CN114187783B - Method for analyzing and predicting potential conflict in airport flight area - Google Patents
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Abstract
The invention provides a method for analyzing and predicting potential conflict in an airport flight area, which comprises the following steps: building a complex network model of a flight area; step two: determining characteristic indexes of a complex network model of the flight area; step three: acquiring a characteristic index time sequence; step four: and predicting the potential conflict of the airport ground flight area. The method for analyzing and predicting the potential conflict of the airport flight zone can accurately predict the potential conflict of the airport ground flight zone, provide help for control personnel, aircrafts or vehicle drivers, improve vigilance when collision is possible, move forward an operation risk port of the airport flight zone, and effectively reduce the possibility of collision.
Description
Technical Field
The invention belongs to the field of airport traffic management, and particularly relates to a method for analyzing and predicting potential conflict in an airport flight area.
Background
Civil airports have been drastically increased in number, scale, traffic, etc. in recent years as air transport infrastructure; the problems of collision conflict among movable targets, potential safety hazards in operation of the flight area and the like are gradually generated, and huge pressure and challenges are brought to the control work of the airport flight area.
Under the prior art condition, the research of applying the complex network theory to the civil aviation field mostly takes airports as nodes, takes the air route between the two airports as sides to carry out complex network modeling, generally takes the air route points as nodes, takes the air segments as sides to establish an air traffic complex network model, and is used for flow analysis, congestion identification and the like of air traffic; the unmanned aerial vehicle is used as a node, the influence relationship is used as a connecting edge, a complex network model of the unmanned aerial vehicle cluster is established, and the mutual influence and conflict generation mechanism in the unmanned aerial vehicle operation process are analyzed.
The airport flying area is a place for taking off, landing and guaranteeing an airplane, the airport flying area is a dynamic evolution open system, randomness and regularity coexist, the flying area active target set is a complex system formed by a plurality of units, the complex system has the characteristics of small world, no scale and the like of a complex network, and the complex network modeling method in the prior art cannot be used for effectively predicting potential conflict of the airport ground flying area.
Disclosure of Invention
In view of the above, the invention aims to provide a method for analyzing and predicting potential conflict in an airport flight area, which can accurately predict the potential conflict in the airport ground flight area, provide assistance for control personnel and aircrafts or vehicle drivers, improve vigilance when a collision is possible, move forward an operation risk port of the airport flight area, and effectively reduce the possibility of collision.
In order to achieve the above purpose, the technical scheme of the invention is realized as follows:
a method for airport flight zone potential conflict analysis prediction, comprising:
step one: building a complex network model of a flight area: in an airport ground flight area, taking an aircraft target geometric center in motion and a vehicle target geometric center in motion as nodes, and dividing a circular area by taking the nodes as centers, wherein when two circular areas are intersected, potential conflict exists between the two targets, and a connecting line between the two nodes with the potential conflict serves as a connecting edge;
step two: determining characteristic indexes of a complex network model of the flight area;
step three: acquiring a characteristic index time sequence: the method comprises the steps of establishing a complex network model of the flight zone in the first step at each time point according to a set time interval for the flight zone needing potential conflict prediction, calculating corresponding characteristic indexes, and forming a time sequence of the characteristic indexes;
step four: predicting potential conflicts in airport ground flight areas: training the long-short-period memory neural network model LSTM by using the obtained characteristic index time sequence to realize the characteristic index prediction of the airport ground flying area in the future.
In the second step, the feature index includes:
average degree ofRepresenting an average value of the number of active targets around each node in the complex network model of the flight area, wherein the average value has potential conflict with the active targets;
average point strengthRepresenting an average value of potential conflict pressures existing at each node in the complex network model of the flight area;
average weighted cluster coefficientRepresenting the aggregation degree of the aircraft targets or the movable targets around the vehicle targets in the complex network model of the flight area;
network density ND: representing the proportion of the number of the connecting edges in the complex network model of the flying area to the maximum number of the connecting edges which can be accommodated in the network.
Compared with the prior art, the method for analyzing and predicting the potential conflict of the airport flight area has the following advantages:
the method for analyzing and predicting the potential conflict of the airport flight zone can accurately predict the potential conflict of the airport ground flight zone, provides help for control personnel and aircrafts or vehicle drivers, improves vigilance when collision is possible, moves forward an operation risk port of the airport flight zone, and effectively reduces the possibility of collision.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the invention.
In the drawings:
FIG. 1 is a schematic diagram illustrating steps of a method for airport flight zone potential conflict analysis and prediction according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a method for airport flight zone potential conflict analysis and prediction according to an embodiment of the present invention;
fig. 3 is a flowchart of a method for analyzing and predicting potential conflict in an airport flight area according to an embodiment of the present invention.
Detailed Description
It should be noted that, without conflict, the embodiments of the present invention and features of the embodiments may be combined with each other.
In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, are merely for convenience in describing the present invention and simplifying the description, and do not indicate or imply that the devices or elements referred to must have a specific orientation, be configured and operated in a specific orientation, and thus should not be construed as limiting the present invention. Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first", "a second", etc. may explicitly or implicitly include one or more such feature. In the description of the present invention, unless otherwise indicated, the meaning of "a plurality" is two or more.
In the description of the present invention, it should be noted that, unless explicitly specified and limited otherwise, the terms "mounted," "connected," and "connected" are to be construed broadly, and may be either fixedly connected, detachably connected, or integrally connected, for example; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communication between two elements. The specific meaning of the above terms in the present invention can be understood by those of ordinary skill in the art in a specific case.
The invention will be described in detail below with reference to the drawings in connection with embodiments.
As shown in fig. 3, a method for airport flight zone potential conflict analysis prediction, comprising:
step one: building a complex network model of a flight area: in an airport ground flight area, taking an aircraft target geometric center in motion and a vehicle target geometric center in motion as nodes, and dividing a circular area by taking the nodes as centers, wherein when two circular areas are intersected, potential conflict exists between the two targets, and a connecting line between the two nodes with the potential conflict serves as a connecting edge;
step two: determining characteristic indexes of a complex network model of the flight area;
step three: acquiring a characteristic index time sequence: the method comprises the steps of establishing a complex network model of the flight zone in the first step at each time point according to a set time interval for the flight zone needing potential conflict prediction, calculating corresponding characteristic indexes, and forming a time sequence of the characteristic indexes;
step four: predicting potential conflicts in airport ground flight areas: training the long-short-period memory neural network model LSTM by using the obtained characteristic index time sequence to realize the characteristic index prediction of the airport ground flying area in the future.
In the second step, the feature index includes:
average degree ofRepresenting an average value of the number of active targets around each node in the complex network model of the flight area, wherein the average value has potential conflict with the active targets;
average point strengthRepresenting an average value of potential conflict pressures existing at each node in the complex network model of the flight area;
as shown in fig. 2, the average weighted cluster coefficientsRepresenting the aggregation degree of the aircraft targets or the movable targets around the vehicle targets in the complex network model of the flight area;
network density ND: representing the proportion of the number of connecting edges existing in the complex network model of the flight area to the maximum number of the connecting edges which can be accommodated in the network;
network efficiency NE: and the average value of the transit times required by any node to another node in the complex network model of the flight area is represented.
In step two:
average degree ofThe calculation method comprises the following steps:
n is the total number of nodes; i and j each represent a node in the network; i, j=1, 2,3,4, …, n and i+.j; alpha ij Is the connecting edge from node i to node j, if there is a connecting edge between node i and node j, then alpha ij =1, otherwise α ij ＝0；
Average point strengthThe calculation method comprises the following steps:
wherein n is the total number of nodes; i and j each represent a node in the network; i, j=1, 2,3,4, …, n and i+.j; alpha ij Is the connecting edge from node i to node j, if there is a connecting edge between node i and node j, then alpha ij =1, otherwise α ij ＝0；ω ij Representing the edge weight from node i to node j;representing the imminent rate of node i to node j; d (D) ij Representing the relative distance between node i and node j, V ij Representing the relative speed between node i and node j;
in the embodiment, the position and speed data of the aircraft and the vehicle are obtained from an advanced scene activity guiding and controlling system A-SMGCS, including the longitude and latitude coordinates of the aircraft and the vehicle, the moving direction and the speed, and the relative distance D between two moving targets is obtained through vector addition and subtraction calculation ij And relative velocity V ij 。
Average weighted cluster coefficientThe calculation method comprises the following steps:
c i the weighted cluster coefficients representing node i are represented, k i is the degree of the node i, j and k are two nodes with a connecting edge with the node i respectively, and j is not equal to k; when k is i <2, c i ＝0；
ω ij Representing the edge weight, ω, of the join from node i to node j jk Representing the edge weight, ω, of the joint edge from node j to node k ik Representing the edge-connecting side weights from the node i to the node k, wherein max (omega) is the maximum value of the edge-connecting side weights in the complex network model of the flight area;
the network density ND calculation method comprises the following steps:
n is the total number of nodes; e represents the number of connected edges in the complex network model of the flight area;
the network efficiency NE calculation method is as follows:
n is the total number of nodes; i and j each represent a node in the network; i, j=1, 2,3,4, …, n and i+.j; d, d ij The shortest path distance from the node i to the node j is shown, wherein the shortest path is the shortest path from the node i to the other node j along the connecting edge, and the path with the minimum sum of the edge weights is the shortest path, and the sum of the edge weights of the shortest paths is the shortest path distance; d when there is no edge between node i and node j ij ＝0。
As shown in fig. 1, in step one: the radius of the aircraft delimiting the circular area is 150 meters and the radius of the vehicle delimiting the circular area is 100 meters.
In step three: the time interval is 10 seconds.
In one embodiment, in a flight area needing potential conflict prediction, the time interval is 10 seconds, the total evolution is 1000 minutes, namely modeling is performed on a complex network model of the flight area for 6000 times, the time sequence of the characteristic indexes of the first 5500 times is used as a training set, the time sequence of the characteristic indexes of the last 500 times is used as a test set, and the long-short-period memory neural network model LSTM is obtained to predict the potential conflict of the characteristic indexes of the ground flight area of the airport in the future, so that the on-site personnel can conveniently schedule or avoid the characteristic indexes in time.
Complex network model characteristic index
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, alternatives, and improvements that fall within the spirit and scope of the invention.
Claims (3)
1. A method for airport flight zone potential conflict analysis prediction, characterized by: comprising the following steps:
step one: building a complex network model of a flight area: in an airport ground flight area, taking an aircraft target geometric center in motion and a vehicle target geometric center in motion as nodes, and dividing a circular area by taking the nodes as centers, wherein when two circular areas are intersected, potential conflict exists between the two targets, and a connecting line between the two nodes with the potential conflict serves as a connecting edge;
step two: determining characteristic indexes of a complex network model of the flight area;
step three: acquiring a characteristic index time sequence: the method comprises the steps of establishing a complex network model of the flight zone in the first step at each time point according to a set time interval for the flight zone needing potential conflict prediction, calculating corresponding characteristic indexes, and forming a time sequence of the characteristic indexes;
step four: predicting potential conflicts in airport ground flight areas: training a long-short-period memory neural network model LSTM by using the obtained characteristic index time sequence to realize the characteristic index prediction of the ground flight area of the airport in the future;
in the second step, the feature index includes:
average degree ofRepresenting an average value of the number of active targets around each node in the complex network model of the flight area, wherein the average value has potential conflict with the active targets;
average point strengthRepresenting an average value of potential conflict pressures existing at each node in the complex network model of the flight area;
average weighted cluster coefficientRepresenting the aggregation degree of the aircraft targets or the movable targets around the vehicle targets in the complex network model of the flight area;
network density ND: representing the proportion of the number of connecting edges existing in the complex network model of the flight area to the maximum number of the connecting edges which can be accommodated in the network;
network efficiency NE: representing the average value of the transfer times required by any one node to another node in the complex network model of the flight area;
average degree ofThe calculation method comprises the following steps:
n is the total number of nodes; i and j each represent a node in the network; i, j=1, 2,3,4, …, n and i+.j; alpha ij Is the connecting edge from node i to node j, if there is a connecting edge between node i and node j, then alpha ij =1, otherwise α ij ＝0；
Average point strengthThe calculation method comprises the following steps:
wherein n is the total number of nodes; i and j each represent a node in the network; i, j=1, 2,3,4, …, n and i+.j; alpha ij Is the connecting edge from node i to node j, if there is a connecting edge between node i and node j, then alpha ij =1, otherwise α ij ＝0；ω ij Representing the edge weight from node i to node j;representing the imminent rate of node i to node j; d (D) ij Representing the relative distance between node i and node j, V ij Representing the relative speed between node i and node j;
average weighted cluster coefficientThe calculation method comprises the following steps:
c i : weighted cluster coefficient, k, representing node i i The degree of the node i, j and k are two nodes with connected edges with the node i respectively, and j is not equal to k; when k is i <2, c i ＝0；
ω ij Representing the edge weight, ω, of the join from node i to node j jk Representing the edge weight, ω, of the joint edge from node j to node k ik Representing the edge-connecting side weights from the node i to the node k, wherein max (omega) is the maximum value of the edge-connecting side weights in the complex network model of the flight area;
the network density ND calculation method comprises the following steps:
n is the total number of nodes; e represents the number of connected edges in the complex network model of the flight area;
the network efficiency NE calculation method is as follows:
n is the total number of nodes; i and j each represent a node in the network; i, j=1, 2,3,4, …, n and i+.j; d, d ij The shortest path distance from the node i to the node j is shown, wherein the shortest path is the shortest path from the node i to the other node j along the connecting edge, and the path with the minimum sum of the edge weights is the shortest path, and the sum of the edge weights of the shortest paths is the shortest path distance; d when there is no edge between node i and node j ij ＝0。
2. A method for airport flight zone potential conflict analysis prediction in accordance with claim 1, wherein: in step one: the radius of the aircraft delimiting the circular area is 150 meters and the radius of the vehicle delimiting the circular area is 100 meters.
3. A method for airport flight zone potential conflict analysis prediction in accordance with claim 1, wherein: in step three: the time interval is 10 seconds.
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| 基于复杂网络的空中交通复杂性识别方法;吴明功;叶泽龙;温祥西;蒋旭瑞;;北京航空航天大学学报;第46卷(第05期);第839-850页 * |
| 基于航空器位置-速度矢量关系的短期冲突检测算法;王运锋;贺文红;刘健波;;四川大学学报(工程科学版)(第05期);全文 * |
| 无人机感知避让技术分析;景晓年;梁晓龙;张佳强;朱磊;;火力与指挥控制(第04期);全文 * |
| 无人机融入空域的冲突感知与避让;樊凯等;第八届中国指挥控制大会论文集》;第693-698页 * |
| 机场终端区航空器飞行冲突风险预测方法研究;高扬;王向章;郑涤滨;;中国安全科学学报(第01期);全文 * |
| 空战飞行对敌目标逼近航迹预测仿真;张振兴;杨任农;张彬超;房育寰;樊蓉;;空军工程大学学报(自然科学版)(第02期);全文 * |
| 非合作大型UAVs飞行冲突预测方法研究;高扬;武文涛;贾晓珊;许铭赫;;中国安全科学学报(第04期);全文 * |
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