Method and system for optimizing aircraft flight plans

Abstract

The invention relates to a method for optimizing aircraft flight plans, the method comprising steps of dividing (42) the airspace into zones, and on the basis of air traffic data including historical data on executed flight plans, determining (44) a maximum air traffic load per zone and per time period over one or more successive time periods, and calculating (46) an optimization threshold per zone and per time period according to the maximum air traffic load; predicting (48), for a plurality of time slots of a subsequent time period, a predicted actual air traffic load, and identifying (50) one or more time slots and associated optimization zones for which the predicted actual air traffic load is below the optimization threshold; using (52, 54) the time slots, the optimization zones and the associated optimization zones to determine at least one optimized flight plan.

Description

[0001] TITLE: Method and system for optimizing aircraft flight plans

[0002] The present invention relates to a method for optimizing aircraft flight plans, an associated aircraft flight plan optimization system, and an associated computer program.

[0003] The invention is in the field of aeronautics, and more particularly in the field of optimizing aircraft flight plans, based on selected efficiency criteria.

[0004] An aircraft flight plan consists of a departure point and a destination point, as well as the 4D route (3D spatial information defining waypoints and associated temporal information) that the aircraft follows to connect them. Examples of flight plans include the ICAO (International Civil Aviation Organization) flight plan as filed by airlines with airspace managers, the flight plan actually implemented during the tactical execution of the flight by the aircraft crew, or the flight plan used by a flight management system such as an FMS (Flight Management System).

[0005] One of the objectives of the invention is to allow dynamic modification of flight plans, in order to allow better occupation of airspace, by optimizing an efficiency criterion, in particular the reduction of fuel consumption and / or the reduction of pollutant emissions into the atmosphere for each flight plan executed (therefore for each trajectory actually flown), while taking into account available resources, such as air traffic control capacity.

[0006] In civil aviation, airlines file flight plans with airspace management authorities. A flight plan typically includes a list of waypoints and associated information defining the aircraft's 3D path between a departure point and a destination point, the aircraft's altitudes and speeds to be maintained, scheduling information, and technical details about the aircraft. Airspace management authorities authorize flight plans based on factors such as the capacity to absorb traffic in the relevant airspace and air traffic control capabilities over the specified time period, in order to ensure the spatial segregation of flights and their safety. Maximum altitude and speed restrictions may be imposed to prevent collisions or to better distribute air traffic loads.

[0007] Traditionally, flight plans are filed in advance and authorized based on static, pre-defined, and conservative rules—that is, rules developed according to maximum traffic limits—to ensure safety. For ad-hoc dynamic optimization, aircraft pilots can request flight plan modifications from air traffic controllers in real time, on a case-by-case basis.

[0008] French patent FR 2104089 describes a communication system between an air traffic control system and an electronic terminal for automatically and systematically negotiating flight plan optimizations based on time slots for environmental optimization. This communication system is advantageous because it allows for the systematization of optimizations offered to aircraft pilots, in conjunction with air traffic controllers. However, for long-haul flights, for example, to achieve end-to-end optimization, it is necessary to deploy this system with each airspace manager responsible for one of the airspaces crossed along the aircraft's trajectory, as airspace managers differ according to regional airspace divisions.

[0009] The present invention aims to overcome the aforementioned constraint and improve the ease of dynamically optimizing flight plans, so as to allow better use of airspace according to available resources.

[0010] To this end, the invention relates to a method for optimizing aircraft flight plans in airspace, the method being implemented by a computing processor of a programmable electronic device, comprising the steps of: obtaining a division of the airspace into geographical sectors, each geographical sector being partitioned vertically and / or laterally into zones, based on air traffic data, including historical data of flight plans executed by aircraft; determining a maximum air traffic load per geographical sector zone and per time period, over one or more successive time periods; calculating an optimization threshold, per geographical sector zone and per time period, as a function of the maximum air traffic load, said optimization threshold being a fraction of the maximum air traffic load.prediction for a plurality of time slots in a subsequent time period, of an effective air traffic load predicted per geographical sector area and per time slot, based at least on historical flight plan data, the air traffic load per geographical sector area and per time period being calculated based on an air traffic volume and / or a level of air traffic complexity in said geographical sector area and during said time period, identification of one or more optimization time slots and associated geographical sector areas, referred to as associated optimization zones, an optimization time slot being a time slot during which, for each associated optimization zone, the predicted effective air traffic load is less than the optimization threshold,

[0011] -use of said optimization time slots and associated optimization zones to determine at least one optimized flight plan.

[0012] Advantageously, the proposed method makes it possible to predict time slots for optimization and associated geographical sector areas, for which the predicted effective air traffic load is below the optimization threshold, and therefore makes it possible to modify flight plans during flight preparation as well as to modify the flight plan actually executed according to the time slots for optimization and associated areas.

[0013] According to other advantageous aspects of the invention, the aircraft flight plan optimization method comprises one or more of the following features, taken individually or in all technically possible combinations.

[0014] The use involves the transmission of said optimization time slots and associated optimization zones, via a communication network, to at least one remote aircraft flight planning system and / or at least one onboard electronic terminal.

[0015] The use involves determining at least one optimized aircraft flight plan, based on an efficiency criterion, according to said optimization time slots and associated optimization zones.

[0016] The efficiency criterion combines one or more of the following criteria: reduction of fuel and / or electricity consumption, limitation of carbon dioxide emissions, limitation of methane emissions, limitation of nitrogen oxide emissions.

[0017] Determining an optimized flight plan involves selecting a previously memorized flight plan that satisfies said efficiency criterion.

[0018] The partitioning is a regular geometric tiling with cells of the same size or an irregular one with cells of varying sizes.

[0019] The prediction of a plurality of time slots also takes into account flight plans filed for the following said time period.

[0020] The prediction of multiple time slots also takes into account contextual elements, including weather forecasts, for the following time period. The application involves determining at least one aircraft flight plan optimized by modifying an initial flight plan based on these optimization time slots and associated optimization zones.

[0021] The modification of an initial flight plan is chosen from a list including at least: a modification of aircraft waypoints, a modification of aircraft altitude, a modification of aircraft speed.

[0022] The invention also relates to a system for optimizing aircraft flight plans in airspace, the system comprising at least one programmable electronic device including a computing processor, the system being configured to implement: a module for obtaining a division of the airspace into geographical sectors, each geographical sector being partitioned vertically and / or laterally into zones, a module for determining, on the basis of air traffic data, including historical data of flight plans executed by aircraft, a maximum air traffic load per geographical sector zone and per time period, over one or more successive time periods, a module for calculating an optimization threshold, per geographical sector zone and per time period, as a function of the maximum air traffic load, said optimization threshold being a fraction of the maximum air traffic load,a prediction module for a plurality of time slots in a subsequent time period, of an effective air traffic load predicted per geographical sector area and per time slot, based at least on historical flight plan data, the air traffic load per geographical sector area and per time period being calculated based on an air traffic volume and / or a level of air traffic complexity in said geographical sector area and during said time period, a module for identifying one or more optimization time slots and associated geographical sector areas, referred to as associated optimization zones, an optimization time slot being a time slot during which, for each associated optimization zone, the predicted effective air traffic load is less than the optimization threshold,and a module for using said optimization time slots and associated optimization zones to determine at least one optimized flight plan. The invention also relates to a computer program comprising software instructions which, when executed by a computer, implement a method for optimizing aircraft flight plans as defined above.

[0023] The invention will become clearer upon reading the following description, given solely by way of non-limiting example, and made with reference to the drawings in which:

[0024] [Fig. 1] Figure 1 schematically represents a flight plan optimization system according to one embodiment;

[0025] [Fig. 2] Figure 2 is a flowchart of the main steps of a process for optimizing aircraft flight plans according to one embodiment;

[0026] [Fig. 3] Figure 3 is an example of division into sectors and associated zones;

[0027] [Fig. 4] Figure 4 is a schematic example of time slot and associated zone effective load predictions.

[0028] Figure 1 schematically represents a system 2 for optimizing aircraft flight plans.

[0029] System 2 includes an aircraft flight planning system 4, for example an airline operations center planning system 4, adapted to generate aircraft flight plans, an electronic terminal 6, for example on board an aircraft (not shown) and a programmable electronic device 8, for example located in a computing center, the programmable electronic device 8 being configured to implement the main steps of a flight plan optimization process as described below.

[0030] The planning system 4, the electronic terminal 6 and the programmable electronic device 8 are adapted to communicate, via bidirectional communication links, for example via wireless communication networks.

[0031] According to one variant, the programmable electronic device 8 is part of the airline's operational center planning system 4.

[0032] In addition, the programmable electronic device 8 is configured to communicate, via a communication network, using wired or wireless communication technology, for example via the Internet, with one or more remote information systems 10.

[0033] The aircraft flight planning system 4 and the electronic terminal 6 are also configured to communicate with an air traffic control system 12, also known as an ATC (Air Traffic Control) system, operated by an airspace management authority. It should be noted that only one electronic terminal 6 has been shown, but it is clear that the system applies to any number of similar electronic terminals 6. Similarly, an aircraft is configured to communicate with each air traffic control system 12 along an effective flight route.

[0034] The objective of system 2 is to optimize aircraft flight plans, according to a chosen efficiency criterion, for flights offered by a specific airline.

[0035] The aircraft flight planning system 4 is configured to manage the planning of a plurality of flights proposed by the airline, in a three-dimensional airspace.

[0036] Flights are planned via flight plans, each containing a list of points with associated information defining a route, between a starting point and a destination point, aircraft altitudes / speeds to be respected along the aircraft route, timetable information, weather data and technical information relating to the aircraft, for example type of aircraft, type of fuel used, technical limitations, onboard mass etc.

[0037] The programmable electronic device 8 is for example a computer, comprising a processor 14 and an electronic memory unit 16, a communication interface 18 and a human-machine interface 20, adapted to communicate via a communication bus 15.

[0038] Processor 14 is configured to implement:

[0039] - a module 30 for obtaining a division of the airspace into 3D geographical sectors, each sector being partitioned vertically and / or laterally into zones;

[0040] - a module 32 for determining a maximum air traffic load per geographical sector area and per time period, over a plurality of successive time periods, from air traffic data, including historical data of flight plans carried out by aircraft;

[0041] - a module 34 calculation of an optimization threshold by geographical sector area and by time period, based on the maximum air traffic load;

[0042] - a module 36 prediction, for a plurality of time slots of a subsequent time period, of an effective air traffic load predicted by geographical sector area and by time slot, based at least on historical data of flight plans carried out;

[0043] - a module 38 for identifying one or more time slots for optimization and associated optimization zones, for each optimization zone, the predicted effective air traffic load during the time slot for optimization is less than the optimization threshold;

[0044] - a module 40 for using optimization time slots and associated optimization zones to determine at least one optimized flight plan.

[0045] According to an optional embodiment, module 40 implements a determination of at least one optimized aircraft flight plan, based on an efficiency criterion, according to said optimization time slots and associated optimization zones.

[0046] According to an optional embodiment, module 40 implements a transmission of optimization time slots and associated optimization zones, via the communication network, to the planning system 4 and / or the on-board electronic terminal 6.

[0047] The term load refers to air traffic load, which can be measured in different ways, as described in more detail below.

[0048] According to one variant, module 40 is implemented by a processor of the aircraft flight planning system 4.

[0049] Modules 30, 32, 34, 36, 38 and 40 are adapted to cooperate, as described in more detail below, to implement the aircraft flight plan optimization method according to the invention and described in more detail below.

[0050] In one embodiment, modules 30, 32, 34, 36, 38 and 40 are implemented as software instructions forming a computer program, which, when executed by a computer, implements a method for optimizing aircraft flight plans according to the invention.

[0051] In an alternative not shown, modules 30, 32, 34, 36, 38 and 40 are each implemented as programmable logic components, such as FPGAs (Field Programmable Gate Arrays), microprocessors, GPGPUs (General-purpose processing on graphemes processing), or dedicated integrated circuits, such as ASICs (Application Specific Integrated Circuits).

[0052] A computer program containing software instructions is also capable of being stored on a computer-readable medium, not shown here. A computer-readable medium is, for example, a medium capable of storing electronic instructions and being connected to a bus of a computer system. Examples of such a readable medium include an optical disc, a magneto-optical disc, ROM, RAM, any type of non-volatile memory (e.g., EPROM, EEPROM, FLASH, NVRAM), a magnetic card, or an optical card.

[0053] Figure 2 is a flowchart of the main steps of a process for optimizing aircraft flight plans. The process includes a step 42 of obtaining a division of the airspace into geographical sectors, each geographical sector being partitioned vertically and / or laterally into zones, implemented by the module 30 for obtaining a division described above.

[0054] Thus, a geographical sector is divided vertically and / or laterally into one or more zones (or layers), each zone being defined by a minimum altitude and an associated maximum altitude.

[0055] For example, in one embodiment, the layers correspond to a vertical division of airspaces or to slices of operational flight levels spaced for example at 2000 ft (or feet).

[0056] An example of airspace segmentation is described with reference to Figure 3, which represents a 45 view of a geographical region, in a horizontal plane, defining geographical sectors P, R, Z, X, N, H which form a map of the geographical region overflown.

[0057] Furthermore, according to the vertical dimension, each geographical sector is divided into zones or layers, each zone of the geographical sector extending vertically between two altitudes.

[0058] For example, and as illustrated in the left part of Figure 3, the geographical sector P is divided into 4 zones, respectively referenced PT1, PT2, PT3, PT4; the geographical sector R is divided into 5 zones, respectively referenced RL1, PL2, RL3, RL4, RL5. The vertical altitude scale indicates the vertical altitude in flight levels or FL (for "Flight Level") of zones PT1, PT2, PT3, PT4.

[0059] The segmentation defines zones in the airspace in which the flights to be optimized will be operated.

[0060] According to one variant, the division is provided by each authority managing the airspace in question.

[0061] According to a second variant, not shown in Figure 3, the partitioning is a geometric tiling formed of regular cells in a 3D reference frame, or an arbitrary tiling, and defined by geographic coordinates in a known reference frame, for example, the Earth's reference frame. For example, a geometric partitioning into parallelepipeds is performed. Advantageously, according to this variant, it is not necessary to obtain information relating to the partitioning of each airspace managing authority.

[0062] According to another variant, the geometric tiling is variable, featuring finer meshes in areas of dense traffic flow and larger meshes outside of dense traffic flow. Step 42, which obtains a segmentation, is advantageously implemented at the initialization of the process, and the segmentation into geographical sectors and associated zones is stored in an electronic memory 16 for later use.

[0063] The process then includes a step 44 for determining a maximum load (or maximum traffic load) per geographical sector zone and per time period, over one or more successive time periods. Step 44 is implemented by module 32 for determining a maximum load per geographical sector zone.

[0064] Preferably, step 44 is implemented regularly, for example over 24-hour (day) periods or subdivisions of 24-hour periods, for example every 6 hours.

[0065] For example, the time periods over which the maximum load determination is carried out are recurring or seasonal time periods, for example days of the week, months of the year, seasons of the year or by 6-hour blocks per day of the week and per season.

[0066] Determination 44 is based at least on historical data of flight plans carried out by aircraft, that is to say, on data previously recorded over one or more analogous time periods in the past.

[0067] For example, historical data is collected over a year, and determination 44 is performed, based on the historical data collected for the previous year, by month of the year. Of course, this example is given simply for illustrative purposes; other time periods are possible.

[0068] For example, historical data is obtained from an external server 10. For example, historical data is ADS-B data (for "Automatic Dependent Surveillance-Broadcast"), or flight plans filed with ATC.

[0069] In one embodiment, during determination step 44, an automatic temporal segmentation is carried out, for example by implementing a partitioning method (in English "clustering") on the historical data.

[0070] The maximum load per time period and per zone is for example measured according to the volume of traffic, the volume of traffic being for example measured by a count of the number of aircraft crossing the zone considered, for example by applying a sliding time window to smooth the result, during the time period considered.

[0071] Preferably, the maximum load is calculated by discarding a percentage of measured traffic volume peaks, for example 5% of the highest peaks having a duration less than a predetermined duration, for example equal to 30 minutes.

[0072] Advantageously, this allows for the elimination of occasional peaks and enables the determination of a more reliable optimization threshold because it closely reflects operational practices. Alternatively, or in addition, the maximum load per time period is also calculated based on a level of traffic complexity, such as the diversity of actual aircraft trajectories and the type of lateral or vertical crossings. For example, the higher the number of aircraft crossing laterally, the higher the level of complexity, according to a pre-established complexity calculation method. This complexity calculation method is preferably adjusted or validated by an expert to adapt to the specific geographical characteristics and traffic patterns encountered locally within a given geographic area.

[0073] Thus, according to one variant, the maximum load is calculated based on a mixture of the two above measures, i.e. the number of aircraft crossing the area and the complexity of the traffic.

[0074] According to one embodiment, determination step 44 is implemented by statistical analysis on historical data.

[0075] According to another embodiment, determination step 44 implements machine learning.

[0076] The process also includes a step 46 of calculating an optimization threshold, by geographical sector area and by time period.

[0077] In one embodiment, the optimization threshold is calculated as a fixed fraction of the maximum load calculated in step 44.

[0078] For example, the fraction is set within a range of 60% to 95%, or for example, the fraction is set at 80%. Thus, when the effective load is less than or equal to 80% of the maximum load, it is considered that there is a possibility of increased traffic, or in other words, as long as the capacity to absorb this increase in traffic has not been reached, flight plan optimizations are possible.

[0079] In another embodiment, the optimization threshold is calculated dynamically, using statistical analysis or machine learning, for example by identifying, based on historical data, the percentage of flights whose actual flight path (corresponding to the executed flight plan) represents an improvement over the filed flight plan. In this embodiment, the optimization thresholds are not the same for every geographical area considered and they can vary over time (within the same day: for example, the thresholds for the 7:00-10:00 slot will typically not be the same as for the 10:00-16:00 slot or the 16:00-20:00 slot) or from one day to the next.

[0080] The process then involves a prediction, for a plurality of time slots in a subsequent time period, of an effective load per geographical sector and per time slot, based at least on historical data from completed flight plans. A time slot has a chosen duration, less than or equal to the duration of the time period over which the optimization threshold is calculated. In other words, the time slot is a unit of time, for example, with a duration set at 1 hour.

[0081] Preferably, in addition to historical data, prediction 48 takes into account current data, for example flight plans filed as well as possibly the actual state of traffic (routes actually flown and volume of traffic) in the minutes preceding the target slot, the time slots on which the prediction is made being future time slots relative to the time at which the prediction is made.

[0082] For example, prediction 48 is performed every half-day, or one day over the next, or one week over the next, depending on the day or half-day. It can also be implemented at a higher frequency (hourly for example), in order, in particular, to refine a prediction made over a larger time and space window for a more restricted geographical area.

[0083] Optionally, prediction 48 takes into account contextual elements, predicted for the following time period. For example, contextual elements are weather forecasts, from weather forecast servers, for time slots in the following time period.

[0084] In one embodiment, prediction 48 is implemented by machine learning, for example machine learning of the parameters of a classification model or a neural network.

[0085] Prediction 48, implemented using machine learning (ML), can, for example, rely on a history of loads for each zone or geographic sector, over a period ranging from a few days to several years. This history could be built from data from an air navigation service provider, or alternatively, from an analysis of flight plans (or therefore flight paths), derived, for example, from ADS-B recordings, from which the number of aircraft crossing each zone at each time step would have been counted. To improve the accuracy of the predictions, the process can add labels ("features") to each historical data point, which can be represented as a time series for each zone, providing contextual information.This contextual information includes, for example, temporal characteristics (day of the week, time, season, holiday period, event period), situational characteristics (airspace closed for political or military reasons, weather conditions), and information published by air navigation managers (NOTAMs or applicable rules). This historical dataset, possibly labeled, can serve as a training basis for machine learning models, adapted, for example, to time series or labeled data.

[0086] This learning process can be supplemented by new data available with each execution of prediction 48. These models can then be used to predict future loads in each zone, over a time horizon ranging from a few hours to several days. These models can be customized for each zone.

[0087] Alternatively, according to one variant, it is planned to aggregate results over several geographical sectors. For example, the predictions of a local area are also a function of the predictions of adjacent areas, or of regions on larger scales such as a FIR (Flight Information Region).

[0088] Figure 4 schematically illustrates, as an example, graphs G1, G2, G3, each graph representing a volume of traffic as a function of time, by time slots, for respective zones S2, S3 and S4.

[0089] In this example, the load is measured by the volume of traffic, and the maximum load corresponds to a maximum volume of traffic.

[0090] The optimization threshold "Threshold", calculated in relation to the maximum load ("Maximum"), is represented on each graph.

[0091] As can be seen visually in the graphs G1 to G3 shown, for certain time slots the predicted effective load is below the optimization threshold. These time slots are those in which air traffic control has, a priori, some flexibility (capacity, workload, etc.), making it conducive to implementing optimization in the relevant geographical sectors during the identified optimization time slots.

[0092] The process includes a step 50 of identifying one or more time slots for optimization and associated optimization zones, for which the predicted effective load is less than the optimization threshold during each optimization slot.

[0093] Identifying 50 zones and time slots for optimization is advantageous for subsequently determining what modifications to flight plans are feasible.

[0094] The associated optimization zones and time slots are memorized and used subsequently.

[0095] Several implementation methods are possible.

[0096] In one embodiment, the use of optimization time slots and associated optimization zones includes the calculation 54 of at least one optimized aircraft flight plan, based on an efficiency criterion, and optionally based on an initial flight plan. In one embodiment, the identified optimization zones and time slots are transmitted (transmission step 52), via a communication network, to an airline planning system 4 and / or an electronic device 6, for direct use.

[0097] For example, when the electronic device 6 is on board, the optimization zones and time slots are displayed on a graphical interface of the electronic device 6, this then allows the pilot to negotiate in real time, with the operators of the control system 12, a modification of the initial flight plan.

[0098] Planning system 4 then exploits the optimization zones and time slots.

[0099] For example, an airspace map, visually identifying areas and time slots for optimization, is displayed on a graphical interface of the planning system 4 to allow operators to visualize, in correlation with initial flight plans, possible flight plan modifications.

[0100] In one embodiment, the planning system 4, or the electronic terminal 6 when it has the initial flight plans, implements step 54, which determines modified, optimized flight plans based on the initial flight plans, according to the optimization zones and time slots, using an efficiency criterion combining one or more of the following objectives: reduced fuel and / or electricity consumption, reduced carbon dioxide emissions, reduced methane emissions, and reduced nitrogen oxide emissions. This list is not exhaustive, and other objectives for reducing fuel consumption and / or environmental pollution can be incorporated.

[0101] For example, step 54 implements an optimization algorithm that calculates the efficiency criterion for each alternative flight plan.

[0102] Initial flight plan modifications typically include changes to waypoint and / or altitude, allowing passage through optimization zones, and may include aircraft speed changes to allow compliance with optimization time slots.

[0103] In one variant, flight plans including routes with satisfactory efficiency levels according to the chosen efficiency criterion are stored, and then, in step 56, the modified flight plan is selected from among the stored flight plans, based on available zones and time slots. In other words, a previously stored flight plan that meets the efficiency criterion is selected.

[0104] As schematically illustrated in Figure 4, in graph 55 (left part of the figure), to make a journey between a starting point (e.g. London) and an arrival point (e.g. Zurich), the initial flight plan Pi is planned with a route crossing the zones S1-S6-S7-S5.

[0105] Following the identification of optimization zones / sectors and time slots, a modified flight plan Pm is calculated, passing through zones S1-S2-S3-S4-S7-S5. The modified flight plan optimizes, for example, an environmental efficiency criterion (e.g., pollution minimization).

[0106] Optionally, the optimized flight plan(s) are transmitted (step 56) to control system 12 for validation. A validated flight plan can be resubmitted, thus replacing the initial flight plan. This is particularly advantageous when flight plan calculations are performed in advance, allowing for anticipated optimizations.

[0107] As an optional addition, in one embodiment, an optimized flight plan is transmitted (step 58) to an electronic terminal 6 onboard an aircraft, for the pilot of the aircraft concerned. This then allows the pilot to negotiate, in real time, with the operators of the control system 12, a modification of the initial flight plan. This is particularly useful when the optimized flight plan is provided in real time.

[0108] Advantageously, the proposed process and system enable the early detection of flight plan optimization opportunities based on selected efficiency criteria, particularly environmental efficiency criteria. Furthermore, it allows for the autonomous calculation of optimized flight plans, which can then be submitted to the air traffic control system, resulting in a faster and more computationally efficient process.

[0109] Furthermore, and advantageously, the early optimization of flight plans reduces the mental workload of pilots during flight, while making the best use of the airspace's traffic absorption capacity.

[0110] Furthermore, advantageously, the early optimization of flight plans reduces the workload of air traffic controllers, as the number of real-time requests for flight plan optimization will be reduced, and limited to flight plans that are feasible a priori.

Claims

DEMANDS 1. A method for optimizing aircraft flight plans in airspace, the method being implemented by a computing processor of a programmable electronic device, and being characterized in that it comprises the steps of: obtaining (42) a division of the airspace into geographical sectors, each geographical sector being partitioned vertically and / or laterally into zones, based on air traffic data, including historical data on flight plans executed by aircraft; determining (44) a maximum air traffic load per geographical sector zone and per time period, over one or more successive time periods; calculating (46) an optimization threshold, per geographical sector zone and per time period, as a function of the maximum air traffic load, said optimization threshold being a fraction of the maximum air traffic load; and predicting,(48) for a plurality of time slots in a subsequent time period, of an effective predicted air traffic load per geographical sector area and per time slot, based at least on historical flight plan data, the air traffic load per geographical sector area and per time period being calculated based on an air traffic volume and / or a level of air traffic complexity in said geographical sector area and during said time period, identification (50) of one or more optimization time slots and associated geographical sector areas, referred to as associated optimization areas, an optimization time slot being a time slot during which, for each associated optimization area, the predicted effective air traffic load is less than the optimization threshold, use (52,54) said time slots for optimization and associated optimization zones to determine at least one optimized flight plan.

2. Method according to claim 1, wherein said use comprises a transmission (52) of said optimization time slots and associated optimization zones, via a communication network, to at least one remote aircraft flight planning system (4) and / or to at least one onboard electronic terminal (6).

3. A method according to claim 1 or 2, wherein said use comprises a determination (54, 56) of at least one aircraft flight plan optimized, based on an efficiency criterion, according to said optimization time slots and zones associated optimization criteria, said efficiency criterion combining one or more of the following criteria: reduction of fuel and / or electricity consumption, limitation of carbon dioxide emissions, limitation of methane emissions, limitation of nitrogen oxide emissions.

4. A method according to claim 3, wherein the determination of an optimized flight plan involves a selection (56) of a previously memorized flight plan satisfying said efficiency criterion.

5. A method according to any one of claims 1 to 4, wherein said cutting is a regular geometric tiling comprising meshes of the same size or an irregular tiling comprising meshes of varying sizes.

6. A method according to any one of claims 1 to 5, wherein the prediction (48) of a plurality of time slots further takes into account flight plans filed for said subsequent time period.

7. A method according to any one of claims 1 to 6, wherein the prediction (48) of a plurality of time slots further takes into account contextual elements, including weather forecasts, for said next time period.

8. A method according to any one of claims 1 to 7, wherein said use (54) comprises determining at least one aircraft flight plan optimized by modifying an initial flight plan according to said optimization time slots and associated optimization zones.

9. Method according to claim 8, wherein the modification of an initial flight plan is chosen from a list comprising at least: a modification of aircraft waypoints, a modification of aircraft altitude, a modification of aircraft speed.

10. Computer program comprising software instructions which, when executed by a programmable electronic device, implement a method for optimizing aircraft flight plans according to claims 1 to 9.

11. Aircraft flight plan optimization system in airspace, the system comprising at least one programmable electronic device including a computing processor, the system being characterized in that it is configured to implement: a module (30) for obtaining a division of the airspace into geographical sectors, each geographical sector being partitioned vertically and / or laterally into zones, 17 a module (32) for determining, on the basis of air traffic data, including historical data of flight plans executed by aircraft, a maximum air traffic load per geographical sector area and per time period, over one or more successive time periods, a module (34) for calculating an optimization threshold, per geographical sector area and per time period, as a function of the maximum air traffic load, said optimization threshold being a fraction of the maximum air traffic load, a module (36) for predicting, for a plurality of time slots of a subsequent time period, an actual predicted air traffic load per geographical sector area and per time slot, as a function at least of the historical data of flight plans executed,the air traffic load per geographical sector area and per time period being calculated based on an air traffic volume and / or a level of air traffic complexity in said geographical sector area and during said time period, a module (38) for identifying one or more time slots for optimization and associated geographical sector areas, referred to as associated optimization zones, an optimization time slot being a time slot during which, for each associated optimization zone, the predicted effective air traffic load is less than the optimization threshold, a module (40) for using said optimization time slots and associated optimization zones to determine at least one optimized flight plan.

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