Method and device for identifying important nodes of airport network

Abstract

The invention relates to the technical field of air transportation network analysis and optimization, in particular to a method and a device for identifying important nodes of an airport network, which can solve the problems of limitation of importance evaluation, attack simulation, practical application and the like in the prior art to a certain extent. The method comprises the following steps: data acquisition and processing: acquiring real-time aviation network data from different data sources, and cleaning, formatting and storing the acquired data to provide preparation for subsequent analysis; network modeling: establishing an airport network model according to the collected data; topology potential calculation: calculating a topology potential value of each node by adopting an improved topology potential formula; key node identification: identifying key nodes in the network based on topology potential centrality; dynamic analysis and optimization are carried out, network data are updated in real time, the topology potential and centrality are periodically recalculated to adapt to the dynamic change of the aviation network, and the network structure and the operation strategy are optimized according to the dynamic change.

Description

Technical Field

[0001] The present application relates to the technical field of aviation transportation network analysis and optimization, and specifically, to a method and device for identifying important nodes in an airport network. Background Art

[0002] With the growth of global air transport demand, the stability and robustness of airport networks are particularly important because they are directly related to the safety and efficiency of air transport. Studying the topological structure of airport networks and their key nodes can help improve the airport's operational efficiency and its ability to respond to emergencies.

[0003] Existing technologies analyze the general structural characteristics of the network by calculating statistical indicators such as degree distribution, average path length and clustering coefficient of the aviation network, and use centrality indicators such as closeness centrality and betweenness centrality to identify the airports that have the greatest impact on network efficiency. The location and connection status of these airports directly affect the transportation efficiency and stability of the network. By simulating failures or attacks on key nodes in the network, the robustness of the network in the face of external interference is evaluated.

[0004] However, existing airport network importance assessment methods, especially those based on centrality indicators, are usually limited to single-dimensional calculations such as the number of static connections and the distance between nodes. Existing studies use uniform or random node deletion methods to simulate network attacks or failures to evaluate the robustness of the network. Existing network robustness analysis methods often focus on theoretical models and mathematical derivations, but rarely verify and apply these models through actual air transport cases, resulting in limitations in importance assessment, attack simulation, and practical application. Summary of the invention

[0005] In order to solve the problems of limitations in the prior art such as importance assessment, attack simulation, and practical application, the present application provides a method and device for identifying important nodes in an airport network.

[0006] The embodiment of the present application is implemented as follows:

[0007] In a first aspect, the present application provides a method for identifying important nodes in an airport network, comprising:

[0008] Data collection and processing, collecting real-time aviation network data from different data sources, and cleaning, formatting and storing the collected data to prepare for subsequent analysis, the aviation network data includes airport and route data in the aviation network;

[0009] Network modeling, based on the collected data, an airport network model is established, where airports are nodes and routes are edges;

[0010] Topological potential calculation, using the improved topological potential formula to calculate the topological potential value of each node;

[0011] Key node identification, based on topological potential centrality, identifies key nodes in the network. Key nodes refer to those airports that have the greatest impact on the operation and stability of the network;

[0012] Dynamic analysis and optimization, real-time update of network data, regular recalculation of topological potential and centrality to adapt to the dynamic changes of the aviation network, and optimize the network structure and operation strategy accordingly.

[0013] In a possible implementation, in the network modeling, the weight of the node is determined by the actual traffic volume of the airport, and the weight of the edge is determined by the passenger volume or cargo volume of the route.

[0014] In a possible implementation, the topological potential calculation further includes:

[0015] Node attribute matrix generation;

[0016] Standardized processing;

[0017] Weight calculation;

[0018] Optimization coefficient;

[0019] Identification of key nodes;

[0020] Dynamic analysis and optimization.

[0021] In a possible implementation, the node attribute matrix generation further includes:

[0022] Assume that the node set is G = (V, E) V = {v1, v2, ... v N}, the edge set is E, in the network, node v i The topological potential of can be expressed as:

[0023]

[0024] Among them, β j Represents node v i The property of d ij Represents node v i With v j is the distance between nodes, and δ is the influence coefficient.

[0025] It also includes an improved topological potential that can fully characterize the attributes of the nodes. Assuming there are n nodes and p attributes in the network, the original attribute matrix is:

[0026]

[0027] Among them, X ijRepresents the j-th attribute value of node i.

[0028] In a possible implementation, the standardization process further includes:

[0029] The attribute matrix is ​​dimensionless, and the formula is as follows:

[0030]

[0031] Among them, x max is the maximum value of attribute j, x min is the minimum value of attribute j.

[0032] In a possible implementation, the weight calculation further includes:

[0033] The variability of an attribute is expressed as follows:

[0034]

[0035] Among them, S j is the standard deviation of attribute j. The standard deviation is used to indicate the degree of change of each attribute. The larger the standard deviation, the greater the difference of the attribute, indicating that the attribute reflects more information and has a greater weight.

[0036] The correlation between attributes can be expressed as:

[0037]

[0038] Among them, r ij is the correlation coefficient between attributes i and j.

[0039] The amount of information contained in the attribute is:

[0040]

[0041] C j The larger it is, the stronger the effect of attribute j is and the greater its weight is;

[0042] Node v i The new properties of can be expressed as:

[0043]

[0044] Based on the above analysis, the improved topological potential (ITP) can be expressed as:

[0045]

[0046] In a possible implementation, the optimization coefficient further includes:

[0047] In order to obtain the optimal coefficient value, this method introduces the topological potential, which is expressed as follows:

[0048]

[0049] in, is the normalization factor. At the same time, the optimization problem of the coefficient δ can be transformed into the minimization problem of the univariate function H(δ), that is, min H(δ);

[0050] When the influence coefficient δ is determined, node v i The topological potential centrality of can be expressed as:

[0051]

[0052] In a possible implementation, the key point identification further includes:

[0053] Based on the topological potential centrality, the key nodes in the network are identified. The key nodes are the airports that have the greatest impact on the operation and stability of the network.

[0054] In a possible implementation, the dynamic analysis and optimization further includes:

[0055] Update network data in real time and recalculate topological potential and centrality regularly to adapt to the dynamic changes of the aviation network and optimize the network structure and operation strategy accordingly.

[0056] In a second aspect, the present application provides an important node identification device for an airport network, comprising:

[0057] Data collection module: used to collect real-time aviation network data from different data sources, including airport information, flight data and passenger flow;

[0058] Data processing module: used to clean, format and store the collected data to prepare for subsequent analysis;

[0059] Network modeling module: used to construct an airport network model using processed data. The nodes in the model represent airports and the edges represent routes.

[0060] Model calculation module: used to perform improved topological potential algorithm and topological potential centrality calculation, used to analyze the structural characteristics of the network and identify key nodes;

[0061] Result output module: used to generate detailed reports and charts based on the analysis results for user evaluation and further decision-making.

[0062] The technical solution provided by this application can at least achieve the following beneficial effects:

[0063] The method for identifying important nodes in an airport network provided in this application significantly improves the accuracy and practicality of airport network analysis by adopting an improved topological potential method and a topological potential centrality model.

[0064] (1) Improved accuracy and comprehensiveness of key node identification:

[0065] The improved topological potential method (ITP) adopted in this paper can comprehensively consider the various network topological structure characteristics of nodes, and can more accurately reflect the actual importance of the airport in the entire network compared to the traditional centrality index, thereby more accurately identifying the key nodes that have the greatest impact on network performance and robustness. Through this method, the structure of the airport network can be effectively optimized, and the network's ability to cope with potential risks can be improved, thereby improving the safety and stability of the entire aviation network.

[0066] (2) The evaluation of network robustness is more in line with the actual situation:

[0067] The topological potential centrality model (TPC) proposed in this paper performs more accurate robustness analysis for different types of network interference and failure scenarios. Compared with traditional models, the TPC model takes into account the differences between nodes, making the robustness evaluation more in line with the complexity and variability of airport networks in actual operations. This improved evaluation model can not only more accurately reflect the vulnerability of the network, but also help managers identify and strengthen key points in the network and reduce network interruptions caused by emergencies.

[0068] (3) Providing efficient decision support tools for air transport management:

[0069] The invention combines complex network theory with actual air transport data to form a comprehensive set of airport network analysis and management tools. This not only enables air transport managers to make more scientific and effective decisions in airport planning, route design, resource allocation and emergency management, but also helps to improve the overall performance of the air transport system, ensure the safety and punctuality of flights, and promote the sustainable development of the aviation industry.

[0070] (4) Improve the versatility and operational convenience of the model:

[0071] The model algorithm of the present invention simplifies the analysis process and can automatically generate analysis results without additional resource investment, which greatly reduces operating costs and improves operating efficiency. At the same time, because the model combines the actual operation data of the airport and the topological characteristics of the network, its analysis results have strong adaptability and universal applicability and are applicable to airport networks of different sizes and types. Therefore, the method of the present invention is not only applicable to large-scale airport networks that have been built, but also to emerging markets or smaller-scale airport networks. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0073] Figure 1 It is a flowchart of a method for identifying important nodes in an airport network shown in an exemplary embodiment of the present application;

[0074] Figure 2 It is a flowchart of an airport network node identification method based on improved topological potential shown in an exemplary embodiment of the present application;

[0075] Figure 3 is a schematic diagram of an airport network topology model shown in an exemplary embodiment of the present application;

[0076] Figure 4 It is a structural schematic diagram of an important node identification device of an airport network shown in an exemplary embodiment of the present application;

[0077] Figure 5 is a schematic diagram of the distribution of network key node identification results shown in an exemplary embodiment of the present application;

[0078] Figure 6 is a schematic diagram of the optimization process of topological potential entropy shown in an exemplary embodiment of the present application;

[0079] Figure 7 is a schematic diagram of the topological potential value of an airport shown in an exemplary embodiment of the present application;

[0080] Figure 8 (a) is a schematic diagram of average path length for robustness analysis of an airport network model in East China against attacks, shown in an exemplary embodiment of the present application:

[0081] Figure 8 (b) is a schematic diagram of network density for robustness analysis of an airport network model in East China against attacks, shown in an exemplary embodiment of the present application;

[0082] Figure 8 (c) is a schematic diagram of network efficiency of an East China airport network model for robustness analysis against attacks, shown in an exemplary embodiment of the present application;

[0083] Figure 8 (d) is a schematic diagram of the maximum connected subgraph size for robustness analysis of the airport network model in East China against attacks, shown in an exemplary embodiment of the present application. DETAILED DESCRIPTION

[0084] In order to make the purpose, implementation mode and advantages of the present application clearer, the exemplary implementation mode of the present application will be clearly and completely described below in conjunction with the drawings in the exemplary embodiments of the present application. Obviously, the described exemplary embodiments are only part of the embodiments of the present application, not all of the embodiments. It should be understood that the specific embodiments described here are only used to explain the present application and are not used to limit the present application.

[0085] It should be noted that the brief description of terms in this application is only for the convenience of understanding the embodiments described below, and is not intended to limit the embodiments of this application. Unless otherwise specified, these terms should be understood according to their common and usual meanings.

[0086] The terms "first", "second", "third", etc. in the specification and claims of this application and the above drawings are used to distinguish similar or similar objects or entities, and do not necessarily mean to limit a specific order or sequence, unless otherwise noted. It should be understood that the terms used in this way can be interchangeable under appropriate circumstances.

[0087] The terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover but not exclude inclusion, for example, a product or device comprising a list of components is not necessarily limited to all the components expressly listed but may include other components not expressly listed or inherent to such product or device.

[0088] Before explaining the method for identifying important nodes in an airport network provided in an embodiment of the present application, the application scenario and implementation environment of the embodiment of the present application are first introduced.

[0089] With the growth of global air transport demand, the stability and robustness of airport networks are particularly important because they are directly related to the safety and efficiency of air transport. Studying the topological structure of airport networks and their key nodes can help improve the airport's operational efficiency and its ability to respond to emergencies.

[0090] Main relevant contents of the prior art

[0091] (1) Network structure analysis

[0092] By calculating statistical indicators such as the degree distribution, average path length, and clustering coefficient of the aviation network, the general structural characteristics of the network are analyzed. For example, researchers often use degree centrality to identify the airports with the most connections in the network, which usually carry a large amount of traffic in the network.

[0093] Degree distribution: The degree of a node in a network refers to the number of edges directly connected to the node. In aviation network analysis, degree distribution can reveal the connection complexity of airports and the overall structure of the network. The formula for degree distribution is:

[0094]

[0095] Among them, P(k) is the proportion of nodes with degree k, n k is the number of nodes with degree k, and N is the total number of nodes in the network.

[0096] Average path length: The average shortest path length between two nodes in the network. This is an important indicator for measuring network efficiency, especially in air transport networks, where shorter average path length means higher transport efficiency. The formula for average path length is:

[0097]

[0098] Among them, d ij is the shortest path length between node i and node j, and n is the total number of nodes in the network.

[0099] Clustering coefficient: The clustering coefficient of a node indicates the degree of actual connection between its neighboring nodes. In the aviation network, airport groups with high clustering coefficients indicate that there are high direct flight connections between these airports. The clustering coefficient formula of node i is:

[0100]

[0101] Among them, E i is the number of edges actually existing between neighbors of node i, k i is the degree of node i.

[0102] (2) Key point identification

[0103] Centrality indicators such as closeness centrality and betweenness centrality are used to identify the airports that have the greatest impact on network efficiency. The location and connectivity of these airports directly affect the transportation efficiency and stability of the network.

[0104] Degree Centrality: It directly reflects how many other airports an airport has direct flight connections with.

[0105]

[0106] where deg(i) is the degree of airport i and n is the total number of airports in the network.

[0107] Closeness Centrality: It measures the average distance from an airport to all other airports. The higher the closeness centrality of an airport, the shorter its average distance to other airports in the network, and the more central its position in the network.

[0108]

[0109] Betweenness Centrality: reflects the "bridge" role of an airport in the airport network, that is, calculating the number of shortest paths passing through the airport.

[0110]

[0111] Among them, σ jk is the number of shortest paths between node j and node k, σ jk (i) is the number of these paths passing through node i.

[0112] (3) Robustness analysis

[0113] Evaluate the robustness of a network in the face of external disturbances by simulating failures or attacks on key nodes in the network. For example, simulate a major airport being temporarily closed due to a natural disaster to observe the impact on the operation of the entire network.

[0114] Simulating a network attack: By removing a certain percentage of airport nodes (selected based on different centrality metrics), we study how the functionality of the entire network is affected. This analysis can assess the resilience of the network in the face of unexpected events (such as natural disasters, terrorist attacks, etc.).

[0115] Network robustness index: Commonly used robustness indexes include the size of the network's connected components, changes in network efficiency, etc. Network efficiency indicates the average transmission efficiency of information or traffic in the network. The formula for network efficiency is:

[0116]

[0117] It can be seen that the prior art has the following problems:

[0118] (1) Limitations of importance assessment. Existing airport network importance assessment methods, especially those based on centrality indicators (such as degree centrality, closeness centrality, and betweenness centrality), are usually limited to single-dimensional calculations such as the number of static connections and the distance between nodes. Although this assessment method is effective in theory, it has certain limitations in practice. For example, airports with high degree centrality indicators may not be the most important airports for the entire network, especially when these airports are mainly connected to some small local airports and have little impact on the global network. Such nodes may not have actual strategic importance. Therefore, traditional centrality indicators may misjudge the true importance of certain airports. Traditional assessment methods ignore the multi-level and multi-dimensional characteristics of airport networks and cannot fully reflect the importance of airports in complex networks.

[0119] (2) Insufficient attack simulation methods. Many existing studies use uniform or random node deletion methods to simulate network attacks or failures in order to evaluate the robustness of the network. This simulation method is too simple and fails to fully consider the complexity of the network under actual circumstances when it is attacked. For example, natural disasters, terrorist activities or other human factors are often not evenly distributed, but have a greater impact on specific regions or specific airport nodes. In reality, geographical location, airport type, the role of aviation hubs, etc. will lead to "non-uniformity" of attacks. Therefore, the robustness analysis based on this uniform deletion model often cannot reflect the actual performance of the network when facing different types of attacks.

[0120] (3) Theoretical research and practical application are out of touch

[0121] Existing network robustness analysis methods often focus on theoretical models and mathematical derivations, but rarely verify and apply these models through actual air transport cases. Most of these models remain at the theoretical level and lack sufficient practical background and application cases to verify their effectiveness and applicability. Although existing studies have proposed a variety of robustness analysis indicators and methods, they often fail to consider the complexity and diversity of airport networks in reality. Therefore, the practical application value of existing technologies is low, and more field cases and data support are needed to prove their feasibility in actual operations.

[0122] In the existing technical system, although there are many valuable theoretical studies and methods, there are often limitations in importance assessment, attack simulation, and practical application when identifying important nodes in the airport network. It is necessary to develop a more comprehensive and comprehensive evaluation method with practical application value to more accurately reflect the robustness of the airport network in the real environment and its ability to respond to emergencies.

[0123] Based on this, this application provides a method and device for identifying important nodes in an airport network, an improved topological potential method (ITP) and a topological potential centrality (TPC) model, and provides a new analysis tool for evaluating the robustness of an airport network. This research not only provides a new perspective for robustness analysis in complex networks, but also has important practical significance for optimizing the air transportation system and improving its safety and efficiency.

[0124] Next, the technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems will be described in detail through embodiments and in combination with the accompanying drawings. The embodiments may be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. Obviously, the described embodiments are part of the embodiments of the present application, not all of them.

[0125] Figure 1It is a flow chart of a method for identifying important nodes in an airport network shown in an exemplary embodiment of the present application.

[0126] In an exemplary embodiment, Figure 1 As shown, a method for identifying important nodes in an airport network is provided. In this embodiment, the method may include the following steps:

[0127] Step 100: Data collection and processing, collecting real-time aviation network data from different data sources, and cleaning, formatting and storing the collected data to prepare for subsequent analysis, wherein the aviation network data includes airport and route data in the aviation network;

[0128] Step 200: Network modeling, based on the collected data, an airport network model is established, where the airports are nodes and the routes are edges;

[0129] Step 300: topological potential calculation, using an improved topological potential formula to calculate the topological potential value of each node;

[0130] Step 400: Identification of key nodes, based on topological potential centrality, identifying key nodes in the network, key nodes are those airports that have the greatest impact on the operation and stability of the network;

[0131] Step 500: Dynamic analysis and optimization, real-time update of network data, regular recalculation of topological potential and centrality to adapt to the dynamic changes of the aviation network, and optimize the network structure and operation strategy accordingly.

[0132] Figure 2 is a flow chart of an airport network node identification method based on improved topological potential shown in an exemplary embodiment of the present application, Figure 3 It is a schematic diagram of an airport network topology model shown in an exemplary embodiment of the present application.

[0133] In one possible implementation, Figure 2 and Figure 3 As shown, the improved topological potential method (ITP) and the topological potential centrality model (TPC) proposed in some implementations of the present application are mainly based on the following steps:

[0134] (1) Data collection and processing: Collect airport and route data in the aviation network, including the geographical location of the airport, route traffic data, etc.

[0135] (2) Network modeling: Based on the collected data, an airport network model is established, with airports as nodes and routes as edges. The weight of the nodes is determined by the actual traffic volume of the airport, and the weight of the edges is determined by the passenger volume or cargo volume of the route.

[0136] (3) Topological potential calculation: The improved topological potential formula is used to calculate the topological potential value of each node. The topological potential formula not only takes into account the degree of the node, but also combines the traffic weight of the node and the distance weight between other nodes. The right part of the figure shows the algorithm flow of the improved topological potential centrality (TPC) model proposed in the present invention, which includes the steps of node attribute matrix generation, standardization processing, weight calculation, optimization coefficient determination, etc., and clearly presents the analysis process of the TPC model.

[0137] ①Node attribute matrix generation

[0138] Assume that the node set is G = (V, E) V = {v1, v2, ... v N}, the edge set is E. In the network, node v i The topological potential of can be expressed as:

[0139]

[0140] Among them, β j Represents node v i The property of d ij Represents node v i With v j is the distance between nodes, and δ is the influence coefficient.

[0141] In order to fully characterize the attributes of nodes, an improved topology potential (ITP) is proposed. Assuming there are n nodes and p attributes in the network, the original attribute matrix is:

[0142]

[0143] Among them, X ij Represents the j-th attribute value of node i.

[0144] ② Standardization

[0145] The attribute matrix is ​​dimensionless, and the formula is as follows:

[0146]

[0147] Among them, x max is the maximum value of attribute j, x min is the minimum value of attribute j.

[0148] ③Weight calculation

[0149] The variability of an attribute is expressed as follows:

[0150]

[0151] Among them, S jis the standard deviation of attribute j. The standard deviation is used to indicate the degree of change of each attribute. The larger the standard deviation, the greater the difference of the attribute, indicating that the attribute reflects more information and the greater the weight given.

[0152] The correlation between attributes can be expressed as:

[0153]

[0154] Among them, r ij is the correlation coefficient between attributes i and j.

[0155] The amount of information contained in the attribute is:

[0156]

[0157] C j The larger it is, the stronger the effect of attribute j is and the greater its weight is.

[0158] Node v i The new properties of can be expressed as:

[0159]

[0160] Based on the above analysis, the improved topological potential (ITP) can be expressed as:

[0161]

[0162] ④Optimization coefficient

[0163] In order to obtain the optimal coefficient value, this method introduces the topological potential, which is expressed as follows

[0164]

[0165] in, is the normalization factor. At the same time, the optimization problem of coefficient δ can be transformed into the minimization problem of the univariate function H(δ), i.e., min H(δ).

[0166] Therefore, when the influence coefficient δ is determined, the node v i The topological potential centrality (TPC) of can be expressed as:

[0167]

[0168] (4) Key node identification: Based on topological potential centrality (TPC), key nodes in the network are identified. Key nodes refer to airports that have the greatest impact on the operation and stability of the network.

[0169] (5) Dynamic analysis and optimization: Update network data in real time and regularly recalculate topological potential and centrality to adapt to the dynamic changes of the aviation network and optimize the network structure and operation strategy accordingly.

[0170] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the instructions, these steps are not necessarily executed in sequence according to the order of the instructions. Unless there is a clear explanation in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the above-mentioned embodiments may include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of the steps or stages in other steps.

[0171] Corresponding to the aforementioned embodiment of the method for identifying important nodes in an airport network, adopting the same technical concept, the present application also provides an embodiment of a device for identifying important nodes in an airport network.

[0172] Figure 4 It is a structural diagram of an important node identification device for an airport network shown in an exemplary embodiment of the present application.

[0173] In an exemplary embodiment, Figure 4 As shown, the airport network important node identification device includes:

[0174] Data collection module: used to collect real-time aviation network data from different data sources, including airport information, flight data and passenger flow;

[0175] Data processing module: used to clean, format and store the collected data to prepare for subsequent analysis;

[0176] Network modeling module: used to construct an airport network model using processed data. The nodes in the model represent airports and the edges represent routes.

[0177] Model calculation module: used to perform improved topological potential algorithm and topological potential centrality calculation, used to analyze the structural characteristics of the network and identify key nodes;

[0178] Result output module: used to generate detailed reports and charts based on the analysis results for user evaluation and further decision-making.

[0179] The connection relationship between these modules is as follows: the data acquisition module transmits the collected data to the data processing module, the processed data is stored and used by the network modeling module, the network model constructed by the network modeling module is sent to the model calculation module for further analysis, and finally, the result output module receives data from the model calculation module and generates a visual report. Through such a structure and process, some embodiments of the present application provide a comprehensive and effective method for analyzing the identification of important nodes in the airport network and improving its operational efficiency and robustness.

[0180] It can be seen that some embodiments of the present application significantly improve the accuracy and practicality of airport network analysis by adopting the improved topological potential method (ITP) and the topological potential centrality (TPC) model. Compared with the prior art, the present invention has the following significant advantages and effects:

[0181] (1) Improved accuracy and comprehensiveness of key node identification

[0182] The improved topological potential method (ITP) adopted in this paper can comprehensively consider the various network topological structure characteristics of nodes, and can more accurately reflect the actual importance of the airport in the entire network compared to the traditional centrality index, thereby more accurately identifying the key nodes that have the greatest impact on network performance and robustness. Through this method, the structure of the airport network can be effectively optimized, and the network's ability to cope with potential risks can be improved, thereby improving the safety and stability of the entire aviation network.

[0183] (2) Network robustness evaluation is more in line with actual conditions

[0184] The topological potential centrality model (TPC) proposed in this paper performs more accurate robustness analysis for different types of network interference and failure scenarios. Compared with traditional models, the TPC model takes into account the differences between nodes, making the robustness evaluation more in line with the complexity and variability of airport networks in actual operations. This improved evaluation model can not only more accurately reflect the vulnerability of the network, but also help managers identify and strengthen key points in the network and reduce network interruptions caused by emergencies.

[0185] (3) Provide efficient decision support tools for air transport management

[0186] The invention combines complex network theory with actual air transport data to form a comprehensive set of airport network analysis and management tools. This not only enables air transport managers to make more scientific and effective decisions in airport planning, route design, resource allocation and emergency management, but also helps to improve the overall performance of the air transport system, ensure the safety and punctuality of flights, and promote the sustainable development of the aviation industry.

[0187] (4) Improve the versatility and operational convenience of the model

[0188] The model algorithm of the present invention simplifies the analysis process and can automatically generate analysis results without the need for additional resource investment, which greatly reduces operating costs and improves operational efficiency. At the same time, because the model combines the actual operating data of the airport and the topological characteristics of the network, its analysis results have strong adaptability and universal applicability and are applicable to airport networks of different sizes and types. Therefore, the method of the present invention is not only applicable to large-scale airport networks that have been built, but also to emerging markets or smaller-scale airport networks.

[0189] For the specific definition of the device for identifying important nodes in an airport network, please refer to the definition of the method for identifying important nodes in an airport network mentioned above, which will not be repeated here. Each module in the above-mentioned device for identifying important nodes in an airport network can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0190] In order to verify the superiority of this application, an Airport Network Model (ANM) of East China was constructed, taking East China as an example. The model regards each airport as a node in the network and the route connection as an edge in the network. The model is further optimized on the basis of undirected weighted graph, and the importance of network nodes is evaluated through the Topological Potential Centrality (TPC) algorithm, so as to effectively identify key nodes and improve the robustness and transportation efficiency of the network.

[0191] Figure 5 is a schematic diagram of the distribution of network key node identification results shown in an exemplary embodiment of the present application, Figure 6 is a schematic diagram of the optimization process of topological potential entropy shown in an exemplary embodiment of the present application, Figure 7 It is a schematic diagram of the topological potential value of an airport shown in an exemplary embodiment of the present application.

[0192] The airport network model and analysis method based on some embodiments of the present application include the following steps:

[0193] (1) Construction of airport network model

[0194] According to the distribution of airports, each airport in eastern China is regarded as a node, and the routes connecting two airports are regarded as edges in the network, forming an undirected weighted graph.

[0195] The weight of an edge is defined as the number of flights per unit time between two airports to characterize their network connectivity and traffic volume.

[0196] like Figure 5 As shown in the figure, the spatial distribution of key nodes identified in the airport network topology, which indicates the topological potential value of each node. The higher the topological potential value of the node, the more critical it is in the network, showing the importance of different nodes in the airport network. The red nodes are the top 20 airports in East China in terms of topological potential value, and the green nodes are the remaining airports.

[0197] (2) Data collection and processing

[0198] Flight data of airports in the eastern region in 2019 were collected from the Civil Aviation Administration of China (CAAC), involving a total of 44 airports. The number of flights and flight capacity information of the airports were used to determine the connection relationship between nodes and the weight of the edges.

[0199] Table 1 Top 20 airports in East China by passenger throughput

[0200]

[0201] (3) Calculation of basic characteristics of airport network

[0202] Based on complex network theory, the topological characteristics of the airport network are calculated, including the number of nodes, the number of edges, the average degree, the clustering coefficient, and the average path length. The results show that the network has an average degree of 8.5, a clustering coefficient of 0.51, and an average path length of 1.88, which conforms to the characteristics of a small-world network.

[0203] Table 2 Basic topological characteristics of the airport network in East China

[0204]

[0205] (4) Analysis of Topological Potential Centrality (TPC) Algorithm

[0206] Through the TPC algorithm, the topological centrality index of each node is calculated, and its degree, betweenness, proximity and eigenvector centrality are integrated to reduce the interference of human factors. The best is obtained by minimizing the topological potential entropy, such as Figure 6 shown.

[0207] The standardized results of different attributes can be obtained by using the improved topological potential algorithm as shown in Table 3. The topological potential values ​​of the airport are as follows: Figure 7 As shown in the figure, the TPC algorithm not only takes into account a single attribute, but also improves the accuracy and robustness of key node identification by optimizing algorithm parameters.

[0208] Table 3 Standardization results of different attributes

[0209]

[0210] (5) Network robustness analysis

[0211] For the airport network, analyzing its robustness can provide insights into the connectivity and structural strength of the airport, which is crucial for the safety and reliability of air transportation. Based on TPC, random and deliberate attacks are applied to ANM to evaluate its robustness. The attacked nodes are ranked based on their degree, betweenness, proximity, eigenvector, and TPC centrality. To evaluate the robustness of the network, we considered metrics such as average path length, density, efficiency, and maximum connected subgraph size. The results are shown in Figure 2. Figure 8 shown.

[0212] Figure 8 (a) is a schematic diagram of average path length for robustness analysis of an airport network model in East China against attacks, shown in an exemplary embodiment of the present application. Figure 8 (b) is a schematic diagram of network density for robustness analysis of an airport network model in East China against attacks, shown in an exemplary embodiment of the present application. Figure 8 (c) is a schematic diagram of network efficiency of an East China airport network model for robustness analysis against attacks, shown in an exemplary embodiment of the present application. Figure 8 (d) is a schematic diagram of the maximum connected subgraph size for robustness analysis of the airport network model in East China against attacks, shown in an exemplary embodiment of the present application.

[0213] Figure 8 (a) shows the change of average path length as the number of attacked nodes increases. From the curve trend, it can be seen that when the number of attacked nodes exceeds 20%, the curve becomes steeper, indicating that the average path length of ANM is more affected. In addition, the proposed TPC centrality has a significant impact on the average path length of ANM. Figure 8 (b) shows that as the number of attacked nodes increases, the density of ANM decreases, indicating that the connectivity between airports is deteriorating. Obviously, the proposed TPC also has a prominent impact on network density. Network efficiency ( Figure 8 (c)) and the maximum connected subgraph size ( Figure 8 (d) The robustness of the model is verified from a structural perspective. Overall, the proposed TPC has a significant impact on the robustness of ANM, highlighting the effectiveness and accuracy of the theory in identifying influential nodes.

[0214] In summary, the TPC method effectively avoids the instability and randomness brought by human attributes, as well as the potential impact of a single attribute. Compared with metrics such as betweenness, degree, or proximity, TPC can evaluate the robustness of ANM more comprehensively and stably.

[0215] By implementing the improved topological potential method (ITP) and topological potential centrality model (TPC) of the present invention in the airport network in East China, the present invention has achieved remarkable results in practical applications. The following are the specific results achieved after the implementation of the technology:

[0216] (1) The accuracy of key node identification is significantly improved

[0217] By adopting the improved topological potential centrality (TPC) model, the present invention can more accurately and comprehensively evaluate the importance of nodes in the airport network. Practical applications show that the model can effectively identify the airports and airport rankings that have the greatest impact on the operation and stability of the entire network, providing a more scientific basis for the optimization decision of the airport network.

[0218] (2) The analysis and evaluation of network robustness are closer to reality

[0219] By simulating and analyzing the robustness of airport networks under random and deliberate attacks, it is found that the evaluation results based on the TPC model are closer to the actual situation. The TPC model can accurately reflect the vulnerability of airport networks under different attack strategies, especially in identifying vulnerable links in the network. This provides scientific support for the formulation of effective defense strategies and emergency plans, and helps to improve the resilience of aviation networks in the face of emergencies.

[0220] (3) Provide practical decision support tools for air transport management

[0221] The present invention combines complex network theory with actual air transport data to form a set of systematic analysis methods and tools. Through this method, aviation managers can obtain more comprehensive and in-depth network structure information, which can assist them in making more scientific and efficient decisions in airport planning, route design, resource allocation and emergency management. This will help improve the scientific and information level of air transport management and promote the sustainable development of the industry.

[0222] (4) The method has good versatility and scalability

[0223] The model of the present invention has been successfully applied to the airport network in East China, proving its strong applicability. Since the method fully considers the topological structure and node attributes of the network, it can be extended and applied to airport networks in other regions or countries, and even to other types of transportation networks or complex network systems. This provides a universal solution for network analysis of different scales and structures, and has broad application prospects.

[0224] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0225] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.

Claims

1. A method for identifying important nodes in an airport network, characterized in that: include: Data collection and processing, collecting real-time aviation network data from different data sources, and cleaning, formatting and storing the collected data to prepare for subsequent analysis, the aviation network data includes airport and route data in the aviation network; Network modeling, based on the collected data, an airport network model is established, where airports are nodes and routes are edges; Topological potential calculation, using the improved topological potential formula to calculate the topological potential value of each node; Key node identification, based on topological potential centrality, identifies key nodes in the network. Key nodes refer to those airports that have the greatest impact on the operation and stability of the network; Dynamic analysis and optimization, real-time update of network data, regular recalculation of topological potential and centrality to adapt to the dynamic changes of the aviation network, and optimize the network structure and operation strategy accordingly.

2. The method for identifying important nodes in an airport network according to claim 1, characterized in that: In the network modeling, the weight of the node is determined by the actual traffic volume of the airport, and the weight of the edge is determined by the passenger volume or cargo volume of the route.

3. The method for identifying important nodes in an airport network according to claim 1, characterized in that: The topological potential calculation further comprises: Node attribute matrix generation; Standardized processing; Weight calculation; Optimization coefficient; Identification of key nodes; Dynamic analysis and optimization.

4. The method for identifying important nodes in an airport network according to claim 3, characterized in that: The node attribute matrix generation further comprises: Assume that the node set is G = (V, E) V = {v1, v2, ... v N }, the edge set is E, in the network, node v i The topological potential of can be expressed as: Among them, β j Represents node v i The property of ij Represents node v i With v j is the distance between nodes, and δ is the influence coefficient. It also includes an improved topological potential that can fully characterize the attributes of the nodes. Assuming there are n nodes and p attributes in the network, the original attribute matrix is: in, X ij Represents the j-th attribute value of node i.

5. The method for identifying important nodes in an airport network according to claim 3, characterized in that: The standardization process further comprises: The attribute matrix is ​​dimensionless, and the formula is as follows: Among them, x max is the maximum value of attribute j, x min is the minimum value of attribute j.

6. The method for identifying important nodes in an airport network according to claim 3, characterized in that: The weight calculation further includes: The variability of an attribute is expressed as follows: in, S j is the standard deviation of attribute j. The standard deviation is used to indicate the degree of change of each attribute. The larger the standard deviation, the greater the difference of the attribute, indicating that the attribute reflects more information and has a greater weight. The correlation between attributes can be expressed as: Among them, r ij is the correlation coefficient between attributes i and j. The amount of information contained in the attribute is: C j The larger it is, the stronger the effect of attribute j is and the greater its weight is; Node v i The new properties of can be expressed as: Based on the above analysis, the improved topological potential (ITP) can be expressed as:

7. The method for identifying important nodes in an airport network according to claim 3, characterized in that: The optimization coefficient further includes: In order to obtain the optimal coefficient value, this method introduces the topological potential, which is expressed as follows: in, is the normalization factor. At the same time, the optimization problem of the coefficient δ can be transformed into the minimization problem of the univariate function H(δ), i.e., minH(δ); When the influence coefficient δ is determined, node v i The topological potential centrality of can be expressed as:

8. The method for identifying important nodes in an airport network as claimed in claim 3, characterized in that: The key point identification further comprises: Based on topological potential centrality (TPC), key nodes in the network are identified. Key nodes are airports that have the greatest impact on the operation and stability of the network.

9. The method for identifying important nodes in an airport network as claimed in claim 3, characterized in that: The dynamic analysis and optimization further includes: Update network data in real time and recalculate topological potential and centrality regularly to adapt to the dynamic changes of the aviation network and optimize the network structure and operation strategy accordingly.

10. An important node identification device for an airport network, characterized in that: include: Data collection module: used to collect real-time aviation network data from different data sources, including airport information, flight data and passenger flow; Data processing module: used to clean, format and store the collected data to prepare for subsequent analysis; Network modeling module: used to construct an airport network model using processed data. The nodes in the model represent airports and the edges represent routes. Model calculation module: used to perform improved topological potential algorithm and topological potential centrality calculation, used to analyze the structural characteristics of the network and identify key nodes; Result output module: used to generate detailed reports and charts based on the analysis results for user evaluation and further decision-making.

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