CN110633875A - Method and device for predicting airway flow and computer storage medium - Google Patents
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Abstract
A method and apparatus for predicting airway flow and a computer storage medium are provided. The method comprises the following steps: obtaining an airway flow prediction data set, wherein the prediction data set comprises at least one of: longitude and latitude, height, flight number, course, vertical speed, ground speed and flight control number; preprocessing the prediction data set; training a recurrent neural network model by adopting the processed prediction data set; and performing route flow prediction by using the trained recurrent neural network. The method improves the accuracy of the prediction of the airway flow. The aircraft traffic flow statistical data and the prediction model obtained by the method can well serve an air traffic flow control system.
Description
Technical Field
The invention belongs to the field of big data mining and machine learning, and particularly relates to a method and a device for predicting route flow and a computer storage medium.
Background
In recent years, people who select more comfortable and faster airplanes to go out more and more, which brings great development to the civil aviation industry, but also brings many problems. In China, civil aviation airspace occupying only one fifth of the national airspace is increasingly difficult to meet the increasing demand of the number of airplanes. The congestion in the airspace makes the operation of the aircraft inefficient, resulting in a series of problems including flight delay, cancellation, etc. And moreover, the safety hidden trouble is inevitably brought to the trip of the airplane.
The widespread use of ADS-B technology has led to the possibility of technological evolution of the overall air traffic control system. More accurate location information than secondary radar technology and more detailed aviation aircraft information allow us to mine more valuable and meaningful data.
The airspace flow control is always an important component of an air traffic control system, and accurate airspace aircraft flow data has important significance for the reasonable division of airspaces and the optimization of flight routes.
Disclosure of Invention
In view of the above-mentioned deficiencies in the prior art, it is an object of the present invention to provide a method, an apparatus and a computer-readable storage medium for improving accuracy of predicting an air route traffic.
The embodiment of the invention discloses a method for predicting route flow, which comprises the following steps: obtaining an airway flow prediction data set, wherein the prediction data set comprises at least one of: longitude and latitude, height, flight number, course, vertical speed, ground speed and flight control number; preprocessing the prediction data set; training a recurrent neural network model by adopting the processed prediction data set; and performing route flow prediction by using the trained recurrent neural network.
In one possible embodiment, obtaining the set of airway traffic prediction data includes collecting ADS-B message data by a broadcast auto-correlation monitoring ADS-B receiving device.
In one possible embodiment, preprocessing the prediction data set comprises: cleaning the ADS-B message data; screening the cleaned data to obtain effective information related to the airway flow statistics, wherein the effective information at least comprises one of longitude and latitude, height and flight number; time slicing is carried out on the screened data and the data are stored; and fusing the screened data with airport data acquired from a network to obtain the aircraft route information.
In one possible embodiment, the aircraft traffic statistics are performed on an hourly basis based on the aircraft route information.
In one possible embodiment, the preprocessed prediction data set includes at least one of: the system comprises aircraft flow, air route information, time vectors including a small time period, a week, a month and a day, and index vectors including seasons, holidays and corresponding air route average flow.
In one possible embodiment, the recurrent neural network model is a long-short memory neural network model, including 4 neural network layers.
The embodiment of the invention also discloses a device for predicting the airway flow, which comprises: an obtaining unit, configured to obtain an airway traffic prediction data set, where the prediction data set includes at least one of: longitude and latitude, height, flight number, course, vertical speed, ground speed and flight control number; a preprocessing unit for preprocessing the prediction data set; a training unit for training the recurrent neural network model using the processed prediction data set; and the prediction unit is used for predicting the airway flow by using the trained recurrent neural network.
In a possible embodiment, the acquiring unit is further configured to collect ADS-B message data by a broadcast auto correlation monitoring ADS-B receiving device.
In one possible embodiment, the preprocessing unit comprises: the cleaning module is used for cleaning the ADS-B message data; the screening module is used for screening the cleaned data to obtain effective information related to the airway airplane flow statistics, and the effective information at least comprises one of longitude and latitude, height and flight number; the storage module is used for time slicing and storing the screened data; and the statistical module is used for fusing the screened data with airport data acquired from a network to obtain the aircraft route information.
In a possible embodiment, the statistical unit is further configured to perform route aircraft traffic statistics with hour granularity according to the aircraft route information.
In one possible embodiment, the preprocessed prediction data set includes at least one of: the system comprises aircraft flow, air route information, time vectors including a small time period, a week, a month and a day, and index vectors including seasons, holidays and corresponding air route average flow.
In one possible embodiment, the recurrent neural network model is a long-short memory neural network model, including 4 neural network layers.
An embodiment of the present invention also discloses a computer storage medium storing a computer program which, when executed, implements the method according to any one of the preceding claims.
The invention has the beneficial effects that:
the aircraft ADS-B message data received by the ground receiving equipment is fused with the data of relevant airports of the Internet, a plurality of pieces of airway data are constructed, the aircraft flow of the airway is counted by taking hour as time granularity, some characteristics are integrated as input on the basis of the airway flow data, a long-short term memory neural network model is used for predicting the airway flow, and the prediction precision is improved. Traffic statistics and prediction work provides important data support for air traffic control departments, including the scheduling of regulatory and future voyages.
Drawings
FIG. 1 is a flow chart of a method according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart of another method according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of an apparatus according to an embodiment of the present invention.
Detailed Description
In order to facilitate understanding of those skilled in the art, the present invention will be further described with reference to the following examples and drawings, which are not intended to limit the present invention.
Referring to fig. 1, a method for predicting an airway flow includes:
s101, acquiring an airway flow prediction data set, wherein the prediction data set at least comprises one of the following data: longitude and latitude, altitude, flight number, course, vertical speed, ground speed and flight control number.
In one embodiment, the acquisition of the air way traffic prediction data set may be performed by a broadcast auto correlation monitoring ADS-B receiving device to collect ADS-B message data.
Specifically, ADS-B is an English abbreviation of broadcast type automatic correlation monitoring, an airplane equipped with the equipment does not need to be manually operated or needs to be inquired like a secondary radar, and the airplane can automatically and uninterruptedly broadcast information such as own position, altitude, course, identification number and the like to other airplanes and ground receiving stations. The integrated and recombined airplane information sent by the broadcast is ADS-B data message.
The ground receiving equipment obtains the relevant information of the airplane by analyzing the received ADS-B message, and the content of the ADS-B message can include: longitude and latitude, altitude, flight number, course, vertical speed, ground speed, flight control number, etc.
S102, preprocessing the prediction data set.
In one embodiment, preprocessing the prediction data set comprises: cleaning, screening, storing and flow counting. Specifically, cleaning ADS-B message data; screening the cleaned data to obtain effective information related to the airway flow statistics, wherein the effective information at least comprises one of longitude and latitude, height and flight number; time slicing is carried out on the screened data and the data are stored; and fusing the screened data with airport data acquired from a network to obtain the aircraft route information.
For example, airport longitude and latitude information acquired through the internet is stored in a dictionary data structure, and the position information of every two airports and the width information of an airway form basic information of the airway. And loading data of a certain time slice, reading relevant data of each frame of the aircraft and verifying whether the data is in a certain air route. And counting the number of the airplanes of each route in each time period.
In one embodiment, the aircraft traffic statistics are performed on hourly granularity according to the aircraft route information.
In one embodiment, the preprocessed prediction data set includes at least one of: the aircraft flow and the air route information comprise time vectors of a small time period, a week, a month and a day, and index vectors corresponding to the average flow of the air route, including seasons, festivals and holidays.
For example, the obtained route traffic data is sorted and unified in format, and feature information such as route number, date, week, holiday, season and the like is added to the data to be input into a subsequent traffic prediction model. Specifically, the input data format may be defined as follows.
The input data may be a data vector
Comprising a time vectortAnd an index vectorp。
For time vectortAnd an index vectorpThe following definitions are made:
in the formula (I), the compound is shown in the specification,
, 
, 
andrespectively, which day of the small period, week, month, or month.
(2)
In the formula (I), the compound is shown in the specification,
, 
and
respectively, a seasonal index, a holiday index and an average flow index of the airway.
Input data vectorThe definition is as follows:
wherein
For the previously mentioned airway flow values,
a number of a route is represented,t，pthe vectors defined in the formulas (1) and (2), respectively.
S103, training the recurrent neural network model by adopting the processed prediction data set.
In one embodiment, the recurrent neural network model is a long-short memory neural network model, including 4 neural network layers.
A time-cycle neural network, a long-short memory network model is selected according to the time sequence characteristics and the non-linear characteristics of the flow data. The network model inputs to outputs are in a sequence-to-sequence format.
In one embodiment, the pre-processed traffic data obtained in step S102 may be split into 67% training set and 33% testing set.
And S104, performing airway flow prediction by using the trained recurrent neural network.
The network architecture of the model only has a long and short memory neural network layer, a full connection layer and a dropout layer in space. Since the data is iterated through an input network in chronological order, it is equivalent in time to increasing the depth of the network by a time-sequential length of the depth of space, i.e., 24 times. The strong nonlinear feature characterization capability exhibited by the network enables the network to well deal with the prediction problem of complex time sequence sequences.
According to the embodiment of the invention, the aircraft ADS-B message data received by the ground receiving equipment is combined with the data of relevant airports of the Internet, a plurality of pieces of airway data are constructed, the aircraft flow of the airway is counted by taking hour as time granularity, some characteristics are integrated as input on the basis of the airway flow data, a long-short term memory neural network model is used for predicting the airway flow, and the prediction precision is improved. In addition, traffic statistics and prediction work provides important data support for air traffic control departments, including the scheduling of regulatory and future voyages.
The method of an embodiment of the present invention is further described below with reference to fig. 2.
The invention uses python3.6 as a development language and a skleran open source library as an algorithm for realization. The method specifically comprises the following steps:
the method comprises the following steps: and receiving ADS-B message data through the ground equipment.
Step 1: ADS-B terrestrial receiving equipment is deployed, including 1090Mhz omnidirectional antenna and storage equipment.
Step 2: and receiving and storing the ADS-B message sent by the aviation aircraft.
Step two:
and 3, step 3: and carrying out format unification and data arrangement on the ADS-B data.
And 4, step 4: the data is cleaned (including removal of outliers and nulls) and stored by time of day.
And 5, step 5: and integrating airport information acquired by the Internet to construct airplane route information.
And 6, step 6: and reading the airplane data obtained after cleaning, checking whether each frame of airplane is in a certain air route, counting the number and outputting a counting result.
Step three:
and 7, step 7: and integrating the flow value and the air route information acquired in the step two to be used as a data vector.
And 8, step 8: the dates, months, weeks, etc. are integrated into a time vector.
Step 9: whether the data is a holiday, average flow of the air route, season and the like are integrated into an index vector.
Step 10: and synthesizing the three vectors into one vector to be used as the input of a subsequent prediction model.
Step four:
and 11, step 11: and establishing a long and short memory neural network model.
Step 12: and training the long and short memory neural network model through the data in the step two.
Step 13: and predicting the airway flow through the model trained in the step 12.
As shown in fig. 3, an embodiment of the present invention further discloses an apparatus 10 for predicting an airway flow, including: an obtaining unit 101, configured to obtain an airway traffic prediction data set, where the prediction data set at least includes one of the following: longitude and latitude, height, flight number, course, vertical speed, ground speed and flight control number; a preprocessing unit 102, configured to preprocess the prediction data set; a training unit 103 for training the recurrent neural network model using the processed prediction data set; and the prediction unit 104 is used for performing the airway flow prediction by using the trained recurrent neural network.
The acquiring unit 101 is further configured to collect ADS-B packet data through a broadcast automatic dependent surveillance-ADS-B receiving device.
The preprocessing unit 102 includes: the cleaning module is used for cleaning the ADS-B message data; the screening module is used for screening the cleaned data to obtain effective information related to the airway airplane flow statistics, and the effective information at least comprises one of longitude and latitude, height and flight number; the storage module is used for time slicing and storing the screened data; and the statistical module is used for fusing the screened data with airport data acquired from a network to obtain the aircraft route information.
In one embodiment, the statistical unit is further configured to perform hourly-granular airway aircraft flow statistics based on the aircraft airway information.
In one embodiment, the preprocessed prediction data set includes at least one of: the system comprises aircraft flow, air route information, time vectors including a small time period, a week, a month and a day, and index vectors including seasons, holidays and corresponding air route average flow.
In one embodiment, the recurrent neural network model is a long-short memory neural network model, including 4 neural network layers.
The apparatus corresponds to the foregoing method embodiment, and specific reference may be made to the description of the method embodiment, which is not repeated herein.
In the several embodiments provided in the present application, it should be understood that the disclosed method and apparatus may be implemented in other ways. For example, the above-described embodiments are merely illustrative, and for example, a division of a unit is merely a division of a logic function, and an actual implementation may have another division, for example, a plurality of units or components may be combined or integrated into another part, or some features may be omitted, or not executed.
Units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment of the present invention.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
While the invention has been described in terms of its preferred embodiments, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention.
Claims (13)
1. A method for predicting an airway flow, comprising:
obtaining an airway flow prediction data set, wherein the prediction data set comprises at least one of: longitude and latitude, height, flight number, course, vertical speed, ground speed and flight control number;
preprocessing the prediction data set;
training a recurrent neural network model by adopting the processed prediction data set;
and performing route flow prediction by using the trained recurrent neural network.
2. The method of claim 1, wherein obtaining the set of airway traffic prediction data comprises collecting ADS-B message data by a broadcast auto-correlation monitoring ADS-B receiving device.
3. The method of claim 2, wherein preprocessing the prediction data set comprises: cleaning the ADS-B message data; screening the cleaned data to obtain effective information related to the airway flow statistics, wherein the effective information at least comprises one of longitude and latitude, height and flight number; time slicing is carried out on the screened data and the data are stored; and fusing the screened data with airport data acquired from a network to obtain the aircraft route information.
4. The method of claim 3, wherein the hourly-sized airway aircraft flow statistics are performed based on the aircraft airway information.
5. The method of claim 1, wherein the pre-processed prediction data set comprises at least one of: the system comprises aircraft flow, air route information, time vectors including a small time period, a week, a month and a day, and index vectors including seasons, holidays and corresponding air route average flow.
6. The method of claim 1, in which the recurrent neural network model is a long-short memory neural network model comprising 4 neural network layers.
7. An apparatus for predicting an airway flow, comprising: an obtaining unit, configured to obtain an airway traffic prediction data set, where the prediction data set includes at least one of: longitude and latitude, height, flight number, course, vertical speed, ground speed and flight control number; a preprocessing unit for preprocessing the prediction data set; a training unit for training the recurrent neural network model using the processed prediction data set; and the prediction unit is used for predicting the airway flow by using the trained recurrent neural network.
8. The apparatus of claim 7, wherein the obtaining unit is further configured to collect ADS-B message data through a broadcast auto-correlation monitoring ADS-B receiving device.
9. The apparatus of claim 8, wherein the pre-processing unit comprises: the cleaning module is used for cleaning the ADS-B message data; the screening module is used for screening the cleaned data to obtain effective information related to the airway airplane flow statistics, and the effective information at least comprises one of longitude and latitude, height and flight number; the storage module is used for time slicing and storing the screened data; and the statistical module is used for fusing the screened data with airport data acquired from a network to obtain the aircraft route information.
10. The apparatus of claim 9, wherein the statistics unit is further configured to perform hourly-granularity airway traffic statistics based on the aircraft airway information.
11. The apparatus of claim 7, wherein the pre-processed prediction data set comprises at least one of: the system comprises aircraft flow, air route information, time vectors including a small time period, a week, a month and a day, and index vectors including seasons, holidays and corresponding air route average flow.
12. The apparatus of claim 7, in which the recurrent neural network model is a long-short memory neural network model comprising 4 neural network layers.
13. A computer storage medium storing a computer program, characterized in that the computer program, when executed, implements the method according to any one of claims 1-6.
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Application publication date: 20191231 |