An emergency under airport emergency response network resilience analysis method, system and storage medium

Abstract

The application provides an airport emergency response network resilience analysis method and system under an emergency and a storage medium, the method comprising: determining key element nodes of airport emergency response under an emergency based on a plan file; creating links between nodes of an airport emergency response network under an emergency according to the relationship between the key element nodes, and representing the links by a binary matrix; constructing an airport emergency response network under an emergency; and performing three-stage resilience measurement analysis on the constructed network. The application integrates multi-dimensional content such as subjects, resources and tasks, uses a meta-network method, constructs an emergency response network model for the operation mechanism of airport emergency response, models and evaluates the emergency plan of the emergency response network from the perspective of overall layout, measures and analyzes the network resilience, clarifies the deficiencies in the plan and makes further improvements, which helps to improve the resilience level of the airport in response to emergencies and makes up for the deficiencies in the existing resilience analysis of airport complex networks.

Description

Technical Field

[0001] This invention belongs to the field of emergency response network resilience analysis technology, and in particular relates to a method for analyzing the resilience of airport emergency response networks under sudden events. Background Technology

[0002] Airport emergencies involve complex interactions and dynamic changes among multiple entities, making them difficult to control. Traditional emergency management models face challenges such as difficulties in systemic adaptation, personnel shortages and misallocations, and lengthy and challenging recovery times. These emergencies can lead to severe impacts, including flight cancellations, delays, prolonged rescue efforts, and significant losses, disrupting the normal operation of the entire airport. Therefore, it is necessary to study the resilience of airport emergency response networks under emergencies to enhance the future resilience of airport infrastructure operations and emergency activities. However, current research on the resilience of complex airport networks mainly focuses on the resilience of the airport's flight area and air traffic network; research on the impact of emergencies on the resilience of airport emergency response networks remains to be conducted. Therefore, it is essential to deeply analyze airport emergency response network resilience indicators under emergencies and establish corresponding response strategies and mechanisms to improve the overall response capability of airports in the face of emergencies and ensure the safe and stable operation of the air transport system. Summary of the Invention

[0003] To address the shortcomings of existing technologies, this invention provides a method, system, and storage medium for analyzing the resilience of airport emergency response networks under sudden events, thus overcoming the deficiencies in existing airport complex network resilience analysis.

[0004] The present invention achieves the above-mentioned technical objectives through the following technical means.

[0005] A method for analyzing the resilience of an airport emergency response network under sudden events includes the following steps:

[0006] Step 1: Identify the key elements and nodes of emergency response in the three stages before, during, and after an airport emergency;

[0007] Step 2: Based on the relationships between the key element nodes, create links between the nodes of the airport emergency response network under emergencies, and represent them using a binary matrix;

[0008] Step 3: Construct an airport emergency response network for sudden events based on a binary matrix;

[0009] Step 4: Perform a three-stage network resilience measurement analysis.

[0010] Furthermore, the key element nodes include: subject (A), resource (R), knowledge (K), and task (T); the binary matrix includes: [AA], [AT], [AR], [AK], [RT], and [KT].

[0011] Furthermore, in step 4, the first-stage resilience measurement analysis is measured using three indicators: natural connectivity, overall task completion, and task resource / knowledge consistency.

[0012] In the second phase of resilience measurement analysis, three indicators are used for measurement: efficiency, performance accuracy, and overall task completion.

[0013] In the third phase of resilience measurement analysis, three indicators are used for measurement: average speed, personnel cost, and cognitive needs.

[0014] Furthermore, the natural connectivity is used to evaluate the network's structural robustness in the face of failure by measuring the redundancy of alternative paths:

[0015]

[0016] Robustness refers to the ability of an element or system to withstand a certain degree of disturbance without losing its functionality; G AA Represents the AA emergency response network; AA represents the binary matrix of the AA emergency response network; λ j (j = 1, ..., m) represent the eigenvalues ​​of matrix [AA]; m represents the total number of eigenvalues; R Rob This indicates the robustness of the network at each stage; G represents the eigenvalue raised to the power of e; maximum robustness represents the condition when organizations are fully connected and cooperate with each other. AA Achieving the highest natural connectivity; while minimum robustness means that G achieves maximum natural connectivity when all stakeholders fail to cooperate. AA Achieve the minimum natural connectivity.

[0017] Furthermore, the overall task completion rate represents the percentage of tasks that an entity with the required resources and knowledge can complete;

[0018]

[0019] Among them, R OTC The overall task completion rate is represented by |T|, which refers to the total number of tasks that need to be completed; |S| represents the overall task completion rate. RK |This represents the number of tasks that a subject cannot complete due to whether it has the necessary resources and knowledge to perform the task;

[0020]

[0021] Where i represents a row in the binary matrix; j represents a column in the binary matrix; N RK This is a knowledge / resource gap matrix, referring to the gap between the knowledge / resources required to complete a task and the knowledge / resources available to the subject; N RK (i,j) is a matrix NRK The element in the i-th row and j-th column; AT is the binary matrix corresponding to the AT network; AT T [AT] is the transpose of matrix [AT]; AR is the binary matrix corresponding to the AR network; AK is the binary matrix corresponding to the AK network; RT is the binary matrix corresponding to the KT network; KT is the binary matrix corresponding to the KT network; [RT KT] T It is the transpose of [RT KT].

[0022] Furthermore, the aforementioned task resource / knowledge consistency refers to the amount of resources and knowledge that the subject does not need to complete the task:

[0023]

[0024] Among them, R TRW R represents the amount of resources that the subject does not need to complete the task. TKW Let represent the amount of knowledge not required for the subject to complete the task, m represent the total number of rows in the binary matrix, K1 represent the demand relationship between tasks and resources within the network, n represent the total number of columns in the binary matrix, and K2 represent the demand relationship between tasks and knowledge within the network. ~RT T Indicates whether the resource is needed to complete a task within the network; ~KT T This indicates whether the knowledge is needed to complete the task within the network; X(i,j) is the element in the i-th row and j-th column of matrix X.

[0025] Furthermore, the efficiency refers to the extent that each component in the network contains the minimum number of links required to maintain a connection:

[0026]

[0027] Penalty=m-n+K

[0028]

[0029] Among them, R EF Efficiency refers to the extent to which each component in the network contains the minimum number of links required to maintain a connection. Penalty represents the number of disconnected links in the network. Let A be a unimodal input network with m edges and n nodes, and let K be the number of elements in A. i Let i be the size of component i.

[0030] Furthermore, the performance precision refers to the degree of refinement with which the measuring subject can accurately execute assigned tasks based on acquired knowledge and resources.

[0031] Let b be the cardinality of the knowledge node set K, N be the transpose of matrix [KT], and S be matrix [AK].

[0032] The correct classification of b relative to task t is defined as follows:

[0033]

[0034] The classification of subject i with respect to task t on b is defined as follows:

[0035]

[0036]

[0037] The main group assigned to task t classifies b using a majority vote:

[0038]

[0039] When gans = tans, the tasks are classified, and the classification is repeated multiple times for each task.

[0040] Where, the true answer represents the correct classification of subject A regarding b relative to task T; N tk Indicates the availability of tasks and knowledge within the network; b k R represents the length of a two-dimensional string of length K; it This indicates that subject i believes that this knowledge is necessary to complete the task; S ij A binary matrix representing the subject and knowledge; answer i gans represents the number of correct classifications of subject A with respect to task T for b; i The group assigned to task t uses majority voting to classify b; |I| represents the number of classifications of b by the group regarding task t; k represents the length of the two-dimensional string; i represents the row in the binary matrix.

[0041] Furthermore, the average speed refers to the average speed at which any two nodes can interact:

[0042]

[0043] Among them, R AS Let G represent the average speed at which any two nodes can interact; n represents the number of columns in the binary matrix; i represents the number of rows in the binary matrix; G AA It is the AA emergency response network; d i It is the minimum path sum:

[0044]

[0045] Where D is G AA The distance matrix; if there is a path between node i and node u, then D(i,u) is the shortest path between node i and node u.

[0046] A system for implementing the above-mentioned airport emergency response network resilience analysis method under sudden events includes:

[0047] The data acquisition and identification module is used to acquire data from the airport's emergency response plan documents and identify four key elements of emergency response nodes: subject, resources, knowledge, and tasks, in the three stages of the airport emergency response before, during, and after the event.

[0048] The link creation module creates links between key element nodes identified by the data acquisition and recognition module, and represents them using a binary matrix.

[0049] The emergency response network construction module constructs a three-stage airport emergency response network based on the contingency plan under emergencies, using binary matrix data created by the link creation module.

[0050] The resilience measurement and analysis module performs resilience measurement and analysis on the three-stage airport emergency response network based on contingency plans under sudden events constructed by the emergency response network construction module.

[0051] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the airport emergency response network resilience analysis method under sudden events as described in claim 1.

[0052] The present invention has the following beneficial effects:

[0053] This invention integrates multi-dimensional operational content such as subjects, resources, and tasks, and uses the meta-network method to construct an emergency response network model under emergencies for the airport emergency response operation mechanism. From the perspective of the overall system layout, it models and evaluates the airport emergency response network plan under emergencies, and measures and analyzes the resilience of the airport emergency response network. Based on the evaluation results, the deficiencies in the plan can be identified and further improvements can be made, which helps to improve the airport's resilience level in response to emergencies and makes up for the shortcomings in the existing airport complex network resilience analysis. Attached Figure Description

[0054] Figure 1 This is a flowchart of the airport emergency response network resilience analysis method under sudden events described in this invention;

[0055] Figure 2 This is a network diagram of the airport's emergency response to unforeseen events, based on the first phase of the contingency plan.

[0056] Figure 3 This is a network diagram of the airport's emergency response to unforeseen events in the second phase, based on contingency plans.

[0057] Figure 4 This is a network diagram of the airport's emergency response to contingencies in the third phase, based on contingency plans. Detailed Implementation

[0058] The present invention will be further described below with reference to the accompanying drawings and specific embodiments, but the scope of protection of the present invention is not limited thereto.

[0059] Example 1:

[0060] The airport emergency response network resilience analysis method under sudden events described in this invention is as follows: Figure 1 As shown, the process includes the following:

[0061] Step 1: Based on the airport's emergency response plan documents, use NVIVO software to identify and determine the four key elements of emergency response in the three stages of the airport emergency response: the subject (A), resources (R), knowledge (K), and tasks (T).

[0062] Step 2: Based on the relationships between the key element nodes, create links between the nodes of the airport emergency response network under emergencies, and describe them using binary matrices [AA], [AT], [AR], [AK], [RT], and [KT].

[0063] Wherein, through the adjacency matrix A = {a ij} represents the connection status of nodes in the network. If any two nodes are connected, then a ij =1; if the two nodes are not connected, then a ij =0;

[0064] In this context, the binary matrix [AA] represents whether there is an interaction relationship between the subjects in the network, where "1" indicates that there is collaboration between the corresponding subject nodes, and "0" indicates that there is no interaction relationship between the subjects; the binary matrix [AT] represents that the subjects in the network need to complete a certain task, where "1" indicates that the corresponding subject needs to be responsible for the task in that column, and "0" indicates that the subject does not undertake the task; the binary matrix [AR] corresponds to the availability between subjects and resources within the network, where "1" represents that the subject can access the resource, and "0" represents that the subject cannot access the resource; the binary matrix [AK] refers to the availability between subjects and knowledge within the network, where "1" represents that the subject can access the knowledge, and "0" represents that the subject cannot access the knowledge; the binary matrix [RT] corresponds to the demand relationship between resources and tasks within the network, where "1" represents that the corresponding resource is required to complete the corresponding task, and "0" represents that the resource is not required to complete the task; the binary matrix [KT] corresponds to the demand relationship between knowledge and tasks within the network, where "1" represents that the corresponding knowledge is required to complete the corresponding task, and "0" represents that the knowledge is not required to complete the task.

[0065] Step 3: Based on the binary matrix established in Step 2, import it into the Organization Risk Analysis (ORA) software to obtain a three-stage airport emergency response network under contingency plans.

[0066] Step 4: Select several meta-network indicators that closely match the conceptual characteristics of each stage to measure the resilience level of airport emergency response organization, emergency response tasks, emergency resources, and emergency information and their collaborative interaction under a sudden event. The specific process is as follows:

[0067] Step 4.1: First-stage toughness measurement analysis and calculation:

[0068] This phase is measured using three indicators: "natural connectivity", "overall task completion", and "task resource / knowledge consistency".

[0069] ① Natural connectivity assesses the structural robustness of a network in the face of failure by measuring the redundancy of alternative paths. As the number of alternative paths increases, stakeholders have more solutions to cope with response tasks, and the resilience of the emergency response network is enhanced.

[0070]

[0071] Robustness refers to the ability of an element or system to withstand a certain degree of disturbance without losing its functionality; G AA λ represents the AA emergency response network, i.e., the emergency response network between entities; AA represents the binary matrix of the AA emergency response network; λ j (j = 1, ..., m) represent the eigenvalues ​​of matrix [AA]; m represents the total number of eigenvalues; R Rob This indicates the robustness of the network at each stage; G represents the eigenvalue raised to the power of e; maximum robustness represents the condition when organizations are fully connected and cooperate with each other. AA Achieving the highest natural connectivity; while minimum robustness means that G achieves maximum natural connectivity when all stakeholders fail to cooperate. AA Achieve the minimum natural connectivity.

[0072] ② Overall task completion rate represents the percentage of tasks that an entity with the necessary resources and knowledge can complete;

[0073]

[0074] Among them, R OTC The overall task completion rate is represented by |T|, which refers to the total number of tasks that need to be completed; |S| represents the overall task completion rate. RK |This represents the number of tasks that a subject cannot complete due to whether it has the necessary resources and knowledge to perform the task;

[0075]

[0076] Where i represents a row in the binary matrix; j represents a column in the binary matrix; N RK This is a knowledge / resource gap matrix, referring to the gap between the knowledge / resources required to complete a task and the knowledge / resources available to the subject; N RK (i,j) is a matrix N RK The element in the i-th row and j-th column; AT is the binary matrix corresponding to the AT network; AT T [AT] is the transpose of matrix [AT]; AR is the binary matrix corresponding to the AR network; AK is the binary matrix corresponding to the AK network; RT is the binary matrix corresponding to the KT network; KT is the binary matrix corresponding to the KT network; [RT KT] T It is the transpose of [RT KT].

[0077] ③ Task resource / knowledge consistency refers to the amount of resources and knowledge that an entity does not need to complete a task. It can quantify the surplus of resources and the oversupply of knowledge for a task.

[0078]

[0079] Among them, R TRW R represents the amount of resources that the subject does not need to complete the task. TKW Let represent the amount of knowledge not required for the subject to complete the task, m represent the total number of rows in the binary matrix, K1 represent the demand relationship between tasks and resources within the network, n represent the total number of columns in the binary matrix, and K2 represent the demand relationship between tasks and knowledge within the network. ~RT T Indicates whether the resource is needed to complete a task within the network; ~KT T This indicates whether the knowledge is needed to complete the task within the network; X(i,j) is the element in the i-th row and j-th column of matrix X.

[0080] Step 4.2: Second-stage toughness measurement analysis and calculation;

[0081] This phase is measured using three indicators: "efficiency," "performance accuracy," and "overall task completion rate."

[0082] ① Efficiency refers to the extent to which each component in a network contains the minimum number of links required to maintain a connection:

[0083]

[0084] Penalty=m-n+K

[0085]

[0086] Among them, REF Efficiency refers to the extent to which each component in the network contains the minimum number of links required to maintain a connection. Penalty represents the number of disconnected links in the network. Let A be a unimodal input network with m edges and n nodes, and let K be the number of elements in A. i Let i be the size of component i.

[0087] ②Performance precision refers to the degree of detail by which the measuring entity can accurately execute assigned tasks based on the acquired knowledge and resources.

[0088] Let b be the cardinality of the knowledge node set K, N be the transpose of matrix [KT], and S be matrix [AK].

[0089] The correct classification of b relative to task t is defined as follows:

[0090]

[0091] The classification of subject i with respect to task t on b is defined as follows:

[0092]

[0093] The main group assigned to task t classifies b using a majority vote:

[0094]

[0095] When gans = tans, the tasks are classified, and the classification is repeated multiple times for each task.

[0096] Where, the true answer represents the correct classification of subject A regarding b relative to task T; N tk Indicates the availability of tasks and knowledge within the network; b k R represents the length of a two-dimensional string of length K; it This indicates that subject i believes that this knowledge is necessary to complete the task; S ij A binary matrix representing the subject and knowledge; answer i gans represents the number of correct classifications of subject A with respect to task T for b; i The group assigned to task t uses majority voting to classify b; |I| represents the number of classifications of b by the group regarding task t; k represents the length of the two-dimensional string; i represents the row in the binary matrix.

[0097] ③ Overall task completion rate refers to the percentage of tasks that an entity with the necessary resources and knowledge can complete.

[0098] The second phase of emergency response mainly involves responding to the disaster. The tasks in this phase will lay the foundation for the recovery and reconstruction phase. Therefore, measuring the overall completion of the tasks in this phase can help to gain a deeper understanding of the resilience level of this phase.

[0099] Step 4.3: Third-stage toughness measurement analysis and calculation;

[0100] This phase is measured using three indicators: "average speed," "personnel costs," and "cognitive needs."

[0101] ① Average speed refers to the average speed at which any two (reachable) nodes can interact; it is the reciprocal of the average shortest path length between node pairs.

[0102]

[0103] Among them, R AS Let G represent the average speed at which any two (reachable) nodes can interact; n represents the number of columns in the binary matrix; i represents the number of rows in the binary matrix; G AA It is the AA emergency response network; v is G. AA Minimum link value; d i It is the sum of the minimum paths from the main node Ai to all other main nodes;

[0104]

[0105] Where D is defined as G AA The distance matrix; if there is a path between node i and node u, then D(i,u) is the shortest path between node i and node u.

[0106] ④ Personnel Costs: The cost of a participant is the sum of the number of input links from other participants and the number of output links to knowledge, resource, and task nodes, scaled by dividing by the total number of links the agent may have to nodes / slave nodes.

[0107] ⑤ Cognitive needs: Measure the total amount of work each agent spends to complete the task.

[0108] This embodiment also provides an airport emergency response network resilience analysis system under sudden events, including:

[0109] The data acquisition and identification module is used to acquire data from the airport's emergency response plan documents and identify four key elements of emergency response nodes: subject, resources, knowledge, and tasks, in the three stages of the airport emergency response before, during, and after the incident.

[0110] The link creation module creates links between key element nodes identified by the data acquisition and recognition module, and represents them using a binary matrix.

[0111] The emergency response network construction module, based on the binary matrix data created by the link creation module, constructs a three-stage airport emergency response network for contingencies under the contingency plan.

[0112] The resilience measurement and analysis module performs resilience measurement and analysis on the three-stage airport emergency response network based on contingency plans under sudden events constructed by the emergency response network construction module.

[0113] This embodiment also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the above-described airport emergency response network resilience analysis method under sudden events.

[0114] Example 2:

[0115] The airport emergency response network resilience analysis method under sudden events described in this invention includes the following process:

[0116] Step 1: Based on the airport's emergency response plan documents, use NVIVO software to identify and determine the four key elements of emergency response in the three stages before, during, and after an airport emergency: the subject (A), resources (R), knowledge (K), and tasks (T). The emergency response plan documents include the "Emergency Response Law of the People's Republic of China", the "Rules for Emergency Rescue Management of Civil Transport Airports", and the "Emergency Response Plan for Zhongwei Shapotou Airport".

[0117] Main node identification:

[0118] According to the emergency response plan, the airport emergency response network under emergencies includes 23 entities, including the Municipal Port and Investment Promotion Office, the District People's Government, and the Emergency Management Bureau, as detailed in Table 1 below. In Table 1, A01 to A23 represent the node numbers of each entity.

[0119] Table 1 Main Nodes of Airport Emergency Response Network under Unexpected Events

[0120]

[0121] Resource node identification:

[0122] Based on the emergency response plan, nine resource nodes were identified in the airport's emergency response network for any unforeseen event. These include emergency supplies, hotel resources, and emergency relief funds, as shown in Table 2. In Table 2, R01 to R09 represent the numbers of each resource node.

[0123] Table 2 Airport Emergency Response Network Resource Nodes under Unexpected Events

[0124]

[0125] Knowledge node identification:

[0126] Based on the emergency response plan documents, 10 knowledge nodes were identified in the airport's emergency response network under sudden events. These nodes include the analysis and judgment of various reported early warning information and the assessment of the situation's development, as shown in Table 3. In Table 3, K01 to K10 represent the numbers of each knowledge node.

[0127] Table 3 Main Nodes of Airport Emergency Response Network under Unexpected Events

[0128]

[0129] Task node identification:

[0130] Based on the analysis of emergency response plan documents, the tasks in the emergency response network under sudden events are identified, and three-stage task nodes are shown in Tables 4, 5, and 6:

[0131] Table 4. Airport Emergency Response Network Task Nodes under Unexpected Events (Phase 1)

[0132]

[0133]

[0134]

[0135] Table 5. Airport Emergency Response Network Task Nodes under Unexpected Events (Phase 2)

[0136]

[0137]

[0138]

[0139] Table 6. Airport Emergency Response Network Task Nodes under Unexpected Events (Phase 3)

[0140]

[0141]

[0142] Step 2: Based on the relationships between nodes in the emergency response plan document, create links between nodes in the airport emergency response network under sudden events, using binary matrices [AA], [AT], [AR], [AK], [RT], and [KT] for description, as shown in Tables 7 and 8 below:

[0143] Table 7 shows the process of establishing some binary matrices.

[0144]

[0145] Table 8. Example of the [AT] matrix in the second stage.

[0146]

[0147]

[0148] Step 3: Based on the binary matrix established in Step 2, use Organization Risk Analysis (ORA) software to obtain a three-stage airport emergency response network under contingency plans, such as... Figure 2 , Figure 3 , Figure 4 As shown, where, Figure 2 , Figure 3 , Figure 4 In this context, Agent represents the subject, Knowledge represents resources, Resource represents knowledge, and Task represents a task.

[0149] Step 4: Select the three-stage meta-network index from Example 1 to measure the three-stage resilience level of airport emergency response organization, emergency response tasks, emergency resources and emergency information and their collaborative interaction under a sudden event;

[0150] (1) First-stage toughness level measurement:

[0151] The overall task completion rate of the airport emergency response network under sudden events based on emergency plan documents is 0.550, which is above average, indicating that the system's response capability is above average. The network's task resource / knowledge consistency is 0.716, which is above average, indicating that the emergency response resources and knowledge are sufficient, and the efficiency and effectiveness of the emergency response are good.

[0152] (2) Second-stage toughness level measurement:

[0153] The efficiency of the airport emergency response network under emergencies based on emergency plan documents is 0.844, reflecting the relatively fast execution speed of tasks in the emergency response network; the performance accuracy is 0.263, reflecting the extent to which the subject can accurately execute the assigned tasks based on the speed of its acquired knowledge and resources; and the overall task completion rate is 0.536, reflecting that the overall response level of the system is above average.

[0154] (3) Third-stage toughness level measurement:

[0155] The personnel cost in the airport emergency response network under emergencies based on the emergency plan document is 0.145, reflecting the low personnel cost in emergency response; the network cognitive demand based on the plan is low at 0.144, therefore, communication and collaboration among various stakeholders need to be enhanced in the improvement of the plan.

[0156] The embodiments described above are preferred embodiments of the present invention, but the present invention is not limited to the above embodiments. Any obvious improvements, substitutions or modifications that can be made by those skilled in the art without departing from the essence of the present invention shall fall within the protection scope of the present invention.

Claims

1. A method for analyzing the resilience of an airport emergency response network under sudden events, characterized in that, The process includes the following: Step 1: Identify the key elements of emergency response in the three stages before, during, and after an airport emergency, including: Agent (A), Resources (R), Knowledge (K), and Task (T); Step 2: Based on the relationships between the key element nodes, create links between the nodes of the airport emergency response network under emergencies, represented using a binary matrix, including [AA], [AT], [AR], [AK], [RT], and [KT]. Step 3: Construct an airport emergency response network for sudden events based on a binary matrix; Step 4: Perform a three-stage network resilience measurement analysis; In step 4, the first-stage resilience measurement analysis is conducted using three indicators: natural connectivity, overall task completion, and task resource / knowledge consistency. In the second phase of resilience measurement analysis, three indicators are used for measurement: efficiency, performance accuracy, and overall task completion. In the third phase of resilience measurement analysis, three indicators are used for measurement: average speed, personnel cost, and cognitive demand. The natural connectivity is used to evaluate the network's structural robustness in the face of failure by measuring the redundancy of alternative paths: Robustness refers to the ability of an element or system to withstand a certain degree of disturbance without losing its functionality; G AA Represents the AA emergency response network; AA represents the binary matrix of the AA emergency response network; λ j (j = 1, ..., m) represent the eigenvalues ​​of matrix [AA]; m represents the total number of eigenvalues; R Rob This indicates the robustness of the network at each stage; G represents the eigenvalue raised to the power of e; maximum robustness represents the condition when organizations are fully connected and cooperate with each other. AA Achieving the highest natural connectivity; while minimum robustness means that G achieves maximum natural connectivity when all stakeholders fail to cooperate. AA Achieve the lowest possible natural connectivity; The overall task completion rate represents the percentage of tasks that an entity with the necessary resources and knowledge can complete. Among them, R OTC The overall task completion rate is represented by |T|, which refers to the total number of tasks that need to be completed; |S| represents the overall task completion rate. RK |This represents the number of tasks that a subject cannot complete due to whether it has the necessary resources and knowledge to perform the task; Where i represents a row in the binary matrix; j represents a column in the binary matrix; N RK This is a knowledge / resource gap matrix, referring to the gap between the knowledge / resources required to complete a task and the knowledge / resources available to the subject; N RK (i,j) is a matrix N RK The element in the i-th row and j-th column; AT is the binary matrix corresponding to the AT network; AT T [AT] is the transpose of matrix [AT]; AR is the binary matrix corresponding to the AR network; AK is the binary matrix corresponding to the AK network; RT is the binary matrix corresponding to the KT network; KT is the binary matrix corresponding to the KT network; [RT KT] T It is the transpose of [RT KT]. The aforementioned task resource / knowledge consistency refers to the amount of resources and knowledge that the subject does not need when completing a task: Among them, R TRW R represents the amount of resources that the subject does not need to complete the task. TKW Let represent the amount of knowledge not required for the subject to complete the task, m represent the total number of rows in the binary matrix, K1 represent the demand relationship between tasks and resources within the network, n represent the total number of columns in the binary matrix, and K2 represent the demand relationship between tasks and knowledge within the network. ~RT T Indicates whether the resource is needed to complete a task within the network; ~KT T This indicates whether the knowledge is needed to complete the task within the network; X(i,j) is the element in the i-th row and j-th column of matrix X; Efficiency refers to the extent that each component in the network contains the minimum number of links required to maintain a connection. Penalty=m-n+K Among them, R EF Efficiency refers to the extent to which each component in the network contains the minimum number of links required to maintain a connection. Penalty represents the number of disconnected links in the network. Let A be a unimodal input network with m edges and n nodes, and let K be the number of elements in A. i The size of component i; The performance precision refers to the degree of detail by which the subject can accurately execute assigned tasks based on the acquired knowledge and resources. Let b be the cardinality of the knowledge node set K, N be the transpose of matrix [KT], and S be matrix [AK]. The correct classification of b relative to task t is defined as follows: The classification of subject i with respect to task t on b is defined as follows: The main group assigned to task t classifies b using a majority vote: When gans = tans, the tasks are classified, and the classification is repeated multiple times for each task. Where, the true answer represents the correct classification of subject A regarding b relative to task T; N tk Indicates the availability of tasks and knowledge within the network; b k R represents the length of a two-dimensional string of length K; it This indicates that subject i believes that this knowledge is necessary to complete the task; S ij A binary matrix representing the subject and knowledge; answer i gans represents the number of correct classifications of subject A with respect to task T for b; i The group assigned to task t uses majority voting to classify b; |I| represents the number of classifications of b by the group regarding task t; k represents the length of the two-dimensional string; i represents the row in the binary matrix. The average speed refers to the average speed at which any two nodes can interact. Among them, R AS Let G represent the average speed at which any two nodes can interact; n represents the number of columns in the binary matrix; i represents the number of rows in the binary matrix; G AA It is the AA emergency response network; d i It is the minimum path sum: Where D is G AA The distance matrix; if there is a path between node i and node u, then D(i,u) is the shortest path between node i and node u; The personnel cost refers to the sum of the number of input links from other participants and the number of output links to knowledge, resource, and task nodes, scaled by dividing the sum by the total number of links the agent may have to nodes / slave nodes. The cognitive requirement refers to measuring the total amount of work each agent expends to complete a task.

2. A system for implementing the airport emergency response network resilience analysis method under sudden events as described in claim 1, characterized in that, include: The data acquisition and identification module is used to acquire data from the airport's emergency response plan documents and identify four key elements of emergency response nodes: subject, resources, knowledge, and tasks, in the three stages of the airport emergency response before, during, and after the event. The link creation module creates links between key element nodes identified by the data acquisition and recognition module, and represents them using a binary matrix. The emergency response network construction module constructs a three-stage airport emergency response network based on the contingency plan under emergencies, using binary matrix data created by the link creation module. The resilience measurement and analysis module performs resilience measurement and analysis on the three-stage airport emergency response network based on contingency plans under sudden events constructed by the emergency response network construction module.

3. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the airport emergency response network resilience analysis method under sudden events as described in claim 1.

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