Method for determining sensitive factors of airport resilience under meteorological disasters

Abstract

This invention discloses a method for determining sensitive factors of airport resilience under meteorological disasters. First, the average resilience and average resilience sub-characteristics of all airports within the analysis area affected by disasters throughout the year are calculated. Then, the significant influencing factors on the average resilience and average resilience sub-characteristics of affected airports within the analysis area under all disaster conditions within the analysis year are identified. Next, the significant influencing factors on the resilience and sub-characteristics of each affected airport within the selected analysis area under a specific disaster condition are determined through calculation. Finally, the sensitive influencing factors on the resilience and sub-characteristics of affected airports within the analysis area under a specific disaster condition are calculated. This method can quickly and accurately determine the sensitive influencing factors on airport resilience within the analysis area under different meteorological disasters, thereby improving the airport's resistance to disasters and its post-disaster recovery capabilities, ultimately improving the airport's operational efficiency and functional level.

Description

Technical Field

[0001] This invention relates to the field of airport engineering, and in particular to a method for determining airport resilience sensitivity factors under meteorological disasters. Background Technology

[0002] Civil aviation airports face various risks during operation, including adverse environmental conditions and unforeseen events, which can reduce or disrupt their operational capacity. Airport resilience is characterized by its ability to adapt to environmental changes, possess strong resistance and necessary redundancy, withstand emergencies, and restore functionality. It allows for the evaluation of airport operational capacity throughout the entire process of an emergency or natural disaster, from occurrence to cessation, and the development of countermeasures to improve airport operational capacity.

[0003] Weather is a significant factor affecting air transport. It not only impacts the normal operation of airports but is also a potential cause of accidents and influences post-disaster recovery, significantly affecting airport resilience. Furthermore, airport resilience is closely related to its inherent characteristics. An airport is a complex, large system comprising environmental, technical, management, and operational subsystems. Therefore, if any characteristic of an airport is unfavorable, it is more susceptible to risks. Through the interrelationships of these subsystems, the overall operational capacity of the airport can be reduced or halted, resulting in a low level of airport resilience.

[0004] Therefore, research on airport resilience under adverse weather conditions, identifying the key influencing factors of airport resilience, and enabling airports to implement targeted preventive measures in advance to minimize the negative impact of severe weather on airports is of profound significance.

[0005] Currently, domestic and international research on the resilience of the civil aviation sector mainly focuses on air transport networks, constructing complex networks with airports as nodes and routes as edges, and evaluating the network performance. In their paper "Resilience Modeling Method of Airport Network Affected by Global Public Health Events" published in Mathematical Problems in Engineering, Guo J et al. studied the impact of the COVID-19 pandemic on the resilience of airport networks. However, COVID-19 is only a specific factor among many influencing factors, and there are many other factors that affect airport resilience.

[0006] In their article "An Improved Model of Civil Aviation Network Vulnerability" published in Transportation Systems Engineering and Information, Li Hang et al. compared and analyzed the different impacts of three types of disasters on the vulnerability of aviation networks. However, research on airport resilience is still in its early stages and no clear definition of airport resilience has been proposed.

[0007] In their paper "Resilience of Networked Infrastructure with Evolving Component Conditions: Pavement Network Application," published in the *Journal of Computing in Civil Engineering*, Levenberg et al. studied the resilience of airport runway and taxiway systems through numerical calculations, but only covered a portion of the airport's facilities. This demonstrates that current research on airport resilience by scholars both domestically and internationally lacks a systematic approach and requires further in-depth study.

[0008] Regarding the assessment method for airport resilience, Chinese patent publication number CN114638075A discloses "An airport cluster system resilience assessment method based on regional control capabilities". In response to the problems existing in the research on airport operational support effectiveness, a network model is established with the airport cluster system as the research object. The following problems exist: the research object is an airport group in a certain area, and the ability to make corresponding assessments for a specific airport needs to be improved. Summary of the Invention

[0009] The purpose of this invention is to overcome the shortcomings of the prior art and propose a method for determining sensitive factors of airport resilience under meteorological disasters. This method can quickly and accurately determine the sensitive influencing factors of airport resilience under different meteorological disasters, thereby enabling targeted prevention and control of these factors and improving the operational efficiency and functional level of the airport.

[0010] To achieve the above objectives, the method for determining airport resilience sensitivity factors under meteorological disasters includes the following steps:

[0011] The present invention provides a method for determining airport resilience sensitivity factors under meteorological disasters, characterized by comprising the following steps:

[0012] s1. Calculate the average resistance, average robustness, average adaptability, average speed, and average resilience of all airports within the analysis area under the influence of disasters throughout the year. The calculation process involves the following steps.

[0013] The first step, for a specific airport within the analysis area, is to collect information on all flights taking off and landing at that airport during the analysis year. This information includes planned and actual departure and arrival times, whether flights were delayed, and whether flights were canceled. Assuming the airport experienced n disasters during the analysis year, each disaster is considered a separate analysis period. The airport's resilience, adaptability, robustness, speed, and tenacity under a specific disaster period are calculated as follows:

[0014] Step 101: Starting from the start time of a certain disaster, at regular intervals, count the total number of flights scheduled to take off and land at the airport and the total number of flights actually taking off and landing at the airport at different times. The total number of flights actually taking off and landing at the airport at a certain time is the total number of flights scheduled to take off and land at the airport at that time minus the number of delayed flights and canceled flights.

[0015] Step 102: For a given disaster event, calculate the airport system performance q(t) at different times t during the disaster event. The formula is:

[0016]

[0017] In the formula, k1(t) represents the total number of flights that actually take off and land at the airport at time t, and k2(t) represents the total number of flights that are scheduled to take off and land at the airport at time t.

[0018] Step 103: Plot the system performance change curve of the airport during the disaster with time as the horizontal axis and q(t) value as the vertical axis;

[0019] Step 104 divides the airport system performance under the influence of a certain disaster into five stages: Stage 1: (0-t1) time period, the airport's normal operation stage; Stage 2: (t1-t2) time period, the airport system performance declines due to the impact of the meteorological disaster; Stage 3: (t2-t3) time period, the airport system performance no longer declines due to the airport's own control capabilities, maintaining a low-performance operation stage; Stage 4: (t3-t4) time period, the airport takes emergency measures, and the airport system performance recovers; Stage 5: (t4-t5) time period, the airport system operation stage after performance recovery.

[0020] Step 105: Resistance R of an airport system to performance changes under the influence of a disaster. 1i Adaptive R 2i Robustness R 3i , speed R 4i and toughness R i The calculations are performed separately, and the formula is as follows:

[0021] Resistance R 1i =(t 2i -t 1i ) / (t 4i -t 1i )·(q 1i -q 3i ) / q 1i ;

[0022] Adaptive R 2i =(t 3i -t 2i ) / (t 4i -t 1i );

[0023] Robustness R 3i =q 3i / q 1i ;

[0024] Rapidity R 4i =(t 4i -t 3i ) / (t 4i -t 1i )·(q 2i -q 3i ) / q 1i ;

[0025] Toughness R i =R 1i ·R 2i ·R 3i ·R 4i ;

[0026] In the formula, the subscript i represents the i-th disaster, i = 1, 2, ..., n; t 1i q represents the end time of the second stage divided according to step 103 under the i-th disaster. 1i t represents the system performance of the airport during normal operation under the i-th disaster; 2i q represents the end time of the second stage divided according to step 103 under the i-th disaster. 3i This indicates the lowest system performance at the airport after being affected by the disaster; t 3i t represents the end time of the third stage under the i-th disaster, as defined in step 103; 4i q represents the time at the end of the fourth stage of the airport under the i-th disaster, as divided according to step 103. 2i This indicates that the airport has restored its system performance to its highest level after taking measures. q 1i q 3i q 2i The values ​​of q1, q3, and q2 corresponding to the airport system performance change curve obtained in step 103 are taken; t 1i t2i t 3i t 4i The time values ​​corresponding to t1, t2, t3, and t4 in the airport system performance change curve obtained in step 103 are taken.

[0027] The second step is to repeat the first step to obtain the resistance, adaptability, robustness, speed and resilience of a certain airport under n disasters;

[0028] The third step is to calculate the average resilience of a given airport under all disaster scenarios during the analysis year. Average robustness Average fitness Average speed and average toughness The formula is:

[0029] Average resistance Average robustness Average fitness Average speed

[0030] Average toughness

[0031] The fourth step involves performing the steps one and two on all airports within the analyzed area to obtain the individual airport conditions under all disaster scenarios within the analyzed year.

[0032] s2, calculate the influencing factors and The correlation coefficient between them is used to determine the impact of all disaster scenarios on affected airports within the analysis area during the analysis year. The significant influencing factors include meteorological data and airport characteristic data, and the specific process is as follows:

[0033] Step 201: Collect airport characteristic factor data of each airport in the analysis area of ​​the analysis year and meteorological factor data of the n disaster occurrences in step s1, and calculate the arithmetic mean of all the same meteorological factor data in the n disaster time periods to obtain the mean of meteorological factor data as the value of meteorological factor data for each airport.

[0034] The airport characteristic factor data includes the longitude, latitude, airport size, altitude, type of navigation aids, runway length, runway width, pavement material, radio type and radio frequency of each airport within the analysis area during the analysis year;

[0035] Meteorological data during the n disaster events include wind speed, visibility, temperature, dew point, air pressure, precipitation, and snow depth data during the n disaster events in step s1.

[0036] Step 202: The average resistance, average robustness, average adaptability, average speed, and average resilience of each airport within the analysis area are used as the first set of data. The airport characteristic factor data and meteorological factor data from Step 201 are used as the second set of data. The first and second sets of data are then imported into PyCharm software. Using the airport name as the keyword, the merge method in Python is used to merge the first and second sets of data. The merged data file is then exported to SPSS software. The data for each airport in the file is then analyzed separately. Using airport characteristic factor data and meteorological factor data from the data file as the dependent variable, a multiple stepwise linear regression analysis was conducted to obtain the correlation coefficients of the influencing factors on the average resistance, average robustness, average adaptability, average speed and average resilience of each airport in the analysis area under the influence of disasters throughout the year. The factors retained in the multiple stepwise linear regression were taken as the significant influencing factors on the average resistance, average robustness, average adaptability, average speed and average resilience of each airport in the analysis area under the influence of disasters throughout the year.

[0037] s3. Through calculation, determine the significant influencing factors on the resistance, robustness, adaptability, speed, and resilience of each affected airport within a selected analysis area under a certain disaster scenario. The specific steps are as follows:

[0038] Step 301: Following the methods in steps 101-103, calculate the system performance change curves of affected airports in the analysis area during the disaster. Following the methods in steps 104 and 105, calculate the resistance R1, adaptability R2, robustness R3, speed R4, and resilience R of each affected airport in the analysis area under a selected disaster.

[0039] Step 302: According to the types of influencing factors in Step 201, collect meteorological factor data and airport characteristic factor data of each airport affected by the disaster, among which the meteorological factor is the value at the worst time during the disaster.

[0040] Step 303: Take the R1, R2, R3, R4, and R values ​​of all affected airports in the analysis area under a certain disaster as the first set of data, and take the meteorological factor data and airport characteristic factor data collected in step 302 as the second set of data. Calculate the correlation coefficients of the influencing factors on the R1, R2, R3, R4, and R values ​​of all affected airports using the same method as in step 202. Then, take the factors retained in the multiple stepwise linear regression as the significant influencing factors on the R1, R2, R3, R4, and R values ​​of all affected airports in the analysis area under the influence of a certain disaster.

[0041] s4. Determine the sensitive influencing factors of resistance, robustness, adaptability, speed, and resilience of affected airports within the analysis area under a specific disaster through calculation. The specific steps are as follows:

[0042] The significant influencing factors of resistance R1, robustness R2, adaptability R3, speed R4, and resilience R of affected airports in the analysis area under a certain disaster occurrence, obtained in step s3, are compared with the average resistance of each airport in the analysis area under the annual disaster impact, obtained in step s2. Average robustness Average fitness Average speed and average toughness The significant influencing factors are compared separately. The factors included in the significant influencing factors obtained in step s3 but not in the significant influencing factors obtained in step s2 are the sensitive influencing factors on the resistance, robustness, adaptability, speed and resilience of the affected airports in the analysis area under the influence of a certain disaster.

[0043] The present invention has the following advantages:

[0044] 1. It can comprehensively consider the entire process of airport system performance changes under meteorological disasters for a specific analysis area containing multiple airports, such as the Beijing-Tianjin-Hebei region, the Yangtze River Delta region, or the entire country. It calculates airport resilience from four aspects: resistance, robustness, adaptability, and speed. The resilience definition is comprehensive and reasonable, and the calculation method is simple and effective.

[0045] 2. The analysis comprehensively considers airport characteristics and weather factors as potential influencing factors of airport resilience. Furthermore, these factors can be further adjusted based on actual circumstances. The comprehensive consideration of factors makes the determination of sensitive factors of airport resilience under disasters more thorough and effective.

[0046] 3. Identify scenarios where different disasters have a significant impact on the airport's resilience, providing decision support and theoretical basis for the airport's disaster prevention, targeted enhancement of airport resilience, and efficient airport operation, enabling the airport to implement targeted preventive measures in advance and minimize the negative impact of severe weather on the airport.

[0047] 4. It can analyze various disasters such as winter storms, blizzards, floods, tropical storms, and tornadoes. By comparing and analyzing the sensitive factors of the same airport under different disasters, it can further clarify the impact of different disasters on the airport's resilience. Attached Figure Description

[0048] Figure 1 This is a flowchart illustrating the method for determining airport resilience sensitivity factors under this meteorological disaster.

[0049] Figure 2A schematic diagram illustrating changes in airport system performance under meteorological disasters;

[0050] Figure 3 This represents partial calculation results for the average resistance, average robustness, average adaptability, average speed, and average resilience of airports affected by all disasters in the United States in 2019.

[0051] Figure 4 The results are calculated based on the average resistance, average robustness, average adaptability, average speed, and average resilience of airports affected by all disasters in the United States in 2019, and the results of multiple stepwise linear regression with the corresponding airport meteorological and airport characteristic data in 2019.

[0052] Figure 5 This is a partial calculation result of the resistance, adaptability, robustness, speed and resilience of airports affected by the snowstorm disaster in the United States in January 2019;

[0053] Figure 6 The results of the calculation of resistance, adaptability, robustness, speed and resilience of some airports affected by the snowstorm disaster in the United States in January 2019 are obtained by multiple stepwise linear regression with the meteorological factors and airport characteristic factors data of the corresponding airports in January 2019. Detailed Implementation

[0054] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments.

[0055] like Figure 1 As shown, the method for determining airport resilience sensitivity factors under meteorological disasters according to the present invention includes the following steps:

[0056] s1. Calculate the average resistance, average robustness, average adaptability, average speed, and average resilience of all airports within the analysis area under the influence of disasters throughout the year. The calculation process involves the following steps.

[0057] The first step, for a specific airport within the analysis area, is to collect information on all flights taking off and landing at that airport during the analysis year. This information includes planned and actual departure and arrival times, whether flights were delayed, and whether flights were canceled. Assuming the airport experienced n disasters during the analysis year, each disaster is treated as a separate analysis period. The airport's resilience, adaptability, robustness, speed, and tenacity under a specific disaster period are calculated. The calculation process is as follows:

[0058] Step 101: Starting from the start time of a certain disaster, at regular intervals (e.g., every 15 minutes), count the total number of flights scheduled to take off and land at the airport at different times and the total number of flights actually taking off and landing at the airport. The total number of flights actually taking off and landing at the airport at a certain time is the total number of flights scheduled to take off and land at the airport at that time minus the number of delayed flights and canceled flights.

[0059] Step 102: For a given disaster event, calculate the q(t) value at different times t during the event. The formula is:

[0060]

[0061] In the formula, t can take values ​​in minutes, and t can be t = 0, 15, 30, ... t end , where t end Let q(t) represent the end time of the disaster analysis, q(t) represent the airport system performance at time t, k1(t) represent the total number of flights that actually take off and land at the airport at time t, and k2(t) represent the total number of flights that are planned to take off and land at the airport at time t.

[0062] Step 103: Plot the system performance change curve of the airport during the disaster with time as the horizontal axis and q(t) value as the vertical axis;

[0063] Step 104 divides the airport system performance under the influence of a certain disaster into five stages, such as... Figure 2 As shown, the airport operates normally during the first stage (0-t1); during the second stage (t1-t2), the airport system performance declines due to the impact of meteorological disasters; during the third stage (t2-t3), the airport system performance no longer declines due to its own control capabilities, maintaining low-performance operation; during the fourth stage (t3-t4), the airport takes emergency measures, and the airport system performance recovers; and during the fifth stage (t4-t5), the airport system resumes operation after performance recovery.

[0064] Step 105: Resistance R of an airport system to performance changes under the influence of a disaster. 1i Adaptive R 2i Robustness R 3i , speed R 4i and toughness R i The calculations are performed separately, and the formula is as follows:

[0065] Resistance R 1i =(t 2i -t 1i ) / (t 4i -t 1i)·(q 1i -q 3i ) / q 1i ;

[0066] Adaptive R 2i =(t 3i -t 2i ) / (t 4i -t 1i );

[0067] Robustness R 3i =q 3i / q 1i ;

[0068] Rapidity R 4i =(t 4i -t 3i ) / (t 4i -t 1i )·(q 2i -q 3i ) / q 1i ;

[0069] Toughness R i =R 1i ·R 2i ·R 3i ·R 4i ;

[0070] In the formula, the subscript i represents the i-th disaster, i = 1, 2, ..., n; t 1i q represents the end time of the second stage divided according to step 103 under the i-th disaster. 1i t represents the system performance of the airport during normal operation under the i-th disaster; 2i q represents the end time of the second stage divided according to step 103 under the i-th disaster. 3i This indicates the lowest system performance at the airport after being affected by the disaster; t 3i t represents the end time of the third stage under the i-th disaster, as defined in step 103; 4i q represents the time at the end of the fourth stage of the airport under the i-th disaster, as divided according to step 103. 2i This indicates that the airport has restored its system performance to its highest level after taking measures. q 1i q 3i q 2i The values ​​of q1, q3, and q2 corresponding to the airport system performance change curve obtained in step 103 are taken; t 1i t 2i t 3i t 4i The time values ​​corresponding to t1, t2, t3, and t4 in the airport system performance change curve obtained in step 103 are taken.

[0071] The second step is to repeat the first step to obtain the resistance, adaptability, robustness, speed and resilience of a certain airport under n disasters;

[0072] The third step is to calculate the average resilience of a given airport under all disaster scenarios during the analysis year. Average robustness Average fitness Average speed and average toughness The formula is:

[0073] Average resistance

[0074] Average robustness

[0075] Average fitness

[0076] Average speed

[0077] Average toughness

[0078] The fourth step involves performing the steps one and two on all airports within the analyzed area to obtain the individual airport conditions under all disaster scenarios within the analyzed year.

[0079] like Figure 3 The example shown illustrates this; the first column of the table contains the three-letter code names of the airports, and columns 2 through 6 contain the airports affected by all types of disasters in the United States throughout 2019. The calculation results.

[0080] s2, calculate the influencing factors and The correlation coefficient between them is used to determine the impact of all disaster scenarios on affected airports within the analysis area during the analysis year. The significant influencing factors include meteorological data and airport characteristic data, and the specific process is as follows:

[0081] Step 201: Collect airport characteristic factor data for each airport within the analysis area of ​​the analysis year, as well as meteorological factor data during the n disaster occurrences in step s1. Calculate the arithmetic mean of all the same meteorological factor data within the n disaster time periods to obtain the mean value of the meteorological factor data for each airport. For example, if an airport experienced three disasters (A, B, and C) during the analysis year, with precipitation amounts of 20mm, 30mm, and 10mm during each disaster, the average precipitation amount for each disaster would be (20+30+10) / 3 = 20mm.

[0082] The airport characteristic factor data includes the longitude, latitude, airport size, altitude, type of navigation aids, runway length, runway width, pavement material, radio type and radio frequency of each airport within the analysis area during the analysis year;

[0083] Meteorological data during the nth disaster occurrence process includes wind speed, visibility, temperature, dew point, air pressure, precipitation, and snow depth data during the nth disaster occurrence process in step s1. The specific types of factors used can be adjusted according to the actual situation of the airport. For example, if there is no snowfall during a summer disaster, the snow depth factor will not be analyzed. That is, the factors that have changed significantly under the disaster compared to the situation without a disaster will be used as the factors for analysis.

[0084] Step 202: The average resistance, average robustness, average adaptability, average speed, and average resilience of each airport within the analysis area are used as the first set of data. The airport characteristic factor data and meteorological factor data from Step 201 are used as the second set of data. The first and second sets of data are then imported into PyCharm software. Using the airport name as the keyword, the merge method in Python is used to merge the first and second sets of data. The merged data file is then exported to SPSS software. The data for each airport in the file is then analyzed separately. Using airport characteristic factor data and meteorological factor data from the data file as the dependent variable, a multiple stepwise linear regression analysis was conducted to obtain the correlation coefficients of the influencing factors on the average resistance, average robustness, average adaptability, average speed and average resilience of each airport in the analysis area under the influence of disasters throughout the year. The factors retained in the multiple stepwise linear regression were taken as the significant influencing factors on the average resistance, average robustness, average adaptability, average speed and average resilience of each airport in the analysis area under the influence of disasters throughout the year.

[0085] like Figure 4The example shown illustrates that the factors without parentheses in the results are those retained after multiple stepwise linear regression. Taking this result as an example, column 6 of the table shows that the significant influencing factors on the average resilience of affected airports under all disasters in the United States in 2019 are radio type, airport latitude, and snow depth.

[0086] s3. Through calculation, determine the significant influencing factors on the resistance, robustness, adaptability, speed, and resilience of each affected airport within a selected analysis area under a certain disaster scenario. The specific steps are as follows:

[0087] Step 301: Following the methods in steps 101-103, calculate the system performance change curves of affected airports in the analysis area during the disaster. Following the methods in steps 104 and 105, calculate the resistance R1, adaptability R2, robustness R3, speed R4, and resilience R of each affected airport in the analysis area under a selected disaster.

[0088] like Figure 5 The example shown illustrates this; the first column of the table contains the three-letter code names of some airports affected by the snowstorm disaster in the United States in January 2019, and the second to sixth columns contain the calculation results of R1, R2, R3, R4, and R for the corresponding airports under the snowstorm disaster in the United States in January 2019.

[0089] Step 302: According to the types of influencing factors in Step 201, collect meteorological factor data and airport characteristic factor data of each airport affected by the disaster. Among the meteorological factors, the values ​​are taken as the values ​​at the worst time during the disaster, such as the maximum value for precipitation and the minimum value for visibility.

[0090] Step 303: Take the R1, R2, R3, R4, and R values ​​of all affected airports in the analysis area under a certain disaster as the first set of data, and take the meteorological factor data and airport characteristic factor data collected in step 302 as the second set of data. Calculate the correlation coefficients of the influencing factors on the R1, R2, R3, R4, and R values ​​of all affected airports using the same method as in step 202. Then, take the factors retained in the multiple stepwise linear regression as the significant influencing factors on the R1, R2, R3, R4, and R values ​​of all affected airports in the analysis area under the influence of a certain disaster.

[0091] like Figure 6 As shown, the factors without parentheses in the results are those retained after multiple stepwise linear regression. Taking this result as an example, column 6 of the table shows that the significant influencing factors on the resilience of US airports under the January 2019 blizzard disaster were radio type, pavement material, and precipitation.

[0092] s4. Determine the sensitive influencing factors of resistance, robustness, adaptability, speed, and resilience of affected airports within the analysis area under a specific disaster through calculation. The specific steps are as follows:

[0093] The significant influencing factors of resistance R1, robustness R2, adaptability R3, speed R4, and resilience R of affected airports in the analysis area under a certain disaster occurrence, obtained in step s3, are compared with the average resistance of each airport in the analysis area under the annual disaster impact, obtained in step s2. Average robustness Average fitness Average speed and average toughness The significant influencing factors are compared separately. Factors included in the significant influencing factors obtained in step s3 but not in the significant influencing factors obtained in step s2 are considered as sensitive influencing factors on the resistance, robustness, adaptability, speed and resilience of affected airports in the analysis area under the influence of a certain disaster. This is to improve airport infrastructure, adjust airport structure and layout, and identify effective improvement measures to enhance airport resistance, robustness, adaptability, speed and resilience under disasters. In this way, the airport's ability to resist disasters and its ability to recover quickly after disasters can be improved, providing effective decision-making references for airport pre-disaster prevention and rapid recovery of operations after disasters.

[0094] by Figure 4 and Figure 6 The examples illustrate that, under the blizzard disaster in January 2019, the sensitive factors for the resilience of US airports were navigation facility type and precipitation, the sensitive factors for robustness were runway length and wind direction, the sensitive factors for adaptability were runway width and precipitation, the sensitive factors for speed were precipitation, and the sensitive factors for toughness were pavement material and precipitation.

Claims

1. A method for determining airport resilience sensitivity factors under meteorological disasters, characterized in that... Includes the following steps: s1. Calculate the average resistance, average robustness, average adaptability, average speed, and average resilience of all airports within the analysis area under the influence of disasters throughout the year. The calculation process involves the following steps. The first step, for a specific airport within the analysis area, is to collect information on all flights taking off and landing at that airport during the analysis year. This information includes planned and actual departure and arrival times, whether flights were delayed, and whether flights were canceled. Assuming the airport experienced n disasters during the analysis year, each disaster is considered a separate analysis period. The airport's resilience, adaptability, robustness, speed, and tenacity under a specific disaster period are calculated as follows: Step 101: Starting from the start time of a certain disaster, at regular intervals, count the total number of flights scheduled to take off and land at the airport and the total number of flights actually taking off and landing at the airport at different times. The total number of flights actually taking off and landing at the airport at a certain time is the total number of flights scheduled to take off and land at the airport at that time minus the number of delayed flights and canceled flights. Step 102: For a given disaster event, calculate the airport system performance q(t) at different times t during the disaster event. The formula is: In the formula, k1(t) represents the total number of flights that actually take off and land at the airport at time t, and k2(t) represents the total number of flights that are scheduled to take off and land at the airport at time t. Step 103: Plot the system performance change curve of the airport during the disaster with time as the horizontal axis and q(t) value as the vertical axis; Step 104 divides the airport system performance under the influence of a certain disaster into five stages: Stage 1: (0-t1) time period, the airport operates normally; Stage 2: (t1-t2) time period, the airport system performance declines due to the impact of the meteorological disaster; Stage 3: (t2-t3) time period, the airport system performance no longer declines due to the airport's own control capabilities, and it maintains a low-performance operation; Stage 4: (t3-t4) time period, the airport takes emergency measures, and the airport system performance recovers. Phase 5: (t4-t5) is the operational phase after the airport system's performance has been restored. Step 105: Resistance R of an airport system to performance changes under the influence of a disaster. 1i Adaptive R 2i Robustness R 3i , speed R 4i and toughness R i The calculations are performed separately, and the formula is as follows: Resistance R 1i =(t 2i -t 1i ) / (t 4i -t 1i )·(q 1i -q 3i ) / q 1i ; Adaptive R 2i =(t 3i -t 2i ) / (t 4i -t 1i ); Robustness R 3i =q 3i / q 1i ; Rapid R 4i = (t 4i -t 3i ) / (t 4i -t 1i )·(q 2i -q 3i ) / q 1i ; Toughness R i =R 1i ·R 2i ·R 3i ·R 4i ; In the formula, the subscript i represents the i-th disaster, i = 1, 2, ..., n; t 1i q represents the end time of the second stage divided according to step 103 under the i-th disaster. 1i t represents the system performance of the airport during normal operation under the i-th disaster; 2i q represents the end time of the second stage divided according to step 103 under the i-th disaster. 3i This indicates the lowest system performance at the airport after being affected by the disaster; t 3i t represents the end time of the third stage under the i-th disaster, as defined in step 103; 4i q represents the time at the end of the fourth stage of the airport under the i-th disaster, as divided according to step 103. 2i This indicates that the airport has restored its system performance to its highest level after taking measures. q 1i q 3i q 2i The values ​​of q1, q3, and q2 corresponding to the airport system performance change curve obtained in step 103 are taken; t 1i t 2i t 3i t 4i The values ​​of time corresponding to t1, t2, t3, and t4 in the airport system performance change curve obtained in step 103 are taken. The second step is to repeat the first step to obtain the resistance, adaptability, robustness, speed and resilience of a certain airport under n disasters; The third step is to calculate the average resilience of a given airport under all disaster scenarios during the analysis year. Average robustness Average fitness Average speed and average toughness The formula is: Average resistance Average robustness Average fitness Average speed Average toughness The fourth step involves performing the steps one and two on all airports within the analyzed area to obtain the individual airport conditions under all disaster scenarios within the analyzed year. s2, calculate the influencing factors and The correlation coefficient between them is used to determine the impact of all disaster scenarios on affected airports within the analysis area during the analysis year. The significant influencing factors include meteorological data and airport characteristic data, and the specific process is as follows: Step 201: Collect airport characteristic factor data of each airport in the analysis area of ​​the analysis year and meteorological factor data of the n disaster occurrences in step s1, and calculate the arithmetic mean of all the same meteorological factor data in the n disaster time periods to obtain the mean of meteorological factor data as the value of meteorological factor data for each airport. The airport characteristic factor data includes the longitude, latitude, airport size, altitude, type of navigation aids, runway length, runway width, pavement material, radio type and radio frequency of each airport within the analysis area during the analysis year; Meteorological data during the n disaster events include wind speed, visibility, temperature, dew point, air pressure, precipitation, and snow depth data during the n disaster events in step s1. Step 202: The average resistance, average robustness, average adaptability, average speed, and average resilience of each airport within the analysis area are used as the first set of data. The airport characteristic factor data and meteorological factor data from Step 201 are used as the second set of data. The first and second sets of data are then imported into PyCharm software. Using the airport name as the keyword, the merge method in Python is used to merge the first and second sets of data. The merged data file is then exported to SPSS software. The data for each airport in the file is then analyzed separately. Using airport characteristic factor data and meteorological factor data from the data file as the dependent variable, a multiple stepwise linear regression analysis was conducted to obtain the correlation coefficients of the influencing factors on the average resistance, average robustness, average adaptability, average speed and average resilience of each airport in the analysis area under the influence of disasters throughout the year. The factors retained in the multiple stepwise linear regression were taken as the significant influencing factors on the average resistance, average robustness, average adaptability, average speed and average resilience of each airport in the analysis area under the influence of disasters throughout the year. s3. Through calculation, determine the significant influencing factors on the resistance, robustness, adaptability, speed, and resilience of each affected airport within a selected analysis area under a certain disaster scenario. The specific steps are as follows: Step 301: Following the methods in steps 101-103, calculate the system performance change curves of affected airports in the analysis area during the disaster. Following the methods in steps 104 and 105, calculate the resistance R1, adaptability R2, robustness R3, speed R4, and resilience R of each affected airport in the analysis area under a selected disaster. Step 302: According to the types of influencing factors in Step 201, collect meteorological factor data and airport characteristic factor data of each airport affected by the disaster, among which the meteorological factor is the value at the worst time during the disaster. Step 303: Take the R1, R2, R3, R4, and R values ​​of all affected airports in the analysis area under a certain disaster as the first set of data, and take the meteorological factor data and airport characteristic factor data collected in step 302 as the second set of data. Calculate the correlation coefficients of the influencing factors on the R1, R2, R3, R4, and R values ​​of all affected airports using the same method as in step 202. Then, take the factors retained in the multiple stepwise linear regression as the significant influencing factors on the R1, R2, R3, R4, and R values ​​of all affected airports in the analysis area under the influence of a certain disaster. s4. The sensitive influencing factors of the resistance, robustness, adaptability, speed, and resilience of affected airports within the analysis area under a certain disaster are determined through calculation. The specific steps are as follows: The significant influencing factors of resistance R1, robustness R2, adaptability R3, speed R4, and resilience R of affected airports in the analysis area under a certain disaster occurrence, obtained in step s3, are compared with the average resistance of each airport in the analysis area under the annual disaster impact, obtained in step s2. Average robustness Average fitness Average speed and average toughness The significant influencing factors are compared separately. The factors included in the significant influencing factors obtained in step s3 but not in the significant influencing factors obtained in step s2 are the sensitive influencing factors on the resistance, robustness, adaptability, speed and resilience of the affected airports in the analysis area under the influence of a certain disaster.

Images