CN110648561A - Air traffic situation risk measurement method based on double-layer multi-level network model - Google Patents
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Abstract
The invention discloses an air traffic situation risk measurement method based on a double-layer multistage network model, which comprises the following four steps: firstly, acquiring air traffic control radar comprehensive track or ADS-B track data, and acquiring aircraft information at each moment in an air traffic situation; secondly, calculating the potential conflict relationship of the aircraft in the two-dimensional horizontal direction and the three-dimensional space, and establishing a double-layer multi-level air traffic situation risk network model; then calculating each statistical index in the risk network; and finally forming air traffic situation risk measurement vectors corresponding to each moment. The method is an objective assessment method for the risk of the air traffic situation, comprehensively depicts two-dimensional structure and three-dimensional structure characteristics of the air traffic situation, occupies less capital, is simple and easy to use, and has an assessment result which is easy to understand.
Description
Technical Field
The invention relates to the field of air traffic management, in particular to an air traffic situation risk measurement method based on a double-layer multistage network model.
Background
A flight conflict may occur when two aircraft are too close to each other to be less than the separation criterion. The core task of air traffic management is to ensure that there is sufficient separation between aircraft to avoid flight conflicts. However, during flight, the flights often cross or overlap in flight path, thereby creating a large number of potential flight conflicts, especially in high density areas. To timely resolve potential flight conflicts, controllers need to keep constant attention to local high risk areas and develop solutions as soon as possible. Therefore, flight conflicts in air traffic situations greatly affect controller workload, are a bottleneck in air traffic system capacity, and are a major source of air traffic situation risk. An air traffic situation network is established based on the potential flight conflict relationship among aircrafts, and the network structure is analyzed, so that the air traffic situation risk can be essentially described. But no relevant research report appears in domestic and foreign literatures.
Therefore, the air traffic situation can be more comprehensively mapped by establishing the double-layer multi-level network model based on the potential conflict relationship, and the description of the network topology indexes is helpful for understanding the essential characteristics of the air traffic risk, so that the defects of the current research are made up, and the theoretical basis is finally provided for the construction of a new generation of air traffic system.
Disclosure of Invention
Aiming at the defects of the current air traffic situation risk description method, the invention divides the air traffic situation analysis into two-dimensional situation risk and three-dimensional situation risk, and accordingly provides an air traffic situation risk measurement method based on a double-layer multistage network model, and the air traffic situation risk is objectively depicted from multiple dimensions.
The technical scheme adopted by the invention is as follows: a method for measuring air traffic situation risk based on a double-layer multi-level network model is characterized by comprising the following steps:
acquiring air traffic control radar comprehensive track or ADS-B track data: dynamically acquiring flight path data according to an original data period, and extracting an aircraft target, a dynamic position and navigation element information thereof; and carrying out primary coarse graining treatment according to a certain period, and averaging longitude, latitude, altitude, speed and course information of all the aircraft targets in the period to obtain the aircraft information of the current period.
Step two, establishing an air traffic situation risk network model according to the result of the step one: and taking the aircrafts as nodes, representing the space potential conflict relationship among the aircrafts by using edges, calculating the potential conflicts among all the aircrafts, and constructing an air traffic situation risk network model.
Step three, calculating each network topology index of the air traffic situation risk: the network scale, namely the number of the aircrafts in the current airspace, is represented by N; the number of edges, i.e. the number of edges in the network, reflects the number of potential conflicts in two-dimensional and three-dimensional air traffic situations, and is denoted by E; the network aggregation coefficient, i.e. the average of the aggregation coefficients of all nodes in the network, is denoted by C.
Step four, forming an air traffic situation risk measure vector based on the calculated indexes: if the level number of the network is 2, the risk vector of the air traffic situation at the time t is M (t) = (N)1,1(t)，E1,1(t)，C1,1(t)，N1,2(t)，E1,2(t)，C1,2(t)，N2,1(t)，E2,1(t)，C2,1(t)，N2,2(t)，E2,2(t)，C2,2(t)), due to N1,1(t)、N1,2(t)、N2,1(t)、N2,2(t) values at the same time are all equal, and N is added1,1(t)、N1,2(t)、N2,1(t)、N2,2(t) the four indexes are unified as N (t); thus, the risk vector M (t) corresponding to the traffic situation at the time t is simplified into (N (t), E1,1(t)，C1,1(t)，E1,2(t)，C1,2(t)，E2,1(t)，C2,1(t)，E2,2(t)，C2,2(t))。
The construction of the air traffic situation risk network model provided by the invention comprises the following substeps:
(1) establishing a single-layer single-stage air traffic situation risk network: if a potential flight conflict exists between the ith aircraft and the jth aircraft at the time t and the predicted conflict time is less than a set level threshold h, unit: minutes, and the vertical distance is less than a predetermined vertical threshold v, in units: rice, the node i and the node j are considered to be connected by one edge; and expressing the air traffic situation risk network structures corresponding to the time t, the horizontal threshold h and the vertical threshold v as follows: gh,v(t)。
(2) Establishing a single-layer multi-level air traffic situation risk network: m threshold combinations { h1, v 1; h2, v 2; ...; hm, vm }, and establishing a corresponding air traffic situation risk network structure by taking each group in the combination as a threshold value, so as to establish m single-layer single-level air traffic situation risk networks; setting Q (t) as a single-layer multi-level air traffic situation risk network corresponding to the air traffic situation at the time t, and combining m single-layer single-level air traffic situation risk networks to form a single-layer multi-level air traffic situation risk network with the number of levels m, namely: q (t) = { Gh1,v1(t),Gh2,v2(t),...,Ghm,vm(t)}。
(3) Establishing a double-layer multi-level air traffic situation risk network: considering the air traffic situation risk from two-dimensional risk and three-dimensional risk, and expanding a single-layer multi-level air traffic situation risk network into a double-layer multi-level air traffic situation risk network; the vertical threshold v of the first layer of air traffic situation risk network is set to be infinite, namely the relationship between the horizontal distance between two airplanes and the set horizontal threshold is only considered, and the two-dimensional traffic situation is corresponded; second floor verticality of air traffic situation risk networkThe threshold value v is set as the minimum vertical interval in the airspace or a multiple thereof and corresponds to the three-dimensional traffic situation; let Qi(t) a multi-level air traffic situation risk network set of the ith layer at time t, Qi(t)={Gi h1,v1(t)，Gi h2,v2(t)，...，Gi hm,vm(t) }, in which Gi hj,vj(t) represents the ith and jth level air traffic situation risk network at the time t, i =1,2, j =1,.. multidot.m, and the horizontal threshold and the vertical threshold are hj and vj respectively.
The method mainly comprises the following steps: (1) collecting comprehensive flight path or ADS-B flight path data of the air traffic control radar, and acquiring aircraft information at each moment; (2) dividing the air traffic situation risk at each moment into a two-dimensional situation risk and a three-dimensional situation risk, respectively calculating the potential conflict relationship of the aircraft in a two-dimensional horizontal direction and a three-dimensional space, and establishing a corresponding double-layer multi-level air traffic situation risk network model; (3) calculating various statistical indexes in the air traffic situation risk network; (4) and integrating all the statistical indexes to form an air traffic situation risk measure vector corresponding to the current moment.
The beneficial effects produced by the invention are as follows: the method can describe the two-dimensional situation risk and the three-dimensional situation risk more comprehensively, all indexes are obtained through actual data calculation, the method is not influenced by human factors, the occupied funds are less, the evaluation method is simple and easy to use, and the evaluation result is easy to understand.
Drawings
FIG. 1 is a basic flow diagram of the present invention;
FIG. 2 is an exemplary schematic illustration of an air traffic situation;
FIG. 3 is a diagram of a risk model of air traffic situation in a two-layer network corresponding to the situation in FIG. 2.
Detailed Description
The invention is further explained with reference to the drawings and the embodiments.
The method comprises the steps of firstly, collecting air traffic control radar comprehensive track or ADS-B track data, and obtaining aircraft information at each moment in air traffic situation; secondly, calculating the potential conflict relationship of the aircraft in the two-dimensional horizontal direction and the three-dimensional space, and establishing a double-layer multi-level air traffic situation risk network model; then calculating various statistical indexes in the air traffic situation risk network; and finally forming the air traffic situation vector corresponding to each moment.
The specific steps are shown in figure 1:
acquiring air traffic control radar comprehensive track or ADS-B track data: acquiring track data every 4 seconds according to the original data display period, and extracting the target and the position information of the aircraft from the track data; carrying out coarse graining treatment once every 10 seconds or a certain period, and averaging longitude, latitude, altitude, speed and heading information of all the aircraft targets in the period to obtain the aircraft information of the current period.
Step two, establishing an air traffic situation risk network model according to the result of the step one: and taking the aircrafts as nodes, representing the space potential conflict relationship among the aircrafts by using edges, calculating the potential conflict among all the aircrafts, and constructing a network model. The construction step can be subdivided into the following three substeps:
(1) establishing a single-layer single-stage air traffic situation risk network: if a potential conflict exists between the ith aircraft and the jth aircraft at the time t, the expected conflict time is less than a set threshold value h (unit: min), and the vertical distance is less than a preset threshold value v (unit: m), the node i and the node j are considered to be connected by an edge; representing the air traffic situation risk network structure corresponding to the time t, the horizontal threshold h and the vertical threshold v as Gh,v(t)。
(2) Establishing a single-layer multi-level air traffic situation risk network: m threshold combinations { h1, v 1; h2, v 2; ...; hm, vm }, and establishing a corresponding air traffic situation risk network structure by taking each group in the combination as a threshold value, so as to establish m single-layer single-level air traffic situation risk networks. Setting q (t) as a single-layer multi-level air traffic situation risk network corresponding to the air traffic situation at the time t, and combining the m single-layer single-level air traffic situation risk networks to form a single-layer multi-level air traffic situation risk network with the number of levels m, namely: q (t) = { Gh1,v1(t),Gh2,v2(t),...,Ghm,vm(t)}。
(3) Establishing a double-layer multi-level air traffic situation risk network: considering the air traffic situation risk from two-dimensional situation risk and three-dimensional situation risk, the single-layer multi-level air traffic situation risk network is expanded into a double-layer multi-level air traffic situation risk network. The vertical threshold v of the first layer of air traffic situation risk network is set to be infinite, namely, only the relation between the horizontal distance between two airplanes and the set threshold is considered, and the two-dimensional traffic situation is corresponded. The vertical interval threshold v of the air traffic situation risk network of the second floor may be set to be the minimum vertical interval in the airspace or a multiple thereof, corresponding to the three-dimensional traffic situation. Let Qi(t) is the ith (i =1,2) layer multi-level air traffic situation risk network set at the time t, Qi(t)={Gi h1,v1(t)，Gi h2,v2(t)，...，Gi hm,vm(t) }, in which Gi hj,vjAnd (t) the ith layer j level air traffic situation risk network at the time t is represented, and the horizontal threshold and the vertical threshold are hj and vj respectively.
Taking an example of establishing a two-level air traffic situation risk network model of a certain traffic situation as an example, an air traffic situation is shown in fig. 2. This example includes 7 aircraft (P1, P2, P3, P4, P5, P6, P7). Here, 3 and 10 minutes are taken as the horizontal threshold values, so that the flight trajectories of 3 and 10 minutes in the future need to be predicted respectively based on the current position and speed of the aircraft, as shown in fig. 2 (in the figure, the triangle symbol represents the aircraft, the broken line represents the flight trajectory of the aircraft within 3 minutes, and the solid line represents the flight trajectory of the aircraft within 10 minutes). And the vertical threshold for the layer 1 air traffic situation risk network is set to 9999 meters (i.e., regardless of aircraft vertical separation), and the vertical threshold for the layer 2 air traffic situation risk network is set to 300 meters. Then Q (t) = { Q1(t)，Q2(t)}，Q1(t) = {G1 h1,v1(t)，G1 h2,v1(t)}，Q2(t) = {G2 h1,v2(t)，G2 h2,v2(t) }, where h1=3 minutes, v1=9999 meters, h2=10 minutes, v2=300 meters (between aircraft in the area)The vertical spacing criterion). As can be seen from fig. 2, the horizontal interval between the aircraft (P1, P4, P5) and (P3, P6, P7) will be smaller than the set interval standard within 3 minutes (i.e., there is a potential conflict in the horizontal direction), so there is one edge between (P1, P4, P5) and (P3, P6, P7) in the level 1 air traffic situation risk network. Moreover, the vertical intervals between (P1, P4, P5) and between (P3, P6, P7) are smaller than the set threshold (i.e., there is a potential conflict in both horizontal and vertical directions), so there is one edge between (P1, P4, P5) and between (P3, P6, P7) in the 2-tier 1-level air traffic situation risk network. And similarly, establishing a layer 1 level 2 level and a layer 2 level air traffic situation risk network model. Thus, a two-layer two-level air traffic situation risk network corresponding to the current air traffic scene is constructed, as shown in fig. 3.
Step three, calculating topological indexes in the air traffic situation risk network: the network scale, namely the number of the aircrafts in the current airspace, is represented by N; the number of edges, i.e. the number of edges in the network, reflects the number of potential conflicts in two-dimensional and three-dimensional air traffic situations, denoted by E. The network aggregation coefficient is the average value of all node aggregation coefficients in the network (the aggregation coefficient of a node, i.e. the proportion of the number of edges existing between the neighboring nodes of the node to all possible edges), and is denoted by C.
Step four, forming an air traffic situation risk measure vector based on the calculated indexes: setting the level number of the air traffic situation risk network as 2, and setting the risk vector of the air traffic situation at the time t as M (t) = (N)1,1(t)，E1,1(t)，C1,1(t)，N1,2(t)，E1,2(t)，C1,2(t)，N2,1(t)，E2,1(t)，C2,1(t)，N2,2(t)，E2,2(t)，C2,2(t)), due to N1,1(t)、N1,2(t)、N2,1(t)、N2,2(t) values at the same time are all equal, and N is added1,1(t)、N1,2(t)、N2,1(t)、N2,2(t) the four indexes are unified as N (t); thus, the risk vector M (t) corresponding to the traffic situation at the time t is simplified into (N (t), E1,1(t)，C1,1(t)，E1,2(t)，C1,2(t)，E2,1(t)，C2,1(t)，E2,2(t)，C2,2(t)). For the scenario in fig. 2, the calculation result of each structural index in the air traffic situation is: n =7, E1,1=6，C1,1=0.86，E1,2=9，C1,2=0.62，E2,1=4，C2,1=0.43，E2,2=6，C2,2= 0.33. The corresponding air traffic situation risk vector is: m (t) = (7, 6, 0.86, 9, 0.62, 4, 0.43, 6, 0.33). The risk of the air traffic situation can be comprehensively measured from two-dimensional and three-dimensional angles based on the traffic situation risk vector.
Claims (2)
1. An air traffic situation risk measurement method based on a double-layer multistage network model is characterized by comprising the following steps:
acquiring air traffic control radar comprehensive track or ADS-B track data: dynamically acquiring flight path data according to an original data period, and extracting an aircraft target, a dynamic position and navigation element information thereof; carrying out primary coarse graining treatment according to a certain period, averaging longitude, latitude, altitude, speed and course information data of all aircraft targets in the period, and taking the averaged data as aircraft information of the current period;
step two, establishing an air traffic situation risk network model according to the result of the step one: taking the aircrafts as nodes, representing the space potential conflict relationship among the aircrafts by edges, calculating the potential conflict among all the aircrafts, and constructing a network model;
step three, calculating each network topology index in the risk network: the network scale, namely the number of the aircrafts in the current airspace, is represented by N; the number of edges, i.e. the number of edges in the network, reflects the number of potential conflicts in two-dimensional and three-dimensional air traffic situations, and is denoted by E; the network aggregation coefficient, namely the average value of the aggregation coefficients of all nodes in the network, is represented by C;
step four, forming a situation risk measure vector based on the calculated indexes: setting the level number of the network as 2, and setting the risk of the air traffic situation at the moment tThe vector is M (t) = (N)1,1(t)，E1,1(t)，C1,1(t)，N1,2(t)，E1,2(t)，C1,2(t)，N2,1(t)，E2,1(t)，C2,1(t)，N2,2(t)，E2,2(t)，C2,2(t)), due to N1,1(t)、N1,2(t)、N2,1(t)、N2,2(t) values at the same time are all equal, and N is added1,1(t)、N1,2(t)、N2,1(t)、N2,2(t) the four indexes are unified as N (t); thus, the risk vector M (t) corresponding to the traffic situation at the time t is simplified into (N (t), E1,1(t)，C1,1(t)，E1,2(t)，C1,2(t)，E2,1(t)，C2,1(t)，E2,2(t)，C2,2(t))。
2. The air traffic situation risk measurement method based on the two-layer multistage network model as claimed in claim 1, wherein the network model building comprises the following substeps:
(1) establishing a single-layer single-stage air traffic situation risk network: if a potential flight conflict exists between the ith aircraft and the jth aircraft at the time t and the predicted conflict time is less than a set level threshold h, unit: minutes, and the vertical distance is less than a predetermined vertical threshold v, in units: rice, the node i and the node j are considered to be connected by one edge; and expressing the air traffic situation risk network structures corresponding to the time t, the horizontal threshold h and the vertical threshold v as follows: gh,v(t)；
(2) Establishing a single-layer multi-level air traffic situation risk network: m threshold combinations { h1, v 1; h2, v 2; ...; hm, vm }, and establishing a corresponding air traffic situation risk network structure by taking each group in the combination as a threshold value, so as to establish m single-layer single-level air traffic situation risk networks; setting Q (t) as a single-layer multi-level air traffic situation risk network corresponding to the air traffic situation at the time t, and combining m single-layer single-level air traffic situation risk networks to form a single-layer multi-level air traffic situation risk network with the number of levels m, namely: q (t) = { Gh1,v1(t),Gh2,v2(t),...,Ghm,vm(t)}；
(3) Establishing a double-layer multi-level air traffic situation risk network: considering the air traffic situation risk from two-dimensional risk and three-dimensional risk, and expanding a single-layer multi-level air traffic situation risk network into a double-layer multi-level air traffic situation risk network; the vertical threshold v of the first layer of air traffic situation risk network is set to be infinite, namely the relationship between the horizontal distance between two airplanes and the set horizontal threshold is only considered, and the two-dimensional traffic situation is corresponded; setting a vertical threshold v of the second-layer air traffic situation risk network as the minimum vertical interval or a multiple thereof in the airspace, and corresponding to the three-dimensional traffic situation; let Qi(t) a multi-level air traffic situation risk network set of the ith layer at time t, Qi(t)={Gi h1,v1(t)，Gi h2,v2(t)，...，Gi hm,vm(t) }, in which Gi hj,vj(t) represents the ith and jth level air traffic situation risk network at the time t, i =1,2, j =1,.. multidot.m, and the horizontal threshold and the vertical threshold are h respectivelyj,vj。
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111429760A (en) * | 2020-03-30 | 2020-07-17 | 中国民用航空飞行学院 | Multidimensional aircraft collision and conflict risk evaluation system |
| CN111951613A (en) * | 2020-07-24 | 2020-11-17 | 北京航空航天大学 | Air-ground cooperative wide-area airspace security situation assessment method |
| CN115527397A (en) * | 2022-09-30 | 2022-12-27 | 中国民用航空飞行学院 | Air traffic control situation feature extraction method and device based on multimode neural network |
| CN116543602A (en) * | 2023-07-04 | 2023-08-04 | 中国民用航空飞行学院 | Situation complexity identification method and system for aircraft clusters |
| CN116935700A (en) * | 2023-09-18 | 2023-10-24 | 四川大学 | Sector traffic situation prediction method based on multi-source features |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103106813A (en) * | 2013-01-18 | 2013-05-15 | 南京航空航天大学 | Traffic situation complexity assessment method based on index system |
| CN104504937A (en) * | 2015-01-07 | 2015-04-08 | 江苏理工学院 | Aircraft trajectory prediction method of air traffic control system |
| CN106169260A (en) * | 2015-01-07 | 2016-11-30 | 江苏理工学院 | Air traffic control method |
| CN106777875A (en) * | 2016-11-18 | 2017-05-31 | 中国民航大学 | A kind of air traffic Complexity Measurement method based on double-deck multistage network model |
-
2019
- 2019-11-04 CN CN201911064743.4A patent/CN110648561A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103106813A (en) * | 2013-01-18 | 2013-05-15 | 南京航空航天大学 | Traffic situation complexity assessment method based on index system |
| CN104504937A (en) * | 2015-01-07 | 2015-04-08 | 江苏理工学院 | Aircraft trajectory prediction method of air traffic control system |
| CN106169260A (en) * | 2015-01-07 | 2016-11-30 | 江苏理工学院 | Air traffic control method |
| CN106205220A (en) * | 2015-01-07 | 2016-12-07 | 江苏理工学院 | Air traffic control method |
| CN106777875A (en) * | 2016-11-18 | 2017-05-31 | 中国民航大学 | A kind of air traffic Complexity Measurement method based on double-deck multistage network model |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111429760A (en) * | 2020-03-30 | 2020-07-17 | 中国民用航空飞行学院 | Multidimensional aircraft collision and conflict risk evaluation system |
| CN111429760B (en) * | 2020-03-30 | 2021-10-08 | 中国民用航空飞行学院 | Multidimensional aircraft collision and conflict risk evaluation system |
| CN111951613A (en) * | 2020-07-24 | 2020-11-17 | 北京航空航天大学 | Air-ground cooperative wide-area airspace security situation assessment method |
| CN115527397A (en) * | 2022-09-30 | 2022-12-27 | 中国民用航空飞行学院 | Air traffic control situation feature extraction method and device based on multimode neural network |
| CN116543602A (en) * | 2023-07-04 | 2023-08-04 | 中国民用航空飞行学院 | Situation complexity identification method and system for aircraft clusters |
| CN116543602B (en) * | 2023-07-04 | 2023-09-01 | 中国民用航空飞行学院 | Situation complexity identification method and system for aircraft clusters |
| CN116935700A (en) * | 2023-09-18 | 2023-10-24 | 四川大学 | Sector traffic situation prediction method based on multi-source features |
| CN116935700B (en) * | 2023-09-18 | 2023-12-05 | 四川大学 | Sector traffic situation prediction method based on multi-source features |
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