Civil aviation information coordination system modeling and evaluation method based on complex network

Abstract

The application discloses a kind of civil aviation information coordination system modeling and evaluation method based on complex network, for the lack of prior art, in combination with the structure and operating characteristics of civil aviation information coordination system, it is abstracted into the networked graph model of multiple types of nodes and its interconnection relationship constitutes, realize system standardization description;Design by network flexibility, structural robustness, information interaction efficiency three aspects multiple key indicators constitute the performance evaluation multilayer index system, and for the subjective weight of the performance evaluation first-level index obtained by fuzzy analytic hierarchy process and the objective weight of the performance evaluation second-level index obtained by inverse entropy weight method, with the principle of minimum deviation to carry out the subjective and objective fusion second-level index weighting, calculate the contribution of each index to the comprehensive performance of civil aviation information coordination system, finally realize the scientific quantification evaluation of the designed civil aviation information coordination system performance.The present application can provide theoretical support for future civil aviation information coordination system construction and optimization from top-level architecture construction.

Description

Technical Field

[0001] This invention relates to a modeling and evaluation method for a civil aviation information collaboration system, and more particularly to a modeling and evaluation method for a civil aviation information collaboration system based on complex networks. Background Technology

[0002] With the rapid development of the civil aviation industry, the contradiction between the ever-increasing air transport load and the air traffic control management and support capabilities has become increasingly prominent. Due to the numerous stakeholders involved in civil aviation and the characteristics of various systems such as cross-regional, distributed, multi-source information, and system heterogeneity, problems such as inadequate data sharing mechanisms, inconsistent data structures and interfaces, and insufficient information sharing are unavoidable, greatly limiting the safety and efficiency of flight operations. To address the enormous pressure on the air traffic control system brought about by the continuously growing demand for air transport and to ensure flight operations, the International Civil Aviation Organization (ICAO) proposed the global ATM operation concept Doc9854 and the Flight and Traffic Information Collaborative Environment (FF-ICE) concept Doc9965. These aim to construct a globally standardized exchange mechanism through Wide Area Information Management (SWIM) to share appropriate data among a wider range of participants, thereby better coordinating ATM and situational awareness operations and promoting collaborative decision-making. China has conducted research on key technologies such as information exchange and sharing standards, information collection and routing, and information platform management, aiming to integrate existing civil aviation operating systems and create an integrated civil aviation information collaboration system. Among them, the overall modeling and evaluation of the civil aviation information collaboration system is an essential means to plan system capabilities, optimize system structure, and ensure the full realization of system effectiveness during the system design phase.

[0003] Regarding system modeling and evaluation, the literature "UAF-based Air Transportation Cooperative Information SoS Modeling under 'Belt and Road Initiative', 2022 IEEE 6th Information Technology and Mechatronics Engineering Conference (ITOEC). IEEE, 2022, vol. 6, pp. 746-754." completes the system architecture modeling of the air transportation environment based on the Unified Structural Framework (UAF) theory, designing multiple views such as strategy, business, and personnel, providing an important means for the efficiency optimization and evaluation of civil aviation information collaborative operation. The literature "Masutti A. Single European Sky - a possible regulatory framework for System Wide Information Management (SWIM), Air and Space Law, 2011, 36(4 / 5)" analyzes the advantages of SWIM application in terms of benefits, information transmission and utilization efficiency, security and reliability, and multi-party collaboration efficiency in response to the ICAO's Air System Block Upgrade (ASBU) plan, and proposes a security and reliability analysis method based on the Analytic Hierarchy Process (AHP). However, the above-mentioned literature describes the civil aviation information collaboration system from a conceptual level, which is conducive to the qualitative evaluation of the system, but does not abstract a model, and therefore it is difficult to give a specific performance indicator system and quantitative evaluation results.

[0004] To address this challenge, in the military field, the literature "Model and effectiveness analysis for C" 4 ISRsystem structure based on complex network,2018 10 th The International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC). IEEE, 2018, vol. 1, pp. 85-88” proposes a C based on complex networks. 4The ISR system structure definition model and its network performance evaluation method abstract system components and their interactions as nodes and edges in a network model, and establish network performance evaluation indicators using relevant statistical characteristics. The literature "Research on Assessment of Technical Importance Based on Weapon Technology System-of-Systems Network Model, 2020 IEEE 15th International Conference of System of Systems Engineering (SoSE). IEEE, 2020:000075-000082" models weapon systems as complex networks, combines three classic node centrality indicators of complex networks with the TOPSIS method to measure node importance, and verifies the feasibility and effectiveness of the method using an unmanned combat scenario as an example. Patent document CN 114491879 A studies the network evolution law of equipment systems considering dynamic reconfiguration based on complex network theory, and systematically studies the evolution network and elasticity index influence law of equipment systems. These studies have verified the correctness and effectiveness of system modeling and evaluation methods based on complex networks through examples, but they have not been applied to the civil aviation information collaboration system, nor have they considered that a completely objective evaluation index system in the evaluation process may overlook the uniqueness of the problem and the disadvantage of decision-makers' preferences.

[0005] In the area of ​​civilian system assessment, patent document CN 107862455 A combines the entropy weight method and the analytic hierarchy process (AHP) to propose a hybrid subjective and objective assessment method for the construction of power system cloud platforms. Patent document CN 112989601 A proposes a submarine cable status assessment method based on a combination of subjective and objective weighting. It uses an improved AHP to determine the subjective weights of each state quantity, while simultaneously using the entropy weight method to determine the objective weights. Then, the subjective and objective weights are combined to assess the status of the submarine cable. These two patent documents effectively avoid the drawbacks of the subjective weighting method (excessive subjectivity of expert opinions) and the objective weighting method (excessive randomness of measurement data). However, when using the entropy weight method to calculate weights, these methods are limited by sample size, resulting in significant sensitivity of weights to differences in indicators. Furthermore, the integration of subjective and objective assessments refers to the weights of each indicator, but the scores for all state indicators still require subjective scoring, making it impossible to avoid biases caused by expert psychological influence. In addition, each assessment requires expert scoring, resulting in poor real-time performance, and horizontal comparisons of system assessments require the same group of experts; otherwise, the reference value is limited. Summary of the Invention

[0006] Purpose of the invention: The technical problem to be solved by the present invention is to provide a modeling and evaluation method for a civil aviation information collaboration system based on complex networks, addressing the shortcomings of existing technologies.

[0007] To address the aforementioned technical problems, this invention discloses a method for modeling and evaluating a civil aviation information collaboration system based on complex networks, comprising the following steps:

[0008] Step 1: Analyze the civil aviation information collaboration system, and based on the ICAO's concept and vision of wide-area information management, define the abstract types of nodes in the civil aviation information collaboration system, and construct a networked graph description method for the civil aviation information collaboration system architecture based on complex network theory;

[0009] Step 2: Oriented to the networked graph model of the civil aviation information collaboration system architecture, construct a multi-dimensional indicator library from an objective perspective based on complex network theory, as a secondary indicator for performance evaluation;

[0010] Step 3: Construct a primary indicator library for performance evaluation from a subjective perspective, and establish a mapping relationship between the primary and secondary indicators of performance evaluation. Based on the fuzzy hierarchical analysis method, assign weights to the primary indicators of performance evaluation from a subjective perspective to obtain the subjective weights of the primary indicators.

[0011] Step 4: Based on the connection probability between nodes, for the three operating conditions of the civil aviation information collaboration system, namely the current operating condition, the full access operation condition of the wide area information management system, and the full access operation condition of the wide area information management system after structural optimization, establish the corresponding networked graph model of the civil aviation information collaboration system structure through a preset number of Monte Carlo simulations, form a model set, and statistically analyze the results of various secondary performance evaluation indicators of all models in the set.

[0012] Step 5: Using the obtained results of the secondary performance evaluation indicators, calculate the weights of each secondary performance evaluation indicator based on the inverse entropy weight method to obtain the objective weights of the secondary indicators. Based on the optimization criterion of minimizing deviation, combine the objective weights of the obtained secondary indicators with the subjective weights of the secondary indicators obtained by decomposing the subjective weights of the primary indicators to calculate the comprehensive weights of the secondary indicators.

[0013] Step 6: Model the designed civil aviation information collaboration system using a network graph description, statistically analyze the results of the secondary performance evaluation indicators of the model, calculate the comprehensive performance of the designed civil aviation information collaboration system based on the comprehensive weights of the secondary indicators calculated in Step 5, and compare the comprehensive performance with the reference average value and upper limit of the comprehensive performance obtained by statistical calculation based on the model set in Step 4. Quantitatively evaluate the designed civil aviation information collaboration system to guide the structural optimization of the system.

[0014] Beneficial effects:

[0015] 1) The civil aviation information collaboration system is abstracted into a networked graph model composed of nodes and edges, realizing a standardized description of the system in formal language and matrix form, which facilitates the comparison and optimization of different system architectures in the top-level design stage; 2) The system nodes are classified according to their actual operation, so that the connection probability between nodes can more accurately reflect the actual connection relationship between various systems, ensuring the accuracy of the system model; 3) Introducing indicators such as the average clustering coefficient from complex network theory into the evaluation of the civil aviation information collaboration system, providing an objective and quantifiable basis for the pre-evaluation of the top-level structure design, improving the interpretability of the evaluation results, and realizing the transformation from the traditional qualitative evaluation based on specific functions to the quantitative evaluation based on the top-level architecture; 4) Compared with the current system architecture design based on multi-view experts such as DoDAF and UAF, The evaluation method in this scheme is based on complex network theory to design a secondary evaluation index for objective effectiveness. It also considers the subjective and objective weights of fuzzy hierarchical analysis and inverse entropy weighting method, and performs fusion weighting based on the optimization criterion of minimizing deviation. The final evaluation result of the civil aviation information collaboration system is more accurate. 5) The civil aviation information collaboration system is divided into three cases, and the results of a preset number of Monte Carlo experiments are used as the benchmark for system effectiveness evaluation. This can intuitively reflect the merits and demerits of the designed system architecture, which is convenient for adjusting and optimizing the system architecture based on the evaluation and conducting evaluation and verification again. 6) After the evaluation model and reference benchmark are established, the adjustment and subsequent evaluation of the designed civil aviation information collaboration system will no longer require expert scoring each time, which improves the real-time performance of the process and saves implementation costs, and has good practical value. Attached Figure Description

[0016] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments, and the advantages of the present invention in the above and / or other aspects will become clearer.

[0017] Figure 1 This is a flowchart of the present invention.

[0018] Figure 2 This is a schematic diagram of the node classification and relationships in the civil aviation information collaboration system.

[0019] Figure 3 This is a schematic diagram of the mapping relationship between subjective and objective evaluation indicators of the civil aviation information collaboration system.

[0020] Figure 4 It is a networked graph model of the civil aviation information collaboration system under the current operating conditions.

[0021] Figure 5 It is a networked graphical model of the civil aviation information collaboration system under the full access operation of the wide area information management system. Detailed Implementation

[0022] A modeling and evaluation method for a civil aviation information collaboration system based on complex networks is proposed. This method enables the standardized description and comprehensive effectiveness evaluation of the civil aviation information collaboration system, supporting the top-level design and structural optimization of the system. The method includes the following steps:

[0023] Step 1: Analyze the civil aviation information collaboration system, and considering the International Civil Aviation Organization's (ICAO) concept and vision of Wide Area Information Management (SWIM), define the abstract types of nodes in the civil aviation information collaboration system, and construct a networked graph description method for the civil aviation information collaboration system architecture based on complex network theory.

[0024] Step 1-1: Analyze the structural composition, operation process, and interaction relationships of the civil aviation information collaboration system, as well as the SWIM concept and vision proposed by ICAO. Based on the stakeholders involved in the operation of the civil aviation information collaboration system, and the functions and properties of the constituent systems and equipment, all elements in the civil aviation information collaboration system can be abstractly modeled into the following five types of nodes:

[0025] Sensor Node (O):

[0026] Functional equipment capable of providing meteorological, navigation, and surveillance information related to flight operations in the area of ​​interest. Examples include weather radar, ADS-B, ADS-C, primary / secondary surveillance radar, and multipoint positioning systems.

[0027] Functional system node (S):

[0028] Independent systems with specialized functions tailored to specific stakeholders. Examples include airport berth systems, remote control tower systems, aeronautical information systems, airline flight planning systems, civil aviation automated message relay systems, arrival / departure management systems, air traffic control automation systems, and advanced surface management systems.

[0029] Fusion Decision System Node (F):

[0030] This functional entity comprehensively processes information from multiple sources and assists executors in making decisions. It receives information from sensor nodes, functional system nodes, and actuator nodes, integrates, analyzes, and processes the information in a timely manner, provides decision-making suggestions, and directs the operation of actuator nodes. Examples include airport information integration systems, airport control and decision-making systems, control and decision-making systems, national air traffic control systems, integrated aviation meteorological service systems, and civil aviation operation data governance platforms.

[0031] Actuator (A):

[0032] Functional entities that perform specific tasks based on plans or instructions provided by system functional nodes or integrated decision nodes, in this system mainly refer to execution units such as aircraft and their onboard flight management systems.

[0033] Information Collaborative Management Platform (P):

[0034] The information management functional entity that supports information service interaction among all parties has the ability to allocate specific sets of needs to specific data items. It is distributed among users, and all users can provide or retrieve the data they need on the platform, ensuring that all stakeholders can access information on demand.

[0035] Among them, O, S, F, and A are four types of subsystem nodes in various civil aviation information collaboration systems that are currently in operation, while P nodes are newly added information management nodes after the implementation of wide-area information management in the future.

[0036] Step 1-2: Based on complex network theory, a graph is used as a tool to describe the network. The civil aviation information collaboration system is abstracted into a networked graph model G = (V, E) consisting of specific functional systems and equipment connected in a certain way. Here, V is the set of nodes, representing the systems and equipment in the actual system, and E is the set of edges between nodes, representing the interaction relationships of data, information, and services between nodes. The various types of nodes defined in Step 1-1 can be one or more, such as O = {o1, o2, o3, ..., o...} n1 The system has n1 sensor nodes, and other types of nodes follow the same pattern. Correspondingly, there exists a set of edges E = {e1, e2, e3, ..., e...}. n2} is used to represent the interactive relationships between nodes, such as information transmission, command issuance, and status feedback. That is, if two nodes (o i ,s j If there is an interaction relationship, then To represent the edges between them, note that the edges here have directionality, and the symbol is... The term "arbitrary" is used. The networked graph model g possesses a unique 0-1 adjacency matrix M. G =[a i,j ] N×B Where, the subscript N represents the number of all nodes in the civil aviation information collaboration system, and the element a in the matrix... i,j This represents the connection between nodes i and j. When a directed connection exists between them, a i,j =1, otherwise a i,j =0.

[0037] Step 2: Based on the networked graph model of the civil aviation information collaboration system architecture, construct a multi-dimensional indicator library from an objective perspective using complex network theory as a secondary indicator for performance evaluation;

[0038] Specifically, the secondary indicators include:

[0039] Average clustering coefficient of architecture The average clustering coefficient of the architecture

[0040] These coefficients reflect the degree of grouping in the system architecture and measure the tightness of connections between member systems. They effectively reflect the self-organization, self-synchronization, and dynamic composition characteristics among system structural units; the larger the coefficient, the tighter the connections between member systems. The specific expressions are as follows:

[0041]

[0042]

[0043] Where V = {v1, v2, ..., v} N} represents the set of all N nodes in the system. and This represents the in-degree and out-degree of the i-th node, i.e., the number of edges in other member systems that have information exchange relationships with node i. and This represents the actual number of edges between all other nodes that enter / exit node i, for node v. i , in-degree out degree

[0044] System connectivity coefficient θ:

[0045] The ability of the system architecture to maintain connectivity under conditions of failure or inability of system nodes and their related information interactions is represented as:

[0046]

[0047] Where U is the number of connected components in the system structure, and N u Let L be the number of nodes in the u-th connected component. u Let be the average path length of the u-th connected component. The fewer the number of connected components and the smaller the average path length of each component, the better the network connectivity. The faster the connectivity coefficient changes after a node or edge fails, and the smaller the changed coefficient value, the worse the system's resilience.

[0048] Architecture average

[0049] The average in-degree and out-degree of all nodes in the architecture is expressed as:

[0050]

[0051] Average path length L of the architecture:

[0052] The average path length of an architecture is defined as the average of the shortest distances between any two units in the architecture, expressed as:

[0053]

[0054] Among them, l i,j For unit v i and v h The shortest distance between, i.e.

[0055]

[0056] Among them, sp(v u ,v h ) represents the shortest path between the two system units, e t The t-th edge segment on the shortest path, ω x (e t ) is the length of the connecting edge, generally defined as ω. x (e t =1. The larger the average path length value, the more layers there are in the system, and the more difficult and inefficient the information flow, sharing and synchronization in the system are.

[0057] Diameter of architecture:

[0058] The maximum value of all shortest paths in the civil aviation information collaboration system can effectively reflect the overall connectivity performance and efficiency of the system architecture, and is expressed as:

[0059]

[0060] Architecture Cost:

[0061] The overhead of all connections in the civil aviation information collaboration system is difficult to quantify directly, but it can be indirectly reflected by the total amount of information in all information interaction relationships within the system architecture:

[0062]

[0063] Here, w(e) is the weight of edge e in the network model G, representing the amount of information on the edge. The greater the amount of information that needs to be exchanged between the nodes of the two systems, the greater the bandwidth of the communication network link required to connect the two systems, and the greater the cost, which also reflects the greater cost of connecting the two system units.

[0064] Step 3: Construct a primary indicator library for performance evaluation from a subjective perspective, and establish a mapping relationship between the primary and secondary performance evaluation indicators. Based on the fuzzy hierarchical analysis method, assign weights to the primary performance evaluation indicators from a subjective perspective to obtain the subjective weights of the primary indicators.

[0065] Step 3-1: Based on the needs of air transport operations, the primary performance indicators for the civil aviation information collaboration system are defined subjectively as three categories: network flexibility (Fl), structural robustness (Rb), and information interaction efficiency (Ef). The mapping relationship between the primary performance indicators and the secondary performance indicators is as follows: Fl: Rb:θ, Ef: L, Diam, Cost.

[0066] Step 3-2: By comparing the importance of each primary performance evaluation indicator through expert surveys, a fuzzy complementary judgment matrix for expert q is constructed. Wherein, the subscript τ represents the number of primary indicators for performance evaluation, and q = 1, 2, ..., Q represents the expert labels. Scale range The specific scaling definitions are shown in Table 1:

[0067] Table 1 Scale Definition Table

[0068]

[0069] Step 3-3: Calculate the fuzzy consistency matrix And based on this, calculate the corresponding indicator weight vector. in,

[0070]

[0071]

[0072] Steps 3-4: Calculate the weight vector of the primary indicators for the comprehensive performance evaluation of all experts:

[0073]

[0074] That is, the subjective weight vector of the primary indicators for performance evaluation.

[0075] Step 4: Based on the connection probability between nodes, for the three operational scenarios of the civil aviation information collaboration system—the current operational scenario, the full access operational scenario of the wide area information management system, and the full access operational scenario of the wide area information management system after structural optimization—a networked graph model of the civil aviation information collaboration system architecture is established through a preset number of Monte Carlo simulations, forming a model set. The results of various secondary performance evaluation indicators of all models in the set are then statistically analyzed. The Monte Carlo method, also known as random sampling or statistical experimentation, is a commonly used method in this field. It is a computational method based on probability and statistical theory, linking the problem to be solved with a certain probability model and using a computer to perform statistical simulation or sampling to obtain an approximate solution. The preset number of simulations is to obtain statistical results close to the probability model through multiple experiments. Considering both accuracy and efficiency, this invention preferably uses a Monte Carlo simulation between 30 and 500 simulations.

[0076] Step 4-1: Divide the civil aviation information collaboration system into three cases:

[0077] Case a. Current operating status, connection probability p between different types of nodes. B,J This can be determined through surveys and statistics, where the subscripts B,J∈{O,S,F,A,P};

[0078] Scenario b. Full access to the Wide Area Information Management System (WAIS) is in operation, which has incorporated the WAIS proposed by the International Civil Aviation Organization (ICAO), but has not cut off the existing inter-system connections in the current civil aviation information collaboration system.

[0079] Scenario c. Full access operation of the wide area information management system after structural optimization. This scenario considers the optimization behavior of the civil aviation information collaboration system architecture after the integration of the wide area information management system. Redundant connections will be pruned, but the number and location of the pruned connections are both factors to be considered in the optimization.

[0080] Step 4-2: Based on the survey results of the current civil aviation information collaboration system in a certain region, obtain the number of nodes of each type at the current stage. The desired number of nodes for the wide area information management system is obtained based on the design goals. And calculate the connection probability p between various types of nodes. B,J .

[0081] Step 4-3: Based on the number of nodes and connection probabilities obtained in Step 4-2, networked graph models of the civil aviation information collaboration architecture for cases a and b are generated using Monte Carlo experiments. For each model in case b, edges between nodes other than those of type P are randomly pruned through mm independent experiments to obtain the networked graph model of the architecture for case c. For the obtained H = n(mm+2) models {Gh}, h=1,2,…,H, Statistically analyze the results of the corresponding secondary performance evaluation indicators: Includes networked graph model G h Average clustering coefficient of the architecture Average clustering coefficient of architecture System connectivity coefficient θ h Average degree of system architecture Average path length L of the architecture h Diam architecture h and architecture cost h .

[0082] Step 5: Using the obtained results of the secondary performance evaluation indicators, calculate the weights of each secondary performance evaluation indicator based on the inverse entropy weight method to obtain the objective weights of the secondary indicators. Based on the optimization criterion of minimizing deviation, combine the objective weights of the obtained secondary indicators with the subjective weights of the secondary indicators obtained by decomposing the subjective weights of the primary indicators to calculate the comprehensive weights of the secondary indicators.

[0083] Step 5-1: Indicator Normalization Process

[0084]

[0085] Obtain the normalized second-order index vector of the efficacy evaluation for the h-th Monte Carlo experiment. Where, β h,d This represents the result of the d-th secondary performance indicator in the h-th Monte Carlo experiment, xx h,d This indicates the result after the index has been normalized.

[0086] Step 5-2: Calculate the proportion of the h-th sample value under the d-th indicator to the total weight of that indicator.

[0087]

[0088] Where D represents the number of index items obtained in a single Monte Carlo experiment.

[0089] Step 5-3: Calculate the inverse entropy value of the d-th index.

[0090]

[0091] Step 5-4: Calculate the weight of each indicator

[0092]

[0093] Obtain the objective weight vector of the secondary indicators for performance evaluation

[0094] Step 5-5: Decompose the subjective weights of the primary performance evaluation indicators to obtain the subjective weight vector of the secondary indicators. in, This is the subjective weight vector of the primary performance evaluation indicators obtained in steps 3-4.

[0095] Steps 5-6: Develop the subjective weight vector for the secondary indicators of performance evaluation. and objective weight vector The fusion yields a comprehensive weight vector for the secondary indicators of performance evaluation. Where α1 + α2 = 1, and α1 and α2 are calculated as follows: based on the optimal strategy, the coefficients α1 and α2 of the linear combination of the subjective and objective weight vectors are optimized so that w f and Minimize the deviation. The subscripts s = 1, 2, that is, through The values ​​of α1 and α2 were obtained. The calculation method can be found in the literature "Navigation safety assessment of waterways based on game theory combination weighting. Journal of Safety and Environment, 2021, 21(06):2430-2437.DOI:10.13637 / j.issn.1009-6094.2020.0639".

[0096] Step 6: Model the designed civil aviation information collaboration system using a network graph description, statistically analyze the results of the secondary performance evaluation indicators of the model, calculate the comprehensive performance of the designed civil aviation information collaboration system based on the comprehensive weights of the secondary indicators calculated in Step 5, and compare the comprehensive performance with the reference average value and upper limit of the comprehensive performance obtained by statistical calculation based on the model set in Step 4. Evaluate the structure of the designed civil aviation information collaboration system to guide the structural optimization of the system.

[0097] It should be noted that in step 4, the civil aviation information collaboration system has been divided into three cases. The "designed civil aviation information collaboration system" in this step belongs to the third case, namely the full access and operation of the wide area information management system after structural optimization. In this case, the optimization behavior needs to consider the number and position of the pruning of the connection. Therefore, the difference in the optimization behavior in this case will lead to the design of different types of civil aviation information collaboration systems. The purpose of the evaluation in this invention is to evaluate the effectiveness of such designed civil aviation information collaboration systems and support the subsequent system architecture optimization.

[0098] Step 6-1: For the set of networked graph models {G} obtained from the H Monte Carlo experiments in Step 4-3 h} Calculate the corresponding comprehensive performance evaluation results And calculate the mean of this evaluation result. and upper limit Imax ,in, It is the result of the normalized secondary performance evaluation index in step 5-1.

[0099] Step 6-2: Establish a networked graph model G for the designed civil aviation information collaboration system. T The vector of various performance evaluation secondary indicators is obtained by statistical analysis. Includes networked graph model G T Average clustering coefficient of the architecture Average clustering coefficient of architecture System connectivity coefficient θ T Average degree of system architecture Average path length L of the architecture T Diam architecture T and architecture cost T The normalized performance evaluation secondary index vector is obtained through step 5-1. Then calculate the comprehensive performance evaluation results.

[0100] Step 6-3: Dimensionlessly transform the comprehensive performance evaluation results to obtain... To characterize the dimensionless comprehensive effectiveness of the designed civil aviation information collaboration system.

[0101] Example:

[0102] A method for modeling and evaluating a civil aviation information collaboration system based on complex networks includes the following steps:

[0103] The first step is to analyze the civil aviation information collaboration system, and considering the International Civil Aviation Organization's (ICAO) concept and vision of Wide Area Information Management (SWIM), define the abstract types of nodes in the civil aviation information collaboration system, and construct a networked graph description method for the civil aviation information collaboration system based on complex network theory.

[0104] (1) Analyze the structural composition, operation process, and interaction relationships of the civil aviation information collaboration system, as well as the ICAO's proposed SWIM concept and vision. Based on the stakeholders involved in the operation of the civil aviation information collaboration system, and the functions and properties of the constituent systems and equipment, all elements in the civil aviation information collaboration system can be abstractly modeled into the following five types of nodes. The node classification and relationships are as follows: Figure 2 As shown.

[0105] Sensor nodes (O) are functional devices that provide meteorological, navigation, and surveillance information related to flight operations in an area of ​​interest. Examples include weather radar, ADS-B, ADS-C, primary / secondary surveillance radar, and multipoint positioning systems.

[0106] Functional system nodes (S) are independent systems with specialized functions tailored to specific stakeholders. Examples include airport berth systems, remote control tower systems, aeronautical information systems, airline flight planning systems, civil aviation automatic communication systems, arrival / departure management systems, air traffic control automation systems, and advanced surface management systems.

[0107] The fusion decision system node (F) is a functional entity that comprehensively processes multi-source information and assists executors in making decisions. It receives information from sensor nodes, functional system nodes, and actuator nodes, integrates, analyzes, and processes the information in a timely manner, provides decision suggestions, and directs the operation of actuator nodes. Examples include airport information integration systems, airport control and decision systems, control and decision systems, national air traffic control systems, aviation meteorological integrated service systems, and civil aviation operation data governance platforms.

[0108] An actuator (A) is a functional entity that performs specific tasks according to plans or instructions provided by system functional nodes or fusion decision nodes. In this system, it mainly refers to the execution units such as aircraft and their onboard flight management systems.

[0109] The information collaboration management platform (P) is an information management functional entity that supports information service interaction among all parties. It has the ability to allocate specific sets of needs to specific data items and is distributed among users. All users can provide or retrieve the data they need on the platform, ensuring that all stakeholders can access information on demand.

[0110] (2) Based on complex network theory, a graph is used as a tool to describe the network. The civil aviation information collaboration system is abstracted into a networked graph model G = (V, E) consisting of specific systems and devices connected together in a certain way. Here, V is the set of nodes, representing the systems and devices in the actual system, and E is the set of edges connecting the nodes, representing the interaction relationships of data, information, and services between the nodes. Each type of node in the system can be one or more, such as O = {o1, o2, o3, ..., o...} n1 The system has n1 sensor nodes, and other types of nodes follow the same pattern. Correspondingly, there exists a set of edges E = {e1, e2, e3, ..., e...}. n2} is used to represent the interactive relationships between nodes, such as information transmission, command issuance, and status feedback. That is, if two nodes (o i ,s j If there is an interaction relationship, then To represent the edges between them, note that the edges here have directionality, and the symbol is... The representation is arbitrary. The networked graph model G possesses a unique 0-1 adjacency matrix M. G =[a i,j ] N×NWhere, the subscript N represents the number of all nodes in the civil aviation information collaboration system, and the element a in the matrix... i,j This represents the connection between nodes i and j. When a directed connection exists between them, a i,j =1, otherwise a i,j =0.

[0111] The second step involves constructing a multi-dimensional indicator library based on complex network theory, using a networked graph model of the civil aviation information collaboration system architecture, as secondary indicators for performance evaluation. Specifically, these secondary indicators include:

[0112] a. Average clustering coefficient of system architecture The average clustering coefficient of the architecture These coefficients reflect the degree of grouping in the system architecture and measure the tightness of connections between member systems. They effectively reflect the self-organization, self-synchronization, and dynamic composition characteristics among system structural units; the larger the coefficient, the tighter the connections between member systems. The specific expressions are as follows:

[0113]

[0114]

[0115] Where V = {v1, v2, ..., v} N} represents the set of all N nodes in the system. and This represents the in-degree and out-degree of the i-th node, i.e., the number of edges in other member systems that have information exchange relationships with node i. and This represents the actual number of edges between all other nodes that enter / exit node i, for node v. i , in-degree out degree

[0116] b. The system architecture connectivity coefficient θ describes the ability of the system architecture to maintain connectivity under conditions where system nodes and their related information interactions fail or are disrupted. It is expressed as...

[0117]

[0118] Where U is the number of connected components in the system structure, and N u Let L be the number of nodes in the u-th connected component. u Let be the average path length of the u-th connected component. The fewer the number of connected components and the smaller the average path length of each component, the better the network connectivity. The faster the connectivity coefficient changes after a node or edge fails, and the smaller the changed coefficient value, the worse the system's resilience.

[0119] c. Architectural average The average in-degree and out-degree of all nodes in the architecture is expressed as:

[0120]

[0121] d. Average path length L of the system architecture, the mean of the shortest distance between any two system nodes in the system, expressed as:

[0122]

[0123] Among them, l i,j For unit v i and v h The shortest distance between, i.e.

[0124]

[0125] sp(v i ,v j ) represents the shortest path between the two system units, e t The t-th edge segment on the shortest path, ω x (e t ) is the length of the connecting edge, generally defined as ω. x (e t =1. The larger the average path length value, the more layers there are in the system, and the more difficult and inefficient the information flow, sharing and synchronization in the system are.

[0126] e. Architecture Diam: The maximum value among all shortest paths in the civil aviation information collaboration system. It effectively reflects the overall connectivity performance and efficiency of the architecture, and is represented as...

[0127]

[0128] f. Architecture Cost: The overhead of all connections in the architecture. Since it is difficult to quantify directly, it can be indirectly reflected by the total amount of information in all information interaction relationships within the architecture.

[0129]

[0130] Here, w(e) is the weight of edge e in the network model G, representing the amount of information on the edge. The greater the amount of information that needs to be exchanged between the nodes of the two systems, the greater the bandwidth of the communication network link required to connect the two systems, and the greater the cost, which also reflects the greater cost of connecting the two system units.

[0131] In this implementation example, the cost matrix between each connection edge is defined as shown in Table 2:

[0132] Table 2. Cost Matrix Definition Table

[0133]

[0134] The third step is to construct a primary indicator library for performance evaluation from a subjective perspective, establish a mapping relationship between the primary and secondary performance evaluation indicators, and assign weights to the primary performance evaluation indicators based on the fuzzy hierarchical analysis method to obtain the subjective weights of the primary indicators.

[0135] (1) Based on the needs of air transport operations, the primary performance indicators for the civil aviation information collaboration system are defined subjectively as three categories: network flexibility (Fl), structural robustness (Rb), and information interaction efficiency (Ef). The mapping relationship between the primary performance indicators and the secondary performance indicators is as follows: Figure 3 As shown.

[0136] (2) By comparing the importance of each indicator through Q = 3 expert surveys, a fuzzy complementary judgment matrix of expert q is constructed. in, Scale range The specific scaling definitions are shown in Table 3 below:

[0137] Table 3 Expert Scoring Scale and its Specific Definitions

[0138]

[0139] The three fuzzy complementary judgment matrices obtained from the expert survey are as follows:

[0140]

[0141] (3) Calculate the fuzzy consistency matrix And based on this, calculate the corresponding indicator weight vector. in,

[0142]

[0143]

[0144] The weight vectors obtained are W 1 =[0.3 0.35 0.35] T W 2 =[0.2833 0.3333 0.3833] T W 3 =[0.3333 0.3333 0.3333] T .

[0145] (4) Calculate the weight vector of the primary indicators for the comprehensive performance evaluation of all experts:

[0146]

[0147] That is, the subjective weight vector of the primary indicators for performance evaluation.

[0148] The fourth step involves establishing a networked graph model of the civil aviation information collaboration system architecture based on the connection probability between nodes, targeting three operational scenarios of the civil aviation information collaboration system: the current operational scenario, the full access operational scenario of the wide area information management system, and the full access operational scenario of the wide area information management system after structural optimization. This is achieved through a predetermined number of Monte Carlo experiments, forming a model set, and statistically analyzing the results of various secondary performance evaluation indicators for all models in the set.

[0149] (1) The civil aviation information collaboration system is divided into three cases:

[0150] Case a. Current operating status, connection probability p between different types of nodes. B,J This can be determined through surveys and statistics, where the subscripts B,J∈{O,S,F,A,P};

[0151] Scenario b. Full access to the Wide Area Information Management System (WAIS) is in operation, which has incorporated the WAIS proposed by the International Civil Aviation Organization (ICAO), but has not cut off the existing inter-system connections in the current civil aviation information collaboration system.

[0152] Scenario c. Full access operation of the wide area information management system after structural optimization. This scenario considers the optimization behavior of the civil aviation information collaboration system architecture after the integration of the wide area information management system. Redundant connections will be pruned, but the number and location of the pruned connections are both factors to be considered in the optimization.

[0153] (2) Based on the survey results of the current civil aviation information collaboration system in a certain region, the number of nodes of each type at the current stage is obtained. The desired number of wide area information management systems is obtained based on the design goals. And calculate the connection probability p between various types of nodes. B,J The node information, quantity, and numbering in this implementation example are shown in Table 4:

[0154] Table 4. Node Information, Quantity, and Numbering

[0155]

[0156]

[0157] The specific connection probability matrices among the stakeholders in scenarios a and b are as follows: Airport connection probability matrix Airport connection probability matrix Where diag[·] represents a diagonal matrix with the vectors within the brackets as the main diagonal elements, representing the airport-airline connection probability matrix. Airport-Air Traffic Control Unit Connection Probability Matrix Airport-meteorological unit link probability matrix Airlines and airport connection probability matrix Airlines' connection probability matrix Connection probability matrix between adjacent airlines Probability matrix of connections between airlines and air traffic control Connection probability matrix between airlines and meteorological units Probability matrix of links between air traffic control and airports Probability matrix of connections between air traffic control and airlines Air pipe self-connection probability matrix Probability matrix of connections between adjacent empty tubes Connection probability matrix between air traffic control and meteorological units Connection probability matrix between meteorological units and airports Connection probability matrix between meteorological units and airlines Probability matrix of connections between meteorological units and air traffic control Meteorological unit self-connection probability matrix Connection probability matrix between meteorological units The elements in the matrices above represent the connection probabilities between two units of nodes O, S, F, and A. In the simulation of system b with the addition of the information collaborative management platform, in addition to the connections generated by the connection probability matrices above, the three information collaborative management platform nodes involved in the system are fully connected. However, due to geographical limitations, the information collaborative management platform is only fully linked to the S, F, and A nodes in its local area. The specific connection relationships are shown in Table 5, where √ indicates that the corresponding objects are fully linked. The network graph models for cases a and b are as follows: Figure 4 and Figure 5 As shown.

[0158] Table 5 Connection Relationship Table

[0159]

[0160] (3) Based on the number of nodes and connection probability, networked graph models of the architecture for cases a and b in the n groups were generated through Monte Carlo experiments. The schematic diagrams of the models for cases a and b are shown below. Figure 4 and Figure 5 As shown; and for each model in case b, by randomly pruning the edges between nodes other than those of type P through mm independent experiments, the network architecture graph model under case c in case mm is obtained. For the obtained H = nn(mm+2) model {G h}, h=1,2,…,H, Statistically analyze the results of the corresponding secondary performance evaluation indicators: Includes networked graph model G h Average clustering coefficient of the architecture Average clustering coefficient of architecture System connectivity coefficient θ h Average degree of system architecture Average path length L of the architecture h Diam architecture h and architecture cost h The Monte Carlo experiment used nn=10 and mm=3, resulting in a total of H=50 sets of performance evaluation secondary index result vectors, as shown in Table 6:

[0161] Table 6 Results of Secondary Indicators for Performance Evaluation

[0162]

[0163]

[0164]

[0165] The fifth step involves using the obtained results of the secondary performance evaluation indicators to calculate the weights of each secondary performance evaluation indicator based on the inverse entropy weight method, thereby obtaining the objective weights of the secondary indicators. Based on the optimization criterion of minimizing deviation, the objective weights of the obtained secondary indicators are combined with the subjective weights of the secondary indicators obtained by decomposing the subjective weights of the primary indicators to calculate the comprehensive weights of the secondary indicators.

[0166] (1) Indicator normalization processing

[0167]

[0168] Obtain the normalized second-order index vector of the efficacy evaluation for the h-th Monte Carlo experiment. Where, β h,d This represents the result of the d-th secondary performance indicator in the h-th Monte Carlo experiment, xx h,d This indicates the result after normalization of the indicators, where D is the number of indicator items obtained in a single Monte Carlo experiment.

[0169] (2) Calculate the proportion of the h-th sample value under the d-th indicator to the total weight of the indicator.

[0170]

[0171] (3) Calculate the inverse entropy value of the d-th index.

[0172]

[0173] (4) Calculate the weight of each indicator.

[0174]

[0175] Obtain the objective weight vector of the secondary indicators for performance evaluation

[0176] (5) Decompose the subjective weights of the primary performance evaluation indicators to obtain the subjective weight vector of the secondary indicators. in, This is the subjective weight vector of the first-level performance evaluation index obtained in step three (4).

[0177] (6) Conduct subjective weight vector analysis of the secondary indicators for performance evaluation. and objective weight vector The fusion yields a comprehensive weight vector for the secondary indicators of performance evaluation. Where α1 + α2 = 1, the parameters α1 and α2 are calculated as follows: based on the optimal strategy, the coefficients α1 and α2 of the linear combination of subjective and objective weight vectors are optimized so that w f and Minimize the deviation. The subscripts s = 1, 2, that is, through We obtain α1 = 0.6807 and α2 = 0.3193, and then calculate w. f =[0.1536,0.1425,0.1608,0.1589,0.1054,0.1069,0.1719] T .

[0178] The sixth step is to model the designed civil aviation information collaboration system using a network graph description. Based on the above indicators and weights, the overall performance of the system is calculated. This overall performance is then compared with the reference average and upper limit of overall performance obtained from the statistical calculation of the model set in the fourth step to evaluate the merits of the designed system architecture and guide its optimization.

[0179] (1) For the set of networked graph models {G} obtained from the Monte Carlo experiment h} Calculate the corresponding comprehensive performance evaluation results And calculate the mean of this evaluation result. and upper limit I max ,in, These are the results of the normalized secondary indicators for performance evaluation.

[0180] (2) Establish a networked graph model G for the designed civil aviation information collaboration system.T The vector of various performance evaluation secondary indicators is obtained by statistical analysis. After normalization, we get Calculate the overall performance evaluation results

[0181] (3) The comprehensive performance evaluation results are dimensionless to obtain... The magnitude of represents the dimensionless overall effectiveness of the designed civil aviation information collaboration system. If the overall effectiveness of the designed system is less than 1, it indicates a gap compared to the highest-efficiency scenario in the previous Monte Carlo experiment, and further optimization and adjustments can be considered based on actual conditions.

[0182] In its specific implementation, this application provides a computer storage medium and a corresponding data processing unit. The computer storage medium is capable of storing a computer program, which, when executed by the data processing unit, can run the invention's content regarding a modeling and evaluation method for a civil aviation information collaborative system based on complex networks, as well as some or all of the steps in various embodiments. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0183] Those skilled in the art will clearly understand that the technical solutions in the embodiments of the present invention can be implemented using computer programs and their corresponding general-purpose hardware platforms. Based on this understanding, the technical solutions in the embodiments of the present invention, or the parts that contribute to the prior art, can be embodied in the form of computer programs, i.e., software products. These computer program software products can be stored in a storage medium and include several instructions to cause a device containing a data processing unit (which may be a personal computer, server, microcontroller, MUU, or network device, etc.) to execute the methods described in various embodiments or certain parts of the embodiments of the present invention.

[0184] This invention provides a modeling and evaluation method for a civil aviation information collaboration system based on complex networks. While there are many specific methods and approaches to implement this technical solution, the above description is merely a preferred embodiment. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of this invention, and these improvements and modifications should also be considered within the scope of protection of this invention. All components not explicitly stated in this embodiment can be implemented using existing technologies.

Claims

1. A method for modeling and evaluating a civil aviation information collaboration system based on complex networks, characterized in that, Includes the following steps: Step 1: Analyze the civil aviation information collaboration system, and based on the ICAO's concept and vision of wide-area information management, define the abstract types of nodes in the civil aviation information collaboration system, and construct a networked graph description method for the civil aviation information collaboration system architecture based on complex network theory; Step 2: Oriented to the networked graph model of the civil aviation information collaboration system architecture, construct a multi-dimensional indicator library from an objective perspective based on complex network theory, as a secondary indicator for performance evaluation; Step 3: Construct a primary indicator library for performance evaluation from a subjective perspective, and establish a mapping relationship between the primary and secondary indicators of performance evaluation. Based on the fuzzy hierarchical analysis method, assign weights to the primary indicators of performance evaluation from a subjective perspective to obtain the subjective weights of the primary indicators. Step 4: Based on the connection probability between nodes, for the three operating conditions of the civil aviation information collaboration system, namely the current operating condition, the full access operation condition of the wide area information management system, and the full access operation condition of the wide area information management system after structural optimization, establish the corresponding networked graph model of the civil aviation information collaboration system structure through a preset number of Monte Carlo simulations, form a model set, and statistically analyze the results of various secondary performance evaluation indicators of all models in the set. Step 5: Using the obtained results of the secondary performance evaluation indicators, calculate the weights of each secondary performance evaluation indicator based on the inverse entropy weight method to obtain the objective weights of the secondary indicators. Based on the optimization criterion of minimizing deviation, combine the objective weights of the obtained secondary indicators with the subjective weights of the secondary indicators obtained by decomposing the subjective weights of the primary indicators to calculate the comprehensive weights of the secondary indicators. Step 6: Model the designed civil aviation information collaboration system using a network graph description, statistically analyze the results of the secondary performance evaluation indicators of the model, calculate the comprehensive performance of the designed civil aviation information collaboration system based on the comprehensive weights of the secondary indicators calculated in Step 5, and compare the comprehensive performance with the reference average value and upper limit of the comprehensive performance obtained by statistical calculation based on the model set in Step 4. Quantitatively evaluate the designed civil aviation information collaboration system to guide the structural optimization of the system. Step 1 includes the following steps: Step 1-1: Analyze the structure, operation, and interaction relationships of the civil aviation information collaboration system, as well as the ICAO's concept and vision for wide-area information management. Abstract and model all elements in the civil aviation information collaboration system into the following five types of nodes: sensor nodes. Functional system nodes Integrated decision-making system nodes Actuator and information collaboration management platform nodes ; Among them, sensor nodes Functional system nodes Integrated decision-making system nodes and actuator These are four types of subsystem nodes in the civil aviation information collaboration system, oriented towards the current operational status, including the information collaboration management platform node. It is a newly added information management node after the implementation of future wide-area information management.

2. The method for modeling and evaluating a civil aviation information collaboration system based on complex networks according to claim 1, characterized in that, Step 1 also includes steps 1-2: Based on complex network theory, this paper uses graphs as a tool to describe networks, abstracting the civil aviation information collaboration system into a networked graph model composed of specific systems and devices connected in a certain way. ;in, The node set represents the systems and equipment within the civil aviation information collaboration system. This is the set of edges between nodes, representing the interaction relationships of data, information, and services between nodes, and the edges are directional; in the civil aviation information collaboration system, the various types of nodes defined in step 1-1 can be one or more; the networked graph model There exists a unique 0-1 adjacency matrix. , where subscript The matrix represents the total number of nodes in the civil aviation information collaboration system, and the elements within the matrix. Indicates the first The node and the first The connection relationships between nodes, when a directed connection exists between two nodes. ,otherwise .

3. The method for modeling and evaluating a civil aviation information collaboration system based on complex networks according to claim 2, characterized in that, The secondary indicators for performance evaluation mentioned in step 2 include: Average clustering coefficient of architecture The average clustering coefficient of the architecture : ； ； in, This indicates all of the civil aviation information collaboration system A set of nodes, and Indicates the first The in-degree and out-degree of each node, i.e., the number of other member systems entering and leaving the node. The number of information interaction relationship edges. and Indicates the entry and exit of the first The actual number of edges that exist between all other nodes of a given node, for a given node , in-degree , out of degree ; System connectivity coefficient : ； in, This refers to the number of connected branches included in the civil aviation information collaboration system. For the first The number of nodes in a connected component. For the first Average path length of each connected branch; Architecture average : The average in-degree and out-degree of all nodes in the civil aviation information collaboration system is expressed as: ； Average path length of architecture : The average of the shortest distances between any two nodes in the civil aviation information collaboration system is expressed as: ； in, For nodes and The shortest distance between them, that is: ； in, This refers to the shortest path between two nodes in the civil aviation information collaboration system. The first on the shortest path Duan Lianbian, It is a connecting edge Length; Architecture Diameter : The maximum value of all shortest paths in the civil aviation information collaboration system is represented as: ； Architecture cost : The overhead of all connections in the civil aviation information collaboration system is indirectly reflected by the total amount of information in all information interaction relationships within this system architecture: ； in, It is a network model Middle of the border The weight represents the amount of information on the edge, and is positively correlated with the bandwidth of the required communication network link.

4. The method for modeling and evaluating a civil aviation information collaboration system based on complex networks according to claim 3, characterized in that, Step 3 includes the following steps: Step 3-1: Based on the needs of air transport operations, define the primary performance indicator for the civil aviation information collaboration system from a subjective perspective as: network flexibility. Structural robustness and the efficiency of information interaction The mapping relationship between the three categories of primary indicators and secondary indicators of performance evaluation is as follows: ； ； ； Step 3-2: Compare the importance of each primary performance evaluation indicator through expert surveys to construct an expert panel. Fuzzy complementary judgment matrix , where subscript The number of primary indicators for performance evaluation. For expert labeling, For the number of experts, Scale range ; Step 3-3: Calculate the fuzzy consistency matrix And based on this, calculate the corresponding indicator weight vector. ,in: ； ； Steps 3-4: Calculate the weight vector of the primary indicators for the comprehensive performance evaluation of all experts: ； That is, the subjective weight vector of the primary indicators for performance evaluation.

5. The method for modeling and evaluating a civil aviation information collaboration system based on complex networks according to claim 4, characterized in that, Step 4 includes the following steps: Step 4-1: Divide the civil aviation information collaboration system into three cases: Scenario a. Current operational status, connection probabilities between different types of nodes. The survey and statistics show that, among them, subscript ; Scenario b. Full access to the wide area information management system, which has been integrated with the wide area information management system proposed by the International Civil Aviation Organization, but the existing inter-system connections of the civil aviation information collaboration system under the current operation have not been cut off; Case c. Full access operation of the optimized wide area information management system. Based on the optimization behavior of the civil aviation information collaboration system architecture after the integration of the wide area information management system, redundant connections are pruned. The number and position of the pruned redundant connections are completed during the optimization. Step 4-2: Based on the survey results of the actual civil aviation information collaboration system, obtain the number of nodes of each type at the current stage. , , , Based on the design goals, the desired number of nodes for the wide-area information management system is obtained. And calculate the connection probability between various types of nodes. ; Step 4-3: Based on the number of nodes and connection probabilities obtained in Step 4-2, generate the following using Monte Carlo experiments: A networked graph model of the civil aviation information collaboration architecture for scenarios a and b, and for each model in scenario b, through... The group of independent experiments was randomly selected. The edges connecting nodes other than node A are obtained A networked graph model of the civil aviation information collaboration architecture under case c; for the obtained... Grouped graph model Statistical analysis of the results of the corresponding secondary performance evaluation indicators: Includes networked graph models Average clustering coefficient of the architecture The average clustering coefficient of the system architecture System connectivity coefficient Average degree of system architecture Average path length of the architecture Architecture diameter and architectural costs .

6. The method for modeling and evaluating a civil aviation information collaboration system based on complex networks according to claim 5, characterized in that, Step 5 includes the following steps: Step 5-1: Indicator normalization processing: ； Get the first Normalized efficacy evaluation secondary index vector of sub-Monte Carlo experiments ,in, Indicates the first The first Monte Carlo experiment Results of secondary indicators in performance evaluation This indicates the result after the index has been normalized. The number of index items obtained in a single Monte Carlo experiment; Step 5-2, calculate the first... The first item under the indicator The proportion of each sample value to the indicator: ； Step 5-3, calculate the first... Inverse entropy value of the item: ； Step 5-4: Calculate the weights of each indicator: ； Obtain the objective weight vector of the secondary indicators for performance evaluation ; Step 5-5: Decompose the subjective weights of the primary performance evaluation indicators to obtain the subjective weight vector of the secondary indicators. ,in, This is the subjective weight vector of the primary performance evaluation indicators obtained in step 3-4; Steps 5-6: Develop the subjective weight vector for the secondary indicators of performance evaluation. and objective weight vector The fusion yields a comprehensive weight vector for the secondary indicators of performance evaluation. ,in, ,parameter and The calculation method is as follows: based on the coefficients of the linear combination of subjective and objective weight vectors according to the optimal strategy. and Optimization processing was performed to make and Minimize the deviation. subscript That is, through get and The value of .

7. The method for modeling and evaluating a civil aviation information collaboration system based on complex networks according to claim 6, characterized in that, Step 6 includes the following steps: Step 6-1: For the set of networked graph models obtained in Step 4-3, calculate the corresponding comprehensive performance evaluation results, and calculate the mean and upper limit of these evaluation results. Step 6-2: Establish a networked graph model for the designed civil aviation information collaboration system and calculate the comprehensive performance evaluation results; Step 6-3: Dimensionlessize the comprehensive performance evaluation results.

8. The method for modeling and evaluating a civil aviation information collaboration system based on complex networks according to claim 7, characterized in that, Step 6-1 specifically includes: Regarding step 4-3 The set of networked graph models obtained from the Monte Carlo experiment Calculate the corresponding comprehensive performance evaluation results. And calculate the mean of this evaluation result. and upper limit ,in, It is the result of the normalized secondary performance evaluation index in step 5-1.

9. The method for modeling and evaluating a civil aviation information collaboration system based on complex networks according to claim 8, characterized in that, Step 6-2 specifically includes: A networked graph model was established for the designed civil aviation information collaboration system. The vector of various performance evaluation secondary indicators is obtained by statistical analysis. Includes networked graph models Average clustering coefficient of the architecture The average clustering coefficient of the system architecture System connectivity coefficient Average degree of system architecture Average path length of the architecture Architecture diameter and architectural costs The normalized performance evaluation secondary index vector is obtained through step 5-1. Then calculate the comprehensive performance evaluation results. .

10. A method for modeling and evaluating a civil aviation information collaboration system based on complex networks as described in claim 9, characterized in that, The dimensionless transformation of the comprehensive performance evaluation results described in step 6-3 is specifically performed as follows: ； Dimensionless results The dimensionless comprehensive effectiveness of the designed civil aviation information collaboration system is characterized.

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