieve the above purpose, the present invention adopts the following technical solution.

[0008] A method for judging the consistency of test and simulation data based on fuzzy mathematics, the specific steps are as follows:

[0009] Step S1: construct an evaluation index vector for consistency between test and simulation data;

[0010] Step S2: Establishing an evaluation set for evaluating the consistency between test and simulation data;

[0011] Step S3: Determine the weight of the evaluation index for consistency between the test and simulation data;

[0012] Step S4: fuzzy comprehensive evaluation calculation of consistency between test and simulation data;

[0013] Step S5: Determine the fuzzy comprehensive evaluation result of the consistency between the test and simulation data.

[0014] Furthermore, in step S1, the evaluation index vector for the consistency between the test and simulation data is constructed, and the specific process is as follows:

[0015] Step S1-1: Determine the influencing factors of consistency evaluation between test and simulation data;

[0016] Step S1-2: characterizing factors affecting consistency between test and simulation data;

[0017] In step S1-1, the factors affecting the consistency evaluation of the test and simulation data are generally the measured physical quantities in the test, such as: the measured value of the single strain gauge of the non-main force transmission path, the maximum measured value of the flower strain gauge of the non-main force transmission path, the minimum measured value of the flower strain gauge of the non-main force transmission path, the displacement measurement value of the non-main force transmission path, the measured value of the single strain gauge of the main force transmission path, the maximum measured value of the flower strain gauge of the main force transmission path, the minimum measured value of the flower strain gauge of the main force transmission path, the displacement measurement value of the main force transmission path, etc. These measured physical quantities constitute the factor set U for evaluating the consistency of the simulation and test data, which is expressed as U={u1,u2,…,u n}, where u i It represents the i-th factor that affects the consistency evaluation of test and simulation data, and is also the i-th indicator of the consistency evaluation of test and simulation data.

[0018] In step S1-2, the factors affecting the consistency of the test measurement and simulation analysis data can be characterized by statistical indicators. The purpose of this step is to characterize the fuzzy information of consistency between data with quantifiable indicators.

[0019] Furthermore, in step S2, the evaluation set of the consistency between the test and simulation data is V = {v1, v2, ..., v m}, where v j The jth evaluation result indicating the consistency between the test and simulation data can be expressed by different grades, comments or numbers.

[0020] Furthermore, in step S3, the weights of various evaluation indicators for consistency between test and simulation data are calculated based on the analytic hierarchy process. The specific process includes the following steps:

[0021] Step S3-1: construct a judgment matrix of test and simulation data consistency evaluation indicators;

[0022] Step S3-2: Calculate the weight vector of each evaluation index of consistency between test and simulation data;

[0023] Step S3-3: Perform consistency check on the constructed judgment matrix.

[0024] Generally, different influencing factors have different degrees of importance for the evaluation results of the consistency between test and simulation data. Therefore, when constructing the judgment matrix of the consistency evaluation index between test and simulation data, different weights should be given to different factors. When giving different weights to different factors, the following principles should be followed: In static test measurement, under normal circumstances, the patch direction of a single strain gauge cannot coincide exactly with the true principal stress direction during test measurement. Therefore, the consistency between the strain test data and the theoretical data measured by the single strain gauge will be poor. This is the limitation of the measurement method itself. Therefore, the weight of the single strain gauge data should be reduced in the overall consistency judgment of the test and simulation data, so as to more truly reflect the data situation. For the flower strain gauge, its maximum and minimum measurement data are converted from the formula, and the weight ratio of the two should be the same. Similarly, in simulation analysis, the simulation of the structure on the main force transmission path is generally closer to the actual situation, while the structure on the non-main force transmission path is usually simplified to varying degrees of mechanics. Therefore, when judging the consistency of test and simulation data, the strain gauge measurement data on the main force transmission path should be given a larger weight ratio.

[0025] Taking into account the relative importance of the factors affecting the consistency of the above test and simulation data, the consistency evaluation index judgment matrix D can be constructed as follows:

[0026]

[0027] Among them, D ij Indicator u i Relative to factor u j The importance of D ij The larger the factor u i Relative to factor u j The more important; D ij =1, indicating that factor u i With factor u j In comparison, both are equally important.

[0028] The specific process of step S3-2 includes the following steps:

[0029] Step S3-2-1: normalize each column of the judgment matrix D, the formula is as follows:

[0030]

[0031] Where: represents the sum of the columns of the judgment matrix, D i ' j Represents the value at the corresponding position of the judgment matrix after normalization, D ij Represents the value at the corresponding position of the original judgment matrix;

[0032] Step S3-2-2: Sum each row of the normalized judgment matrix to obtain the eigenvector λ = (λ1, λ2, ..., λ n );

[0033] Step S3-2-3:

[0034] Assume that the weight vector of each evaluation index (i.e., each factor) is

[0035] where a i represents the weight of the i-th evaluation index, a i ≥0,

[0036] The weights of the evaluation indicators for consistency between test and simulation data can be calculated according to formula (2):

[0037]

[0038] Where: a i represents the weight of each evaluation index of the consistency between test and simulation data, λ represents the feature vector, λ j represents the value of each element of the feature vector, Represents the sum of the values ​​of each element of the eigenvector.

[0039] The specific process of step S3-3 includes the following steps:

[0040] Step S3-3-1: Calculate the maximum characteristic root γ of the above judgment matrix D max ;

[0041] Step S3-3-2: Perform a consistency check on the above judgment matrix D. The formula is as follows:

[0042]

[0043] Where: n is the number of factors; CI is the consistency index of the judgment matrix. When CI = 0, it has complete consistency; when CI is close to 0, it has good consistency; the larger the CI value, the worse the consistency. If the consistency of the consistency judgment matrix of the test and simulation data does not meet the requirements, the judgment matrix needs to be modified until it passes the consistency test.

[0044] Furthermore, in step S4, the specific process of the fuzzy comprehensive evaluation calculation of the consistency between the test and simulation data includes the following steps:

[0045] Step S4-1: Determine the membership function and establish a fuzzy comprehensive evaluation matrix;

[0046] Through the above steps S1 and S2, the factor set U={u1,u2,…u n}, and the evaluation set V = {v1,v2,…,v m Then in step S4-1, based on the membership function, a fuzzy comprehensive evaluation matrix is ​​constructed. as follows:

[0047]

[0048] Where: 0≤r ij ≤1, r ij Represents the factor u i With comments j The degree of i , the object to be judged belongs to the comment v j the extent of; The fuzzy relationship between the factor set U and the evaluation set V that maps the consistency of the test and simulation data.

[0049] Step S4-2: Select a fuzzy operator and perform fuzzy operation on the consistency between the test and simulation data;

[0050] Through the fuzzy operator weight set and fuzzy comprehensive evaluation matrix Perform fuzzy operation, see formula (4), and then we can get the evaluation result vector

[0051]

[0052] Where: represents a fuzzy operator in a general sense; is the evaluation result vector.

[0053] Furthermore, in step S5, the evaluation result vector of the consistency between the test data and the simulation data obtained by the fuzzy operation is interpreted.

[0054] From step S4, the evaluation result vector The results are explained by the weighted average principle, see formula (5), b wa The corresponding rating is the comprehensive evaluation result of the consistency between the test and simulation data;

[0055]

[0056] Where: v j represents the jth evaluation result of the consistency between the test and simulation data, b j Represents the evaluation result vector The j-th element of .

[0057] Beneficial effects of the present invention: The consistency of test measurement and simulation analysis data is affected by a variety of factors, which are often ambiguous and difficult to quantify, and the importance of various factors is also different, which increases the complexity of the consistency judgment problem of test and simulation data. The traditional method of directly using statistical indicators to judge the consistency of test and simulation data generally calculates statistical indicators such as the correlation coefficient, mean square error or relative deviation between the test data and the simulation data, and compares the index with the size of its limit to judge the consistency of the test and simulation data. This process only focuses on the test and simulation data itself, and does not take into account the objective existence of fuzzy factors such as strain gauge measurement direction, structural force transmission characteristics, etc. that have a greater impact on the consistency of test and simulation data, resulting in a large difference between its judgment results and the actual situation.

[0058] In contrast, the positive effect of the present invention is that the fuzzy mathematics comprehensive evaluation theory is introduced in the process of consistency judgment of test measurement and simulation analysis data. First, the objective fuzzy factors that affect the consistency judgment of test and simulation data are comprehensively considered, and the fuzzy factors are quantitatively characterized by statistical indicators; then, combined with the inherent technical characteristics of the test measurement and the force transmission characteristics of the structure itself, the relative importance of each fuzzy factor is considered and different weights are given, so as to obtain a more objective and realistic test and simulation data consistency judgment result.

[0059] The invention has a simple principle and significant effect, and can be widely used in evaluating the consistency of structural test measurement and simulation analysis data. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 This is a flow chart for consistency assessment of test and simulation data based on fuzzy mathematics;

[0061] Figure 2 This is the graph and formula of the trapezoidal distribution membership function. DETAILED DESCRIPTION

[0062] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not 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.

[0063] The present invention discloses for the first time a method for judging the consistency of static test measurement and simulation analysis data based on fuzzy mathematics, comprehensively considers multiple factors affecting the consistency of test and simulation data, and establishes a data consistency evaluation model using fuzzy comprehensive evaluation theory.

[0064] The method for checking the consistency of test and simulation data based on fuzzy mathematics proposed by the present invention will be further described below with reference to examples. This embodiment is exemplary and intended to be used to explain the present invention, but should not be construed as limiting the present invention.

[0065] See attached Figure 1 In a certain project, strain test measurements (single strain gauge and flower strain gauge) and finite element simulation analysis were performed on the test piece. After preliminary analysis, the test piece has a single force transmission path, so here we mainly consider the influence of three types of data on the consistency of test and simulation data, namely the measurement value of the single strain gauge, the maximum value measured by the flower strain gauge, and the minimum value measured by the flower strain gauge. The consistency of these three types of data is ambiguous. The specific steps are as follows:

[0066] 1) Determine the factor set

[0067] First, factors affecting the consistency of simulation and test data are determined. This embodiment mainly considers three aspects, namely, the measurement value u1 of the single strain gauge, the maximum measurement value u2 of the flower strain gauge, and the minimum measurement value u3 of the flower strain gauge. The factor set for evaluating the consistency of simulation and test data can be expressed as U = {u1, u2, u3}.

[0068] 2) Characterization factor set

[0069] The factors affecting the consistency of test measurement and simulation analysis data can be characterized by statistical indicators. As a preference, the regression coefficient K is selected to characterize the consistency of test and simulation data. Then the factor set U can be expressed as U = [K1, K2, K3], where:

[0070] K1 represents the consistency between single strain gauge test and simulation data;

[0071] K2 represents the consistency between the maximum value test and simulation data of the flower strain gauge;

[0072] K3 characterizes the consistency between the minimum value test and simulation data of the flower strain gauge;

[0073] Substituting the experimental measurement data and simulation analysis data of the single strain gauge and the flower strain gauge into the regression coefficient calculation formula (6) respectively, all the values ​​in the factor set can be obtained.

[0074]

[0075] Where: x i is the measured value of strain data, y i is the theoretical value of strain data; x avg is the average value of strain data measurement, y avg is the theoretical average value of strain data.

[0076] Calculation shows that K1: 0.002446, K2: 0.806394, K3: 0.859257.

[0077] Then the factor set U = [0.0024460.8063940.859257].

[0078] Other statistical indicators can also be used to characterize the factors affecting the consistency of test and simulation data, such as correlation coefficient.

[0079] 3) Determine the evaluation set

[0080] Regarding the evaluation criteria for consistency between test measurement and simulation analysis data, as a preferred approach, three evaluation levels V={v1, v2, v3} are set, where v1 represents poor, v2 represents good, and v3 represents excellent. At the same time, the quantitative value of each element in the evaluation set is specified to be v1=30, v2=60, and v3=90.

[0081] 4) Determine the weight vector

[0082] As mentioned above, since the patch direction of a single strain gauge cannot exactly coincide with the true principal stress direction during test measurement, the consistency between the strain test data and theoretical data measured by the single strain gauge is poor, so the weight of the single strain gauge data should be reduced in the overall consistency judgment of the test and simulation data. Based on this, the evaluation index judgment matrix D can be constructed as follows:

[0083]

[0084] Normalizing the judgment matrix D according to formula (1) yields D i ' j for:

[0085]

[0086] By summing each row of the normalized judgment matrix, we can get the eigenvector: λ = [3 / 7 9 / 7 9 / 7]. From formula (2), we can get the weight vector:

[0087] The obtained weight vector To perform a consistency check:

[0088] The maximum eigenvalue of the judgment matrix D is γ max =3, then the CI value calculated according to formula (3) is 0, which has complete consistency.

[0089] 5) Select the membership function and determine the membership function parameters

[0090] There are many ways to determine the membership function in fuzzy mathematics. As a preferred method, the fuzzy assignment method is used, that is, a fixed fuzzy distribution form is selected. As a preferred method, the membership functions of the three factors K1, K2, and K3 are trapezoidal distribution membership functions, such as Figure 2 Among them, factors K1, K2, and K3 correspond to small, medium, and large sizes, respectively, and their corresponding membership function parameters are shown in Table 1.

[0091] Other membership functions may also be used, such as triangular membership function, normal membership function, etc.

[0092] Table 1 Membership function parameters of three evaluation factors

[0093]

[0094] 6) Construct fuzzy comprehensive evaluation matrix

[0095] Substituting the statistical indicators K1, K2, and K3 into the membership function described in Section 5), we can get the fuzzy comprehensive evaluation matrix for:

[0096]

[0097] 7) Calculate fuzzy operations and determine fuzzy comprehensive evaluation results

[0098] Select the fuzzy operator and perform fuzzy operation according to formula (4).

[0099]

[0100] In the formula, is a fuzzy operator in a general sense. As a preferred choice, the Zadeh fuzzy operator is used here.

[0101] Other fuzzy operators, such as weighted average operator, can also be used.

[0102] The comprehensive evaluation result matrix can be calculated by formula (4):

[0103] According to formula (5), the weighted average principle is used to interpret the results as follows:

[0104]

[0105] b wa =57.72786, corresponding to the good in the evaluation set (v2=60), so the final fuzzy comprehensive evaluation result is obtained: after fuzzy comprehensive evaluation, the consistency between the test data and the simulation data is good.

[0106] The present invention proposes a method for judging the consistency of test and simulation data by introducing fuzzy mathematics. By calculating the relevant statistical indicators of the test and simulation data, characterizing the factors affecting the consistency between the two, establishing a fuzzy comprehensive evaluation model and performing fuzzy transformation, an objective and true evaluation of the consistency of the test measurement and simulation analysis data is achieved, and guidance and basis are provided for the verification, correction and test design of the simulation model.

[0107] The above is only a specific embodiment of the present invention, and the present invention is described in detail. The unexplained part is a conventional technology. However, the protection scope of the present invention is not limited to this. Any changes or substitutions that can be easily thought of by any technician familiar with the technical field within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. The protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A method for judging consistency between test and simulation data based on fuzzy mathematics, characterized in that: The discrimination method comprises the following steps: Step S1: construct an evaluation index vector for consistency between test and simulation data; Step S2: Establishing an evaluation set for evaluating the consistency between test and simulation data; Step S3: Determine the weight of the evaluation index for consistency between the test and simulation data; Step S4: fuzzy comprehensive evaluation calculation of consistency between test and simulation data; Step S5: Determine the fuzzy comprehensive evaluation result of the consistency between the test and simulation data.

2. A method for determining consistency between test and simulation data based on fuzzy mathematics as claimed in claim 1, characterized in that: In step S1, the method for constructing an evaluation index vector for consistency between test and simulation data includes: Step S11: determining the influencing factors of consistency evaluation between test and simulation data; Step S12: Characterize factors affecting the consistency of test and simulation data.

3. A method for determining consistency between test and simulation data based on fuzzy mathematics as claimed in claim 2, characterized in that: The factors that affect the consistency of test and simulation data are generally the measured physical quantities in the test. These measured physical quantities constitute the factor set U for evaluating the consistency of simulation and test data, which is expressed as U = {u1,u2,…,u n }, where u i It represents the i-th factor that affects the consistency evaluation of test and simulation data, and is also the i-th indicator of the consistency evaluation of test and simulation data.

4. A method for determining consistency between test and simulation data based on fuzzy mathematics as claimed in claim 2, characterized in that: The factors affecting the consistency between experimental measurement and simulation analysis data are characterized by statistical indicators.

5. A method for determining consistency between test and simulation data based on fuzzy mathematics as claimed in claim 1, characterized in that: In step S2, the evaluation set of the consistency between the test and simulation data is V = {v1, v2, ..., v m }, where v j The jth evaluation result indicating the consistency between the test and simulation data can be expressed by different grades, comments or numbers.

6. A method for determining consistency between test and simulation data based on fuzzy mathematics as claimed in claim 5, characterized in that: In step S3, the weights of various evaluation indicators for consistency between test and simulation data are calculated based on the hierarchical analysis method; the specific process includes the following steps: Step S31: constructing a judgment matrix of test and simulation data consistency evaluation indicators; Step S32: Calculate the weight vector of each evaluation index of consistency between test and simulation data; Step S33: performing consistency check on the constructed judgment matrix; When constructing the judgment matrix of test and simulation data consistency evaluation indexes, try to give different weights to different factors; comprehensively consider the relative importance of the factors affecting the consistency of test and simulation data, and construct the consistency evaluation index judgment matrix D as follows: Among them, D ij Indicator u i Relative to factor u j The importance of D ij The larger the factor u i Relative to factor u j The more important; D ij =1, indicating that factor u i With factor u j In comparison, both are equally important; The specific process of step S32 includes the following steps: Step S321: normalize each column of the judgment matrix D, the formula is as follows: Where: represents the sum of the columns of the judgment matrix, D i ' j Represents the value at the corresponding position of the judgment matrix after normalization, D ij Represents the value at the corresponding position of the original judgment matrix; Step S322: Sum each row of the normalized judgment matrix to obtain the eigenvector λ = (λ1, λ2, ..., λ n ); Step S323: Assume that the weight vector of each evaluation index is where a i represents the weight of the i-th evaluation index, a i ≥0, The weights of the evaluation indicators for consistency between test and simulation data can be calculated according to formula (2): Where: a i represents the weight of each evaluation index of the consistency between test and simulation data, λ represents the feature vector, λ j represents the value of each element of the feature vector, Represents the sum of the values ​​of each element of the eigenvector; The specific process of step S33 includes the following steps: Step S331: Calculate the maximum eigenvalue γ of the above judgment matrix D max ; Step S332: Perform a consistency check on the above judgment matrix D. The formula is as follows: Wherein: n is the number of factors; CI is the consistency index of the judgment matrix. When CI=0, it has complete consistency; when CI is close to 0, it has good consistency; the larger the CI value, the worse the consistency.

7. A method for determining consistency between test and simulation data based on fuzzy mathematics as claimed in claim 6, characterized in that: When assigning different weights to different factors, the following principles should be followed: In static test measurements, the direction of a single strain gauge during test measurement cannot exactly coincide with the true principal stress direction, so the consistency between the strain test data and theoretical data measured by the single strain gauge will be poor. The weight of the single strain gauge data should be reduced in the overall consistency judgment of the test and simulation data; For the flower strain gauge, its maximum and minimum measurement data are converted from the formula, and the weight ratio of the two should be the same; in the simulation analysis, the simulation of the structure on the main force transmission path is closer to the actual situation, while the structure on the non-main force transmission path is usually mechanically simplified to varying degrees. When judging the consistency of the test and simulation data, the strain gauge measurement data on the main force transmission path should be given a larger weight ratio.

8. A method for determining consistency between test and simulation data based on fuzzy mathematics as claimed in claim 6, characterized in that: If the consistency of the test and simulation data consistency judgment matrix does not meet the requirements, it is necessary to modify the judgment matrix until it passes the consistency test.

9. A method for determining consistency between test and simulation data based on fuzzy mathematics as claimed in claim 6, characterized in that: In step S4, the specific process of fuzzy comprehensive evaluation calculation of the consistency between test and simulation data includes the following steps: Step S41: Determine the membership function and establish a fuzzy comprehensive evaluation matrix; Through the above steps S1 and S2, the factor set U={u1,u2,…u n }, and the evaluation set V = {v1,v2,…,v m }; Then in step S41, based on the membership function, a fuzzy comprehensive evaluation matrix is ​​constructed as follows: Where: 0≤r ij ≤1, r ij Represents the factor u i With comments j The degree of i , the object to be judged belongs to the comment v j the extent of; The fuzzy relationship between the factor set U and the evaluation set V that maps the consistency between the test and simulation data; Step S42: Select a fuzzy operator and perform fuzzy operation on the consistency between the test and simulation data; Through the fuzzy operator weight set and fuzzy comprehensive evaluation matrix Perform fuzzy operation, see formula (4), and then we can get the evaluation result vector Where: "ο" represents a fuzzy operator in a general sense; is the evaluation result vector.

10. A method for determining consistency between test and simulation data based on fuzzy mathematics as claimed in claim 9, characterized in that: In step S5, the evaluation result vector can be obtained from step S4 The results are explained by the weighted average principle, see formula (5), b wa The corresponding rating is the comprehensive evaluation result of the consistency between the test and simulation data; Where: v j represents the jth evaluation result of the consistency between the test and simulation data, b j Represents the evaluation result vector The j-th element of .

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