Method and electronic device for assisting in the piloting of an aircraft by monitoring at least one operational criterion, computer program and associated aircraft

Abstract

Method and electronic device for assisting the piloting of an aircraft via the monitoring of at least one operational criterion, computer program and associated aircraft. This method for assisting the piloting of an aircraft, via the monitoring of at least one operational criterion of a mission, is implemented by an electronic device and comprises, for each operational criterion: - determination (100) of a value of each characteristic quantity of a set of characteristic quantity(ies) associated with said operational criterion, the set being specific and predefined for each operational criterion, each characteristic quantity being determined from at least one avionics variable; - estimation (110) of a value of the operational criterion from each determined value of characteristic quantity associated with said criterion and via the implementation of an artificial intelligence algorithm;- calculation (120) of a difference between the estimated value and a desired value of the operational criterion, and of a characteristic quantity, called the causal quantity, which is the main cause of said difference; - if the calculated difference is greater than a predefined threshold, performance (130) of at least one action among: displaying, on a display system, the estimated value of the operational criterion, the calculated difference and an indication of the causal quantity; issuing an alert based on the calculated difference; and generating a command instruction for an avionics system based on the estimated value of the operational criterion.

Description

Title of the invention: Method and electronic device for assisting in the piloting of an aircraft via the monitoring of at least one operational criterion, computer program and associated aircraft

[0001] The present invention relates to a method of assisting the piloting of an aircraft, via the monitoring of at least one operational criterion of a mission of the aircraft during the execution of said mission, the method being implemented by an electronic piloting assistance device.

[0002] The invention also relates to a computer program comprising software instructions which, when executed by a computer, implement such a piloting assistance method.

[0003] The invention also relates to an electronic flight assistance device via the monitoring of at least one such operational criterion, and an aircraft comprising such a flight assistance device.

[0004] The invention relates more particularly to an airplane, while being applicable to any type of aircraft, such as a helicopter or a drone.

[0005] The invention relates to the field of aircraft piloting assistance, in particular to reduce the cognitive load for the aircraft pilot during the execution of an aircraft mission.

[0006] A system and method for enhanced human-machine dialogue is known from French document FR 3 111 210 B1. This system and method includes bidirectional translations between a user, such as a pilot, and the aircraft, notably through the translation, via a downward translator, of human commands into a form that can be manipulated by the machine, and conversely, through the translation, via an upward translator, of results produced by the machine into a form intelligible to the human. This document also describes the display of parts of intermediate reasoning performed by the machine, in order to provide an explanation of causes to the user.

[0007] However, with such a system and such a method, the cognitive load for the aircraft pilot sometimes remains relatively significant.

[0008] The aim of the invention is therefore to propose a method, and an associated electronic device, for assisting in the piloting of an aircraft which makes it possible to further reduce the cognitive load for the pilot of the aircraft.

[0009] To this end, the invention relates to a method for assisting in the piloting of an aircraft, via the monitoring of at least one operational criterion of an aircraft mission during the execution of said mission,

[0010] the process being implemented by an electronic piloting assistance device and including, for each operational criterion, the following steps:

[0011] - determination of a value for each characteristic quantity of a set of characteristic quantity(ies) associated with said operational criterion, the set of characteristic quantity(ies) being specific to each operational criterion and predefined for each operational criterion, each characteristic quantity being determined from at least one avionics variable, each avionics variable being acquired from a source chosen from an avionics system, a sensor and a database;

[0012] - estimation of a value of the operational criterion from each determined value of characteristic magnitude associated with said operational criterion and via the implementation of an artificial intelligence algorithm;

[0013] - calculation of a difference between the estimated value of the operational criterion and a value desired of said operational criterion, and of a characteristic magnitude, called causal magnitude, which is the main cause of said deviation, said causal magnitude being calculated via the artificial intelligence algorithm;

[0014] - if the calculated difference is greater than a predefined threshold, at least one action is performed chosen from the group consisting of: displaying, on a display system, the estimated value of the operational criterion, the calculated deviation and an indication of the causal magnitude; issuing an alert based on the calculated deviation; and generating a command instruction for an avionics system based on the estimated value of the operational criterion.

[0015] The piloting assistance method according to the invention then makes it possible to help the pilot to assess the chances of success of the aircraft's mission or the need to replan it, by estimating the value of each operational criterion, then displaying the estimated value of each operational criterion, and / or issuing an alert based on the estimated value of each operational criterion.

[0016] The pilot assistance method according to the invention thus makes it possible to provide significant assistance to the pilot and to reduce the cognitive load required to diagnose the chances of mission success. By regularly calculating the difference between the estimated value of the operational criterion and a desired value of said operational criterion, the pilot assistance method according to the invention, in other words, provides a guardian angel function for piloting the aircraft, in particular by drawing the user's attention, such as a member of the aircraft crew, in the form of a visual and / or audible alert if the calculated difference is greater than the predefined threshold.

[0017] The piloting assistance method according to the invention also makes it possible to provide the user with an indication as to the characteristic quantity that is the main cause of said deviation, also called the causal quantity, that is to say, the one which, among the set of characteristic quantity(ies) associated with said operational criterion, is primarily responsible for The origin of this discrepancy, or in other words, its primary source, is identified. This allows for a clearer explanation to the user of the cause of the reported discrepancy, thereby reducing the cognitive load required to determine how to address it. In other words, the artificial intelligence algorithm used to identify the causal factor makes the diagnosis more understandable for the user.

[0018] Preferably, for each operational criterion, the set of characteristic quantity(ies) associated with said operational criterion can be consulted and modified by a user, and this control aid can then be adapted by and for the user.

[0019] Preferably, the artificial intelligence algorithm implemented to estimate the value of the operational criterion includes a fuzzy logic decision tree, which makes the diagnosis made even more understandable for the user.

[0020] Preferably, the pilot assistance method according to the invention helps the pilot to identify symptoms of a situation by monitoring several operational criteria, including safety, punctuality, comfort, and environmental impact; and to take into account the consequences of a change in context, such as a modification of the aircraft's environment, a change in the pilot's intention for at least one operational criterion, and a pilot action different from a planned action. The pilot assistance method according to the invention then allows the user to better assess the impact of this change in context relative to an initially defined performance, that is, relative to the desired value of each operational criterion.

[0021] According to other advantageous aspects of the invention, the piloting assistance method comprises one or more of the following features, taken individually or in all technically possible combinations:

[0022] - the method further comprises the detection of at least one change among the group consisting of: modification of an aircraft environment, change in the desired value of a respective operational criterion, and pilot action different from a planned action; the determination, estimation and calculation steps then being implemented again following this detection and according to at least one detected change;

[0023] - the determination, estimation and calculation steps are repeated regularly,

[0024] the determination, estimation and calculation steps being preferably repeated periodically,

[0025] the period between two successive iterations of the determination, estimation and calculation steps being preferably still less than 10 seconds;

[0026] - for each operational criterion, the set of characteristic quantity(ies) associated with audit operational criterion and / or the desired value of said operational criterion are viewable and modifiable by a user;

[0027] - several operational criteria are monitored;

[0028] the operational criteria being preferably monitored simultaneously;

[0029] - each operational criterion is chosen from the group consisting of: security, punctuality, comfort, and ecology;

[0030] if several operational criteria are monitored, the operational criteria are preferably all the operational criteria of the group consisting of: safety, punctuality, comfort, and ecology;

[0031] - the set of characteristic quantity(ies) associated with safety comprises: a aircraft lift, a ratio between the amount of fuel available and the amount of fuel required, and an indicator quantifying the aircraft's adherence to a flight plan;

[0032] - the set of characteristic quantity(ies) associated with punctuality comprises: a an indicator quantifying a delay of the aircraft on arrival, a ratio of the number of passengers who missed a connecting flight on arrival to the total number of passengers on the delayed flight, an indicator quantifying a delay of a subsequent flight of the aircraft due to the delay of the current flight of the aircraft;

[0033] - the set of characteristic quantity(ies) associated with comfort comprises: an in a delayed takeoff indicator, a number of vertical acceleration(s) exceeding a predefined threshold during the flight and a cumulative duration of vertical acceleration(s) exceeding a predefined threshold during the flight;

[0034] - the set of characteristic quantity(ies) associated with ecology comprises: a quantity of carbon dioxide emitted during the flight, an indicator of the use of favorable air currents to modify the aircraft's trajectory from an initially planned trajectory, a level of noise generated on the ground during landing, a ratio between a quantity of carbon dioxide emitted during the flight and a number of passengers carried;

[0035] - the artificial intelligence algorithm includes a logic decision tree blurry,

[0036] the fuzzy logic decision tree preferably including at least one fuzzy inference system, each fuzzy inference system being configured to receive as input at least one determined value of characteristic quantity and to deliver as output a unit evaluation value; for each fuzzy inference system, a correspondence between input(s) and output being established by fuzzy logic; the value of the operational criterion then being estimated from the unit evaluation value(s) calculated for the set of characteristic quantity(ies) associated with said operational criterion;

[0037] - the method further comprises a preliminary learning step the artificial intelligence algorithm based on training data;

[0038] the preliminary learning of the artificial intelligence algorithm being preferably supervised learning;

[0039] if the artificial intelligence algorithm includes a fuzzy logic decision tree, the preliminary learning of the fuzzy logic decision tree is preferably still carried out via the implementation of a genetic algorithm.

[0040] The invention also relates to a computer program comprising software instructions which, when executed by a computer, implement a piloting assistance method, as defined above.

[0041] The invention also relates to an electronic device for piloting an aircraft, by monitoring at least one operational criterion of an aircraft mission during the execution of said mission, the device comprising:

[0042] - a determination module configured to determine, for each operational criterion rational, a value of each characteristic quantity of a set of characteristic quantity(ies) associated with said operational criterion, the set of characteristic quantity(ies) being specific to each operational criterion and predefined for each operational criterion, each characteristic quantity being determined from at least one avionics variable, each avionics variable being acquired from a source chosen from an avionics system, a sensor and a database;

[0043] - an estimation module configured to estimate, for each operational criterion, an operational criterion value from each determined value of characteristic quantity associated with said operational criterion and via the implementation of an artificial intelligence algorithm;

[0044] - a calculation module configured to calculate a difference between the estimated value of the operational criterion and a desired value of said operational criterion, and a characteristic quantity, called causal quantity, which is the main cause of said deviation, said causal quantity being calculated via the artificial intelligence algorithm;

[0045] - an implementation module configured to, if the calculated deviation is greater than a threshold predefined, perform at least one action chosen from the group consisting of: displaying, on a display system, the estimated value of the operational criterion, the calculated deviation and an indication of the causal magnitude, issuing an alert based on the calculated deviation, and generating a command instruction for an avionics system based on the estimated value of the operational criterion and the calculated deviation.

[0046] The invention also relates to an aircraft comprising an electronic device for piloting an aircraft, the piloting aid device being as defined above.

[0047] These features and advantages of the invention will become clearer upon reading the following description, given solely by way of example and not limiting, and made with reference to the attached drawings, on which:

[0048] [Fig.1] [Fig.1] is a schematic representation of an aircraft comprising an electronic flight aid device according to the invention, connected to avionics systems, one or more sensors, a database, and a display system;

[0049] [Fig.2] [Fig.2] is a schematic representation of a logic decision tree fuzzy logic included in an artificial intelligence algorithm, implemented by the flight assistance system of [Fig. 1] to estimate the value of an operational criterion from the value of each quantity in a set of characteristic quantity(ies) associated with said operational criterion, the flight assistance system then allowing the monitoring of at least one operational criterion of an aircraft mission; and

[0050] [Fig.3] [Fig.3] is a flowchart of a pilot assistance process according to the invention, the method being implemented the piloting assistance device of the [Fig.1].

[0051] In the description, the expression "approximately equal to" denotes a relationship of equality to plus or minus 10%, preferably to plus or minus 5%.

[0052] In [Fig.1], an aircraft 10 comprises several avionics systems 12, one or more databases 14, several sensors 16, one or more display systems 18, and an electronic flight aid device 20 connected to the avionics systems 12, the database(s) 14, the sensors 16 and the display system(s) 18.

[0053] The aircraft 10 is, for example, an airplane, such as a commercial airliner. Alternatively, the aircraft 10 is a helicopter, a drone remotely piloted by a pilot, or an autonomous aircraft without an operator. Those skilled in the art will note that if the aircraft 10 is an autonomous aircraft without an operator, it preferably does not include a display system.

[0054] Each avionics system 12 is carried on board the aircraft 10, is known in itself, and is configured to implement one or more respective avionics functions.

[0055] Each avionics system 12 is capable of transmitting to the electronic flight assistance device 20 different avionics data, for example so-called "aircraft" data, such as the position, speed, acceleration, orientation, heading or altitude of the aircraft 10, and / or so-called "navigation" data, such as a flight plan, an estimated time of arrival, a number of passengers.

[0056] Each avionics system 12 is, for example, chosen from the group consisting of: a flight management system, also called FMS (Flight Management System); a guidance system, or FG (Flight Guidance); a flight control system, or FCS (Flight Control System); a positioning system satellite navigation, such as a GPS (Global Positioning System); an inertial reference system, also known as an 1RS (Inertial Reference System); an ILS (Instrument Landing System) or MLS (Microwave Landing System); an active runway overrun prevention system, also known as a ROPS (Runway Overrun Prevention System); and a radio altimeter, also known as an RA (Radio Altimeter).

[0057] Optionally, certain avionics systems 12 are also capable of receiving instructions, or commands, from the electronic flight assistance device 20. These avionics systems 12 capable of receiving instructions are, for example:

[0058] - the flight control system, also noted as FCS or FBW (from the English Fly By Wire), to act on a set of aircraft control surfaces and actuators. In the case of a fixed-wing aircraft, the control surfaces are, for example, ailerons, the elevator, or the rudder. In the case of a rotary-wing aircraft, the control surfaces are, for example, the collective pitch, the cyclic pitch, or the tail rotor pitch;

[0059] - an engine control system, also noted as ECU (Engine Control Unit) to vary the energy delivered by an aircraft engine, such as a jet engine, a turboprop engine or a turbine;

[0060] - at least one guidance system, such as an autopilot device, also noted AFCS (from the English Auto-Flight Control System), also called autopilot and noted PA or AP (from the English Automatic Pilot), or also such as the aircraft flight management system (FMS).

[0061] Each database 14 is optional, known in itself, is for example chosen from the group consisting of:

[0062] - a navigation database, also called NAVDB (from the English NA- Vigation Data Base), containing in particular data relating to prohibited flight areas or zones, data relating to landing strips on which the aircraft 10 is likely to land, this data typically being a position of a threshold of the landing strip, an orientation of the landing strip, a length of the strip, an altitude or a decision point, etc;

[0063] - a database of terrain elevations, containing information relating to the height and altitude of the Earth's surface;

[0064] - a performance database, also called PERFDB (from English PERformance Data Base), containing information on aircraft performance 10, such as speed, fuel consumption, altitude, range, etc;

[0065] - a maintenance database, containing information on the re preparations, maintenance and inspections carried out on the aircraft;

[0066] - a passenger database, containing information on passengers, such as their name, age, nationality, passport number, etc.; and

[0067] - a meteorological database, containing information on conditions meteorological data, such as temperature, pressure, wind speed and direction, visibility, etc., are used for flight planning and passenger safety.

[0068] These databases are typically interconnected and fed at least in part by sensors 16.

[0069] In the example of [Fig.1], the databases 14 are external databases to the electronic piloting aid device 20. Alternatively, not shown, the databases 14 are at least partly internal to the electronic piloting aid device 20.

[0070] The sensors 16 are capable of measuring various quantities associated with the aircraft 10 and / or the environment of the aircraft 10, and include, for example, at least one sensor from among: a laser remote sensing device, better known as lidar (light detection and ranging); a radar (radio detection and ranging); a laser (light amplification by stimulated emission of radiation); a rangefinder; a radio altimeter; an accelerometer; an inertial measurement unit (IMU); a Doppler sensor; a satellite positioning sensor, such as a GPS (Global Positioning System) sensor, a Galileo sensor, a Glonass sensor; one or more stereoscopic cameras; and an atmospheric data sensor, such as pressure, temperature.

[0071] Each electronic sensor 16 is known in itself, and the data measured by each sensor 16 are intended to be acquired by the electronic piloting aid device 20, to which it is connected.

[0072] The display system(s) 18 are, for example, a head-down display system and / or a head-up display system, also known as a HUD (Head-Up Display). The head-down display system is, for example, a navigation data display system. Alternatively or in addition, the display system 18 is a remote display system, in particular a display system external to the aircraft 10, such as a display system in a ground station, or the remote control or goggles of a drone operator.

[0073] The electronic flight aid device 20 is intended to be carried on board the aircraft 10, when the aircraft 10 is an airplane or a helicopter. Alternatively, the electronic piloting aid device 20 is intended to be installed on the ground, while being connected to the avionics systems 12 on board the aircraft 10, when the aircraft 10 is a drone remotely piloted by a pilot or an autonomous aircraft without an operator.

[0074] The electronic flight assistance device 20 is intended to assist the pilot of the aircraft 10 by monitoring at least one operational criterion of a mission of the aircraft 10 during the execution of said mission, thereby reducing the cognitive load on the pilot. This monitoring is preferably performed regularly, by regularly estimating a new value for each monitored operational criterion. Each operational criterion is, for example, chosen from the group consisting of: safety, punctuality, comfort, and environmental impact.

[0075] In addition, the electronic flight control system 20 is configured to monitor several operational criteria, including several operational criteria from among the aforementioned operational criteria, and for example, all operational criteria from the group consisting of: safety, punctuality, comfort, and environmental impact. According to this addition, the electronic flight control system 20 is preferably configured to simultaneously monitor several operational criteria. In other words, the plurality of operational criteria is then monitored simultaneously, with said operational criteria being monitored in parallel with one another.

[0076] In the example of [Fig. 1], the electronic flight assistance device 20 is an autonomous electronic device, external to the avionics systems 12, the database(s) 14, the sensors 16 and the display system(s) 18. In other words, in this example, the flight assistance device 20 is distinct and separate from each of the avionics systems 12. In an alternative not shown, the electronic flight assistance device 20 is integrated into one of the avionics systems 12, i.e. included in one of the avionics systems 12, such as the flight management system or FMS.

[0077] The electronic flight control system 20 includes a module 22 for determining, for each operational criterion, a value of each characteristic quantity Ki, K2, K3, K4, K5 of a set of characteristic quantity(ies) associated with said operational criterion. The determination module 22 is connected to at least one of the avionics systems 12, databases 14, and sensors 16 to acquire the information and / or measured values ​​necessary for determining each value of characteristic quantity Kb K2, K3, K4, K5.

[0078] The electronic piloting aid device 20 also includes a module 24 for estimating a value of each operational criterion, from each determined value of characteristic quantity Kh K2, K3, K4, K5 associated with said operational criterion and via the implementation of an artificial intelligence algorithm 26. The artificial intelligence algorithm 26 includes, for example, a fuzzy logic decision tree 28, visible in [Fig.2]. The estimation module 24 is connected to the output of the determination module 22.

[0079] The electronic flight control aid 20 also includes a module 30 for calculating the difference between the estimated value of the operational criterion and a desired value of said operational criterion, and a characteristic quantity, called the causal quantity, which is the main cause of said difference. The calculation module 30 is connected to the output of the estimation module 24.

[0080] The electronic flight control aid 20 includes an implementation module 32 which, if the calculated deviation exceeds a predefined threshold, performs at least one of the following actions: displaying the estimated value of the operational criterion, the calculated deviation, and an indication of the causal magnitude; issuing an alert based on the calculated deviation; and generating a command instruction for an avionics system based on the estimated value of the operational criterion and the calculated deviation. The implementation module 32 is connected to the output of the calculation module 30.

[0081] Advantageously, the electronic pilot aid device 20 is configured to regularly, for example periodically, launch the implementation of the determination 22, estimation 24 and calculation 30 modules, in order to regularly estimate a new value of each monitored CO operational criterion, and to regularly calculate a new difference between the new estimated value and the desired value of each monitored CO operational criterion.

[0082] When this implementation of the determination 22, estimation 24 and calculation 30 modules is repeated periodically, the period between two successive iterations of the implementations of the determination 22, estimation 24 and calculation 30 modules is typically less than 10 seconds, preferably less than 5 seconds, and preferably even less than 1 second.

[0083] As an optional addition, the electronic flight control system 20 further comprises a module 34 for detecting at least one change among a modification of the aircraft environment 10, a change in the desired value of a respective operational criterion CO, and / or a pilot action different from a planned action. According to this optional addition, the detection module 34 is then configured to, upon detection of at least one change, reactivate the determination 22, estimation 24, and calculation 30 modules in order to estimate a new value for each monitored operational criterion CO and to calculate a new difference, for each monitored operational criterion CO, between the new estimated value and the desired value of said operational criterion CO.

[0084] In the example of [Fig. 1], the electronic piloting aid device 20 comprises an information processing unit 40 formed, for example, of a memory 42 and a processor 44 associated with memory 42.

[0085] In the example of [Fig.1], the determination module 22, the estimation module 24, the calculation module 30 and the realization module 32, as well as the optional detection module 34, are each implemented in the form of a software program, or a software component, executable by the processor 44.The memory 42 of the electronic flight assistance device 20 is then capable of storing software for determining, for each operational criterion, a value for each characteristic quantity; software for estimating a value for each operational criterion; software for calculating the causal quantity and the difference between the estimated and desired values ​​of the operational criterion; and software for performing at least one action among the following: displaying the estimated value of the operational criterion, the calculated difference, and the indication of the causal quantity; issuing an alert based on the calculated difference; and generating the command instruction to the avionics system. As an optional addition, the memory 42 of the electronic flight assistance device 20 is then capable of storing software for detecting at least one change.The processor 44 is then capable of running each of the software programs among the determination software, the estimation software, the calculation software and the implementation software, as well as, optionally, the detection software.

[0086] In variant, not shown, the database 14 is an internal database of the electronic piloting aid device 20, it is typically suitable for being stored in a memory of the electronic piloting aid device 20, such as memory 42.

[0087] In an alternative not shown, the determination module 22, the estimation module 24, the calculation module 30 and the realization module 32, as well as the optional detection module 34, are each implemented 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 Specified Integrated Circuit).

[0088] When the electronic control aid device 20 is implemented in the form of one or more software programs, i.e., in the form of a computer program, it is also capable of being stored on a computer-readable medium (not shown). The computer-readable medium is, for example, a medium capable of storing electronic instructions and being connected to a bus of a computer system. For example, the readable medium is an optical disc, a magneto-optical disc, a ROM, a RAM, any type of non-volatile memory (e.g., EPROM, EEPROM, FLASH, NVRAM), a magnetic card, or an optical card. A computer program comprising software instructions is then stored on the readable medium.

[0089] The determination module 22 is configured to determine, for each operational criterion CO, a value for each characteristic quantity Kb K2, K3, K4, K5 of a set of characteristic quantities associated with said operational criterion CO. The set of characteristic quantities is specific to each operational criterion CO and predefined for each operational criterion CO.

[0090] Advantageously, for each operational criterion CO, the set of characteristic quantity(ies) associated with said operational criterion CO and / or the desired value of said operational criterion CO can be consulted and modified by a user.

[0091] The set of characteristic quantity(ies) KH K2, K3, K4, K5 associated with safety includes, for example, a lift of the aircraft 10, a ratio between quantity of fuel available and quantity of fuel required, and an indicator quantifying the compliance by the aircraft 10 with a flight plan.

[0092] The set of characteristic quantity(ies) KH K2, K3, K4, K5 associated with punctuality typically includes an indicator quantifying a delay of aircraft 10 on arrival, a ratio of a number of passengers who missed a connection on arrival and a total number of passengers on the delayed flight, an indicator quantifying a delay of a subsequent flight of aircraft 10 due to the delay of the current flight of aircraft 10.

[0093] The set of characteristic quantity(ies) Kb K2, K3, K4, K5 associated with comfort includes, for example, a takeoff delay indicator, a number of vertical acceleration(s) greater than a predefined threshold during the flight and a cumulative duration of vertical acceleration(s) greater than a predefined threshold during the flight.

[0094] The set of characteristic quantity(ies) KH K2, K3, K4, K5 associated with ecology typically includes a quantity of carbon dioxide emission during the flight, an indicator of the use of favorable air currents to modify the trajectory of the aircraft 10 compared to an initially planned trajectory, a level of noise generated on the ground during landing, a ratio between a quantity of carbon dioxide emitted during the flight and a number of passengers transported.

[0095] Each characteristic quantity KH K2, K3, K4, K5 is determined from at least one avionics variable, each avionics variable being acquired from a source chosen from the avionics systems 12, the database(s) 14 and the sensors 16. The determination of each characteristic quantity KH K2, K3, K4, K5 from at least one avionics variable is known in itself.

[0096] The estimation module 24 is configured to estimate, for each operational criterion CO, a value of the operational criterion CO from each determined value of characteristic quantity Kh K2, K3, K4, K5 associated with said operational criterion CO and via the implementation of the artificial intelligence algorithm 26, and for example of the fuzzy logic decision tree 28.

[0097] The fuzzy logic decision tree 28, also called GFT (from the English Generalized Fuzzy Tree), allows decisions to be made even with uncertain or imprecise data. Unlike traditional decision trees that use binary rules to make decisions (true / false), the fuzzy logic decision tree 28 uses linguistic variables to represent concepts such as "very likely" or "somewhat likely".

[0098] The fuzzy logic decision tree 28 operates by evaluating the input variables, namely the determined value(s) of characteristic quantity(ies) K1, K2, K3, K4, K5 associated with the respective operational criterion CO, these input variables being quantitative or qualitative data, and then converting them into degree of membership values ​​for the corresponding linguistic variables. For example, if the input variable is the indicator quantifying adherence to the flight plan or, for example, the indicator of takeoff delay, the value of this variable is translated into a degree of membership of linguistic variables, such as "low", "medium", or "high".

[0099] The fuzzy logic decision tree 28 then uses fuzzy rules to evaluate these membership degrees and make decisions. These rules are generally defined by experts in the field or by historical data. The fuzzy rules are typically represented in the form of "if...then" statements with linguistic variables.

[0100] As an optional complement, the fuzzy logic decision tree 28 uses inference methods to calculate the final output by combining the results of several rules. One such inference method is, for example, the Mamdani method, which uses the weighted average of the rules to calculate the output.

[0101] The fuzzy logic decision tree 28 then makes it possible to estimate the value of the corresponding operational criterion CO in uncertain or imprecise environments using linguistic concepts and fuzzy rules, rather than rigid binary rules.

[0102] Advantageously, the fuzzy logic decision tree 28 includes at least one fuzzy inference system FISi, FIS2, FIS3, FIS4, FIS5 (FIS for Fuzzy Inference System), each fuzzy inference system FISi, FIS2, FIS3, FIS4, FIS5 being configured to receive as input at least one determined value of characteristic quantity K2, K3, K4, K5 and to deliver as output a unit evaluation value; for each fuzzy inference system FISi, FIS2, FIS3, FIS4, FIS5, a correspondence between input(s) and output being established by fuzzy logic; the value of the operational criterion CO then being estimated from the unit evaluation value(s) calculated for the set of characteristic quantity(ies) associated with said operational criterion CO.

[0103] In the example in [Fig.2], the fuzzy logic decision tree 28 is then re presented as a graph of fuzzy inference systems FISi, FIS2, FIS3, FIS4, FIS5, each with an associated weighting coefficient ai, a2, a3, a4, a5. In this example, the fuzzy logic decision tree 28 comprises five fuzzy inference systems FISi, FIS2, FIS3, FIS4, FIS5, namely a first fuzzy inference system FISi with a first weighting coefficient ab, a second fuzzy inference system FIS2 with a second weighting coefficient a2, a third fuzzy inference system FIS3 with a third weighting coefficient a3, a fourth fuzzy inference system FIS4 with a fourth weighting coefficient a4, and a fifth fuzzy inference system FIS5 with a weighting coefficient a5.

[0104] In this example, the fuzzy inference systems are distributed over three levels, namely a lower level corresponding to the first, second and third fuzzy inference systems FISi, FIS2, FIS3 receiving the input variables, i.e. the determined values ​​of the set of characteristic quantity(ies) Kb K2, K3, K4, K5 associated with the corresponding operational criterion CO; an intermediate level corresponding to the fourth fuzzy inference system FIS4 connected at the output of the first and second fuzzy inference systems FISi, FIS2; and a higher level corresponding to the fifth fuzzy inference system FIS5 connected at the output of the third and fourth fuzzy inference systems FIS3, FIS4, the fifth fuzzy inference system FIS5 then being configured in this example to deliver at its output the estimated value of the operational criterion CO.

[0105] Each fuzzy inference system FISb FIS2, FIS3, FIS4, FIS5 is a structure for formalizing the fuzzy rules that govern the decision-making of the decision tree 28. Each fuzzy inference system FISb FIS2, FIS3, FIS4, FIS5 includes, for example, one or more input variables, each typically divided into a number of linguistic categories, called "fuzzy sets"; one or more membership functions, namely mathematical functions assigning a degree of membership value to each input for each fuzzy set; one or more fuzzy rules governing decision-making, typically of the form "If the input is in fuzzy set A AND the input is in fuzzy set B, then the output is in fuzzy set C"; one or more inference functions combining the degrees of membership of the input fuzzy sets to determine the degrees of membership of the output fuzzy sets;one or more output variables representing the final decision, each typically divided into a number of fuzzy sets, analogous to the input variables; and one or more aggregation functions combining the degrees of membership of the output fuzzy sets to determine the final output value. The aggregation function is, for example, a weighted sum.

[0106] The fuzzy logic decision tree 28 was previously trained during a step preliminary training of the artificial intelligence algorithm 26 from training data.

[0107] Advantageously, the preliminary training of the artificial intelligence algorithm 26 is supervised learning. Those skilled in the art will observe that supervised learning is not direct. Indeed, the operator annotates a result, while the artificial intelligence algorithm 26, in particular the fuzzy logic decision tree 28, takes characteristic quantities as input. To build the training set, it is therefore necessary to provide a set of contextualized results; then, for each result in this set, evaluate the characteristic quantities; and finally, for each result in this set, have it annotated by a user in the operational semantics.

[0108] Supervised learning of the fuzzy logic decision tree 28 begins with the collection of input and output training data. The input data are typically features or attributes that describe a situation or problem, while the output data represent the expected outcomes for each situation or problem. The logical rules of the fuzzy logic decision tree 28 are then constructed from the training data.

[0109] The preliminary learning of the fuzzy logic decision tree 28 is preferably carried out via the implementation of a genetic algorithm. For said genetic algorithm learning, a set of individuals is created, each individual representing a potential fuzzy logic decision tree. Each decision tree is evaluated according to its accuracy in decision-making, which is measured using an activity function (from the English word "fitness"). Individuals with a higher activity function are selected to reproduce and produce offspring. Reproduction involves combining characteristics of the parents, while adding some variation to encourage the exploration of new solutions. The offspring produced are then subjected to an activity function evaluation to determine whether they are better or worse than their parents.The best individuals are retained for the next generation, while the least successful are eliminated. This process is repeated for several generations until a satisfactory fuzzy logic decision tree is found. Once the genetic algorithm has converged to a solution, the trained fuzzy logic decision tree is used to make decisions based on new input data. The activity function calculates, for example, the average of the differences between the output of the model under training and an operational, typically high-level, semantic annotation. This activity function must be minimized during the training process.

[0110] The calculation module 30 is configured to calculate a difference between the estimated value of the operational criterion CO and a desired value of said operational criterion CO, and a characteristic quantity, called causal quantity, which is the main cause of said deviation.

[0111] The calculated deviation is, for example, the difference between the desired value and the estimated value of the operational criterion CO, the calculated deviation then being a relative number. Alternatively, the calculated deviation is the absolute difference between the estimated value and the desired value of the operational criterion CO, and the calculated deviation is then a positive number.

[0112] The causal magnitude is calculated via the fuzzy logic decision tree 28, taking into account the weighting coefficient ab a2, a3, a4, a5 associated respectively with each fuzzy inference system FISi, FIS2, FIS3, FIS4, FIS5.

[0113] The implementation module 32 is configured to, if the calculated deviation is greater than a predefined threshold, perform at least one action chosen from the group consisting of: displaying, on a display system 18, the estimated value of the operational criterion CO, the calculated deviation and an indication of the causal magnitude, issuing an alert based on the calculated deviation, and generating a command instruction for an avionics system 12 based on the estimated value of the operational criterion CO and the calculated deviation.

[0114] As an optional addition, the implementation module 32 is configured to display the estimated value of the operational criterion and the calculated deviation regardless of the value of the calculated deviation, in particular also if the calculated deviation is less than or equal to the predefined threshold.

[0115] The operation of the electronic piloting aid device 20 will now be described with reference to [Fig.3] representing a flowchart of the piloting aid process according to the invention, implemented by the electronic piloting aid device 20.

[0116] During a preliminary learning step, not shown, the artificial intelligence algorithm 26 is trained from the training data, preferably in a supervised manner, and preferably still via the implementation of a genetic algorithm, as described above.

[0117] After this preliminary training of the artificial intelligence algorithm 26, during an initial step 100, the electronic flight assistance device 20 determines, via its determination module 22 and for each operational criterion CO, the value of each characteristic quantity Kb K2, K3, K4, K5 of the set of characteristic quantities associated with said operational criterion CO. Each characteristic quantity Kb K2, K3, K4, K5 is then determined in a manner known per se from at least one avionics variable, each being acquired from a source chosen from among the avionics systems 12, the database(s) 14 and the sensors 16.

[0118] At the end of the determination step 100, the piloting aid device 20 proceeds to the next step 110 during which it estimates, via its estimation module 24 putting Implementing the artificial intelligence algorithm 26, the value of the operational criterion CO is determined from each determined value of characteristic quantity Kb K2, K3, K4, K5 associated with said operational criterion CO. This estimation is carried out more specifically via the fuzzy logic decision tree 28, as described previously.

[0119] After the estimation step 110, the pilot support device 20 calculates, during the next step 120 and via its calculation module 30, the gap, such as relative difference or absolute value difference, between the estimated and desired values ​​of the operational criterion CO.

[0120] At the end of the calculation step 120, if the calculated deviation is greater than the predefined threshold, the flight assistance device 20, during the following step 130 and via its implementation module 32, displays relevant information on the display system 18, including the estimated value of the operational criterion CO, the calculated deviation and the indication of the possible causal quantity; and / or issues the alert based on the calculated deviation; or even generates the corresponding avionics system 12 command instruction based on the estimated value of the operational criterion CO and the calculated deviation.

[0121] The person skilled in the art will observe that, as an optional supplement, the estimated value of the operational criterion and the calculated deviation are displayed on the display system 18 during the implementation step 130 regardless of the value of the calculated deviation, and in particular also if the calculated deviation is less than or equal to the predefined threshold.

[0122] Advantageously, as an optional complement, a trend indicator is also displayed to indicate to the operator in which direction the estimated value of the operational criterion CO has evolved, compared to a previous estimated value of said operational criterion CO.

[0123] The determination 100, estimation 110 and calculation 120 steps are repeated regularly, and for example periodically, so that at the end of the calculation 120 step or the implementation 130 step, the pilot support device 20 returns to the initial determination 100 step in order to estimate a new value for each monitored operational criterion CO.

[0124] In addition or alternatively, the determination steps 100, estimation 110 and calculation 120 are implemented again, i.e. reiterated, following the detection, during a supplementary detection step, not shown, of at least one development among the modification of the aircraft environment 10, the change in the desired value of the respective operational criterion CO and a pilot action different from that planned.

[0125] A change in the environment of aircraft 10 is, for example, a change in the meteorological environment, or the receipt of a NOTAM (Notice to Airmen), i.e., a message to aircrew, ge generally published by government air navigation control agencies for the purpose of informing pilots of infrastructure developments.

[0126] Thus, the flight assistance device 20 according to the invention provides significant assistance to the user, such as the pilot of the aircraft 10, by enabling them to more effectively assess the chances of success of the aircraft 10's mission or the need to replan it, by estimating the value of each operational criterion (OC) associated with the mission, and then displaying the estimated value and variations of each operational criterion (OC). This reduction in cognitive load for the user then improves the flight safety of the aircraft 10.

[0127] By regularly calculating, preferably periodically, the difference between the estimated and desired values ​​of the operational criterion CO, the flight assistance device 20 also acts as a guardian angel for piloting the aircraft 10, typically by drawing the user's attention in the form of a visual and / or audible alert if the calculated difference is greater than the predefined threshold.

[0128] The piloting assistance device 20 also provides the user with an indication of the causal factor that is primarily responsible for this deviation. This allows the user to better understand the cause of the reported deviation and thus further reduce their cognitive load, particularly regarding how to react in order to limit this deviation. The fuzzy logic decision tree 28 then makes the diagnosis more understandable for the user.

[0129] Furthermore, the pilot assistance device 20 helps the pilot to identify symptoms of a situation by monitoring several operational criteria simultaneously, assessed in parallel, such as safety, punctuality, comfort, and environmental impact. This multi-criteria monitoring is therefore even more relevant and useful for the user, allowing them to further reduce their cognitive load.

[0130] Furthermore, the flight assistance device 20 is capable of taking into account the consequences of a change in context, such as a modification of the aircraft's environment, a change in the pilot's intention for at least one operational criterion, and a pilot action different from a planned action. The flight assistance device 20 then allows the user to better assess the impact of this change in context with respect to the desired value of each operational criterion CO, i.e., the initially defined performance of the aircraft 10 during its mission.

Claims

Demands

1. A method for assisting the piloting of an aircraft (10), via the monitoring of at least one operational criterion (OC) of a mission of the aircraft (10) during the execution of said mission, the method being implemented by an electronic piloting assistance device (20) and comprising, for each operational criterion (OC), the following steps: - determination (100) of a value of each characteristic quantity (Ki, K2, K3, K4, K5) of a set of characteristic quantity(ies) associated with said operational criterion (OC), the set of characteristic quantity(ies) being specific to each operational criterion (OC) and predefined for each operational criterion (OC), each characteristic quantity (Ki, K2, K3, K4, K5) being determined from at least one avionics variable, each avionics variable being acquired from a source chosen from an avionics system (12), a sensor (16) and a database (14); - estimation (110) of a value of the operational criterion (OC) from each determined value of characteristic quantity (Kb K2, K3, K4, K5) associated with said operational criterion (OC) and via the implementation of an artificial intelligence algorithm (26); - calculation (120) of a difference between the estimated value of the operational criterion (OC) and a desired value of said operational criterion (OC), and of a characteristic quantity, called causal quantity, which is the main cause of said difference, said causal quantity being calculated via the artificial intelligence algorithm (26); - if the calculated deviation is greater than a predefined threshold, at least one action chosen from the group consisting of: displaying, on a display system (18), the estimated value of the operational criterion (OC), the calculated deviation and an indication of the causal magnitude; issuing an alert based on the calculated deviation; and generating a command instruction for an avionics system (12) based on the estimated value of the operational criterion (OC), the at least one action performed including the display, on the display system (18), of the estimated value of the operational criterion (OC), the calculated deviation and an indication of the causal magnitude.

2. A method according to claim 1, wherein the method further comprises the following step: - detection of at least one change among the group consisting of: modification of an aircraft environment (10), change in the desired value of a respective operational criterion (OC), and pilot action different from a planned action; the determination (100), estimation (110) and calculation (120) steps then being implemented again following this detection and according to the at least one change detected.

3. A method according to any one of the preceding claims, wherein the determination (100), estimation (110) and calculation (120) steps are repeated regularly; the determination (100), estimation (110) and calculation (120) steps preferably being repeated periodically; the period between two successive iterations of the determination (100), estimation (110) and calculation (120) steps preferably being less than 10 seconds.

4. A method according to any one of the preceding claims, wherein for each operational criterion (OC), the set of characteristic quantity(ies) associated with said operational criterion (OC) and / or the desired value of said operational criterion (OC) are available for consultation and modification by a user.

5. A method according to any one of the preceding claims, wherein several operational criteria (OCs) are monitored; the operational criteria (OCs) preferably being monitored simultaneously.

6. A method according to any one of the preceding claims, wherein each operational criterion (OC) is chosen from the group consisting of: safety, punctuality, comfort, and ecology; if several operational criteria (OC) are monitored, the operational criteria (OC) are preferably all the operational criteria from the group consisting of: safety, punctuality, comfort, and ecology.

7. A method according to claim 6, wherein the set of characteristic quantity(ies) associated with safety comprises: the lift of the aircraft (10), a ratio between the quantity of fuel available and the quantity of fuel required, and an indicator quantifying the aircraft's (10) adherence to a flight plan; wherein the set of characteristic quantity(ies) associated with punctuality comprises: an indicator quantifying a delay of the aircraft (10) upon arrival, a ratio of the number of passengers who missed a connectionresponse to arrival and a total number of passengers on the delayed flight, an indicator quantifying a delay of a subsequent flight of the aircraft (10) due to the delay of the current flight of the aircraft (10); in which the set of characteristic quantity(ies) associated with comfort includes: a takeoff delay indicator, a number of vertical accelerations greater than a predefined threshold during the flight and a cumulative duration of vertical accelerations greater than a predefined threshold during the flight; and in which the set of characteristic quantity(ies) associated with ecology includes: a quantity of carbon dioxide emission during the flight, an indicator of the use of favorable air currents to modify the trajectory of the aircraft (10) relative to an initially planned trajectory, a level of noise generated on the ground during landing, a ratio between a quantity of carbon dioxide emitted during the flight and a number of passengers carried.

8. A method according to any one of the preceding claims, wherein the artificial intelligence algorithm (26) comprises a fuzzy logic decision tree (28), the fuzzy logic decision tree (28) preferably including at least one fuzzy inference system (FISi, FIS2, FIS3, FIS4, FIS5), each fuzzy inference system (FISi, FIS2, FIS3, FIS4, FIS5) being configured to receive as input at least one determined value of characteristic magnitude (Ki, K2, K3, K4, K5) and to deliver as output a unit evaluation value; for each fuzzy inference system (FISi, FIS2, FIS3, FIS4, FIS5), an input(s) and output correspondence being established by fuzzy logic; the value of the operational criterion (OC) is then estimated from the unit value(s) of evaluation calculated for the set of characteristic quantity(ies) associated with said operational criterion (OC).

9. A method according to any one of the preceding claims, wherein the method further comprises a preliminary step of training the artificial intelligence algorithm (26) from training data; the preliminary training of the artificial intelligence algorithm (26) preferably being supervised learning; if the artificial intelligence algorithm (26) includes a fuzzy logic decision tree (28), the preliminary training of the fuzzy logic decision tree (28) preferably being further performed via the implementation of a genetic algorithm.

10. A computer program comprising software instructions which, when executed by a computer, implement a method according to any one of the preceding claims.

11. Electronic piloting aid device (20) for an aircraft (10), by monitoring at least one operational criterion (OC) of a mission of the aircraft (10) during the execution of said mission, the device (20) comprising: - a determination module (22) configured to determine, for each operational criterion (OC), a value of each characteristic quantity (Ki, K2, K3, K4, K5) of a set of characteristic quantity(ies) associated with said operational criterion (OC), the set of characteristic quantity(ies) being specific to each operational criterion (OC) and predefined for each operational criterion (OC), each characteristic quantity (Kb K2, K3, K4, K5) being determined from at least one avionics variable, each avionics variable being acquired from a source chosen from an avionics system (12), a sensor and a database;- an estimation module (24) configured to estimate, for each operational criterion (OC), a value of the operational criterion (OC) from each determined value of characteristic quantity (Kb K2, K3, K4, K5) associated with said operational criterion (OC) and via the implementation of an artificial intelligence algorithm (26); - a calculation module (30) configured to calculate a difference between the estimated value of the operational criterion (OC) and a desired value of said operational criterion (OC), and a characteristic quantity, called causal quantity, which is the main cause of said difference, said causal quantity being calculated via the artificial intelligence algorithm (26);- an implementation module (32) configured to, if the calculated deviation is greater than a predefined threshold, perform at least one action chosen from the group consisting of: displaying, on a display system (18), the estimated value of the operational criterion (OC), the calculated deviation and an indication of the causal magnitude, issuing an alert based on the calculated deviation, and generating a command instruction for an avionics system (12) based on the estimated value of the operational criterion (OC) and the calculated deviation; at least one action performed including the display, on the display system (18), of the estimated value of the operational criterion (OC); the calculated difference and an indication of the causal magnitude.

12. Aircraft (10) comprising an electronic device (20) for piloting an aircraft (10), the piloting aid device (20) being in accordance with the preceding claim.

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