WO2023000666A1 - 一种基于不安全事件的航班运行风险网络构建与控制方法 - Google Patents

一种基于不安全事件的航班运行风险网络构建与控制方法 Download PDF

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WO2023000666A1
WO2023000666A1 PCT/CN2022/077287 CN2022077287W WO2023000666A1 WO 2023000666 A1 WO2023000666 A1 WO 2023000666A1 CN 2022077287 W CN2022077287 W CN 2022077287W WO 2023000666 A1 WO2023000666 A1 WO 2023000666A1
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risk
unsafe
nodes
network
flight operation
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王岩韬
赵昕颐
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中国民航大学
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    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0635—Risk analysis of enterprise or organisation activities
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/40—Business processes related to the transportation industry

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  • the invention relates to an analysis method for flight operation risk generation, propagation and control, in particular to a flight operation risk network construction and control method based on unsafe events.
  • the classic SIR model believes that the probability of disease transmission between people is the same, that is, the risk transmission probability ⁇ between all nodes is a certain value, but in the actual flight operation risk network, the risk is not at the same probability in the node In recent years, there have been millions of flights every year. Compared with the number of statistical unsafe incidents, the probability of occurrence is very small, and the probability of corresponding node risk occurrence and inter-node transmission should be relatively small. , and it is more reasonable to distinguish the probability of risk propagation between nodes according to the frequency of different events.
  • this method proposes a method for constructing and controlling flight operation risk network based on unsafe events.
  • This method first constructs a flight operation risk network based on the existing network construction technology, based on civil aviation unsafe event data and theoretical knowledge; based on the SIR virus propagation model to improve the risk propagation and control process, according to the occurrence frequency of different unsafe events, different node Differentiate the risk propagation probability among them, and simulate the risk propagation process in this network. Control the spread of risks in the network by controlling nodes and network attacks, and reduce the probability of unsafe incidents by establishing defenses for important nodes, thereby proposing a reliable and effective flight operation risk control scheme.
  • the construction of flight operation risk network helps to understand the relationship between flight risk factors, risks and accidents, and the use of SIR virus transmission model can simulate the flight risk transmission and control process.
  • the technical solution adopted by the present invention is: a method for building and controlling a flight operation risk network based on an unsafe event, characterized in that the method has the following steps:
  • Step 1 Construction of flight operation risk network
  • the construction of flight operation risk network is divided into the following four sub-steps:
  • the types of civil aviation unsafe incidents and the causes of unsafe incidents are counted, and various unsafe incidents are set as unsafe incident nodes in the network, and the causes of unsafe incidents are set as risk factor nodes.
  • risk factor nodes and risk factor nodes, risk factor nodes and unsafe event nodes, unsafe event nodes and unsafe event nodes are all connected by directed edges, and the propagation direction is from cause to result, indicating the mutual relationship between nodes. relationship; based on the accident causation theory, connect each node according to the unsafe event record, when the unsafe event record shows that there is a functional relationship between the nodes, the directed edge connection is performed, otherwise no connection is made; This establishes the link relationship between nodes, first constructs a sub-network of each type of unsafe event, and finally forms several sub-networks into an overall flight operation risk network.
  • Step 2 Determine the risk propagation probability of the flight operation risk network
  • the nodes are divided into three states, namely, the susceptible state is recorded as S, the infected state is recorded as I and the recovery state is recorded as R; the node in the susceptible state S will be Denoted as ⁇ with a certain risk propagation probability, it is transformed into the infected state I, and at the same time, the node in the infected state I has a certain defense ability, and it is transformed into the restored state R with a certain recovery probability denoted as ⁇ ; on the basis of the SIR model
  • the risk propagation probability ⁇ is distinguished above, and the steps of the distinction are as follows:
  • the frequency of risk communication of various types of unsafe incidents is divided into five grades: high frequency of risk communication, high frequency of risk communication, medium frequency of risk communication, risk communication
  • the frequency of occurrence is low, and the frequency of risk transmission is extremely low.
  • the risk propagation probability takes the maximum value.
  • the flight operation risk network control is divided into two methods.
  • One method is to control the flight operation risk network by controlling the effective control nodes, and set the risk propagation probability and recovery probability for the selected risk factor nodes; search for all controllable nodes combination; and then select the effective control node corresponding to the effective control scheme.
  • the centralization index includes degree centrality as DC and betweenness centrality as BC, where:
  • Degree centrality DC is the number of other nodes directly connected to a node. If a node is directly connected to many nodes, then the node has a high degree centrality; the calculation formula of degree centrality DC is:
  • i and j are the nodes in the flight operation risk network
  • ⁇ jk is the weight sum of all shortest paths from node j to node k
  • ⁇ jk (i) is the weight sum of the path passing through node i in the shortest path from node j to node k
  • the ratio is called the betweenness centrality of node i.
  • Method 1 Remove the node with the highest degree centrality DC value of all nodes in the flight operation risk network.
  • Method 2 Remove the node with the highest betweenness centrality BC value of all nodes in the flight operation risk network.
  • the frequency ranges of the risk communication of various types of unsafe incidents in the five grades are respectively set as:
  • the frequency of unsafe incidents with a high frequency of unsafe incident risk transmission is greater than or equal to 1,000 per year.
  • the annual frequency of unsafe incidents is greater than or equal to 500 and less than 1,000 if the frequency of unsafe incident risk transmission is high.
  • the frequency of risk transmission of unsafe incidents is medium.
  • the annual frequency of unsafe incidents is greater than or equal to 100 and less than 500.
  • the annual frequency of unsafe incidents is greater than or equal to 10 and less than 100 if the risk transmission frequency of unsafe incidents is low.
  • the frequency of unsafe incident risk transmission is extremely low and the frequency of unsafe incidents is less than 10 times per year.
  • the risk propagation probability ranges corresponding to the risk propagation frequencies of various types of unsafe events in the five grades are respectively set as:
  • the risk transmission probability range of high frequency of unsafe event risk transmission is 0.8 to 1.0.
  • the risk transmission probability range of the higher frequency of unsafe event risk transmission is 0.6-0.8.
  • the medium risk communication frequency ranges from 0.4 to 0.6.
  • the risk transmission probability range of low frequency of unsafe event risk transmission is 0.2 to 0.4.
  • the risk transmission probability range of the extremely low frequency of unsafe event risk transmission is 0 to 0.2.
  • the risk control method is: the controllable node combination
  • the risk propagation probability of the risk factor node is set below 0.01; the recovery probability is set above 0.99; and the recovery probability of other risk factor nodes and unsafe event nodes outside the controllable node combination is set below 0.2.
  • the effective control plan is the risk control plan that minimizes the frequency of unsafe events after control.
  • the risk factor nodes in the effective control plan are called is an effective control node.
  • n ⁇ 3000 is set.
  • the beneficial effects of the present invention are: 1.
  • the aviation operation risk network based on aviation unsafe events can effectively reflect the contextual correlation between each risk element, showing the mutual influence between various types of unsafe events, and each item in the network There are actual cases as the basis, which is more in line with the actual operation of the flight; the logical supplementary connection of the network through professional theoretical knowledge can make the network more comprehensive and complete.
  • the present invention distinguishes the risk propagation probability between network nodes through the occurrence probability of unsafe events, so that the risk propagation process is more in line with the actual flight operation situation, and the risk control scheme determined through this network propagation process Higher reliability.
  • Fig. 1 is a schematic diagram of a communication interruption sub-network constructed in an embodiment of the present invention
  • Fig. 2 is the schematic diagram of the total network of flight operation risks constructed in the embodiment of the present invention.
  • Figure 3 is a schematic diagram of flight operation risk propagation mechanism.
  • Step 1 Construction of flight operation risk network
  • the construction of flight operation risk network is divided into the following four sub-steps:
  • the typical types of events that occur frequently in civil aviation include heavy landing, aborted approach/go-around, communication interruption, bird strike, lightning strike, foreign object injury, dangerous approach/flight conflict, lost/yaw, turbulence in the air, rush/ 16 types of events, such as runway deviation, aircraft collision with obstacles, aborted takeoff, engine shutdown, tire blowout/puncture/delamination, emergency loss of pressure and emergency fuel level, are set as unsafe event nodes.
  • the main causes of unsafe incidents are inadequate prevention and control of bird damage at the airport, thunderstorms/rain and snow, low visibility, low-altitude wind shear, strong winds, thunderstorms/rain and snow, strong crosswinds, air turbulence, ice accumulation, crew violations, and crew violations.
  • risk factor nodes and risk factor nodes, risk factor nodes and unsafe event nodes, unsafe event nodes and unsafe event nodes are all connected by directed edges, and the propagation direction is from cause to result, indicating the mutual relationship between nodes. relationship; based on the accident causation theory, connect each node according to the unsafe event record, when the unsafe event record shows that there is a functional relationship between the nodes, the directed edge connection is performed, otherwise no connection is made; To establish the link relationship between nodes, first construct the sub-network of each type of unsafe event, as shown in Figure 1; finally, several sub-networks are composed of the overall flight operation risk network, as shown in Figure 2.
  • risk nodes and risk nodes, risk nodes and unsafe event nodes, unsafe event nodes and unsafe event nodes are all connected by directed edges, and the propagation direction is from cause to result, indicating the relationship between nodes.
  • the communication interruption shown in Figure 1 is a sub-network in the flight operation risk network.
  • the sub-network consists of thirteen nodes pointing to the result of communication interruption through twelve directed edges.
  • the box in the communication interruption sub-network represents the network nodes, and the directed arrows between the nodes represent the impact from one node to the next node, and the numbers on the directed edge connection represent the number of node associations in this sub-network (that is, there are The number of the edge), and this number corresponds to the connection basis of the communication interruption subnetwork, see the table below.
  • Step 2 Determine the risk propagation probability of the flight operation risk network
  • the nodes are divided into three states, respectively, the susceptible state (Susceptible) is recorded as S, the infected state (infected) is recorded as I and recovery
  • the state (recovered) is denoted as R;
  • ⁇ is the probability of risk propagation between nodes, and
  • ⁇ is the recovery probability of nodes.
  • the meaning of Figure 3 is: a node in the susceptible state S will be transformed into an infected state I with a certain risk propagation probability ⁇ , and at the same time, a node in the infected state I has a certain defense ability, and will be transformed with a certain recovery probability ⁇ to restore state R.
  • the frequency of risk communication of various types of unsafe incidents is divided into five grades: high frequency of risk communication, high frequency of risk communication, medium frequency of risk communication, risk communication
  • the frequency of occurrence is low, and the frequency of risk transmission is extremely low.
  • the risk propagation probability takes the maximum value.
  • the frequency ranges of risk communication of various types of unsafe incidents in five grades are set as follows:
  • the frequency of unsafe incidents with a high frequency of unsafe incident risk transmission is greater than or equal to 1,000 per year.
  • the annual frequency of unsafe incidents is greater than or equal to 500 and less than 1,000 if the frequency of unsafe incident risk transmission is high.
  • the frequency of risk transmission of unsafe incidents is medium.
  • the annual frequency of unsafe incidents is greater than or equal to 100 and less than 500.
  • the annual frequency of unsafe incidents is greater than or equal to 10 and less than 100 if the frequency of unsafe incident risk transmission is low.
  • the frequency of unsafe incident risk transmission is extremely low and the frequency of unsafe incidents is less than 10 times per year.
  • the risk propagation probability ranges corresponding to the risk propagation frequencies of each type of unsafe event in the five grades are respectively set as:
  • the risk transmission probability range of high frequency of unsafe event risk transmission is 0.8 to 1.0.
  • the risk transmission probability range of the higher frequency of unsafe event risk transmission is 0.6-0.8.
  • the medium risk communication frequency ranges from 0.4 to 0.6.
  • the risk transmission probability range of low frequency of unsafe event risk transmission is 0.2 to 0.4.
  • the risk transmission probability range of the extremely low frequency of unsafe event risk transmission is 0 to 0.2.
  • the specific value of the infection probability can be determined within a certain interval according to the actual needs, and only a distinction is made here to distinguish the risk propagation probability between different nodes, that is, the subnetwork nodes with a high frequency of unsafe events have a high probability of spreading between nodes, and unsafe events
  • the transmission probability between subnetwork nodes with low frequency of occurrence is small.
  • a connection between nodes appears in different sub-networks take a larger value.
  • the infection probability between two nodes is set to 0.9; the average number of engine shutdowns caused by bird strikes and engine shutdowns caused by foreign object injuries per year is If the number of digits is 0.05, the inter-node infection probability is set to 0.05. The average annual occurrence of emergency depressurization caused by bird strikes and foreign object damage is single digits, and the inter-node infection probability is set to 0.05.
  • the flight operation risk network control is divided into two methods.
  • One method is to control the flight operation risk network by controlling the effective control nodes, and set the risk propagation probability and recovery probability for the selected risk factor nodes; search for all controllable nodes combination; and then select the effective control node corresponding to the effective control scheme.
  • the risk control method is: the controllable node combination
  • the risk propagation probability of the risk factor node is set to 0.01 (indicating that the risk factor node is not easy to be infected); the recovery probability is set to 0.99; and the recovery probability of other risk factor nodes and unsafe event nodes outside the controllable node combination Set to 0.2.
  • the effective control plan is the risk control plan that minimizes the frequency of unsafe events after control.
  • the risk factor nodes in the effective control plan are called is an effective control node.
  • the overall propagation process of the flight risk network for each control scheme is simulated 3,000 times, the number of times the risk spreads in the network is set to 20, and the frequency of infection risks of various aviation unsafe incidents is counted.
  • the centralization index includes degree centrality as DC and betweenness centrality as BC, where:
  • Degree centrality DC is the number of other nodes directly connected to a node. If a node is directly connected to many nodes, then the node has a high degree centrality; the calculation formula of degree centrality DC is:
  • i and j are the nodes in the flight operation risk network
  • ⁇ jk is the weight sum of all shortest paths from node j to node k
  • ⁇ jk (i) is the weight sum of the path passing through node i in the shortest path from node j to node k
  • the ratio is called the betweenness centrality of node i.
  • Method 1 Remove 1 to 4 nodes with the highest degree centrality DC value of all nodes in the flight operation risk network.
  • Method 2 Remove 1 to 4 nodes with the highest betweenness centrality BC value of all nodes in the flight operation risk network.
  • the network propagation results under different attack strategies are respectively obtained, and the best attack strategy is selected by comparing the propagation results.
  • the result of cyber attacks is to remove risk nodes.
  • Corresponding to the actual operation control it is necessary to change the information transmission process or change the work procedure. This takes time to rebuild and has undergone risk assessment, so it is more suitable for long-term mid-term and long-term control.
  • Program design or optimization For the immediate and short-term risk management and control needs of a certain flight, based on the existing system and working procedures, it is still an important and feasible control method to suppress risk transmission by controlling key nodes.

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Abstract

一种基于不安全事件的航班运行风险网络构建与控制方法。该方法基于民航不安全事件数据构建航班运行风险网络，根据航班运行实际业务对网络进行逻辑补充连接；基于SIR病毒传播模型改进风险传播与控制过程，依据不同不安全事件的发生频率对不同节点间的风险传播概率加以区分，模拟风险在此网络中的传播过程。通过控制节点和网络攻击两种方法来控制风险在网络中的传播，并通过对重要节点建立防御来降低不安全事件发生风险的概率，从而提出可靠有效的航班运行风险控制方案。通过构建航班运行风险网络有助于了解航班风险因素之间、风险与事故之间的联系，使风险传播过程更加符合实际航班运行情况，确定的风险控制方案可靠度更高。

Description

一种基于不安全事件的航班运行风险网络构建与控制方法
本发明要求于2021年7月19日向中国专利局提交的申请号为202110812937.9、发明名称为“一种基于不安全事件的航班运行风险网络构建与控制方法”中国专利申请的优先权，其全部内容通过引用结合在本申请中。
技术领域
本发明涉及一种航班运行风险产生、传播与控制的分析方法，特别涉及一种基于不安全事件的航班运行风险网络构建与控制方法。
背景技术
目前，对于航班运行风险的研究主要停留在风险分析与量化评估等方向，而航班运行风险的产生条件、传播过程、控制消散方法等问题尚缺乏足够深入的研究。现有研究方案中存在一种通用假设，即风险因素间关系是树状结构。但运行风险实则与气象条件、机组资质、机场设施、障碍物、飞机维修甚至政策保障等多因素相关，而各因素间通过业务逻辑、数据流转等相互作用，错综交汇形成网状结构。
目前复杂网络理论多用于交通网络、社会网络、生物网络等研究，在民航中最早应用于全国航线网、机场网，并依此分析延误与飞行冲突等，在航班运行风险管理方面应用较少。例如，王岩韬团队于2019年首次提出基于复杂网络的航班运行风险传播分析方法并加以改进，分别运用Spearman相关系数法和偏秩相关系数法构建航班运行风险网络。但由相关系数建立的航班运行风险网络会与实际情况有所出入，如Spearman相关系数难以区分直接相关与间接相关，往往导致网络中“假相关”的连边出现，会出现较多与业务逻辑不符的连边。鉴于此，2020年王岩韬团队根据2009-2014年民航安全监察中特殊事件、工作差错及不安全事件总计25401起的统计资料，当事件记录中显示节点间存在作用关系时，即进行连线，首次构建了航班运行风险的有向加权网络，但基于实际事件建立的网络仍存在覆盖不全面的问题，需要做进一步的补充改进。
经典的SIR模型认为疾病在人与人之间传播的概率是相同的，即所有节点间风险传播概率β为一定值，但在实际的航班运行风险网络中，风险并不是以同样的概率在节点之间传播，并且近些年的航班架次每年都有百万架次，统计的不安全事件数量与之相比发生的概率很小，相对应的节点风险发生和节点间传播的概率都应该比较小，且根据不同事件发生频率将节点间风险传播的概率有所区分更加合理。
发明内容
鉴于现有技术状况，本方法提出一种基于不安全事件的航班运行风险网络构建与控制方法。该方法首先根据现有网络构建技术，基于民航不安全事件数据，结合理论知识补充构建航班运行风险网络；基于SIR病毒传播模型改进风险传播与控制过程，依据不同不安全事件的发生频率对不同节点间的风险传播概率加以区分，模拟风险在此网络中的传播过程。通过控制节点和网络攻击两种方法来控制风险在网络中的传播，并通过对重要节点建立防御来降低不安全事件发生风险的概率，从而提出可靠有效的航班运行风险控制方案。通过构建航班运行风险网络有助于了解航班风险因素之间、风险与事故之间的联系，而使用SIR病毒传播模型可模拟航班风险的传播与控制过程。
本发明采取的技术方案是：一种基于不安全事件的航班运行风险网络构建与控制方法，其特征在于，所述方法有以下步骤：
步骤一、航班运行风险网络构建
航班运行风险网络构建分成以下四个子步骤：
(1)、网络中不安全事件节点和风险因素节点选取
统计民航不安全事件类型及导致不安全事件发生的原因，将各类不安全事件设置为网络中的不安全事件节点，将导致不安全事件发生的原因设置为风险因素节点。
(2)、构建航班运行风险网络
在网络中，风险因素节点与风险因素节点、风险因素节点与不安全事件节点、不安全事件节点与不安全事件节点间均通过有向边相连，传播方向由原因指向结果，表示节点间的相互关系；以事故致因理论为基础，依据不安全事件记录为每个节点连线，当不安全事件记录中显示节点间存在作用关系时，即进行有向边连 线，否则不连线；以此建立节点间的链接关系，先构建每一个不安全事件类型的子网络，最后将若干个子网络组成整体航班运行风险网络。
(3)、完善网络结构，根据航班运行实际业务对航班运行风险网络进行补充有向边连线。
步骤二、确定航班运行风险网络的风险传播概率
使用SIR病毒传播模型建立航班运行风险传播机制：将节点分为三种状态，分别是易感染状态记作S，已感染状态记作I和恢复状态记作R；处于易感染状态S的节点将以一定的风险传播概率记作β转化为已感染状态I，同时处于已感染状态I的节点自身有一定的防御能力，将以一定的恢复概率记作γ转化为恢复状态R；在SIR模型基础上对风险传播概率β进行区分，区分步骤如下：
a、首先，依据平均每年发生不安全事件频次，将各类型不安全事件风险传播发生频率划分为五个档次：风险传播发生频率高、风险传播发生频率较高、风险传播发生频率中等、风险传播发生频率低、风险传播发生频率极低。
b、然后，依据各类型不安全事件所划分的档次，设定五个档次的各类型不安全事件风险传播频率所对应的风险传播概率；不安全事件发生频率高的子网节点间风险传播概率大，不安全事件发生频率低的子网节点间风险传播概率小。
c、如果某两节点间有向边在不同的子网络中都有出现，则风险传播概率取最大值。
步骤三、航班运行风险网络控制
航班运行风险网络控制分为两种方法，一种方法是采取控制有效控制节点对航班运行风险网络进行控制，对选取的风险因素节点分别设定风险传播概率、恢复概率；搜寻所有的可控节点组合；然后挑选出有效控制方案对应的有效控制节点。
另一种方法是采用移除中心化指数高的节点对航班运行风险网络进行控制，中心化指数包括度数中心度记作DC和中间中心度记作BC，其中：
(1)、度数中心度DC是与某节点直接相连的其他节点的个数，如果一个节点与许多节点直接相连，那么该节点具有较高的度数中心度；度数中心度DC计算公式：
DC(i)＝Σ j≠ka(i，j)‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐(1)
式(1)中，i和j为航班运行风险网络中的节点，DC(i)为节点i的度数中心度，当节点i与节点j存在有向边相连时，a(i,j)＝1，否则a(i,j)＝0。
(2)、中间中心度BC计算公式：
Figure PCTCN2022077287-appb-000001
式(2)中，σ jk为节点j到节点k的所有最短路径的权值和；σ jk(i)为节点j到节点k的最短路径中经过节点i的路径的权值和；两者比值称为节点i的中间中心度。
(3)、根据计算出的航班运行风险网络中每个节点的度数中心度DC值、中间中心度BC值，采取以下两种方法移除中心化指数高的节点进行控制：
方法一：移除航班运行风险网络中所有节点的度数中心度DC值最高的节点。
方法二：移除航班运行风险网络中所有节点的中间中心度BC值最高的节点。
所述五个档次的各类型不安全事件风险传播的发生频次范围分别设定为：
①、不安全事件风险传播频率高的每年发生不安全事件频次大于等于1000。
②、不安全事件风险传播频率较高的每年发生不安全事件频次大于等于500且小于1000。
③、不安全事件风险传播频率中等的每年发生不安全事件频次大于等于100且小于500。
④、不安全事件风险传播频率低的每年发生不安全事件频次大于等于10且小于100。
⑤、不安全事件风险传播频率极低的每年发生不安全事件频次小于10次。
所述五个档次的各类型不安全事件风险传播频率所对应的风险传播概率范围分别设定为：
①、不安全事件风险传播频率高的风险传播概率范围为0.8～1.0。
②、不安全事件风险传播频率较高的风险传播概率范围为0.6～0.8。
③、不安全事件风险传播频率中等的风险传播频率范围为0.4～0.6。
④、不安全事件风险传播频率低的风险传播概率范围为0.2～0.4。
⑤、不安全事件风险传播频率极低的风险传播概率范围为0～0.2。
所述采取控制有效控制节点对航班运行风险网络进行控制的方法有以下子步骤：
(一)、使用SIR病毒传播模型对航班运行风险网络进行n次仿真传播，统计n次仿真传播中各类型航空不安全事件感染风险的频次，作为初始感染频次。
(二)、将航班运行风险网络中的风险因素节点作为可控节点；随机选取三个以上风险因素节点作为一个可控节点组合，进行风险控制，其风险控制方法为：将可控节点组合内的风险因素节点风险传播概率设定为0.01以下；将恢复概率设定为0.99以上；并将可控节点组合外的其他风险因素节点和不安全事件节点的恢复概率设定为0.2以下。
(三)、搜寻网络内所有可控节点组合，每一个可控节点组合对应的风险控制方法作为一个风险控制方案，将每一种风险控制方案对应的航班运行风险网络使用SIR病毒传播模型进行n次仿真传播，统计n次仿真中各类型航空不安全事件感染风险的频次，作为风险控制后频次。
(四)、在所有风险控制方案中为每一类不安全事件挑选出有效控制方案，有效控制方案是使不安全事件的控制后频次最低的风险控制方案，有效控制方案内的风险因素节点称为有效控制节点。
(五)、多次重复(一)至(四)步骤，以避免偶然情况的发生。
所述n次仿真传播中设定n≥3000。
所述移除航班运行风险网络中所有节点的度数中心度DC值最高的1～4个节点进行控制。
所述移除航班运行风险网络中所有节点的中间中心度BC值最高的1～4个节点进行控制。
本发明的有益效果是：①以航空不安全事件为基础的航空运行风险网络可以有效体现出各个风险要素之间的前后关联，表现出各类型不安全事件间的相互影响，并且网络中每条边都有实际案例作为依据，更加符合航班实际运行情况；通过专业理论知识对网络进行逻辑补充连接，可以使得网络更加全面完整。
②与现有SIR模型固定传播概率相比，本发明通过不安全事件的发生概率区分网络节点间风险传播概率，使风险传播过程更加符合实际航班运行情况，通过此网络传播过程确定的风险控制方案可靠度更高。
③应用此网络可针对不同的不安全事件类型，搜寻风险控制方案，提出可靠有效的风险控制方法。
附图说明
图1为本发明实施例中构建的通讯中断子网络示意图；
图2为本发明实施例中构建的航班运行风险总网络示意图；
图3为航班运行风险传播机制示意图。
具体实施方式
以下结合附图和实施例对本发明作进一步说明。
步骤一、航班运行风险网络构建
航班运行风险网络构建分成以下四个子步骤：
(1)、网络中不安全事件节点和风险因素节点选取
根据每年民航局发布的《民航不安全事件统计分析报告》《中国民航安全信息统计分析报告》以及公开的民航不安全事件调查报告，统计民航不安全事件类型及导致不安全事件发生的原因，将各类不安全事件设置为网络中的不安全事件节点，将导致不安全事件发生的原因设置为风险因素节点。
如民航发生频率较大的典型事件类型有重着陆、中止进近/复飞、通讯中断、鸟击、雷击、外来物击伤、危险接近/飞行冲突、迷航/偏航、空中颠簸、冲/偏出跑道、航空器撞障碍物、中断起飞、发动机停车、爆胎/扎破/脱层、紧急失压和紧急油量等16类型事件，设置为不安全事件节点。
导致不安全事件的原因主要有机场鸟害防治不到位、雷暴/雨雪、低能见度、低空风切变、强风、雷暴/雨雪、大侧风、空中湍流、积冰、机组违规违章、机组资源管理差、机组情景意识差、机组训练不足、机组应急决策不当、机组疲劳、机组未建立目视、机长违规离开驾驶舱、机组安全意识差、机组交流沟通差、机组团队合作差、空管设备原因、空管情报欠缺或偏差、管制员违规违章、管制员应急决策不当、管制员疲劳、管制员口误、管制员通话用语不规范、道面维护不到位、道面维护不到位、地面监管不到位、油路堵塞等机械故障、飞机设计缺陷等44类原因，均设置为风险因素节点。
(2)、构建航班运行风险网络
在网络中，风险因素节点与风险因素节点、风险因素节点与不安全事件节点、不安全事件节点与不安全事件节点间均通过有向边相连，传播方向由原因指向结果，表示节点间的相互关系；以事故致因理论为基础，依据不安全事件记录为每 个节点连线，当不安全事件记录中显示节点间存在作用关系时，即进行有向边连线，否则不连线；以此建立节点间的链接关系，先构建每一个不安全事件类型的子网络，如图1所示；最后将若干个子网络组成整体航班运行风险网络，如图2所示。
因为在实际情况中，一个风险因素会导致一个或多个不安全事件的发生，同时也会导致其他风险因素的产生，而一个不安全事件的发生又会导致其他不安全事件的发生。故在复杂网络中风险节点与风险节点、风险节点与不安全事件节点、不安全事件节点与不安全事件节点间均通过有向边相连，传播方向由原因指向结果，表示节点间的相互关系。
以事故致因理论为基础，依据上述不安全事件调查报告为每个节点连线，当事件记录中显示节点间存在作用关系时，即进行连线，否则不连线。例如在一个事故报告中有如下描述：A321飞机在哈尔滨23号跑道起飞过程中，由于鸟击导致发动机停车中断起飞。则在网络中描述为“鸟击→发动机停车→中断起飞”。以此建立节点间的链接关系，构建每类型不安全事件的子网络(如图1所示)。
图1所示的通讯中断为航班运行风险网络中的一个子网络。该子网络由十三个节点通过十二条有向边均指向通讯中断结果。通讯中断子网络中方框内表示网络节点，节点间有向箭头表示从某一节点对下一节点产生影响，有向边连线上的数字表示在此子网络中的节点关联关系编号(即有向边的编号)，并且此编号与通讯中断子网络连接依据相对应，见下表。
Figure PCTCN2022077287-appb-000002
Figure PCTCN2022077287-appb-000003
(3)、完善网络结构，根据航班运行实际业务对航班运行风险网络进行补充有向边连线。建网后会发现节点之间的逻辑关系、以及节点有向边连线不准确的情况， 对网络进行逻辑补充有向边连接，可以使得网络更加全面完整。
步骤二、确定航班运行风险网络的风险传播概率
使用SIR病毒传播模型建立航班运行风险传播机制(如图3所示)：将节点分为三种状态，分别是易感染状态(Susceptible)记作S，已感染状态(infected)记作I和恢复状态(recovered)记作R；β为节点间风险传播概率，γ为节点的恢复概率。图3含义为：处于易感染状态S的节点将以一定的风险传播概率β转化为已感染状态I，同时处于已感染状态I的节点自身有一定的防御能力，将以一定的恢复概率γ转化为恢复状态R。
在SIR模型基础上对风险传播概率β进行区分，区分步骤如下：
a、首先，依据平均每年发生不安全事件频次，将各类型不安全事件风险传播发生频率划分为五个档次：风险传播发生频率高、风险传播发生频率较高、风险传播发生频率中等、风险传播发生频率低、风险传播发生频率极低。
b、然后，依据各类型不安全事件所划分的档次，设定五个档次的各类型不安全事件风险传播频率所对应的风险传播概率；不安全事件发生频率高的子网节点间风险传播概率大，不安全事件发生频率低的子网节点间风险传播概率小。
c、如果某两节点间有向边在不同的子网络中都有出现，则风险传播概率取最大值。
在本实施例中，依据2010-2019平均每年发生频次，将五个档次的各类型不安全事件风险传播的发生频次范围分别设定为：
①、不安全事件风险传播频率高的每年发生不安全事件频次大于等于1000。
②、不安全事件风险传播频率较高的每年发生不安全事件频次大于等于500且小于1000。
③、不安全事件风险传播频率中等的每年发生不安全事件频次大于等于100且小于500。
④、不安全事件风险传播频率低的每年发生不安全事件频次大于等于10且小于100。
⑤、不安全事件风险传播频率极低的每年发生不安全事件频次小于10次。
五个档次的各类型不安全事件风险传播频率所对应的风险传播概率范围分别设定为：
①、不安全事件风险传播频率高的风险传播概率范围为0.8～1.0。
②、不安全事件风险传播频率较高的风险传播概率范围为0.6～0.8。
③、不安全事件风险传播频率中等的风险传播频率范围为0.4～0.6。
④、不安全事件风险传播频率低的风险传播概率范围为0.2～0.4。
⑤、不安全事件风险传播频率极低的风险传播概率范围为0～0.2。
感染概率具体数值可根据实际需求在一定区间范围内确定，在此只做区分，以区别不同节点间的风险传播概率，即不安全事件发生频率高的子网节点间传播概率大，不安全事件发生频率低的子网节点间传播概率小。另外，如果某一节点间连线在不同的子网络中都有出现，则取较大值。如冲/偏出跑道与爆胎/扎破/脱层几乎百分之百相关，则两节点间感染概率设为0.9；鸟击导致的发动机停车和外来物击伤导致的发动机停车平均每年发生次数为个位数，则设置节点间感染概率为0.05，鸟击导致的紧急失压和外来物击伤导致的紧急失压平均每年发生次数为个位数，则设置节点间感染概率为0.05。
为不同节点设置不同风险传播概率后，在建好的航班运行风险网络中，运用Matlab等软件根据现有SIR传播模型模拟航班运行风险传播过程。
步骤三、航班运行风险网络控制
航班运行风险网络控制分为两种方法，一种方法是采取控制有效控制节点对航班运行风险网络进行控制，对选取的风险因素节点分别设定风险传播概率、恢复概率；搜寻所有的可控节点组合；然后挑选出有效控制方案对应的有效控制节点。
采取控制有效控制节点对航班运行风险网络进行控制的方法有以下子步骤：
(一)、使用SIR病毒传播模型对航班运行风险网络进行n次仿真传播(n≥3000)，统计n次仿真传播中各类型航空不安全事件感染风险的频次，作为初始感染频次。
(二)、将航班运行风险网络中的风险因素节点作为可控节点；随机选取三个以上风险因素节点作为一个可控节点组合，进行风险控制，其风险控制方法为：将可控节点组合内的风险因素节点风险传播概率设定为0.01(表示该风险因素节点不易被感染)；将恢复概率设定为0.99；并将可控节点组合外的其他风险因素节点和不安全事件节点的恢复概率设定为0.2。
(三)、搜寻网络内所有可控节点组合，每一个可控节点组合对应的风险控制方法作为一个风险控制方案，将每一种风险控制方案对应的航班运行风险网络使用SIR病毒传播模型进行n次仿真传播，统计n次仿真中各类型航空不安全事件感染风险的频次，作为风险控制后频次。
(四)、在所有风险控制方案中为每一类不安全事件挑选出有效控制方案，有效控制方案是使不安全事件的控制后频次最低的风险控制方案，有效控制方案内的风险因素节点称为有效控制节点。
(五)、重复(一)至(四)步骤六次来避免偶然情况的发生。
在搜寻风险控制方案过程中，对每一种控制方案的航班风险网络整体传播过程模拟3000次，设置风险在网络中的传播次数20次，统计各类航空不安全事件感染风险的频次。
综合航班风险网络控制模拟得到的结果，挑选出每一航空不安全事件相对应的有效控制节点，通过对有效控制节点的风险控制来降低不安全事件的发生频率。
另一种方法是采用移除中心化指数高的节点对航班运行风险网络进行控制，中心化指数包括度数中心度记作DC和中间中心度记作BC，其中：
(1)、度数中心度DC是与某节点直接相连的其他节点的个数，如果一个节点与许多节点直接相连，那么该节点具有较高的度数中心度；度数中心度DC计算公式：
DC(i)＝Σ j≠ka(i，j)‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐(1)
式(1)中，i和j为航班运行风险网络中的节点，DC(i)为节点i的度数中心度，当节点i与节点j存在有向边相连时，a(i,j)＝1，否则a(i,j)＝0。
(2)、中间中心度BC计算公式：
Figure PCTCN2022077287-appb-000004
式(2)中，σ jk为节点j到节点k的所有最短路径的权值和；σ jk(i)为节点j到节点k的最短路径中经过节点i的路径的权值和；两者比值称为节点i的中间中心度。
(3)、根据计算出的航班运行风险网络中每个节点的度数中心度DC值、中 间中心度BC值，选采取以下两种方法移除中心化指数高的节点进行控制：
方法一：移除航班运行风险网络中所有节点的度数中心度DC值最高的1～4个节点。
方法二：移除航班运行风险网络中所有节点的中间中心度BC值最高的1～4个节点。
通过攻击网络内度数中心度DC值、中间中心度BC值高的节点，分别得到不同攻击策略下网络传播结果，对比传播结果，选取效果最佳的攻击策略。
网络攻击的结果是移除风险节点，对应于实际运行管控，则需要更改信息传递流程或变更工作程序，这是需要时间再造并且经过风险评估的，因此更适合于时间较长的中远期管控方案设计或优化。而对于某一航班的即时与短期风险管控需要，基于现有体制与工作程序，采取控制关键节点的方式抑制风险传播仍是重要且可行的管控手段。

Claims (7)

  1. 一种基于不安全事件的航班运行风险网络构建与控制方法，其特征在于，所述方法有以下步骤：
    步骤一、航班运行风险网络构建
    航班运行风险网络构建分成以下四个子步骤：
    (1)、网络中不安全事件节点和风险因素节点选取
    统计民航不安全事件类型及导致不安全事件发生的原因，将各类不安全事件设置为网络中的不安全事件节点，将导致不安全事件发生的原因设置为风险因素节点；
    (2)、构建航班运行风险网络
    在网络中，风险因素节点与风险因素节点、风险因素节点与不安全事件节点、不安全事件节点与不安全事件节点间均通过有向边相连，传播方向由原因指向结果，表示节点间的相互关系；以事故致因理论为基础，依据不安全事件记录为每个节点连线，当不安全事件记录中显示节点间存在作用关系时，即进行有向边连线，否则不连线；以此建立节点间的链接关系，先构建每一个不安全事件类型的子网络，最后将若干个子网络组成整体航班运行风险网络；
    (3)、完善网络结构，根据航班运行实际业务对航班运行风险网络进行补充有向边连线；
    步骤二、确定航班运行风险网络的风险传播概率
    使用SIR病毒传播模型建立航班运行风险传播机制：将节点分为三种状态，分别是易感染状态记作S，已感染状态记作I和恢复状态记作R；处于易感染状态S的节点将以一定的风险传播概率记作β转化为已感染状态I，同时处于已感染状态I的节点自身有一定的防御能力，将以一定的恢复概率记作γ转化为恢复状态R；在SIR模型基础上对风险传播概率β进行区分，区分步骤如下：
    a、首先，依据平均每年发生不安全事件频次，将各类型不安全事件风险传播发生频率划分为五个档次：风险传播发生频率高、风险传播发生频率较高、风险传播发生频率中等、风险传播发生频率低、风险传播发生频率极低；
    b、然后，依据各类型不安全事件所划分的档次，设定五个档次的各类型不安全事件风险传播频率所对应的风险传播概率；不安全事件发生频率高的子网节点间 风险传播概率大，不安全事件发生频率低的子网节点间风险传播概率小；
    c、如果某两节点间有向边在不同的子网络中都有出现，则风险传播概率取最大值；
    步骤三、航班运行风险网络控制
    航班运行风险网络控制分为两种方法，一种方法是采取控制有效控制节点对航班运行风险网络进行控制，对选取的风险因素节点分别设定风险传播概率、恢复概率；搜寻所有的可控节点组合；然后挑选出有效控制方案对应的有效控制节点；另一种方法是采用移除中心化指数高的节点对航班运行风险网络进行控制，中心化指数包括度数中心度记作DC和中间中心度记作BC，其中：
    (1)、度数中心度DC是与某节点直接相连的其他节点的个数，如果一个节点与许多节点直接相连，那么该节点具有较高的度数中心度；度数中心度DC计算公式：
    DC(i)＝Σ j≠ka(i，j)‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐(1)
    式(1)中，i和j为航班运行风险网络中的节点，DC(i)为节点i的度数中心度，当节点i与节点j存在有向边相连时，a(i,j)＝1，否则a(i,j)＝0；
    (2)、中间中心度BC计算公式：
    Figure PCTCN2022077287-appb-100001
    式(2)中，σ jk为节点j到节点k的所有最短路径的权值和；σ jk(i)为节点j到节点k的最短路径中经过节点i的路径的权值和；两者比值称为节点i的中间中心度；
    (3)、根据计算出的航班运行风险网络中每个节点的度数中心度DC值、中间中心度BC值，采取以下两种方法移除中心化指数高的节点进行控制：
    方法一：移除航班运行风险网络中所有节点的度数中心度DC值最高的节点；
    方法二：移除航班运行风险网络中所有节点的中间中心度BC值最高的节点。
  2. 根据权利要求1所述的一种基于不安全事件的航班运行风险网络构建与控制方法，其特征在于，所述五个档次的各类型不安全事件风险传播的发生频次范围分别设定为：
    ①、不安全事件风险传播频率高的每年发生不安全事件频次大于等于1000；
    ②、不安全事件风险传播频率较高的每年发生不安全事件频次大于等于500且小于1000；
    ③、不安全事件风险传播频率中等的每年发生不安全事件频次大于等于100且小于500；
    ④、不安全事件风险传播频率低的每年发生不安全事件频次大于等于10且小于100；
    ⑤、不安全事件风险传播频率极低的每年发生不安全事件频次小于10次。
  3. 根据权利要求2所述的一种基于不安全事件的航班运行风险网络构建与控制方法，其特征在于，所述五个档次的各类型不安全事件风险传播频率所对应的风险传播概率范围分别设定为：
    ①、不安全事件风险传播频率高的风险传播概率范围为0.8～1.0；
    ②、不安全事件风险传播频率较高的风险传播概率范围为0.6～0.8；
    ③、不安全事件风险传播频率中等的风险传播频率范围为0.4～0.6；
    ④、不安全事件风险传播频率低的风险传播概率范围为0.2～0.4；
    ⑤、不安全事件风险传播频率极低的风险传播概率范围为0～0.2。
  4. 根据权利要求1所述的一种基于不安全事件的航班运行风险网络构建与控制方法，其特征在于，所述采取控制有效控制节点对航班运行风险网络进行控制的方法有以下子步骤：
    (一)、使用SIR病毒传播模型对航班运行风险网络进行n次仿真传播，统计n次仿真传播中各类型航空不安全事件感染风险的频次，作为初始感染频次；
    (二)、将航班运行风险网络中的风险因素节点作为可控节点；随机选取三个以上风险因素节点作为一个可控节点组合，进行风险控制，其风险控制方法为：将可控节点组合内的风险因素节点风险传播概率设定为0.01以下；将恢复概率设定为0.99以上；并将可控节点组合外的其他风险因素节点和不安全事件节点的恢复概率设定为0.2以下；
    (三)、搜寻网络内所有可控节点组合，每一个可控节点组合对应的风险控制方法作为一个风险控制方案，将每一种风险控制方案对应的航班运行风险网络使用SIR病毒传播模型进行n次仿真传播，统计n次仿真中各类型航空不安全事件感染风险的频次，作为风险控制后频次；
    (四)、在所有风险控制方案中为每一类不安全事件挑选出有效控制方案，有效控制方案是使不安全事件的控制后频次最低的风险控制方案，有效控制方案内的风险因素节点称为有效控制节点；
    (五)、多次重复(一)至(四)步骤，以避免偶然情况的发生。
  5. 根据权利要求4所述的一种基于不安全事件的航班运行风险网络构建与控制方法，其特征在于，所述n次仿真传播中设定n≥3000。
  6. 根据权利要求1所述的一种基于不安全事件的航班运行风险网络构建与控制方法，其特征在于，所述移除航班运行风险网络中所有节点的度数中心度DC值最高的1～4个节点进行控制。
  7. 根据权利要求1所述的一种基于不安全事件的航班运行风险网络构建与控制方法，其特征在于，所述移除航班运行风险网络中所有节点的中间中心度BC值最高的1～4个节点进行控制。
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