Aircraft assembly quality influence factor analysis method based on fuzzy evaluation

Abstract

The invention discloses an aircraft assembly quality influence factor analysis method based on fuzzy evaluation, and the method comprises the steps: analyzing aircraft assembly quality influence factor indexes, and constructing an assembly quality fluctuation influence factor evaluation index system; according to the quality fluctuation influence factor evaluation index system, an analytic hierarchy process is adopted to calculate an index weight vector W; constructing an assembly quality fluctuation evaluation factor set P, and converting n influence factors in the index system into an evaluation index set P = {p1, p2,..., pn}; calculating the evaluation grade membership degree of each evaluation index to form an evaluation set G; calculating a fuzzy comprehensive judgment membership degree matrix from the factor set P to the evaluation set G to form a fuzzy relation matrix R; and according to the index weight vector W and the fuzzy relation matrix R obtained by the analytic hierarchy process, calculating a comprehensive evaluation vector K, and realizing fuzzy comprehensive evaluation of the assembly quality influence factors. According to the technical scheme provided by the invention, the problems of difficulty in tracing the assembly quality fluctuation influence factors, difficulty in quality control in the assembly process and the like caused by numerous influence factors and the characteristics of uncertainty, coupling and the like in the aircraft assembly process are solved.

Description

Technical Field

[0001] The present invention relates to the field of, but is not limited to, the technical field of quality fluctuation analysis in an aircraft assembly process, and in particular to a method for analyzing factors affecting aircraft assembly quality based on fuzzy evaluation. Background Art

[0002] Aircraft assembly quality is an important indicator that determines whether aircraft production meets standards. In the existing technology, the inspection of aircraft assembly quality is mainly based on the conformity inspection of design indicators, that is, the design indicators are considered to meet the quality requirements if they are within a certain tolerance range. The above inspection method is effective for the processing quality control of parts with a single quality indicator requirement.

[0003] However, when faced with inconsistent tolerance requirements for multiple quality indicators and coupled correlations between the indicators, a simple conformity test design indicator cannot guarantee optimal evaluation of product quality. In addition, due to the coupling of multiple influencing factors, product quality fluctuations are difficult to detect and quality control measures are delayed, resulting in poor product consistency and even quality abnormalities. Summary of the invention

[0004] Purpose of the present invention: In order to solve the above technical problems, an embodiment of the present invention provides an analysis method of factors affecting aircraft assembly quality based on fuzzy evaluation, so as to solve the problem that during the aircraft assembly process, key quality influencing factors cannot be identified and assembly quality is inconsistent due to the requirement to verify the assembly quality using multiple quality indicator parameters.

[0005] Technical solution of the present invention: In a first aspect, an embodiment of the present invention provides a method for analyzing factors affecting aircraft assembly quality based on fuzzy evaluation, comprising:

[0006] Step 1: Analyze the influencing factors of aircraft assembly quality and construct an evaluation index system of influencing factors of assembly quality fluctuation, wherein the evaluation index system includes multiple indicators affecting aircraft assembly quality and quality influencing factors corresponding to each indicator;

[0007] Step 2: According to the evaluation index system of factors affecting assembly quality fluctuation, the entropy weight method is used to calculate the weight vector W of each index;

[0008] Step 3: Construct the assembly quality fluctuation evaluation factor set P, and transform the n quality influencing factors in the evaluation index system into the evaluation index set P = {p1, p2, ..., p n};

[0009] Step 4: Set the evaluation level according to the requirements of each indicator to form the evaluation set G;

[0010] Step 5: Calculate the fuzzy comprehensive judgment membership matrix of the evaluation index set P to the evaluation set G to form the membership matrix R;

[0011] Step 6: Calculate the comprehensive evaluation vector K based on the weight vector W and the fuzzy relationship matrix R to determine the grade score of each influencing factor;

[0012] Step 7: Based on the comprehensive evaluation vector K, a comprehensive evaluation is performed according to the maximum membership vector principle to obtain the importance of each influencing factor.

[0013] Optionally, in the aircraft assembly quality influencing factors analysis method based on fuzzy evaluation as described above, in the step one, the constructed evaluation index system of assembly quality fluctuation influencing factors is an evaluation index system model with a hierarchical structure, the first level of the evaluation index system model is the quality inspection index, and the second level is the quality influencing factors that affect the quality inspection index.

[0014] Optionally, in the above-mentioned aircraft assembly quality influencing factor analysis method based on fuzzy evaluation, in the step 2, the weight vector W of each indicator is calculated by a weight calculation method based on information entropy, and the calculation process includes:

[0015] Step 21, collect the quality inspection data of the aircraft assembly process, which is defined as the original data matrix F, which is expressed as follows:

[0016]

[0017] Among them, m represents the sample size of quality inspection data, n represents the number of assembly quality indicators, and f ij represents the quality inspection data of the jth indicator of the i-th sample, i=1,2,...,m; j=1,2,...,n;

[0018] Step 22, standardized data processing, includes: calculating the weight of each indicator, and obtaining the indicator weight matrix A as follows:

[0019]

[0020] in,

[0021] a ij Indicates the proportion of the jth index value of the i-th sample;

[0022] Step 23, calculating the information utility value D of the indicator;

[0023] Step 24, calculate the weight W of each indicator.

[0024] Optionally, in the above-mentioned aircraft assembly quality influencing factor analysis method based on fuzzy evaluation, the calculation method of the indicator information utility value D in step 23 is:

[0025]

[0026] Among them, E represents the information entropy of the indicator, e j ,j=1,2,...,n represents the information entropy of the jth indicator; d j ,j=1,2,...,n represents the information utility value of the jth indicator.

[0027] Optionally, in the above-mentioned aircraft assembly quality influencing factor analysis method based on fuzzy evaluation, the weight W of each indicator in step 24 is calculated as follows:

[0028]

[0029] Among them, wj is the weight of the j-th indicator.

[0030] Optionally, in the above-mentioned aircraft assembly quality influencing factor analysis method based on fuzzy evaluation, the evaluation set G in step 4 is a comment set based on the Likert scale method, which is expressed as:

[0031] G = {G1 is very important, G2 is important, G3 is average, G4 is not important, G5 is very unimportant};

[0032] Among them G k , k=1,2,...5 represents each level in the evaluation set, and the corresponding scores of the importance of each level in the evaluation set G are: 5, 4, 3, 2, 1.

[0033] Optionally, in the above-mentioned aircraft assembly quality influencing factor analysis method based on fuzzy evaluation, the membership matrix R is calculated in step 5 by using a trapezoidal membership function calculation method, and the calculation process includes:

[0034] Step 51, according to the evaluation set G, the level intervals of each indicator are divided into five intervals: [0, a], (a, b], (b, c], (c, d], (d, +∞];

[0035] Step 52: Calculate the grade score of each indicator using a trapezoidal function according to the interval range of the quality inspection data corresponding to each indicator T = [T1, T2, ..., T n ], expressed as:

[0036]

[0037] Among them, T jFor each level G in the evaluation set G corresponding to the jth indicator in the evaluation indicator set P k The score, t ik , i=1,2,..m;k=1,2,...5 indicates that the score value of the kth level of the jth indicator in the i-th sample is calculated by using the trapezoidal membership function, m indicates the data sample size of each indicator, and k indicates the number of evaluation levels;

[0038] Step 53: According to the grade score of each indicator, T = [T1, T2, ..., T n ], calculate the membership matrix of each index R = [R1, R2, ..., R n ]′, calculated as:

[0039]

[0040] in, Indicates the membership degree of the j-th indicator to the k-th evaluation level.

[0041] Optionally, in the above-mentioned aircraft assembly quality influencing factor analysis method based on fuzzy evaluation, the trapezoidal membership function calculation formula is:

[0042]

[0043] According to the interval position of the quality inspection data corresponding to each indicator, when the interval position is in the first interval and the second interval, the small-size calculation formula is used; when the interval position is in the third interval, the intermediate calculation formula is used; when the interval position is in the fourth interval and the fifth interval, the large-size calculation formula is used.

[0044] Optionally, in the above-mentioned aircraft assembly quality influencing factor analysis method based on fuzzy evaluation, the method of calculating the comprehensive evaluation vector K in step 6 is:

[0045]

[0046] Among them, K is the factor weight set, W is the weight of each indicator, and R is the membership matrix.

[0047] In a second aspect, the present invention further provides a computer-readable storage medium, comprising: a memory and a processor;

[0048] The memory is configured to store executable instructions;

[0049] The processor is specifically configured to implement any one of the above-mentioned methods for analyzing factors affecting aircraft assembly quality based on fuzzy evaluation when executing the executable instructions stored in the memory.

[0050] Beneficial effects of the present invention: In view of the problems such as difficulty in tracing quality fluctuations in the assembly process, delayed quality control and poor quality consistency caused by the coupling of multiple factors in the product assembly process, the embodiment of the present invention proposes a method for analyzing factors affecting aircraft assembly quality based on fuzzy evaluation. The method analyzes quality indicators and quality influencing factors of the assembly process, constructs a quality influencing factor model with a hierarchical structure, uses information entropy to calculate the weight of each indicator, establishes a fuzzy comprehensive evaluation indicator based on the weight, realizes the analysis of key influencing factors of aircraft assembly quality based on fuzzy evaluation, can effectively identify key indicators and factors affecting assembly quality, and timely control quality fluctuation factors, effectively solves the problem of difficulty in tracing quality fluctuations in the assembly process, improves assembly quality, and provides theoretical support for aircraft assembly process quality control. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] The accompanying drawings are used to provide a further understanding of the technical solution of the present invention and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the technical solution of the present invention and do not constitute a limitation on the technical solution of the present invention.

[0052] Figure 1 A schematic diagram of an aircraft component docking assembly quality evaluation index system in an embodiment of the present invention;

[0053] Figure 2 Schematic diagram of the evaluation index system model of assembly quality fluctuation influencing factors in an embodiment of the present invention. DETAILED DESCRIPTION

[0054] In order to make the purpose, technical solution and advantages of the present invention more clear, the embodiments of the present invention will be described in detail with reference to the accompanying drawings. It should be noted that the embodiments and features in the embodiments of the present application can be combined with each other arbitrarily without conflict.

[0055] It has been explained in the above background technology that the existing assembly quality inspection method cannot guarantee the optimal evaluation of product quality when faced with inconsistent tolerance requirements of multiple quality indicators and coupled correlations between the indicators; in addition, due to the coupling of multiple indicator influencing factors, product quality fluctuations are difficult to detect and quality control measures are delayed, resulting in poor product consistency and even quality abnormalities.

[0056] In view of the above problems, it is of great significance to quantitatively evaluate the importance of each indicator of the product assembly process, clarify the key influencing factors of the assembly process, and effectively control the key indicators of the assembly process to improve the product assembly quality.

[0057] Based on the above analysis, an embodiment of the present invention provides an analysis of key influencing factors of aircraft assembly quality based on fuzzy evaluation.

[0058] The present invention provides the following specific embodiments which can be combined with each other. The same or similar concepts or processes may not be described in detail in some embodiments.

[0059] The embodiment of the present invention proposes a method for analyzing factors affecting aircraft assembly quality based on fuzzy evaluation, comprising the following steps:

[0060] Step 1: Analyze the influencing factors of aircraft assembly quality and construct an evaluation index system of influencing factors of assembly quality fluctuation, wherein the evaluation index system includes multiple indicators affecting aircraft assembly quality and quality influencing factors corresponding to each indicator;

[0061] Step 2: According to the evaluation index system of factors affecting assembly quality fluctuation, the entropy weight method is used to calculate the weight vector W of each index;

[0062] Step 3: Construct the assembly quality fluctuation evaluation factor set P, and transform the n quality influencing factors in the evaluation index system into the evaluation index set P = {p1, p2, ..., p n};

[0063] Step 4: Set the evaluation level according to the requirements of each indicator to form the evaluation set G;

[0064] Step 5: Calculate the fuzzy comprehensive judgment membership matrix of the evaluation index set P to the evaluation set G to form the membership matrix R;

[0065] Step 6: Calculate the comprehensive evaluation vector K based on the weight vector W and the fuzzy relationship matrix R to determine the grade score of each influencing factor;

[0066] Step 7: Based on the comprehensive evaluation vector K, a comprehensive evaluation is performed according to the maximum membership vector principle to obtain the importance of each influencing factor.

[0067] In one implementation of the present invention, in the above step 1, the evaluation index system of factors affecting assembly quality fluctuations is a hierarchical evaluation index system model, wherein the first level of the evaluation index system model is the quality inspection index, and the second level is the quality influencing factors affecting the quality inspection index. Figure 2 The evaluation index system model of factors affecting assembly quality fluctuation is shown.

[0068] In an implementation of the embodiment of the present invention, in the above step 2, the weight vector W of each indicator is calculated by a weight calculation method based on information entropy, and the calculation process includes:

[0069] Step 21, collect the quality inspection data of the aircraft assembly process, which is defined as the original data matrix F, which is expressed as follows:

[0070]

[0071] Among them, m represents the sample size of quality inspection data, n represents the number of assembly quality indicators, and f ij represents the quality inspection data of the jth indicator of the i-th sample, i=1,2,...,m; j=1,2,...,n;

[0072] Step 22, standardized data processing, includes: calculating the weight of each indicator, and obtaining the indicator weight matrix A as follows:

[0073]

[0074] in,

[0075] a ij Indicates the proportion of the jth index value of the i-th sample;

[0076] Step 23, calculating the information utility value D of the indicator;

[0077] Step 24, calculate the weight W of each indicator.

[0078] In this implementation, the calculation method of the indicator information utility value D in the above step 23 is:

[0079]

[0080] Among them, E represents the information entropy of the indicator, e j ,j=1,2,...,n represents the information entropy of the jth indicator; d j ,j=1,2,...,n represents the information utility value of the jth indicator

[0081] In this implementation, the weight W of each indicator in the above step 24 is calculated as follows:

[0082]

[0083] Among them, w j is the weight of the jth indicator.

[0084] In an implementation of the embodiment of the present invention, the evaluation set G in the above step 4 is a comment set based on the Likert scale method, which is expressed as:

[0085] G = {G1 is very important, G2 is important, G3 is average, G4 is not important, G5 is very unimportant};

[0086] Among them G k , k=1,2,...5 represents each level in the evaluation set, and the corresponding scores of the importance of each level in the evaluation set G are: 5, 4, 3, 2, 1.

[0087] In one implementation of the embodiment of the present invention, in step 5, the membership matrix R is calculated based on a trapezoidal membership function calculation method, and the calculation process includes:

[0088] Step 51, according to the evaluation set G, the level intervals of each indicator are divided into five intervals: [0, a], (a, b], (b, c], (c, d], (d, +∞];

[0089] Step 52: Calculate the grade score of each indicator using a trapezoidal function according to the interval range of the quality inspection data corresponding to each indicator T = [T1, T2, ..., T n ], expressed as:

[0090]

[0091] Among them, T j For each level G in the evaluation set G corresponding to the jth indicator in the evaluation indicator set P k The score, t ik , i=1,2,..m;k=1,2,...5 indicates that the score value of the kth level of the jth indicator in the i-th sample is calculated by using the trapezoidal membership function, m indicates the data sample size of each indicator, and k indicates the number of evaluation levels;

[0092] Step 53: According to the grade score of each indicator, T = [T1, T2, ..., T n ], calculate the membership matrix of each index R = [R1, R2, ..., R n ]′, calculated as:

[0093]

[0094] in, Indicates the membership degree of the j-th indicator to the k-th evaluation level.

[0095] The calculation formula of the trapezoidal membership function is:

[0096]

[0097] According to the interval position of the quality inspection data corresponding to each indicator, when the interval position is in the first interval and the second interval, the small-size calculation formula is used; when the interval position is in the third interval, the intermediate calculation formula is used; when the interval position is in the fourth interval and the fifth interval, the large-size calculation formula is used.

[0098] In one implementation of the embodiment of the present invention, the method of calculating the comprehensive evaluation vector K in step 6 is:

[0099]

[0100] Among them, K is the factor weight set, W is the weight of each indicator, and R is the membership matrix.

[0101] In view of the problems such as the difficulty in tracing the quality fluctuations in the assembly process, delayed quality control and poor quality consistency caused by the coupling of multiple factors in the product assembly process, the embodiment of the present invention proposes a method for analyzing factors affecting aircraft assembly quality based on fuzzy evaluation. The method analyzes the quality indicators and quality influencing factors of the assembly process, constructs a quality influencing factor model with a hierarchical structure, uses information entropy to calculate the weight of each indicator, establishes a fuzzy comprehensive evaluation indicator based on the weight, realizes the analysis of key influencing factors of aircraft assembly quality based on fuzzy evaluation, can effectively identify the key indicators and factors affecting the assembly quality, and timely control the quality fluctuation factors, effectively solves the problem of the difficulty in tracing the quality fluctuations in the assembly process, improves the assembly quality, and provides theoretical support for the quality control of the aircraft assembly process.

[0102] Based on the aircraft assembly quality influencing factor analysis method based on fuzzy evaluation provided by the embodiment of the present invention, the embodiment of the present invention also provides a computer-readable storage medium, including: a memory and a processor;

[0103] The memory is configured to store executable instructions;

[0104] The processor is specifically configured to implement the aircraft assembly quality influencing factor analysis method based on fuzzy evaluation as provided in any one of the above embodiments when executing the executable instructions stored in the memory.

[0105] The following is a schematic illustration of an implementation of the aircraft assembly quality influencing factors analysis method based on fuzzy evaluation provided by an embodiment of the present invention through an application case.

[0106] Application Cases

[0107] This application case provides a method for analyzing key factors affecting aircraft assembly quality based on fuzzy evaluation. This application case takes the analysis of factors affecting the quality of aircraft component docking assembly as an example. This application case takes the aircraft component docking assembly quality inspection indicators as the object, and based on multiple sets of quality inspection data, uses the fuzzy comprehensive evaluation method to construct a comprehensive evaluation index for assembly quality, identify key factors in the assembly process, and provide theoretical support for the quality control of the docking assembly process, thereby improving the consistency of assembly quality. The specific algorithm includes the following steps:

[0108] Step 1: Analyze the quality inspection requirements for the docking assembly of aircraft parts, build an evaluation index system model for factors affecting assembly quality fluctuations, and divide the docking assembly quality evaluation indicators into two levels: the first level is the quality inspection index, and the second level is the product key factors that affect the inspection index, such as Figure 1 The figure shows the aircraft component docking assembly quality evaluation index system.

[0109] Step 2: According to the evaluation index system of factors affecting quality fluctuation, the index weight vector W is calculated using the entropy weight method;

[0110] Step 21, collect the quality inspection data of the docking assembly process, mainly the data of the feature points in the second level of the evaluation system model, a total of 31 groups of data samples, each sample contains 20 index values, thus forming a 31×20 size docking assembly process quality inspection original data matrix F, which can be expressed as follows:

[0111]

[0112] Step 22: Since the ideal deviation value of the indicator is 0, the indicator value calculation is carried out using the absolute value of the matrix elements. According to the formula Perform standardized data processing on matrix F, calculate the weight of each indicator, and obtain the indicator weight matrix as follows:

[0113]

[0114] Step 23, according to the formula and D = {d1, d2, ..., d j}=1-e j Calculate the information entropy value E and information utility value D of each indicator, and the results are as follows:

[0115] E=[0.9213,0.9267,...,0.9413] 1×20 ;

[0116] D=[0.0787,0.0733,...,0.0587] 1×20 ;

[0117] Step 23, according to the formula Calculate the weight W of each indicator, and the results are as follows:

[0118] W=[0.0534,0.0497,...,0.0398] 1×20

[0119] Step 3: Construct the evaluation factor set P of assembly quality fluctuation, and transform the n influencing factors in the index system into the evaluation index set P = {p1, p2, ..., p n};

[0120] Step 4: Calculate the evaluation grade membership of each evaluation index to form an evaluation set G; G = {G1 is very important, G2 is important, G3 is average, G4 is not important, G5 is very not important};

[0121] Among them G k, k=1,2,...5 represents the importance of each level in the evaluation set, and the corresponding scores are: 5,4,3,2,1.

[0122] Step 5: Calculate the fuzzy comprehensive judgment membership matrix from the factor set P to the evaluation set G to form the fuzzy relationship matrix R.

[0123] Step 51: Based on the evaluation set G, the level intervals of each indicator are divided into five intervals: [0, 0.5], (0.5, 1.25], (1.25, 1.5], (1.5, 2], (2, +∞]

[0124] Step 52, according to the interval range of each indicator data, the grade score T = [T1, T2, ..., T n ], the evaluation grade scores corresponding to each indicator are calculated as follows:

[0125]

[0126] ……;

[0127]

[0128] R1 represents the evaluation grade score P of the first influencing factor in the aircraft assembly quality evaluation system model 11 , R 20 It represents the evaluation grade score P of the first influencing factor in the aircraft assembly quality evaluation system model. 310 .

[0129] Step 53, according to the grade score of each indicator, the membership matrix of each indicator is calculated as follows:

[0130]

[0131] Step 6: Calculate the comprehensive evaluation vector K of each indicator based on the weight vector W and the fuzzy relationship matrix R:

[0132]

[0133] The final evaluation set of each indicator is as follows, and the importance of each indicator is shown in Table 1:

[0134] Table 1 The level of membership of each influencing factor

[0135]

[0136]

[0137] Step 7: Based on the comprehensive evaluation vector K, a comprehensive evaluation is performed according to the maximum membership vector principle to obtain the importance ranking of each influencing factor (from large to small):

[0138] {P 37 ,P 26 ,P 25 ,P 39 ,P 13 ,P 33 ,P 35 ,P 310 ,P 38 ,P 23 ,P 24 ,P 32 ,P 36 ,P 11 ,P 14 ,P 22 ,P 34 ,P 31 ,P 12 ,P 21}.

[0139] In this application implementation case, the docking assembly of large aircraft components is taken as the object. The docking assembly quality inspection data is used to analyze the key factors affecting the aircraft assembly quality, solve the problems of difficult identification of key factors in the docking assembly process of aircraft components, delayed quality process control, etc., and a method for analyzing the factors affecting the quality of aircraft assembly based on entropy weight-fuzzy comprehensive evaluation is proposed. The algorithm is used to analyze 31 sets of aircraft docking assembly inspection data. The evaluation level of each factor is shown in Table 1. It can be seen that the inspection indicators selected in the process of component docking assembly are all very important. The importance of each influencing factor is obtained by ranking by membership degree. Among them, the factors that have a greater impact on the evaluation of assembly quality are distributed in the two indicators of the anhedral angle P37 and the installation angle P26 and P25, that is, the feature points 7#-11#, 9#-10#, and 11#-12#, which are consistent with the actual docking assembly process control factors and verify the effectiveness of the algorithm.

[0140] Although the embodiments disclosed in the present invention are as above, the contents are only embodiments adopted to facilitate understanding of the present invention and are not intended to limit the present invention. Any technician in the field to which the present invention belongs can make any modifications and changes in the form and details of implementation without departing from the spirit and scope disclosed in the present invention, but the patent protection scope of the present invention shall still be subject to the scope defined in the attached claims.

Claims

1. A method for analyzing factors affecting aircraft assembly quality based on fuzzy evaluation, characterized in that: include: Step 1: Analyze the influencing factors of aircraft assembly quality and construct an evaluation index system of influencing factors of assembly quality fluctuation, wherein the evaluation index system includes multiple indicators affecting aircraft assembly quality and quality influencing factors corresponding to each indicator; Step 2: According to the evaluation index system of factors affecting assembly quality fluctuation, the entropy weight method is used to calculate the weight vector W of each index; Step 3: Construct the assembly quality fluctuation evaluation factor set P, and transform the n quality influencing factors in the evaluation index system into the evaluation index set P = {p1, p2, ..., p n }; Step 4: Set the evaluation level according to the requirements of each indicator to form the evaluation set G; Step 5: Calculate the fuzzy comprehensive judgment membership matrix of the evaluation index set P to the evaluation set G to form the membership matrix R; Step 6: Calculate the comprehensive evaluation vector K based on the weight vector W and the fuzzy relationship matrix R to determine the grade score of each influencing factor; Step 7: Based on the comprehensive evaluation vector K, a comprehensive evaluation is performed according to the maximum membership vector principle to obtain the importance of each influencing factor.

2. The method for analyzing factors affecting aircraft assembly quality based on fuzzy evaluation according to claim 1 is characterized in that: In the step 1, the constructed evaluation index system of factors affecting assembly quality fluctuation is an evaluation index system model with a hierarchical structure, wherein the first level of the evaluation index system model is the quality inspection index, and the second level is the quality influencing factors affecting the quality inspection index.

3. The method for analyzing factors affecting aircraft assembly quality based on fuzzy evaluation according to claim 1 is characterized in that: In the step 2, the weight vector W of each indicator is calculated by a weight calculation method based on information entropy, and the calculation process includes: Step 21, collect the quality inspection data of the aircraft assembly process, which is defined as the original data matrix F, which is expressed as follows: Among them, m represents the sample size of quality inspection data, n represents the number of assembly quality indicators, and f ij represents the quality inspection data of the jth indicator of the i-th sample, i=1,2,...,m; j=1,2,...,n; Step 22, standardized data processing, includes: calculating the weight of each indicator, and obtaining the indicator weight matrix A as follows: in, a ij Indicates the proportion of the jth index value of the i-th sample; Step 23, calculating the information utility value D of the indicator; Step 24, calculate the weight W of each indicator.

4. The method for analyzing factors affecting aircraft assembly quality based on fuzzy evaluation according to claim 3 is characterized in that: The calculation method of the indicator information utility value D in step 23 is: Among them, E represents the information entropy of the indicator, e j ,j=1,2,...,n represents the information entropy of the jth indicator; d j ,j=1,2,...,n represents the information utility value of the jth indicator.

5. The method for analyzing factors affecting aircraft assembly quality based on fuzzy evaluation according to claim 3 is characterized in that: The weight W of each indicator in step 24 is calculated as follows: Among them, w j is the weight of the jth indicator.

6. The method for analyzing factors affecting aircraft assembly quality based on fuzzy evaluation according to any one of claims 1 to 5, characterized in that: The evaluation set G in step 4 is a comment set based on the Likert scale method, which is expressed as: G = {G1 is very important, G2 is important, G3 is average, G4 is not important, G5 is very unimportant}; Among them G k , k=1,2,...5 represents each level in the evaluation set, and the corresponding scores of the importance of each level in the evaluation set G are: 5, 4, 3, 2, 1.

7. The method for analyzing factors affecting aircraft assembly quality based on fuzzy evaluation according to any one of claims 1 to 5, characterized in that: In step 5, the membership matrix R is calculated based on the trapezoidal membership function calculation method, and the calculation process includes: Step 51, according to the evaluation set G, the level intervals of each indicator are divided into five intervals: [0, a], (a, b], (b, c], (c, d], (d, +∞]; Step 52, according to the interval range of the quality inspection data corresponding to each indicator, use the trapezoidal function to calculate the grade score of each indicator T = [T1, T2, ..., T n ], expressed as: Among them, T j For each level G in the evaluation set G corresponding to the jth indicator in the evaluation indicator set P k The score, t ik , i=1,2,..m;k=1,2,...5 indicates that the score value of the kth level of the jth indicator in the i-th sample is calculated by using the trapezoidal membership function, m indicates the data sample size of each indicator, and k indicates the number of evaluation levels; Step 53: According to the grade score of each indicator, T = [T1, T2, ..., T n ], calculate the membership matrix of each index R = [R1, R2, ..., R n ]′, calculated as: in, j=1,2,..,n;k=1,2,...,5, indicating the membership degree of the jth indicator to the kth evaluation level.

8. The method for analyzing factors affecting aircraft assembly quality based on fuzzy evaluation according to claim 7 is characterized in that: The calculation formula of the trapezoidal membership function is: According to the interval position of the quality inspection data corresponding to each indicator, when the interval position is in the first interval and the second interval, the small-size calculation formula is used; when the interval position is in the third interval, the intermediate calculation formula is used; when the interval position is in the fourth interval and the fifth interval, the large-size calculation formula is used.

9. The method for analyzing factors affecting aircraft assembly quality based on fuzzy evaluation according to claim 8 is characterized in that: The method for calculating the comprehensive evaluation vector K in step 6 is: Among them, K is the factor weight set, W is the weight of each indicator, and R is the membership matrix.

10. A computer-readable storage medium, characterized in that: include: Memory and processor; The memory is configured to store executable instructions; The processor is specifically configured to implement the aircraft assembly quality influencing factor analysis method based on fuzzy evaluation according to any one of claims 1 to 9 when executing the executable instructions stored in the memory.

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