A method, device, equipment and medium for evaluating airport importance based on information fusion

Abstract

The invention provides an airport importance evaluation method, device, equipment and medium based on information fusion, which relates to the field of aviation technology. Firstly, a civil aviation transportation network is constructed by using a flight schedule; then, a centrality measurement calculation model is constructed based on the airport nodes in the civil aviation transportation network; next, based on the centrality measurement calculation model, the degree centrality, proximity centrality and betweenness centrality of the airport nodes are obtained, and a basic probability distribution function of the airport node centrality is constructed; then, based on the D-S evidence theory combination rule and the basic probability of each centrality of the airport node, an information fusion model of the airport node importance is constructed; finally, according to the information fusion model of the airport node importance, the basic probability value of the optimal evaluation of the airport fusion centrality is obtained, and the importance of each airport is judged according to the ranking result of the importance of each airport by the basic probability value; the importance of the airport can be evaluated comprehensively, accurately and efficiently.

Description

Technical Field

[0001] The present invention relates to the field of aviation technology, and in particular to an airport importance assessment method, device, equipment and medium based on information fusion. Background Art

[0002] With the vigorous development of the civil aviation industry, the operating network of airlines has become increasingly large, greatly improving the convenience of passenger travel. However, this growth is also accompanied by challenges, especially in a complex and changing operating environment, such as large-scale flight delays caused by extreme weather, which has a significant negative impact on the aviation network. Therefore, in-depth analysis and accurate assessment of the importance of each airport in the aviation network has become the key to optimizing route layout, predicting risks and formulating response strategies.

[0003] At present, the airport importance assessment framework based on complex network theory mainly focuses on three indicators: node degree centrality, proximity centrality and betweenness centrality. Although these indicators have their own unique advantages, they also have several limitations that limit their comprehensiveness and accuracy:

[0004] (1) Degree centrality: As a basic evaluation indicator, degree centrality is widely favored because of its simple calculation and intuitive understanding. However, its limitation is that it only focuses on the number of direct connections of nodes, that is, local characteristics, ignoring the overall topological structure of the network and the indirect influence between nodes, making it difficult to fully reflect the comprehensive status of nodes in the network.

[0005] (2) Proximity centrality: This metric evaluates the centrality of a node by measuring the average shortest path length from a node to all other nodes, and is intended to reflect the global accessibility of a node. However, in a segmented network area, if a node is completely isolated from another area, its proximity centrality will not be able to accurately measure its influence in the entire network, especially in the face of network fragmentation or local disruption, its effectiveness is greatly reduced.

[0006] (3) Betweenness centrality: This metric evaluates the control and bridging role of a node by calculating the frequency of its appearance in all shortest paths, taking into account the global structure and indirect connections of the network. However, in real-world aviation networks, a large number of nodes do not directly participate in the shortest path between any two points, resulting in extremely low or even zero betweenness centrality values ​​for these nodes, which obscures their potential importance in specific scenarios. In addition, the high computational cost of betweenness centrality also limits its real-time application in large networks.

[0007] In summary, although degree centrality, proximity centrality, and betweenness centrality provide a basic framework for evaluating the importance of airports in aviation networks, each of them has its own limitations;

[0008] In addition, according to a patent document on a flight plan optimization method based on a timing network model with publication number CN116882590A, although the flight plan data is analyzed, the timing network model is used for pre-deduction, and corresponding thresholds are set at routes and airports, and finally the route congestion points and situations exceeding the peak service capacity of the hub airport that appear in the flight plan deduction are identified and warned; however, this method cannot give an effective assessment of the importance of the airport.

[0009] Therefore, we are required to combine multiple methods in practical applications, and even introduce new evaluation dimensions, in order to build a more comprehensive, accurate and efficient evaluation system, so as to better cope with the complex challenges faced by the aviation transportation sector. Summary of the invention

[0010] The purpose of the present invention is to provide an airport importance assessment method, device, equipment and medium based on information fusion, which can comprehensively, accurately and efficiently assess the importance of the airport.

[0011] The embodiments of the present invention are implemented by the following technical solutions:

[0012] A method for airport importance assessment based on information fusion, including

[0013] Use flight schedules to build a civil aviation transport network;

[0014] Based on the airport nodes in the civil aviation transportation network, a centrality measurement calculation model is constructed;

[0015] Based on the centrality measurement calculation model, the degree centrality, proximity centrality and betweenness centrality of airport nodes are obtained, and the basic probability distribution function of airport node centrality is constructed.

[0016] Based on the combination rules of DS evidence theory and the basic probability of each centrality of airport nodes, an information fusion model of airport node importance is constructed;

[0017] According to the information fusion model of airport node importance, the basic probability value of the optimal evaluation of airport fusion centrality is obtained, and the importance of each airport is judged according to the ranking result of the importance of each airport according to the basic probability value.

[0018] Furthermore, the steps of constructing the civil aviation transportation network include:

[0019] According to the obtained flight schedule, obtain the flight operation information of each airport;

[0020] According to the connection between airports, a civil aviation transportation network G is constructed with airports as nodes and routes as edges.

[0021] Furthermore, based on the operation information of airport flights, assuming that the total number of airports is n, the size of the civil aviation transportation network is, where the elements are:

[0022] ;

[0023] It is the total number of flights from airport i to airport j, and 0 means there is no flight between airport i and airport j;

[0024] The constructed civil aviation transportation network G is expressed as:

[0025] .

[0026] Furthermore, for routes without flights, the corresponding elements in the civil aviation transportation network G are assigned a value of 0;

[0027] For routes with flights, the corresponding elements in the civil aviation transportation network G are assigned the number of flights on the route.

[0028] Furthermore, the steps of constructing the centrality measurement calculation model include:

[0029] Calculate the degree centrality of airport i based on the civil aviation transportation network G ;

[0030] The proximity centrality of airport i is calculated by using the shortest distance from airport node i to the other airport nodes in the civil aviation transportation network G. ;

[0031] Analyze the situation where the shortest path between airport nodes in the civil aviation transportation network G passes through airport node i, and calculate the betweenness centrality of airport i .

[0032] Furthermore, the steps of constructing the basic probability distribution function of the airport node centrality include:

[0033] Calculate the degree centrality, proximity centrality and betweenness centrality of airport i and the maximum Euclidean distance between the maximum degree centrality, proximity centrality and betweenness centrality of airports in the civil aviation transportation network G respectively. , , ;

[0034] Calculate the minimum Euclidean distance between the degree centrality, proximity centrality and betweenness centrality of airport i and the minimum degree centrality, proximity centrality and betweenness centrality of airports in the civil aviation transportation network G respectively. , , ;

[0035] Based on the maximum and minimum Euclidean distances of the centralities of airport i, the median Euclidean distances of degree centrality, proximity centrality, and betweenness centrality of airport i are calculated respectively. , , ;

[0036] The optimal evaluation basic probability of the airport i-degree centrality is obtained by using the maximum Euclidean distance, minimum Euclidean distance and median Euclidean distance of the airport i-degree centrality. , Worst Assessment Basic Probability and medium assessment base probability ;

[0037] The optimal evaluation basic probability of proximity centrality of airport i is obtained by using the maximum Euclidean distance, minimum Euclidean distance and median Euclidean distance of proximity centrality of airport i. , Worst Assessment Basic Probability and medium assessment base probability ;

[0038] The optimal evaluation basic probability of airport i betweenness centrality is obtained by using the maximum Euclidean distance, minimum Euclidean distance and median Euclidean distance of airport i betweenness centrality. , Worst Assessment Basic Probability and medium assessment base probability .

[0039] Furthermore, the steps of constructing the information fusion model of the importance of the airport nodes include:

[0040] Based on the basic probabilities of each evaluation level of the degree centrality and proximity centrality of airport i, the node importance support evidence conflict coefficient of the degree centrality and proximity centrality of airport i is obtained. ;

[0041] Support evidence conflict coefficient based on node importance , and obtain the optimal evaluation basic probability after integrating the importance of degree centrality and proximity centrality of airport i , Worst Assessment Basic Probability and medium assessment base probability ;

[0042] Based on the basic probability of each evaluation level of airport i's betweenness centrality and the basic probability of each evaluation level after the fusion of degree centrality-closeness centrality importance, the node importance support evidence conflict coefficient after the fusion of airport i's degree centrality, closeness centrality and betweenness centrality importance is calculated. ;

[0043] Support evidence conflict coefficient based on node importance , and obtain the optimal evaluation basic probability after integrating the importance of betweenness centrality, degree centrality and closeness centrality of airport i , Worst Assessment Basic Probability and medium assessment base probability ;

[0044] According to the basic probability of each evaluation level after the fusion of betweenness centrality, degree centrality and closeness centrality importance, the optimal basic probability of fusion centrality evaluation of airport i is obtained. , and used as the evaluation result of the importance of airport i in the civil aviation transportation network.

[0045] An airport importance assessment device based on information fusion, comprising:

[0046] The acquisition module is used to obtain the flight schedule, and according to the flight schedule, the flight operation information of each airport can be obtained to build a civil aviation transportation network;

[0047] Model building module, used to build centrality measurement calculation model, basic probability distribution function of airport node centrality, and information fusion model of airport node importance;

[0048] The processing calculation module is used to perform calculation processing according to the above-mentioned importance evaluation method, and for the calculation of each model or function, the corresponding model or function in the model construction module is called through the model calling module.

[0049] An electronic device comprises: a processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor, and when the electronic device is running, the processor and the memory communicate via the bus, and the machine-readable instructions are executed by the processor to execute the steps of the above-mentioned airport importance assessment method.

[0050] A computer-readable storage medium having a computer program stored thereon, wherein the computer program executes the steps of the above-mentioned airport importance assessment method when executed by a processor.

[0051] The technical solution of the embodiment of the present invention has at least the following advantages and beneficial effects:

[0052] The present invention introduces DS evidence theory into the field of aviation technology to evaluate the importance of airports.

[0053] The basic probability distribution function is constructed using the metrics of airport node degree centrality, proximity centrality and betweenness centrality, which effectively expresses the supporting evidence of each centrality for the importance of the airport.

[0054] Secondly, the fusion of basic probability distribution is achieved through information combination rules to characterize the comprehensive importance of airports, providing an effective decision-making basis for the optimization and control of airport business in the civil aviation transportation network. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0056] Figure 1 This is a schematic diagram of the method flow provided in Example 1 of the present invention. DETAILED DESCRIPTION

[0057] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0058] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0059] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0060] In the description of the present invention, it should be noted that if the terms "center", "up", "down", "left", "right", "vertical", "horizontal", "inside", "outside" and the like appear, the orientation or position relationship indicated is based on the orientation or position relationship shown in the accompanying drawings, or is the orientation or position relationship in which the product of the application is usually placed when used. It is only for the convenience of describing the present invention and simplifying the description, and does not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention.

[0061] In the description of the present invention, it is also necessary to explain that, unless otherwise clearly specified and limited, the terms "set", "install", "connect", and "connect" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0062] Example 1

[0063] A method for evaluating airport importance based on information fusion, combined with the attached Figure 1 ,include

[0064] S1. Use flight schedules to build a civil aviation transportation network;

[0065] The steps for building a civil aviation transport network include:

[0066] S11. According to the obtained flight schedule, the flight operation information of each airport is obtained, that is, the departure time and arrival time of each flight, the specific route of the flight, whether the flight stops at certain airports, and the number of flights on a specific route;

[0067] S12. According to the connection between airports, a civil aviation transportation network G is constructed with airports as nodes and routes as edges;

[0068] In addition, based on the operation information of airport flights, assuming that the total number of airports is n, the size of the civil aviation transportation network is, where the elements are:

[0069] ;

[0070] It is the total number of flights from airport i to airport j, and 0 means there is no flight between airport i and airport j;

[0071] The constructed civil aviation transportation network G is expressed as:

[0072] ;

[0073] In addition, for routes without flights, the corresponding elements in the civil aviation transportation network G are assigned a value of 0;

[0074] For routes with flights, the corresponding elements in the civil aviation transportation network G are assigned the number of flights on the route.

[0075] S2. Construct a centrality measurement calculation model based on airport nodes in the civil aviation transportation network;

[0076] This model is used to calculate the degree centrality, proximity centrality and betweenness centrality of airport nodes. The degree centrality of an airport node refers to the sum of the number of neighbors of a node. It reflects that if a node has more neighbors, the more influence the node has.

[0077] The closeness centrality of an airport node represents the inverse of the sum of the shortest distances between the node and all other nodes in the network. The smaller the average distance between a node and other nodes, the greater the closeness centrality of the node, which can be expressed as the greater the importance of the node.

[0078] The betweenness centrality of the airport node indicates that among the shortest paths of all node pairs in the network, the more shortest paths pass through a certain node, the greater the importance of this node.

[0079] S3. Based on the centrality measurement calculation model, the degree centrality, proximity centrality and betweenness centrality of the airport node are obtained, and the basic probability distribution function of the airport node centrality is constructed;

[0080] When constructing the basic probability distribution function of the airport node centrality, the maximum Euclidean distance, minimum Euclidean distance and median Euclidean distance of each centrality are calculated first according to the degree centrality, proximity centrality and betweenness centrality of the airport node. The Euclidean distance is used to quantify the position of the degree centrality, proximity centrality and betweenness centrality of the airport node relative to the maximum and minimum centrality values ​​in the entire network. That is, calculating these Euclidean distances can obtain the relative position information of the airport node under different centrality measures. Specifically, the maximum Euclidean distance reflects the difference between the airport node and the most prominent node in the network in terms of centrality measure, while the minimum Euclidean distance reveals the difference between the airport node and the weakest node in the network. Through these two distances, the median Euclidean distance can be further calculated.

[0081] This helps determine the optimal basic probability, the worst basic probability and the medium basic probability of evaluation of the airport node in terms of degree centrality, proximity centrality and betweenness centrality; this allows for a more accurate evaluation of the importance of the airport node in the network, providing a scientific basis for airport business optimization and control.

[0082] S4. Based on the combination rules of DS evidence theory and the basic probability of each centrality of airport nodes, an information fusion model of airport node importance is constructed;

[0083] S5. According to the information fusion model of airport node importance, the basic probability value of the optimal evaluation of airport fusion centrality is obtained, and the importance of each airport is judged according to the ranking result of the importance of each airport based on the basic probability value.

[0084] Example 2

[0085] Furthermore, the steps of constructing the centrality measurement calculation model include:

[0086] S21. Calculate the degree centrality of airport i based on the civil aviation transportation network G ;

[0087] The specific calculation formula is:

[0088] ;

[0089] in, is an element in the adjacency matrix G of the civil aviation transport network, and n is the total number of airports in the network.

[0090] S22. Using the shortest distance from airport node i to the other airport nodes in the civil aviation transportation network G, calculate the proximity centrality of airport i ;

[0091] The specific calculation formula is:

[0092] ;

[0093] Where n is the total number of airports in the network. is the shortest distance between airport i and airport j, which can be expressed as:

[0094] ;

[0095] h is the intermediate airport on the shortest path between airport i and airport j, is the distance between airport i and the middle airport h on the path, is the distance between the intermediate airport h and airport j on the path.

[0096] S23. Analyze the situation where the shortest path between airport nodes in the civil aviation transportation network G passes through airport node i, and calculate the betweenness centrality of airport i ;

[0097] The specific calculation formula is:

[0098] ;

[0099] in, is the sum of the number of all shortest paths from node s to node t, is the sum of the number of shortest paths from node s to node t that pass through node i, and n is the total number of airports in the network.

[0100] Example 3

[0101] Furthermore, the steps of constructing the basic probability distribution function of the airport node centrality include:

[0102] Calculate the degree centrality, proximity centrality and betweenness centrality of airport i and the maximum Euclidean distance between the maximum degree centrality, proximity centrality and betweenness centrality of airports in the civil aviation transportation network G respectively. , , ;

[0103] Calculate the minimum Euclidean distance between the degree centrality, proximity centrality and betweenness centrality of airport i and the minimum degree centrality, proximity centrality and betweenness centrality of airports in the civil aviation transportation network G respectively. , , ;

[0104] Based on the maximum and minimum Euclidean distances of the centralities of airport i, the median Euclidean distances of degree centrality, proximity centrality, and betweenness centrality of airport i are calculated respectively. , , ;

[0105] The optimal evaluation basic probability of the airport i-degree centrality is obtained by using the maximum Euclidean distance, minimum Euclidean distance and median Euclidean distance of the airport i-degree centrality. , Worst Assessment Basic Probability and medium assessment base probability ;

[0106] The optimal evaluation basic probability of proximity centrality of airport i is obtained by using the maximum Euclidean distance, minimum Euclidean distance and median Euclidean distance of proximity centrality of airport i. , Worst Assessment Basic Probability and medium assessment base probability ;

[0107] The optimal evaluation basic probability of airport i betweenness centrality is obtained by using the maximum Euclidean distance, minimum Euclidean distance and median Euclidean distance of airport i betweenness centrality. , Worst Assessment Basic Probability and medium assessment base probability .

[0108] In this embodiment, the calculation process of each parameter is as follows:

[0109] The degree centrality of airport i, the maximum Euclidean distance to the maximum degree centrality of airports in the network , minimum Euclidean distance and median Euclidean distance They are:

[0110] ;

[0111] ;

[0112] ;

[0113] in, is the maximum degree centrality among all nodes in the network, is the minimum degree centrality among all nodes in the network;

[0114] The optimal evaluation basic probability of airport i-degree centrality , Worst Assessment Basic Probability and medium assessment base probability They can be expressed as:

[0115] ;

[0116] ;

[0117] ;

[0118] The maximum Euclidean distance between the proximity centrality of airport i and the maximum proximity centrality of airports in the network is obtained respectively. , minimum Euclidean distance and median Euclidean distance :

[0119] ;

[0120] ;

[0121] ;

[0122] in, is the maximum proximity centrality among all nodes in the network, is the minimum proximity centrality among all nodes in the network.

[0123] Basic probability of optimal evaluation of proximity centrality of airport i , Worst Assessment Basic Probability and medium assessment base probability They can be expressed as:

[0124] ;

[0125] ;

[0126] ;

[0127] The maximum Euclidean distance between the betweenness centrality of airport i and the maximum betweenness centrality of airports in the network is obtained respectively. , minimum Euclidean distance and median Euclidean distance :

[0128] ;

[0129] ;

[0130] ;

[0131] in, is the maximum betweenness centrality among all nodes in the network, is the minimum betweenness centrality among all nodes in the network.

[0132] Basic probability of optimal evaluation of airport i betweenness centrality , Worst Assessment Basic Probability and medium assessment base probability They can be expressed as:

[0133] ;

[0134] ;

[0135] .

[0136] Example 4

[0137] Furthermore, the steps of constructing the information fusion model of the importance of the airport nodes include:

[0138] Based on the basic probabilities of each evaluation level of the degree centrality and proximity centrality of airport i, the node importance support evidence conflict coefficient of the degree centrality and proximity centrality of airport i is obtained. ;

[0139] Support evidence conflict coefficient based on node importance , and obtain the optimal evaluation basic probability after integrating the importance of degree centrality and proximity centrality of airport i , Worst Assessment Basic Probability and medium assessment base probability ;

[0140] Based on the basic probability of each evaluation level of airport i's betweenness centrality and the basic probability of each evaluation level after the fusion of degree centrality-closeness centrality importance, the node importance support evidence conflict coefficient after the fusion of airport i's degree centrality, closeness centrality and betweenness centrality importance is calculated. ;

[0141] Support evidence conflict coefficient based on node importance , and obtain the optimal evaluation basic probability after integrating the importance of betweenness centrality, degree centrality and closeness centrality of airport i , Worst Assessment Basic Probability and medium assessment base probability ;

[0142] According to the basic probability of each evaluation level after the fusion of betweenness centrality, degree centrality and closeness centrality importance, the optimal basic probability of fusion centrality evaluation of airport i is obtained. , and used as the evaluation result of the importance of airport i in the civil aviation transportation network;

[0143] Among them, in this embodiment, the calculation method of the information fusion model based on the importance of airport nodes is:

[0144] In the DS evidence theory combination rule, the conflict coefficient between two sets of evidence A and B is It can be expressed as:

[0145] ;

[0146] in, and They are the basic probability distribution of evidence A and the basic probability distribution of evidence B respectively.

[0147] Based on the above conflict coefficients, the node importance supporting evidence conflict coefficients of airport i-degree centrality and proximity centrality It is expressed as:

[0148] ;

[0149] In the DS evidence theory combination rule, the fusion method of two sets of evidence A and B can be expressed as:

[0150] ;

[0151] Based on the above fusion method, the basic probability distribution function obtained by fusion of the node importance of airport i-degree centrality and proximity centrality is expressed as:

[0152] ;

[0153] ;

[0154] ;

[0155] Further obtain the node importance support evidence conflict coefficient of airport i-degree centrality-closeness centrality and betweenness centrality for:

[0156] ;

[0157] Obtain the node importance fusion of airport i-degree centrality-closeness centrality and betweenness centrality:

[0158] ;

[0159] ;

[0160] ;

[0161] Finally, the optimal basic probability of fusion centrality evaluation of airport i is obtained :

[0162] ;

[0163] The importance of each airport is ranked according to the obtained optimal evaluation basic probability value of airport fusion centrality. The larger the optimal evaluation basic probability value, the more important the airport is.

[0164] An airport importance assessment device based on information fusion, comprising:

[0165] The acquisition module is used to obtain the flight schedule, and according to the flight schedule, the flight operation information of each airport can be obtained to build a civil aviation transportation network;

[0166] Model building module, used to build centrality measurement calculation model, basic probability distribution function of airport node centrality, and information fusion model of airport node importance;

[0167] The processing calculation module is used to perform calculation processing according to the above-mentioned importance evaluation method, and for the calculation of each model or function, the corresponding model or function in the model construction module is called through the model calling module.

[0168] An electronic device comprises: a processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor, and when the electronic device is running, the processor and the memory communicate via the bus, and the machine-readable instructions are executed by the processor to execute the steps of the above-mentioned airport importance assessment method.

[0169] A computer-readable storage medium having a computer program stored thereon, wherein the computer program executes the steps of the above-mentioned airport importance assessment method when executed by a processor.

[0170] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for evaluating airport importance based on information fusion, characterized by: include, Use flight schedules to build a civil aviation transport network; Based on the airport nodes in the civil aviation transportation network, a centrality measurement calculation model is constructed; Based on the centrality measurement calculation model, the degree centrality, proximity centrality and betweenness centrality of airport nodes are obtained, and the basic probability distribution function of airport node centrality is constructed. The steps of constructing the basic probability distribution function of the airport node centrality include: Calculate the degree centrality, proximity centrality and betweenness centrality of airport i and the maximum Euclidean distance between the maximum degree centrality, proximity centrality and betweenness centrality of airports in the civil aviation transportation network. , , ; Calculate the minimum Euclidean distance between the airport i-degree centrality, proximity centrality and betweenness centrality and the minimum degree centrality, proximity centrality and betweenness centrality of the airport in the civil aviation transportation network respectively , , ; Based on the maximum and minimum Euclidean distances of the centralities of airport i, the median Euclidean distances of degree centrality, proximity centrality, and betweenness centrality of airport i are calculated respectively. , , ; in: ； ； ； ； ； ； ； ； ； The optimal evaluation basic probability of the airport i-degree centrality is obtained by using the maximum Euclidean distance, minimum Euclidean distance and median Euclidean distance of the airport i-degree centrality. , Worst Assessment Basic Probability and medium assessment base probability ; The optimal evaluation basic probability of proximity centrality of airport i is obtained by using the maximum Euclidean distance, minimum Euclidean distance and median Euclidean distance of proximity centrality of airport i. , Worst Assessment Basic Probability and medium assessment base probability ; The optimal evaluation basic probability of airport i betweenness centrality is obtained by using the maximum Euclidean distance, minimum Euclidean distance and median Euclidean distance of airport i betweenness centrality. , Worst Assessment Basic Probability and medium assessment base probability ; ； ； ； ； ； ； ； ； ； Based on the combination rules of DS evidence theory and the basic probability of each centrality of airport nodes, an information fusion model of airport node importance is constructed; The steps of constructing the information fusion model of the importance of the airport nodes include: Based on the basic probabilities of each evaluation level of the degree centrality and proximity centrality of airport i, the node importance support evidence conflict coefficient of the degree centrality and proximity centrality of airport i is obtained. ; ； Support evidence conflict coefficient based on node importance , and obtain the optimal evaluation basic probability after the integration of the degree centrality and proximity centrality importance of airport i , Worst Assessment Basic Probability and medium assessment base probability ; ； ； ； Based on the basic probability of each evaluation level of airport i's betweenness centrality and the basic probability of each evaluation level after the fusion of degree centrality-closeness centrality importance, the node importance support evidence conflict coefficient after the fusion of airport i's degree centrality, closeness centrality and betweenness centrality importance is calculated. ; ； Support evidence conflict coefficient based on node importance , and obtain the optimal evaluation basic probability after integrating the importance of betweenness centrality, degree centrality and closeness centrality of airport i , Worst Assessment Basic Probability and medium assessment base probability ; ； ； ； According to the basic probability of each evaluation level after the fusion of betweenness centrality, degree centrality and closeness centrality importance, the optimal basic probability of fusion centrality evaluation of airport i is obtained. , and used as the evaluation result of the importance of airport i in the civil aviation transportation network; ； According to the information fusion model of airport node importance, the basic probability value of the optimal evaluation of airport fusion centrality is obtained, and the importance of each airport is judged according to the ranking result of the importance of each airport according to the basic probability value.

2. The airport importance assessment method based on information fusion as claimed in claim 1 is characterized by: The steps of constructing the civil aviation transportation network include: According to the obtained flight schedule, obtain the flight operation information of each airport; According to the connection between airports, a civil aviation transportation network G is constructed with airports as nodes and routes as edges.

3. The airport importance assessment method based on information fusion as claimed in claim 2 is characterized by: According to the operation information of airport flights, assuming that the total number of airports is n, the size of the civil aviation transportation network is, where the elements are: ； It is the total number of flights from airport i to airport j, and 0 means there is no flight between airport i and airport j; The constructed civil aviation transportation network G is expressed as: 。 4. The airport importance assessment method based on information fusion as claimed in claim 2 is characterized by: For routes without flights, the corresponding elements in the civil aviation transportation network G are assigned a value of 0; For routes with flights, the corresponding elements in the civil aviation transportation network G are assigned the number of flights on the route.

5. The airport importance assessment method based on information fusion as claimed in claim 1 is characterized by: The steps of constructing the centrality measurement calculation model include: Calculate the degree centrality of airport i based on the civil aviation transportation network ; The proximity centrality of airport i is calculated by using the shortest distance from airport node i to the other airport nodes in the civil aviation transportation network. ; Analyze the shortest path between airport nodes in the civil aviation transportation network passing through airport node i, and calculate the betweenness centrality of airport i .

6. An airport importance assessment device based on information fusion, comprising: The acquisition module is used to obtain the flight schedule, and according to the flight schedule, the flight operation information of each airport can be obtained to build a civil aviation transportation network; Model building module, used to build centrality measurement calculation model, basic probability distribution function of airport node centrality, and information fusion model of airport node importance; A processing calculation module is used to perform calculation processing according to the importance assessment method as described in any one of claims 1-5, and for the calculation of each model or function, the corresponding model or function in the model construction module is called through the model calling module.

7. An electronic device comprising: A processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor, and when the electronic device is running, the processor and the memory communicate through the bus, and the machine-readable instructions are executed by the processor to execute the steps of the airport importance assessment method as described in any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, executes the steps of the airport importance assessment method according to any one of claims 1 to 5.

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