Civil airport emergency guarantee capability assessment method and system

Abstract

The invention provides a civil airport emergency guarantee capability assessment method and system for solving the problems that civil airport emergency guarantee capability index weight assignment lacks rationality, is greatly influenced by subjective and objective factors, is not uniform in assessment standards and lacks transparency in the assessment process. The method comprises the following steps: 1, constructing a civil airport emergency guarantee capability evaluation index system; 2, determining the comprehensive weight of each index in the civil airport emergency guarantee capability evaluation index system based on a multi-expert order-information entropy weight combination method; 3, based on the obtained comprehensive weight of each index, performing civil airport emergency guarantee capability evaluation on each index by adopting a fuzzy comprehensive evaluation method; and step 4, outputting a guarantee capability evaluation result. Experimental results show that the method is simple and efficient in calculation process, and verification results accord with actual conditions.

Description

Technical Field

[0001] The present invention relates to the technical field of emergency support for civil airports, and in particular to a method and system for evaluating the emergency support capability of civil airports. Background Art

[0002] As an important transportation hub, the safe operation of civil airports is directly related to the safety of life and property of the public. The emergency support capability assessment can comprehensively check the airport's preparedness, response speed and handling capabilities in dealing with emergencies, thereby ensuring that the airport can take quick and effective actions in an emergency to protect the lives of passengers and employees and reduce property losses.

[0003] In China, many experts have published many relevant papers on the comprehensive evaluation of support capabilities. The document "Song Yunxue, Li Sha. Evaluation of emergency support capabilities of civil airports [J]. Journal of Civil Aviation University of China, 2022, 40(06): 37-44" constructs an airport emergency evaluation index system, which covers five first-level indicators and is further subdivided into 30 second-level indicators. The evaluation is based on the analytic hierarchy process and factor analysis method. The results show that the established evaluation index system and the adopted evaluation method can play an effective role and provide strong support for the evaluation of emergency support capabilities of civil airports. The document "Li Gailing, Huang Tao, Li Angjun. Equipment support capability evaluation based on group decision-making-cloud theory [J]. Science, Technology and Engineering, 2024, 24(11): 4488-4494" aims at the complexity, randomness and fuzziness of equipment support effectiveness evaluation, and establishes an equipment support capability evaluation model based on group decision-making-cloud theory. The simulation shows that this method can effectively solve the fuzziness and randomness in the evaluation process. The document "Wang Tao, Zhou Wenya, Guo Jitang, et al. Improved Gaussian cloud model and its application in equipment support system capability assessment" proposes an improved Gaussian cloud model to address the problem that cloud models are prone to poor atomization characteristics during the use of assessment methods and the assessment results cannot be directly used. The model is compared with the assessment results obtained with the traditional cloud model, proving the feasibility and effectiveness of the improved Gaussian cloud model and providing a reference for its application in assessment methods. Literature "Wang Jinguo, Wang Yabin, Guo Yurong, et al. Research on evaluation of wartime maintenance equipment supply and support capability based on CM-FCE-AHP[J]. Acta Armamentarii Sinica, 2024, 45(03):1010-1024." In view of the high complexity, randomness and dynamics of wartime maintenance equipment supply and support, which leads to the uncertainty and ambiguity of wartime maintenance equipment supply and support evaluation, a hierarchical analysis method and fuzzy comprehensive evaluation method based on cloud model are proposed to objectively evaluate the wartime maintenance equipment supply and support capability. The evaluation results show that the evaluation method can effectively solve the high ambiguity and uncertainty in the evaluation of wartime maintenance equipment supply and support capability, and improve the robustness and objectivity of the evaluation results. Reference "Zhou Wenming, Cui Dekang, Zhou Jingyi, et al. Hybrid algorithm for evaluating the support and guarantee capability of storage and supply bases [J]. Systems Engineering and Electronics, 2022, 44(09): 2832-2839." In view of the lack of effective model algorithms for evaluating the storage and supply capability of wartime materials, this paper proposes a comprehensive use of definition quantification, assessment and evaluation, fuzzy algorithm, hierarchical analysis, queuing network and index synthesis algorithms to calculate the values ​​of indicators such as personnel quality, warehousing, loading and unloading, delivery, command and control, and information guarantee. The example analysis verifies the effectiveness of the proposed algorithm, which provides a basis for auxiliary decision-making and information system design for the storage and supply guarantee of combat materials in wartime.Literature "He Huafeng, Wang Yifan, He Yaomin, et al. Comprehensive performance evaluation method of missile weapon system time synchronization network [J]. Acta Aeronautica Sinica, 2021, 42(06): 541-555." In view of the problems that the existing evaluation methods of missile weapon system time synchronization network are not comprehensive and objective, a comprehensive performance evaluation method of missile weapon system time synchronization network based on weight coefficient optimization is proposed. The effectiveness of the above evaluation process is verified through cases. The results show that the evaluation system can effectively reflect the availability of each subsystem and equipment in the missile weapon system time synchronization network, and is measurable and operable.

[0004] Through the analysis of the above literature, it can be seen that there are many shortcomings in the evaluation of the comprehensive emergency support capability of civil airports. In terms of the construction of the index system, there is a lack of comprehensive and overall evaluation of civil airport emergencies. In terms of practical application, it lacks certain versatility and operability. In terms of determining the weight of the index system, there are problems such as over-reliance on subjective factors, insufficient data accuracy, unreasonable quantification of relevant indicators, and lack of reasonable basis for the assignment of indicator weights. In terms of support capability assessment, there are problems such as relatively simple assessment methods, unrealistic assessment results, and strong subjectivity of assessment results. Summary of the invention

[0005] In view of the shortcomings of the above-mentioned prior art, the present invention proposes a civil airport emergency support capability assessment method for evaluating from both qualitative and quantitative perspectives, improving the assessment accuracy, and providing scientific and reasonable assessment results for civil airport emergency management and decision-making.

[0006] The present invention achieves the above-mentioned purpose through the following technical solutions, and proposes a civil airport emergency support capability assessment method comprising:

[0007] Step 1: Construct an evaluation index system for emergency support capabilities of civil airports;

[0008] Step 2: Determine the comprehensive weight of each indicator in the civil airport emergency support capability evaluation index system based on the multi-expert sequence-information entropy weight combination method;

[0009] Step 3: Based on the comprehensive weights of each indicator, the fuzzy comprehensive evaluation method is used to evaluate the emergency support capability of civil airports for each indicator;

[0010] Step 4: Output the assurance capability assessment results.

[0011] Furthermore, the civil airport emergency support capability assessment index system constructed in step 1 includes six secondary indicators: airport operation capability guarantee, staff guarantee, material spare parts guarantee, emergency equipment guarantee, emergency plan quality guarantee and airport environment guarantee. The secondary indicators include a total of 42 tertiary indicators.

[0012] Furthermore, the specific steps of step 2 include:

[0013] Step 2.1: Score the 6 secondary indicators and 42 tertiary indicators respectively through the expert scoring method;

[0014] Step 2.2: Determine the fusion subjective weights of all experts for each indicator based on the multi-expert sequence analysis method;

[0015] Step 2.3: Determine the objective weights of all experts for each indicator based on the information entropy weight method;

[0016] Step 2.4: By introducing the preference coefficient, the subjective weight and the objective weight are integrated to obtain the comprehensive weight of each indicator.

[0017] Furthermore, the specific steps of step 2.2 include:

[0018] Step 2.2.1: According to the expert scoring results, construct the order relation sets {x 1 ,x 2 ,x 3 ,...,x 6} and {x 1 ,x 2 ,x 3 ,...,x 42};

[0019] Step 2.2.2: For the secondary and tertiary indicators respectively, select the most important one and mark it as Then filter out the next most important indicators and Finally, we get the initial order relationship set of evaluation index importance and

[0020] Step 2.2.3: Based on the initial order relationship set of evaluation index importance, calculate the importance ratio r between adjacent indicators according to the following formula: k :

[0021]

[0022] in, is the initial importance value of the kth indicator, is the initial importance value of the k-1th indicator, which is assigned by experts;

[0023] And, r k The following formula must be satisfied:

[0024] r k × k-1>1,k=n,n-1,n-2,...,3,2;

[0025] Step 2.2.4: According to the importance ratio, the subjective weight coefficient of the kth indicator is obtained by the following formula:

[0026]

[0027] Next, based on The subjective weight coefficients of the remaining indicators are calculated by the following formula:

[0028]

[0029] Step 2.2.5: Determine whether the judgment results of the experts are consistent in the process of judging the initial sequence relationship. If so, proceed to step 2.2.6; otherwise, proceed to step 2.2.7;

[0030] Step 2.2.6: Calculate the fused subjective weight value of the kth indicator by the expert group composed of J experts using the following formula:

[0031]

[0032] Among them, ω″ k is the fused weight value of the kth indicator;

[0033] Next, the fused subjective weight values ​​of the remaining indicators are calculated using the following formula:

[0034]

[0035] In the formula, is the importance ratio of the fusion weight of the same indicator by different experts, and:

[0036]

[0037] Among them, r k,j Represents adjacent evaluation index and The importance ratio between them;

[0038] Step 2.2.7: If there is J 0 Experts on the indicator {x 1 ,x 2 ,x 3 ,...,x n} are completely consistent, then first calculate these J 0 The subjective weight coefficients of the n indicators given by the experts Then calculate the remaining JJ 0 Each for The weight coefficient ω′k,j ,in Indicates the evaluation results of adjacent evaluation indicators;

[0039] Next, put this JJ 0 The weight coefficients obtained by the experts are averaged to obtain this JJ 0 The fusion subjective weight value ω′ of the experts k :

[0040]

[0041] Finally, the fused subjective weight of all J experts is calculated by the following formula:

[0042]

[0043] Among them, ω″ k is the fusion subjective weight value of all J experts.

[0044] Furthermore, the specific operation steps of step 2.3 include:

[0045] Step 2.3.1: Construct the object set F based on the secondary indicators, and construct the evaluation indicator set based on the tertiary indicators, and establish the evaluation information matrix R′=(r′ ij ) m×n , where r′ ij represents the evaluation value of the i-th object on the j-th indicator, and uses the Min-max normalization method to normalize r′ ij Perform normalization processing;

[0046] Step 2.3.2: Calculate the information entropy of the i-th indicator by the following formula:

[0047]

[0048] Among them, P ij is the proportion of the index value of the jth third-level index under the i-th second-level index. ij =0

[0049] When P ij ×lnP ij =0;

[0050] and

[0051]

[0052] Step 2.3.2: Calculate the objective weight of the ith indicator according to the information entropy of the ith indicator by the following formula:

[0053]

[0054] Among them, vi is the objective weight of the ith indicator.

[0055] Furthermore, the specific operation steps of step 2.4 include:

[0056] Step 2.4.1: Based on step 2.2, the subjective weight vector of the evaluation index of the i-th expert is obtained as follows:

[0057] ω″ i =(ω″ i1 ,ω″ i2 ,ω″ i3 ,...,ω″ im )

[0058] Among them, 0<ω″ im <1 and m is the number of indicators, Q is the total number of experts;

[0059] Step 2.4.2: Based on step 2.3, the objective weight vector of the evaluation index is:

[0060] v=(v 1 ,v 2 ,v 3 ,...,v m )

[0061] Among them, 0 <v i <1 and

[0062] Step 2.4.3: Introduce the preference coefficient β and calculate the comprehensive weight vector by the following formula:

[0063]

[0064] Among them, W i is the comprehensive weight vector.

[0065] Furthermore, the specific operation steps of step 3 include:

[0066] Step 3.1: G indicators in the evaluation index form an evaluation factor set, and The evaluation factor set is denoted as U = {u 1 ,u 2 ,u 3 ,...,u n};

[0067] Step 3.2: Select different rating levels to construct a comprehensive evaluation review set H = {h 1 ,h 2 ,h 3 ,...,h p};

[0068] Step 3.3: Assign the comprehensive weight vector W to the n elements in the factor set U;

[0069] Step 3.4: For the i-th element in the evaluation factor set U, its membership to the j-th element in the comment set H is recorded as e ij , thus constructing the fuzzy comprehensive evaluation matrix E:

[0070]

[0071] In the formula, E p represents the evaluation result of the pth factor; e gp represents the membership of the g-th factor to the p-th evaluation level; g represents the number of elements in the evaluation factor set; p represents the number of levels in the comprehensive evaluation comment set;

[0072] Step 3.5: Determine different membership functions according to the type of indicator;

[0073] Step 3.6: Evaluate each factor in the evaluation factor set U through a multi-level fuzzy comprehensive evaluation model to obtain the evaluation score of the overall performance of the system;

[0074]

[0075] Among them, F represents the final score of the system evaluation; Represents fuzzy subsets The normalized value of the jth element in Q j represents the grade value corresponding to the jth evaluation factor;

[0076] Step 3.7: Based on the evaluation scores obtained in step 3.6, the evaluation results of the civil airport emergency support capability are obtained.

[0077] Furthermore, the specific steps of step 3.5 include:

[0078] Step 3.5.1: If the evaluation index is indicator data, proceed to step 3.5.2; otherwise, proceed to step 3.5.3;

[0079] Step 3.5.2: Use expert evaluation statistics to establish a statistical membership function:

[0080]

[0081] Among them, c ij The approval indicator u i Belongs to the evaluation level H j , n is the number of experts participating in the evaluation;

[0082] Step 3.5.3: For four different evaluation levels h jEstablish fuzzy membership functions based on normal distribution respectively:

[0083] When j = 1, the membership function is:

[0084]

[0085] in, is the maximum value among all indicators;

[0086] When j = 2, 3, the membership function is:

[0087]

[0088] in, is the minimum value among all indicators;

[0089] When j=4, the membership function is:

[0090]

[0091] in,

[0092]

[0093] Furthermore, the specific steps of step 3.6 include:

[0094] Step 3.6.1: For the evaluation factor set U, divide it into f subsets:

[0095]

[0096] Among them, U i = {U ik},(i=1,2,...,n;k=1,2,...,f),U i represents the kth subset, each of which contains i evaluation factors;

[0097] Step 3.6.2: For each i-th evaluation factor in each subset, the evaluation is performed according to the single-level fuzzy comprehensive evaluation model, and finally the comprehensive evaluation result of the i-th subset is obtained:

[0098]

[0099] in, represents the fuzzy synthesis operator, w i is the weight set of the judgment factors determined in the single-level fuzzy comprehensive evaluation model. 1 ,w 2 ,w 3 ,...,w n ) T , E iRepresents the subset U i The fuzzy comprehensive evaluation matrix, b if represents the membership of the i-th target to the j-th comment set;

[0100] Step 3.6.3: Perform comprehensive evaluation on all f subsets and construct a comprehensive evaluation decision matrix:

[0101] B * =W×E

[0102] Among them, B * It is the comprehensive evaluation result of all evaluation factors in U;

[0103] Step 3.6.4: Comprehensive evaluation decision matrix B * After normalization, the processed fuzzy subset is expressed as Based on B * The overall performance evaluation score F of the system is calculated by the following formula:

[0104]

[0105] in, is a fuzzy subset The normalized value of the jth element in , which indicates the importance or contribution of the jth evaluation factor in the overall evaluation; Q j is the grade score corresponding to the jth evaluation factor.

[0106] A civil airport emergency support capability assessment system, characterized in that it is implemented based on the civil airport emergency support capability assessment method, and the system includes:

[0107] Evaluation index system construction module, used to construct the evaluation index system of civil airport emergency support capability;

[0108] The indicator weight determination module is used to determine the comprehensive weight of each indicator in the civil airport emergency support capability evaluation indicator system based on the multi-expert sequence-information entropy weight combination method;

[0109] The support capability evaluation module is used to evaluate the emergency support capability of civil airports based on the comprehensive weights of various indicators using the fuzzy comprehensive evaluation method;

[0110] The assessment result output module is used to output the assurance capability assessment results.

[0111] Therefore, the present invention adopts the above-mentioned civil airport emergency support capability assessment method, which has the following beneficial effects:

[0112] First, the present invention can comprehensively reflect the comprehensive capabilities of civil airport emergency response in ensuring travel safety by constructing a multi-dimensional and multi-level evaluation system;

[0113] Second, the present invention combines practical experience with objective data and proposes a multi-expert sequence relationship-information entropy weight combination method to determine the weight of the indicator system, which not only retains the actual insight of experts but also utilizes the objectivity of data, making the weight setting more in line with actual needs and improving the accuracy and reliability of indicator weight determination;

[0114] Third, the present invention constructs a fuzzy comprehensive evaluation model, which can be used to process the uncertainty or fuzzy information in the emergency support capabilities of civil airports, conduct evaluations from both qualitative and quantitative perspectives, improve evaluation accuracy, and provide scientific and reasonable evaluation results for civil airport emergency management and decision-making.

[0115] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0116] Figure 1 The invention is a civil airport emergency support capability index system.

[0117] Figure 2 Flowchart for determining weights of support capability assessment indicators.

[0118] Figure 3 This is a flow chart of support capability assessment based on fuzzy comprehensive evaluation.

[0119] Figure 4a This is the comparison result of the secondary indicator weights of the three methods: multi-expert ranking method, information entropy weight method and multi-expert ranking-information entropy weight combination method.

[0120] Figure 4b This is the comparison result of the three-level indicator weights of the three methods: multi-expert ranking method, information entropy weight method and multi-expert ranking-information entropy weight combination method.

[0121] Figure 5a This is the comparison result of the weights of technical support capability indicators of three methods: multi-expert sequence method, information entropy weight method and multi-expert sequence-information entropy weight combination method.

[0122] Figure 5b The comparison results of indicator weights are guaranteed for technicians of three methods: multi-expert sequence method, information entropy weight method and multi-expert sequence-information entropy weight combination method.

[0123] Figure 5c The comparison results of material spare parts guarantee index weights of three methods: multi-expert sequence method, information entropy weight method and multi-expert sequence-information entropy weight combination method.

[0124] Figure 5d The comparison results of the weights of security protection indicators of three methods: multi-expert sequence method, information entropy weight method and multi-expert sequence-information entropy weight combination method.

[0125] Figure 5e This is the comparison result of the weights of quality assurance indicators of emergency plans among the three methods: multi-expert sequence method, information entropy weight method and multi-expert sequence-information entropy weight combination method.

[0126] Figure 5f This is the comparison result of the weights of environmental protection level indicators using three methods: multi-expert sequence method, information entropy weight method, and multi-expert sequence-information entropy weight combination method.

[0127] Figure 6 It is the membership degree of the qualitative assessment of emergency response for three types of civil airports.

[0128] Figure 7 It is the final score of the quantitative evaluation of emergency response for three types of civil airports. DETAILED DESCRIPTION

[0129] In the description of the present invention, it is also necessary to explain that, unless otherwise clearly specified and limited, these embodiments are only used to illustrate the present invention and are not used to limit the scope of the present invention. In addition, it should be understood that after reading the content taught by the present invention, those skilled in the art make various changes or modifications to the present invention, and these equivalent forms also fall within the scope limited by the claims attached to the application.

[0130] The present invention proposes a method for evaluating the emergency support capability of a civil airport, which comprises the following steps:

[0131] Step 1: Construct an evaluation index system for emergency support capabilities of civil airports;

[0132] Step 2: Determine the comprehensive weight of each indicator in the civil airport emergency support capability evaluation index system based on the multi-expert sequence-information entropy weight combination method;

[0133] Step 3: Based on the comprehensive weights of each indicator, the fuzzy comprehensive evaluation method is used to evaluate the emergency support capability of civil airports for each indicator;

[0134] Step 4: Output the assurance capability assessment results.

[0135] The key technologies of each of the above steps are introduced below:

[0136] 1. Civil airport emergency support capability assessment index system

[0137] The evaluation of the emergency support capability of civil airports is of great significance for ensuring the safe operation of airports, improving the efficiency of emergency response, promoting continuous improvement of airports, meeting regulatory requirements and enhancing public confidence. Therefore, constructing a scientific, comprehensive and quantifiable evaluation index system can accurately reflect the actual effectiveness of civil airport emergencies in all aspects and provide decision makers with strong data support and decision-making basis. The establishment of the evaluation index system follows the principles of systematicity, scientificity, rationality and independence to ensure that the indicators can comprehensively cover all aspects of the emergency support capability of civil airports, while avoiding cross-repetition between indicators and improving the accuracy and reliability of the evaluation results. Based on this, the present invention constructs an evaluation index system for the emergency support capability of civil airports from the perspective of coping with complex security challenges. The index system includes six secondary indicator dimensions: airport operation capability guarantee, staff guarantee, material spare parts guarantee, emergency equipment guarantee, plan quality guarantee and airport environment guarantee. The secondary indicator dimension also includes a total of 42 specific third-level indicator dimensions.

[0138] Figure 1 This is the indicator system of emergency support capability of civil airports. As can be seen from the figure, the airport operation capability support indicators include flight regularity rate, daily average flight support total duration, daily maximum flight support total duration, airport peak hour average flight, airport peak hour average daily proportion, daily maximum support flight, daily average support flight, annual flight support volume, number of routes and average delay time; the staff support indicators include the proportion of licensed personnel, the number of emergency support personnel per unit area, the average duration of professional skills training, the average number of people on duty per day, the average number of actual drills, the average daily working hours, theoretical examination results, and skill level results; the material and spare parts support indicators include oil storage and water storage. , spare parts guarantee, supporting crew tools, civil airport information and technical information of important equipment; the emergency equipment guarantee index includes the performance of emergency facilities, the proportion of overdue equipment, the average maintenance period, the average service life, the number of emergency guarantee equipment per unit area, the matching of safety equipment or software, and the update of safety data; the quality assurance index of the emergency plan includes the comprehensive emergency guarantee plan, the special emergency guarantee plan, the material and equipment plan, the on-site handling procedure plan, the emergency handling plan, and the risk assessment report; the airport environment guarantee index includes the proportion of complex weather, the proportion of unit available airspace, the proportion of airport parking spaces, the proportion of runway assessment capacity, and the proportion of electromagnetic environment interference.

[0139] 2. Multi-expert sequence-information entropy weight combination method

[0140] As a key indicator to measure whether the emergency system of civil airports can operate effectively and achieve predetermined goals under specific conditions, the determination of the weights of each indicator is crucial to the accuracy of the evaluation results. It not only directly affects the rationality and effectiveness of the evaluation results, but also affects the scientific nature of decision-making. The present invention combines the advantages of subjective weighting method and objective weighting method, and proposes a multi-expert sequence-information entropy weight combination method to determine the indicator weights, which combines expert knowledge and objective data analysis, thereby improving the accuracy and scientific nature of the weights.

[0141] (1) Multi-expert sequence relationship method

[0142] The multi-expert order relationship method includes the following steps:

[0143] Step 1: Construct the initial relationship set;

[0144] Specifically, experts in the field of study are identified, an expert assessment team is formed, and the experts' prior knowledge and expertise are used to conduct an assurance capability assessment.

[0145] First, according to the indicator system of each layer of civil airport emergency support capability evaluation, the order relationship set of indicators is constructed respectively, and the order relationship set constructed by each layer of indicators is recorded as: {x 1 ,x 2 ,x 3 ,...,x n},against Figure 1 The indicator system shown in the figure is to construct 6 secondary indicators to construct the order relationship set {x 1 ,x 2 ,x 3 ,...,x 6} and 42 third-level indicators to construct the order relation set {x 1 ,x 2 ,x 3 ,...,x 42};

[0146] Then, through expert consultation, questionnaire survey and data analysis, the most important indicator in each level is selected and recorded as: Then select the next most important indicator and record it as Until all indicators at this level are screened out, they are finally recorded as That is, the secondary and tertiary indicators are screened respectively to obtain and in, The specific score of the expert, i.e. the initial value, can refer to Table 1 below when the expert scores;

[0147] Finally, after n-1 screenings, the initial order relationship set of evaluation index importance is obtained, as shown in formula (1):

[0148]

[0149] In the formula, x i >x j Represents the indicator x in the evaluation system i X j important;

[0150] Step 2: Determine the relative importance of adjacent indicators in the initial order relationship set of each evaluation indicator's importance;

[0151] The initial importance value variable of each indicator is defined as The initial importance value is the subjective weight coefficient of each indicator. get;

[0152] Next, the importance ratio r between adjacent indicators is calculated according to formula (2): k :

[0153]

[0154] Moreover, the initial importance ratio must satisfy formula (3):

[0155]

[0156] Table 1 is a reference table for assigning indicator importance. According to this table, the importance evaluation result between two adjacent indicators can be obtained.

[0157] Table 1 Reference table for indicator importance assignment

[0158]

[0159] Step 3: Calculate the subjective weight coefficient;

[0160] Initial order relation set according to the importance of evaluation index and importance ratio, the subjective weight coefficient of the kth indicator is obtained by formula (4):

[0161]

[0162] Next, based on the subjective weight coefficient of the kth indicator And the importance ratio of the kth indicator and its adjacent indicators, the subjective weight coefficients of the remaining indicators are calculated by formula (5):

[0163]

[0164] in, is the subjective weight coefficient of the k-1th indicator;

[0165] Step 4: Weight fusion based on expert groups;

[0166] Since the experts in the expert group have different professional backgrounds, their understanding depth and emphasis on various evaluation indicators vary, which may lead to differences of opinion during the evaluation process. Therefore, effectively integrating the insights of each expert becomes a core link in weight analysis.

[0167] Assume that there is an expert group composed of J experts who jointly participate in the screening and weight allocation of n evaluation indicators. In order to obtain a unified, integrated weight value ω″ k (where k = 1, 2, ..., n), the following two situations need to be analyzed respectively:

[0168] 1) Experts’ initial order relation set judgment is consistent

[0169] In the process of experts judging the initial order relationship, if it is believed that the judgments of the experts are consistent, the evaluation results of expert j (j∈{1,2,3,...,J}) for adjacent evaluation indicators are set and The importance ratio between them is r k,j ,Right now Here and They represent the initial importance evaluation of expert j on the kth and k-1th indicators respectively. Based on this setting, the weight calculation formula after fusion can be further derived (Formula 6-Formula 7), through which the weight value after fusion can be calculated;

[0170] First, the weight value of the kth indicator is calculated by formula (6):

[0171]

[0172] Among them, r i * It represents the importance ratio of the fusion weight of different experts on the same indicator (i-th indicator);

[0173] Secondly, the remaining fusion weights are calculated based on the weight value of the kth indicator and the ratio of each weight coefficient. The calculation formula of the fusion weights is shown in formula (7):

[0174]

[0175] Among them, the fusion weight importance ratio for:

[0176]

[0177] 2) The experts’ initial order relation set judgment is inconsistent

[0178] Assume that J 0 Experts on the indicator {x 1 ,x 2 ,x 3 ,...,x n The order relation judgment of} is completely consistent. According to formula (4), these J 0 The weight coefficients corresponding to the experts are recorded as

[0179] On the other hand, there is JJ 0 The initial order relationship judgments of the experts are not consistent. They each give a different ranking in the form of where j = 1, 2, ..., JJ 0 .

[0180] Since the ordered set {x k} and indicator importance There is a clear mapping relationship between them, so for this JJ 0 For each of the experts, we can use formula (4) to find their respective The weight coefficient is denoted as ω′ k,j (where k = 1, 2, ..., n). In order to integrate this 0 The opinions of the experts are calculated by arithmetically averaging the weight coefficients obtained by each of them, and the result is used as the 0 The fusion weight value of the experts is uniformly recorded as ω′ k :

[0181]

[0182] This process ensures that even if there are differences of opinion, representative weight coefficients can be integrated through scientific methods.

[0183] In summary, when the initial order of experts is inconsistent, the weight after fusion can be obtained by the following formula, denoted as ω″ k :

[0184]

[0185] in, Indicates J 0 The weight coefficient corresponding to each expert, ω′ k Indicates JJ 0 The fusion weight of the experts, ω″ k is the fusion weight value of all J experts;

[0186] Through the analysis of the above two situations, we can deal with the differences among expert groups in the weight allocation process in a more systematic and scientific way, ensuring that the final fusion weight not only reflects the collective wisdom of the experts, but also has a certain degree of objectivity and rationality.

[0187] (2) Information Entropy Weight Method

[0188] The information entropy weight method is an objective weighting method based on the principle of information entropy. In the comprehensive evaluation, if the variation of a certain evaluation index is small, that is, the values ​​of each evaluation object on this index are relatively close, then the amount of information provided by this index is small, and the impact on the comprehensive evaluation is also small, so it should be given a lower weight; on the contrary, if the variation of a certain evaluation index is large, that is, the values ​​of each evaluation object on this index are very different, then the amount of information provided by this index is large, and the impact on the comprehensive evaluation is also greater, so it should be given a higher weight. It includes the following steps:

[0189] Step 1: Standardize the initial data;

[0190] Set two sets, namely object set F and indicator set C. Object set F includes all objects that need to be evaluated, such as F 1 ,F 2 ,F 3 ,...,F n , where n is the total number of objects. The metric set C contains all the metrics used to evaluate these objects, such as C 1 ,C 2 ,C 3 ,...,C m , where m is the total number of indicators.

[0191] Construct the following evaluation information matrix:

[0192]

[0193] Among them, the element r′ ij represents the evaluation value of the i-th object on the j-th indicator, where i = 1, 2, 3, ..., m; j = 1, 2, 3, ..., n;

[0194] Each object has an evaluation value according to each indicator, and a complete evaluation information matrix is ​​constructed to provide a basis for subsequent data analysis. This method ensures the integrity and consistency of the data and provides important reference information for decision makers.

[0195] In the system, since the dimensions of each factor are different and the magnitude of the values ​​may be significantly different, it is not convenient to directly compare these data. Therefore, the Min-max normalization method is used to normalize the original data. The Min-max normalization method includes:

[0196] For the case where the larger the index value, the better, then:

[0197]

[0198] For the case where the smaller the index value, the better, then:

[0199]

[0200] Among them, max{r′ ij} and min{r′ ij} respectively represent the maximum and minimum values ​​of the i-th row in the evaluation information matrix R′; r ij represents the normative value of the i-th row and j-th column, which can form the normative matrix R:

[0201]

[0202] Among them, i=1,2,3,…,m; j=1,2,3,…,n.

[0203] The matrix R is usually used to convert data of different dimensions or orders of magnitude into a common evaluation standard.

[0204] Step 2: Calculate the information entropy of the indicator;

[0205] In the core of solving the multi-index decision-making model, the key lies in establishing the weight distribution relationship between various indicators. When a factor shows a significant difference in its contribution to a specific evaluation indicator, the evaluation indicator should be given a higher weight, that is, the entropy weight, and the same logic is reversed. By incorporating the concept of entropy weight into the decision-making process, the unique contribution of different factors to each indicator can be effectively highlighted, while the similarity between indicators can be weakened. Furthermore, the entropy weight is combined with the initial indicator weight assigned by experts to form a comprehensive weight system, which can not only fully absorb the professional insights of experts, but also enhance the ability to identify the contribution of evaluation indicators with the help of the entropy weight mechanism, thereby making the final evaluation results more objective and scientific.

[0206] The proportion P of the index value of the jth object under the calculation of the i-th index ij When (for example, when calculating the proportion of the index value of the first object suitable temperature under the sixth index environmental protection level index P 61 ), first we need to determine the sum (or total amount) of all objects under this indicator. Assuming there are n objects, we can calculate the proportion of the value of the jth object under the i-th indicator to the total of this indicator, that is:

[0207]

[0208] The proportion of the index value P ijIt is a number between 0 and 1, reflecting the relative importance or contribution of the object under the indicator. The sum of the weights of all objects (under the same indicator) is equal to 1, that is:

[0209]

[0210] According to formula (15), the entropy S of the i-th indicator is obtained i for:

[0211]

[0212] Among them, when P ij =0, P ij ×lnP ij =0.

[0213] In multi-criteria decision analysis, the concept of information entropy is used to measure the amount of information provided by each indicator. i The smaller the value, the more information the indicator provides, and therefore the more important the indicator is in the decision-making process.

[0214] Step 3: Determine objective weights;

[0215] According to formula (17), the information entropy of the ith indicator is obtained, and the objective weight v of the ith indicator is calculated using the information entropy according to formula (18): i :

[0216]

[0217] Weight v i It reflects the relative importance of this indicator among all indicators.

[0218] Based on the above-mentioned information entropy weight method and combined with the multi-expert sequence relationship analysis method, a multi-expert sequence-information entropy weight combination method is obtained.

[0219] In order to balance the advantages of multi-expert sequence relationship analysis method and information entropy weight method, and avoid bias caused by experience limitations or data errors when used alone, this paper adopts a combined strategy based on multi-expert sequence-information entropy weight method. This strategy aims to integrate subjective judgment and objective laws to ensure the comprehensiveness and rationality of the evaluation weights.

[0220] like Figure 2 As shown in the figure, let the total number of invited experts be Q and the number of indicators to be evaluated be m. First, based on the multi-expert sequence relationship analysis method, the subjective weight vector of the i-th expert on the evaluation indicator is ω″ i =(ω″ i1 ,ω″ i2 ,ω″ i3 ,...,ω″ im), where each weight satisfies 0<ω″ im <1 and the sum of all weights is 1, that is

[0221] Secondly, the entropy weight method is used to calculate the objective weight vector v of the evaluation index = (v 1 ,v 2 ,v 3 ,...,v m ), similarly, v needs to satisfy 0 <v i <1 and the sum is 1, that is

[0222] Finally, by introducing the preference coefficient (denoted as β, representing the proportion of the subjective weight of the multi-expert ranking method in the combined weight), the above two weights are effectively combined to establish the multi-expert ranking method-information entropy weight method, and the comprehensive weight vector W = (w 1 ,w 2 ,w 3 ,...,w q ) T The calculation formula of the comprehensive weight vector is:

[0223]

[0224] Through the above formula, while respecting the subjective opinions of experts, the objective characteristics of the data are also fully considered, thereby improving the scientificity and accuracy of the evaluation weights.

[0225] 3. Evaluation of emergency support capabilities of civil airports based on fuzzy comprehensive evaluation

[0226] The comprehensive evaluation of the emergency support capabilities of civil airports aims to comprehensively and systematically evaluate the comprehensive capabilities of civil airports in terms of ensuring the safety of the masses and responding to emergencies through scientific methods and means. However, in actual operations, there are still problems such as inconsistent evaluation standards, difficulty in data collection, lack of transparency in the evaluation process, and insufficient application of evaluation results. Therefore, the present invention uses a fuzzy comprehensive evaluation method on the basis of determining the weights of each indicator based on the expert sequence-information entropy weight combination method to deal with the situation where there is a fuzzy relationship between the evaluation indicators and the weights are difficult to determine. Ultimately, a scientific, reasonable, and practical quantitative evaluation can be made for the emergency support capabilities of civil airports that contain information and are ambiguous. This method fully takes into account the complexity and uncertainty of the evaluation factors, so that the evaluation results are more in line with the actual situation. It specifically includes the following steps:

[0227] Step 1: Establish a set of evaluation factors;

[0228] The evaluation factor set is a collection of all factors that may affect the evaluation results of the evaluation object. For the evaluation object of civil airport emergency support capability, all secondary indicators and G indicators in the tertiary indicators constitute the evaluation factor set, and The evaluation factor set U = {u 1 ,u 2 ,u 3 ,...,u n In this set, each element ui represents a specific influencing factor, which is part of the comprehensive consideration of the evaluation object. These influencing factors are often not completely clear or certain, but have varying degrees of ambiguity or uncertainty.

[0229] Step 2: Construct a comprehensive evaluation review set;

[0230] The review set is a collection of a series of evaluation results given by the evaluator based on the performance of the evaluation object. These evaluation results are divided into multiple levels in increasing (or decreasing) order of intensity, quality or quantity. In order to describe these levels in detail, we select p evaluation levels that meet the actual situation and form a set H of these levels, denoted as H = {h 1 ,h 2 ,h 3 ,...,h p In view of the evaluation requirements of civil airport emergency support capabilities, four evaluation levels were selected to form the comment set, namely H = {excellent, good, medium, poor} = {h 1 ,h 2 ,h 3 ,h 4 As shown in Table 2, the evaluation level of the evaluation object can be obtained according to the evaluation interval of the evaluation result score.

[0231] Table 2 Classification of support capability assessment levels

[0232]

[0233] Step 3: Construct indicator weight set;

[0234] In the process of fuzzy comprehensive evaluation, due to the differences in the importance of evaluation factors, it is necessary to 1 ,u 2 ,u 3 ,...,u n} are assigned a weight to quantify their importance. These weights together constitute a weight vector, which is expressed as W = (w 1 ,w 2 ,w 3 ,...,w n ) T, the weight vector is calculated by formula (19). This weight set is formally expressed as a fuzzy set W in fuzzy evaluation theory, where each w i Represents the corresponding factor u i degree of importance.

[0235] Step 4: Construct a comprehensive evaluation matrix;

[0236] For the i-th element in the evaluation factor set U, if its j The membership degree of the jth element in (j=1,2,…,p) is e ij , then the result of the element in the single factor evaluation can be expressed by fuzzy set E i ={e i1 ,e i2 ,e i3 ,...,e ip}, and E i As a fuzzy subset of the comprehensive evaluation comment set H. 1 ,E 2 ,…,E p-1 ,E p Combination can form a fuzzy comprehensive evaluation matrix E:

[0237]

[0238] In the formula, E p represents the evaluation result of the pth factor; e gp It represents the membership of the g-th factor to the p-th evaluation level, revealing the fuzzy relationship between the evaluation factor and the evaluation level expressed by the membership. This membership reflects the extent to which the evaluation factor belongs to or conforms to a specific evaluation level, thus reflecting the uncertainty and fuzziness in the evaluation process. g represents the number of elements in the evaluation factor set; p represents the number of levels in the comprehensive evaluation comment set.

[0239] According to different indicator types, select the appropriate membership function. If the indicator is indicator data, use the statistical membership function; if the indicator is a value that cannot be directly quantified (only good, medium, or poor grades can be given), use the fuzzy membership function based on normal distribution. The specific steps to determine the membership function are as follows:

[0240] 1) Establish a statistical membership function through expert evaluation statistics;

[0241] Assume that there are n experts participating in the evaluation, and each expert will directly evaluate the indicator based on the relevant parameters of the indicator. ij For the factor of approval i (where i = 1, 2, ..., n) belongs to the evaluation level Hj (j=1,2,…,p) experts, then the membership degree e ij Calculated as:

[0242]

[0243] Similarly, for statistical indicators based on historical data, the historical data range can be evenly divided into corresponding intervals according to the evaluation level, and then the frequency in each interval can be calculated and used as the degree of membership of the indicator relative to each evaluation level.

[0244] 2) Establish a fuzzy membership function based on normal distribution;

[0245] First, the indicator data needs to be normalized. According to historical records or expert settings, the maximum value of each indicator is selected. and minimum value And the interval Divide into p-1 subintervals according to the number of evaluation levels;

[0246] Next, define the approval factor u i (i=1,2,...,n) belongs to evaluation level h j The membership function of (j=1, 2, ..., n) is different for different j (n=4) corresponding to different evaluation levels (excellent, good, medium, poor):

[0247] When j = 1, the membership function is:

[0248]

[0249] When j = 2, 3, the membership function is:

[0250]

[0251] When j=4, the membership function is:

[0252]

[0253] in,

[0254]

[0255] Step 5: Fuzzy comprehensive evaluation;

[0256] Since fuzzy comprehensive evaluation involves many factors, and the weight assigned to each factor is often very small, this may cause the comprehensive evaluation to fail to achieve the expected effect. Therefore, the present invention adopts an evaluation method of a multi-level model.

[0257] (1) Single-level fuzzy comprehensive evaluation model

[0258] First, determine the weight set of the first-level factors W = (w 1 ,w 2 ,w 3 ,...,w n ) T ,in

[0259] Then, the membership degree of each factor is obtained through fuzzy transformation Among them, b in represents the membership degree of the nth item of civil airport emergency i, E i Represents the fuzzy comprehensive evaluation matrix of the first-level factors;

[0260] Finally, the first-level comprehensive evaluation result B of U is obtained:

[0261]

[0262] Where, ° represents the fuzzy synthesis operator; b j (i=1, 2, ..., p) represents the degree of membership of the target to the j comment sets.

[0263] (2) Multi-level fuzzy comprehensive evaluation model

[0264] like Figure 3 As shown, for the evaluation factor set U, according to a specific attribute, it is divided into f subsets U f , these subsets should satisfy the following conditions:

[0265]

[0266] The factors within each subset have similar attributes or characteristics, so the second-level evaluation factor set U = {u 1 ,u 2 ,...,u f}. U in formula (27) i = {U ik}, (i=1,2,...,n;k=1,2,...,f) represents the kth subset, each of which contains i evaluation factors.

[0267] For each i-th evaluation factor in each subset, the evaluation is performed according to the single-level fuzzy comprehensive evaluation model. i The factor weights in are assigned as W i , its fuzzy comprehensive evaluation matrix is ​​E i , then the comprehensive evaluation result of the i-th subset is obtained:

[0268]

[0269] Among them, bif represents the membership of the i-th target to the j-th comment set;

[0270] Then, all f subsets are comprehensively evaluated, and the evaluation decision matrix is:

[0271] B * =W×E ​​(29)

[0272] Among them, B * It is both the comprehensive evaluation result of U and the comprehensive evaluation result of all evaluation factors in U.

[0273] If there are still many factors in U, it can be further divided to obtain more levels of fuzzy comprehensive evaluation models. The multi-level fuzzy comprehensive evaluation model can not only reflect the different levels of evaluation factors, but also avoid the problem of difficulty in allocating weights due to too many factors.

[0274] Finally, the final evaluation score is determined. Assuming that the multi-level fuzzy comprehensive evaluation results in a score of B * , for B * After normalization, the processed fuzzy subset is expressed as Based on this fuzzy subset, the evaluation result of the overall system performance is calculated by formula (31):

[0275]

[0276] Among them, F represents the final score of the system evaluation, which comprehensively reflects the performance of the system in various evaluation factors; is a fuzzy subset The normalized value of the jth element in , which indicates the importance or contribution of the jth evaluation factor in the overall evaluation; Q j It is the grade score corresponding to the jth evaluation factor. This score is determined according to the evaluation criteria or the preset grade system, and reflects the performance level of the factor under specific conditions.

[0277] By calculating the weighted sum of the fuzzy subsets and the factor grade scores, the information of multiple evaluation factors is effectively integrated, providing a quantitative and comprehensive method for evaluating the overall performance of the system.

[0278] Finally, by comparing the total system score with the grade classification table (Table 2), the emergency support capability of civil airports can be accurately assessed.

[0279] As can be seen from the above, given that the civil airport emergency support capability evaluation system involves many evaluation factors, if only the first-level comprehensive evaluation model is used, it will not only face the problem of numerous evaluation factors and complex weight distribution, but also may lead to small values ​​in the evaluation matrix E, making it difficult to effectively distinguish the order of advantages and disadvantages between the evaluation objects, and thus unable to obtain evaluation results with practical significance. Therefore, in actual operation, in order to improve the accuracy and effectiveness of the evaluation, a hierarchical evaluation strategy can be adopted: first, the evaluation factors are reasonably divided into several categories; then, an independent fuzzy comprehensive evaluation is carried out for each category; finally, based on these classification evaluation results, a higher-level fuzzy comprehensive evaluation is carried out to integrate the evaluation results of each category and form a comprehensive and in-depth evaluation of the overall safety situation.

[0280] Example

[0281] The multi-expert sequence-information entropy weight combination method and the fuzzy comprehensive evaluation method proposed in the present invention are verified below.

[0282] 1. Verification of multi-expert sequence-information entropy weight combination method

[0283] The above-mentioned civil airport emergency support capability evaluation index system includes 6 secondary indicators and 42 tertiary indicators. Based on this index system, the index weight determination based on the multi-expert sequence-information entropy weight combination method is verified. Taking the calculation of secondary indicator weights as an example, Table 3 shows the initial scoring results of 5 experts on 6 secondary indicators.

[0284] According to formulas (1) to (3), the initial order relationship ranking of the six secondary indicators by each expert and the importance ratio between adjacent indicators are obtained, as shown in Table 4. According to Table 4, each expert has different understandings of the importance of secondary indicators based on his or her own professional knowledge and experience.

[0285] Table 3 Expert scores of secondary indicators

[0286]

[0287] Table 4 Importance ratio of experts' scores for secondary indicators

[0288]

[0289] according to Figure 4a It can be seen that for the six secondary indicators, in the multi-expert ranking method, the material spare parts guarantee C 3 The indicator weight value is the largest, staff security C 2 The index weight value is the smallest; in the information entropy weight method, emergency equipment guarantee C 4 The indicator weight value is the largest, staff security C 2The index weight value is the smallest; in the multi-expert sequence-information entropy weight combination method, the airport operation capacity guarantee C 1 The indicator weight value is the largest, staff security C 2 The indicator weight value is the smallest.

[0290] When considering the impact of secondary indicators on tertiary indicators, Figure 4b It can be seen that for the 42 third-level indicators, in the multi-expert ranking method, complex weather accounts for C 61 The indicator weight value is the largest, and the theoretical examination situation is C 23 and the average duration of professional skills training C 27 The indicator weight value is the smallest; in the information entropy weight method, the natural disaster plan C 54 The indicator weight value is the largest, and the theoretical examination situation is C 23 The index weight value is the smallest; in the multi-expert sequence-information entropy weight combination method, complex weather accounts for C 61 The indicator weight value is the largest, and the theoretical examination situation is C 23 The indicator weight value is the smallest. Through the analysis of the results, among the three methods, the staff guarantees C 2 The indicator has the lowest importance, which is in line with the emergency support operation mechanism of civil airports. All three methods evaluate the theoretical test situation C 23 The index weight value is the smallest, and the consistency is reached. For the maximum weights of the secondary and tertiary indexes, the multi-expert sequence-information entropy weight combination method combines the empiricism of the expert sequence method and the objectivity of the information entropy weight method, so that the weight determination process not only takes into account the professional judgment of experts, but also avoids the subjectivity and limitations of a single method. Through the expert's experience guidance and quantitative analysis of information entropy, the disparity in weight determination caused by the subjective and objective factors of the multi-expert sequence method and the information entropy weight method is balanced, which can more accurately reflect the actual role of each indicator in the emergency technical support capability of civil airports and improve the accuracy and scientificity of the weights.

[0291] When the impact of secondary indicators on tertiary indicators is not considered, Figure 5a-5f The comparison of the weights of the indicators based on the three methods. Under the airport operation capacity guarantee indicator, the three methods all evaluate the total time of the highest flight support in a single day, C 14 The indicator has the largest weight, and the average daily proportion of airport peak hours is C 15 The index weight is the smallest, and the number of routes evaluated by the traditional expert ranking method is C 19 The indicator weight is the smallest. Under the staff support indicator, all three methods evaluate the number of emergency support personnel per unit area C 22 The indicator has the largest weight, and the theoretical examination situation is C 23 The indicator weight is the smallest, and the traditional expert ranking method evaluates the average daily working hours C 27 The indicator weight is the smallest. Under the material spare parts guarantee indicator, all three methods evaluate the spare parts guarantee C 33The indicator has the largest weight, and the supporting unit tool C 34 The indicator weight is the smallest. Under the emergency equipment guarantee indicator, all three methods evaluate the number of emergency guarantee equipment per unit area C 44 The indicator has the largest weight, and the average service life is C 45 The indicator weight is the smallest. Under the quality assurance indicator of the number of emergency plans, the traditional expert ranking method evaluates the special emergency plan C 52 The indicator with the largest weight is the comprehensive emergency support plan C 51 and emergency response plan C 55 The index weight is the smallest, while the traditional information entropy weight method and the expert sequence method-information entropy weight combination method both evaluate the on-site disposal procedure plan C 54 The indicator with the largest weight is the comprehensive emergency support plan C 51 The indicator weight is the smallest. Under the airport environmental protection indicator, all three methods evaluate the proportion of complex weather C 61 The indicator has the largest weight, and the number of airport parking spaces accounts for C 62 The indicator weight is the smallest. For the third-level indicators under the same second-level indicators, the evaluation results of the three methods are basically consistent, but the weight values ​​are different. 5 Hierarchical indicators, the traditional information entropy weight method and the proposed method are based on a large amount of actual data, and the evaluation results are in the same state, which shows that the proposed combination method can combine expert experience and objective data to improve the accuracy and scientificity of the weights.

[0292] 2. Verification of fuzzy comprehensive evaluation method

[0293] Through the investigation of airport data in recent years and the consultation of relevant aviation industry experts, 42 index data were divided into intervals, and three types of civil airport emergency data were randomly generated: Airport 1, Airport 2 and Airport 3. The civil airport emergency support capability index system constructed according to the present invention is based on the three types of civil airport emergency data for comparative analysis.

[0294] Since the matrix of 42 three-level indicators is relatively large, the airport environment protection C in Airport 1 is used as the 6 As an example, according to the fuzzy comprehensive evaluation model, a qualitative evaluation can be performed to obtain the evaluation matrix E of the five third-level indicators under the airport environmental protection subset. C6 .

[0295]

[0296] Based on the multi-expert sequence-information entropy weight combination method, the weights of the five third-level indicators under the airport environmental protection subset of Airport 1 are obtained. C6 :

[0297]

[0298] According to formulas (28) to (30), the membership matrix of airport environmental protection of airport 1 is determined as follows:

[0299]

[0300] Similarly, the degree of subordination of the secondary indicators of airport 1, namely, airport operation support capability, staff support, material spare parts support, emergency equipment support, and quality assurance of emergency plans, can be obtained and combined into E 工程1 .

[0301]

[0302] The weights are determined by the multi-expert sequence-information entropy weight combination method to obtain the secondary indicator weight matrix W of airport 1 工程1 :

[0303] W 工程1 =[0.221582 0.103493 0.192147 0.156996 0.184514 0.141268]

[0304] Therefore, the membership matrix of the final support capability evaluation of Airport 1 is:

[0305]

[0306] Similarly, the membership matrix of the final support capability evaluation of Airport 2 and Airport 3 can be obtained as follows:

[0307]

[0308] Figure 6 is the membership of the three types of engineering support capabilities. Among the memberships of Airport 1, which are excellent, medium and poor, the excellent membership is the highest (0.5602), followed by the good membership (0.4252). The excellent and good memberships account for a large proportion, accounting for 98.74% of the overall membership, which matches the strong support capabilities of some indicators in Airport 1, but there are still some indicators that are weak, so Airport 1 belongs to the excellent level. In the support capability membership of Airport 2, the medium and poor memberships are the highest and the same (0.3902), followed by the good membership (0.2150), and the medium and poor memberships account for 78.04% of the overall membership, which matches the weak support capabilities of most indicators in Airport 2, so Airport 2 belongs to the medium or poor level. Among the degrees of support capability of Airport 2, the excellent degree is the highest (0.7217), followed by the good degree (0.1796), and the excellent and good degrees account for 90.13% of the overall degree, which matches the better overall indicator support capability of Airport 3, so Airport 3 belongs to the excellent level.

[0309] After qualitative membership evaluation and analysis, we understand the overall status of the three types of projects. Now we conduct a quantitative analysis to convert the comprehensive capability indicators into final support capability scores. Combined with the support capability evaluation level classification in Table 2 in the aforementioned comprehensive evaluation comments, the weighted average method is used to assign membership values, thereby obtaining the final scores of the support capabilities of the three types of projects.

[0310]

[0311] Figure 7 This is the final score of the quantitative evaluation of the three types of projects. According to the ranking of support capabilities, Airport 3 is the best, followed by Airport 1. Both Airport 3 and Airport 1 are of excellent quality with strong support capabilities, while Airport 1 is of poor quality with weak support capabilities. The overall evaluation results are consistent with the actual situation.

[0312] From the above calculation results, it can be seen that for the emergency support capability assessment of civil airports, the fuzzy comprehensive evaluation proposed in the present invention, based on the determination of the indicator weights by the multi-expert sequence-information entropy weight comprehensive method, can better realize the combination of qualitative and quantitative methods, improve the accuracy of the assessment, make the assessment results more comprehensive and in line with reality, and provide a reference for the overall improvement of the support capability.

[0313] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solution of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solution to deviate from the spirit and scope of the technical solution of the present invention.

Claims

1. A method for evaluating the emergency support capability of a civil airport, characterized in that: The following steps are involved: Step 1: Construct an evaluation index system for emergency support capabilities of civil airports; Step 2: Determine the comprehensive weight of each indicator in the civil airport emergency support capability evaluation index system based on the multi-expert sequence-information entropy weight combination method; Step 3: Based on the comprehensive weights of each indicator, the fuzzy comprehensive evaluation method is used to evaluate the emergency support capability of civil airports for each indicator; Step 4: Output the assurance capability assessment results.

2. A method for evaluating emergency support capabilities of a civil airport as claimed in claim 1, characterized in that: The civil airport emergency support capability assessment index system constructed in step 1 includes six secondary indicators: airport operation capability guarantee, staff guarantee, material spare parts guarantee, emergency equipment guarantee, emergency plan quality guarantee and airport environment guarantee. The secondary indicators include a total of 42 tertiary indicators.

3. A method for evaluating emergency support capabilities of a civil airport as claimed in claim 2, characterized in that: The specific steps of step 2 include: Step 2.1: Score the 6 secondary indicators and 42 tertiary indicators respectively through the expert scoring method; Step 2.2: Determine the fusion subjective weights of all experts for each indicator based on the multi-expert sequence analysis method; Step 2.3: Determine the objective weights of all experts for each indicator based on the information entropy weight method; Step 2.4: By introducing the preference coefficient, the subjective weight and the objective weight are integrated to obtain the comprehensive weight of each indicator.

4. A method for evaluating emergency support capabilities of a civil airport as claimed in claim 3, characterized in that: The specific steps of step 2.2 include: Step 2.2.1: According to the expert scoring results, construct the order relation sets {x1,x2,x3,...,x6} and {x1,x2,x3,...,x6} of the secondary indicators and the tertiary indicators respectively. 42 }; Step 2.2.2: For the secondary and tertiary indicators respectively, select the most important one and mark it as Then filter out the next most important indicators and Finally, we get the initial order relationship set of evaluation index importance and Step 2.2.3: Based on the initial order relationship set of evaluation index importance, calculate the importance ratio r between adjacent indicators according to the following formula: k : in, is the initial importance value of the kth indicator, is the initial importance value of the k-1th indicator, which is assigned by experts; And, r k The following formula must be satisfied: r k ×r k-1 >1,k=n,n-1,n-2,...,3,2; Step 2.2.4: According to the importance ratio, the subjective weight coefficient of the kth indicator is obtained by the following formula: Next, based on The subjective weight coefficients of the remaining indicators are calculated by the following formula: Step 2.2.5: Determine whether the judgment results of the experts are consistent in the process of judging the initial sequence relationship. If so, proceed to step 2.2.6; otherwise, proceed to step 2.2.7; Step 2.2.6: Calculate the fused subjective weight value of the kth indicator by the expert group composed of J experts using the following formula: Among them, ω k ″ is the weight value of the kth indicator after fusion; Next, the fused subjective weight values ​​of the remaining indicators are calculated using the following formula: In the formula, is the importance ratio of the fusion weight of the same indicator by different experts, and: Among them, r k,j Represents adjacent evaluation index and The importance ratio between them; Step 2.2.7: If there are J0 experts on the indicators {x1,x2,x3,...,x n } are completely consistent, then we first calculate the subjective weight coefficients of these J0 experts for these n indicators. Then calculate the remaining J-J0 for each The weight coefficient ω k ' ,j ,in Indicates the evaluation results of adjacent evaluation indicators; Next, the weight coefficients obtained by these J-J0 experts are arithmetic averaged to obtain the fusion subjective weight value ω of these J-J0 experts. k ′: Finally, the fused subjective weight of all J experts is calculated by the following formula: Among them, ω k ″ is the fusion subjective weight value of all J experts.

5. A method for evaluating emergency support capabilities of a civil airport as claimed in claim 4, characterized in that: The specific steps of step 2.3 include: Step 2.3.1: Construct the object set F based on the secondary indicators, and construct the evaluation indicator set based on the tertiary indicators, and establish the evaluation information matrix R′=(r ij ′) m×n , where r ij ′ represents the evaluation value of the i-th object on the j-th index, and the Min-max normalization method is used to normalize r ij 'Normalization processing; Step 2.3.2: Calculate the information entropy of the i-th indicator by the following formula: Among them, P ij is the proportion of the index value of the jth third-level index under the i-th second-level index. ij =0 When P ij ×lnP ij =0; and Step 2.3.2: Calculate the objective weight of the ith indicator according to the information entropy of the ith indicator by the following formula: Among them, v i is the objective weight of the ith indicator.

6. A method for evaluating emergency support capabilities of a civil airport as claimed in claim 4, characterized in that: The specific steps of step 2.4 include: Step 2.4.1: Based on step 2.2, the subjective weight vector of the evaluation index of the i-th expert is obtained as follows: oh i ″=(ω i ′1′,ω i ″2,h i ″3,...,oh i ″ m ) Among them, 0<ω i ″ m <1 and i=1,2,3,...,Q;j=1,2,3,...,m, m is the number of indicators, Q is the total number of experts; Step 2.4.2: Based on step 2.3, the objective weight vector of the evaluation index is: v=(v1,v2,v3,...,v m ) Among them, 0 <v i <1 and i=1,2,3,...,Q; Step 2.4.3: Introduce the preference coefficient β and calculate the comprehensive weight vector by the following formula: Among them, W i is the comprehensive weight vector.

7. A method for evaluating emergency support capabilities of a civil airport as claimed in claim 4, characterized in that: The specific steps of step 3 include: Step 3.1: G indicators in the evaluation index form an evaluation factor set, and The evaluation factor set is denoted as U = {u1,u2,u3,...,u n }; Step 3.2: Select different rating levels to construct a comprehensive evaluation comment set H = {h1,h2,h3,...,h p }; Step 3.3: Assign the comprehensive weight vector W to the n elements in the factor set U; Step 3.4: For the i-th element in the evaluation factor set U, its membership to the j-th element in the comment set H is recorded as e ij , thus constructing the fuzzy comprehensive evaluation matrix E: In the formula, E p represents the evaluation result of the pth factor; e gp represents the membership of the g-th factor to the p-th evaluation level; g represents the number of elements in the evaluation factor set; p represents the number of levels in the comprehensive evaluation comment set; Step 3.5: Determine different membership functions according to the type of indicator; Step 3.6: Evaluate each factor in the evaluation factor set U through a multi-level fuzzy comprehensive evaluation model to obtain the evaluation score of the overall performance of the system; Among them, F represents the final score of the system evaluation; Represents fuzzy subsets The normalized value of the jth element in Q j represents the grade value corresponding to the jth evaluation factor; Step 3.7: Based on the evaluation scores obtained in step 3.6, the evaluation results of the civil airport emergency support capability are obtained.

8. A method for evaluating emergency support capabilities of a civil airport as claimed in claim 7, characterized in that: The specific steps of step 3.5 include: Step 3.5.1: If the evaluation index is indicator data, proceed to step 3.5.2; otherwise, proceed to step 3.5.3; Step 3.5.2: Use expert evaluation statistics to establish a statistical membership function: Among them, c ij The approval indicator u i Belongs to the evaluation level H j , n is the number of experts participating in the evaluation; Step 3.5.3: For four different evaluation levels h j Establish fuzzy membership functions based on normal distribution respectively: When j=1, the membership function is: in, is the maximum value among all indicators; When j = 2, 3, the membership function is: in, is the minimum value among all indicators; When j=4, the membership function is: in, 9. A method for evaluating emergency support capabilities of a civil airport as claimed in claim 7, characterized in that: The specific steps of step 3.6 include: Step 3.6.1: For the evaluation factor set U, divide it into f subsets: Among them, U i = {U ik },(i=1,2,...,n;k=1,2,...,f),U i represents the kth subset, each of which contains i evaluation factors; Step 3.6.2: For each i-th evaluation factor in each subset, the evaluation is performed according to the single-level fuzzy comprehensive evaluation model, and finally the comprehensive evaluation result of the i-th subset is obtained: in, represents the fuzzy synthesis operator, w i is the weight set W of the judgment factors determined in the single-level fuzzy comprehensive evaluation model = (w1, w2, w3, ..., w n ) T , E i Represents the subset U i The fuzzy comprehensive evaluation matrix, b if represents the membership of the i-th target to the j-th comment set; Step 3.6.3: Perform comprehensive evaluation on all f subsets and construct a comprehensive evaluation decision matrix: B * =W×E Among them, B * It is the comprehensive evaluation result of all evaluation factors in U; Step 3.6.4: Comprehensive evaluation decision matrix B * After normalization, the processed fuzzy subset is expressed as Based on B * The overall performance evaluation score F of the system is calculated by the following formula: in, is a fuzzy subset The normalized value of the jth element in , which indicates the importance or contribution of the jth evaluation factor in the overall evaluation; Q j is the grade score corresponding to the jth evaluation factor.

10. A civil airport emergency support capability assessment system, characterized in that: Based on the civil airport emergency support capability assessment method according to any one of claims 1 to 9, the system comprises: Evaluation index system construction module, used to construct the evaluation index system of civil airport emergency support capability; The indicator weight determination module is used to determine the comprehensive weight of each indicator in the civil airport emergency support capability evaluation indicator system based on the multi-expert sequence-information entropy weight combination method; The support capability evaluation module is used to evaluate the emergency support capability of civil airports based on the comprehensive weights of various indicators using the fuzzy comprehensive evaluation method; The assessment result output module is used to output the assurance capability assessment results.

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