CN115527397B - Air traffic control situation feature extraction method and device based on multimode neural network - Google Patents
Air traffic control situation feature extraction method and device based on multimode neural network Download PDFInfo
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- CN115527397B CN115527397B CN202211237546.XA CN202211237546A CN115527397B CN 115527397 B CN115527397 B CN 115527397B CN 202211237546 A CN202211237546 A CN 202211237546A CN 115527397 B CN115527397 B CN 115527397B
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G5/00—Traffic control systems for aircraft, e.g. air-traffic control [ATC]
- G08G5/0043—Traffic management of multiple aircrafts from the ground
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G5/00—Traffic control systems for aircraft, e.g. air-traffic control [ATC]
- G08G5/0073—Surveillance aids
- G08G5/0082—Surveillance aids for monitoring traffic from a ground station
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G5/00—Traffic control systems for aircraft, e.g. air-traffic control [ATC]
- G08G5/06—Traffic control systems for aircraft, e.g. air-traffic control [ATC] for control when on the ground
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G5/00—Traffic control systems for aircraft, e.g. air-traffic control [ATC]
- G08G5/06—Traffic control systems for aircraft, e.g. air-traffic control [ATC] for control when on the ground
- G08G5/065—Navigation or guidance aids, e.g. for taxiing or rolling
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Abstract
The invention discloses a method and a device for extracting empty pipe situation characteristics based on a multimode neural network, wherein the method comprises the following steps: firstly, acquiring empty pipe monitoring data and preprocessing; then, classifying the monitoring data, classifying longitude, latitude, course and horizontal speed data into two-dimensional attitude data according to two modes of a convolutional neural network and a fully-connected neural network, and classifying aircraft identification number, altitude and vertical speed data into other effective data; and drawing a two-dimensional attitude graph of the empty pipe by using the two-dimensional attitude data as input of a convolutional neural network, extracting the horizontal attitude characteristics of the empty pipe, constructing two-dimensional structured data by using the rest effective data, and extracting the number and height attitude characteristics of the air-handling aircraft. And finally, outputting the extracted empty pipe situation characteristics through characteristic fusion. The invention can be applied to the air traffic management process, extracts comprehensive empty pipe situation characteristics and improves the level of empty pipe automation and intellectualization.
Description
Technical Field
The invention relates to the field of air traffic management, in particular to an air management situation feature extraction method and device based on a multimode neural network.
Background
The air traffic control process refers to that a controller makes reasonable control decisions according to the observed empty pipe situation, and directs and guides the aircrafts in the controlled empty area. Air traffic management is developing to automation and intellectualization, and comprehensive, accurate and efficient air traffic situation feature extraction can effectively relieve the pressure of a controller and improve aviation safety and operation efficiency.
The air traffic situation has the characteristics of a large number of aircrafts, dynamic change and high parameter dimension of the aircrafts, so that the existing method is difficult to comprehensively extract the characteristics of the air traffic situation. The method for processing the number of dynamic aircrafts can limit the parameter dimension of the aircrafts and indirectly limit the use scene of the method. The method for processing the high-dimensional aircraft parameters has the limitation of input scale and can be only applied to scenes with a small number of aircraft and no change. Therefore, it is necessary to study a high-level empty pipe situation feature extraction method.
The deep neural network has extremely strong feature extraction capability, and subversion research results are obtained in various fields. There are also many studies in the field of air traffic management, such as incoming and outgoing flight sequencing, air traffic flow prediction, aircraft conflict resolution, etc. However, a single type of neural network is not sufficient for extracting the empty pipe situation characteristics in the current complex airspace environment, and the structure of the neural network needs to be improved in a targeted manner according to the empty pipe situation characteristics. The deep neural network with the multimode structure is a novel neural network structure, situation data are classified according to feature extraction preferences of different neural networks, corresponding features are extracted by using different neural networks respectively and then fused, and finally comprehensive situation features are output.
Disclosure of Invention
According to the air traffic situation feature extraction method and device, different air traffic situation features are respectively extracted by classifying air traffic situations and using neural networks with different characteristics, air traffic situation feature data are output after fusion processing, and comprehensive air traffic situation features are extracted in an air traffic situation scene with large number and dynamic change of aircrafts.
In order to achieve the above purpose, the invention adopts the following technical scheme:
a method for extracting empty pipe situation features based on a multimode neural network comprises the following steps:
step one, monitoring data input; acquiring air pipe monitoring data acquired by air pipe monitoring equipment;
step two, monitoring data preprocessing; preprocessing the acquired air traffic control monitoring data to obtain preprocessed air traffic control monitoring data;
step three, monitoring data classification; classifying the preprocessed air traffic control monitoring data into categories consistent with the neural network modes according to different modes of the neural network to obtain classified air traffic control monitoring data;
step four, data item processing; converting the classified air traffic control monitoring data into input data of a multimode neural network, and respectively extracting features by the multimode neural network;
fifthly, carrying out feature fusion treatment; and fusing the characteristics extracted by the multimode neural network, and outputting the fused empty pipe situation characteristics.
In the second step, the preprocessing is performed on the obtained air traffic control monitoring data, including deleting invalid repeated points in the air traffic control monitoring data, interpolating and supplementing missing points in the air traffic control monitoring data, and converting coordinates of the aircraft in a longitude and latitude coordinate system into coordinates in a cartesian coordinate system.
Further, in the third step, the modes of the neural network include a convolutional neural network and a fully-connected neural network; the monitoring data are divided into two types, including two-dimensional attitude data and other effective data, wherein the two-dimensional attitude data comprises longitude, latitude, heading and horizontal speed, and the other effective data comprises aircraft identification number, altitude and vertical speed.
In a fourth step, the classified air traffic control monitoring data is converted into input data of the multimode neural network, and the multimode neural network performs feature extraction respectively, and specifically includes the following steps:
drawing a two-dimensional situation map of the empty pipe by using the two-dimensional gesture data;
constructing two-dimensional structured data by using the rest effective data, wherein each row represents an aircraft, and each column of data sequentially comprises an identification number, a height and a vertical speed of the aircraft;
taking the blank pipe two-dimensional situation map as input of a convolutional neural network, wherein the output of the convolutional neural network is blank pipe horizontal plane situation characteristics of a one-dimensional vector format;
and taking the two-dimensional structured data as the input of a fully-connected neural network, wherein the output of the fully-connected neural network is the number and height situation characteristics of the air-handling aircraft in a one-dimensional vector format.
In a fifth step, the feature extracted by the multimode neural network is fused by using a neural network, and the method specifically includes the following steps:
the method comprises the steps of taking the empty pipe horizontal plane situation characteristics of a one-dimensional vector format output by a convolutional neural network and the number and height situation characteristics of air pipe aircrafts of the one-dimensional vector format output by a fully-connected neural network as inputs of the neural network;
the output of the neural network is the empty pipe situation characteristic of a one-dimensional vector format.
An empty pipe situation feature extraction device applying an empty pipe situation feature extraction method based on a multimode neural network, comprising one or more processors and a storage device, wherein the storage device is used for storing one or more programs, and when the one or more programs are executed by the one or more processors, the one or more processors implement the method of any one of claims 1 to 5.
Compared with the prior art, the invention has the following advantages and effects:
(1) The invention utilizes the convolutional neural network to extract the horizontal situation characteristics of the empty pipe, utilizes the fully-connected neural network to extract the number and height situation characteristics of the empty pipe aircrafts, and can output the comprehensive situation characteristics of the empty pipe after fusion.
(2) The invention perfects the basic links of intelligent air traffic control, and the comprehensive air traffic situation characteristics are helpful for the research and use of other link automation methods and intelligent methods in the air traffic control process.
Drawings
FIG. 1 is a flow chart of a method for extracting empty pipe situation features based on a multimode neural network;
FIG. 2 is a schematic view of aircraft monitoring data according to the present embodiment;
FIG. 3 is a two-dimensional situation map of the hollow tube of the present embodiment;
fig. 4 is a diagram of a multimode neural network according to the present embodiment.
Detailed Description
The technical solution of the present invention will be described in further detail with reference to the accompanying drawings, but the scope of the present invention is not limited to the following description.
For the purpose of making the technical solution and advantages of the present invention more apparent, the present invention will be further described in detail with reference to the accompanying drawings and examples. It should be understood that the particular embodiments described herein are illustrative only and are not intended to limit the invention, i.e., the embodiments described are merely some, but not all, of the embodiments of the invention. The components of the embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the invention, as presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be made by a person skilled in the art without making any inventive effort, are intended to be within the scope of the present invention. It is noted that relational terms such as "first" and "second", and the like, are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions.
Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises the element.
The features and capabilities of the present invention are described in further detail below in connection with the examples.
As shown in fig. 1, the empty pipe situation feature extraction method based on the multimode neural network comprises the following steps:
step one, monitoring data input; acquiring air pipe monitoring data acquired by air pipe monitoring equipment;
step two, monitoring data preprocessing; preprocessing the acquired air traffic control monitoring data to obtain preprocessed air traffic control monitoring data;
step three, monitoring data classification; classifying the preprocessed air traffic control monitoring data into categories consistent with the neural network modes according to different modes of the neural network to obtain classified air traffic control monitoring data;
step four, data item processing; converting the classified air traffic control monitoring data into input data of a multimode neural network, and respectively extracting features by the multimode neural network;
fifthly, carrying out feature fusion treatment; and fusing the characteristics extracted by the multimode neural network, and outputting the fused empty pipe situation characteristics.
And step two, preprocessing the acquired air traffic control monitoring data, which comprises deleting invalid repeated points in the air traffic control monitoring data, interpolating and supplementing the missing points in the air traffic control monitoring data, and converting coordinates of the aircraft in a longitude and latitude coordinate system into coordinates in a Cartesian coordinate system.
In the third step, the modes of the neural network comprise a convolutional neural network and a fully-connected neural network; the monitoring data are divided into two types, including two-dimensional attitude data and other effective data, wherein the two-dimensional attitude data comprises longitude, latitude, heading and horizontal speed, and the other effective data comprises aircraft identification number, altitude and vertical speed.
In the fourth step, the classified air traffic control monitoring data are converted into input data of the multimode neural network, and the multimode neural network performs feature extraction respectively, and specifically comprises the following steps:
drawing a two-dimensional situation map of the empty pipe by using the two-dimensional gesture data;
constructing two-dimensional structured data by using the rest effective data, wherein each row represents an aircraft, and each column of data sequentially comprises an identification number, a height and a vertical speed of the aircraft;
taking the blank pipe two-dimensional situation map as input of a convolutional neural network, wherein the output of the convolutional neural network is blank pipe horizontal plane situation characteristics of a one-dimensional vector format;
and taking the two-dimensional structured data as the input of a fully-connected neural network, wherein the output of the fully-connected neural network is the number and height situation characteristics of the air-handling aircraft in a one-dimensional vector format.
In the fifth step, a neural network is used to fuse the features extracted by the multimode neural network, and the method specifically comprises the following steps:
the method comprises the steps of taking the empty pipe horizontal plane situation characteristics of a one-dimensional vector format output by a convolutional neural network and the number and height situation characteristics of air pipe aircrafts of the one-dimensional vector format output by a fully-connected neural network as inputs of the neural network;
the output of the neural network is the empty pipe situation characteristic of a one-dimensional vector format.
An empty pipe situation feature extraction device applying an empty pipe situation feature extraction method based on a multimode neural network, comprising one or more processors and a storage device, wherein the storage device is used for storing one or more programs, and when the one or more programs are executed by the one or more processors, the one or more processors implement the method of any one of claims 1 to 5.
Specifically, (1) monitoring data input, acquiring air-traffic monitoring data acquired by air-traffic monitoring equipment;
in this embodiment, there are three aircraft in total, and each row represents one piece of monitoring information, and time number, transponder code for identifying aircraft, longitude, latitude, horizontal speed, vertical speed, altitude, and heading are sequentially from left to right as shown in fig. 2.
(2) Preprocessing the monitoring data, namely preprocessing the acquired air traffic control monitoring data to obtain preprocessed air traffic control monitoring data;
in this embodiment, coordinate conversion is performed, in which the region center is used as the origin of coordinates, the north direction is the y-axis, the east direction is the x-axis, and longitude and latitude information is converted into cartesian coordinates.
(3) Classifying the monitoring data, namely classifying the preprocessed air traffic control monitoring data into categories consistent with the modes of the neural network according to different modes of the neural network to obtain classified air traffic control monitoring data;
in this embodiment, the modes of the neural network include two types of convolutional neural networks and fully-connected neural networks; the surveillance data are divided into two classes, namely longitude, latitude, heading and horizontal speed are two-dimensional attitude data, and the transponder code for identifying the aircraft, altitude and vertical speed are the rest effective data.
(4) Data item division processing, namely converting the classified air traffic control monitoring data into input data of a multimode neural network, and respectively extracting features by the multimode neural network;
in this embodiment, two-dimensional attitude data are used to draw a two-dimensional attitude diagram of an empty pipe, as shown in fig. 3, a solid circle is used to represent an aircraft, the position of the solid circle in the diagram represents the horizontal position of the aircraft in an airspace, a solid straight line is used to represent the heading and speed of the aircraft, the tail of the solid straight line has no arrow, the connection represents the center of the solid circle of the aircraft, the head of the solid straight line has an arrow, the horizontal direction of the aircraft is represented, the length of the solid straight line represents the horizontal speed of the aircraft, and the positions, the relative positions, the headings and the horizontal speeds of three aircraft in this embodiment are all contained in one two-dimensional diagram;
constructing two-dimensional structured data by using the rest effective data, wherein each row represents an aircraft, and each column of data sequentially comprises transponder codes for identifying the aircraft, height and vertical speed;
in this embodiment, two kinds of neural networks are used to extract the air traffic control horizontal plane situation feature and the air traffic control aircraft number and altitude situation feature, respectively, as shown in fig. 4;
taking the blank pipe two-dimensional situation map as input of a convolutional neural network, wherein the output of the convolutional neural network is blank pipe horizontal plane situation characteristics of a one-dimensional vector format;
and taking the two-dimensional structured data as the input of a fully-connected neural network, wherein the output of the fully-connected neural network is the number and height situation characteristics of the air-handling aircraft in a one-dimensional vector format.
(5) Feature fusion processing, namely fusing the features extracted by the multimode neural network and outputting the fused empty pipe situation features;
in this embodiment, the empty pipe horizontal plane situation feature of the one-dimensional vector format output by the convolutional neural network and the number and height situation features of the air-handling aircraft of the one-dimensional vector format output by the fully-connected neural network are fused by using one fully-connected neural network as the input of the neural network, as shown in fig. 4. The output of the neural network is the empty pipe situation characteristic of a one-dimensional vector format.
Based on the unified invention concept, the invention also provides a device for extracting the empty pipe situation characteristics based on the multimode neural network, which comprises one or more processors and a storage device, wherein the storage device is used for storing one or more programs, and when the one or more programs are executed by the one or more processors, the one or more processors realize the method of the invention.
Specifically, for example, the empty pipe situation feature extraction device includes: the system comprises a monitoring data acquisition module, a monitoring data preprocessing module, a two-dimensional attitude data processing module, a structured data processing module and an empty pipe situation characteristic fusion output module. Moreover, the modules are each capable of reading data in a storage device, and each module has one or more processors.
The above examples illustrate only one embodiment of the invention, which is described in more detail and is not to be construed as limiting the scope of the invention. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the invention, which are all within the scope of the invention. Accordingly, the protection scope of the present invention is subject to the claims.
The foregoing is merely a preferred embodiment of the invention, and it is to be understood that the invention is not limited to the form disclosed herein but is not to be construed as excluding other embodiments, but is capable of numerous other combinations, modifications and environments and is capable of modifications within the scope of the inventive concept, either as taught or as a matter of routine skill or knowledge in the relevant art. And that modifications and variations which do not depart from the spirit and scope of the invention are intended to be within the scope of the appended claims.
Claims (2)
1. The empty pipe situation feature extraction method based on the multimode neural network is characterized by comprising the following steps of:
step one, monitoring data input; acquiring air pipe monitoring data acquired by air pipe monitoring equipment;
step two, monitoring data preprocessing; preprocessing the acquired air traffic control monitoring data to obtain preprocessed air traffic control monitoring data;
step three, monitoring data classification; classifying the preprocessed air traffic control monitoring data into categories consistent with the neural network modes according to different modes of the neural network to obtain classified air traffic control monitoring data;
step four, data item processing; converting the classified air traffic control monitoring data into input data of a multimode neural network, and respectively extracting features by the multimode neural network;
fifthly, carrying out feature fusion treatment; fusing the characteristics extracted by the multimode neural network, and outputting the fused empty pipe situation characteristics;
step two, preprocessing the acquired air traffic control monitoring data, which comprises deleting invalid repeated points in the air traffic control monitoring data, interpolating and supplementing the missing points in the air traffic control monitoring data, and converting coordinates of the aircraft in a longitude and latitude coordinate system into coordinates in a Cartesian coordinate system;
in the third step, the modes of the neural network comprise a convolutional neural network and a fully-connected neural network; the monitoring data are divided into two types, including two-dimensional attitude data and other effective data, wherein the two-dimensional attitude data comprises longitude, latitude, heading and horizontal speed, and the other effective data comprises aircraft identification number, altitude and vertical speed;
in the fourth step, the classified air traffic control monitoring data are converted into input data of the multimode neural network, and the multimode neural network performs feature extraction respectively, and specifically comprises the following steps:
drawing a two-dimensional situation map of the empty pipe by using the two-dimensional gesture data;
constructing two-dimensional structured data by using the rest effective data, wherein each row represents an aircraft, and each column of data sequentially comprises an identification number, a height and a vertical speed of the aircraft;
taking the blank pipe two-dimensional situation map as input of a convolutional neural network, wherein the output of the convolutional neural network is blank pipe horizontal plane situation characteristics of a one-dimensional vector format;
taking the two-dimensional structured data as input of a fully-connected neural network, wherein the output of the fully-connected neural network is the number and height situation characteristics of air-handling aircrafts in a one-dimensional vector format;
in the fifth step, a neural network is used to fuse the characteristics extracted by the multimode neural network, and the method specifically comprises the following steps:
the method comprises the steps of taking the empty pipe horizontal plane situation characteristics of a one-dimensional vector format output by a convolutional neural network and the number and height situation characteristics of air pipe aircrafts of the one-dimensional vector format output by a fully-connected neural network as inputs of the neural network;
the output of the neural network is the empty pipe situation characteristic of a one-dimensional vector format.
2. An empty pipe situation feature extraction device applying the method for extracting empty pipe situation features based on a multimode neural network according to claim 1, characterized by comprising one or more processors, and a storage device, wherein the storage device is configured to store one or more programs, and when the one or more programs are executed by the one or more processors, the one or more processors implement the method according to claim 1.
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