The present invention relates to the field of electronic air traffic control systems,
typically electronic air traffic management (ATM) systems. Such a system provides the
interfacing between an air traffic controller on the one hand, for example responsible for a
given geographical sector, and on the other hand the aircraft located within the
geographical sector or other air traffic controllers, in particular those responsible for the neighboring geographical sectors.
Such a system receives data from outside systems (weather data, aircraft flight
plans, radar detection, messages from air traffic controllers from neighboring sectors,
etc.), processes this data, optionally combines it, etc., then retrieves, via an MMI (manmachine
interface), this data or the information resulting from the processing operations
for the air traffic controller. The air traffic controller, based on this data and information,
determines instructions (commands intended for the aircraft, messages for the
neighboring controllers including information, data, commands to execute additional
functions of the system, etc.) and enters them via the MMI. The system next processes
these commands.
Currently, no solution is offered making it possible for air traffic controllers to
benefit from the experience acquired over time in the field of air traffic control, whether in
the context of training or during operational air traffic control situations.
For example, air traffic controller training is done through test programs having a
certain number of drawbacks: they offer a very limited number of scenarios, are not very
upgradable and therefore find it difficult to be representative of changes to air traffic
control systems.
To that end, according to a first aspect, the invention proposes a training and/or
assistance platform for air management via an air traffic management electronic system,
said training platform including:
- said air traffic management electronic system, adapted for obtaining input data
representative at least of the state of air traffic, to deliver, to the air traffic controller
operating the system, information relative to the air traffic and established as a function at
least of said obtained input data, to receive, from an air traffic controller, instructions as a
function of said delivered information and to process said instructions;
- a block for automatically determining instructions based on input data representative of
at least the state of air traffic;
2
- an electronic processing module adapted for collecting said input data obtained by the
electronic air traffic control system, to provide it to the automatic determining block, to
collect at least an instruction determined automatically based on said provided input data
and to command the delivery of said instruction to the air traffic controller operating the
system;
and said block includes a neural network derived from learning carried out by computer,
based on sets of elements of a first history of sets of elements, each set of elements being
associated with a respective aerial situation corresponding to a respective moment from
among a plurality of respective moments in the history and including at least input data
obtained by an electronic air traffic control system at the respective moment and the
received air traffic control instruction(s) received by said system following the delivery of
information established based on said input data.
The invention thus makes it possible to provide a reliable training and/or
assistance platform for air traffic management, taking advantage of real situations and
their variety, and which is dynamic.
In embodiments, the training and/or assistance platform for air traffic management
according to the invention further includes one or more of the following features:
- the platform includes a memory adapted for storing a second history of sets of
elements, and the processing module is adapted for extracting input data from said
database, in order to determine training input data at least based on extracted
input data, and for providing said training data to the air traffic management
electronic system and the automatic determining block; said air traffic
management electronic system being adapted for delivering information relative to
the air traffic established based on said training input data, in order to receive,
from an air traffic controller training on the platform, at least one air traffic
management instruction based on said delivered information and to provide it to
the processing module; said processing module being adapted for comparing said
received instruction with the collected automatically determined instruction and for
determining a message based on said comparison, said platform being adapted
for displaying the message;
- the processing module is adapted for determining, based on the instruction
determined automatically by the algorithmic model, a message intended to guide
the air traffic controller through training toward said instruction, the platform being
adapted for displaying said message;
- the processing module is adapted for receiving information indicating a training
theme and for extracting the input data based on said information.
3
According to a second aspect, the present invention proposes a training and/or
assistance method for air traffic management by an air traffic management electronic
system, adapted for obtaining input data representative at least of the state of air traffic, to
deliver, to the air traffic controller operating the system, information relative to the air
traffic and established as a function at least of said obtained input data, to receive, from
an air traffic controller, instructions as a function of said delivered information and to
process said instructions, said method being carried out on a training and/or assistance
platform including:
- said electronic system;
- an automatic determination block based on input data representative of at least the state
of a derived air traffic, said block including a neural network derived from learning carried
out by computer, based on sets of elements of a first history of sets of elements, each set
of elements being associated with a respective aerial situation corresponding to a
respective moment from among a plurality of respective moments in the history and
including at least input data obtained by an electronic air traffic control system at the
respective moment and the received air traffic control instruction(s) received by said
system following the delivery of information established based on said input data;
- an electronic processing module;
said method comprising the following steps:
- obtaining input data via the air traffic management electronic system;
- by the processing module: collecting said input data and providing said
collected input data to the automatic determination block;
- automatic determination by said automatic determination block based on said
provided input data of at least one instruction;
- command by said processing module of the delivery of said instruction to the
air traffic controller operating the system.
In embodiments, the training and/or assistance method for air traffic management
according to the invention further includes one or more of the following features:
- the platform includes a memory adapted for storing a second history of sets of elements,
said method comprising the following steps:
- by the processing module: extracting input data from said database; determining,
based on at least the extracted input data, training input data and providing said
training data to the electronic control system of the air traffic and the automatic
determination block;
- by said air traffic management electronic system: delivering information relative to
the air traffic established based on said training input data; receiving, from an air
4
traffic controller training on the platform, at least one air traffic control instruction
established based on said delivered information; and provision to the processing
module;
- comparison by the processing module of said received instruction with the
collected instruction determined automatically and determination of a message
based on said comparison;
- display of the message by said platform;
- the method comprises the determination by the processing module, based on the
instruction determined automatically by the algorithmic model, of a message intended to
guide the air traffic controller through training toward said instruction, and the display of
the message by the platform;
- the method comprises the following steps, carried out by the processing module:
- receiving information indicating a training theme, and
- extracting input data based on said information.
These features and advantages of the invention will appear upon reading the
following description, provided solely as an example, and done in reference to the
appended drawings, in which:
- figure 1 shows a view of an air traffic management electronic system
considered in one embodiment of the invention;
- figure 2 shows a view of a training platform in one embodiment of the
invention;
- figure 3 is a flowchart of steps implemented in one embodiment of the
invention;
- figure 4 is a flowchart of steps implemented in one embodiment of the
invention.
Figure 1 shows an air traffic management electronic system 2, called ATM system
2, connected by telecommunication links to a set 4 of outside systems 4_1, 4_2, ..., 4_n.
The ATM system 2 includes a memory 20, an MMI unit 21 and a processor 22.
The MMI unit 21 for example includes display screens, which may or may not be
touch-sensitive, a speaker system, a keyboard, a microphone, etc.
The outside systems 4_1, 4_2, ..., 4_n for example include aircraft, radars,
weather stations, telecommunication devices of other air traffic controllers, airport control
rooms, etc.
The ATM system 2 is adapted for receiving, and storing in its memory 20, input
data (and the timestamp of such data).
5
This input data includes external data, i.e., which is delivered to it by the outside
systems 4_1, 4_2, ..., 4_n of the set 4; this external data represents the current or future
state of the airspace (or a given sector of the airspace) for example and non-exhaustively
including:
- for each aircraft currently or imminently in the sector: coordinates in 3
dimensions (3D), aircraft type, its heading, its speed, its flight plan;
- current and future weather information;
- air traffic density indicators (such as the number of airplanes in the sector,
turbulence), data defining the current and future structure of the airspace (such
as the presence of military no-fly zones, air corridors), current and future
configuration of the airport (open runways, wind direction, available taxiways,
etc.), surveillance data (as derived from primary and secondary radars, ADS-B,
WAM, etc. data);
- coordination messages with air traffic controllers operating on ATM systems
relative to adjacent sectors.
The ATM system 2 is further adapted for retrieving this data for the air traffic
controller, via the MMI unit 21.
The external data can further be processed (for example averaged, verified,
combined, analyzed, etc.) before retrieval.
The input data generally includes this processed data and its timestamp.
The ATM system 2 is further adapted for generating data internal to the ATM
system 2, representative of its current state. It is in particular generated using probes
installed in the ATM system 2 and for example, non-exhaustively, includes:
- a queue of messages, shared data, logs, technical layouts, a focus action on a
flight tag, zoom in or zoom out factor on a given zone, voice conversation
transcription between a controller and an aircraft pilot, etc.
The internal data is accessible to an operational air traffic controller operating the
ATM system 2 via the MMI 21. In one embodiment, the input data stored in the memory
20 also includes this internal data, with the timestamp corresponding to said data.
In one embodiment, the ATM system 2 is adapted for implementing simple
functions (such as zooming in or out on an area chosen by the air traffic controller) and
more dynamic functions using the external data and generating internal data. These
functions are carried out for example using computer programs implementing the function,
which are stored in the memory 20 and executed on the processor 22. The dynamic
functions for example include detecting conflicts, identifying a collision risk between an
aircraft and another aircraft, or a risk of an aircraft entering a zone during a grounding
6
period in the zone, etc., or determining a solution making it possible to resolve the conflict.
The results of these functions are retrieved via the MMI 21, for the air traffic controller.
In a known manner, an air traffic controller operating the ATM system 2 an
operational mode can thus learn, via the MMI 21, at each moment, of the current or future
air traffic situation, as a function of external data and/or as a function of internal data,
including the results of the upgraded functions. These data and results are provided to
him via the MMI 21. Based on these elements, the air traffic controller then makes
decisions that he provides to the ATM system 2 in the form of instructions via the MMI 21
(in text or visual or voice form, etc.).
These decisions include function commands of the ATM system 2 (for example
commands to zoom in or out on an area displayed on the MMI block 21, and/or
instructions intended for aircraft and/or ATM systems of controllers of adjacent sectors:
they can thus include target flight altitude commands, target speed commands (horizontal,
vertical), target heading commands, target claim or descent gradient commands, etc.
Figure 2 shows a view of a training platform to the air traffic controller 1 in one
embodiment of the invention.
This training platform for the air traffic controller 1 includes, in the considered case,
a training system 10 and an ATM system 2 operating as described above and which here
is used this time, not in traditional operational use, but to train air traffic controllers.
The training system 10 includes a memory 11, a processor 12, a processing
module 13 and an electronic block for automatically determining instructions 14, in the
present case, a neural network derived from learning, as described later.
The memory 11 includes a history 15 of element sets. Each element set includes
an input element. Each input element describes an aerial situation having taken place at a
given moment in the history and includes external data of the ATM systems defining this
aerial situation, and includes, in embodiments, internal data of ATM systems and/or
results of functions implemented by ATM systems. Each set of elements includes,
associated with the input element, an output element including the instruction(s) provided
to the ATM system by the air traffic controllers in light of this respective input element at
the given moment.
This history is for example made from the collection over several months, from the
ATM system 2, or another ATM system similar to the ATM system 2 or from several
operational ATM systems, of all of these elements and their storage.
The processing module 13 includes a scenario generator block 130 and a virtual
assistant block 131.
7
The scenario generator block 130 is adapted for selecting at least one set of
elements, in order to determine, based on the input element(s) of the set of selected
element(s) of a training scenario, i.e., training input data including external input data, or
even associated internal input data and to provide them to the ATM system 2 in the neural
network 14 of the platform 1.
The virtual assistant block 131 is adapted for collecting a decision provided to the
ATM system 2 by the controller undergoing training and collecting a decision provided by
the neural network 14, following the provision of the same training input data to the ATM
system 2 and to the neural network 14, in order to compare them, in order to determine a
message based on said comparison.
Figure 4 is a flowchart of steps implemented in one embodiment of the invention,
in order to obtain the neural network 14 after learning.
In a preliminary phase 101_0 for obtaining a programmed neural network, learning
is carried out, by the neural network 14, of the behavior of the air traffic controller(s), from
a history of input elements including external data of the ATM systems, and including, in
embodiments, internal data of the ATM systems and/or results of functions, and from a
history of output elements including the decisions provided to the ATM systems by the air
traffic controllers in light of these respective input elements. This history is for example the
history 15, or an excerpt from the history 15.
It will be noted that this preliminary phase 101_0 for learning of the neural network
14, according to the embodiments is carried out on the platform 1, or
is carried out on a specific neural network learning platform (not shown), equipped with its
own memory and computing resources.
In one embodiment, the preliminary phase 101_0 includes a preparation phase
101_01 for the input and output elements identifying which are (the extracts of) those of
the input and output elements useful for the learning of the neural network, in the
decision-making, as well as for example the minimum duration of the history.
In a known manner, the preparation of these elements can include the
segmentation of the collected elements, the detection of missing elements and operations,
the reduction of the dimensions of the elements, the extraction of groups, the identification
of causes and relationships, and to finish, the definition of a set of learning elements
including input learning elements and associated output elements. For each test
configuration, the preliminary phase 101_0 includes a learning phase 101_02 strictly
speaking for the neural network 14 from input and output learning elements associated
with the set of learning elements. A “learned” neural network is thus determined.
8
These principles of the definition of sets of learning data and use of neural
networks are well known, cf. for example:
- Tolk, A. (2015, July). The next generation of modeling & simulation: integrating
big data and deep learning. In Proceedings of the Conference on Summer
Computer Simulation (pp. 1-8). Society for Computer Simulation International;
- Akerkar, R. (2014). Analytics on Big Aviation Data: Turning Data into Insights.
IJCSA, 11(3),116-127;
- Boci, E., & Thistlethwaite, S. (2015, April). A novel big data architecture in
support of ADS-B data analytic. In Integrated Communication, Navigation, and
Surveillance Conference (ICNS), 2015 (pp. C1-1). IEEE;
- Bengio, Y. (2009). Learning deep architectures for AI. Foundations and
trends® in Machine Learning, 2(1), 1-127.
The result of the learning phase 101_02 is the delivery 101_1 of a trained neural
network 14, also called instruction automatic determination electronic block 14.
Any type of artificial neural networks can be used. For example, a deep learning
network, a convolutional neural network (CNN) are used. The number of input nodes will
be chosen to be equal to the number of input elements and the number of output nodes
will be chosen to be equal to the number of output elements for each test configuration.
An ATM system 2 evolving regularly, the updates, both functional (introduction of
new functions or modified functions) and technical (changes of hardware, operating
system, etc.), are to be taken into account in the model 14. In such a situation, in one
embodiment, the neural network 14 corresponding to the ATM system 2 before update is
completed to account for these updates. Thus in reference to figure 4, in a step 102_0,
input elements including external data of the ATM systems, or even internal data of the
ATM systems, and including output elements including the decisions made by the air
traffic controllers in light of these respective input elements, are recorded and stored
during validation sessions done by air traffic controllers on the ATM system 2. The step
101_0 for obtaining a programmed neural network is then carried out based on these
input and output elements, and leads to the delivery of an air traffic network controller
model targeted on the part of the ATM system 2 that is updated. In step 101_1, a
combination of the algorithmic model corresponding to the ATM system 2 before update
and the algorithmic model of the ATM system 2 targeted on the updated aspects is done
(for example, in embodiments, by a concatenation), thus making it possible to deliver a
complete algorithmic model corresponding to the updated ATM system 2.
Furthermore, in one embodiment, in a securing step 100_0, rules, principles,
constraints, conditions and prohibitions implemented by the air traffic controllers in the
9
application of their trade (for example corresponding to the ICAO standards as defined in
document 4444) are formalized in algorithmic form.
For example, these rules include that:
- a1/ an air traffic controller cannot provide commands regarding aircraft outside
the sector for which it is responsible,
- a2/ except for certain exceptions specifically defined by the conditions Cond1,
Cond2, Cond3;
- a3/ in a given situation corresponding to a given aircraft speed and altitude, a
commanded change in flight level altitude must be below a given threshold
depending on said speed and altitudes.
The resulting securing algorithm is, in one embodiment, implemented in a step
100_1 on the input and output learning elements associated with the set of learning
elements prior to the building of an air traffic controller model, which makes it possible to
detect the elements not conforming to the standardized practice, and next either to
eliminate them from the set of learning elements, or to assign them to a "bad practice"
class allowing the model to better learn the behavior of the controller according to a "good
practice".
In one embodiment, in a step 100_1, these rules, principles, constraints, conditions
and prohibitions implemented by the air traffic controllers in the application of their trade
are also provided as learning data to an artificial neural network and, at the end of the
learning phase, a model encompassing these rules is delivered, hereinafter called
securing model.
In a step 101_3, each decision next made by the controller model 14 during a
learning process as illustrated in figure 3 is provided to this securing model, which either
validates the decision as conforming to good practices (in particular validates that it is in
the acceptable dynamic range for the output decisions), or invalidates the decision, which
is then not taken into account in the context of tests and for example then enriches the
class of "bad practice", which results in reinforcing the air traffic controller model.
In one embodiment of the invention, the steps, illustrated in figure 3, for carrying
out training of an air traffic controller using the learning form phase 1 are carried out.
In a preliminary step for choosing a learning scenario 200, the scenario generator
block 130 selects at least one input element from the set of elements and determines,
based on the selected element(s), a learning scenario, i.e., learning input data including
external input data, or even internal input data, and it provides them to the ATM system 2
of the platform 1 and to the neural network 14 of the platform 1.
10
This selection can take several forms. For example, the choice can be random, or
can be increasing or decreasing depending on the corresponding moment of the
occurrence of the aerial situation described by the input element.
In one embodiment, a trainer provides a keyword to the learning system 10, via a
man-machine interface block (not shown) of the system 10. The keyword can for example
be a desired training theme, such as "conflict between airplanes", "heavy traffic", etc. The
scenario generator block 130 is adapted for identifying the sets of elements whereof the
input elements and/or the output elements correspond to said keyword and for selecting
the input element(s) of these identified sets of elements.
In a step 201 for determining the training scenario, the scenario generator block
130 determines a training scenario based on at least the selected input element.
For example, when several input elements have been selected, each one is
processed independently, as a part of the scenario, or a single one is ultimately selected,
after designation by the trainer from among the set of selected input elements or a
selection by the scenario generator block 130 based on predefined criteria; or a
combination of several input elements is done by the scenario generator block 130 in
order to obtain a single input element (by averaging, interpolation, etc.).
Then, for example, each selected input element is processed by the scenario
generator 130. For example, an extraction step is carried out where only some of the
parameters of the input element, in particular related to the selection theme if applicable,
are kept.
A conversion step is for example carried out, in order to bring the data format into
compliance with the formats accepted by the ATM system 2. The learning input data thus
processed (for example in the form of data vectors including, in one case, the position of
an airplane, its speed, its direction, the weather, the messages sent by the other sectors,
etc.) are then provided as learning scenario to the ATM system 2 of the platform 1 and to
the neural network 14 of the platform 1.
In a step 202 for training of an air traffic controller on the platform 1, the ATM
system 2 receives the learning input data provided by the scenario generator 130. It is
optionally processed, then the internal data received, optionally processed, is retrieved via
the MMI 21, for the air traffic controller being trained.
The neural network 14 automatically determines, based on the learning input data,
the corresponding output data, i.e., one or several instructions intended for the ATM 2 and
provides them to the processing module 13. These instructions represent the fruit of the
experience of air traffic controllers pooled within the neural network 14.
11
In one embodiment, the virtual assistant block 131 is adapted for determining,
based on output data provided by the neural network 14, content intended to guide the air
traffic controller so that he determines this output data. This content for example includes
advice on the points of the learning input data to which he should pay attention, or to
propose a choice of several alternative lots of instructions among which the lot of
instruction provided by the neural network appears. In such a case, the virtual assistant
block 131 next commands that the content be retrieved for the air traffic controller, for
example by display on a screen of the MMI 21, or any other screen with which the
platform 1 is equipped and which is visible by the air traffic controller.
The air traffic controller training on the platform 1, in light of the learning internal
data delivered to him by the MMI 21, completed if applicable by additional content coming
from the virtual assistant block 131, then makes the decision and provides it as
instruction(s) to the ATM system 2 via the MMI block 21: for example, in case of training
theme relative to conflicts between airplanes, flight level change commands for at least
one aircraft.
The ATM system 2 of the platform 1 then sends this or these instruction(s) to the
processing block 13.
The virtual assistant block 131 then makes a comparison between the instruction
provided by the ATM system and coming from the air traffic controller and that provided by
the neural network 14 (or the instructions between them when there are several), then it
determines the content of the message based on this comparison.
This message for example indicates whether the instruction provided by the air
traffic controller is correct (if it corresponds to that provided by the neural network 14) or
incorrect, and the virtual assistant block then commands the display of this message on
the platform 1 for the air traffic controller. If the response is incorrect, the message can
further include additional information to guide the behavior of the controller toward the
instruction to be determined.
In one embodiment, the virtual assistant block 131 performs, at the end of the
training of the air traffic controller, a summary of the “correct instructions” provided by the
air traffic controller and for example calculates his training score.
A training platform according to the invention like the platform 1 contributes to a
targeted and dynamic training of air traffic controllers, benefitting from the capitalization
and pooling of the considered history.
In the considered embodiment, the neural network 14 and the processing module
13 are made in the form of algorithms including software instructions stored in the memory
11 and executed on the processor 12. When they are executed, these instructions lead to
12
the implementation of the steps described above as falling to the neural network 14,
respectively to the processing module 13.
In another embodiment, the processing module 13 is made in the form of a
programmable logic component, such as an FPGA (Field Programmable Gate Array), or
in the form of a dedicated integrated circuit, such as an ASIC (Application Specific
Integrated Circuit), and/or the neural network 14 is made in the form of a programmable
logic component, such as a GPU or multi-GPU (Graphics Processing Unit) card.
The invention has been described above in the case of the training field. It also has
applications in the field of assistance for air traffic controllers in operational use.
In such an application, the ATM system 2 as shown in figure 2 is completed by an
assistance system similar to the training system 10, except that it has no history 15 in its
memory. This assistance system includes a memory, a processor, an instruction
automatic determination block similar in all points to the block 14 and, in place of the
processing module 13, a processing module, hereinafter called assistance module. The
assistance module is adapted for collecting current input data of the ATM system 2, to
provide it as input to the instruction automatic determination block, to collect the
instruction delivered as output by this instruction automatic determination block based on
current input data and to command the provision to the air traffic controller operating the
ATM system 2, for example by display on a display screen of the ATM system 2 or on an
additional screen, visible by the air traffic controller. Thus, when the air traffic controller
becomes aware of the state of the air traffic corresponding to the current input data as
retrieved on the MMI 21, he further has determined instruction(s) as resulting from the
practice learned via the neural network of the instruction automatic determination block
and is able to make his own decision regarding the instruction to be entered on the MMI
21 by benefiting from this additional expertise.
The steps are then the following, in the operational phase:
The ATM system 2 of figure 1 receives external input data, generates internal input
data. It stores them in its memory 20 and provides them, optionally after certain additional
processing, to the air traffic controller via the MMI 21.
In parallel, the module collects the current input data from the ATM system 2,
provides it as input for the instruction automatic determination block, obtains the delivered
instruction as output by the latter and commands the provision thereof to the air traffic
controller operating the ATM system 2, for example by display on a display screen of the
ATM system 2 or on an additional screen, visible by the air traffic controller. The air traffic
controller becomes aware of the state of the air traffic corresponding to the current input
data as retrieved on the MMI 21, and further has determined instruction(s) as resulting
13
from the practice learned via the neural network of the instruction automatic determination
block. He makes his own decision regarding the instruction by benefiting from this
additional expertise and enters it on the MMI 21. The ATM system 2 then processes this
instruction, by performing the function of the system if such a function is commanded in
the instruction, or by sending the instruction by telecommunication, to an aircraft or to the
ATM system of another concerned sector.

CLAIMS
1.- A training and/or assistance platform (1) for air management via an air traffic
management electronic system (2) adapted for being operated by an air traffic controller,
said training platform including:
- said air traffic management electronic system (2), adapted for obtaining input data
representative at least of the state of air traffic, to deliver, to the air traffic controller
operating the system, information relative to the air traffic and established as a function at
least of said obtained input data, to receive, from an air traffic controller, instructions as a
function of said delivered information and to process said instructions;
- an automatic determination block (14) for automatically determining instructions based
on input data representative of at least the state of air traffic;
- an electronic processing module (13) adapted for collecting said input data obtained by
the electronic air traffic control system, to provide it to the automatic determining block
(14), to collect at least an instruction determined automatically based on said provided
input data and to command the delivery of said instruction to the air traffic controller
operating the system;
wherein said block includes a neural network derived from learning carried out by
computer, based on sets of elements of a first history of sets of elements, each set of
elements being associated with a respective aerial situation corresponding to a respective
moment from among a plurality of respective moments in the history and including at least
input data obtained by an electronic air traffic control system at the respective moment
and the received air traffic control instruction(s) received by said system following the
delivery of information established based on said input data.
2.- The training and/or assistance platform (1) according to claim 1, including
- a memory (11) adapted for storing a second history (15) of sets of elements,
- the processing module (13) being adapted for extracting input data from said database,
in order to determine training input data at least based on extracted input data, and for
providing said training data to the air traffic management electronic system and the
automatic determining block (14);
said air traffic management electronic system being adapted for delivering information
relative to the air traffic established based on said training input data, in order to receive,
15
from an air traffic controller training on the platform, at least one air traffic management
instruction based on said delivered information and to provide it to the processing module;
said processing module (13) being adapted for comparing said received instruction with
the collected automatically determined instruction and for determining a message based
on said comparison, said platform being adapted for displaying the message.
3.- The training and/or assistance platform (1) according to claim 1 or 2, wherein the
processing module (13) is adapted for determining, based on the instruction determined
automatically by the algorithmic model, a message intended to guide the air traffic
controller through training toward said instruction, the platform being adapted for
displaying said message.
4.- The training and/or assistance platform (1) according to claim 2 or 3, wherein the
processing module (13) is adapted for receiving information indicating a training theme
and for extracting the input data based on said information.
5.- A training and/or assistance method for air traffic management by an air traffic
management electronic system (2), adapted for obtaining input data representative at
least of the state of air traffic, to deliver, to the air traffic controller operating the system,
information relative to the air traffic and established as a function at least of said obtained
input data, to receive, from an air traffic controller, instructions as a function of said
delivered information and to process said instructions, said method being carried out on a
training and/or assistance platform (1) including:
- said electronic system (2);
- an automatic determination block (14) based on input data representative of at least the
state of a derived air traffic, said block including a neural network derived from learning
carried out by computer, based on sets of elements of a first history of sets of elements,
each set of elements being associated with a respective aerial situation corresponding to
a respective moment from among a plurality of respective moments in the history and
including at least input data obtained by an electronic air traffic control system at the
respective moment and the received air traffic control instruction(s) received by said
system following the delivery of information established based on said input data;
- an electronic processing module (13);
said method comprising the following steps:
- obtaining input data via the air traffic management electronic system;
16
- by the processing module (13): collecting said input data and providing said
collected input data to the automatic determination block (14);
- automatic determination by said automatic determination block (14) based on
said provided input data of at least one instruction;
- command by said processing module of the delivery of said instruction to the
air traffic controller operating the system.
6.- The training and/or assistance method according to claim 5, the platform (1) including
a memory (11) adapted for storing a second history (15) of sets of elements, said method
comprising the following steps:
- by the processing module (13): extracting input data from said database; determining,
based on at least the extracted input data, training input data and providing said training
data to the electronic control system of the air traffic and the automatic determination
block (14);
- by said air traffic management electronic system: delivering information relative to the air
traffic established based on said training input data; receiving, from an air traffic controller
training on the platform, at least one air traffic control instruction established based on
said delivered information; and provision to the processing module;
- comparison by the processing module (13) of said received instruction with the collected
instruction determined automatically and determination of a message based on said
comparison;
- display of the message by said platform.
7.- The training and/or assistance method according to claim 5 or 6, comprising the
determination by the processing module (13), based on the instruction determined
automatically by the algorithmic model, of a message intended to guide the air traffic
controller through training toward said instruction, and the display of the message by the
platform.
8.- The training and/or assistance method according to claim 6 or 7, comprising the
following steps, carried out by the processing module (13):
- receiving information indicating a training theme, and
- extracting input data based on said information.