CN110796901A - Air traffic situation risk hotspot identification method - Google Patents
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Abstract
The invention discloses a method for identifying air traffic situation risk hot spots, which comprises the following four steps: firstly, acquiring comprehensive track data of an aircraft, and acquiring aircraft information at each moment in an air traffic situation; secondly, establishing an air traffic situation network based on the position relation between the aircrafts; then searching a community structure in the network model to form an initial hot spot sub-region; and finally, verifying and integrating the initial hot spot sub-area to form an air traffic situation risk hot spot. By adopting the method, the aircrafts related to the risk hot spot area in the air traffic situation can be automatically identified, the occupied funds are less, and the evaluation method is simple, rapid and easy to use.
Description
Technical Field
The invention relates to the field of air traffic management, in particular to an air traffic situation risk hotspot identification method based on a network community structure idea.
Background
Controllers in actual air traffic control operations often classify aircraft into different levels of risk depending on the spatial separation of the aircraft from other aircraft in order to reduce the amount of operational memory. Aircraft that are closer to each other and more susceptible to collisions are considered to be more risky aircraft and are divided into different groups according to spatial location. This particular set of aircraft reflects some local, high risk areas in the airspace, which may also be referred to as risk hot spots. The more the risk hot spot areas are, the larger the scale is, the longer the existing time is, and the corresponding air traffic situation risk degree is higher. At present, controllers mainly observe radar screens through naked eyes, and identify risk hot spot areas through judging intervals among aircrafts on the radar screens manually. The manual identification method has low efficiency, is easy to identify error hot spots by mistake, increases the workload of controllers, and finally influences the air traffic safety level. Therefore, in the air traffic management operation, it is highly required to realize the automatic identification of the risk hot spot area through an automatic system.
Disclosure of Invention
Aiming at the current situation that an automatic identification method for air traffic situation risk hotspots is lacked at present, the invention provides an identification method for air traffic situation risk hotspots, and the automatic identification of risk hotspot areas in air traffic situations is realized.
The method comprises four steps: firstly, acquiring comprehensive track data of an aircraft, and acquiring aircraft information at each moment in an air traffic situation; secondly, establishing an air traffic situation network based on the position relation between the aircrafts; then searching a community structure in the network model to form an initial hot spot sub-region; and finally, verifying the initial hot spot sub-area, and comprehensively forming the air traffic situation risk hot spot.
The technical scheme adopted by the invention is as follows: a method for identifying air traffic situation risk hot spots is realized by assistance of a computer system, wherein the computer system mainly comprises a client/server, and is characterized by comprising the following steps:
step 1, leading and processing aircraft track data: receiving the comprehensive track data of the aircraft in real time according to the sending frequency of the original data, wherein the comprehensive track data comprises longitude, latitude, altitude, speed and course information of the aircraft at each moment;
step 2, establishing an air traffic situation network model according to the result of the step 1: firstly, setting a horizontal distance threshold value as S1 and a vertical distance threshold value as S2, and constructing a network model by taking the aircrafts as nodes and representing the spatial position relation among the aircrafts by edges at each moment; let the current time betThe total number of the aircrafts in the air traffic situation at the moment isnCalculating the space distance between all the aircrafts to generaten×nIf the horizontal distance between two aircrafts is less than or equal to S1 and the vertical distance is less than S2, the aircrafts obtain the horizontal distance matrix D1, the vertical distance matrix D2 and the adjacent matrix A of the network corresponding to the air traffic situation at the momentiAircraft and aircraftjThere is a proximity relationship between them, thena i,j =1, otherwisea i,j =0, toa i,j For elements of the adjacency matrix A, i.e. thetA temporal air traffic situation network model;
step 3, searching a community structure in the network model at each moment to form an initial risk hotspot sub-region: first generate 1nThe initial value of each element in the tag array is 0, and the following process can be subdivided into the following steps:
step 3.1 search for a vertex in the adjacency matrix AV i Satisfies Flag (f)i) =0, if the search fails, i.e. all aircraft are visited, go to step 4, otherwise set Flag: (1)i) =1, in nodesV i Establishing an initial risk hotspot for the initial point search, marked asC；
Step 3.2 if for all verticesV j (0<j, j is less than or equal to n, j is not equal to i) are alla i,j =0, if the aircraft node is not adjacent to the aircraft, returning to the step 3.1 to reselect a new node, otherwise, continuing;
step 3.3 create a new risk hotspot sub-area, marked ascInitial value ofc={V i }；
Step 3.4 search for nodesV i Set of all non-visited contiguous aircraftV ’={V ’ i,...,V ’ jAnd updating risk hotspot sub-areasc=c∪V ’Setting an access mark of a corresponding node as 1;
step 3.5 searchV ’Set of contiguous aircraft of all unvisited aircraftV ’’= {V ’’ i , ...,V ’’ j And updating risk hotspot sub-areasc=c∪V ’’Setting the access flag of the corresponding node to 1, performing recursive search, and finally generating the first nodekIndividual risk hot spot sub-areac k =cReturning to the step 3.1;
step 4, verifying the initial hotspot sub-area to form an air traffic situation hotspot: firstly, deleting initial risk hot spot sub-regions with the number of nodes less than 3, and further integrating the initial risk hot spot sub-regions to constructtRisk hot spot in air traffic situation at momentC(t) I.e. byC(t) = {c(t)1,c(t)2,...,c(t) i ,...,c(t) k Therein ofc(t) i Is thatC(t) To middleiIndividual risk hot spot sub-regions.
The invention has the following beneficial effects: by adopting the air traffic situation risk hotspot identification method based on the network community structure idea, aircrafts related to risk hotspot areas in the air traffic situation can be automatically and quickly identified, the identification time of the risk hotspots in a single sector is less than 2 seconds, the method is not influenced by human factors, the workload of controllers for manually judging the risk hotspot areas can be effectively reduced, the workload of the controllers is not additionally increased, the occupied funds are less, the evaluation method is simple, quick and easy to use, the evaluation result is easy to understand, and the air traffic safety level is improved.
Drawings
FIG. 1 is a basic process flow of the present invention;
fig. 2 is a schematic diagram of a risk hot spot in an air traffic situation according to the present invention.
Detailed Description
The invention is further explained with reference to the drawings and the embodiments.
An air traffic situation risk hotspot identification method comprises the following specific steps, as shown in fig. 1:
step 1, leading and processing aircraft track data: receiving the comprehensive track data of the aircraft in real time according to the sending frequency of the original data, wherein the comprehensive track data comprises longitude, latitude, height, speed and course information of all aircrafts in an airspace at each moment;
step 2, establishing an air traffic situation network model according to the result of the step 1: first, the horizontal distance threshold is set to S1, and the vertical distance threshold is set to S2. At each moment, the aircraft is taken as a node, the spatial position relation between the aircraft is represented by an edge, and a network model is constructed. Let the current time betThe total number of the aircrafts in the air traffic situation at the moment isnCalculating the space distance between all the aircrafts to generaten×nThe aircraft horizontal distance matrix D1, the vertical distance matrix D2, and the adjacency matrix a of the network corresponding to the air traffic situation at that moment. If the horizontal distance between the two aircrafts is smaller than or equal to S1 and the vertical distance is smaller than S2, the aircraftsiAircraft and aircraftjThere is a proximity relationship between them, thena i,j =1, otherwisea i,j =0. Further on the basis ofa i,j For elements of the adjacency matrix A, i.e. thetAnd (3) a network model of air traffic situation at a moment. Examples are: if the horizontal distance threshold value S1=20 km and the vertical distance threshold value S2=600 m are set, the aircraftiAircraft and aircraftjAt a horizontal distance of 15 km and a vertical distance of 500 m, the aircraftiAnd an aircraftjThere is a close relationship between them, i.e.a i,j =1; if the aircraft isiAnd an aircraftjAt a horizontal distance of 15 km and a vertical distance of 1000 m, the aircraftiAnd an aircraftjThere is no proximity relation between them, i.e.a i,j =0. In practical application, the interval standard issued by the civil aviation bureau can be set after being properly amplified.
Step 3, searching a community structure in the network model at each moment to form an initial risk hotspot sub-region: first generate 1nThe initial value of each element in the tag array is 0, and the following process can be subdivided into the following steps:
step 3.1 search for a vertex in the adjacency matrix AV i Satisfies Flag (f)i) And = 0. If the search fails, i.e. all aircraft are visited, go to step 4. Otherwise, set Flag: (i) =1, in nodesV i Establishing an initial risk hotspot for the initial point search, marked asC。
Step 3.2 if for all verticesV j (0<j, j is less than or equal to n, j is not equal to i) are alla i,j =0, i.e. the aircraft node is not adjacent to the aircraft, return to step 3.1 to reselect a new node, otherwise continue.
Step 3.3 create a new risk hotspot sub-area, marked ascInitial value ofc={V i }。
Step 3.4 search for nodesV i Set of all non-visited contiguous aircraftV ’={V ’ i,...,V ’ jAnd updating risk hotspot sub-areasc=c∪V ’The access flag of the corresponding node is set to 1.
Step 3.5 searchV ’Set of contiguous aircraft of all unvisited aircraftV ’’= {V ’’ i , ...,V ’’ j And updating risk hotspot sub-areasc=c∪V ’’Setting the access flag of the corresponding node to 1, performing recursive search, and finally generating the first nodekIndividual risk hot spot sub-areac k =cAnd returning to the step 3.1.
Step 4, verifying the initial hotspot sub-area to form an air traffic situation hotspot: firstly, deleting initial risk hotspot sub-regions with the number of nodes less than 3; further integrating the initial risk hot spot sub-regions to constructtRisk hot spot in air traffic situation at momentC(t) I.e. byC(t) = {c(t)1,c(t)2,...,c(t) i ,...,c(t) k Therein ofc(t) i Is thatC(t) To middleiIndividual risk hot spot sub-regions.
Fig. 2 shows a schematic diagram of the automatic identification result of the hot spot of the air traffic situation risk at a certain time, the air traffic situation has 10 aircrafts, and the mapped network model has 10 nodes. Assuming that the horizontal distance threshold S1=20 km and the vertical distance threshold S2=600 m, the horizontal distance and the vertical distance between the nodes are calculated and compared with S1 and S2. The risk hotspot in this situation comprises two hotspot sub-areas, denoted as risk hotspot sub-area 1 and risk hotspot sub-area 2. The risk hot spot sub-area 1 comprises three nodes N3, N5 and N6, wherein the three nodes correspond to three aircrafts P3, P5 and P6 respectively, the horizontal distances between N3 and N5, between N3 and N6 and between N5 and N6 are respectively 18, 14 and 12, and the vertical distances are respectively 300, 300 and 0. The risk hot spot sub-area 2 comprises three nodes N8, N9 and N10, wherein the three nodes correspond to three aircrafts P8, P9 and P10 respectively, the horizontal distances between N8 and N9, between N8 and N10 and between N9 and N10 are respectively 17, 11 and 10, and the vertical distances are respectively 400, 100 and 500. The other 4 aircraft N1, N2, N4, N7 in this situation are not risk hotspots.
Claims (1)
1. A method for identifying air traffic situation risk hot spots is realized by assistance of a computer system, wherein the computer system mainly comprises a client/server, and is characterized by comprising the following steps:
step 1, leading and processing aircraft track data: receiving the comprehensive track data of the aircraft in real time according to the sending frequency of the original data, wherein the comprehensive track data comprises longitude, latitude, altitude, speed and course information of the aircraft at each moment;
step 2, establishing an air traffic situation network model according to the result of the step 1: firstly, setting a horizontal distance threshold value as S1 and a vertical distance threshold value as S2, and constructing a network model by taking the aircrafts as nodes and representing the spatial position relation among the aircrafts by edges at each moment; let the current time betThe total number of the aircrafts in the air traffic situation at the moment isnCalculating the space distance between all the aircrafts to generaten×nIf the horizontal distance between two aircrafts is less than or equal to S1 and the vertical distance is less than S2, the aircrafts obtain the horizontal distance matrix D1, the vertical distance matrix D2 and the adjacent matrix A of the network corresponding to the air traffic situation at the momentiAircraft and aircraftjThere is a proximity relationship between them, thena i,j =1, otherwisea i,j =0, toa i,j For elements of the adjacency matrix A, i.e. thetA temporal air traffic situation network model;
step 3, searching a community structure in the network model at each moment to form an initial risk hotspot sub-region: first generate 1nThe initial value of each element in the tag array is 0, and the following process can be subdivided into the following steps:
step 3.1 search for a vertex in the adjacency matrix AV i Satisfies Flag (f)i) =0, if the search fails, i.e. all aircraft are visited, go to step 4, otherwise set Flag: (1)i) =1, in nodesV i Establishing initial risk for an origin searchHot spots, marked asC；
Step 3.2 if for all verticesV j (0<j, j is less than or equal to n, j is not equal to i) are alla i,j =0, if the aircraft node is not adjacent to the aircraft, returning to the step 3.1 to reselect a new node, otherwise, continuing;
step 3.3 create a new risk hotspot sub-area, marked ascInitial value ofc={V i }；
Step 3.4 search for nodesV i Set of all non-visited contiguous aircraftV ’={V ’ i,...,V ’ jAnd updating risk hotspot sub-areasc=c∪V ’Setting an access mark of a corresponding node as 1;
step 3.5 searchV ’Set of contiguous aircraft of all unvisited aircraftV ’’= {V ’’ i , ...,V ’’ j And updating risk hotspot sub-areasc=c∪V ’’Setting the access flag of the corresponding node to 1, performing recursive search, and finally generating the first nodekIndividual risk hot spot sub-areac k =cReturning to the step 3.1;
step 4, verifying the initial hotspot sub-area to form an air traffic situation hotspot: firstly, deleting initial risk hot spot sub-regions with the number of nodes less than 3, and further integrating the initial risk hot spot sub-regions to constructtRisk hot spot in air traffic situation at momentC(t) I.e. byC(t) = {c(t)1,c(t)2,...,c(t) i ,...,c(t) k Therein ofc(t) i Is thatC(t) To middleiIndividual risk hot spot sub-regions.
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