Device and method for assisting in monitoring the status of a mission involving at least one aircraft

Abstract

Device and method for assisting in monitoring the status of a mission of at least one aircraft. The present invention relates to a method (50) for assisting in monitoring the status of a mission of at least one aircraft, via the monitoring of at least one operational criterion during the execution of said mission, comprising the following steps: - determination (52) of a polynomial function representing said at least one operational criterion, each monomial of which is a product of constants and / or variables, each variable being a characteristic quantity of a set of characteristic quantity(ies) associated with said operational criterion; - by partial derivative of said polynomial function, determination (54) of the impact of the variation of each characteristic quantity on the value of said operational criterion;- restitution (56) of the impact of each characteristic quantity to an operator of said aircraft, capable of using said impact to identify at least one source of anomaly in the execution of said mission. Figure for the abbreviation: Figure 3;

Description

Title of the invention: Device and method for assisting in monitoring the status of a mission of at least one aircraft

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

[0002] The invention also relates to a computer program comprising software instructions which, when executed by a computer, implement such a method for assisting in monitoring the status of a mission of at least one aircraft.

[0003] The invention also relates to an electronic device for assisting in monitoring the status of a mission of at least one aircraft, and an aircraft comprising such a device for assisting in monitoring the status of a mission of at least one aircraft.

[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 tactical assistance to the pilot of an aircraft in planning / replanning an avionics mission, in particular in order to reduce the cognitive load for the pilot of the aircraft during the execution of said aircraft mission.

[0006] A method, and an associated electronic device, for piloting an aircraft is known from the document, whose filing number is FR 2303810. This method reduces the cognitive load on the aircraft pilot by monitoring at least one operational criterion of the mission of at least one aircraft during the execution of the mission. This solution allows the operator, in particular, to realize whether the operational criteria are deteriorating or improving.

[0007] However, identifying the origin of the change remains a task for the operator which involves an additional cognitive load, particularly in complex environments.

[0008] The aim of the invention is then to propose a solution to help identify the origin of a change in the value of the operational criteria of a mission.

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

[0010] the method being implemented by an electronic device for monitoring the status of a mission of at least one aircraft, and comprising, for each operational criterion, and comprising the following steps:

[0011] - determination of a polynomial function representing said at least one criterion operational criterion where each monomial is a product of constants and / or variables, each variable being a characteristic quantity of a set of N characteristic quantity(ies) associated with said operational criterion;

[0012] - by partial derivative of said polynomial function, determination of the impact of the variation of each characteristic quantity of said set associated with said operational criterion on the value of said operational criterion;

[0013] - rendering the impact of each characteristic quantity, via an interface man-machine, to an operator of said aircraft, capable of using said impact to identify at least one source of anomaly in the execution of said mission.

[0014] The method for assisting in monitoring the status of a mission of at least one aircraft according to the invention then makes it possible to help the pilot to assess the chances of success of the mission of said at least one aircraft or the need to re-plan it, by improving his understanding of the impact of each characteristic quantity associated with the monitored operational criterion and this throughout the mission in progress.

[0015] The piloting assistance method according to the invention then makes it possible to provide significant assistance to the pilot and to reduce his cognitive load necessary to make a diagnosis on the chances of success of the mission, and to identify the source of problems occurring during the mission, in particular in the presence of a changing environment.

[0016] The solution according to the present invention complements the aforementioned solution of the document whose filing number is FR 2303810, by providing the pilot with keys (i.e. the restored impacts) to identify the cause or root causes of a variation in operational criteria detected via the aforementioned solution of the document whose filing number is FR 2303810, which makes it possible to further reduce the cognitive load of the operator (e.g. the pilot).

[0017] According to other advantageous aspects of the invention, the method for assisting in monitoring the status of a mission of at least one aircraft comprises one or more of the following features, taken individually or in all technically possible combinations:

[0018] - the impact of the variation of each characteristic quantity of said associated assembly audit operational criterion on the value of said operational criterion depends on the sign and value of the partial derivative associated with said characteristic quantity;

[0019] - the process further comprises:

[0020] - the receipt of at least one piloting intention to be monitored and associated with audit operational criterion;

[0021] - depending on said impact of each characteristic quantity of said associated assembly audit operational criterion and of said at least one intention, the determination (60) of the type of variation of said characteristic quantity to be monitored according to a reference variation value, the type of variation being an upward or downward variation;

[0022] - the return, via said human-machine interface, to an operator of said aircraft, of said type of variation to be returned to the operator for each characteristic quantity of said set associated with said operational criterion;

[0023] The process further comprises:

[0024] - determining a difference between a current value of the operational criterion and a previous value of said operational criterion, each current or previous value of said operational criterion being obtained from each determined value of characteristic quantity associated with said operational criterion and via the implementation of an artificial intelligence algorithm;

[0025] - based on the value of said deviation, and said impact of each characteristic quantity said set associated with said operational criterion, identification of at least one of the characteristic quantities associated with said operational criterion that is responsible for said discrepancy.

[0026] - the method further comprises:

[0027] - the construction and rendering of a visual representation corresponding to the projection of the aircraft's trajectory into an M-dimensional space where each point is associated with a cost value with respect to said operational criterion, a correspondence being established beforehand between each cost value and the value(s) of each characteristic quantity of said set associated with said operational criterion;

[0028] - from said visual representation, identification of the portions of trajectories impacted by said at least one of the characteristic quantities associated with said operational criterion specific to be responsible for said deviation;

[0029] - the artificial intelligence algorithm includes a logic decision tree fuzzy, the fuzzy logic decision tree including at least one fuzzy inference system, each fuzzy inference system being configured to receive as input at least one determined value of a 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 being then estimated from the unit evaluation value(s) calculated for the set of characteristic quantity(ies) associated with said operational criterion,

[0030] and said polynomial function representing said at least one operational criterion is equivalent to said at least one fuzzy inference system of said fuzzy logic decision tree;

[0031] - the artificial intelligence algorithm comprises a fuzzy function network based radial including at least one fuzzy inference system, each fuzzy inference system being configured to receive as input at least one determined value of a 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,

[0032] and wherein said polynomial function representing said at least one operational criterion is equivalent to said at least one fuzzy inference system of said radially based fuzzy function network.

[0033] The invention also relates to a computer program comprising software instructions which, when executed by a computer, implement a method for assisting in monitoring the status of a mission of at least one aircraft as defined above.

[0034] The invention also relates to an electronic device for assisting in monitoring the status of a mission of at least one aircraft by monitoring at least one operational criterion of a mission of said at least one aircraft during the execution of said mission, the device comprising:

[0035] - a first determination module configured to determine a function polynomial representing said at least one operational criterion where each monomial is a product of constants and / or variables, each variable being a characteristic quantity of a set of N characteristic quantity(ies) associated with said operational criterion;

[0036] - a second determination module configured to determine, by derivative partial of said polynomial function, the impact of the variation of each characteristic quantity of said set associated with said operational criterion on the value of said operational criterion;

[0037] - a restitution module configured to restore the impact of each quantity characteristic, via a human-machine interface, to an operator of said aircraft, capable of using said impact to optimize the piloting of said aircraft.

[0038] The invention also relates to an aircraft comprising an electronic device for monitoring the status of a mission of at least one aircraft, the electronic monitoring device being as defined above.

[0039] These features and advantages of the invention will become clearer upon reading the following description, given solely by way of non-limiting example, and made with reference to the accompanying drawings, in which:

[0040] [Fig-1] [Fig.1] is a schematic representation of an aircraft comprising an electronic device for monitoring the status of a mission of at least one aircraft according to the invention, connected to avionics systems, to one or more sensors, to a database, and to a display system;

[0041] [Fig.2] [Fig.2] is a schematic representation of a fuzzy logic decision tree included in an artificial intelligence algorithm, implemented by the electronic device for monitoring the mission status of at least one aircraft according to the invention of [Fig.1]; and

[0042] [Fig.3] [Fig.3] is a flowchart of a method for assisting in monitoring the status of a mission of at least one aircraft according to the invention, the method being implemented by the device for assisting in monitoring the status of a mission of at least one aircraft of [Fig.1].

[0043] 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 device 20 for monitoring the status of a mission of said aircraft connected to the avionics systems 12, the database(s) 14, the sensors 16 and the display system(s) 18.

[0044] 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.

[0045] 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.

[0046] Each avionics system 12 is capable of transmitting to the electronic device 20 for monitoring the status of a mission of at least one aircraft, various 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.

[0047] 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 system of satellite positioning, such as a GPS (Global Positioning System); an inertial reference system, also called an 1RS (Inertial Reference System); an ILS (Instrument Landing System) or MLS (Microwave Landing System); an active runway overrun prevention system, also called a ROPS (Runway Overrun Prevention System); and a radio altimeter, also denoted RA (Radio Altimeter).

[0048] Optionally, certain avionics systems 12 are also capable of receiving instructions, or commands, via (i.e., through) a human-machine interface associated with said electronic device 20 for monitoring the status of a mission of at least one aircraft. These avionics systems 12 capable of receiving instructions are, for example:

[0049] - 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;

[0050] - 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;

[0051] - 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).

[0052] Each database 14 is optional, known in itself, and for example chosen from the group consisting of:

[0053] - a navigation database, also called NAVDB (from English NAVigation 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;

[0054] - a database of terrain elevations, containing information relating to the height and altitude of the Earth's surface;

[0055] - a performance database, also called PERFDB (from English PERformance Data Base), containing information on the performance of aircraft 10, such as speed, fuel consumption, altitude, range, etc;

[0056] - a maintenance database, containing information on the repairs, maintenance and inspections carried out on the aircraft;

[0057] - a passenger database, containing information on passengers, such as their name, age, nationality, passport number, etc.; and

[0058] - 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.

[0059] These databases are typically interconnected and fed at least in part by the sensors 16.

[0060] In the example of [Fig. 1], the databases 14 are external to the electronic device 20 for monitoring the status of a mission of at least one aircraft. Alternatively, and not shown, the databases 14 are at least partly internal to the electronic device 20 for monitoring the status of a mission of at least one aircraft.

[0061] The sensors 16 are capable of measuring various quantities associated with the aircraft 10 and / or associated with 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), Galileo, or Glonass sensor; one or more stereoscopic cameras; and an atmospheric data sensor, such as pressure and temperature.

[0062] Each electronic sensor 16 is known in itself, and the data measured by each sensor 16 are intended to be acquired by the electronic device 20 for monitoring the status of a mission of at least one aircraft, to which it is connected.

[0063] The display system(s) 18 are, for example, a head-down display system and / or a head-up display system, also called 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 system display in a ground station, or even the remote control or vision goggles of a drone operator.

[0064] The electronic device for monitoring the status of a mission of at least one aircraft 20 is intended to be carried on board the aircraft 10, when the aircraft 10 is an airplane or a helicopter. Alternatively, the electronic device 20 for monitoring the status of a mission of at least one aircraft is intended to be installed on the ground, while being connected to the avionics systems 12 carried on board the aircraft 10, when the aircraft 10 is a remotely piloted drone or an unmanned autonomous aircraft.

[0065] The electronic device 20 for monitoring the status of a mission of at least one aircraft is intended to assist the pilot of the aircraft 10 by monitoring at least one operational criterion of said mission of said at least one aircraft 10 during the execution of said mission, thereby reducing the cognitive load for 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.

[0066] In addition, the electronic device 20 for monitoring the status of a mission involving at least one aircraft is configured to monitor several operational criteria simultaneously, 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 device 20 for monitoring the status of a mission involving at least one aircraft 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.

[0067] In the example of [Fig. 1], the electronic device for monitoring the status of a mission of at least one aircraft 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 electronic device 20 for monitoring the status of a mission of at least one aircraft is distinct and separate from each of the avionics systems 12. In an alternative not shown, the electronic device 20 for monitoring the status of a mission of at least one aircraft 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.

[0068] The electronic device 20 for monitoring the status of a mission of at least one aircraft includes a first determination module 22 configured to determine a polynomial function representing said at least one operational criterion, each monomial of which is a product of constants and / or variables, each variable being a characteristic quantity K1, K2, ..., KN of a set of N characteristic quantity(ies) associated with said operational criterion CO, N being an integer.

[0069] The electronic device 20 for monitoring the status of a mission of at least one aircraft also includes a second determination module 24 configured to determine, by partial derivative of said polynomial function, the impact of the variation of each characteristic quantity of said set associated with said operational criterion CO on the value of said operational criterion CO.

[0070] The electronic device 20 for monitoring the status of a mission of at least one aircraft also includes a feedback module 26 configured to provide feedback on the impact of each characteristic quantity, via a human-machine interface, to an operator of said aircraft, capable of using said impact to optimize the piloting of said aircraft.

[0071] Such a rendering module 26 is capable of visually rendering said impact in particular by using a human-machine interface corresponding to one of the aforementioned display systems 18, or audibly via a human-machine interface including a microphone.

[0072] As an optional supplement (represented in dotted lines), the electronic device 20 for monitoring the status of a mission of at least one aircraft also includes a module 28 for receiving at least one piloting intention to be monitored and associated with said operational criterion CO, in particular via the aforementioned human-machine interface.

[0073] According to this optional supplement, the electronic device 20 for monitoring the status of a mission of at least one aircraft also includes a third determination module 30 configured to determine, based on said impact of each characteristic quantity of said assembly associated with said operational criterion CO and said at least one piloting intention, the type of variation of said characteristic quantity to be monitored according to a reference variation value, the type of variation being an upward or downward variation.

[0074] According to this optional supplement, the rendering module 26 is then further configured to render, via said human-machine interface, to an operator of said aircraft, said type of variation, to be rendered to the operator for each characteristic quantity of said assembly associated with said operational criterion CO.

[0075] According to another optional supplement (suitable for combination with the preceding optional supplement), the electronic device 20 for state monitoring assistance of a mission of at least one aircraft also includes a fourth determination module 32 configured to determine a difference between a current value of the operational criterion CO and a previous value of said operational criterion CO, each current or previous value of said operational criterion CO being obtained from each determined value of characteristic quantity Kl, K2, ..., KN associated with said operational criterion CO and via the implementation of an artificial intelligence algorithm 26.

[0076] According to a preferred example, the fourth determination module 32 is suitable for implementing the aforementioned solution of the document with filing number FR 2303810 to obtain the current value and the previous value of said operational criterion CO. In other words, the aforementioned solution of the document with filing number FR 2303810 is suitable for being used twice, first to determine the previous value of said operational criterion CO, and then a second time to re-evaluate this value and obtain the current value of said operational criterion CO, particularly following a change in the mission context or the environment of said aircraft.

[0077] The modification (i.e. the change) of the environment of aircraft 10 is for example a modification of the meteorological environment, or the receipt of a NOTAM (Notice to AirMeri) message, i.e. a message to airmen, generally published by government air navigation control agencies in order to inform pilots of infrastructure developments.

[0078] According to this other optional supplement, the electronic device 20 for monitoring the status of a mission of at least one aircraft also includes a first identification module 34 configured to identify, from the value of said deviation, and of said impact of each characteristic quantity of said set associated with said operational criterion CO, at least one of the characteristic quantities K1, K2, ..., KN associated with said operational criterion CO that is responsible for said deviation.

[0079] According to yet another optional supplement (suitable to be combined with the two previous optional supplements), the electronic device 20 for monitoring the status of a mission of at least one aircraft also includes a construction and rendering module 36 configured to construct and render a visual representation corresponding to the projection of the aircraft's trajectory in an M-dimensional space, each point of which is associated with a cost value with respect to said operational criterion, a correspondence being established beforehand between each cost value and the value(s) of each characteristic quantity of said set associated with said operational criterion CO.

[0080] According to this other optional supplement, the electronic device 20 for monitoring the status of a mission of at least one aircraft further comprises a second identification module 38 configured to identify, from said visual representation, portions of trajectories impacted by said at least one of the characteristic quantities Kl, K2, ..., KN associated with said operational criterion CO, which is responsible for said deviation from said operational criterion CO.

[0081] In the example of [Fig.1], the electronic device 20 for monitoring the status of a mission of at least one aircraft includes an information processing unit 40 formed for example of a memory 42 and a processor 44 associated with the memory 42.

[0082] In the example of [Fig.1], the first determination module 22, the second determination module 24 and the restitution module 26, as well as optionally the reception module 28, the third determination module 30, the fourth determination module 32, the first identification module 34, the construction module 36 and the second identification module 38 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 device 20 for monitoring the status of a mission of at least one aircraft is then capable of storing a first determination software program, a second determination software program; a restitution software program.As an optional addition, the memory 42 of the electronic device 20 for monitoring the status of a mission of at least one aircraft is then capable of storing a reception software, a third determination software, a fourth determination software, a first identification software, a construction software and a second identification software. The processor 44 is then capable of executing each of the software among the first determination software, the second determination software, the restitution software, as well as, as an optional addition, the reception software, the third determination software, the fourth determination software, the first identification software, the construction software and the second identification software.

[0083] In variant, not shown, the database 14 is an internal database of the electronic device 20 for monitoring the status of a mission of at least one aircraft, it is typically suitable for being stored in a memory of the electronic device 20 for monitoring the status of a mission of at least one aircraft, such as memory 42.

[0084] In an alternative not shown, the first determination module 22, the second determination module 24 and the output module 26, as well as optionally the receiving module 28, the third determination module 30, the fourth determination module 32, the first identification module 34, the construction module 36 and the second identification module 38, are each implemented as a programmable logic component, such as an FPGA (of (English: Field Programmable Gate Array); or in the form of a dedicated integrated circuit, such as an ASIC (from the English: Application Specifies Integrated Circuit).

[0085] When the electronic device 20 for monitoring the status of a mission of at least one aircraft 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. By way of 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.

[0086] The first determination module 22 is configured to determine a polynomial function representing said at least one operational criterion, each monomial of which is a product of constants and / or variables, each variable being a characteristic quantity K1, K2, ..., KN of a set of N characteristic quantity(ies) associated with said operational criterion CO.

[0087] 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.

[0088] The set of characteristic quantity(ies) 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.

[0089] The set of characteristic quantity(ies) Kb K2, K3, K4, K5 associated with punctuality typically includes an indicator quantifying a delay of aircraft 10 on arrival, a ratio of the number of passengers who missed a connection on arrival to the total number of passengers on the delayed flight, and an indicator quantifying a delay of a subsequent flight of aircraft 10 due to the delay of the current flight of aircraft 10.

[0090] The set of characteristic quantity(ies) 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.

[0091] The set of characteristic quantity(ies) Kb K2, K3, K4, K5 associated with ecology typically includes a quantity of carbon dioxide emission during flight, an indicator of the use of favorable air currents to modify the trajectory of 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 carried.

[0092] Each characteristic quantity K, 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 Kb K2, K3, K4, K5 from at least one avionics variable is known in itself.

[0093] As indicated previously, each current or previous value of said operational criterion (OC) is obtained from each determined value of characteristic quantity (Kl, K2, ..., KN) associated with said operational criterion (OC) and via the implementation of an artificial intelligence algorithm.

[0094] According to a first variant, as illustrated by [Fig.2], the artificial intelligence algorithm 46 comprises a fuzzy logic decision tree 48, the fuzzy logic decision tree preferably including at least one fuzzy inference system FIS1, FIS2, FIS3, FIS4, FIS5, each fuzzy inference system FIS1, FIS2, FIS3, FIS4, FIS5 being configured to receive as input at least one determined value of characteristic magnitude K1, K2, K3, K4, K5 and to deliver as output a unit evaluation value; for each fuzzy inference system FIS1, FIS2, FIS3, FIS4, FIS5, a correspondence between input(s) and output being established by fuzzy logic;the value of the operational criterion CO is then estimated from the unit value(s) of evaluation calculated for the set of characteristic quantity(ies) associated with said operational criterion CO, and said polynomial function representing said at least one operational criterion is equivalent to said at least one fuzzy inference system of said fuzzy logic decision tree. ;

[0095] The fuzzy logic decision tree 48, also known as GFT (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 48 uses linguistic variables to represent concepts such as "very likely" or "somewhat likely".

[0096] The fuzzy logic decision tree 48 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 even by Example of a takeoff delay indicator, the value of this variable is translated into a degree of belonging to linguistic variables, such as "low", "medium" or "high".

[0097] The fuzzy logic decision tree 48 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.

[0098] As an optional complement, the fuzzy logic decision tree 48 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.

[0099] The fuzzy logic decision tree 48 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.

[0100] Advantageously, the fuzzy logic decision tree 48 includes at least one fuzzy inference system FISi, FIS2, FIS3, FIS4, FIS5 (FIS from the English 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 Ki, 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.

[0101] In the example in [Fig. 2], the fuzzy logic decision tree 48 is then represented as a graph of fuzzy inference systems FISI, FIS2, FIS3, FIS4, FIS5, each with an associated weighting coefficient a1, a2, a3, a4, a5. In this example, the fuzzy logic decision tree 48 comprises five fuzzy inference systems FISI, FIS2, FIS3, FIS4, FIS5, namely a first fuzzy inference system FISI with a first weighting coefficient a1, 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.

[0102] 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 FIS1, FIS2, FIS3 receiving the input variables, i.e. the determined values ​​of the set of characteristic quantity(ies) K1, 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 FIS1, 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.

[0103] Each fuzzy inference system FIS1, FIS2, FIS3, FIS4, FIS5 is a structure for formalizing the fuzzy rules that govern the decision-making of the decision tree 48. Each fuzzy inference system FIS1, 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.

[0104] The fuzzy logic decision tree 48 was previously trained during a preliminary learning step of the artificial intelligence algorithm 46 from training data.

[0105] Advantageously, the preliminary training of the artificial intelligence algorithm 46 is supervised learning. A person 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 48, 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 an expert operator of the system in operational semantics.

[0106] Supervised learning of the fuzzy logic decision tree 48 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 48 are then constructed from the training data.

[0107] The preliminary learning of the fuzzy logic decision tree 48 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.

[0108] According to a second embodiment, the artificial intelligence algorithm comprises a radially based fuzzy function network including at least one fuzzy inference system FIS1, FIS2, FIS3, FIS4, FIS5, each fuzzy inference system FIS1, FIS2, FIS3, FIS4, FIS5 being configured to receive as input at least one determined value of characteristic quantity K1, K2, K3, K4, K5 and to output a unit evaluation value; for each fuzzy inference system FIS1, FIS2, FIS3, FIS4, FIS5, an input-output correspondence is established by fuzzy logic; the value of the operational criterion CO is then estimated from the unit evaluation value(s) calculated for the set of characteristic quantity(ies) associated with said operational criterion CO, said polynomial function representing said at least one operational criterion is equivalent to at least one fuzzy inference system of said radially based fuzzy function network.

[0109] In other words, the present invention proposes, according to the first variant, to implement a transition from a fuzzy logic decision tree also called GFT (from the English Generalized Fuzzy Tree) to a polynomial decision tree, as described in particular by Timothy J. Arnett in the document entitled "Iteratively Increasing Complexity During Optimization for Formally Verifiable Fuzzy Systems", or according to the second variant, to implement a transition from a fuzzy inference system to a radially based fuzzy function network (artificial neural networks called radially based fuzzy function networks or FRBFNs (Fuzzy Radial Basis Function Networks), a solution for implementing such a transition being described in the patent application whose filing number is FR 2412499.

[0110] Note that these documents only disclose how to determine a polynomial function from a fuzzy decision tree or from an artificial neural network known as radially based but do not apply it to represent an operational criterion of said aircraft mission as proposed according to the present invention.

[0111] For example, we consider the aircraft mission during which an aircraft is required to search for and find a diversion airport due to weather degradation, and the operational criterion CO to be monitored is the safety of the flight path, this criterion depending, according to this example, on two characteristic quantities K1, for example the speed of the aircraft, and K2, for example the ratio between the quantity of fuel available and the quantity of fuel required.

[0112] Such an operational criterion to be monitored, corresponding to the safety S of the flight path, is then, according to the present invention, expressed in the following polynomial form, obtained, according to a first variant from a fuzzy logic decision tree GFT illustrated by [Fig.2], or according to a second variant from a fuzzy inference system of a radial basis fuzzy function network FRBFN:

[0113] S = f(Kl, K2) with f a polynomial function equivalent to the fuzzy inference system of a fuzzy logic decision tree GFT or a radial basis fuzzy function network FRBFN, with for example S = 3 - 3*K1 + 1*K2.

[0114] The second determination module 24 is configured to determine, by partial derivative of said polynomial function, the impact of the variation of each characteristic quantity of said set associated with said operational criterion CO on the value of said operational criterion CO.

[0115] Taking up the above example of monitoring the operational criterion CO to be monitored corresponding to the safety S of the flight path, by partial derivative, the second determination module 24 is able to identify (determine) the impact of the characteristic quantity Kl, for example the speed of the aircraft, and

[0116]

[0117] K2, for example the ratio between the amount of fuel available and the amount of fuel required on the safety S of the flight path. Indeed, according to this example, the second modulus 24 of determination determines that JL _ _ 3, which implies that when Kl, for example the speed, increases, the safety S decreases, and JL _ ] which implies that when K2, for example the ratio between quantity of fuel available and quantity of fuel required, increases, the safety S increases. The feedback module 26 is configured to provide the impact of each characteristic quantity, via a human-machine interface, to an operator of said aircraft, for example the value -3 for K1 and 1 for K2, which then allows said operator of said aircraft to easily identify the causes of an increase or decrease in safety value.

[0118] The characteristic quantity Ki at the origin of the positive or negative variation of the operational criterion to be monitored is thus identified.

[0119] It should be noted that, in relation to the aforementioned document whose filing number is FR 2303810 where, by means of the weighting coefficients a1, a2, a3, a4, a5, it was only possible to identify which fuzzy inference system FIS was associated with a variation of operational criterion, the present invention proposes to go further, via the associated polynomial function, and to further reduce the cognitive load of the operator, by going back (i.e. from the English backpropagation) to a higher level of refinement (i.e. in [Fig.2] going down to the lowest level of refinement that of the inputs Ki), that of the characteristic quantities Ki corresponding to the individual inputs of the fuzzy inference systems FIS of [Fig.2].

[0120] In other words, the present invention proposes to use a gradient technique identifying the modification of technical parameters (i.e. characteristic magnitude, elements of the tactical situation) Ki (i.e. "low level" because corresponding to the "low level" inputs of the fuzzy inference system FIS) having an impact on an operational criterion to be monitored, and likely to be the root cause of a change in the value of said operational criterion to be monitored.

[0121] For example, for an operational criterion corresponding to comfort, according to the aforementioned document, registration number FR 2303810, a fuzzy logic decision tree including two fuzzy inference systems (FIS), corresponding respectively to "comfort at destination" and "comfort during flight," would be suitable for use, and, according to that document, only the causal quantity between these two fuzzy inference systems (FIS) would be suitable for determination, whereas, according to the present invention, via the equivalent representation in the form of a function Using polynomial analysis and the application of partial derivatives, it is possible to identify the Ki(s) responsible for an anomaly in the monitored operational criterion.

[0122] Indeed, the impact of the variation of each characteristic quantity of said set associated with said operational criterion CO, on the value of said operational criterion CO, depends on the sign and value of the partial derivative associated with said characteristic quantity. Directly identifying the characteristic quantity Ki that is the source of the mission execution anomaly is directly more "meaningful" to the operator compared to the less precise identification of an involved fuzzy inference system (FIS).

[0123] Note that in the case of several characteristic quantities Ki associated with the same sign of partial derivative, the characteristic quantity Ki having the highest value of partial derivative is the one which, among the said characteristic quantities Ki associated with the same sign of partial derivative, has the most impact on the operational criterion considered.

[0124] As an optional complement, module 28 for receiving at least one piloting intention to be monitored and associated with said operational criterion CO, the third module 30 for determination mentioned above are for example used to further reduce the cognitive load of the operator in the presence of the received piloting intention.

[0125] For example, taking up the previous example relating to an operational criterion corresponding to comfort, the fuzzy inference system corresponding to "comfort at destination" CD is for example equivalent to the polynomial function expressed by the following equation: CD = 1.55*K1*K2 + 0.14*K1 - 1.76*K2 + 0.63, with Kl the availability of hotel rooms, and K2 the availability of food at the destination, Kl and K2 being known at each iteration implemented by the electronic device 20 for monitoring the status of a mission of at least one aircraft, with for example, at the current time of implementation, Kl = 0.5 and K2 = 1.0.The partial derivative, with respect to Kl varying, with K2 constant is: 0.14 + 1.55*K2, while the partial derivative, with respect to K2 varying, with Kl constant is: -1.76 + 1.55*K1, so knowing Kl and K2, the partial derivative, with respect to Kl varying is then equal to 1.69 (K2 being equal to 1.0) and the partial derivative, with respect to K2 varying is then equal to -0.985 (Kl being equal to 0.5).

[0126] If the piloting intention to be monitored received by the receiving module 28 is to maximize comfort at destination, the partial derivative, with respect to K2 varying, being negative, to maximize comfort at destination, it is necessary to reduce K2, whereas the partial derivative, with respect to Kl varying, being positive, to maximize comfort at destination it is necessary to increase Kl.

[0127] According to this optional add-on and this example, the feedback module 26 is therefore configured to provide (i.e., signal / indicate) to the operator a recommended reduction of K2 and a recommended increase of Kl to satisfy the piloting intent received, and the operator (or another supervisory function), having knowledge of this feedback, will be able to make a decision on the Ki that it can optimize.

[0128] As an optional complement, the fourth determination module 32 configured to determine a difference between a current value of the operational criterion CO and a previous value of said operational criterion CO, and the first identification module 34, make it possible to take advantage of the present invention, for example in the presence of at least one change in the value of the monitored operational criterion, among the modification of the environment of the aircraft 10, the change in the desired value of the operational criterion CO and an action of the pilot different from that intended, by using the impact of each characteristic quantity to explain to the operator the root cause of said change.

[0129] As an optional complement, the construction and restitution module 36 and the second identification module 38 allow the addition of a visual representation and the specification of portions of trajectories impacted by said at least one of the characteristic quantities Kl, K2, ..., KN associated with said operational criterion CO, itself responsible for a deviation of said operational criterion CO.

[0130] More specifically, the construction and rendering module 36 is configured to construct and render a visual representation corresponding to the projection of the aircraft trajectory in an M-dimensional space (for example, a 2D two-dimensional space, a 3D three-dimensional space, or even a 4D four-dimensional space suitable for also taking into account the inclination of the aircraft) where each point is associated with a cost value with respect to said operational criterion, a correspondence being established beforehand between each cost value and the value(s) of each characteristic quantity of said set associated with said operational criterion CO.

[0131] The cost value for all points in space is a spatial representation with a value (i.e., a floating-point number) for each point associated with a characteristic quantity ("minimap" - matrix or tensor depending on the space considered, from the English minimap), and is used to project the trajectory only onto the representation of the characteristic quantity Ki (for each operational criterion) which is impacted.

[0132] When the predetermined function g is differentiable and corresponds for example to the Hadamard product between the MM cost map i and the identity map of the trajectory trajectory^ it is then possible to identify by derivative: the pixels (of coordinates x and y) on the cost map at the origin of the evolution of the value of the operational criterion, and the portions of critical trajectory passing through pixels having consequences on the value of our Ki and indirectly on the value of the operational criterion.

[0133] The georeferenced spatial identification implemented by the second identification module 38 is then suitable for making it possible to identify, for example, whether the aircraft passes through a dangerous cumulonimbus and what portion of the critical trajectory linked to this danger is responsible for a low value of the operational safety criterion.

[0134] The operation of the electronic device 20 for monitoring the state of a mission of said aircraft will now be described with reference to [Fig.3] representing a flowchart of the method 50 for monitoring the state of a mission of said aircraft according to the invention, implemented by the electronic device 20 for monitoring the state of a mission of said aircraft.

[0135] During a first step 52, the electronic device 20 for monitoring the state of a mission of said aircraft determines D_F a polynomial function representing said at least one operational criterion, each monomial of which is a product of constants and / or variables, each variable being a characteristic quantity K1, K2, ..., KN of a set of N characteristic quantity(ies) associated with said operational criterion CO.

[0136] Then, during a step 54, the electronic device 20 for monitoring the status of a mission of said aircraft determines D_Imp, by partial derivative, said polynomial function, determination of the impact of the variation of each characteristic quantity of said set associated with said operational criterion CO on the value of said operational criterion CO.

[0137] Then, during a step 56, the electronic device 20 for monitoring the status of a mission of said aircraft provides Rest-Imp of the impact of each characteristic quantity, via a human-machine interface, to an operator of said aircraft, capable of using said impact to identify at least one source of anomaly in the execution of said mission.

[0138] According to an optional supplement (represented in dotted lines), during a step 58, the electronic device 20 for monitoring the status of a mission of said aircraft receives R_Int at least one piloting intention to be monitored and associated with said operational criterion CO, in particular via said aforementioned human machine interface.

[0139] According to this optional supplement, during a step 60, the electronic device 20 for monitoring the status of a mission of said aircraft determines D_V, as a function of said impact (from step 54) of each characteristic quantity of said assembly associated with said operational criterion (OC) and of said at least one intention, the type of variation of said characteristic quantity to be monitored according to a reference variation value, the type of variation being an upward or downward variation.

[0140] According to this optional supplement, during step 62, the electronic device 20 for monitoring the status of a mission of said aircraft, restores Rest-V, via said human-machine interface, to an operator of said aircraft, said type of variation to be returned to the operator for each characteristic quantity of said assembly associated with said operational criterion CO.

[0141] According to another optional complement (suitable to be combined with the preceding optional complement), during a step 64, the electronic device 20 for monitoring the status of a mission of at least one aircraft determines D_EC a difference between a current value Vs of the operational criterion CO and a previous value Ve of said operational criterion CO, each current or previous value of said operational criterion CO being obtained from each determined value of characteristic quantity Kl, K2, ..., KN associated with said operational criterion CO and via the implementation of an artificial intelligence algorithm.

[0142] According to this other optional supplement, the electronic device 20 for monitoring the status of a mission of at least one aircraft, during a step 66, identifies ID_G, from the value of said deviation, and of said impact (from step 54) of each characteristic quantity of said set associated with said operational criterion CO at least one of the characteristic quantities K1, K2, ..., KN associated with said operational criterion CO which is responsible for said deviation Ec.

[0143] According to yet another optional supplement (suitable to be combined with the two preceding optional supplements), the electronic device 20 for monitoring the status of a mission of at least one aircraft, during a step 68, constructs C_RV and provides a visual representation corresponding to the projection of the aircraft's trajectory in an M-dimensional space where each point is associated with a cost value with respect to said operational criterion (and where appropriate taking into account the piloting intention to be monitored received during step 58), a correspondence being established beforehand between each cost value and the value(s) of each characteristic quantity of said set associated with said operational criterion CO.

[0144] According to this other optional supplement, the electronic device 20 for monitoring the status of a mission of at least one aircraft, during a stage 70, identifies ID_P, from said visual representation, the portions of trajectories impacted by said at least one of the characteristic quantities Kl, K2, ..., KN associated with said operational criterion CO which is responsible for said deviation.

[0145] A person skilled in the art will understand that the invention is not limited to the embodiments described, nor to the particular examples of the description, the embodiments and variants mentioned above being capable of being combined with each other to generate new embodiments of the invention.

[0146] The present invention thus makes it possible to inform the aircraft operator of the impact of each characteristic quantity on an operational criterion to be monitored, each operational criteria being chosen for example from the group consisting of: safety, punctuality, comfort, and ecology.

[0147] Such impact information is also suitable for use in automatically and directly identifying the root cause of a change (i.e. variation) in the operational criterion under surveillance, which effectively relieves the cognitive load of the operator.

Claims

Demands

1. A method (50) for assisting in monitoring the status of a mission of at least one aircraft, by monitoring at least one operational criterion (OC) of a mission of said at least one aircraft (10) during the execution of said mission, the method being implemented by an electronic device (20) for assisting in monitoring the status of a mission of at least one aircraft, and comprising, for each operational criterion (OC), the following steps: - determining (52) a polynomial function representing said at least one operational criterion, each monomial of which is a product of constants and / or variables, each variable being a characteristic quantity (K1, K2, ..., KN) of a set of N characteristic quantity(ies) associated with said operational criterion (OC); - by partial derivative of said polynomial function, determination (54) of the impact of the variation of each characteristic quantity of said set associated with said operational criterion (OC) on the value of said operational criterion (OC); - restitution (56) of the impact of each characteristic quantity, via a human-machine interface, to an operator of said aircraft, suitable for using said impact to identify at least one source of anomaly in the execution of said mission.

2. Method (50) according to claim 1, wherein the impact of the variation of each characteristic quantity of said assembly associated with said operational criterion (OC) on the value of said operational criterion (OC) depends on the sign and value of the partial derivative associated with said characteristic quantity.

3. A method (50) according to claim 1 or 2, wherein the method (50) further comprises: - receiving (58) at least one pilot intention to be monitored and associated with said operational criterion (OC); - depending on said impact of each characteristic quantity of said assembly associated with said operational criterion (OC) and of said at least one intention, determining (60) the type of variation of said characteristic quantity to be monitored according to a reference variation value, the type of variation being an upward or downward variation; - the return (62), via said man-machine interface, to an operator of said aircraft, of said type of variation to be returned to the operator for each characteristic quantity of said assembly associated with said operational criterion (OC).

4. A method (50) according to any one of the preceding claims, wherein the method further comprises: - determining (64) a deviation between a current value of the operational criterion (OC) and a previous value of said operational criterion (OC), each current or previous value of said operational criterion (OC) being obtained from each determined value of characteristic quantity (Kl, K2, ..., KN) associated with said operational criterion (OC) and via the implementation of an artificial intelligence algorithm (46); - from the value of said deviation, and of said impact of each characteristic quantity of said set associated with said operational criterion (OC), identification (66) of at least one of the characteristic quantities (Kl, K2, ..., KN) associated with said operational criterion (OC) that is responsible for said deviation.

5. A method (50) according to any one of the preceding claims, wherein the method further comprises: - the construction (68) and the rendering of a visual representation corresponding to the projection of the aircraft trajectory in an M-dimensional space, each point of which is associated with a cost value with respect to said operational criterion, a correspondence being established beforehand between each cost value and the value(s) of each characteristic quantity of said set associated with said operational criterion (OC); - from said visual representation, identification (70) of the portions of trajectories impacted by said at least one of the characteristic quantities (K1, K2, ..., KN) associated with said operational criterion (OC) which is responsible for said deviation.

6. A method (50) according to any one of claims 4 or 5, wherein the artificial intelligence algorithm (46) comprises a fuzzy logic decision tree (48), the fuzzy logic decision tree (48) 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 quantity (Kb K2, K3, K4, K5) and to deliver as output a unit value of evaluation; 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 (OC) then being estimated from the unit value(s) of evaluation calculated for the set of characteristic quantity(s) associated with said operational criterion (OC), and in which said polynomial function representing said at least one operational criterion is equivalent to said at least one fuzzy inference system of said fuzzy logic decision tree.

7. A method (50) according to any one of the preceding claims, wherein the artificial intelligence algorithm (46) comprises a radially based fuzzy function network 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 quantity (K1, 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-output correspondence being established by fuzzy logic;the value of the operational criterion (OC) then being estimated from the unit evaluation value(s) calculated for the set of characteristic quantity(ies) associated with said operational criterion (OC), and in which said polynomial function representing said at least one operational criterion is equivalent to said at least one fuzzy inference system of said radially based fuzzy function network.

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

9. An electronic device (20) for assisting in monitoring the status of a mission of at least one aircraft (10), by monitoring at least one operational criterion (OC) of a mission of said at least one aircraft (10) during the execution of said mission, the device (20) comprising: - a first determination module (22) configured to determine a polynomial function representing said at least one criterion an operational function in which each monomial is a product of constants and / or variables, each variable being a characteristic quantity (K1, K2, ..., KN) of a set of N characteristic quantity(ies) associated with said operational criterion (OC); - a second determination module configured to determine, by partial derivative of said polynomial function, the impact of the variation of each characteristic quantity of said set associated with said operational criterion (OC) on the value of said operational criterion (OC); - a rendering module configured to render the impact of each characteristic quantity, via a human-machine interface, to an operator of said aircraft, suitable for using said impact to optimize the piloting of said aircraft.

10. 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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