Multi-region environment aviation maintenance support capability assessment method and system

Abstract

The invention relates to the field of equipment maintenance support, and relates to a multi-region environment aviation maintenance support capability evaluation method and system, and the method comprises the steps: constructing an aviation maintenance support capability evaluation index system; calculating the relative weight of each first-level index and the relative weight of each second-level index by using an analytic hierarchy process (AHP), and further calculating the comprehensive weight of each second-level index; calculating the combined weight of each secondary index; constructing a multi-target decision matrix based on the measured data matrix of each regional environment, performing weighting processing on the multi-target decision matrix by using the combined weight of the secondary indexes in each regional environment to obtain a weighted decision matrix, and calculating the assessment closeness of each regional environment by using an approximate ideal solution sorting method based on the weighted decision matrix, and sorting the aviation maintenance support capability of each regional environment according to the evaluation closeness. By adopting the method, the scientificity and the credibility of the aviation maintenance support capability evaluation result can be improved.

Description

A method and system for assessing aircraft maintenance support capabilities in multiple geographical environments Technical Field

[0001] This invention relates to the field of equipment maintenance and support. More specifically, this invention relates to a method and system for assessing aviation maintenance and support capabilities in multi-regional environments. Background Technology

[0002] Aviation maintenance support capability is a crucial logistical support for maintaining the availability of aviation equipment and ensuring the success rate of flight missions. With the diversification of mission requirements, aviation equipment frequently performs missions across regions, facing challenges from various complex geographical environments such as plateaus, deserts, cold regions, and islands. Different geographical environments, through physical factors such as temperature, humidity, and air pressure, have significant and varying impacts on equipment performance, the physiological and psychological state of maintenance personnel, and the efficiency of material resupply. However, existing aviation maintenance support capability assessment technologies suffer from the following main problems: First, the existing assessment indicator system is not well-developed, often employing general evaluation models that fail to fully consider the coupling and constraints of specific geographical environments on the maintenance support system. In other words, existing technologies lack a set of indicator systems that can systematically reflect the interaction between "environment-maintenance-equipment," resulting in insufficient specificity and sensitivity of assessment results in cross-regional scenarios.

[0003] Secondly, in terms of indicator weighting, existing methods mostly employ a single weighting model. The Analytic Hierarchy Process (AHP), which relies solely on expert scoring, is easily influenced by subjective preferences and lacks support from objective data. While the Entropy Method (EWM), which relies solely on data statistics, is objective, it may overlook certain indicators (expert experience) that have small data fluctuations but are crucial in practical assurance. The lack of a combined weighting mechanism that can effectively balance subjective experience and objective measured data leads to an unscientific allocation of weights.

[0004] Finally, existing technologies lack methods for horizontal comparison and quantitative ranking of support capabilities across multiple geographical regions. Traditional assessments often score individual units or environments, making it difficult to simultaneously rank the support capabilities of multiple different geographical environments at a macro level. Commanders cannot intuitively understand the differences in support shortcomings across various geographical environments, making it difficult to formulate targeted cross-regional support resource allocation plans. Summary of the Invention

[0005] To address the technical problems described in the background section, the present invention provides solutions in the following aspects.

[0006] In a first aspect, the present invention provides a method for assessing aviation maintenance support capabilities in multiple geographical environments, comprising: constructing an aviation maintenance support capability assessment index system, including primary indicators and secondary indicators belonging to the primary indicators, wherein the primary indicators include equipment status, maintenance support work, personnel status, and resource support; calculating the relative weights of each primary indicator and each secondary indicator using the analytic hierarchy process (AHP), and then calculating the comprehensive weight of each secondary indicator; and establishing a measured data matrix for each geographical environment, wherein for a given geographical environment, the expression of the measured data matrix is: In the formula, This is the measured data of the j-th secondary indicator of the i-th maintenance unit in this regional environment, where m represents the number of maintenance units in this regional environment and n represents the number of secondary indicators corresponding to a single maintenance unit. For each regional environment, based on the measured data matrix, the objective weight of each secondary indicator is calculated using the entropy method, and based on the comprehensive weight and objective weight of the secondary indicators, the combined weight of each secondary indicator is calculated using the multiplication combination method. A multi-objective decision matrix is ​​constructed based on the measured data matrix of each regional environment. The multi-objective decision matrix is ​​weighted using the combined weight of the secondary indicators in each regional environment to obtain a weighted decision matrix. Based on the weighted decision matrix, the assessment closeness of each regional environment is calculated using the approximation ideal solution ranking method, and the aviation maintenance support capabilities of each regional environment are ranked according to the assessment closeness.

[0007] Preferably, the secondary indicators set under the equipment status include: equipment failure rate, repair availability rate, deployment response rate, and equipment protection difficulty; the secondary indicators set under the maintenance support work include: maintenance support complexity, organizational management difficulty, work quality pass rate, and maintenance support support level; the secondary indicators set under the personnel status include: professional and technical qualification rate, personnel error rate, psychological adaptability, and physiological health; the secondary indicators set under the resource support include: maintenance equipment availability rate, timeliness of aviation material and spare parts supply, accessibility of material and equipment transportation, and maintenance facility perfection rate.

[0008] Preferably, calculating the relative weights of each primary indicator and each secondary indicator includes: grouping all primary indicators into one group, grouping secondary indicators under the same primary indicator into the same group, and calculating the corresponding relative weights for the indicators within each group; for a given group of indicators, the method for calculating the relative weights includes: obtaining a comparison matrix of judgments from m experts on the group of indicators from the database; multiplying the elements at the same position in each expert's comparison matrix and taking the mth root to obtain new integrated data, and then obtaining the judgment integration matrix; based on the judgment integration matrix, calculating the relative weights of the group of indicators using the square root method, the calculation expression is: In the formula, This represents the relative weight of the i-th indicator within the group of indicators. This represents the element in the i-th row and j-th column of the integration matrix; calculate the consistency ratio CR of the comparison matrix, and if CR < 0.1, confirm that the relative weights are valid.

[0009] Preferably, the formula for calculating the comprehensive weight of each secondary indicator is as follows: In the formula, This represents the combined weight of the j-th secondary indicator under the i-th primary indicator. This represents the relative weight of the i-th primary indicator. This represents the relative weight of the j-th secondary indicator under the i-th primary indicator.

[0010] Preferably, the consistency ratio CR of the comparison matrix is ​​calculated using the formula CR = CI / RI; where RI is the average random consistency index; and CI is the consistency index, calculated using the following formula: In the formula, To determine the largest eigenvalue of the comparison matrix, where n is the order of the comparison matrix, the expression for calculating the largest eigenvalue of the comparison matrix is: In the formula, To determine the integrated matrix, This represents the relative weight vector of the indicators at this level. For matrix The j-th component.

[0011] Preferably, before calculating the objective weights, the method further includes: standardizing the objective measured data, wherein the standardization formula is: for positive indicators, the expression for calculating the standardized value is: For negative indicators, the standardized value is calculated as follows: In the formula, This represents the standardized value of the j-th secondary indicator for the i-th repair unit. This represents the measured data of the j-th secondary indicator for the i-th repair unit. This represents the minimum value of the j-th evaluation index among all maintenance units in a specific geographical environment. This represents the maximum value of the j-th evaluation index among all maintenance units in a certain geographical environment.

[0012] Preferably, the calculation of the objective weights of each secondary indicator using the entropy method includes: calculating the standardized value defined for each secondary indicator, expressed as: In the formula, Let represent the percentage of the j-th secondary indicator for the i-th repair unit; calculate the entropy value of the secondary indicator using the following expression: ;formula, Let represent the entropy value of the j-th secondary indicator; calculate the information entropy redundancy of the secondary indicator using the following expression: In the formula, Let represent the information entropy redundancy of the j-th secondary indicator; calculate the objective weight of the secondary indicator using the following expression: In the formula, This represents the objective weight of the j-th secondary indicator.

[0013] Preferably, the formula for calculating the combined weights of the secondary indicators is as follows: In the formula, This represents the combined weight of the j-th secondary indicator. This represents the comprehensive weight of the j-th secondary indicator. This represents the objective weight of the j-th secondary indicator.

[0014] Preferably, the number of geographical environments is p, and constructing the multi-objective decision matrix includes: calculating the average value of each secondary indicator in each geographical environment, wherein the average value of the j-th secondary indicator in a certain geographical environment is calculated by averaging the j-th secondary indicator of each maintenance unit in that geographical environment; constructing a multi-objective decision matrix D based on the average value of each secondary indicator in each geographical environment, with the expression:

[0015] In the formula, Let represent the average value of the j-th secondary indicator in the i-th regional environment; the weighted decision matrix is ​​obtained by weighting the multi-objective decision matrix using the combined weights of the secondary indicators in each regional environment, including: standardizing and normalizing the elements of the multi-objective decision matrix to obtain the processed multi-objective decision matrix; and multiplying the elements in the processed multi-objective decision matrix by the combined weights of the corresponding secondary indicators to obtain the weighted decision matrix.

[0016] In a second aspect, the present invention provides a multi-regional environment aviation maintenance support capability assessment system, the multi-regional environment aviation maintenance support capability assessment system comprising a processor and a memory, the memory storing computer program instructions, which, when executed by the processor, implement the multi-regional environment aviation maintenance support capability assessment method of the present invention.

[0017] The beneficial effects of this invention are as follows: The method of this invention overcomes the problem of existing models not fully considering the impact of regional environmental differences by constructing an indicator system that includes equipment status, maintenance and support work, personnel status, and resource support. It can comprehensively reflect the multidimensional constraints of different regional environments (such as plateaus, frigid zones, and islands) on the aviation maintenance and support system. Furthermore, by comprehensively utilizing subjective weights calculated using the Analytic Hierarchy Process (AHP) and objective weights calculated using the Entropy Method (EWM), and fusing them through a multiplicative combination method, this method retains the rich experience of experts while reflecting the objective variation information of measured data, avoiding the one-sidedness of a single weighting method and improving the scientificity and credibility of the evaluation results. Moreover, by using the Top-Approximation Ideal Solution Ranking (TOPSIS) method to process the multi-objective decision matrix, it can calculate the degree of closeness between each regional environment and the ideal state, thereby enabling quantitative ranking and horizontal comparison of aviation maintenance and support capabilities under multiple different regional environments, providing commanders with an intuitive decision-making basis for cross-domain support force deployment. Attached Figure Description

[0018] Figure 1 is a schematic flowchart illustrating the multi-regional environment aviation maintenance support capability assessment method according to an embodiment of the present invention; Figure 2 is a schematic flowchart illustrating the multi-regional environment aviation maintenance support capability assessment method according to an embodiment of the present invention; Figure 3 is a schematic diagram illustrating the environment-maintenance-equipment three-ring model according to an embodiment of the present invention; Figure 4 is a schematic diagram illustrating the indicator system according to an embodiment of the present invention; Figure 5 is a schematic diagram illustrating the combined weight calculation results of the secondary indicators according to an embodiment of the present invention; Figure 6 is a schematic diagram illustrating the proximity of the four types of regional environments according to an embodiment of the present invention; Figure 7 is a schematic diagram illustrating the structure of the multi-regional environment aviation maintenance support capability assessment system according to an embodiment of the present invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] The specific embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0021] Example of a Multi-Regional Environment Aviation Maintenance Support Capability Assessment Method: As shown in Figures 1 and 2, the multi-regional environment aviation maintenance support capability assessment method of the present invention includes: S101, constructing an aviation maintenance support capability assessment index system, specifically: constructing an aviation maintenance support capability assessment index system, including primary indicators and secondary indicators belonging to the primary indicators. The primary indicators include equipment status, maintenance support work, personnel status, and resource support. In this embodiment, the aviation maintenance support capability assessment index system is established based on the environment-maintenance-equipment three-ring model, as shown in Figure 3. Based on the environment-maintenance-equipment three-ring model, the regional environment is taken as the outer driving factor, and then the impact of the maintenance support system is mapped to the impact of personnel status performance and resource support. Finally, the impact of equipment is mapped to the equipment status manifestation in the regional environment. Therefore, four primary assessment indicators can be constructed: equipment status (U1), maintenance support work (U2), personnel status (U3), and resource support (U4). The specific mapping relationship is shown in Figure 3.

[0022] Individual equipment, weapon systems, and equipment systems all have environmental adaptability requirements. In a fixed geographical environment, aviation equipment is the core target of maintenance and support. Maintenance and support personnel, with the help of maintenance and support resources, ensure the integrity of aircraft through maintenance and support work with varying requirements. Once entering a different geographical environment, the balance of this existing maintenance and support system can easily be disrupted, thereby reducing maintenance and support capabilities. The geographical environment, maintenance and support personnel, and support resources constitute the most closely related components of the maintenance and support system. Firstly, the coupling effect of multiple environmental factors such as temperature, humidity, wind, sandstorms, rain, snow, altitude, terrain, and transportation directly affects maintenance personnel, aviation materials, and maintenance equipment, thus impacting the performance status of core aviation equipment under inefficient maintenance and support system operation. Therefore, a three-ring model of "environment-maintenance-equipment" can be constructed (as shown in Figure 3) to present the operational mechanism of aviation maintenance and support in a geographical environment, laying the foundation for a subsequent aviation maintenance and support capability assessment index system.

[0023] In this embodiment, the specific indicator system is shown in Figure 4. The secondary indicators set under the equipment status include: equipment failure rate, repair availability rate, deployment response rate, and equipment protection difficulty; the secondary indicators set under the maintenance support work include: maintenance support complexity, organizational management difficulty, work quality pass rate, and maintenance support support level; the secondary indicators set under the personnel status include: professional and technical qualification rate, personnel error rate, psychological adaptability, and physiological health; the secondary indicators set under the resource support include: maintenance equipment availability rate, timeliness of aircraft parts supply, accessibility of material and equipment transportation, and maintenance facility completeness rate.

[0024] Among the secondary indicators, five indicators—equipment failure susceptibility (U11), equipment protection difficulty (U14), maintenance and support complexity (U21), organizational management difficulty (U22), and error susceptibility (U32)—are inverse indicators, while the others are positive indicators. The greater the weight of an indicator, the more important it is in the assessment, and the more prominent its impact on aviation maintenance and support capabilities is in the context of geographical environment. These indicators require special attention in maintenance and support decision-making.

[0025] The primary indicators are further refined into specific secondary indicators such as "equipment failure rate," "maintenance and support complexity," "professional and technical qualification rate," and "timeliness of aircraft material and spare parts supply." This detailed approach covers the entire chain, from equipment physical performance to personnel physical and mental well-being, and then to logistics resources. This makes the evaluation system highly targeted and operable, enabling it to accurately pinpoint specific shortcomings that constrain support capabilities in particular geographical environments.

[0026] S102. Calculate the comprehensive weight of each secondary indicator, specifically by using the analytic hierarchy process (AHP) to calculate the relative weight of each primary indicator and the relative weight of each secondary indicator, and then calculating the comprehensive weight of each secondary indicator. In this embodiment, the expression for calculating the comprehensive weight of each secondary indicator is: In the formula, This represents the combined weight of the j-th secondary indicator under the i-th primary indicator. This represents the relative weight of the i-th primary indicator. This represents the relative weight of the j-th secondary indicator under the i-th primary indicator.

[0027] This embodiment clarifies the calculation path for the comprehensive weight, which combines the relative weight of the secondary indicator with the relative weight of its corresponding primary indicator. This step transforms the weight from a local perspective into a global perspective, ensuring that all secondary indicators are on the same order of magnitude and evaluation benchmark when subsequently combined with objective weights.

[0028] S103. Construct the measured data matrix for each regional environment. Specifically, establish the measured data matrix for each regional environment. For a given regional environment, the expression for the measured data matrix is: In the formula, S104. Calculate the combined weights of each secondary indicator in each regional environment. Specifically, for each regional environment, based on the measured data matrix, calculate the objective weights of each secondary indicator using the entropy method. Then, based on the comprehensive weights and objective weights of the secondary indicators, calculate the combined weights of each secondary indicator using the multiplication combination method. The entropy method determines the indicator weights based on the degree of variation of each indicator value. Compared with the subjective assignment method, it has higher accuracy and stronger objectivity, and can better interpret the results. If the degree of variation of an indicator is smaller, the amount of information it reflects is smaller, and the corresponding weight will not be too high. Conversely, if the degree of variation of an indicator is large, the amount of information it reflects should be given a higher weight.

[0029] There are many methods for calculating combined weights, such as the commonly used additive and multiplicative methods, as well as game theory methods, distance function methods, and range maximization methods, each with its own advantages, disadvantages, and applicable scenarios. Due to the large number of indicators to be evaluated (16 secondary indicators in the indicator system, UII-U44), to highlight the differences in importance between indicators and maintain consistency between the AHP and EWM weights, this embodiment selects the relatively suitable multiplicative combination method to calculate the combined weights of the indicators.

[0030] In this embodiment, the formula for calculating the combined weight of the secondary indicators is as follows: In the formula, This represents the combined weight of the j-th secondary indicator. This represents the comprehensive weight of the j-th secondary indicator. This represents the objective weight of the j-th secondary indicator.

[0031] The multiplicative combination method is used to calculate the combination weights. Compared with the additive combination method, the multiplicative combination method has a "multiplier effect," meaning that the final weight will only be significantly higher when both subjective perception and objective data deem the indicator important. This mechanism can more effectively highlight core constraints, making the weight allocation more in line with actual combat support needs.

[0032] S105. The aviation maintenance support capabilities of different regional environments are ranked, specifically as follows: a multi-objective decision matrix is ​​constructed based on the measured data matrix of each regional environment; the multi-objective decision matrix is ​​weighted by the combined weights of secondary indicators under each regional environment to obtain a weighted decision matrix; the assessment closeness of each regional environment is calculated by the approximation ideal solution ranking method based on the weighted decision matrix; and the aviation maintenance support capabilities of each regional environment are ranked according to the assessment closeness.

[0033] To effectively compare and evaluate aviation maintenance support capabilities in multiple geographical environments, it is necessary to address the real-world problems of multiple objective geographical environments and multiple evaluation indicators. The Top-Ideal Solution Ranking Method (TOPSIS) is widely used to handle multi-indicator and multi-scheme problems. It ranks a finite number of evaluation objects according to their proximity to the ideal objective (optimal solution and worst solution), and is suitable for evaluating the relative merits of multiple schemes.

[0034] This invention overcomes the problem of existing models not fully considering the impact of regional environmental differences by constructing an indicator system that includes equipment status, maintenance and support work, personnel status, and resource support. It comprehensively reflects the multidimensional constraints of different regional environments (such as plateaus, frigid zones, and islands) on the aviation maintenance and support system. Furthermore, it integrates subjective weights calculated using the Analytic Hierarchy Process (AHP) and objective weights calculated using the Entropy Method (EWM), and fuses them through a multiplicative combination method. This method retains the rich experience of experts while reflecting the objective variation information of measured data, avoiding the one-sidedness of a single weighting method and improving the scientific rigor and credibility of the evaluation results. Moreover, by using the Top-Approximation-Ideal-Solution Ranking (TOPSIS) method to process the multi-objective decision matrix, it can calculate the degree of closeness between each regional environment and the ideal state, thereby enabling quantitative ranking and horizontal comparison of aviation maintenance and support capabilities under multiple different regional environments, providing commanders with intuitive decision-making basis for cross-domain support force deployment.

[0035] In one embodiment, calculating the relative weights of each primary indicator and each secondary indicator includes: grouping all primary indicators into one group, grouping secondary indicators under the same primary indicator into the same group, and calculating the corresponding relative weights for the indicators within each group; the relative weight calculation method for a certain group of indicators includes: S201, establishing a hierarchical structure model.

[0036] In this embodiment, the established hierarchical structure model is shown in Figure 4.

[0037] S202. Obtain the judgment comparison matrix of m experts for this group of indicators from the database; compare the relative importance of this group of indicators pairwise, assign values ​​using the 1-9 scaling method, and thus construct the judgment comparison matrix A, the corresponding expression of which is: In the formula, is the relative importance scale value between the former indicator i and the latter indicator j, and k is the total number of indicators at this level.

[0038] S203. Multiply the elements at the same position in the judgment comparison matrix of each expert and take the m-th root to obtain new integrated data, and then obtain the judgment integration matrix; the corresponding calculation expression is: In the formula, Indicates the judgment integration matrix, This represents the integrated data corresponding to the element in the i-th row and j-th column of the comparison matrix.

[0039] For example: Suppose there are 3 experts. The element in the i-th row and j-th column of the judgment comparison matrix of the first expert has a value of 1, the element in the i-th row and j-th column of the judgment comparison matrix of the second expert has a value of 2, and the element in the i-th row and j-th column of the judgment comparison matrix of the third expert has a value of 3. Then the integrated data corresponding to the element in the i-th row and j-th column of the judgment comparison matrix is: .

[0040] S204. Based on the judgment integration matrix, calculate the relative weights of this group of indicators using the square root method. The calculation expression is as follows: In the formula, This represents the relative weight of the i-th indicator within the group of indicators. This represents the element in the i-th row and j-th column of the integration matrix; S205, calculate the consistency ratio CR of the comparison matrix. If CR < 0.1, the relative weight is confirmed to be valid.

[0041] In this embodiment, if CR ≥ 0.1, the relative weight is determined to be invalid, and the judgment comparison matrix of this level of index is corrected.

[0042] In this embodiment, the consistency ratio CR of the comparison matrix is ​​calculated using the formula CR = CI / RI; where RI is the average random consistency index; and CI is the consistency index, calculated using the following formula: In the formula, To determine the largest eigenvalue of the comparison matrix, To determine the order of the comparison matrix, the expression for calculating the largest eigenvalue of the comparison matrix is: In the formula, To determine the integrated matrix, This represents the relative weight vector of the indicators at this level. For matrix The j-th component.

[0043] In this embodiment, if the order of the comparison matrix is ​​4, the average random consistency index RI is 0.89. The value of the average random consistency index RI is related to the order of the comparison matrix. The relationships between them are shown in Table 1.

[0044] Table 1

[0045] In this embodiment, by obtaining a comparison matrix of multiple experts' judgments on the indicators and using the geometric mean method (multiplying elements and taking the m-th power) to generate an integrated matrix, the randomness and bias of individual expert subjective judgments are effectively reduced. Simultaneously, a consistency ratio (CR) verification mechanism is introduced to ensure the rigor of the expert scoring logic and guarantee the validity of the basic weight data.

[0046] In one embodiment, before calculating the objective weights, the method further includes: standardizing the objective measured data, using the following formula: For positive indicators, the standardized value is calculated as follows: For negative indicators, the standardized value is calculated as follows: In the formula, This represents the standardized value of the j-th secondary indicator for the i-th repair unit. This represents the measured data of the j-th secondary indicator for the i-th repair unit. This represents the minimum value of the j-th evaluation index among all maintenance units in a specific geographical environment. This represents the maximum value of the j-th evaluation index among all maintenance units in a certain geographical environment.

[0047] In this embodiment, to ensure the effectiveness of the standardized index, the standardized value is increased by 0.0001.

[0048] Before calculating objective weights, the measured data are standardized, with different processing formulas used for positive and negative indicators. This eliminates differences in units (such as percentage, time, frequency) and orders of magnitude between different indicators, unifies the data direction, and ensures that multi-source heterogeneous data can be comprehensively calculated.

[0049] In one embodiment, the calculation of the objective weights of each secondary indicator using the entropy method includes: S301, calculating the standardized value of the secondary indicator by definition, the calculation expression being: In the formula, This represents the percentage of the j-th secondary indicator for the i-th repair unit; S302, calculate the entropy value of the secondary indicator, the calculation expression is: ;formula, S303. Calculate the information entropy redundancy of the j-th secondary indicator; the calculation expression is: In the formula, S304. Calculate the objective weight of the secondary indicator. The calculation expression is: In the formula, This represents the objective weight of the j-th secondary indicator.

[0050] The entropy method is used to determine objective weights by calculating the entropy value and information entropy redundancy of indicators. This method is entirely based on the dispersion of data and can automatically identify key indicators with large data differences and a large amount of information, overcoming the blind spots that may exist in manual weighting.

[0051] In one embodiment, the number of geographical environments is p, and constructing the multi-objective decision matrix includes: S401, calculating the average value of each secondary indicator in each geographical environment, wherein the average value of the j-th secondary indicator in a certain geographical environment is calculated by averaging the j-th secondary indicator of each maintenance unit in that geographical environment; S402, constructing the multi-objective decision matrix D based on the average value of each secondary indicator in each geographical environment, with the expression:

[0052] In the formula, This represents the average value of the j-th secondary indicator of the i-th regional environment.

[0053] By calculating the mean of maintenance unit data under different regional environments to construct a multi-objective decision matrix, the transformation from micro-level "maintenance unit" data to macro-level "regional environment" characteristics was successfully achieved. This allows the TOPSIS algorithm to directly evaluate and rank the abstract object of "regional environment," meeting the needs of cross-regional macro-level decision-making.

[0054] The weighted decision matrix is ​​obtained by weighting the multi-objective decision matrix using the combined weights of secondary indicators under various regional environments. This includes: S501, standardizing and normalizing the elements of the multi-objective decision matrix to obtain the processed multi-objective decision matrix; if the average value of the j-th secondary indicator in the i-th regional environment... As a positive indicator, its standardized calculation expression is as follows: In the formula, This represents the standardized value of the j-th secondary indicator of the i-th regional environment. This represents the value corresponding to the smallest element in the multi-objective decision matrix. This represents the value corresponding to the element with the largest value in the multi-objective decision matrix.

[0055] If the average value of the j-th secondary indicator of the i-th regional environment As a negative indicator, its standardized calculation expression is as follows: .

[0056] The standardized value of the j-th secondary indicator of the i-th regional environment is normalized, and the corresponding calculation expression is: In the formula, This represents the normalized value of the j-th secondary indicator of the i-th regional environment.

[0057] S502. Multiply the elements in the processed multi-objective decision matrix with the combined weights of the corresponding secondary indicators to obtain the weighted decision matrix.

[0058] The corresponding expression is: In the formula, Z represents the weighted decision matrix. This represents the element at the i-th row and j-th column of the weighted decision matrix (i.e., the weighted value of the normalized value of the j-th secondary indicator of the i-th regional environment). Let p represent the combined weight of the j-th secondary indicator of the i-th regional environment, p represent the number of regional environments, and n represent the number of secondary indicators corresponding to a single maintenance unit.

[0059] For example: Suppose that the element at the i-th row and j-th column of the processed multi-objective decision matrix is ​​5 (that is, the weighted value of the normalized value of the j-th secondary indicator of the i-th regional environment is 5), and the combined weight of the j-th secondary indicator of the i-th regional environment is 0.1, then the element at that position (i-th row and j-th column) in the obtained weighted decision matrix is ​​0.5.

[0060] In one embodiment, calculating the assessment closeness of each regional environment based on the weighted decision matrix using the approximation ideal solution ranking method, and ranking the aviation maintenance support capabilities of each regional environment according to the assessment closeness includes: S601, determining the optimal solution vector of the weighted decision matrix. and worst solution vector The corresponding calculation expression is: ; ; (This vector represents the maximum value selected for each indicator based on a comprehensive comparison of all regional environments.) (This vector represents the minimum value selected for each indicator based on a comprehensive comparison of all regional environments); the formula, This represents the element in the p-th row and j-th column of the weighted decision matrix. This represents the maximum value of the element in the j-th column of the weighted decision matrix. This represents the minimum value of the element in the j-th column of the weighted decision matrix.

[0061] S602. Calculate the distances between each regional environment and the optimal and worst solutions using Euclidean distance. The expression for calculating the distance between the i-th regional environment and the optimal solution is: The expression for calculating the distance between the i-th regional environment and the worst solution is: S603. Calculate the proximity T of each typical regional environment based on distance values. i The calculation expression is: In the formula, T iThis represents the proximity of the i-th regional environment.

[0062] Finally, the proximity Ti of aviation maintenance support capabilities in each typical regional environment was ranked. The closer Ti is to 1, the stronger the aviation maintenance support capability in that regional environment, the better the equipment and personnel status, the more favorable the maintenance support work, and the lower the difficulty and better the effect of resource support.

[0063] The following specific examples verify the method of this invention: Aviation maintenance support is usually based at airports. To compare and evaluate aviation maintenance support capabilities in multiple geographical environments, based on the relatively prominent geographical characteristics of my country, four types of airports located in different climatic and geographical environments are selected for the example. Airport A is a high-altitude airport, representing a typical plateau environment; Airport B is an airport on the edge of a desert, representing a typical desert environment; Airport C is an airport located in the north, representing a typical cold region environment; and Airport D is an airport located at sea, representing an island environment.

[0064] (1) Calculate the AHP weights.

[0065] Based on the established indicator system, the relative importance of the four primary indicators and the four groups of secondary indicators were scaled and assigned values. Each group of indicators formed 15 judgment comparison matrices. Then, the relative weights of the 20 indicators were calculated sequentially according to formulas (2)–(3). The CR of the five groups of indicators was less than 0.1, and the consistency was verified. The AHP comprehensive weight of the secondary indicators of aviation maintenance support capability under the comprehensive regional environment was calculated according to formula (6). The calculation results are shown in Table 2.

[0066] Table 2

[0067] (2) Calculate the EWM weights (i.e., objective weights).

[0068] Entropy weights of aviation maintenance support capability indicators derived in four typical regional environments As shown in Table 3.

[0069] Table 3

[0070] (3) Analysis of combined weights and results of AHP-EWM method.

[0071] The combined weights of AHP and EWM in each regional environment are combined and weighted separately to calculate the combined weight wj in each regional environment. The combined weight calculation results of the first-level indicators are shown in Table 4, and the combined weight calculation results of the second-level indicators are shown in Figure 5.

[0072] Table 4

[0073] As shown in Table 4 and Figure 5, among the common regional environmental issues, resource support (U4) has the largest weight (47.26%-57.57%), indicating that resource supply support has the most significant impact on aviation maintenance support capabilities, especially its secondary indicators, the timeliness of spare parts supply (U42) and the accessibility of material and equipment transportation (U43). It is necessary to improve the efficiency of spare parts supply and strengthen transportation infrastructure. Secondly, personnel status (U3) has the next largest weight (23.95%-30.16%), particularly the secondary indicator, personnel error susceptibility (U32). This indicates that when personnel are subjected to significant changes in climate and environment, they cannot adapt, leading to an imbalance in their work status and inducing various errors, indirectly affecting aviation maintenance support capabilities. Equipment status (U1) has a relatively low weight (12.24%-16.25%), verifying that with the development of aviation equipment and the improvement of reliability, aircraft have seen significant optimization and improvement in adapting to complex regional environments. The fact that maintenance support work (U2) accounts for the lowest proportion indicates that the improvement of the supportability of aviation equipment, the development of maintenance technology, and the standardization of the aviation maintenance support system have reduced the differences in maintenance support under different climates, geography and other environments.

[0074] From the perspective of regional environmental differences, as shown in Figure 5, the equipment failure susceptibility (U11) and equipment protection difficulty (U14) are weighted higher in high-altitude environments than in the other three environments. This indicates that the probability of airborne equipment failure is higher in high-altitude environments, and the complex and changeable climate of high-altitude areas places greater pressure on aircraft protection. In desert environments, the timeliness of spare parts supply (U42) is significantly higher than in other environments. However, this does not mean that the timeliness of spare parts supply is the worst; rather, it indicates that other indicators still perform well in desert environments, but the efficiency of spare parts supply becomes a relative weakness. In cold-region environments, the maintenance support support (U24) and personnel error susceptibility (U32) are weighted higher than in the other three environments. This indicates that prolonged low-temperature environments have a significant impact on personnel's condition. Reduced operational flexibility or frostbite often leads to higher error rates, and the demanding support capabilities are more challenging. Therefore, U24 and U32 require special attention in cold-region environments. In island environments, the combined weights of professional technical compliance rate (U31), material and equipment transportation accessibility (U43), and maintenance facility improvement rate (U44) are higher than in other environments, indicating that the ability to meet the corrosion prevention technical requirements of islands needs to be strengthened. Secondly, due to the distance of islands from the mainland and the susceptibility to extreme weather, the supply of materials and equipment is difficult, which restricts resource turnover. The conditions for construction on islands are limited, and the impact of inadequate maintenance facilities on maintenance working conditions and equipment protection needs to be considered.

[0075] (4) Comprehensive evaluation and analysis of AHP-EWM-TOPSIS.

[0076] Calculate the Euclidean distance and overall proximity T between the secondary indicators and the optimal solution H+ and the worst solution H- under four different regional environments. i A comprehensive comparative evaluation of aircraft maintenance support capabilities in multi-objective geographical environments was conducted. The final results are shown in Figure 6.

[0077] As shown in Figure 6, in terms of distance from the optimal solution H+, the plateau environment is the furthest, followed by islands, cold regions, and deserts, while the opposite is true for the distance from the worst solution H-. Based on the proximity T... i The results show that a larger value indicates a higher degree of superiority in terms of approaching the optimal solution and moving away from the worst solution. Therefore, it can be concluded that the aviation maintenance support capability in the four types of geographical environments, from high to low, is: Desert (T i =0.881) > Cold region (T i =0.699) > Island (T) i =0.088) > Plateau (T) i =0.022), with desert and cold regions showing significantly better performance than island and plateau environments.

[0078] Implementation Example of a Multi-Regional Environment Aviation Maintenance Support Capability Assessment System: This invention also provides a multi-regional environment aviation maintenance support capability assessment system. As shown in Figure 7, the multi-regional environment aviation maintenance support capability assessment system includes a processor and a memory. The memory stores computer program instructions, which, when executed by the processor, implement the multi-regional environment aviation maintenance support capability assessment method according to the first aspect of this invention.

[0079] The multi-regional environment aviation maintenance support capability assessment system also includes other components well known to those skilled in the art, such as communication buses and communication interfaces. Their settings and functions are known in the art and will not be described in detail here.

[0080] While this specification has shown and described numerous embodiments of the invention, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Many modifications, alterations, and alternatives will occur to those skilled in the art without departing from the spirit and essence of the invention. It should be understood that various alternatives to the embodiments of the invention described herein may be employed in the practice of this invention.

Claims

1. A method for assessing aviation maintenance support capabilities in multi-regional environments, characterized in that, include: An evaluation index system for aviation maintenance support capabilities is constructed, comprising primary indicators and secondary indicators belonging to the primary indicators. The primary indicators include equipment status, maintenance support work, personnel status, and resource support. The relative weights of each primary indicator and each secondary indicator are calculated using the analytic hierarchy process (AHP), and then the comprehensive weight of each secondary indicator is calculated. A measured data matrix is ​​established for various regional environments. For a given regional environment, the expression for the measured data matrix is ​​as follows: In the formula, For the measured data of the j-th secondary indicator of the i-th maintenance unit in this regional environment, m represents the number of maintenance units in this regional environment, and n represents the number of secondary indicators corresponding to a single maintenance unit; for each regional environment, based on the measured data matrix, the objective weight of each secondary indicator is calculated using the entropy method, and based on the comprehensive weight and objective weight of the secondary indicators, the combined weight of each secondary indicator is calculated using the multiplication combination method. A multi-objective decision matrix is ​​constructed based on the measured data matrix of each regional environment. The multi-objective decision matrix is ​​weighted by the combined weights of secondary indicators under each regional environment to obtain a weighted decision matrix. Based on the weighted decision matrix, the assessment closeness of each regional environment is calculated by the approximation ideal solution ranking method. The aviation maintenance support capabilities of each regional environment are ranked according to the assessment closeness.

2. The method for assessing aviation maintenance support capabilities in multiple geographical environments as described in claim 1, characterized in that, The secondary indicators set under the equipment status include: equipment failure rate, repair and availability rate, deployment response rate, and equipment protection difficulty; the secondary indicators set under the maintenance and support work include: maintenance and support complexity, organizational and management difficulty, work quality pass rate, and maintenance and support support level; the secondary indicators set under the personnel status include: professional and technical qualification rate, personnel error rate, psychological adaptability, and physiological health; the secondary indicators set under the resource support include: maintenance equipment integrity rate, timeliness of aviation material and spare parts supply, accessibility of material and equipment transportation, and maintenance facility perfection rate.

3. The method for assessing aviation maintenance support capabilities in multiple geographical environments as described in claim 1, characterized in that, The calculation of the relative weights of each primary indicator and each secondary indicator includes: grouping all primary indicators into one group, grouping secondary indicators under the same primary indicator into another group, and calculating the corresponding relative weights for the indicators within each group; the method for calculating the relative weights of a certain group of indicators includes: obtaining the judgment comparison matrix of m experts for that group of indicators from the database; multiplying the elements at the same position in each expert's judgment comparison matrix and taking the mth root to obtain new integrated data, and then obtaining the judgment integration matrix; based on the judgment integration matrix, calculating the relative weights of that group of indicators using the square root method, the calculation expression is: In the formula, This represents the relative weight of the i-th indicator within the group of indicators. This represents the element in the i-th row and j-th column of the integration matrix; calculate the consistency ratio CR of the comparison matrix, and if CR < 0.1, confirm that the relative weights are valid.

4. The method for assessing aviation maintenance support capabilities in multiple geographical environments as described in claim 1, characterized in that, The formula for calculating the comprehensive weight of each secondary indicator is as follows: In the formula, This represents the combined weight of the j-th secondary indicator under the i-th primary indicator. This represents the relative weight of the i-th primary indicator. This represents the relative weight of the j-th secondary indicator under the i-th primary indicator.

5. The method for assessing aviation maintenance support capabilities in multiple geographical environments as described in claim 3, characterized in that, The consistency ratio CR of the comparison matrix is ​​calculated using the formula CR = CI / RI; where RI is the average random consistency index; and CI is the consistency index, calculated using the following formula: In the formula, To determine the largest eigenvalue of the comparison matrix, where n is the order of the comparison matrix, the expression for calculating the largest eigenvalue of the comparison matrix is: In the formula, To determine the integrated matrix, This represents the relative weight vector of the indicators at this level. For matrix The j-th component.

6. The method for assessing aviation maintenance support capabilities in multiple geographical environments as described in claim 1, characterized in that, Before calculating the objective weights, the process also includes: standardizing the objective measured data using the following formula: For positive indicators, the standardized value is calculated as follows: For negative indicators, the standardized value is calculated as follows: In the formula, This represents the standardized value of the j-th secondary indicator for the i-th repair unit. This represents the measured data of the j-th secondary indicator for the i-th repair unit. This represents the minimum value of the j-th evaluation index among all maintenance units in a specific geographical environment. This represents the maximum value of the j-th evaluation index among all maintenance units in a certain geographical environment.

7. The method for assessing aviation maintenance support capabilities in multiple geographical environments as described in claim 6, characterized in that, The calculation of the objective weights of each secondary indicator using the entropy method includes: calculating the standardized value defined for each secondary indicator, expressed as: In the formula, Let represent the percentage of the j-th secondary indicator for the i-th repair unit; calculate the entropy value of the secondary indicator using the following expression: ;formula, Let represent the entropy value of the j-th secondary indicator; calculate the information entropy redundancy of the secondary indicator using the following expression: In the formula, Let represent the information entropy redundancy of the j-th secondary indicator; calculate the objective weight of the secondary indicator using the following expression: In the formula, This represents the objective weight of the j-th secondary indicator.

8. The method for assessing aviation maintenance support capabilities in multiple geographical environments as described in claim 1, characterized in that, The formula for calculating the combined weights of the secondary indicators is as follows: In the formula, This represents the combined weight of the j-th secondary indicator. This represents the comprehensive weight of the j-th secondary indicator. This represents the objective weight of the j-th secondary indicator.

9. The method for assessing multi-regional aviation maintenance support capabilities as described in any one of claims 1 to 8, characterized in that, The number of geographical environments is p. Constructing the multi-objective decision matrix includes: calculating the average value of each secondary indicator in each geographical environment; wherein the average value of the j-th secondary indicator in a certain geographical environment is calculated by averaging the j-th secondary indicator of each maintenance unit in that geographical environment; and constructing the multi-objective decision matrix D based on the average values ​​of each secondary indicator in each geographical environment, with the expression: In the formula, Let represent the average value of the j-th secondary indicator in the i-th regional environment; the weighted decision matrix is ​​obtained by weighting the multi-objective decision matrix using the combined weights of the secondary indicators in each regional environment, including: standardizing and normalizing the elements of the multi-objective decision matrix to obtain the processed multi-objective decision matrix; and multiplying the elements in the processed multi-objective decision matrix by the combined weights of the corresponding secondary indicators to obtain the weighted decision matrix.

10. A multi-regional environment aviation maintenance support capability assessment system, the multi-regional environment aviation maintenance support capability assessment system comprising a processor and a memory, the memory storing computer program instructions, characterized in that, When the computer program instructions are executed by the processor, the multi-regional environment aviation maintenance support capability assessment method according to any one of claims 1 to 9 is implemented.

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