Method and system for evaluating effectiveness of aviation support system based on combined weighting and cloud barycenter theory

Abstract

The invention provides an aviation support system efficiency evaluation method and system based on a combined weighting and cloud gravity center theory. The method comprises the following steps: determining an aviation guarantee efficiency evaluation object, and establishing an aviation guarantee efficiency evaluation index system; the method comprises the following steps: based on an aviation guarantee efficiency evaluation index system, acquiring data of quantitative indexes through simulation deduction, rating the qualitative indexes by experts, converting the qualitative indexes into quantitative data through an evaluation grade scale table, standardizing the index data, and calculating weights of the indexes by adopting a combined weighting method combining an analytic hierarchy process and an entropy weight method; the method comprises the following steps: determining digital characteristics of an index cloud based on aviation guarantee efficiency evaluation index data, calculating a comprehensive cloud gravity center, determining a cloud gravity center position in an ideal state based on standardized index data, and calculating a weighted deviation degree; and calculating an efficiency evaluation value of the aviation guarantee system based on the comprehensive weight and the weighted deviation degree of the aviation guarantee efficiency evaluation index, and forming a final evaluation conclusion.

Description

Technical Field

[0001] This invention relates to the field of performance evaluation technology, and more particularly to a method and system for evaluating the performance of aviation support systems based on combined weighting and cloud center of gravity theory. Background Technology

[0002] The aircraft carrier aviation support system is a crucial component of modern naval combat systems. Its main functions include ensuring the smooth operation of various carrier-based aircraft, such as mission planning, efficient sortie, safe recovery, aircraft transport, fuel, water, gas, and electricity supply, ammunition transfer and loading, and maintenance support. This system comprises multiple subsystems, including catapult launch system, arrested recovery system, landing guidance system, aircraft transport system, mission command and control system, and deck support system, exhibiting complex interactive processes. The effectiveness of the aviation support system directly determines the combat capability of carrier-based aircraft and the overall combat effectiveness of the aircraft carrier; therefore, a comprehensive and objective evaluation of the aviation support system's effectiveness is of paramount importance. Existing methods for evaluating the effectiveness of aviation support systems typically employ either qualitative or quantitative approaches. Qualitative evaluation methods rely heavily on expert experience and judgment, using logical analysis and comparative studies to subjectively assess aviation support effectiveness. This approach is suitable for situations where evaluation indicators are difficult to quantify or where data is insufficient, but it is highly subjective and easily influenced by personal biases. Quantitative evaluation methods, on the other hand, objectively quantify aviation support effectiveness by establishing mathematical models or utilizing statistical data, reducing the influence of subjective factors and improving the accuracy and objectivity of the evaluation. However, aviation support systems involve a wide variety of support equipment and processes, and not all influencing factors can be quantitatively analyzed using computers or mathematical models; therefore, relying solely on quantitative evaluation methods also has limitations. Existing methods for evaluating the effectiveness of aviation support systems have several shortcomings. On the one hand, traditional qualitative evaluation methods, relying heavily on expert experience, are prone to subjectivity, leading to inaccurate and subjective results. On the other hand, while quantitative evaluation methods offer some objectivity, they often struggle to comprehensively consider all influencing factors, especially those qualitative factors that are difficult to quantify, when dealing with complex systems. Furthermore, most existing evaluation methods fail to effectively combine qualitative and quantitative indicators, making it difficult to simultaneously meet the evaluation needs of the complexity and diversity of aviation support systems, and thus unable to comprehensively and objectively reflect the actual effectiveness of these systems. Therefore, an evaluation method that comprehensively considers both qualitative and quantitative indicators is needed to improve the accuracy and objectivity of aviation support system effectiveness evaluation. Summary of the Invention

[0003] To address the aforementioned technical problems, this invention provides a method and system for evaluating the effectiveness of aviation support systems based on combined weighting and cloud center of gravity theory. This invention achieves a scientific and objective evaluation of the effectiveness of aviation support systems by quantifying qualitative indicators, combining subjective and objective weight allocation, and utilizing the cloud center of gravity evaluation method to comprehensively consider the uncertainty and ambiguity of data.

[0004] The technical means employed in this invention are as follows: A method for evaluating the effectiveness of aviation support systems based on combined weighting and cloud center of gravity theory includes: 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, quantitative index data are obtained through simulation and deduction, qualitative indexes are rated by experts, and quantitative data are converted through the evaluation level scale table. After standardizing the index data, the weight of the index is calculated by a combination of the analytic hierarchy process and the entropy weight method. S3. Based on the aviation support effectiveness evaluation index data, determine the digital characteristics of the index cloud, then calculate the comprehensive cloud center of gravity, and determine the cloud center of gravity position under the ideal state based on the standardized index data, thereby calculating the weighted deviation. S4. Calculate the performance evaluation value of the aviation support system based on the comprehensive weight and weighted deviation of the aviation support performance evaluation indicators, and form the final evaluation conclusion.

[0005] Furthermore, in step S1, the established aviation support effectiveness evaluation index system is a criterion used to measure the quality of aviation support work, and the principles it meets include simplicity, independence, objectivity, relevance, integrity, and measurability.

[0006] Further, step S2 specifically includes: S21. Calculate the subjective weights of the indicators using the analytic hierarchy process. S22. The objective weights of the indicators are calculated using the entropy weight method. S23. Based on the calculated subjective and objective weights of the indicators, the combined weighting method is used to calculate the comprehensive weight of the indicators.

[0007] Further, step S21 specifically includes: S211. Construct a hierarchical model of evaluation indicators; S212. Based on the relative importance of each evaluation indicator under the same upper-level indicator in the hierarchical evaluation indicator model, construct a judgment matrix between each pair of factors, transforming the qualitative problem of subjective judgment based on expert experience into a quantitative problem, and quantifying the differences between each factor. S213. Based on the importance of each indicator, establish an importance judgment matrix based on prior knowledge. The formula is as follows:

[0008] in, Indicators relative to evaluation indicators The importance of , Indicates the number of evaluation indicators; S214, Importance Judgment Matrix After normalization, the subjective weights of the indicators are obtained, as shown in the following formula:

[0009] in, Indicates the subjective weight of the indicator; S215, Importance Judgment Matrix Find the largest eigenvalue The formula is as follows:

[0010] S216. Importance Judgment Matrix A consistency check is performed to examine whether there are any contradictions in the importance levels among the indicators. If the consistency requirements are not met, the judgment matrix needs to be readjusted, and the above steps need to be repeated. The consistency indicators include... The calculation formula is as follows:

[0011] When satisfied When the importance judgment matrix is ​​considered to be... The consistency requirement is met, and the smaller the value, the higher the reliability of the calculation result.

[0012] Further, step S22 specifically includes: S221, Regarding the indicators Select an acceptable result range based on the test scenario. ;when If the test falls within this range, the metrics are considered to have passed and are processed for consistency, then converted to... Indicators within the range When the indicator When the indicator is extremely large, the indicator satisfy When the indicator When it is a very small indicator, the indicator satisfy ; S222, Normalization of indicator data, the formula is as follows:

[0013] S223. Calculate the information entropy of the indicator data, using the following formula:

[0014] S224. Calculate the coefficient of difference, using the following formula:

[0015] S225. Based on the calculated difference coefficient, determine the objective weight of the indicator, using the following formula:

[0016] in, The objective weights of the indicators for evaluating the effectiveness of aviation support systems reflect the relative importance of the indicators.

[0017] Further, step S23 specifically includes: The combined weighting method is used to calculate the comprehensive weight of subjective and objective values. The calculation formula is as follows: .

[0018] Further, step S3 specifically includes: S31, Use quantitative indicators and A cloud with all zeros is represented by the mathematical characteristic of... The qualitative indicators of cloud mathematics are The set of indicators, composed of quantitative and qualitative indicators, forms a judgment matrix. Each quantitative indicator is represented by a single cloud model as follows:

[0019]

[0020] S32, Each cloud model represents one indicator, which can be represented by a one-dimensional comprehensive cloud as follows:

[0021]

[0022] When the indicator is a precise numerical value The values ​​for each indicator; when the indicator is described using qualitative language, For the expected value of the indicator cloud model, The entropy of the indicator cloud model; S33, will The core of the integrated cloud Use one A dimensional vector is represented as:

[0023] in, , The expected value reflects the information center value of the corresponding fuzzy concept, i.e., the location of the cloud centroid; The height of the cloud's center of gravity, i.e., its weight value, reflects the importance of the corresponding cloud. When the system changes, its center of gravity changes as follows:

[0024] S34. Assuming an ideal state... The comprehensive cloud centroid position vector is Cloud center of gravity height In an ideal state, the cloud centroid vector ,in, , As the indicator weight; S35. Normalize the comprehensive cloud centroid vector using the following formula:

[0025] S36. After normalization, the comprehensive cloud centroid vector representing the system state is a dimensionless value with magnitude and direction. Multiplying the normalized vector values ​​of each index by their weight values ​​and then summing them gives the weighted deviation. The formula is as follows:

[0026] Among them, weighted deviation It is used to measure the difference in the overall cloud centroid under two states. For the first The weight value of each individual indicator will be obtained. The efficiency value can be obtained by comparing the results of inputting the cloud generator with the ideal state.

[0027] Further, step S4 specifically includes: S41. Set the system's comment set as follows: , These represent the range, very poor, extremely poor, poor, average, good, fairly good, very good, excellent, and superb states, respectively. Superb represents the ideal state, i.e., the weighted deviation of the system from the ideal state in a specific condition. The smaller the value, the more comments are marked on a continuous language value scale, and the cloud model is used to implement them, forming a cloud generator for qualitative evaluation. S42, based on weighted deviation Attribution to the assessment results The formula is as follows:

[0028] S43. Calculate system performance using the following formula:

[0029] in, For system efficiency; Weights for criteria layer indicators; The number of indicators at the criteria layer; Weights for indicators at the scheme layer; This is the cloud centroid vector after normalization of the scheme-level indicators. This represents the number of indicators at the scheme layer.

[0030] This invention also provides an aviation support system performance evaluation system based on the above-mentioned aviation support system performance evaluation method based on combined weighting and cloud center of gravity theory, comprising: an indicator system construction module, a data acquisition and weight calculation module, a cloud center of gravity calculation module, and a performance evaluation module, wherein: The indicator system construction module is used to clarify the objects of aviation support effectiveness evaluation and establish an aviation support effectiveness evaluation indicator system. The data acquisition and weight calculation module is used to acquire quantitative indicator data through simulation based on the aviation support effectiveness evaluation index system, have experts rate the qualitative indicators, convert them into quantitative data through the evaluation level scale table, and calculate the weight of the indicators by a combination of the analytic hierarchy process and the entropy weight method after standardizing the indicator data. The cloud center of gravity calculation module is used to determine the digital characteristics of the indicator cloud based on the aviation support effectiveness evaluation index data, and then calculate the comprehensive cloud center of gravity. Based on the standardized index data, it determines the cloud center of gravity position under the ideal state, and then calculates the weighted deviation. The performance evaluation module is used to calculate the performance evaluation value of the aviation support system based on the comprehensive weight and weighted deviation of the aviation support performance evaluation indicators, and form the final evaluation conclusion.

[0031] Compared with the prior art, the present invention has the following advantages: 1. This invention maps qualitative indicator comments to quantitative data through a scaling table, enabling qualitative indicators that are originally difficult to quantify to participate in subsequent calculations in numerical form, thereby providing a foundation for the scientific evaluation of qualitative indicators and improving the comprehensiveness and operability of the evaluation.

[0032] 2. This invention employs a combined weighting method that integrates the Analytic Hierarchy Process (AHP) and the entropy weighting method, comprehensively considering both subjective experience and objective data, resulting in a more scientific and reasonable allocation of indicator weights. The AHP method reflects expert experience, while the entropy weighting method objectively assigns weights based on the dispersion of data. The combination of the two effectively reduces the limitations of a single weighting method and improves the accuracy and reliability of weight allocation.

[0033] 3. This invention introduces a cloud center of gravity assessment method, which uses three numerical characteristics of the cloud model—expectation, entropy, and hyperentropy—to characterize the uncertainty of the indicators, combining qualitative concepts with quantitative values. This method can effectively handle the fuzziness and randomness of data, and intuitively reflect changes in the system state through changes in the cloud center of gravity, thereby achieving an accurate assessment of the effectiveness of the aviation support system. Attached Figure Description

[0034] 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.

[0035] Figure 1 This is a flowchart illustrating the overall evaluation process of the present invention.

[0036] Figure 2 This is a diagram of the cloud generator for evaluating the present invention.

[0037] Figure 3 A structural diagram of the performance evaluation index for the aviation support system provided in this embodiment of the invention.

[0038] Figure 4 Evaluation cloud diagram of index X4 provided in the embodiments of the present invention.

[0039] Figure 5 Evaluation cloud diagrams of various indicators for Scheme 1 provided in the embodiments of the present invention.

[0040] Figure 6 Evaluation cloud diagrams for schemes 1-4 provided in the embodiments of the present invention. Detailed Implementation

[0041] 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.

[0042] It should be noted that the terms "comprising" and "having" and any variations thereof in the specification, claims and accompanying drawings of this invention are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or units that are not explicitly listed or that are inherent to such processes, methods, products or devices.

[0043] like Figure 1 As shown, this invention provides a method for evaluating the effectiveness of aviation support systems based on combined weighting and cloud center of gravity theory, including: 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, quantitative index data are obtained through simulation and deduction, qualitative indexes are rated by experts, and quantitative data are converted through the evaluation level scale table. After standardizing the index data, the weight of the index is calculated by a combination of the analytic hierarchy process and the entropy weight method. S3. Based on the aviation support effectiveness evaluation index data, determine the digital characteristics of the index cloud, then calculate the comprehensive cloud center of gravity, and determine the cloud center of gravity position under the ideal state based on the standardized index data, thereby calculating the weighted deviation. S4. Calculate the performance evaluation value of the aviation support system based on the comprehensive weight and weighted deviation of the aviation support performance evaluation indicators, and form the final evaluation conclusion.

[0044] In specific implementation, as a preferred embodiment of the present invention, in step S1, the established aviation support effectiveness evaluation index system is a criterion used to measure the quality of aviation support work. A scientifically sound index system should meet the following principles: simplicity, independence, objectivity, relevance, comprehensiveness, and measurability. Among them: Simplicity: While meeting the evaluation requirements, the system's security performance should be evaluated using as few indicators as possible to avoid unnecessary complexity.

[0045] 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.

[0046] Objectivity: The indicators should be able to objectively reflect the characteristics of the aviation support system, respect the objectivity of the aviation support system, and not be mixed with personal subjective will.

[0047] Targeted: The indicators should be able to effectively reflect the characteristics and content of various aviation support scenarios, and reflect the system characteristics of carrier-based aircraft take-off and landing, support, etc.

[0048] Holistic approach: Since the aviation support system is a typical complex large system, the selected indicators should be able to fully reflect the overall effectiveness of the system.

[0049] 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, so as to facilitate subsequent evaluation, analysis, and comparison.

[0050] In this embodiment, the indicator system serves as the foundation for performance evaluation. Due to the differences in attributes among different indicators, it is necessary to start from the actual situation of carrier-based aircraft aviation support. Based on 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. The principles for constructing the aviation support performance evaluation indicator system are clarified, performance indicators are screened, analyzed, and decided upon, key evaluation elements are extracted to build an orderly hierarchical structure, and thus the aviation support performance evaluation indicator system is constructed.

[0051] In a specific implementation, as a preferred embodiment of the present invention, step S2 specifically includes: S21. Calculate the subjective weights of the indicators using the analytic hierarchy process. S22. The objective weights of the indicators are calculated using the entropy weight method. S23. Based on the calculated subjective and objective weights of the indicators, the combined weighting method is used to calculate the comprehensive weight of the indicators.

[0052] In a specific implementation, as a preferred embodiment of the present invention, step S21 specifically includes: S211. Construct a hierarchical model of evaluation indicators; In this embodiment, the carrier-based aircraft aviation support evaluation model has a significant hierarchical structure, which is divided into three levels.

[0053] S212. Based on the relative importance of each evaluation indicator under the same upper-level indicator in the hierarchical evaluation indicator model, construct a judgment matrix between each pair of factors, transforming the qualitative problem of subjective judgment based on expert experience into a quantitative problem, and quantifying the differences between each factor. S213. Based on the importance of each indicator, establish an importance judgment matrix based on prior knowledge. The formula is as follows:

[0054] in, Indicators relative to evaluation indicators The degree of importance is given by the corresponding description in Table 1. , Indicates the number of evaluation indicators; Table 1 Importance Judgment Table

[0055] S214, Importance Judgment Matrix After normalization, the subjective weights of the indicators are obtained, as shown in the following formula:

[0056] in, Indicates the subjective weight of the indicator; S215, Importance Judgment Matrix Find the largest eigenvalue The formula is as follows:

[0057] S216. Importance Judgment Matrix A consistency check is performed to examine whether there are any contradictions in the importance levels among the indicators. If the consistency requirements are not met, the judgment matrix needs to be readjusted, and the above steps need to be repeated. The consistency indicators include... The calculation formula is as follows:

[0058] When satisfied When the importance judgment matrix is ​​considered to be... The consistency requirement is met, and the smaller the value, the higher the reliability of the calculation results. The values ​​of the average random consistency index CR are shown in the table below.

[0059] Table 2. Average Random Consistency Index (CR) Values

[0060] In this embodiment, a 1-9 scale method is used to establish a hierarchical judgment matrix for the performance evaluation indicators of the aviation support system, and then the indicator weights are obtained. Based on the hierarchical structure of the constructed performance evaluation indicator system for the aviation support system, the subjective weights of the indicators are determined using the Analytic Hierarchy Process (AHP). The AHP is a hierarchical weighted decision analysis method. This method combines qualitative analysis and quantitative calculation, determining subjective weights by comparing and calculating the importance of each indicator pairwise. It is highly scientific and still widely used in the field of performance evaluation. The AHP is suitable for complex systems composed of numerous interrelated and mutually restrictive factors. Through the judgment matrix, it effectively measures the superiority or inferiority relationships between interrelated elements, providing a useful tool for simplifying system analysis and calculation.

[0061] In a specific implementation, as a preferred embodiment of the present invention, step S22 specifically includes: S221. In this embodiment, because the evaluation scales and directions of various types of indicators are different, it is difficult to combine the various indicators and conduct an overall evaluation. Therefore, before conducting a comprehensive evaluation, it is necessary to standardize the various types of indicators to ensure that their evaluation directions and evaluation scales are consistent. The specific conversion method is as follows: for indicators... Select an acceptable result range based on the test scenario. ;when If the test falls within this range, the metrics are considered to have passed and are processed for consistency, then converted to... Indicators within the range When the indicator When the indicator is extremely large, the indicator satisfy When the indicator When it is a very small indicator, the indicator satisfy ; S222, Normalization of indicator data, the formula is as follows:

[0062] S223. Calculate the information entropy of the indicator data, using the following formula:

[0063] S224. Calculate the coefficient of difference, using the following formula:

[0064] S225. Based on the calculated difference coefficient, determine the objective weight of the indicator, using the following formula:

[0065] in, The objective weights of the indicators for evaluating the effectiveness of aviation support systems reflect the relative importance of the indicators.

[0066] In this embodiment, the entropy weight method is an objective weighting method based on information entropy theory, mainly used to determine the weight of each indicator in multi-indicator comprehensive evaluation problems. This method determines the degree of dispersion of each indicator by calculating its entropy value, thereby determining the influence weight of each indicator in the comprehensive evaluation. It effectively avoids interference from human factors and has strong objectivity.

[0067] In a specific implementation, as a preferred embodiment of the present invention, step S23 specifically includes: The combined weighting method is used to calculate the comprehensive weight of subjective and objective values. The calculation formula is as follows: .

[0068] In a specific implementation, as a preferred embodiment of the present invention, step S3 specifically includes: S31. The performance indicator system for aviation support systems includes both indicators expressed with precise numerical values ​​and indicators described with qualitative language. Quantitative indicators should be expressed using... and A cloud with all zeros is represented by the mathematical characteristic of... The qualitative indicators of cloud mathematics are The set of indicators, composed of quantitative and qualitative indicators, forms a judgment matrix. Each quantitative indicator is represented by a single cloud model as follows:

[0069]

[0070] S32, Each cloud model represents one indicator, which can be represented by a one-dimensional comprehensive cloud as follows:

[0071]

[0072] When the indicator is a precise numerical value The values ​​for each indicator; when the indicator is described using qualitative language, For the expected value of the indicator cloud model, The entropy of the indicator cloud model; S33, Each performance metric is used To describe it using a cloud model, then The state of the system reflected by each indicator can be represented by a single indicator. This is represented by the integrated cloud platform. When the system state reflected by each indicator changes, this As the shape of the cloud changes, its center of gravity also shifts. In other words, changes in the cloud's center of gravity can reflect changes in the system's state information. The core of the integrated cloud Use one A dimensional vector is represented as:

[0073] in, , The expected value reflects the information center value of the corresponding fuzzy concept, i.e., the location of the cloud centroid; The height of the cloud's center of gravity, i.e., its weight value, reflects the importance of the corresponding cloud. When the system changes, its center of gravity changes as follows:

[0074] S34. Assuming an ideal state... The comprehensive cloud centroid position vector is Cloud center of gravity height In an ideal state, the cloud centroid vector ,in, , As the indicator weight; S35. Normalize the comprehensive cloud centroid vector using the following formula:

[0075] S36. After normalization, the comprehensive cloud centroid vector representing the system state is a dimensionless value with magnitude and direction. Multiplying the normalized vector values ​​of each index by their weight values ​​and then summing them gives the weighted deviation. The formula is as follows:

[0076] Among them, weighted deviation It is used to measure the difference in the overall cloud centroid under two states. For the first The weight value of each individual indicator will be obtained. The efficiency value can be obtained by comparing the results of inputting the cloud generator with the ideal state.

[0077] In this embodiment, the cloud model uses expectations (Expected Value), Entropy (Entropy) and hyperentropy (Hyper Entropy) Three numerical features are used to characterize the basic properties of the cloud, with the expectation of The expected value defines the central location of the cloud; entropy. It can be used to describe the span of clouds, reflecting the dispersion of cloud droplets, and is used to measure the uncertainty of qualitative concepts; hyperentropy. Used to measure entropy Uncertainty reflects the relationship between fuzziness and randomness. Representing the overall characteristics of a concept using three numerical features is a quantitative characteristic of a qualitative concept, which is crucial for the conversion between qualitative concepts and quantitative values. In the in-depth analysis and processing of aviation support effectiveness evaluation index data, the first step is to determine the numerical characteristics of the indicator cloud based on the index data, thereby deriving the specific location of the comprehensive cloud centroid. On this basis, to further measure the gap between actual indicators and the ideal state, it is necessary to determine the cloud centroid location under the ideal state based on standardized index data. This step requires unified dimensional processing and normalization of all indicators to eliminate errors and deviations caused by inconsistencies in dimensions between different indicators. Through scientific and reasonable methods, the optimal values ​​that each indicator should achieve under ideal conditions and the corresponding cloud centroids can be determined. Finally, using the obtained comprehensive cloud centroid and ideal cloud centroid location information, a weighted deviation algorithm is applied for calculation. This algorithm comprehensively considers the importance and weight allocation of each indicator, quantitatively evaluating the deviation between actual and ideal indicators, thereby deriving a weighted deviation that comprehensively reflects the degree of difference between aviation support effectiveness and the ideal state.

[0078] In a specific implementation, as a preferred embodiment of the present invention, step S4 specifically includes: S41. Set the system's comment set as follows: , These represent the range, very poor, extremely poor, poor, average, good, fairly good, very good, excellent, and superb states, respectively. Superb represents the ideal state, i.e., the weighted deviation of the system from the ideal state in a specific condition. The smaller, please refer to Figure 2 Eleven comments are marked on a continuous language value scale and implemented using a cloud model to form a cloud generator for qualitative evaluation. S42, based on weighted deviation Attribution to the assessment results The formula is as follows:

[0079] S43. Calculate system performance using the following formula:

[0080] in, For system efficiency; Weights for criteria layer indicators; The number of indicators at the criteria layer; Weights for indicators at the scheme layer; This is the cloud centroid vector after normalization of the scheme-level indicators. This represents the number of indicators at the scheme layer.

[0081] In this embodiment, based on this comprehensive performance evaluation value, combined with preset evaluation criteria and thresholds, a final evaluation conclusion can be formed. This conclusion includes a quantitative assessment of the aviation support system's performance and an intuitive qualitative evaluation cloud map.

[0082] This invention also provides an aviation support system performance evaluation system based on the above-mentioned aviation support system performance evaluation method based on combined weighting and cloud center of gravity theory, comprising: an indicator system construction module, a data acquisition and weight calculation module, a cloud center of gravity calculation module, and a performance evaluation module, wherein: The indicator system construction module is used to clarify the objects of aviation support effectiveness evaluation and establish an aviation support effectiveness evaluation indicator system. The data acquisition and weight calculation module is used to acquire quantitative indicator data through simulation based on the aviation support effectiveness evaluation index system, have experts rate the qualitative indicators, convert them into quantitative data through the evaluation level scale table, and calculate the weight of the indicators by a combination of the analytic hierarchy process and the entropy weight method after standardizing the indicator data. The cloud center of gravity calculation module is used to determine the digital characteristics of the indicator cloud based on the aviation support effectiveness evaluation index data, and then calculate the comprehensive cloud center of gravity. Based on the standardized index data, it determines the cloud center of gravity position under the ideal state, and then calculates the weighted deviation. The performance evaluation module is used to calculate the performance evaluation value of the aviation support system based on the comprehensive weight and weighted deviation of the aviation support performance evaluation indicators, and form the final evaluation conclusion.

[0083] The embodiments of the present invention are described simply because they correspond to those in the embodiments above. For any similarities, please refer to the descriptions in the embodiments above, which will not be elaborated here.

[0084] Example 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.

[0085] Next, the subjective weights of the indicators are determined using the Analytic Hierarchy Process (AHP): Experts were invited to compare the importance of the primary and secondary indicators, and the resulting fuzzy judgment matrix for the primary and secondary indicators is shown below:

[0086] , , , ; To determine the matrix A For example, the weight vector is obtained as follows: The largest eigenvalue is Consistency index is ;and The consistency test is satisfied. Similarly, based on the judgment matrix of the secondary indicators, the subjective weights of the secondary indicators can be obtained, and the comprehensive weights of the secondary indicators relative to the overall goal can be calculated. The results are shown in Table 3.

[0087] Table 3 Subjective Evaluation Weights

[0088] Entropy weight method for determining objective weights: Since the evaluation index system for the effectiveness of aviation support systems includes qualitative indicators, experts who participated in the evaluation process were invited to provide a rating scale for each qualitative indicator, as shown in Table 4.

[0089] Table 4. Rating scale of evaluation indicators

[0090] Qualitative indicators were graded by experts, and their ideal values ​​were obtained through simulation experiments. The objective weights of the acquired indicator data were calculated using the entropy weight method, as shown in Table 5. Table 5. Objective Evaluation Weights of Indicators

[0091] Determining the overall weight using combined weighting: The overall weights obtained using the combined weighting method are shown in Table 6; Table 6 Overall Weight of Indicators

[0092] 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. Experts were invited to provide evaluation levels and degrees of ambiguity for the qualitative indicators based on the evaluation level scale in Table 4. 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 7.

[0093] Table 7 Raw Data of Indicators

[0094] Step 1: Represent each indicator using a cloud model.

[0095] Taking X4 in Scheme 1 as an example, the original data is standardized, and experts score the qualitative indicators according to the evaluation level scale to give the degree of fuzziness of the qualitative indicators. Ideally, the cloud center of gravity location is... The expected value and entropy of the X4 index cloud model are shown in Table 8.

[0096] Table 8. Expected Value, Entropy, and Ideal Expected Value of Indicator X4

[0097] Step 2, use n The system status is represented by a comprehensive cloud.

[0098] Taking X4 in Scheme 1 as an example, according to the cloud center of gravity theory, ,in The position of the cloud's centroid is the expected value. , The height of the cloud's center of gravity is represented by its weight value. , From the comprehensive cloud centroid vector, a 3D comprehensive cloud centroid vector can be obtained. and the cloud centroid vector under ideal conditions , and by formula The comprehensive cloud centroid deviation vector is obtained. As shown in Table 9: Table 9 Cloud centroid vector values ​​for index X4

[0099] Step 3: Calculate the weighted deviation.

[0100] The weighted deviation can be obtained from the formula. Attribution of evaluation results Input into the established cloud generator, such as Figure 4 As shown, both "Very Good" and "Excellent" clouds will be activated, with a preference for "Excellent".

[0101] Step 4: Assess aviation support effectiveness.

[0102] Similarly, taking Scheme 1 as an example, the weighted deviation and assessment result attribution of the indicators "ship deck support capability," "mission completion capability," and "takeoff support capability" can be calculated, as shown in Table 10. These values ​​are then input into the established cloud generator. Figure 5 As shown.

[0103] Table 10: Performance Evaluation of Each Indicator in Scheme 1: Attribution and Comments

[0104] By combining the comprehensive weights of the primary indicators, the overall effectiveness value of aviation support under Scheme 1 can be calculated as follows:

[0105] Similarly, the weighted deviation and evaluation attribution of the primary indicators for the four schemes can be obtained. Combined with the comprehensive weight of the primary indicators, the overall effectiveness value and qualitative evaluation of the aviation support for each scheme can be calculated according to the formula, as shown in Table 11. This information is then input into the established cloud generator. Figure 6 As shown.

[0106] Table 11. Performance Evaluation Attributes and Comments for Schemes 1-4

[0107] The results show that the optimal solution is solution 4, with an evaluation value of 0.929, and the worst solution is solution 2, with an evaluation value of 0.784.

[0108] In summary, addressing the complex issue of effectively evaluating qualitative indicators in aviation support effectiveness, this study employs scientific quantitative methods to accurately convert qualitative indicator evaluations into measurable quantitative data. This process begins with a designed scaling table, which maps qualitative evaluations to corresponding numerical values ​​according to certain rules, providing a foundation for subsequent processing. Regarding weight allocation, a combined weighting strategy integrating the Analytic Hierarchy Process (AHP) and entropy weighting is adopted. The AHP constructs a hierarchical model, utilizing expert experience to compare elements at each level pairwise to derive subjective weights; while the entropy weighting method, based on the principle of information entropy, calculates objective weights according to the dispersion or variation of the data. By integrating these two weighting methods, both subjective and objective factors are considered, resulting in a more scientific and reasonable weight allocation that more accurately reflects the importance of each indicator. Finally, the cloud center of gravity assessment method is used to further calculate the overall system effectiveness. This method fully considers the uncertainty and fuzziness of the data, establishing a cloud model to describe the distribution characteristics of each indicator and calculating the cloud center of gravity as a comprehensive representation of system effectiveness. Meanwhile, in order to more intuitively display the system's status and performance, the method also calculates the uncertainty of different indicators and presents them in the form of cloud maps, realizing qualitative and quantitative evaluation of the aviation support system and providing strong support for aviation support system decision-making.

[0109] 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 combined weighting and cloud center of gravity theory, characterized in that, include: 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, quantitative index data are obtained through simulation and deduction, qualitative indexes are rated by experts, and quantitative data are converted through the evaluation level scale table. After standardizing the index data, the weight of the index is calculated by a combination of the analytic hierarchy process and the entropy weight method. S3. Based on the aviation support effectiveness evaluation index data, determine the digital characteristics of the index cloud, then calculate the comprehensive cloud center of gravity, and determine the cloud center of gravity position under the ideal state based on the standardized index data, thereby calculating the weighted deviation. S4. Calculate the performance evaluation value of the aviation support system based on the comprehensive weight and weighted deviation of the aviation support performance evaluation indicators, and form the final evaluation conclusion.

2. The method for evaluating the effectiveness of an aviation support system based on combined weighting and cloud center of gravity theory as described in claim 1, characterized in that, In step S1, the established aviation support effectiveness evaluation index system is a criterion used to measure the quality of aviation support work. The principles it meets include simplicity, independence, objectivity, relevance, integrity, and measurability.

3. The method for evaluating the effectiveness of an aviation support system based on combined weighting and cloud center of gravity theory according to claim 1, characterized in that, Step S2 specifically includes: S21. Calculate the subjective weights of the indicators using the analytic hierarchy process. S22. The objective weights of the indicators are calculated using the entropy weight method. S23. Based on the calculated subjective and objective weights of the indicators, the combined weighting method is used to calculate the comprehensive weight of the indicators.

4. The method for evaluating the effectiveness of an aviation support system based on combined weighting and cloud center of gravity theory according to claim 3, characterized in that, Step S21 specifically includes: S211. Construct a hierarchical model of evaluation indicators; S212. Based on the relative importance of each evaluation indicator under the same upper-level indicator in the hierarchical evaluation indicator model, construct a judgment matrix between each pair of factors, transforming the qualitative problem of subjective judgment based on expert experience into a quantitative problem, and quantifying the differences between each factor. S213. Based on the importance of each indicator, establish an importance judgment matrix based on prior knowledge. The formula is as follows: in, Indicators relative to evaluation indicators The importance of , Indicates the number of evaluation indicators; S214. Importance Judgment Matrix After normalization, the subjective weights of the indicators are obtained, as shown in the following formula: in, Indicates the subjective weight of the indicator; S215, Importance Judgment Matrix Find the largest eigenvalue The formula is as follows: S216. Importance Judgment Matrix A consistency check is performed to examine whether there are any contradictions in the importance levels among the indicators. If the consistency requirements are not met, the judgment matrix needs to be readjusted, and the above steps need to be repeated. The consistency indicators include... The calculation formula is as follows: When satisfied When the importance judgment matrix is ​​considered to be... The consistency requirement is met, and the smaller the value, the higher the reliability of the calculation result.

5. The method for evaluating the effectiveness of an aviation support system based on combined weighting and cloud center of gravity theory according to claim 3, characterized in that, Step S22 specifically includes: S221, Regarding the indicators Select an acceptable result range based on the test scenario. ;when If the test falls within this range, the metrics are considered to have passed and are processed for consistency, then converted to... Indicators within the range When the indicator When the indicator is extremely large, the indicator satisfy When the indicator When it is a very small indicator, the indicator satisfy ; S222. Normalization of indicator data, the formula is as follows: S223. Calculate the information entropy of the indicator data, using the following formula: S224. Calculate the coefficient of difference, using the following formula: S225. Based on the calculated difference coefficient, determine the objective weight of the indicator, using the following formula: in, The objective weights of the indicators for evaluating the effectiveness of aviation support systems reflect the relative importance of the indicators.

6. The method for evaluating the effectiveness of an aviation support system based on combined weighting and cloud center of gravity theory according to claim 3, characterized in that, Step S23 specifically includes: The combined weighting method is used to calculate the comprehensive weight of subjective and objective values. The calculation formula is as follows: 。 7. The method for evaluating the effectiveness of an aviation support system based on combined weighting and cloud center of gravity theory according to claim 1, characterized in that, Step S3 specifically includes: S31, Use quantitative indicators and A cloud with all zeros is represented by the mathematical characteristic of... The qualitative indicators of cloud mathematics are The set of indicators, composed of quantitative and qualitative indicators, forms a judgment matrix. Each quantitative indicator is represented by a single cloud model as follows: S32, Each cloud model represents one indicator, which can be represented by a one-dimensional comprehensive cloud as follows: When the indicator is a precise numerical value The values ​​for each indicator; when the indicator is described using qualitative language, For the expected value of the indicator cloud model, The entropy of the indicator cloud model; S33, will The core of the integrated cloud Use one A dimensional vector is represented as: in, , The expected value reflects the information center value of the corresponding fuzzy concept, i.e., the location of the cloud centroid; The height of the cloud's center of gravity, i.e., its weight value, reflects the importance of the corresponding cloud. When the system changes, its center of gravity changes as follows: S34. Assuming an ideal state... The comprehensive cloud centroid position vector is Cloud center of gravity height In an ideal state, the cloud centroid vector ,in, , As the indicator weight; S35. Normalize the comprehensive cloud centroid vector using the following formula: S36. After normalization, the comprehensive cloud centroid vector representing the system state is a dimensionless value with magnitude and direction. Multiplying the normalized vector values ​​of each index by their weight values ​​and then summing them gives the weighted deviation. The formula is as follows: Among them, weighted deviation It is used to measure the difference in the overall cloud centroid under two states. For the first The weight value of each individual indicator will be obtained. The efficiency value can be obtained by comparing the results of inputting the cloud generator with the ideal state.

8. The method for evaluating the effectiveness of an aviation support system based on combined weighting and cloud center of gravity theory according to claim 1, characterized in that, Step S4 specifically includes: S41. Set the system's comment set as follows: , These represent the range, very poor, extremely poor, poor, average, good, fairly good, very good, excellent, and superb states, respectively. Superb represents the ideal state, i.e., the weighted deviation of the system from the ideal state in a specific condition. The smaller the value, the more comments are marked on a continuous language value scale, and the cloud model is used to implement them, forming a cloud generator for qualitative evaluation. S42, based on weighted deviation Attribution to the assessment results The formula is as follows: S43. Calculate system performance using the following formula: in, For system efficiency; Weights for criteria layer indicators; The number of indicators at the criteria layer; Weights for indicators at the scheme layer; This is the cloud centroid vector after normalization of the scheme-level indicators. This represents the number of indicators at the scheme layer.

9. An aviation support system performance evaluation system based on the combined weighting and cloud center of gravity theory, implemented by the aviation support system performance evaluation method based on any one of claims 1-8, characterized in that, include: The module comprises an indicator system construction module, a data acquisition and weight calculation module, a cloud centroid calculation module, and a performance evaluation module, among which: The indicator system construction module is used to clarify the objects of aviation support effectiveness evaluation and establish an aviation support effectiveness evaluation indicator system. The data acquisition and weight calculation module is used to acquire quantitative indicator data through simulation based on the aviation support effectiveness evaluation index system, have experts rate the qualitative indicators, convert them into quantitative data through the evaluation level scale table, and calculate the weight of the indicators by a combination of the analytic hierarchy process and the entropy weight method after standardizing the indicator data. The cloud center of gravity calculation module is used to determine the digital characteristics of the indicator cloud based on the aviation support effectiveness evaluation index data, and then calculate the comprehensive cloud center of gravity. Based on the standardized index data, it determines the cloud center of gravity position under the ideal state, and then calculates the weighted deviation. The performance evaluation module is used to calculate the performance evaluation value of the aviation support system based on the comprehensive weight and weighted deviation of the aviation support performance evaluation indicators, and form the final evaluation conclusion.

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