A method for evaluating the robustness of airway networks

Abstract

This invention discloses a method for evaluating the robustness of airway networks, belonging to the technical field of airway network robustness evaluation. The method includes: determining airway network nodes based on the target airway network and segment traffic, wherein the airway network nodes include critical nodes and ordinary nodes; determining a suitable cascading failure model based on the characteristics of the target airway network; performing a network attack on the airway network nodes based on the cascading failure model; and calculating the robustness index of the airway network based on the network attack results. This invention specifically identifies critical nodes in the airway network based on the airspace airway network structure and segment traffic, combines a suitable cascading failure model, attacks ordinary and critical nodes of the airway network through network attacks, calculates the robustness index of the target airway based on the network attack results, and uses this to evaluate the robustness of the airway network after the network attack. The robustness evaluation results are realistic, consistent with the actual situation of my country's airspace, and beneficial for airway planning.

Description

[0001] This invention relates to a method for evaluating the robustness of airway networks, belonging to the technical field of airway network robustness evaluation. Background Technology

[0002] With the rapid development of civil aviation, the problem of airspace resource scarcity is becoming increasingly prominent. The air route network's resilience to various emergencies is also weakening, leading to increasingly serious problems. According to statistics from the Civil Aviation Administration of China, in 2020, weather-related factors accounted for 56.88% of flight irregularities. The air route network, as the carrier of air passenger and cargo transport and the physical space for realizing air traffic, is a crucial factor affecting the stability of air traffic flow. Air route network robustness assessment is an important component of air route network design, and it is of great significance for eliminating air route bottlenecks, improving airspace capacity, and rationally allocating routes.

[0003] The primary issue in assessing the robustness of airway networks is to begin with airspace modeling and reconstruction. Due to the relatively late start of my country's civil aviation industry and the presence of numerous restricted areas in the airspace, the overall airspace fragmentation is high. Furthermore, the insufficient redundancy design of the airway network results in a lack of capacity to respond to unforeseen airspace events. Therefore, assessing the robustness of the airway network, scientifically allocating and utilizing airspace resources, eliminating airway bottlenecks, and preventing accidents are of paramount importance at present.

[0004] Based on practical considerations, on the one hand, my country has a vast airspace, and the importance of local route structures to the overall route network is increasing; on the other hand, compared to the coordination issues of airspace resources in Europe, my country has a stronger ability to coordinate airspace resources, thus making route assessments more applicable. To further promote the development of my country's civil aviation transport industry, it is crucial to conduct a reasonable assessment of the robustness of the route network, taking into account my country's airspace environment. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the prior art and provide a robustness assessment method for airway networks. This method combines static airway structure and segment traffic for robustness assessment, which is consistent with the actual situation of my country's airspace. The robustness assessment results are realistic, which is beneficial to airway planning and improves economic efficiency. It solves the problem that the current robustness assessment of airways does not match the actual situation and the assessment results are not very useful for airway planning.

[0006] To achieve the above objectives, the present invention is implemented using the following technical solution:

[0007] A method for evaluating the robustness of a route network includes:

[0008] Based on the obtained target route network and target route segment traffic, determine the route network node type;

[0009] Based on the characteristics of the target airway network, determine a cascading failure model that matches the node capacity and load of the target airway network.

[0010] Based on the cascading failure model, network attacks are carried out on different types of airway network nodes;

[0011] Calculate the robustness index of the airway network based on the results of the cyberattack;

[0012] The evaluation results of the target route are obtained based on the robustness index of the route network.

[0013] Furthermore, determining the route network node type based on the target route network and segment traffic includes:

[0014] Construct the topology of the target air route network;

[0015] Based on the topology and segment traffic of the target airway network, select key node identification indicators for the airway network;

[0016] Based on the identification indicators of key nodes in the airway network, the types of airway network nodes are determined, including key nodes and ordinary nodes.

[0017] Furthermore, determining the type of airway network node based on the key node identification indicators of the airway network includes:

[0018] Obtain static and dynamic indicators for identifying key nodes in the airway network. Static indicators include node degree, node coefficient, clustering coefficient, and neighbor degree product, while dynamic indicators include typical daily traffic and airway network change rate.

[0019] The objective weights of each indicator in static and dynamic indicators are calculated using the entropy weight method.

[0020] The subjective weights of each indicator in the static and dynamic indicators are calculated using network analysis.

[0021] The importance of each node is determined by combining objective and subjective weights;

[0022] Based on the importance of each node, critical nodes and ordinary nodes in the air route network are distinguished.

[0023] Furthermore, the calculation of the objective weights of each indicator in the static and dynamic indicators using the entropy weight method includes:

[0024] Construct the original data judgment matrix R :

[0025] Normalize each indicator: or

[0026] Calculate the numerical weight of the j-th node for the i-th indicator:

[0027] Calculate the objective weight of the i-th indicator :

[0028] in: Indicates the first i The first indicator j The normalized index value of each node Indicates the first i The first indicator j The index values ​​of each node, Indicates the first i The first indicator j The numerical weight of each node i Indicates the first i One indicator, j Indicates the first j 1 node m Indicates the number of nodes. n Indicates the number of indicators.

[0029] Furthermore, the calculation of the subjective weights of each indicator in the static and dynamic indicators using network analysis includes:

[0030] Each indicator is compared pairwise to construct a judgment matrix, including:

[0031] Based on the judgment matrix, calculate the feature vector corresponding to each indicator;

[0032] Based on the feature vectors corresponding to each indicator, calculate the subjective weight of each indicator, including:

[0033] in: Indicates the first i The subjective weight of each indicator, Indicates the first i The feature vector corresponding to each indicator n Indicates the number of indicators.

[0034] Furthermore, by combining objective and subjective weights, the importance of each node is determined, including:

[0035] in: Indicates the importance of each node. This indicates the proportion of objective weights involved in the weighting process. Indicates the first i The objective weight of each indicator, Indicates the first i Subjective weighting of each indicator.

[0036] Furthermore, the network attack based on the cascading failure model, targeting different types of route network nodes, includes:

[0037] Select the capacity-load cascading failure model;

[0038] Determine the node load conditions in the capacity-load cascading failure model;

[0039] Based on the node load conditions in the capacity-load cascading failure model, determine the node load redistribution method;

[0040] Identify the methods of cyberattacks, which include random attacks and deliberate attacks.

[0041] Based on node load redistribution, network attacks target different types of route network nodes.

[0042] Furthermore, determining the node load status of the capacity-load cascading failure model includes:

[0043] Determine the initial load of the target route network nodes:

[0044] Determine the maximum capacity of each node:

[0045] in: Indicates the first i Initial load of each node, Indicates the first i The maximum capacity of a node, a Indicates an adjustable parameter. Indicates an adjustable parameter. Indicates the first i The degree of each node, This indicates the node's ability to handle additional load. N This indicates the number of nodes in the target route network.

[0046] Furthermore, determining the node load redistribution method based on the node load situation in the capacity-load cascading failure model includes:

[0047] Determine the remaining load capacity of the node:

[0048] When a node is attacked and fails, the load on that failed node is transferred to other nodes in the target route network. The redistribution of load on the failed node and the probability of redistribution are determined.

[0049] in: Represents a node i Real-time load; Represents a node i The remaining load capacity, when At that time, node i Failure; Represents a node i Incremental load updates This indicates the load of the failed node. This represents the set of all adjacent nodes of the failed node. Represents a node i The probability of load redistribution;

[0050] when When node i crashes, the target route network node load is redistributed.

[0051] Furthermore, the calculation of robustness indicators of the airway network based on the network attack results includes:

[0052] After each network attack, robustness metrics of the target route network are calculated, including the maximum connected subgraph, global network efficiency, and global clustering coefficient.

[0053] Calculate the maximum connected subgraph, including:

[0054] Calculating global network efficiency includes:

[0055] Calculating the global clustering coefficient includes:

[0056] in: Represents the maximum connected subgraph. Indicates global network efficiency. B This represents the mean of the global clustering coefficients. Indicates the first i The maximum capacity of a node, This represents the clustering coefficient of node i; m Indicates the number of nodes. n Indicates the number of indicators. Represents a node i With nodes j The shortest path between, Represents a node i The number of existing edges in the neighbors, Indicates the first i The degree of each node.

[0057] Compared with the prior art, the beneficial effects achieved by the present invention are as follows:

[0058] This invention identifies key nodes in the airway network based on the airspace route network structure and segment traffic. According to the characteristics of the target route network, and combined with a suitable cascading failure model, it attacks both ordinary and key nodes of the route network via cyberattacks. Based on the cyberattack results, it calculates the robustness index of the target route and uses this to evaluate the robustness of the route network after the cyberattack. The robustness assessment results are realistic and consistent with the actual situation of my country's airspace, which is beneficial for route planning and improves economic efficiency. It solves the problem that current route robustness assessments are inconsistent with reality and have low reference value for route planning. Attached Figure Description

[0059] Figure 1 This is a flowchart of an embodiment of a route network robustness evaluation method provided by the present invention;

[0060] Figure 2 This is a schematic diagram of the judgment matrix in an embodiment of a route network robustness evaluation method provided by the present invention. Detailed Implementation

[0061] The present invention will be further described below with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and should not be used to limit the scope of protection of the present invention. Example 1

[0062] like Figure 1 As shown, a method for evaluating the robustness of a route network includes:

[0063] Based on the target route network and segment traffic, the route network node types are determined. These include critical route network nodes and ordinary route network nodes. Specifically:

[0064] Construct the topology of the target air route network;

[0065] Based on the topology and segment traffic of the target airway network, select key node identification indicators for the airway network;

[0066] Based on the key node identification indicators of the airway network, the types of airway network nodes are determined, including:

[0067] Obtain static and dynamic indicators for identifying key nodes in the airway network. Static indicators include node degree, node coefficient, clustering coefficient, and neighbor degree product, while dynamic indicators include typical daily traffic and airway network change rate.

[0068] The objective weights of each indicator in static and dynamic indicators are calculated using the entropy weight method, including:

[0069] Construct the original data judgment matrix R:

[0070] Normalize each indicator:

[0071] or

[0072] Calculate the first i The first indicator j The numerical weight of each node:

[0073] Calculate the first i Objective weight of each indicator :

[0074] in: Indicates the first i The first indicator j Normalized index values ​​for each node Indicates the first i The first indicator j The index values ​​of each node, Indicates the first i The first indicator j The numerical weight of each node i Indicates the first i One indicator, j Indicates the first j 1 node m Indicates the number of nodes. n Indicates the number of indicators;

[0075] The subjective weights of each indicator in the static and dynamic indicators are calculated using network analysis, including:

[0076] like Figure 2 As shown in the figure, each indicator is compared pairwise to construct a judgment matrix. c ef This indicates a comparison and assignment of values ​​to indicators e and f, where e = 1.…n and f = 1.…n, including: ;

[0077] Construct a normalized judgment matrix: ;

[0078] Calculate the largest eigenvalue of the normalized judgment matrix and the corresponding normalized feature vector , b n Represents the eigenvectors of the judgment matrix;

[0079] When comparing each indicator pairwise, a consistency test is required. Only after passing the consistency test is the comparison valid. This includes:

[0080]

[0081] Indicators of consistency This represents the largest eigenvalue of the normalized judgment matrix. n Indicates the number of indicators. Indicates the average random consistency rate. Indicates the consistency ratio;

[0082] Based on the judgment matrix, calculate the feature vector corresponding to each indicator;

[0083] Based on the feature vectors corresponding to each indicator, calculate the subjective weight of each indicator, including:

[0084] Indicates the first i The subjective weight of each indicator, Indicates the first i The normalized feature vectors corresponding to each indicator;

[0085] By combining objective and subjective weights, the importance of each node is determined, including:

[0086] in: Indicates the importance of each node. This represents the proportion of objective weights involved in the weighting process;

[0087] Based on the importance of each node, critical nodes and ordinary nodes in the air route network are distinguished.

[0088] Based on the characteristics of the target airway network, determine a cascading failure model that matches the node capacity and load of the target airway network.

[0089] Based on the cascading failure model, network attacks are launched against different types of route network nodes, specifically:

[0090] Select the capacity-load cascading failure model;

[0091] Determine the node load conditions in the capacity-load cascading failure model, including: determining the initial load of the target route network nodes:

[0092] Determine the maximum capacity of each node:

[0093] in: Indicates the first i Initial load of each node, Indicates the first i The maximum capacity of a node, a Indicates an adjustable parameter. Indicates an adjustable parameter. Indicates the first i The degree of each node, This indicates the node's ability to handle additional load. , N Indicates the number of nodes in the target route network;

[0094] Based on the node load conditions in the capacity-load cascading failure model, determine the node load redistribution method, including: when At that time, node i Crash, target route network node load redistribution:

[0095] Determine the remaining load capacity of the node:

[0096] When a node fails, the load on that failed node is transferred to other nodes in the target route network. The redistribution of the load on the failed node and the probability of redistribution are determined.

[0097] in: Represents a node i Real-time load; Represents a node i The remaining load capacity, when At that time, node i Failure; Represents a node i Incremental load updates This indicates the load of the failed node. This represents the set of all adjacent nodes of the failed node. Represents a node i The probability of load redistribution;

[0098] The network attack method was identified, and the network attack was a random attack:

[0099] Random attacks refer to attacks that do not consider the difference in importance between nodes; critical nodes and ordinary nodes have the same probability of being attacked, and the attacks are not correlated.

[0100] Based on node load redistribution, network attacks target airway networks.

[0101] Based on the results of the network attack, the robustness index of the route network is calculated, including: after each network attack, the robustness index of the target route network is calculated, including the maximum connected subgraph, global network efficiency and global clustering coefficient.

[0102] Calculate the maximum connected subgraph, including:

[0103] Calculating global network efficiency includes:

[0104] Calculating the global clustering coefficient includes:

[0105] in: Represents the maximum connected subgraph. Indicates global network efficiency. B This represents the mean of the global clustering coefficients. Indicates the first i The maximum capacity of a node, This represents the clustering coefficient of node i; m Indicates the number of nodes. n Indicates the number of indicators. Represents a node i With nodes j The shortest path between, Represents a node i The number of existing edges in the neighbors, Indicates the first i The degree of each node;

[0106] By calculating and comparing the changes in the robustness index of the route network under random attack methods, the robustness of the route network under different conditions is measured, and the evaluation results of the target route are obtained.

[0107] Example 2

[0108] like Figure 1 As shown, a method for evaluating the robustness of a route network includes:

[0109] Based on the target route network and segment traffic, the route network node types are determined. These include critical route network nodes and ordinary route network nodes. Specifically:

[0110] Construct the topology of the target air route network;

[0111] Based on the topology and segment traffic of the target airway network, select key node identification indicators for the airway network;

[0112] Based on the key node identification indicators of the airway network, the types of airway network nodes are determined, including:

[0113] Obtain static and dynamic indicators for identifying key nodes in the airway network. Static indicators include node degree, node coefficient, clustering coefficient, and neighbor degree product, while dynamic indicators include typical daily traffic and airway network change rate.

[0114] The objective weights of each indicator in static and dynamic indicators are calculated using the entropy weight method, including:

[0115] Construct the original data judgment matrix R:

[0116] Normalize each indicator:

[0117] or

[0118] Calculate the first i The first indicator j The numerical weight of each node:

[0119] Calculate the first i Objective weight of each indicator :

[0120] in: Indicates the first i The first indicator j Normalized index values ​​for each node Indicates the first i The first indicator j The index values ​​of each node, Indicates the first i The first indicator j The numerical weight of each node i Indicates the first i One indicator, j Indicates the first j 1 node m Indicates the number of nodes. n Indicates the number of indicators;

[0121] The subjective weights of each indicator in the static and dynamic indicators are calculated using network analysis, including:

[0122] like Figure 2 As shown in the figure, each indicator is compared pairwise to construct a judgment matrix. c ef This indicates a comparison and assignment of values ​​to indicators e and f, where e = 1.…n and f = 1.…n, including: ;

[0123] Construct a normalized judgment matrix: ;

[0124] Calculate the largest eigenvalue of the normalized judgment matrix and the corresponding normalized feature vector , b n Represents the eigenvectors of the judgment matrix;

[0125] When comparing each indicator pairwise, a consistency test is required. Only after passing the consistency test is the comparison valid. This includes:

[0126]

[0127] Indicators of consistency This represents the largest eigenvalue of the normalized judgment matrix. n Indicates the number of indicators. Indicates the average random consistency rate. Indicates the consistency ratio;

[0128] Based on the judgment matrix, calculate the feature vector corresponding to each indicator;

[0129] Based on the feature vectors corresponding to each indicator, calculate the subjective weight of each indicator, including:

[0130] Indicates the first i The subjective weight of each indicator, Indicates the first i The normalized feature vectors corresponding to each indicator;

[0131] By combining objective and subjective weights, the importance of each node is determined, including:

[0132] in: Indicates the importance of each node. This represents the proportion of objective weights involved in the weighting process;

[0133] Based on the importance of each node, critical nodes and ordinary nodes in the air route network are distinguished.

[0134] Based on the characteristics of the target airway network, determine a cascading failure model that matches the node capacity and load of the target airway network.

[0135] Based on the cascading failure model, network attacks are launched against different types of route network nodes, specifically:

[0136] Select the capacity-load cascading failure model;

[0137] Determine the node load conditions in the capacity-load cascading failure model, including: determining the initial load of the target route network nodes:

[0138] Determine the maximum capacity of each node:

[0139] in: Indicates the first iInitial load of each node, Indicates the first i The maximum capacity of a node, a Indicates an adjustable parameter. Indicates an adjustable parameter. Indicates the first i The degree of each node, This indicates the node's ability to handle additional load. , N Indicates the number of nodes in the target route network;

[0140] Based on the node load conditions in the capacity-load cascading failure model, determine the node load redistribution method, including: when At that time, node i Crash, target route network node load redistribution:

[0141] Determine the remaining load capacity of the node:

[0142] When a node fails, the load on that failed node is transferred to other nodes in the target route network. The redistribution of the load on the failed node and the probability of redistribution are determined.

[0143] in: Represents a node i Real-time load; Represents a node i The remaining load capacity, when At that time, node i Failure; Represents a node i Incremental load updates This indicates the load of the failed node. This represents the set of all adjacent nodes of the failed node. Represents a node i The probability of load redistribution;

[0144] Identify the methods of cyberattacks, which include random attacks and deliberate attacks:

[0145] Random attacks refer to attacks that do not consider the difference in importance between nodes; critical nodes and ordinary nodes have the same probability of being attacked, and the attacks are not correlated.

[0146] Intentional attacks refer to prioritizing attacks on nodes of higher importance. Attacks target critical nodes and ordinary nodes in a specific order, and the attacks are continuous and interconnected.

[0147] Based on node load redistribution, network attacks target airway networks.

[0148] Based on the results of the network attack, the robustness index of the route network is calculated, including: after each network attack, the robustness index of the target route network is calculated, including the maximum connected subgraph, global network efficiency and global clustering coefficient.

[0149] Calculate the maximum connected subgraph, including:

[0150] Calculating global network efficiency includes:

[0151] Calculating the global clustering coefficient includes:

[0152] in: Represents the maximum connected subgraph. Indicates global network efficiency. B This represents the mean of the global clustering coefficients. Indicates the first i The maximum capacity of a node, This represents the clustering coefficient of node i; m Indicates the number of nodes. n Indicates the number of indicators. Represents a node i With nodes j The shortest path between, Represents a node i The number of existing edges in the neighbors, Indicates the first i The degree of each node;

[0153] By calculating and comparing the changes in the robustness index of the route network under random attack methods, the robustness of the route network under different conditions is measured, and the evaluation results of the target route are obtained.

[0154] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage CD-ROMs, optical storage, etc.) containing computer-usable program code.

[0155] This application is described with reference to flowchart illustrations of methods, apparatus (systems), and computer program products according to embodiments of this application. It should be understood that each step in the flowchart can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the processes. Figure 1 One or more processes or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0156] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 The function specified in one or more processes.

[0157] These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable apparatus for implementing the process. Figure 1 Steps of a specified function in one or more processes.

[0158] The embodiments of the present invention have been described above with reference to the accompanying drawings. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of the present invention without departing from the spirit and scope of the claims. All of these forms are within the protection scope of the present invention.

Claims

1. A method for evaluating the robustness of a route network, characterized in that, include: Based on the target route network and segment traffic, route network nodes are determined. These nodes include critical route network nodes and ordinary route network nodes, including: Construct the topology of the target air route network; Based on the topology and segment traffic of the target airway network, select key node identification indicators for the airway network; Based on the key node identification indicators of the airway network, key nodes and ordinary nodes of the airway network are determined, including: Obtain static and dynamic indicators for identifying key nodes in the airway network. Static indicators include node degree, node coefficient, clustering coefficient, and neighbor degree product, while dynamic indicators include typical daily traffic and airway network change rate. The objective weights of each indicator in static and dynamic indicators are calculated using the entropy weight method. The subjective weights of each indicator in the static and dynamic indicators are calculated using network analysis. The importance of each node is determined by combining objective and subjective weights; Based on the importance of each node, distinguish between critical nodes and ordinary nodes in the air route network; Based on the characteristics of the target route network, determine a suitable cascading failure model; Based on the cascading failure model, network attacks are carried out on route network nodes. Based on the results of the cyberattack, robustness metrics of the airway network are calculated, including: After each network attack, robustness metrics of the target route network are calculated, including the maximum connected subgraph, global network efficiency, and global clustering coefficient. Calculate the maximum connected subgraph, including: ； Calculating global network efficiency includes: ； Calculating the global clustering coefficient includes: ； ； in: Represents the maximum connected subgraph. Indicates global network efficiency. This represents the mean of the global clustering coefficients. Indicates the first i The maximum capacity of a node, This represents the clustering coefficient of node i; m Indicates the number of nodes. n Indicates the number of indicators. d ij Represents a node i With nodes j The shortest path between, E i Represents a node i The number of existing edges in the neighbors, Indicates the first i The degree of each node; The evaluation results of the target route are obtained based on the robustness index of the route network; Based on the importance of each node, critical nodes and ordinary nodes in the air route network are distinguished.

2. The route network robustness evaluation method according to claim 1, characterized in that, The calculation of the objective weights of each indicator in the static and dynamic indicators using the entropy weight method includes: Construct the original data judgment matrix R: ； Normalize each indicator: or ; Calculate the first i The first indicator j The numerical proportion of each node : ； Calculate the first i Objective weight of each indicator : ； in: Indicates the first i The first indicator j The normalized index value of each node Indicates the first i The first indicator j The index values ​​of each node, Indicates the first i The first indicator j The numerical weight of each node i Indicates the first i One indicator, j Indicates the first j 1 node m Indicates the number of nodes. n Indicates the number of indicators.

3. The route network robustness evaluation method according to claim 1, characterized in that, The calculation of the subjective weights of each indicator in the static and dynamic indicators using network analysis includes: Each indicator is compared pairwise to construct a judgment matrix, including: ； Based on the judgment matrix, calculate the feature vector corresponding to each indicator; Based on the feature vectors corresponding to each indicator, calculate the subjective weight of each indicator, including: ； in: Indicates the first i The subjective weight of each indicator, Indicates the first i The feature vector corresponding to each indicator n Indicates the number of indicators.

4. The route network robustness evaluation method according to claim 1, characterized in that, By combining objective and subjective weights, the importance of each node is determined, including: ； in: This indicates the importance of each node, where This indicates the proportion of objective weights involved in the weighting process. Indicates the first i The objective weight of each indicator, Indicates the first i Subjective weighting of each indicator.

5. The route network robustness evaluation method according to claim 1, characterized in that, The network attack on route network nodes based on the cascading failure model includes: Select the capacity-load cascading failure model; Determine the node load conditions in the capacity-load cascading failure model; Based on the node load conditions in the capacity-load cascading failure model, determine the node load redistribution method; Identify the methods of cyberattacks, which include random attacks and deliberate attacks. Based on node load redistribution, network attacks target airway networks.

6. The route network robustness evaluation method according to claim 5, characterized in that, The determination of node load conditions in the capacity-load cascading failure model includes: Determine the initial load of the target route network nodes: ； Determine the maximum capacity of each node: ； in: Indicates the first i Initial load of each node, Indicates the first i The maximum capacity of a node, a Indicates an adjustable parameter. Indicates an adjustable parameter. Indicates the first i The degree of each node, This indicates the node's ability to handle additional load. N represents the number of nodes in the target route network.

7. The route network robustness evaluation method according to claim 6, characterized in that, The step of determining the node load redistribution method based on the node load status of the capacity-load cascading failure model includes: when When node i crashes, the target route network node load is redistributed: Determine the remaining load capacity of the node: ； When a node fails, the load on that failed node is transferred to other nodes in the target route network. The redistribution of the load on the failed node and the probability of redistribution are determined. ； ； in: Represents a node i Real-time load; Represents a node i The remaining load capacity, when At that time, node i Failure; Represents a node i Incremental load updates This indicates the load of the failed node. This represents the set of all adjacent nodes of the failed node. Represents a node i The probability of load redistribution.

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