An improved variable weight TOPSIS-based effectiveness evaluation method for aviation support system

Abstract

This invention discloses an aviation support system performance evaluation method based on an improved variable weight TOPSIS. It establishes an aviation support performance evaluation index system, constructs a judgment matrix based on the importance of indicators, and calculates the constant weights of the indicators. Based on the aviation support performance evaluation index system, it collects and standardizes data for each indicator to obtain the optimal and worst value sets. The projected values ​​of the differences between the indicator's disadvantage and advantage information are converted into indicator dominance, constructing an indicator dominance matrix. A state-variable weight function is constructed, and the performance evaluation results of the aviation support system are calculated based on the relative closeness of each indicator to the absolute ideal solution. This method significantly improves aviation support performance through a dynamic adjustment mechanism, thus possessing both scientific validity and adaptability.

Description

Technical Field

[0001] This invention relates to the field of performance evaluation, and more particularly to a method for evaluating the performance of aviation support systems based on an improved variable weight TOPSIS. Background Technology

[0002] The aircraft carrier aviation support system is the sum of technologies that ensure various carrier-based aircraft can perform mission planning, efficient sorties, safe recovery, aircraft transport, fuel, water, gas, and electricity supply, ammunition transfer and loading, and maintenance support. This technology involves catapult launch systems, arrested recovery systems, landing guidance systems, carrier-based aircraft transport systems, mission command and control systems, and deck support systems, and consists of multiple subsystems with complex interactive processes. To ensure that air power can maintain high operational efficiency in complex and ever-changing battlefield environments, a comprehensive and objective assessment of the aviation support system's effectiveness is essential.

[0003] Currently, a large body of literature has been dedicated to researching effectiveness evaluation methods. However, existing methods generally have two limitations: first, they are difficult to effectively handle situations where the evaluation values ​​of indicators are mixed with multiple data types (such as precise numbers and fuzzy numbers); second, the indicator weights obtained by traditional methods are usually fixed across different schemes, failing to reflect the dynamic changes in the evaluation values ​​of each indicator for different samples. Summary of the Invention

[0004] The purpose of this invention is to provide an improved variable-weight TOPSIS-based method for evaluating the effectiveness of aviation support systems. This method aims to address the problem of invariant indicator weights under different mission scenarios in aviation support effectiveness evaluation, and is adaptable to different types of indicator evaluation problems. It constructs an dominance matrix by calculating indicator dominance; then, it constructs a state-variable weight function by combining indicator dispersion and adjustment, dynamically correcting the constant weights determined by the analytic hierarchy process (AHP) to obtain variable weights; finally, it combines the variable weights with the TOPSIS method, calculating the final aviation support effectiveness evaluation result based on the relative closeness of each scenario to the ideal solution.

[0005] This invention provides a method for evaluating the effectiveness of aviation support systems based on an improved variable-weight TOPSIS, the method comprising the following steps: S1: Define the objects of aviation support effectiveness assessment and establish an aviation support effectiveness assessment indicator system; S2: Based on the aviation support effectiveness evaluation index system, obtain the data of each index, convert the projection value of the difference between the index disadvantage information and the advantage information into the index advantage degree, and construct the index advantage degree matrix. S3: Construct a state-variable weight function, combine the constant weights of the indicators obtained by the analytic hierarchy process, calculate the variable weights of each indicator, and measure the rationality of the state-variable weight function by calculating the dispersion and adjustment degree of the indicator factors based on the indicator dominance matrix. S4: Based on the variable weights of each indicator and combined with the TOPSIS method, the effectiveness evaluation results of the aviation support system are calculated according to the relative closeness of each objective to the absolute ideal solution.

[0006] Furthermore, as the foundation for performance evaluation, the indicator system, due to the differences in attributes among different indicators, needs to be based on the actual situation of carrier-based aircraft support. According to the carrier-based aircraft support mission process, the aviation support system can be divided into subsystems such as the dispatch and command system, takeoff support system, transfer support system, arrested landing system, and deck support system. The various subsystems within the carrier-based aircraft support system are not independent; there are coupling effects between different subsystems. Therefore, it is necessary to clarify the construction principles of the aviation support performance evaluation indicator system, screen, analyze, and make decisions on performance indicators, extract key evaluation elements to build an orderly hierarchical structure, and thus construct the aviation support performance evaluation indicator system.

[0007] Furthermore, based on the hierarchical structure of the constructed aviation support system performance evaluation index system, the constant weights of the indicators are determined using the Analytic Hierarchy Process (AHP). The AHP method is a hierarchical weighted decision analysis method that determines subjective weights by comparing and calculating the importance of each indicator pairwise. It is highly scientific and applicable to complex systems composed of numerous interconnected and mutually restrictive factors. Through a judgment matrix, it effectively measures the superiority or inferiority relationships between interconnected elements, providing a valuable tool for simplifying system analysis and calculation.

[0008] Furthermore, the collected indicator data is standardized to obtain a standardized evaluation matrix. Based on the gray target decision-making concept, the distance from the indicator value to the positive and negative target centers is defined as indicator advantage information and indicator disadvantage information. The Gaussian criterion is used to convert the projected value of the difference between the indicator advantage information and the disadvantage information into indicator dominance degree, and the indicator dominance degree matrix is ​​then constructed. for:

[0009] in, , These are information on the indicator's weaknesses and information on its strengths. The distance from the positive target center to the negative target center. This is the dominance adjustment coefficient, and .

[0010] Furthermore, the key to constructing the variable weight vector lies in constructing the equilibrium function. Based on the definition of the equilibrium function and the axiomatic definition of the variable weight theory, the hybrid state variable weight function is constructed as follows:

[0011] in, The standard deviation of the indicator factors. The factor state value of the indicator. For the number of indicators, These are the critical values ​​for the indicator states of punishment and incentive, respectively. To increase the severity of punishment, These are the constant weights of each indicator calculated using the analytic hierarchy process (AHP).

[0012] Therefore, the variable weight of the j-th indicator in the i-th evaluation scheme is:

[0013] Since different variable weight equilibrium functions have different applicable ranges, the concepts of dispersion and adjustment degree are introduced into variable weight. Dispersion measures the degree of equilibrium of the factor group, and adjustment degree measures the ability of the state variable weight vector to adjust the weight. The lower the degree of equilibrium of the factor group, the greater the adjustment force of the state variable weight vector is required, and vice versa.

[0014] If the state value of the indicator factor is The dispersion is then:

[0015] The degree of adjustment is:

[0016] in, The constant weights of each indicator, The variable weights of the indicators are obtained from the state variable weight function.

[0017] Furthermore, the standardized evaluation matrix The standardized data and the ideal solution are assigned corresponding variable weights to obtain the weighted standardized decision matrix. Using Euclidean distance To measure the relative closeness of the i-th solution to the positive and negative ideal solutions. :

[0018] in, Let be the distance from the i-th solution to the corresponding positive ideal solution. Let be the distance from the i-th solution to the corresponding negative ideal solution. In the performance evaluation of aviation support systems, the relative proximity is used... The comprehensive effectiveness evaluation value of scheme i is used to form the final evaluation conclusion.

[0019] This invention also provides a method for evaluating the effectiveness of aviation support systems based on an improved variable-weight TOPSIS. The advantage of this method lies in constructing a hybrid state-variant weighting function, which significantly enhances the scientific rigor and adaptability of the aviation support effectiveness evaluation system through a dynamic adjustment mechanism. This function can adjust the weight allocation of each indicator factor based on its actual state value, thereby more accurately reflecting the system's true effectiveness. Specifically, when the dominance of an indicator is low, even if it accounts for a large proportion in the conventional weight allocation, the dynamic weight adjustment mechanism will significantly reduce its contribution to overall effectiveness. Conversely, when an indicator exhibits high dominance, even if its conventional weight is low, the variable weighting function will correspondingly increase its weight. This dynamic adjustment characteristic is particularly helpful for in-depth analysis of the characteristics of various aviation support system schemes, clearly identifying the strengths and weaknesses of each scheme, and providing a clear direction for scheme optimization.

[0020] Furthermore, this invention uses the dispersion and adjustment degree in variable weighting theory as key conditions for evaluating the rationality of state variable weighting functions. The lower the equilibrium degree of the factor group's state, that is, the greater the dispersion of the index state vector, the stronger the adjustment capability of the required state variable weighting function should be. Establishing this correspondence makes the construction of variable weighting functions more scientific and reasonable, enabling the application of appropriate adjustment force to index state vectors with different discrete characteristics.

[0021] Finally, this invention organically integrates the aforementioned innovative variable weighting mechanism with the TOPSIS evaluation method, forming a complete evaluation system. By calculating the relative closeness of each scheme to the ideal solution using the TOPSIS method, and with the support of variable weighting theory, this relative closeness can more realistically and comprehensively reflect the overall level of the scheme, providing strong support for the optimal selection of schemes in aviation support systems. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 This is a flowchart of the evaluation method of the present invention; Figure 2 This is a structural diagram of the performance evaluation indicators for the aviation support system of the present invention; Figure 3 This is a comparison chart of dispersion and adjustment degree in an embodiment of the present invention; Figure 4 This is a comparison chart of the evaluation results of constant weights and variable weights in an embodiment of the present invention. Detailed Implementation

[0024] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0025] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0026] like Figure 1 As shown, this invention provides a method for evaluating the effectiveness of aviation support systems based on an improved variable weight TOPSIS, specifically including the following steps: The evaluation objects of aviation support effectiveness are clearly defined, an evaluation index system for aviation support effectiveness is established, a judgment matrix is ​​constructed based on the degree of importance among the indicators, and the constant weights of the indicators are calculated based on the judgment matrix using the analytic hierarchy process. Based on the aviation support effectiveness evaluation index system, data of each index are collected and standardized to obtain the optimal value set and the worst value set of the index. The projected value of the difference between the index disadvantage information and the advantage information is converted into the index advantage degree, and an index advantage degree matrix is ​​constructed. A state-variable weighting function is constructed, and the dispersion and adjustment degree of the indicator factors are calculated based on the indicator dominance matrix to measure the rationality of the state-variable weighting function. The constant weights of the indicators are obtained based on the analytic hierarchy process, and then the variable weights of each indicator are calculated. Based on the variable weights of each indicator and combined with the TOPSIS method, the set of optimal values ​​and the set of worst values ​​of all indicators are defined as the absolute ideal solution. The effectiveness evaluation results of the aviation support system are calculated based on the relative closeness of each indicator to the absolute ideal solution.

[0027] Furthermore, aviation support effectiveness evaluation indicators are criteria used to measure the quality of aviation support work. A scientific and reasonable indicator system should meet the following principles: Simplicity. When the evaluation requirements can be met, the system's security performance should be evaluated using as few indicators as possible to avoid unnecessary complexity.

[0028] Independence. The indicators in the indicator system should be as independent as possible, and overlap and strong statistical correlation between indicators should be avoided as much as possible.

[0029] Objectivity. The indicators should objectively reflect the characteristics of the aviation support system, respecting the objectivity of the aviation support system, rather than being influenced by personal subjective opinions.

[0030] Targeted. The indicators should effectively reflect the characteristics and content of various aviation support scenarios, and demonstrate the system characteristics of carrier-based aircraft take-off and landing, support, etc.

[0031] Holistic approach. Since the aviation support system is a typical complex and large system, the selected indicators should be able to fully reflect the overall effectiveness of the system.

[0032] Measurability. Indicators that are easy to measure, determine, and quantify should be selected as much as possible, avoiding indicators with unavailable data or excessive measurement difficulty, in order to facilitate subsequent evaluation, analysis, and comparison.

[0033] Furthermore, in the process of evaluating the effectiveness of complex systems, in order to eliminate the influence of different dimensions on the weighting of indicators and comprehensive evaluation, it is usually necessary to standardize the original indicator data.

[0034] Data of each underlying indicator Composition of evaluation matrix Then the corresponding The value of is in the form of:

[0035] in, It can be in three forms: precise number, interval number, and fuzzy number, corresponding to respectively The same evaluation indicator takes the same value type under different evaluation objects.

[0036] Because each evaluation indicator has a different physical meaning, the definition is... and Divided into subscript sets representing extremely large and extremely small indicators, the standardized matrix is .

[0037] For precise numbers Consistency handling is as follows:

[0038] in, .

[0039] For interval numbers Consistency handling is as follows:

[0040] in, .

[0041] For fuzzy numbers Consistency handling is as follows:

[0042] in, .

[0043] Because there are multiple types of indicators, it is difficult to directly construct a state-weighted function using them as variables. Therefore, this invention, referencing the basic idea of ​​the gray target decision-making method, defines the positive and negative targets as ideal solutions, and introduces the distance from the indicator evaluation value to the positive target as indicator disadvantage information, and the distance to the negative target as indicator advantage information. Based on this, the Gaussian criterion is further applied to project the difference between disadvantage and advantage information into a unified indicator advantage degree, thereby achieving a comprehensive measurement of the indicator's superiority or inferiority. set up To standardize the evaluation matrix, the positive and negative bullsimiles for different types of indicators are defined as follows: , ; in, The corresponding standardized indicator data are:

[0044] in, The corresponding standardized indicator data are:

[0045] Therefore, the indicator dominance matrix for:

[0046] in, , These are information on the indicator's weaknesses and information on its strengths. The distance from the positive target center to the negative target center. This is the dominance adjustment coefficient, and , The larger the value, the greater the adjustment for the indicator's dominance; and the distance is calculated as follows: Precision number

[0047]

[0048] Interval number

[0049]

[0050] Fuzzy Number

[0051]

[0052] Furthermore, based on the definition of the equilibrium function and the axiomatic definition of the change weight theory, a state equilibrium function is constructed. The basic principle of the change weight theory is that a set of m-dimensional change weights corresponds to m mappings. , ,satisfy: Normalization: that is ; Continuity: that is Continuity with respect to each variable; Monotonicity: that is about Decreasing (punitive contingency) or increasing (incentive contingency); Therefore, the hybrid state-weighted function constructed in this invention is as follows:

[0053] in, The standard deviation of the indicator factors. The factor state value of the indicator. For the number of indicators, These are the critical values ​​for the indicator states of punishment and incentive, respectively. The K-means algorithm was used to determine that the indicator state factor data were clustered into three categories. To increase the severity of punishment, The constant weights of each indicator calculated by the analytic hierarchy process are verified to satisfy the properties of continuity and monotonicity. The resulting state-variable weight vector satisfies the properties defined in the definition and also meets the requirements of increasing attention to extreme indicators, ensuring that the incentive magnitude is less than the penalty magnitude, and not deviating from the constant weight constraint.

[0054] Furthermore, the variable weight of the j-th indicator in the i-th evaluation scheme is:

[0055] Since different variable weight equilibrium functions have different applicable ranges, the concepts of dispersion and adjustment degree are introduced into variable weight. Dispersion measures the degree of equilibrium of the factor group, and adjustment degree measures the ability of the state variable weight vector to adjust the weight. The lower the degree of equilibrium of the factor group, the greater the adjustment force of the state variable weight vector is required, and vice versa.

[0056] If the state value of the indicator factor is The dispersion is then:

[0057] The degree of adjustment is:

[0058] in, The constant weights of each indicator, The variable weights of the indicators are obtained from the state variable weight function.

[0059] When state values ​​are equal, the factor states maintain absolute equilibrium. Therefore, dispersion reflects the degree of deviation between the factor state vector and absolute equilibrium; the larger the dispersion value, the lower the configuration equilibrium. The magnitude of the accommodation reflects the weight adjustment capability of the state-variable vector; the larger the accommodation, the more weights are transferred.

[0060] Furthermore, the TOPSIS method is used to evaluate the effectiveness of the aviation support system. TOPSIS is a multi-objective decision analysis method that measures the distance between the evaluated objective and the positive and negative ideal solutions in the research sample, calculating the proximity (relative distance to the positive and negative ideal solutions) of each objective. The closer the proximity is to 1, the closer the evaluated object is to the optimal level; the closer it is to 0, the closer the object is to the worst level. The specific calculation steps are as follows: Establish the initial evaluation matrix Suppose there are n task plans, each with m evaluation metrics, resulting in an initial evaluation matrix A:

[0061] Establish a standardized evaluation matrix A standardized evaluation matrix is ​​obtained based on the standardization of the three indicator types in S2. ; Establish a weighted standardized decision matrix Weighted Standardized Decision Matrix From the standardized decision matrix Variable weighting of indicators Multiplying them together gives:

[0062] Calculate the distance between each evaluation scheme and the positive and negative ideal solutions. Using Euclidean distance To measure the relative closeness of the i-th solution to the positive and negative ideal solutions:

[0063] The relative similarity of each evaluation scheme is calculated as the evaluation result. The relative similarity of each task plan is calculated as follows:

[0064] This method compares the positive and negative ideal solutions of the data itself. The relative closeness is not sensitive to the absolute value of the data and has good robustness to outliers. This makes the evaluation results still maintain good stability when the data distribution changes. Therefore, the relative closeness is used as the comprehensive evaluation result of the effectiveness evaluation of the aviation support system.

[0065] Please refer to Figure 3 By reviewing relevant literature and analyzing factors affecting carrier-based aircraft aviation support, the effectiveness of the aviation support system is divided into four components: deck support capability, mission completion capability, takeoff support capability, and arrested landing support capability, which serve as primary indicators. The aviation support system effectiveness evaluation indicator system is as follows: Figure 3 As shown, the quantitative indicators are obtained from simulation calculations, while some qualitative indicators are determined manually.

[0066] Next, the constant weights of each bottom-level indicator were determined using the analytic hierarchy process, as shown in Table 1.

[0067] Table 1. Constant Weights of Indicators

[0068] The high-intensity exercises conducted by the USS Nimitz aircraft carrier in 1997 were selected as the evaluation object. To ensure scientific rigor, four scenarios were randomly selected, as shown in the table. These four scenarios were chosen as the evaluation objects. Quantitative indicators were obtained through simulation experiments, and experts were invited to assign evaluation levels to the qualitative indicators based on the evaluation level scale in Table 3. The scores were used to obtain the levels of the qualitative indicators, which were then used as the numerical values ​​of the indicators, as shown in Table 2.

[0069] Table 2 Initial data for indicators

[0070] Table 3. Rating and Fuzzy Number Scale

[0071] The above-mentioned index values ​​can be standardized according to the standardization formulas for each type of index in the invention. In this embodiment, Scheme 1 is used as an example. The standardized index values ​​are shown in Table 3-4: Table 3 Standardized values ​​of indicators for Scheme 1

[0072] Table 4 Standardized values ​​of indicators for Scheme 1

[0073] Next, construct the indicator dominance matrix. as follows:

[0074] Cluster analysis of the indicator dominance matrix using the K-means algorithm yields the following results: The values ​​are -0.017 and 0.026 respectively. The index weights can then be calculated using the state-weighted function, as shown in Table 5.

[0075] Table 5. Variable Weights of Indicators

[0076] Simultaneously, the dispersion and adjustment degree of the four weighting schemes can be calculated. Please refer to [reference needed]. Figure 3 As can be seen, the greater the dispersion of the index state vector, the greater the degree of adjustment. Therefore, the state weighting function of this invention is more reasonable.

[0077] Based on the obtained variable weights of the indicators, and combined with the TOPSIS method, the evaluation results of each scheme can be calculated. Please refer to [link / reference needed]. Figure 4 Compared with the results calculated using constant weights, the evaluation results of Schemes 1 and 3 are improved, while the evaluation results of Schemes 2 and 4 are decreased. During the weighting process, the inferior indicators were penalized and the superior indicators were incentivized. Since the superiority of indicators X31, X32, and X43 in Scheme 4 is lower than that in Schemes 1 and 3, in order to reflect the impact of the inferior indicators on the overall performance reduction, a penalty mechanism was adopted to increase the weight of these indicators, so that the overall performance after the weight adjustment is lower than that of Schemes 1 and 3.

[0078] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0079] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0080] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for evaluating the effectiveness of aviation support systems based on an improved variable weight TOPSIS, characterized in that: The evaluation objects of aviation support effectiveness are clearly defined, an evaluation index system for aviation support effectiveness is established, a judgment matrix is ​​constructed based on the degree of importance among the indicators, and the constant weights of the indicators are calculated based on the judgment matrix using the analytic hierarchy process. Based on the aviation support effectiveness evaluation index system, data of each index are collected and standardized to obtain the optimal value set and the worst value set of the index. The projected value of the difference between the index disadvantage information and the advantage information is converted into the index advantage degree, and the index advantage degree matrix is ​​constructed. A state-variable weighting function is constructed, and the constant weights of the indicators are obtained based on the analytic hierarchy process (AHP). Then, the variable weights of each indicator are calculated. The dispersion and regulation of the indicator factors are calculated based on the indicator dominance matrix to measure the rationality of the state-variable weighting function. Based on the variable weights of each indicator and combined with the TOPSIS method, the set of optimal values ​​and the set of worst values ​​of all indicators are defined as the absolute ideal solution. The effectiveness evaluation results of the aviation support system are calculated based on the relative closeness of each indicator to the absolute ideal solution. The collected index data is standardized to obtain an optimal value set and a worst value set of the index, and a standardized evaluation matrix is obtained Based on the grey target decision-making idea, the distance from the index value to the positive and negative target centers is defined as the index advantage information and the index disadvantage information, the projection value of the difference between the index advantage information and the disadvantage information is converted into the index advantage degree by using the Gaussian criterion, and the index advantage degree matrix Is: wherein, , are index disadvantage information and index advantage information, respectively, is a distance from a positive bullseye to a negative bullseye, is an advantage degree adjustment coefficient, and ; Based on the definition of the equilibrium function and the axiomatic definition of the change weighting theory, the following state change weighting function is constructed: in, The standard deviation of the indicator factors. The factor state value of the indicator. For the number of indicators, These are the critical values ​​for the indicator states of punishment and incentive, respectively. To increase the severity of punishment, These are the constant weights of each indicator calculated using the analytic hierarchy process (AHP). No. i The first evaluation scheme j The variable weights for each indicator are: Since different variable weight equilibrium functions have different applicable ranges, the concepts of dispersion and adjustment degree are introduced into variable weight. Dispersion is used to measure the degree of equilibrium of factor group, and adjustment degree is used to measure the ability of state variable weight vector to adjust weight. If the state value of the indicator factor is The dispersion is then: The degree of adjustment is: in, The constant weights of each indicator, The variable weights of the indicators are obtained from the state variable weight function.

2. The method for evaluating the effectiveness of an aviation support system based on an improved variable weight TOPSIS according to claim 1, characterized in that: The principles for constructing the aviation support effectiveness evaluation index system are clarified. The effectiveness indicators are screened, analyzed, and decided upon. Key evaluation elements are extracted to build an orderly hierarchical relationship, thereby constructing the aviation support effectiveness evaluation index system.

3. The method for evaluating the effectiveness of an aviation support system based on an improved variable weight TOPSIS according to claim 2, characterized in that: Standardized evaluation matrix The standardized data and the ideal solution are assigned corresponding variable weights to obtain the weighted standardized decision matrix. Using Euclidean distance To measure the relative closeness of the i-th solution to the positive and negative ideal solutions. : in, Let be the distance from the i-th solution to the corresponding positive ideal solution. For the first i The distance from each solution to the corresponding negative ideal solution is used in the performance evaluation of aviation support systems to determine the relative proximity. As a solution i The comprehensive performance evaluation value forms the performance assessment result of the aviation support system.

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