Analysis method for airport infrastructure network resilience under adverse weather disasters

Abstract

The application discloses a method for analyzing the disaster resistance and resilience of an airport infrastructure network under adverse weather disasters. The method comprises the following steps: establishing an airport network model, analyzing the characteristics of the airport network model by using Ucinet software; obtaining a service efficiency function of the network model by taking the number of flights and the distance of routes as parameters; calculating the importance of the airport nodes; obtaining the number of failed airport nodes in the network model under random interference and deliberate destruction respectively; and determining the optimal recovery sequence of the failed nodes based on a genetic algorithm. The method has the following advantages: by establishing the airport network model, considering two destruction modes of random interference and deliberate destruction, taking the recovery time of the airport infrastructure network and the airport support resources as optimization targets, and analyzing and determining the optimal recovery sequence of the failed nodes under different destruction actions, the airport network resilience is improved, the service efficiency of the airport network is ensured, and the method provides an analysis means for improving the disaster resistance and resilience of the airport infrastructure network.

Description

Technical Field

[0001] This invention relates to a method for analyzing the disaster resilience of airport infrastructure networks under adverse meteorological disasters, belonging to the technical field of airport network resilience analysis. Background Technology

[0002] Air transport, as a vital mode of transportation, plays a significant role in national social and economic development, and airport infrastructure is a crucial component of air transport. Adverse weather conditions such as heavy rain and blizzards are major causes of flight delays. These disasters not only affect normal aircraft operations but also cause airport infrastructure malfunctions, leading to flight delays, reduced airport network service efficiency, and even network paralysis. Therefore, it is necessary to study the resilience of airport networks under adverse weather conditions such as heavy rain and blizzards to reduce the proportion of flight delays caused by such disasters.

[0003] However, current research on the impact of adverse weather disasters on air transport mainly focuses on the impact on flight routes. The impact of adverse weather disasters such as rainstorms and snowstorms on the resilience of airport infrastructure needs further research, and no analytical method has yet been established for analyzing the resilience of airport infrastructure networks under adverse weather disasters. Summary of the Invention

[0004] To address the aforementioned problems, the present invention aims to provide a simple and easy-to-use method for analyzing the disaster resilience of airport infrastructure networks under adverse meteorological disasters.

[0005] To achieve the above objectives, the method for analyzing the disaster resilience of airport infrastructure networks under adverse meteorological disasters provided by this invention includes the following steps performed in sequence:

[0006] (1) Obtain airport infrastructure data and airport flight data under adverse meteorological disasters such as rainstorms and snowstorms as basic analysis data. Take airport infrastructure as nodes and the routes between airport nodes as edges. Consider the functional characteristics of airport infrastructure to establish an airport network model. Then, based on the airport network model, use Ucinet software to establish an adjacency matrix. Use the adjacency matrix and complex network analysis methods to analyze the characteristics of the airport network model and obtain the node degree and cluster coefficient.

[0007] (2) Using the airport network model and basic analysis data obtained in step (1), the number of flights and route distance between airport nodes per unit time are used as parameters to obtain the service efficiency function of the airport network model, and the service efficiency is obtained and normalized using the service efficiency function.

[0008] (3) Using the service efficiency function of the airport network model obtained in step (2), the airport network model is set to fail to take into account the impact of adverse meteorological disasters such as rainstorms and snowstorms on the function of airport nodes. The importance of the airport node is calculated by the change in service efficiency of the airport network model before and after the failure of the airport node.

[0009] (4) Considering the two destruction modes of random interference and deliberate sabotage, with the help of the degree and cluster coefficient obtained in step (1), the two destruction modes are applied to the airport network model respectively, so that some airport nodes in the airport network model are ineffective. The number of airport nodes in the airport network model that are ineffective under the two modes of random interference and deliberate sabotage is obtained respectively, and a new adjacency matrix is ​​formed.

[0010] (5) Using the importance of airport nodes obtained in step (3) and the number of failed airport nodes and new adjacency matrix in the airport network model under different damage modes obtained in step (4), the service efficiency of the airport network model is optimized based on the genetic algorithm to determine the optimal recovery order of failed nodes, thereby improving the resilience of the airport network model.

[0011] In step (1), the method of obtaining airport infrastructure data and airport flight data under adverse meteorological disasters including rainstorms and snowstorms as basic analysis data, using airport infrastructure as nodes and air routes between airport nodes as edges, and considering the functional characteristics of airport infrastructure to establish an airport network model, and then using Ucinet software to establish an adjacency matrix based on the airport network model is as follows: obtain airport infrastructure data and airport flight data under adverse meteorological disasters including rainstorms and snowstorms through a flight data platform, use airport infrastructure as nodes and air routes between airport nodes as edges, consider the functional characteristics of airport infrastructure to establish an airport network model, and then use Ucinet software to establish an adjacency matrix A based on the airport network model;

[0012] The airport network model is G = (V, E).

[0013] V is the set of airport nodes, V = {1,2,...,N}, where N is the total number of airport nodes in the airport network model;

[0014] Let E be the edge set, E = {e ij When airport nodes i and j are directly connected, edge e is used to connect them. ij =1, otherwise it is 0;

[0015] Adjacency matrix A = (a ij ) N*N In the airport network model G, the connection status of each airport node is defined. When there is a connecting edge between airport nodes i and j, element a... ij =1; otherwise element aij =0.

[0016] In step (2), the method of using the airport network model and basic analysis data obtained in step (1), taking the number of flights and route distances between airport nodes per unit time as parameters, to obtain the service efficiency function of the airport network model, and then using the service efficiency function to obtain the service efficiency and perform normalization processing is as follows:

[0017] The number of flights f between airport nodes per unit time is obtained using basic analysis data. ij The normalized flight path distance between airport nodes is d. ij The service efficiency function R between airport nodes i and j is obtained. s,ij :

[0018] R s,ij =f ij / d ij

[0019] This leads to the service efficiency function R of the entire airport network model. s (G), and normalized to obtain the normalized service efficiency function R. Ns (G):

[0020] Among them, R s (G)=1 / (N(N-1))∑ (i≠j) R (s,ij) =1 / (N(N-1))∑ (i≠j) f ij / d ij

[0021] R Ns (G)=R s (G) / max i≠j R s,ij

[0022] In step (3), the method for calculating the importance of airport nodes by using the service efficiency function of the airport network model obtained in step (2), setting airport node failures in the airport network model to consider the impact of adverse meteorological disasters such as rainstorms and snowstorms on the functions of airport nodes, and calculating the importance of airport nodes by the change in service efficiency of the airport network model before and after airport node failure is as follows:

[0023] Importance K of airport nodes i The expression is:

[0024] K i =(R Ns (G)-R Ns,i (G)) / R Ns (G)

[0025] In the formula, R Ns,i (G) represents the service efficiency of the airport network model after airport node i fails, and R represents the service efficiency of the airport network model. Ns (G) represents the normalized service efficiency of the airport network model.

[0026] In step (4), the method of considering two destruction modes, random interference and deliberate sabotage, and using the node degree and cluster coefficient obtained in step (1), applying the two destruction modes to the airport network model to make some airport nodes in the airport network model fail, and obtaining the number of airport nodes that fail in the airport network model under the two modes of random interference and deliberate sabotage, and forming a new adjacency matrix, is as follows: Consider two destruction modes, random interference and deliberate sabotage, for the established airport network model. The random interference mode is to randomly generate n airport nodes in the obtained airport network model to interfere with them using the randperm function of MATLAB. The deliberate sabotage mode is to select the n airport nodes with the largest node degree value and the largest cluster coefficient in the airport network model to sabotage them. The value of n depends on the total number of airport nodes in the airport network model. Then, the random interference and deliberate sabotage are applied to the airport network model respectively to obtain the number of airport nodes that fail in the airport network model under the two modes of random interference and deliberate sabotage, and form a new adjacency matrix.

[0027] In step (5), the method of using the importance of airport nodes obtained in step (3) and the number of failed airport nodes and the new adjacency matrix in the airport network model under different damage modes obtained in step (4) to optimize the service efficiency of the airport network model based on the genetic algorithm and determine the optimal recovery order of the failed nodes, thereby improving the resilience of the airport network model, is as follows: Based on the importance of airport nodes obtained in step (3) and the number of failed airport nodes and the new adjacency matrix in the airport network model determined by the analysis in step (4), with the airport infrastructure network recovery time and airport support resources as optimization objectives, the genetic algorithm is used to optimize the service efficiency of the airport network model and determine the optimal recovery order of the failed nodes, thereby improving the disaster resistance resilience of the airport network.

[0028] The advantages of the airport infrastructure network disaster resilience analysis method provided by this invention are as follows: By establishing an airport network model, considering two damage modes, random interference and deliberate sabotage, and taking airport infrastructure network recovery time and airport support resources as optimization objectives, the optimal recovery sequence of failed nodes under different damage effects is analyzed and determined to improve airport network resilience, thereby ensuring the service efficiency of the airport network and providing an analytical means for improving the disaster resilience of airport infrastructure networks. Attached Figure Description

[0029] Figure 1This is a flowchart of the disaster resilience analysis method for airport infrastructure networks under adverse meteorological disasters provided by the present invention. Detailed Implementation

[0030] The following detailed description, in conjunction with the accompanying drawings and embodiments, illustrates the method for analyzing the disaster resilience of airport infrastructure networks under adverse meteorological disasters provided by this invention.

[0031] like Figure 1 As shown, the disaster resilience analysis method for airport infrastructure networks under adverse meteorological disasters provided by this invention includes the following steps performed in sequence:

[0032] (1) Obtain airport infrastructure data and airport flight data under adverse meteorological disasters such as rainstorms and snowstorms as basic analysis data. Take airport infrastructure as nodes and the routes between airport nodes as edges. Consider the functional characteristics of airport infrastructure to establish an airport network model. Then, based on the airport network model, use Ucinet software to establish an adjacency matrix. Use the adjacency matrix and complex network analysis methods to analyze the characteristics of the airport network model and obtain the node degree and cluster coefficient.

[0033] By acquiring airport infrastructure data and airport flight data under adverse weather disasters such as rainstorms and snowstorms through the flight data platform, an airport network model is established with airport infrastructure as nodes and air routes between airport nodes as edges, taking into account the functional characteristics of airport infrastructure. Then, based on the airport network model, the adjacency matrix A is established using Ucinet software.

[0034] The airport network model is G = (V, E).

[0035] V is the set of airport nodes, V = {1,2,...,N}, where N is the total number of airport nodes in the airport network model;

[0036] Let E be the edge set, E = {e ij When airport nodes i and j are directly connected, edge e is used to connect them. ij =1, otherwise it is 0;

[0037] Adjacency matrix A = (a ij ) N*N In the airport network model G, the connection status of each airport node is defined. When there is a connecting edge between airport nodes i and j, element a... ij =1; otherwise element a ij =0.

[0038] (2) Using the airport network model and basic analysis data obtained in step (1), the number of flights and route distance between airport nodes per unit time are used as parameters to obtain the service efficiency function of the airport network model, and the service efficiency is obtained and normalized using the service efficiency function.

[0039] The number of flights f between airport nodes per unit time is obtained using basic analysis data. ij The normalized flight path distance between airport nodes is d. ij The service efficiency function R between airport nodes i and j is obtained. s,ij :

[0040] R s,ij =f ij / d ij

[0041] This leads to the service efficiency function R of the entire airport network model. s (G), and normalized to obtain the normalized service efficiency function R. Ns (G):

[0042] Among them, R s (G)=1 / (N(N-1))∑ (i≠j) R (s,ij) =1 / (N(N-1))∑ (i≠j) f ij / d ij

[0043] R Ns (G)=R s (G) / max i≠j R s,ij

[0044] (3) Using the service efficiency function of the airport network model obtained in step (2), the airport network model is set to fail to take into account the impact of adverse meteorological disasters such as rainstorms and snowstorms on the function of airport nodes. The importance of the airport node is calculated by the change in service efficiency of the airport network model before and after the failure of the airport node.

[0045] Importance K of airport node i The expression is:

[0046] K i =(R Ns (G)-R Ns,i (G)) / R Ns (G)

[0047] In the formula, R Ns,i (G) represents the service efficiency of the airport network model after airport node i fails, and R represents the service efficiency of the airport network model. Ns(G) represents the normalized service efficiency of the airport network model.

[0048] (4) Considering the two destruction modes of random interference and deliberate sabotage, the node degree and cluster coefficient obtained in step (1) are used to apply the two destruction modes to the airport network model respectively, so that some airport nodes in the airport network model become ineffective. The number of airport nodes that fail in the airport network model under the two modes of random interference and deliberate sabotage is obtained respectively, and a new adjacency matrix is ​​formed.

[0049] Two disruption modes are considered for the established airport network model: random interference and deliberate sabotage. In the random interference mode, n airport nodes are randomly generated in the obtained airport network model using the randperm function in MATLAB to interfere with the network. In the deliberate sabotage mode, n airport nodes with the largest degree value and the largest cluster coefficient are selected and sabotaged respectively (the value of n depends on the total number of airport nodes in the airport network model). Then, random interference and deliberate sabotage are applied to the airport network model respectively to obtain the number of airport nodes that fail in the airport network model under the two modes, and a new adjacency matrix is ​​formed.

[0050] (5) Using the importance of airport nodes obtained in step (3) and the number of failed airport nodes and new adjacency matrix in the airport network model under different damage modes obtained in step (4), the service efficiency of the airport network model is optimized based on the genetic algorithm to determine the optimal recovery order of failed nodes, thereby improving the resilience of the airport network model.

[0051] Based on the importance of the airport nodes obtained in step (3), and the number of failed airport nodes and the new adjacency matrix in the airport network model determined by step (4), the airport infrastructure network recovery time and airport support resources are optimized as optimization objectives. The genetic algorithm is used to optimize the service efficiency of the airport network model and determine the optimal recovery order of the failed nodes, thereby improving the disaster resilience of the airport network.

Claims

1. A method for analyzing the disaster resilience of airport infrastructure networks under adverse meteorological disasters, characterized in that: The method for analyzing the resilience of airport infrastructure networks under adverse meteorological disasters includes the following steps performed in sequence: (1) Obtain airport infrastructure data and airport flight data under adverse meteorological disasters such as rainstorms and snowstorms as basic analysis data. Take airport infrastructure as nodes and the routes between airport nodes as edges. Consider the functional characteristics of airport infrastructure to establish an airport network model. Then, based on the airport network model, use Ucinet software to establish an adjacency matrix. Use the adjacency matrix and complex network analysis methods to analyze the characteristics of the airport network model and obtain the node degree and cluster coefficient. (2) Using the airport network model and basic analysis data obtained in step (1), the number of flights and route distance between airport nodes per unit time are used as parameters to obtain the service efficiency function of the airport network model, and the service efficiency is obtained by using the service efficiency function and normalized. (3) Using the service efficiency function of the airport network model obtained in step (2), the airport network model is set to fail to take into account the impact of adverse meteorological disasters such as rainstorms and snowstorms on the function of airport nodes. The importance of the airport node is calculated by the change in service efficiency of the airport network model before and after the failure of the airport node. (4) Considering the two destruction modes of random interference and deliberate sabotage, with the help of the degree and cluster coefficient obtained in step (1), the two destruction modes are applied to the airport network model respectively, so that some airport nodes in the airport network model are ineffective. The number of airport nodes in the airport network model that are ineffective under the two modes of random interference and deliberate sabotage is obtained respectively, and a new adjacency matrix is ​​formed. (5) Using the importance of airport nodes obtained in step (3) and the number of failed airport nodes and new adjacency matrix in the airport network model under different damage modes obtained in step (4), the service efficiency of the airport network model is optimized based on the genetic algorithm to determine the optimal recovery order of failed nodes, thereby improving the resilience of the airport network model.

2. The method for analyzing the disaster resilience of airport infrastructure networks under adverse meteorological disasters according to claim 1, characterized in that: In step (1), the method of obtaining airport infrastructure data and airport flight data under adverse meteorological disasters including rainstorms and snowstorms as basic analysis data, using airport infrastructure as nodes and air routes between airport nodes as edges, considering the functional characteristics of airport infrastructure to establish an airport network model, and then using Ucinet software to establish an adjacency matrix based on the airport network model is as follows: Obtain airport infrastructure data and airport flight data under adverse meteorological disasters including rainstorms and snowstorms through a flight data platform, using airport infrastructure as nodes and air routes between airport nodes as edges, considering the functional characteristics of airport infrastructure to establish an airport network model, and then using Ucinet software to establish an adjacency matrix A based on the airport network model; The airport network model is G=(V,E); V is the set of airport nodes, V={1,2,...,N}, and N is the total number of airport nodes in the airport network model; E is the edge set, E={𝑒 𝑖𝑗 When airport nodes i and j are directly connected, edge 𝑒 is used. 𝑖𝑗 =1, otherwise it is 0; Adjacency matrix A = ( ) 𝑖𝑗 ) 𝑁∗𝑁 In the airport network model G, the connection status of each airport node is defined. When there is a connecting edge between airport nodes i and j, element 𝑎 𝑖𝑗 =1; otherwise element 𝑎 𝑖𝑗 =0.

3. The method for analyzing the disaster resilience of airport infrastructure networks under adverse meteorological disasters according to claim 2, characterized in that: In step (2), the method of using the airport network model and basic analysis data obtained in step (1), taking the number of flights and route distances between airport nodes per unit time as parameters, to obtain the service efficiency function of the airport network model, and then using the service efficiency function to obtain the service efficiency and perform normalization processing is as follows: The number of flights per unit time between airport nodes is obtained using basic analysis data. 𝑖𝑗 The normalized flight path distance between airport nodes is d. 𝑖𝑗 The service efficiency function R between airport nodes i and j is obtained. s,𝑖𝑗 : R s,𝑖𝑗 =𝑓 𝑖𝑗 / d 𝑖𝑗 ; This leads to the service efficiency function R of the entire airport network model. s (G), and normalized to obtain the normalized service efficiency function R. Ns (G): Among them, R s (G) = 1 / (𝑁(𝑁−1))∑ (𝑖≠𝑗) R (s,𝑖𝑗) = 1 / (𝑁(𝑁−1))∑ (𝑖≠𝑗) 𝑓 𝑖𝑗 / d 𝑖𝑗 ; R Ns (G)=R s (G) / 𝑚𝑎𝑥 𝑖≠𝑗 R s,𝑖𝑗 。 4. The method for analyzing the disaster resilience of airport infrastructure networks under adverse meteorological disasters according to claim 1, characterized in that: In step (3), the method for calculating the importance of airport nodes by using the service efficiency function of the airport network model obtained in step (2), setting airport node failures in the airport network model to consider the impact of adverse meteorological disasters such as rainstorms and snowstorms on the functions of airport nodes, and calculating the importance of airport nodes by the change in service efficiency of the airport network model before and after airport node failure is as follows: Importance K of airport node 𝑖 The expression is: K 𝑖 =(R Ns (G)−R Ns,𝑖 (G)) / R Ns (G); In the formula, R Ns,𝑖 (G) represents the service efficiency of the airport network model after airport node i fails, and R represents the service efficiency of the airport network model. Ns (G) represents the normalized service efficiency of the airport network model.

5. The method for analyzing the disaster resilience of airport infrastructure networks under adverse meteorological disasters according to claim 1, characterized in that: In step (4), the method of considering two destruction modes, random interference and deliberate sabotage, and using the node degree and cluster coefficient obtained in step (1), applying the two destruction modes to the airport network model respectively, so that some airport nodes in the airport network model become ineffective, and obtaining the number of airport nodes in the airport network model under the two modes of random interference and deliberate sabotage respectively, and forming a new adjacency matrix, is as follows: Consider two destruction modes, random interference and deliberate sabotage, for the established airport network model. The random interference mode is to randomly generate n airport nodes in the obtained airport network model to interfere with them using the randperm function of MATLAB. The deliberate sabotage mode is to select the n airport nodes with the largest node degree value and the largest cluster coefficient in the airport network model to sabotage them respectively. The value of n depends on the total number of airport nodes in the airport network model. Then, the random interference and deliberate sabotage are applied to the airport network model respectively, and the number of airport nodes in the airport network model under the two modes of random interference and deliberate sabotage is obtained, and a new adjacency matrix is ​​formed.

6. The method for analyzing the disaster resilience of airport infrastructure networks under adverse meteorological disasters according to claim 1, characterized in that: In step (5), the method of using the importance of airport nodes obtained in step (3) and the number of failed airport nodes and the new adjacency matrix in the airport network model under different damage modes obtained in step (4) to optimize the service efficiency of the airport network model based on the genetic algorithm and determine the optimal recovery order of the failed nodes, thereby improving the resilience of the airport network model, is as follows: Based on the importance of airport nodes obtained in step (3) and the number of failed airport nodes and the new adjacency matrix in the airport network model determined by the analysis in step (4), with the recovery time of airport infrastructure network and airport support resources as optimization objectives, the genetic algorithm is used to optimize the service efficiency of the airport network model and determine the optimal recovery order of the failed nodes, thereby improving the disaster resistance resilience of the airport network.

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