CN113657813B - Airport network risk assessment method under severe weather - Google Patents
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Abstract
The invention discloses an airport network risk assessment method under severe weather, which comprises the steps of acquiring historical data of an airport network affected by severe weather, calculating the single airport vulnerability value of each airport under severe weather, and calculating the vulnerability assessment value of the whole airport network under severe weather according to the single airport vulnerability value; respectively calculating an operation efficiency drop value and a capacity drop value of the airport network in severe weather, and coupling the operation efficiency drop value and the capacity drop value to obtain a functional loss index evaluation value of the airport network; and coupling the vulnerability evaluation value and the functional loss index value of the airport network to obtain a macroscopic risk evaluation value of the airport network, comprehensively considering various risk inducements to evaluate the macroscopic risk of the airport network, and obtaining a numerical risk evaluation value which is visual and reliable.
Description
Technical Field
The invention relates to the technical field of civil aviation risk assessment, and particularly discloses an airport network risk assessment method under severe weather.
Background
With the rapid development of the field of civil aviation in China, the contradiction between limited aviation resources and rapidly-growing aviation transportation is increasingly intensified, and the problem of operation safety risk of an airport network as a carrier of the aviation transportation is increasingly highlighted along with the deepening of the contradiction. Due to the fact that the aviation network is large in scale, high in complexity and numerous in risk inducement, the fact that severe weather is the most main risk inducement causing the abnormality of civil aviation flights and accident symptoms in China is found through investigation on the current situation of civil aviation operation risk in China, the severe weather is used as a hazard with space characteristics, functional losses such as operation efficiency and capacity reduction can be caused to the whole network through damage to airport nodes in a certain range of the airport network, and further the airport network operation safety risk under high-density and high-rhythm is caused. Meanwhile, the influence frequency of the aviation network is very high due to the daily property and the high incidence of severe weather, and airports influenced by the severe weather exist in the aviation network almost every day, so that the normal operation of the aviation network is hindered.
At present, aiming at the problem of operation risk caused by severe weather to an airport network, on one hand, quantification and evaluation are carried out on single damage of the airport network caused by weather, and the method mainly comprises delayed damage evaluation and capacity damage evaluation. The delay hazard assessment mainly assesses the large-area delay of the airport network caused by single-machine field delay or delay spread due to severe weather, and mainly researches the occurrence mechanism and range of the large-area delay. And the capacity hazard assessment is mainly used for assessing the capacity reduction of airports and the capacity reduction of airspace sectors caused by weather. In the above research, when the airport network hazard caused by severe weather is evaluated, the selected evaluation factor is too single, and most of the evaluation factors are influences on the airport network caused by single delay or capacity reduction factor caused by the severe weather, a comprehensive airport network risk evaluation index system under the severe weather considering multiple risk inducements is not provided, and a risk evaluation method facing to the airport network layer under the severe weather is not formed, so that the guidance significance on the overall airport network risk management and control is insufficient.
On the other hand, in the existing method for performing aviation network risk assessment aiming at natural disasters such as severe weather, the risk assessment mainly focuses on severe loss caused by severe disasters or risks caused by damage to airport infrastructures, but the occurrence frequency and predictability of severe disasters have strong limitations, the risk control and guidance capability of aviation transportation is limited, the traditional research lacks operation risks caused by aviation system service function loss due to daily severe weather events, and the characteristic of influence of severe weather on an aviation network is not fully considered.
Disclosure of Invention
In view of the above, the present invention provides a method for assessing airport network risk in severe weather, so as to solve the problem of operational risk caused by a single assessment factor and lack of aviation system service function loss due to a daily severe weather event in the prior art.
In order to achieve the purpose, the invention provides the following technical scheme:
a network risk assessment method for an airport under severe weather specifically comprises the following steps:
s1: acquiring historical data of an airport network affected by severe weather, calculating the single airport vulnerability value of each airport under severe weather, and calculating the vulnerability evaluation value of the whole airport network under severe weather according to the single airport vulnerability value;
s2: respectively calculating an operation efficiency drop value and a capacity drop value of the airport network in severe weather, and coupling the operation efficiency drop value and the capacity drop value to obtain a functional loss index evaluation value of the airport network;
s3: and coupling the vulnerability assessment value and the functional loss index value of the airport network to obtain a macroscopic risk assessment value of the airport network.
Further, regarding each airport in the whole airport network as an independent airport node, the specific step of step S1 is:
s101: collecting historical data of the field network affected by severe weather within a preset time;
s102: analyzing the descending rate of the traffic capacity of the airport nodes under different severe weather types based on the historical data, and calculating the relative damage severity and single airport vulnerability values of each airport node when being affected by severe weather;
s103: selecting airport nodes directly affected by severe weather, analyzing to obtain an affected curve of the airport network under the severe weather, and calculating to obtain an estimated vulnerability value of the whole airport network under the severe weather according to the affected curve.
Further, the specific method for obtaining the damage relative severity and the single-airport vulnerability value in step S102 is as follows:
analyzing the traffic capacity reduction rate of all airport nodes and single airport reception under different severe weather types by adopting a single-factor variance analysis method, and calculating the damage relative severity of each airport node when being affected by severe weather according to the mean value of the corresponding traffic capacity reduction rate:
wherein: viThe relative severity of damage shown when the airport node i is affected by severe weather, i is 1,2, …, n, n is the number of airport nodes; z is a certain weather type suffered by the airport node i;
a set of severe weather types suffered by airport node i; mzNeutral value for z weather type;
analyzing a result value for the single-factor variance of the airport node i under the z weather type;
according to the relative severity of damage of each airport node when the airport node is affected by severe weather, combining the historical damage frequency of the corresponding airport node, calculating the single airport vulnerability value of each airport in the airport network under severe weather:
wherein: VUiThe airport node is a single airport vulnerability value of the airport node i in severe weather, wherein i is 1,2, …, n is the number of the airport nodes; fiThe frequency with which airport node i is affected by severe weather.
Further, the specific method for calculating and obtaining the vulnerability assessment value of the whole airport network in severe weather in step S103 is as follows:
based on the single airport vulnerability values of each airport calculated in the step S102, sequentially selecting airport nodes directly affected by severe weather according to the single airport vulnerability values, counting the total number of the airport nodes directly and indirectly affected to form a scatter point distribution diagram, and fitting the scatter points to obtain an affected curve of the airport network in severe weather;
according to the affected curve of the airport network in severe weather, a proportional straight line is taken as a neutral line, the proportion of the damaged airport nodes of the airport network in a certain day is given, and the vulnerability assessment value of the whole airport network in severe weather is calculated:
VAd＝ISP(rd)-IBM(rd)；
wherein: VAdA vulnerability assessment value for the airport network on day d; r isdThe proportion of damaged airports of the airport network on day d; I.C. ASPInfluenced music in bad weather for airport networkA wire; i isBMIs the neutral line of the affected curve.
Further, regarding each airport in the entire airport network as an independent airport node, the specific step of step S2 is:
s201: setting an initial connecting edge weight between two airport nodes with a route, calculating the initial transportation efficiency of an airport network, updating the connecting edge weight between the two corresponding airport nodes, recalculating the transportation efficiency of the airport network at the corresponding moment, and iterating by using the updated connecting edge weight until the maximum iteration times are reached or the transportation efficiency of the airport network is not reduced any more, stopping iteration and obtaining the operating efficiency reduction value of the airport network;
s202: introducing a virtual outside airport node, establishing bidirectional connection with all airport nodes in an airport network by using the virtual outside airport node, and calculating the initial capacity of the airport network and the change value of the total capacity of the airport network affected by severe weather so as to obtain the capacity reduction value of the airport network;
s203: and calculating a corresponding descending sequence based on the operation efficiency descending value and the capacity descending value of the airport network, and weighting the descending sequence to obtain an efficiency descending index weight and a capacity loss index weight of the airport network, thereby obtaining a final functional loss index value of the airport network.
Further, the specific method for obtaining the operating efficiency degradation value of the airport network in step S201 is as follows:
s2011: setting initial connecting edge weights of two airport nodes with routes in an airport network at an initial moment, calculating the initial transportation efficiency of the airport network when the airport network is not influenced by severe weather, and determining initial loads and load capacity thresholds of the airport nodes;
s2012: updating the first connecting edge weights of the two airport nodes, re-determining the path with the highest transport efficiency between the two airport nodes, calculating the corresponding transport efficiency between the two airport nodes at the moment, and determining the load of the corresponding airport node at the moment;
s2013: judging whether the load of the airport node in the step S2012 is greater than the load capacity threshold of the airport node, if so, repeatedly executing the step S2012 until the maximum iteration number is reached, then executing the step S2014, otherwise, continuously executing the step S2014;
s2014: taking the transportation efficiency calculated in the step S2012 as the final transportation efficiency of the airport network, and combining the initial transportation efficiency calculated in the step S2011 to obtain the operation efficiency drop value of the airport network when the airport network is affected by severe weather:
wherein: echargeThe reduction value of the operation efficiency of the airport network under the influence of severe weather is obtained; einitialThe initial transport efficiency of the airport network when the airport network is not affected by severe weather; eafterThe ultimate operating efficiency of the airport network when subjected to severe weather.
Further, considering the airport network as an N × N adjacency matrix, the transportation efficiency of the airport network is calculated by the following formula:
wherein: e is the transport efficiency of the airport network; n is the number of vertexes of the adjacency matrix; i and j are two airport nodes respectively, and G is a set of airport nodes with air routes; epsilonijThe path with the highest transportation efficiency from the airport node i to the airport node j is defined;
the first connecting edge weights of the two airport nodes are obtained by updating the following formula:
wherein: e.g. of the typeij(t +1) is the first connecting right between two airport nodes directly affected by severe weather from the airport node i to the airport node jA value; e.g. of the typeij(0) The initial connecting edge weight from the airport node i to the airport node j is obtained; l isi(t) the loading of airport node i at time t; ciIs the initial capacity threshold for airport node i.
Further, in step S202, an input-output model is used to calculate the capacity change before and after the airport network is affected by severe weather, and the specific method is as follows:
s2021: introducing a virtual external airport node into the airport network, and establishing bidirectional connection with all airport nodes in the airport network by using the virtual external airport node;
s2022: the virtual external airport nodes are the final consumers in the input-output model, and the total output flow of each airport node is calculated, so that the initial capacity of the airport network when the airport network is not affected by severe weather is obtained;
s2023: calculating a flow value of an airport node affected by severe weather, and calculating the capacity of an airport network under the influence of severe weather based on the flow value;
s2024: and calculating to obtain a capacity reduction value of the airport network under the influence of severe weather by combining the initial capacity of the airport network under the influence of severe weather and the capacity of the airport network after the influence of severe weather:
wherein: ccutThe capacity drop value of the airport network under the influence of severe weather is obtained; i, k are airport nodes, and n is the number of airport nodes in the airport network; xiThe total output flow of the airport node i;
the flow value of the airport node i under the influence of severe weather.
Further, the specific method for obtaining the efficiency reduction index weight and the capacity loss index weight of the airport network in step S203 is as follows:
respectively calculating daily operating efficiency descending values and daily capacity descending values when the airport network is influenced by severe weather in a certain preset time period in the past to form corresponding historical efficiency descending sequences and historical capacity descending sequences, weighting the historical efficiency descending sequences and the historical capacity descending sequences to obtain efficiency descending index weights and capacity loss index weights of the airport network, and coupling the efficiency descending index weights and the capacity loss index weights of the airport network to obtain a functional loss index evaluation value of the airport network:
wherein:
evaluating the functional loss index of the airport network on the d day;
for the operating efficiency degradation value, w, of the airport network on day deThe efficiency drop index weight of the airport network;
capacity drop value, w, for day d of airport networkcAnd the capacity reduction index weight of the airport network is obtained.
Further, in step S3, the macroscopic risk assessment value of the airport network is expressed as:
wherein: rdA macroscopic risk assessment value for the airport network on day d;
evaluating the functional loss index of the airport network on the d day; VAdAnd evaluating the vulnerability of the airport network on the d day.
The method comprehensively considers the cascade vulnerability of a single airport and all airports in the airport network under severe weather and the potential safety hazard caused by the risk of the daily money of the airport network caused by the severe weather, utilizes the reduction of the operating efficiency of the airport network and the capacity loss to measure the functional loss under the influence of the severe weather, synthesizes the direct influence and the indirect influence of the severe weather, focuses on the potential operating risk, and truly reflects the operating risk of the airport network caused by the severe weather; meanwhile, evolution of airport network numerical risk assessment values along with time can be formed based on historical data, and guidance information is provided for aviation risk management and control.
Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention. The objectives and other advantages of the invention may be realized and attained by the means of the instrumentalities and combinations particularly pointed out hereinafter.
Drawings
FIG. 1 is a flowchart of an airport network risk assessment method under severe weather according to the present invention.
Fig. 2 is a flowchart of step S1 in fig. 1.
Fig. 3 is a schematic diagram of an affected curve and a neutral line of the airport network of step S103 in fig. 2.
Fig. 4 is a schematic diagram of airport network cascade failure in step S2 in fig. 1.
Fig. 5 is a flowchart of step S2 in fig. 1.
Fig. 6 is a flowchart of step S201 in fig. 5.
Fig. 7 is a detailed flowchart of the step S201 of fig. 6 for calculating the operation efficiency degradation value of the airport network.
Fig. 8 is a flowchart of step S202 in fig. 5.
Detailed Description
The following is further detailed by way of specific embodiments:
examples
As shown in fig. 1, a flowchart of an airport network risk assessment method under severe weather according to the present invention includes the following steps:
s1: and calculating the vulnerability assessment value of the airport network.
The airport network uses an airport as a node of the network (marked as an airport or an airport node), and an air route between two airport nodes is used as an edge of the network. The vulnerability is the degree to which the subject (i.e., the airport network in this embodiment) is vulnerable to external risk inducement, and can be used as a measure of the probability of damage to the airport network in severe weather.
Specifically, historical data of the airport network affected by severe weather is obtained, the single airport vulnerability value of each airport in severe weather is calculated, and the vulnerability assessment value of the whole airport network in severe weather is calculated according to the single airport vulnerability value.
As shown in fig. 2, the specific steps of step S1 are:
s101: historical data of the airport network is collected.
According to the evaluation requirement, a period of time is set as a preset time (generally data of the past year) for calculating the vulnerability evaluation value of the airport network, and historical data of the airport network affected by severe weather in the preset time is collected, wherein the historical data comprises the overall historical data of the whole airport network and the historical data of the single airport, and specifically comprises the daily traffic capacity reduction rate under the influence of severe weather, the airline data of the airport network, the historical damage frequency of the airport, the proportion of damaged airports and the like.
S102: and calculating the damage severity of the single airport and the corresponding single airport vulnerability value.
Firstly, a single-factor variance analysis method is adopted to analyze the difference of the traffic capacity decline rate when all airports and a single airport are influenced by different types of severe weather. Calculating the relative severity V of damage of each airport when the airport is affected by severe weather according to the mean value of the traffic capacity reduction rate of all the airports and the single airport when the airport is affected by the severe weather, which is obtained by the analysis of the variance of the single factori：
Wherein: viThe relative severity of damage shown when the airport node i is affected by severe weather, i is 1,2, …, n, n is the number of airport nodes; z is a certain weather type suffered by the airport node i;
a set of severe weather types suffered by airport node i; mzNeutral value for z weather type;
and analyzing the result value for the one-factor variance of the airport node i under the z weather type.
Then, according to the relative severity of damage of each airport under the influence of severe weather, combining the historical damage frequency of the corresponding airport, calculating the single airport vulnerability value VU of each airport in the airport network under the severe weatheri：
Wherein: VUiThe airport node is a single airport vulnerability value of the airport node i in severe weather, wherein i is 1,2, …, n is the number of the airport nodes; fiThe frequency with which airport node i is affected by severe weather.
S103: and calculating the vulnerability assessment value of the whole airport network in severe weather.
And sequentially selecting airports directly affected by the severe weather according to the single airport vulnerability value of the single airport in the airport network under the severe weather, which is obtained by calculation in the step S102, wherein the higher the single airport vulnerability value is, the higher the probability that the corresponding airport is selected is. Arranging the proportion of airports directly affected by severe weather from 0% to 100% in an increasing mode, and sequentially carrying out the influence association degree experiment on the airports of each proportion section for a preset number of times; in this example, the number of experiments is 1000. According to the experimental results, the total number of airports affected directly and indirectly is counted to form a scatter distribution diagram.
As shown in fig. 3, the scatter distribution diagram is fitted by using a least square method to obtain an affected curve of the airport network in severe weather, and a 45-degree direct proportion straight line is taken as a neutral line.
Proportion r of damaged airports on day d for a given airport networkdAnd calculating to obtain an assessment value of the vulnerability of the whole airport network in severe weather:
VAd＝ISP(rd)-IBM(rd) (3)
wherein: VAdA vulnerability assessment value for the airport network on day d; r is a radical of hydrogendThe proportion of damaged airports for the airport network on day d; i isSPIs the affected curve of the airport network in severe weather; i isBMIs the neutral line of the affected curve.
S2: and calculating the functional loss index evaluation value of the airport network in severe weather.
Because severe weather is a daily high risk inducement, potential safety hazards caused by the daily potential risk of the airport network caused by the severe weather need to be considered during risk assessment, and the functional loss index of the airport network influenced by the severe weather is measured by utilizing the running efficiency reduction and the capacity loss of the airport network and is used as the measurement index of the severity of the damage risk of the airport network under the severe weather.
Specifically, an operation efficiency drop value and a capacity drop value of the airport network in severe weather are respectively calculated, and the operation efficiency drop value and the capacity drop value are coupled to obtain a functional loss index evaluation value of the airport network.
As shown in fig. 4-5, the specific steps of step S2 are:
s201: and calculating the operation efficiency degradation value of the airport network.
And evaluating the degree of reduction of the operation efficiency of the airport network under severe weather by using an improved cascade failure model. Specifically, an initial connecting edge weight value between two airport nodes with a route is set, the initial transportation efficiency of the airport network is calculated, the connecting edge weight value between the two corresponding airport nodes is updated, the transportation efficiency of the airport network at the corresponding moment is recalculated, iteration is carried out by the updated connecting edge weight value until the maximum iteration times is reached or the transportation efficiency of the airport network is not reduced any more, and the iteration is stopped to obtain the running efficiency reduction value of the airport network.
For ease of analysis, an NXN adjacency matrix { e } is usedijDenotes an airport network (where N is the number of vertices of the adjacency matrix, e)ijAnd setting the connecting edge weight value between the airport node i and the airport node j as the transmission efficiency of the corresponding route from the airport i to the airport j in the airport network in the embodiment). If an air route exists between the airport node i and the airport node j, the connecting edge weight e between the airport node i and the airport node jij(i.e., the transmission efficiency from airport i to airport j) is the interval (0, 1)]Any value in between, if no course exists, then eij＝0。
Specifically, as shown in fig. 6 to 7, the specific method for obtaining the operating efficiency degradation value of the airport network in step S201 is as follows:
s2011: an initial shipping efficiency for the airport network is calculated.
Because the airport network is not affected by severe weather at the starting moment, namely t is 0, the airport network keeps normal operation, and the initial connecting edge weight value between all two airports with routes of the airport network is set to be 1.
Calculating the initial transport efficiency E of an airport network at the present moment (i.e. when not subjected to adverse weather influences)initial：
Wherein: n is the number of vertexes of the adjacency matrix; i and j are two airport nodes respectively, and G is a set of airport nodes with air routes; epsilonijFor airport nodesi to airport node j.
In this embodiment, the route epsilon having the highest transportation efficiency from the airport node i to the airport node j is usedijIs defined as:
wherein: h is the path epsilon with the highest transport efficiency from the airport node i to the airport node jijThe airport node through which it passes; α is an adjustable parameter, and in the present embodiment, α is 1.2.
Determining initial load L of each airport node in the airport network based on the path with the highest transportation efficiencyi(0) The initial load Li(0) The number of paths through airport node i in the most efficient transportation path between all pairs of airport nodes (i.e., two airport nodes where an airline exists) in the airport network.
Calculating a load capacity threshold C of the airport network from the initial load of said airport networki. Load capacity threshold C of the airport networkiAt the initial moment, the load L of the corresponding airport node is a determined and unchangeable valuei(t) will vary over time, thus the threshold value of the load capacity C of the airport networkiExpressed as:
Ci＝β·Li(0) (6)
wherein: i is an airport node, i is 1,2, and n is the number of airport nodes (or airports) in the airport network; beta is tolerance margin of the airport network, and in the embodiment, beta is more than or equal to 1; l isi(0) Is the initial load for airport node i (or airport i).
S2012: and updating the side connection weight and calculating the transportation efficiency at the corresponding moment.
When part of airport nodes in the airport network are affected by severe weather, the descending of the traffic capacity of the airport nodes can cause cascade failure of the airport network, the number of flights which can take off and land in the airport network in unit time is reduced, and the overstocked and delayed flights are caused, so that the transportation time spent by one flight is greatly increased, and the transportation efficiency of a flight line connected with the airport affected by severe weather is reduced.
Traffic capacity reduction value of airport node affected by severe weather
(
The communication capacity reduction value of the airport node i on the day d) is used for updating the connecting edge weight of the airport node directly influenced by severe weather, namely the connecting edge weight is
Due to the fact that the partial connecting edge weight values are changed, the path with the highest transportation efficiency between the airport node pairs is correspondingly changed, the load of the airport node is changed accordingly, the connecting edge weight values between the airport node pairs need to be updated, and the updated connecting edge weight values are recorded as the first connecting edge weight values. The first connection edge weight value can be updated by the following formula (7):
wherein: e.g. of the typeij(t +1) is a first connecting edge weight between two airport nodes from the airport node i to the airport node j which are directly influenced by severe weather; e.g. of the typeij(0) The initial connecting edge weight from the airport node i to the airport node j is obtained; l isi(t) the load of the airport node i at time t (i.e. the load of the airport node i under the influence of severe weather); ciIs the initial capacity threshold for airport node i.
Calculating the transport efficiency E of the airport network at the current moment under the influence of severe weather by utilizing a formula (4) based on the updated first connecting edge weightafter。
S2013: and judging whether the load of the airport node is larger than the load capacity threshold value of the airport node.
It is determined whether the load of the airport node is greater than the load capacity threshold of the airport node in step S2012.
If the load of the airport node is greater than the load capacity threshold of the airport node, step S2012 is repeatedly executed, the edge connection weight of the airport node in a new round is updated, and the transportation efficiency of the airport network with cascade failure in the new round is calculated, until the maximum iteration number is reached or the transportation efficiency of the airport network is reduced to a certain value and then remains unchanged, the iteration is stopped, and step S2014 is then executed.
If the load of the airport node is less than or equal to the threshold of the load capacity of the airport node, step S2014 is directly performed.
S2014: and calculating the operating efficiency degradation value of the airport network when the airport network is influenced by severe weather.
Taking the transportation efficiency calculated in the step S2012 as the final transportation efficiency E of the airport networkafterAnd combining the initial transportation efficiency E calculated in the step S2011initialCalculating the operating efficiency degradation value E of the airport network when it is affected by bad weathercharge：
Wherein: echargeThe method is characterized in that the method is an operation efficiency reduction value when the airport network is influenced by severe weather; einitialThe initial transport efficiency of the airport network when the airport network is not affected by severe weather; eafterThe ultimate operating efficiency of the airport network when subjected to severe weather.
S202: and calculating the capacity reduction value of the airport network.
And calculating the capacity change of the airport network before and after being influenced by severe weather by using an input-output model. Specifically, a virtual outside airport node is introduced, bidirectional connection is established between the virtual outside airport node and all airport nodes in the airport network, the initial capacity of the airport network and the change value of the total capacity of the airport network affected by severe weather are calculated, and the capacity reduction value of the airport network is obtained.
As shown in fig. 8, the specific steps of step S202 are:
s2021: and introducing virtual outside airport nodes and establishing corresponding bidirectional airport connection.
For a closed flow network such as an airport network, a standard input-output model cannot be directly applied, and therefore, in this embodiment, a virtual external airport node is introduced into the airport network, and the input-output model is applied to the closed flow network by establishing bidirectional connection between the virtual external airport node and all airport nodes in the airport network. In this embodiment, the setting of the virtual outside airport node follows the following rule:
wherein:
for the actual outflow of airport node i, j ∈ (1, 2.. n) and i ≠ j;
setting the inflow of the virtual external airport node to an airport node i in the airport network as the actual outflow of the airport node i
The sum of (a);
actual inflow for airport node i;
setting the outflow of the virtual external airport node to an airport node i in the airport network as the actual inflow of the airport node i
The sum of (a) and (b).
S2022: initial capacity of a computer airport network when not subjected to severe weather.
Suppose that n +1 airport nodes coexist in the airport network, wherein the first n airport nodes are airport nodes inside the airport network, and the n +1 airport nodes are introduced virtual outside airport nodes. According to an input-output model, using
Representing the flow output by the airport node i to the airport node j, the total output flow X of the airport node iiExpressed as:
input-output coefficient among the airport nodes
Expressed as:
the input-output coefficient represents a flow value which is required to be obtained from an airport node i in the airport network for an airport node j to output a unit of total flow. If the virtual external airport node is the final consumer in the input-output model, the total output flow X of the airport node iiCan be further expressed as:
and then the output flow of the first n airport nodes can be obtained:
X[-(n+1)]＝B[-(n+1)]X[-(n+1)]+Y[-(n+1)] (13)
wherein: x[-(n+1)]An X vector of the input-output model after the n +1 th airport node (namely the virtual outside airport node) is deleted; y is[-(n+1)]The method comprises the steps of deleting an n +1 th airport node (namely a virtual outside airport node) to obtain a Y vector of an input-output model; b is[-(n+1)]The input-output matrix of the input-output model after the n +1 th airport node (i.e. the virtual outside airport node) is deleted is an n × n square matrix.
The above equation is simplified by (13):
X[-(n+1)]＝(1-B[-(n+1)])-1Y[-(n+1)] (14)
at this time, the initial capacity C of the airport network when not affected by severe weather is obtainedinitialComprises the following steps:
s2023: capacity of a computer airport network when exposed to severe weather.
The traffic capacity of part of airport nodes is reduced when the airport network is affected by severe weather. To characterize the severe weather effects that an airport network is subjected to, improved virtual elimination is used. When the traffic capacity of a certain airport node k on the d day is decreased by the rate
In time, the non-zero values in the k rows and k columns of the response in the initial input-output matrix are multiplied by the throughput reduction rate
Multiplying the kth element in the initial Y vector by the traffic capacity reduction rate
Obtaining a changed input-output matrix B(-n-k)And the changed Y vector Y(-n-k)Substituting the calculation result in the formula (14) to obtain the weather influenceX vector of change:
Xcut＝(1-B(-n-k))-1Y(-n-k) (16)
and capacity C of airport network when it is affected by bad weatherafter：
S2024: and calculating the capacity degradation value of the airport network under the influence of severe weather.
And calculating to obtain a capacity reduction value of the airport network under the influence of severe weather by combining the initial capacity of the airport network under the influence of severe weather and the capacity of the airport network after the influence of severe weather:
wherein: ccutThe capacity of the airport network is reduced under the influence of severe weather; i, k are airport nodes, and n is the number of airport nodes in the airport network; xiThe total output flow of the airport node i;
and the X vector is changed when the airport node i is influenced by severe weather.
S203: and calculating a functional loss index value of the airport network based on the operation efficiency drop value and the capacity drop value.
Specifically, the daily operating efficiency drop value and the daily capacity drop value of the airport network affected by severe weather in a certain preset time period in the past are respectively calculated to form a corresponding historical efficiency drop sequence and a corresponding historical capacity drop sequence, wherein the sequence length is the total days of the past year. And weighting the historical efficiency decline sequence and the historical capacity decline sequence by adopting a CRITIC model to obtain an efficiency decline index weight and a capacity loss index weight of the airport network. Coupling the efficiency reduction index weight and the capacity loss index weight of the airport network by using a linear weighting method to obtain a functional loss index evaluation value of the airport network:
wherein:
evaluating the functional loss index of the airport network on the d day;
for the operating efficiency degradation value, w, of the airport network on day deThe efficiency drop index weight of the airport network;
for the capacity drop value, w, of the airport network on day dcAnd the capacity reduction index weight of the airport network is obtained.
S3: and calculating a macroscopic risk assessment value of the airport network.
Multiplying the vulnerability assessment value of the airport network on the d day by the functional loss index assessment value, and coupling to obtain a macroscopic risk assessment value of the airport network: the macroscopic risk assessment value for the airport network is expressed as:
wherein: rdA macroscopic risk assessment value for the airport network on day d;
evaluating the functional loss index of the airport network on the d day; VAdAnd evaluating the vulnerability of the airport network on the d day.
Therefore, when a certain proportion of airport nodes of a given airport network are affected by severe weather, the potential safety risk of the whole airport network is evaluated, the numerical risk evaluation value of the airport network on the day d is obtained, meanwhile, the evolution of the numerical risk evaluation value of the airport network along with time can be formed on the basis of historical data, and guidance information is provided for aviation risk management and control.
In the airport network risk assessment method under severe weather of this embodiment, when the method is specifically implemented, there is no strict precedence order between step S1 and step S2 and between step S201 and step S202, in other embodiments, step S1 and step S3 may be implemented in reverse order or simultaneously, and step S201 and step S202 may also be implemented in reverse order or simultaneously.
Compared with the prior art, the airport network risk assessment method under severe weather provided by the invention has the following advantages:
(1) the vulnerability of the airport network in severe weather is fully considered, when different networks are harmed to the same degree, the damage to the network with higher vulnerability is more serious than the damage to the network with lower vulnerability, and therefore when the risk evaluation of the airport network in severe weather is carried out, the risk can be more scientifically quantified by considering the vulnerability. Meanwhile, when the whole vulnerability of the airport network is calculated, the simple superposition of the single-airport vulnerability is not carried out, but the vulnerability curve of the airport network is obtained according to a plurality of experiments, and the absolute vulnerability of the network is obtained by combining a neutral curve, so that the method is a novel method for more objectively evaluating the vulnerability of the airport network.
(2) When the airport network risk indexes are selected in severe weather, the influence characteristics and action mechanism of the severe weather on the airport network are fully considered, and the risk assessment is based on the risk assessment of the influence mechanism of the severe weather on the airport network. Through a complex network cascade failure principle and an input-output theory, the degree of overall network functional loss is finally caused by evaluating severe weather through directly influencing airports in the range and further influencing more airports in the network, the direct influence and the indirect influence of the severe weather are considered, potential operation risks are concerned, and the operation risks of the severe weather on the airport network are reflected more truly.
(3) When the airport network capacity decline risk assessment is carried out, an input-output model is introduced, lightweight network overall capacity assessment is carried out, theoretical experience is contributed to subsequent aviation network capacity assessment and risk assessment method research based on capacity decline indexes, and the method has important significance in theory and practice.
The foregoing is merely an example of the present invention and common general knowledge of known specific structures and features of the embodiments is not described herein in any greater detail. It should be noted that, for those skilled in the art, without departing from the structure of the present invention, several variations and modifications can be made, which should also be regarded as the scope of the present invention, and these do not affect the effect of the implementation of the present invention and the practicability of the present invention.
Claims (6)
1. An airport network risk assessment method under severe weather is characterized by comprising the following steps:
s1: acquiring historical data of an airport network affected by severe weather, calculating the single airport vulnerability value of each airport under severe weather, and calculating the vulnerability evaluation value of the whole airport network under severe weather according to the single airport vulnerability value;
s2: respectively calculating an operation efficiency drop value and a capacity drop value of the airport network in severe weather, and coupling the operation efficiency drop value and the capacity drop value to obtain a functional loss index evaluation value of the airport network;
regarding each airport in the whole airport network as an independent airport node, the specific step of step S2 is:
s201: setting an initial connecting edge weight between two airport nodes with a route, calculating the initial transportation efficiency of the airport network, updating the connecting edge weight between the two corresponding airport nodes, recalculating the transportation efficiency of the airport network at the corresponding moment, and iterating by using the updated connecting edge weight until the maximum iteration times are reached or the transportation efficiency of the airport network is not reduced any more, stopping iteration and obtaining the running efficiency reduction value of the airport network;
the specific method for obtaining the operating efficiency degradation value of the airport network in step S201 is as follows:
s2011: setting initial connecting edge weights of two airport nodes with routes in an airport network at an initial moment, calculating the initial transportation efficiency of the airport network when the airport network is not influenced by severe weather, and determining initial loads and load capacity thresholds of the airport nodes;
s2012: updating the first connecting edge weights of the two airport nodes, re-determining the path with the highest transport efficiency between the two airport nodes, calculating the corresponding transport efficiency between the two airport nodes at the moment, and determining the load of the corresponding airport node at the moment;
s2013: judging whether the load of the airport node in the step S2012 is greater than the load capacity threshold of the airport node, if so, repeatedly executing the step S2012 until the maximum iteration number is reached, then executing the step S2014, otherwise, continuously executing the step S2014;
s2014: taking the transportation efficiency calculated in the step S2012 as the final transportation efficiency of the airport network, and combining the initial transportation efficiency calculated in the step S2011 to obtain the operation efficiency drop value of the airport network when the airport network is affected by severe weather:
wherein: echargeThe method is characterized in that the method is an operation efficiency reduction value when the airport network is influenced by severe weather; einitialThe initial transport efficiency of the airport network when the airport network is not affected by severe weather; eafterThe final operation efficiency of the airport network under the influence of severe weather;
s202: introducing a virtual outside airport node, establishing bidirectional connection with all airport nodes in an airport network by using the virtual outside airport node, and calculating the initial capacity of the airport network and the change value of the total capacity of the airport network affected by severe weather so as to obtain the capacity reduction value of the airport network;
in step S202, an input-output model is used to calculate the capacity change before and after the airport network is affected by severe weather, and the specific method is as follows:
s2021: introducing a virtual external airport node into the airport network, and establishing bidirectional connection with all airport nodes in the airport network by using the virtual external airport node;
s2022: the virtual external airport nodes are the final consumers in the input-output model, and the total output flow of each airport node is calculated, so that the initial capacity of the airport network when the airport network is not affected by severe weather is obtained;
s2023: calculating a flow value of an airport node affected by severe weather, and calculating the capacity of an airport network under the influence of severe weather based on the flow value;
s2024: and calculating to obtain a capacity reduction value of the airport network under the influence of severe weather by combining the initial capacity of the airport network under the influence of severe weather and the capacity of the airport network after the influence of severe weather:
wherein: ccutThe capacity of the airport network is reduced under the influence of severe weather; i, k are airport nodes, and n is the number of the airport nodes in the airport network; xiThe total output flow of the airport node i;
the flow value is the flow value of the airport node i under the influence of severe weather;
s203: calculating a corresponding descending sequence based on the running efficiency descending value and the capacity descending value of the airport network, and weighting the descending sequence to obtain an efficiency descending index weight and a capacity loss index weight of the airport network, so as to obtain a final functional loss index value of the airport network;
the specific method for obtaining the efficiency reduction index weight and the capacity loss index weight of the airport network in step S203 is as follows:
respectively calculating daily operating efficiency descending value and daily capacity descending value when the airport network is influenced by severe weather in a certain preset time period in the past to form a corresponding historical efficiency descending sequence and a corresponding historical capacity descending sequence, weighting the historical efficiency descending sequence and the historical capacity descending sequence to obtain an efficiency descending index weight and a capacity loss index weight of the airport network, and coupling the efficiency descending index weight and the capacity loss index weight of the airport network to obtain a functional loss index evaluation value of the airport network:
wherein:
evaluating the functional loss index of the airport network on the d day;
for the operating efficiency degradation value, w, of the airport network on day deA weight value is an efficiency reduction index of the airport network;
capacity drop value, w, for day d of airport networkcA capacity reduction index weight value of the airport network;
s3: and coupling the vulnerability assessment value and the functional loss index value of the airport network to obtain a macroscopic risk assessment value of the airport network.
2. The method for assessing risk of an airport network under severe weather according to claim 1, wherein each airport in the whole airport network is regarded as an independent airport node, and the specific steps of step S1 are:
s101: collecting historical data of the field network influenced by severe weather within a preset time;
s102: analyzing the descending rate of the traffic capacity of the airport nodes under different severe weather types based on the historical data, and calculating the relative damage severity and single airport vulnerability values of each airport node when being affected by severe weather;
s103: selecting airport nodes directly affected by severe weather, analyzing to obtain an affected curve of the airport network under the severe weather, and calculating to obtain an estimated vulnerability value of the whole airport network under the severe weather according to the affected curve.
3. The method for assessing airport network risk under severe weather of claim 2, wherein the specific method for obtaining the damage relative severity and the single airport vulnerability value in the step S102 is as follows:
analyzing the traffic capacity reduction rate of all airport nodes and a single airport node under different severe weather types by adopting a single-factor variance analysis method, and calculating the damage relative severity of each airport node when being affected by severe weather according to the mean value of the corresponding traffic capacity reduction rate:
wherein: viThe relative severity of damage shown when the airport node i is affected by severe weather, i is 1,2, …, n, n is the number of airport nodes; z is a certain weather type suffered by the airport node i;
a set of severe weather types suffered by airport node i; m is a group ofzNeutral value for z weather type;
analyzing a result value for the single-factor variance of the airport node i under the z weather type;
according to the relative severity of damage of each airport node when the airport node is affected by severe weather, and by combining the historical damage frequency of the corresponding airport node, calculating the single airport vulnerability value of each airport node in the airport network under severe weather:
wherein: VUiThe method is characterized in that the number of airport nodes is a single airport vulnerability value of the airport node i in severe weather, wherein i is 1,2, …, n is the number of the airport nodes; fiThe frequency with which airport node i is affected by severe weather.
4. The method for assessing airport network risks in severe weather according to claim 2, wherein the specific method for calculating the vulnerability assessment value of the whole airport network in severe weather in step S103 is as follows:
based on the single airport vulnerability values of each airport node calculated in the step S102, sequentially selecting airport nodes directly affected by severe weather according to the single airport vulnerability values, counting the total number of the airport nodes directly and indirectly affected to form a scatter point distribution diagram, and fitting the scatter points to obtain an affected curve of the airport network in severe weather;
according to the affected curve of the airport network in severe weather, a proportional straight line is taken as a neutral line, the proportion of the damaged airport nodes of the airport network in a certain day is given, and the vulnerability assessment value of the whole airport network in severe weather is calculated:
VAd＝ISP(rd)-IBM(rd)；
wherein: VAdA vulnerability assessment value for the airport network on day d; r isdThe proportion of damaged airports of the airport network on day d; i isSPIs the affected curve of the airport network in severe weather; i isBMIs the neutral line of the affected curve.
5. The method of claim 1, wherein the airport network is considered as an N x N adjacency matrix, and the transportation efficiency of the airport network is calculated by the following formula:
wherein: e is the transport efficiency of the airport network; n is the number of vertexes of the adjacency matrix; i and j are two airport nodes respectively, and G is a set of airport nodes with air routes; epsilonijThe path with the highest transportation efficiency from the airport node i to the airport node j is defined;
the first connecting edge weights of the two airport nodes are obtained by updating the following formula:
wherein: e.g. of a cylinderij(t +1) is a first connecting edge weight between two airport nodes directly affected by severe weather from the airport node i to the airport node j; e.g. of the typeij(0) The initial connecting edge weight from the airport node i to the airport node j is obtained; l isi(t) the loading of airport node i at time t; ciIs the initial capacity threshold for airport node i.
6. The method for assessing risk of an airport network under severe weather as claimed in claim 1, wherein in said step S3, the macroscopic risk assessment value of said airport network is expressed as:
wherein: r isdA macroscopic risk assessment value for the airport network on day d;
evaluating the functional loss index of the airport network on the d day; VAdAnd evaluating the vulnerability of the airport network on the d day.
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