An Airport Apron Operation Risk Assessment Method Based on Complex Networks

Abstract

The present invention discloses a method for risk assessment of airport apron operations based on complex networks, belonging to the technical field of risk control. The present invention includes the following steps: analyzing the apron safety report by using the event chain analysis method to construct a complex network; determining the static risk indicators of each node; simulating the dynamic propagation process of risks in the complex network based on the SIRS model to determine the dynamic risk indicators of each node; taking the dynamic and static indicators of each node as features, using the spectral clustering algorithm to classify the nodes, calculating the average risk comprehensive value of each cluster of nodes respectively, and determining the risk level of each cluster of nodes based on the quantile; obtaining the risk events of the airport apron at a certain moment, determining the risk comprehensive value and risk level of each risk event, and conducting hierarchical classification management on them. The present invention constructs a multi-dimensional risk assessment index system to evaluate the risk propagation ability and blocking ability of nodes, realizes the classification and grading of risk nodes, and can provide scientific decision-making support for airport managers.

Description

Technical Field

[0001] The present invention relates to the technical field of risk control, and particularly to an airport apron operation risk assessment method based on complex networks. Background Art

[0002] The airport apron is an important part of the air transportation system. Its operation process needs to coordinate multiple personnel, equipment and processes, with high complexity and high risk. Traditional apron safety management methods often focus on the statistical analysis of individual risk events, such as the accident occurrence frequency, accident cause classification, etc. However, this static analysis is difficult to reveal the correlation and propagation path between risk events, especially in complex scenarios with multi-factor coupling, and there are great limitations.

[0003] In recent years, complex network theory has been gradually applied to the field of aviation risk management. For example, through network analysis, the risk propagation behavior of pilots in flight tasks is studied; through constructing an interaction network, the impact of the operation behavior of air traffic controllers on the overall operation safety is analyzed. However, most of these studies focus on the dynamic interaction analysis at the pilot and controller levels, and few studies apply complex network theory to ground operations, especially the risk propagation and management of airport apron operations.

[0004] The risks of airport apron operations are unique: there are a large number of participating personnel and equipment types, the operation scenarios are highly dynamic, and multiple risk factors (such as equipment failures, human errors, environmental factors) interact with each other. These characteristics determine that it is difficult to accurately evaluate the risks in apron operations simply relying on static indicators. Summary of the Invention

[0005] To solve the above problems, the present invention provides an airport apron operation risk assessment method based on complex networks, which adopts multi-dimensional risk assessment indicators that integrate static and dynamic characteristics, improving the comprehensiveness and accuracy of risk assessment.

[0006] To achieve the above object, the solution provided by the present invention is as follows:

[0007] An airport apron operation risk assessment method based on complex networks, comprising the following steps:

[0008] S1. Analyze the apron safety report by using the event chain analysis method and construct a complex network; the complex network takes events as nodes and establishes directed edges according to the dependency relationship between events, and the nodes with an out-degree value of zero are top event nodes;

[0009] S2. Based on the complex network, determine the static risk indicators of each node. The static risk indicators include the accident near-neighbor risk rate and the target-oriented centrality. The accident near-neighbor risk rate of a certain node is the ratio of the proportion of top event nodes among the out-degree neighbor nodes of this node to the average value of the proportion of top event nodes among the out-degree neighbor nodes of all nodes in the entire complex network, which is used to evaluate the direct impact degree of this node on the occurrence of accidents, that is, to measure the possibility of this node directly triggering an accident and belongs to a local indicator; the target-oriented centrality is used to measure the importance of the node i as an intermediate node s in all the shortest paths from the node t to the top event node

[0010] and is a global indicator for identifying key nodes that have an important indirect impact on the occurrence of accidents in the network;

[0011] ;

[0012] In the formula, C og ( i ) is the target-oriented centrality of the node i ; Г is the set of top event nodes; П is the set of non-top event nodes, that is, the set of all nodes in the network except the top event nodes; δ st ( i ) represents the number of paths passing through the node s in all the shortest paths from the node t along the directed edge to the node i , δ st represents the total number of all the shortest paths from the node s along the directed edge to the node t ;

[0013] S3. Based on the SIRS (Susceptible-Infected-Recovered-Susceptible again) model, simulate the dynamic propagation process of risks in the complex network, and determine the dynamic risk indicators of each node. The dynamic risk indicators include the average peak infection proportion and the average peak infection period, which are used to quantify the risk propagation ability of each node and evaluate the influence range and propagation speed of each node on the entire network; the state of the node evolves in the order of susceptible state-infected state-recovered state in the SIRS model. Among them, the node in the infected state infects the susceptible state node into the infected state at the infection rate β and recovers to the recovered state at the recovery rate γ , and the node in the recovered state is converted into the susceptible state again at the rate of becoming susceptible again μ , thus causing the risk to spread again;

[0014] S4. Using the static and dynamic indicators of each node in the complex network as features, the spectral clustering algorithm is used to classify the nodes, and the average risk comprehensive value of each cluster of nodes is calculated respectively, and the risk level of each cluster of nodes is determined based on the quantile;

[0015] The calculation formula of the average value of the risk comprehensive value is as follows:

[0016] ;

[0017] ;

[0018] SC ( i ) is the average risk comprehensive value of the i th cluster of nodes; N i is the set of the i th cluster of nodes; SCI ( j ) is the risk comprehensive value of node j , and the greater the risk comprehensive value, the greater the risk; S k ( j ) is the normalized eigenvalue of the j th feature of node k . Among them, the normalized eigenvalue of the average peak infection period is obtained by reverse min-max normalization, and other normalized eigenvalues are obtained by min-max normalization;

[0019] S5. Obtain the risk events on the airport apron at a certain moment, determine the risk comprehensive value and risk level of each risk event, give priority to dealing with the risk events with high risk comprehensive value, and each risk event adopts corresponding management strategies according to its risk level.

[0020] In step S1, through event chain analysis, the risk events recorded in the airport apron operation are transformed into a unified directed complex network, so as to comprehensively reflect the causal relationship and risk propagation path between risk events. As a specific implementation manner of the present invention, step S1 includes:

[0021] S11. Extract all the risky events in a certain apron safety report and determine the sub-network of the event development in the apron safety report based on the event chain analysis method. In the sub-network, the events are used as nodes, and each event is divided into three categories: initial event, development event and top event according to its position in the event chain. Among them, the initial event is the starting point of the event chain, the development event is the propagation link of the event chain, and the top event is the end point of the event chain. The node corresponding to the top event is the top event node, and it has no directed edge pointing to other nodes; in the sub-network, each node is connected by a directed edge, and the direction of the directed edge is determined according to the dependency relationship between events;

[0022] S12. Repeat step S11 to establish a sub-network for each apron safety report respectively, and then integrate and link all the sub-networks to form a complex network.

[0023] As a specific embodiment of the present invention, the calculation formula of the accident neighbor risk rate is as follows:

[0024] ;

[0025] ;

[0026] ;

[0027] ;

[0028] In the formula, APR( i ) is the accident neighbor risk rate of node i ; P nb ( i ) is the probability that the out-degree neighbor node of node i directly causes an accident;

[0029] is the average probability that the out-degree neighbor nodes of all nodes in the complex network directly cause an accident; N is the total number of nodes in the complex network; δ ( u , Г) is the indicator function indicating that node u belongs to the top event node set Г; Nb( i ) is the set of out-degree neighbor nodes of node i ; N( i ) is the total number of nodes in Nb( i );

[0030] As a specific embodiment of the present invention, obtaining the average peak infection proportion and average peak infection period of each node includes the following steps:

[0031] S31. Initialize the parameters and determine the infection rate set;

[0032] S32. Select a non-top event node from the complex network, set the selected node to the infected state, and set the remaining nodes to the susceptible state;

[0033] S33. Select an infection rate from the infection rate set and substitute it into the SIRS model, repeat the simulation of the virus transmission process of the selected node, count the peak infection proportion in the entire complex network and the time corresponding to the peak infection proportion during each virus transmission process, and then calculate the average value respectively as the first peak infection proportion and the first infection period of the selected node at this infection rate;

[0034] S34. Repeat step S33 to traverse all the infection rates in the infection rate set, obtain the first peak infection ratio and the first infection cycle of the selected node at different infection rates, and then calculate the average values respectively as the average peak infection ratio and the average peak infection cycle of the selected node;

[0035] S35. Repeat steps S32 - S34 to traverse all non - top - event nodes in the complex network.

[0036] In step S5, the corresponding management strategies for different risk levels can be formulated as needed. For example, different monitoring frequencies can be adopted for different risk levels, or targeted management measures can be taken by combining various dynamic and static indicators.

[0037] Beneficial effects:

[0038] The present invention constructs a multi - dimensional risk assessment index system to evaluate the risk propagation ability and blocking ability of nodes, realizes the classification and grading of risk nodes, and can provide scientific decision - making support for airport managers.

[0039] The present invention selects the SIRS model to simulate the dynamic risk propagation process in apron operation. This selection is based on the particularity of aviation operation. Different from general operations, relevant units in the aviation field usually adopt specific risk management strategies to suppress potential risks and ensure the normal operation of the system. However, over time, the effect of risk management may gradually weaken, resulting in some nodes facing risk exposure again. In the SIRS model of the present invention, nodes in the recovery state are μ transformed into the susceptible state again at the re - susceptibility rate, which can better reflect the risk propagation characteristics in apron operation. Brief description of the drawings

[0040] Figure 1 is a schematic diagram of the complex network in the embodiment of the present invention;

[0041] Figure 2 is a two - dimensional visualization graph after the high - dimensional data is dimension - reduced. Detailed implementation manners

[0042] The following combines the embodiments and the drawings to further elaborate on the present invention in detail, but the implementation manners of the present invention are not limited thereto.

[0043] A method for risk assessment of airport apron operation based on a complex network includes the following steps:

[0044] S1. Analyze the apron safety report by using the event - chain analysis method and construct a complex network; the complex network takes events as nodes and establishes directed edges according to the dependency relationships between events. A node with a total amount of directed edges pointing to other nodes being zero is a top - event node. Specifically, it includes the following steps:

[0045] S11. Extract all risky events in a certain apron safety report and determine the sub-network of the event development in the apron safety report based on the event chain analysis method. In the sub-network, events are used as nodes, and each event is divided into three categories: initial event, development event, and top event according to its position in the event chain. Among them, the initial event is the starting point of the event chain, the development event is the propagation link of the event chain, and the top event is the end point of the event chain. The node corresponding to the top event is the top event node. In this embodiment, the initial events and development events in the sub-network are the risk factors for the occurrence of the top event. These risk factors are divided into four categories: personnel, equipment, environment, and management. Each category includes several specific events, specifically referring to Table 1; in the sub-network, each node is connected by a directed edge, and the direction of the directed edge is determined according to the dependence relationship between events. There are two types of dependence relationships, namely causal dependence and conditional dependence. Among them, causal dependence means that one event directly triggers another event, and there is a clear one-way relationship. Conditional dependence means that one event creates conditions for the occurrence of another event but does not directly cause it.

[0046] S12. Repeat step S11 to establish sub-networks for each apron safety report, and then link and integrate all sub-networks to form a complex network.

[0047] Table 1 Risk Node Statistics Table

[0048]

[0049] The complex network constructed in this embodiment is as Figure 1 shown, which contains 82 nodes and 244 directed edges, and comprehensively reflects the risk propagation paths between risk events.

[0050] S2. Based on the complex network, determine the risk static indicators of each node. The risk static indicators include the accident neighbor risk rate and the goal-oriented centrality. The accident neighbor risk rate is the ratio of the proportion of top event nodes among the out-degree neighbor nodes of this node to the average value of the proportion of top event nodes among the out-degree neighbor nodes of all nodes in the entire complex network;

[0051] The calculation formula of the goal-oriented centrality is as follows:

[0052] ;

[0053] In the formula, C og ( i ) is the goal-oriented centrality of node i ; Г is the set of top event nodes; П is the set of non-top event nodes, that is, the set of all nodes in the network except the top event nodes; δ st ( i ) represents from nodes Among all the shortest paths along the directed edge to the node t , the number of paths passing through the node i ; δ st denotes the total number of all the shortest paths from the node s along the directed edge to the node t ;

[0054] The calculation formula of the accident near - neighbor risk rate is as follows:

[0055] ;

[0056] ;

[0057] ;

[0058] ;

[0059] In the formula, APR( i ) is the accident near - neighbor risk rate of the node i ; P nb ( i ) is the probability that the out - degree neighbor nodes of the node i directly cause an accident; is the average probability that the out - degree neighbor nodes of all nodes in the complex network directly cause an accident; N is the total number of nodes in the complex network; δ ( u , Г) is the indicator function indicating that the node u belongs to the top - event node set Г; Nb( i ) is the set of out - degree neighbor nodes of the node i ; N( i ) is the total number of nodes in Nb( i );

[0060] S3. Simulate the dynamic propagation process of risks in the complex network based on the SIRS (Susceptible - Infected - Recovered - Susceptible) model, determine the risk dynamic indicators of each node. The risk dynamic indicators include the average peak infection proportion and the average peak infection period, which are used to quantify the risk propagation ability of each node and evaluate the influence range and propagation speed of each node on the entire network;

[0061] The state of the node evolves in the order of susceptible state - infected state - recovered state in the SIRS model. Among them, the node is in one of the susceptible state, infected state, and recovered state. The susceptible state is a healthy state that is easily infected, the infected state is the state of being infected, and the recovered state is the healthy state that has recovered from the infected state and is not easily infected. The node in the infected state has an infection rateβ Infect the susceptible nodes into infected nodes and, according to the recovery rate γ recover to the recovered state. The nodes in the recovered state are converted into susceptible nodes again at the re-susceptibility rate μ and then cause the risk to spread again;

[0062] The dynamic changes of the SIRS model can be described by the following system of equations:

[0063] ;

[0064] ;

[0065] In the formula, S ( t )、 I ( t )、 R ( t ) are the proportions of nodes in the susceptible state, infected state, and recovered state at time t respectively.

[0066] Obtaining the average peak infection proportion and average peak infection period of each node includes the following steps:

[0067] S31. Initialize the parameters and determine the set of infection rates B = { β j | j ∈ [1, k}, k is the total number of infection rates in set B;

[0068] S32. Select a non-top event node from the complex network, put the selected node in the infected state, and put the remaining nodes in the susceptible state;

[0069] S33. Select an infection proportion from the set of infection rates and substitute it into the SIRS model. Repeat the simulation of the virus transmission process of the selected node, count the peak infection proportion in the entire complex network and the time corresponding to the peak infection proportion during each virus transmission process, and then calculate the average values respectively as the first peak infection proportion and the first infection period of the selected node at this infection rate;

[0070] ;

[0071] ;

[0072] ;

[0073] ;

[0074] In the formula, Ipeam,m ( v i , β j ) is the node v i at the infection rate β j for the m th simulation, the peak infection proportion during the entire simulation process; T is the total time; max(*) is the maximum value function; I m ( t ; v i , β j ) is the node v i at the infection rate β j for the m th simulation, the infection proportion of the entire complex network at time t ; argmax(*) is the return function that returns the time when the function takes the maximum value; T peam,m ( v i , β j ) is I m ( t ; v i , β j ) corresponding time; is the node v i at the infection rate β j for the first peak infection proportion; M is the node v i at the infection rate β j for the total number of simulations; is the node v i at the infection rate β j for the first infection cycle;

[0075] S34. Repeat step S33, traverse all the infection rates in the infection rate set, obtain the first peak infection proportion and the first infection cycle of the selected node at different infection rates, and then calculate the average value respectively as the average peak infection proportion and the average peak infection cycle of the selected node;

[0076] ;

[0077] ;

[0078] Wherein, is the average peak infection ratio of node v i ; is the average infection period of node v i ;

[0079] S35. Repeat steps S32 - S34 to traverse all non - top - event nodes in the complex network;

[0080] S4. Using the static and dynamic indicators of each node in the complex network as features, adopt the spectral clustering algorithm to classify the nodes, calculate the average comprehensive risk value of each cluster of nodes respectively, and determine the risk level of each cluster of nodes based on the quantile;

[0081] The calculation formula of the average comprehensive risk value is as follows:

[0082] ;

[0083] ;

[0084] SC ( i ) is the average comprehensive risk value of the i th cluster of nodes; N i is the set of the i th cluster of nodes; SCI ( j ) is the comprehensive risk value of node j , and the greater the comprehensive risk value, the greater the risk; S k ( j ) is the normalized eigenvalue of the j th feature of node k . Among them, the normalized eigenvalue of the average peak infection period is obtained by reverse min - max normalization, and other normalized eigenvalues are obtained by min - max normalization.

[0085] In order to compare the adaptability of various clustering algorithms, in this embodiment, the Calinski - Harabasz index (CH index for short) is used to compare multiple clustering algorithms. The comparison results are shown in Table 2. It can be seen from Table 2 that the spectral clustering algorithm has the best effect.

[0086] Table 2 CH indices of three clustering models

[0087]

[0088] The spectral clustering algorithm is used to divide the nodes into four categories. To more intuitively display the spectral clustering results and analyze the distribution of different risk nodes in the reduced-dimensional space, in this embodiment, Uniform Manifold Approximation and Projection (UMAP for short) is used to perform dimensionality reduction on the high-dimensional data, as Figure 2 shown. Figure 2 shows the distribution of the four categories of risk nodes in the two-dimensional space after the dimensionality reduction of the high-dimensional data, and the distribution characteristics of the 4 categories of risk nodes in the reduced-dimensional space can be observed more clearly. The average values of the risk indicators and the number of nodes for each category are shown in Table 3.

[0089] Table 3 Average values of risk indicators and number of nodes for each category of nodes

[0090]

[0091] The comprehensive risk values of each node are shown in Table 4, and the average comprehensive risk values of each cluster of nodes are shown in Table 5.

[0092] Table 4 Comprehensive risk values of each node

[0093]

[0094] Table 5 Average comprehensive risk values of each cluster of nodes

[0095]

[0096] In this embodiment, the rules for dividing the risk levels of each cluster of nodes are as follows: Using 75% and 25% as quantiles, if the average comprehensive risk value is greater than or equal to the comprehensive risk value corresponding to the 75% quantile, it is a high risk; if the average comprehensive risk value is less than the comprehensive risk value corresponding to the 25% quantile, it is a low risk; otherwise, it is a medium risk.

[0097] According to the above classification rules, the comprehensive risk value corresponding to the 75% quantile is 0.41, and the comprehensive risk value corresponding to the 25% quantile is 0.17. Therefore, cluster 1 is a high risk, clusters 2 and 3 are medium risks, and cluster 4 is a low risk.

[0098] S5. Obtain the risk events on the airport apron at a certain moment, determine the comprehensive risk values and risk levels of each risk event, give priority to dealing with the risk events with high comprehensive risk values, and each risk event adopts corresponding management strategies according to its risk level.

[0099] For low-risk clusters, only basic monitoring and optimized resource allocation are required to avoid unnecessary management cost input. For medium- and high-risk clusters, static and dynamic indicators are combined for ranking, and corresponding management strategies are matched: for the cluster with the highest accident near-neighbor risk rate, the possibility of an accident occurring is the greatest, and real-time monitoring and strict operation specifications are required to prevent individual violations from directly causing accidents. Management measures include strengthening on-site inspections, increasing safety training, and establishing a mechanism for holding violators accountable; for the cluster with the highest target-oriented centrality, which plays a key intermediary role in the accident propagation chain, process management needs to be optimized to improve the stability of key nodes and prevent the entire system from being affected by individual node failures. Measures that can be adopted include multi-level decision-making reviews and optimizing cross-departmental coordination mechanisms to ensure accurate information transmission; for the cluster with the highest average peak infection ratio, which has the greatest impact on accident propagation, a propagation isolation mechanism needs to be established to prevent risk diffusion. Measures that can be taken include optimizing the information transmission method, adjusting the process to isolate key nodes with high propagation risks, and establishing a rapid response mechanism; for the cluster with the lowest average peak infection period, it means that risks spread rapidly in a short period of time, and rapid intervention and optimized emergency response processes are required. The management focus is on shortening the emergency response time to ensure that sudden risks can be detected and handled in a timely manner.

[0100] For this embodiment, as can be seen from Table 3 and Table 5, cluster 4 is of low risk and only basic monitoring and optimized resource allocation are required. The remaining three clusters are of medium to high risk. Their four indicators are ranked respectively. The average accident near-neighbor risk rate of cluster 1 is the highest, and the average peak infection period is the shortest. Management measures include strengthening on-site inspections, increasing safety training, establishing a mechanism for holding violators accountable, and optimizing the emergency response process to shorten the emergency response time; the target-oriented centrality of cluster 2 is the highest, and management measures include multi-level decision-making reviews and optimizing cross-departmental coordination mechanisms to ensure accurate information transmission; the average peak infection ratio of cluster 3 is the highest, and management measures include optimizing the information transmission method, adjusting the process to isolate key nodes with high propagation risks, and establishing a rapid response mechanism.

[0101] The above is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by those skilled in the art within the technical scope disclosed in the embodiments of the present invention should be covered by the protection scope of the present invention.

Claims

1. A method for risk assessment of apron operations at an airport based on complex networks, characterized in that, It includes the following steps: S1. Analyze the apron safety report using the event chain analysis method and construct a complex network; the complex network takes events as nodes and establishes directed edges according to the dependency relationships between events, and the nodes with an out-degree value of zero are top event nodes; S2. Based on the complex network, determine the risk static indicators of each node; the risk static indicators include the accident neighbor risk rate and the goal-oriented centrality. The accident neighbor risk rate of a certain node is the ratio of the proportion of top event nodes among the out-degree neighbor nodes of this node to the average value of the proportion of top event nodes among the out-degree neighbor nodes of all nodes in the entire complex network; The calculation formula of the goal-oriented centrality is as follows: In the formula, C og ( i ) is the target-oriented centrality of the node i ; Γ is the set of top event nodes; Π is the set of non-top event nodes; δ st ( i ) represents the number of paths passing through node s along the directed edge to node t among all the shortest paths from node i ; δ st represents the total number of all shortest paths from node s along the directed edge to node t ; The calculation formula of the accident neighbor risk rate is as follows: ； ； ； ; where APR( i ) is the accident near-neighbor risk rate of the node i ; P nb ( i ) is the probability that the out-degree neighbor nodes of the node i directly cause an accident; is the average probability that the out-degree neighbor nodes of all nodes in the complex network directly cause an accident; N is the total number of nodes in the complex network; δ ( u , Г) is the indicator function indicating that the node u belongs to the top event node set Г; Nb( i ) is the set of out-degree neighbor nodes of the node i ; N( i ) is the total number of nodes in Nb( i ); S3. Simulate the dynamic propagation process of risks in the complex network based on the SIRS model, and determine the risk dynamic indicators of each node; the risk dynamic indicators include the average peak infection ratio and the average peak infection period, and the state of the node evolves in the order of susceptible state - infected state - recovered state in the SIRS model. Among them, the nodes in the infected state are infected from the susceptible state nodes to the infected state according to the infection rate β and recover to the recovered state according to the recovery rate γ . The nodes in the recovered state are converted back to the susceptible state at the re-susceptible rate μ again; S4. Using the static indicators and dynamic indicators of each node in the complex network as features, adopt the spectral clustering algorithm to classify the nodes, calculate the average risk comprehensive value of each cluster of nodes respectively, and determine the risk level of each cluster of nodes based on the quantile; The calculation formula of the average value risk comprehensive value is as follows: ； ; SC ( i ) is the average risk composite value of the i cluster node; N i is the i set of cluster nodes; SCI ( j ) is the risk composite value of node j , and the greater the risk composite value, the greater the risk; S k ( j ) is the normalized eigenvalue of the j th k feature of the node, where the normalized eigenvalue of the average peak infection period is obtained by reverse min-max normalization, and other normalized eigenvalues are obtained by min-max normalization; S5. Obtain the risk events on the airport apron at a certain moment, determine the risk comprehensive value and risk level of each risk event, give priority to dealing with the risk events with a high risk comprehensive value, and each risk event adopts corresponding management strategies according to its risk level.

2. The airport apron operation risk assessment method based on complex network according to claim 1, wherein Step S1 includes: S11. Extract all risky events in a certain apron safety report and determine the sub-network of the event development in this apron safety report based on the event chain analysis method; in the sub-network, take events as nodes, and divide each event into three categories: initial event, development event, and top event according to the position of the event in the event chain. Among them, the initial event is the starting point of the event chain, the development event is the propagation link of the event chain, and the top event is the end point of the event chain. Each node is connected by a directed edge, and the direction of the directed edge is determined according to the dependency relationship between events; S12. Repeat step S11 to establish sub-networks for each apron safety report respectively, and then link and integrate all sub-networks to form a complex network.

3. The method for evaluating the operation risk of an airport apron based on a complex network according to claim 1, wherein Obtaining the average peak infection proportion and average peak infection period of each node includes the following steps: S31. Initialize the parameters and determine the infection rate set; S32. Select a non-top event node from the complex network, set the selected node to the infected state, and the remaining nodes to the susceptible state; S33. Select an infection rate from the infection rate set and substitute it into the SIRS model, repeat the simulation of the virus propagation process of the selected node, count the peak infection proportion in the entire complex network and the time corresponding to the peak infection proportion during each virus propagation process, and then calculate the average value respectively as the first peak infection proportion and the first infection period of the selected node at this infection rate; S34. Repeat step S33, traverse all infection rates in the infection rate set, obtain the first peak infection proportion and the first infection period of the selected node at different infection rates, and then calculate the average value respectively as the average peak infection proportion and the average peak infection period of the selected node; S35. Repeat steps S32 - S34 to traverse all non-top event nodes in the complex network, and obtain the average peak infection ratio and average peak infection period of each node.

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