• 专利附录
  • 专利说明
  • 权利要求
  • 类似技术
  • 同族专利
  • 引用文献
  • 法律状态
  • 优先权
    • 专利摘要:
    The invention relates to a method and a system for viewing weather risks on board an aircraft. The system (300) includes communication means (330) for receiving meteorological information relating to a given area, a processor (310) for determining at each point in that area the future instant at which the aircraft would reach that point, a expert system (340) for estimating, at each point of the zone, from the meteorological information and the future instant, the meteorological hazard at this point, and a graphical interface (360) for displaying, at each point of the zone , the meteorological risk thus estimated.
    公开号:FR3022341A1
    申请号:FR1455336
    申请日:2014-06-12
    公开日:2015-12-18
    发明作者:Gago Isidro Bas；Armesto Oscar Dominguez
    申请人:Gtd Sist S De Informacion；
    IPC主号:
    专利说明:

    [0001] TECHNICAL FIELD The present invention relates generally to the field of weather monitoring and more particularly to the visualization of meteorological risks on board a vehicle such as an aircraft. STATE OF THE PRIOR ART Weather conditions can severely affect the safety of an aircraft during its flight phase or its take-off and approach phases. In particular, stormy phenomena often associated with turbulence in the atmosphere and heavy rainfall or hail, may require changes to the route. These changes result in increased workload for air traffic controllers and pilots. The available airspace is reduced, which requires putting some flights on hold. This frequently results in delays, additional operating costs (the amount of fuel consumed) and a general deterioration in the quality of service. In addition, aircraft routes are sometimes unnecessarily altered to the extent that adverse weather conditions would have disappeared or become remote had the aircraft continued its initial course. In an aircraft, meteorological hazard monitoring is usually carried out using a weather radar embedded in the tip. It uses conventionally a mobile antenna along two axes so as to perform a mechanical scan of the radar beam in a wide angular range in azimuth and for discrete angular elevation values. These weather radars make it possible to represent in real time the meteorological conditions at the front of the aircraft in an angular sector of about 1600 and with a range of about 320 NM (nautical miles). When the weather risk is at a short distance (eg less than 120 NM), the pilot has little time to anticipate and engage in an avoidance maneuver. It is therefore necessary to be able to anticipate these avoidance maneuvers as soon as possible. It has been proposed in US-B-7109913 to merge meteorological information obtained by radar reflectometry with pressure and temperature measurements taken on board the aircraft as well as satellite meteorological information retransmitted by ground stations. Although this system makes it possible to improve an instantaneous assessment of meteorological risks in a given area, it does not make it possible to easily anticipate the evolution of these risks for the aircraft.
    [0002] Moreover, as a general rule, the pilot has before departure from weather forecasts in the form of maps. It can receive subsequent alerts on the evolution of meteorological conditions by means of messages transmitted by the ground, in particular by Airline Operational Control (AOC) air traffic control centers or Air Traffic Control (ATC) air traffic. These messages, however, give only imprecise information on the location, extent and evolution of risks. The pilot is frequently forced to follow an alternative route to that initially planned without having a precise overview of the meteorological risks actually incurred. The international application WO-A-2013/130897 describes a method of rationalizing the exchange of meteorological information between pilots, air traffic controllers and airlines to more accurately represent risks of turbulence. However, this method does not allow the pilot to assess and anticipate the risks along his trajectory, as shown in Figs. 1A-1B and 2A-2B. Figs. 1A and 1B represent a first example of meteorological conditions in a given zone at the front of the aircraft, respectively at a given first time, t0, and a given second time, t1, subsequent to t0. Cb has been called a storm formation (cumulonimbus) developing in this zone. Note that it has moved between moments t1 and t1, so that if it did not present a risk for the aircraft A moving along its route R at time t0, it represents a risk real at the instant t1.
    [0003] Figs. 2A and 2B represent a second example of meteorological conditions in the area in question, with the same notation conventions as before. In this example, the pilot has changed his / her route / because of the presence of the risk created by Cb on his route, identified at time to. However, one realizes that this maneuver of avoidance was useless since at the moment t1, the risk created by Cb no longer threatens the trajectory initially chosen. The purpose of the present invention is therefore to propose a method and an onboard weather visualization system that allow the pilot to anticipate and estimate the evolution of these risks in a clear and reliable manner, and in the case where it is necessary to change the route early enough to avoid the risks involved. PRESENTATION OF THE INVENTION The present invention is defined by a system for viewing weather risks in a predetermined zone, said system being intended to be on board a vehicle and comprising: communication means for receiving meteorological information in each point of said zone; a processor for determining, for each point of said area, from the current position of the vehicle and said navigation parameters, a future time at which the vehicle would reach that point; an expert system for estimating, at each point of said zone, from said meteorological information, a meteorological risk at this point at said future instant; a graphical interface to represent, at each point of said zone, the meteorological risk thus estimated by the expert system. According to a first embodiment, the predetermined zone is a corridor around a predetermined route of the vehicle.
    [0004] According to a second embodiment, the predetermined zone is an angular sector whose apex is located at the current position of the vehicle. The meteorological information may relate to a situation observed at a present time and / or predicted situations for a plurality of future times, said meteorological information being further stored in a database. The expert system is advantageously based on a probabilistic model, using a Bayesian network. The meteorological risk may include the presence of a weather phenomenon delimited by a border.
    [0005] In this case, the Bayesian network can estimate the meteorological hazard at a point P of said zone and at the future time, t1, at which the aircraft would reach this point, based on at least meteorological information predicted in said zone for the moment t1, the distance d between the current position A of the aircraft and the point P, the distance between the line (AP) and the boundary of the meteorological phenomenon, and the age of said predicted meteorological information. Alternatively, the Bayesian network can estimate the meteorological risk at a point P of said zone and at the future instant, t1, at which the aircraft would reach this point, based on at least meteorological information observed or predicted in said zone for the current time ro, the time necessary for the aircraft to reach this point, the speed and direction of the meteorological phenomenon, the type of its evolution and the age of said weather information observed or predicted. In any case, the Bayesian network could also take into account the confidence of meteorological information when it is predicted. The display system may further include means for selecting an instant within a predetermined time range. In a second mode of operation, the processor is adapted to calculate a position of the aircraft corresponding to a selected instant in this range and, conversely to calculate a time for a selected position of the aircraft, the processor being further adapted to display on the graphical interface the calculated or selected position of the aircraft as well as weather conditions at the selected or calculated time.
    [0006] The invention also relates to a method of visualization of meteorological risks incurred by a vehicle, within a predetermined zone, said method comprising: a step of receiving meteorological information at each point of said zone; a calculation step, for each point of said zone, from the current position of the vehicle and said navigation parameters, of a future instant at which the vehicle would reach this point; an estimation step using an expert system for estimating, at each point of the zone considered, from said meteorological information, a meteorological risk at this point at said future instant; a step of displaying, at each point of the zone considered, the meteorological risk thus estimated in the previous step. According to a first embodiment, the predetermined zone is a corridor around a predetermined route of the vehicle. According to a second embodiment, the predetermined zone is an angular sector whose apex is located at the current position of the vehicle. Finally, the meteorological information may relate to a situation observed at a current moment and / or to predicted situations for a plurality of future instants. BRIEF DESCRIPTION OF THE DRAWINGS Other features and advantages of the invention will appear on reading a preferred embodiment of the invention described with reference to the appended figures among which: FIGS. 1A and 1B represent a first example of meteorological conditions in a given zone, respectively in a first and a second instants; Figs. 2A and 2B show a second example of meteorological conditions in a given area, respectively in a first and a second instants; Fig. 3 schematically shows a weather hazard display system according to one embodiment of the invention; Figs. 4A and 4B are two examples of weather hazard viewing areas for the system of FIG. 3; Fig. 5A is a first example of a Bayesian network for the expert system of FIG. 3; Fig. 5B represents a meteorological situation in which the Bayesian network of FIG. 5A can operate; Figs. 5C and 5D represent meteorological situations for learning the Bayesian network of FIG. 5A; Fig. 6A is a second example of a Bayesian network for the expert system of FIG. 3; Fig. 6B represents a meteorological situation in which the Bayesian network of FIG. 6A can operate; Fig. 7 represents in the form of a flow chart a method for viewing weather risks according to a first mode of operation of the invention; Fig. 8 represents an example of a screen on which weather risks have been visualized according to the visualization method of FIG. 7; Fig. 9 is a flowchart of a weather visualization method according to a second mode of operation of the system of FIG. 3; Figs. 10A and 10B represent a window of the graphical interface displaying the meteorological conditions of a given area at a present moment and in a future instant, by means of the method of FIG. 9. DETAILED DESCRIPTION OF PARTICULAR EMBODIMENTS In the remainder of the presentation, we will consider an on-board weather risk visualization system on board an aircraft. However, one skilled in the art will understand that the invention is likely to apply also to other types of vehicle such as a boat for example. This weather hazard visualization system can be advantageously used as a navigation aid system insofar as the driver of the vehicle can change his route so as to avoid the risks in question. Fig. 3 represents an on-board weather visualization system, 300. This system comprises a processor 310, coupled with a navigation system 320, communication means 340, an expert system 340 and a mass memory. 350 and a graphical interface (GUI), 360. The navigation system, 320, is adapted to provide the processor 310 at each instant, the current position of the aircraft and its main navigation parameters, including its speed, direction and if necessary, its route, given for example by means of waypoints. The communication means, 330, make it possible to receive meteorological information via the uplink. This meteorological information is for example collected and transmitted by a ground station, 390, connected to the ACARS communication network, to the internet network or to the System Wide Information Management (SWIM) network developed within the European framework of SESAR (Single European Sky ATM Research). and NextGen (Next Generation Air Transportation System.) Weather information can be provided by Weather Data Proyiders, which in turn process data from ground-based weather stations or meteorological satellites (for example METEOSAT satellites) Knowing the position of the aircraft, the processor 310 can selectively download the meteorological information via the uplink in an area of interest, which information can relate to observed weather conditions or predicted by meteorological conditions in particular the presence of storms, turbulence, icing or volcanic ash, convection or wind phenomena, wind speeds, precipitation levels, types of precipitation (rain, hail), height and thickness cloud layers, cloud types (especially cumulonimbus clouds), this list is of course in no way exhaustive. Weather conditions may be likely to create weather hazards for aircraft. Meteorological risk is a risk to the aircraft (or its passengers) due to weather conditions in a given location. It is understood that only certain weather conditions at or near the location where the aircraft is located are likely to make him run a risk justifying an avoidance maneuver. The observed and / or predicted weather information (nowcast or forecast) in a given area is stored by the processor in the mass memory 350. The meteorological information is indexed by spatial coordinates (preferably three-dimensional) and a time variable. In other words, the mass memory contains a spatio-temporal meteorological database. For a given weather type, when more recent observed or predicted data is available, it overwrites the older data. The estimation of the meteorological risks for the aircraft is carried out by the expert system on the one hand, meteorological information, stored in the mass memory, and, on the other hand, the position and the navigation parameters of the aircraft (including its speed, direction and, if applicable, its route), provided by the processor, 310. The expert system, 340, estimates at each point a predetermined area or a selected area by the pilot, the level of meteorological risk incurred by the aircraft.
    [0007] More specifically, the processor 310 determines for each given point of this zone, the future time at which the point could be reached taking into account the current position and the navigation parameters of the aircraft. The expert system then estimates the meteorological risk that the aircraft would incur if it were at this point in the future instant. It is essential to note that the estimated risk is not that which the aircraft would experience if it were at the point considered at the present time but rather the one that the aircraft would incur at that point at the time it was 'reach. Thus, the estimate of the risk takes into account the evolution of the meteorological conditions during the time of movement of the aircraft. The estimation of the meteorological risk is carried out using a probabilistic model as described below. FIGS. 4A and 4B represent a weather risk estimate for two example areas at the front of an aircraft. In the case of FIG. 4A, the estimation of meteorological risks is carried out for the entirety of a sector whose summit is substantially centered on the position of the aircraft, without assumption of a particular trajectory. The sector is assumed to be located in the horizontal plane of the aircraft. Alternatively, another altitude could be considered, taking into account the time required to change altitude. The sector is generally located in the half-plane at the front of the aircraft (angular range less than 1801. However, a sector of angular upper range can be envisaged.
    [0008] In FIG. 4A, the aircraft occupies a current position, denoted A0, at a current instant t0. Given its speed V the aircraft can reach the concentric circles C1, C2, ..., CN at times ti, t2, ..., tN. Of course, the different points of the same circle can be achieved by a change of course of the aircraft. It was simply considered here that the time required for the change of course was negligible compared to St = ti - to. In general, it will be understood that the points that the aircraft can rally after a time interval θt are located on a sheet in three-dimensional space, this sheet being not necessarily spherical because of the time required for the change of course and altitude. The expert system will advantageously proceed from a ribbon web, so as to process sequentially all the points corresponding to the same time shift θt. Thus, in the case of FIG. 4A, the points of the sectoral zone are treated according to successive concentric circles. For a circle Cn, the expert system extracts the predicted meteorological conditions for the corresponding time tn from the mass memory and estimates the meteorological risk in each of the points of this circle. Alternatively, the expert system extracts from the mass memory the current meteorological conditions (observed or predicted) and a prediction of their evolution to evaluate the risk at each point of the circle. The risk can be represented according to a grayscale intensity scale or a color scale. In the case of FIG. 4A, the maximum risk is represented in the form of gray levels. Thus, in the present case, the aircraft must modify its route I so as not to incur a maximum meteorological risk. It can for example choose the route I 'to avoid the risk in question. In the case of FIG. 4B the weather risk estimate is made for a predetermined route I of the aircraft, or for a relatively narrow corridor, of width A on either side of this trajectory. The width of the lane may be chosen by the pilot, for example by selecting from a predetermined set of widths or simply by moving the edges of the lane, 450, with the mouse pointer. In any case, the processor then determines the times at which the aircraft would wait respectively the points Pi, ..., PN of the trajectory /, given its flight plan. As before, the expert system extracts from the mass memory the predicted weather conditions for times ti, ..., tN and deduces the meteorological risk at the points considered. Alternatively, it will be able to extract from the mass memory the current meteorological conditions at the points considered and a forecast of their evolution. In the illustrated case, the trajectory I 'represents a good alternative to avoid risks. This variant has the advantage of requiring less calculation and allows to simply validate a trajectory. If a meteorological risk is present on this trajectory, the pilot can opt for the first variant and then obtain a risk map over the entire desired zone. Conversely, after choosing a route using the first variant, the pilot can then simply visualize the risks along the chosen route. The expert system estimates from the current position of the aircraft, from a possible position thereof at a future time in the area and weather predicted for that moment, a level of weather risk. To do this, the expert system advantageously uses a probabilistic model of meteorological risk. This probabilistic model is advantageously represented by a Bayesian network. An introduction to Bayesian networks can be found in the article by T.A.
    [0009] Stephenson, "An Introduction to Bayesian Network Theory and Use," IDIAP Research Report, Feb. 2000. It is recalled that a Bayesian network is an acyclic oriented graph whose nodes represent random variables (generally discrete) and whose arcs represent the causal relations. An arc E between a starting node (random variable) u and arrival node (random variable) is assigned a weight representing the conditional probability P (v1u). Thus, if a node has for parents a plurality of nodes up. ", UI 'in other words if the random variable is conditionally r dependent on the random variables up ..., u,', we have: NP (v) - np (v luk) k = 1 The weights of the arcs, that is to say the conditional probabilities between random variables, are obtained by means of a learning phase In the absence of a priori knowledge of the structure of the graph , we can start learning with a large number of random variables and simplify the graph by removing arcs.For example, the arcs whose weights are lower than a threshold value can be deleted.The Bayesian network (and more generally the probabilistic model) may be a function of the region overflown, as it is conceivable that the risks may be different in a tropical region and a temperate region, and may also depend on the type of aircraft, and some aircraft may be more susceptible at certain weather conditions than others. It may still depend on the flight phase in which the aircraft is. Other parameters of the Bayesian network may be considered by those skilled in the art.
    [0010] A typical example of meteorological risk for an aircraft is that of encountering a cumulonimbus on its trajectory. Indeed, this type of cloud may be the seat of violent electrical events such as lightning but also hail, heavy rainfall, icing and strong wind shear. The main danger of cumulonimbus clouds comes generally from downward gusts (particularly in the approach phase) and from strong turbulence that can occur in the high layers of the cloud. The presence of cumulonimbus may be detected by a ground-based forecast station by comparing satellite weather observations, relative to the upper layers of the atmosphere and ground weather observations. Alternatively, the presence of these cumulonimbus may be detected by the onboard system based on these observations, after they have been transmitted to the aircraft on the uplink. Fig. 5A represents a first example of a Bayesian network evaluating a meteorological risk related to the meeting of a cumulonimbus.
    [0011] It is assumed that we know the current position A0 of the aircraft and the forecasts of the meteorological conditions for the time t1 = to + d IV where to denotes the current moment, d is the distance between the current position A of the aircraft and the point P of the area where it is desired to estimate the meteorological risk, and V is the speed of the aircraft. It is shown in FIG. 5B the relative situation of the aircraft and the cumulonimbus. The position and the extension of the cumulonimbus represented here are those relative to the time t1, that is to say for the moment when the aircraft reaches the point P. The zone in dark gray located inside the cumulonimbus Cb is the one with a high level of precipitation. This zone can be detected by means of radar reflectometry measurements (radar reflection coefficient higher than a predetermined threshold, for example). The distance between the line (AP) and the cumulonimbus boundary and p the distance from the line (AP) to the zone of high precipitation level was noted. The Bayesian network of FIG. 5A involves here the distances d and Ô as well as the age of the prediction = t1 -ç where tw is the moment at which the prediction was made. If necessary, other random variables may be taken into account, such as the distance p defined above, the traveling direction of the cumulonimbus or its displacement (provided by the weather forecast station). The degree of confidence r7 of the weather prediction (for example in the form of a standard deviation) may also be a parameter taken into consideration by the Bayesian network. In practice, the continuous random variables involved in the prediction, such as, for example, the distances d and δ are discretized (by integrating their probability density on the quantization steps) so as to always be reduced to discrete random variables. As indicated above, the conditional probabilities relating to the different arcs of the graph are obtained during a learning phase. When the database is complete, the learning phase can consist in comparing, by means of a meteorological database, predicted situations with those actually observed. On the other hand, if the database is not complete, the so-called EM (expectation-maximization) method can be used to obtain the conditional probabilities of the Bayesian network, in a manner known per se. Fig. 5C illustrates a predicted situation at a point of interest P located at the distance d of an aircraft at the current position A0. The prediction seniority is Ti and the distance to the cumulonimbus boundary in the predicted situation at time t1 is 5.
    [0012] This predicted situation is compared at time tw with that which has actually been observed at time t1, represented in FIG. 5D. We see in this case that the prediction was correct since the trajectory of the aircraft actually crosses the cumulonimbus. For each correct prediction one assigns a given score, and for each erroneous prediction one assigns a null score. Conditional probabilities are obtained by averaging the scores obtained on a large number of predicted situations characterized by triplets (d, g, r). Fig. 6A represents a second example of a Bayesian network evaluating a meteorological risk related to the meeting of a cumulonimbus.
    [0013] This Bayesian network differs from the previous one in that the base of the probabilistic computation is not the meteorological situation predicted for the moment t1 where the aircraft arrives at the point P but that observed at the present moment to (or which had been predicted for this moment). Fig. 6B represents the relative situation of the aircraft and the cumulonimbus. It will be noted that the position and the extension of the cumulonimbus represented here are indeed those relating to the time t0. The random variables, that is to say the nodes of the graph of FIG. 6A, here are the time St = cliV necessary for the aircraft to travel the distance AP to the point of interest, the distance Ô from the line (AP) to the cumulonimbus boundary, the speed and direction of the cumulonimbus, the type of cumulonimbus evolution (increasing, decreasing), the age of the prediction 1-0 = t0 -t ',. Here again, other random variables may be taken into consideration, as well as the confidence level r7 of the prediction. Continuous random variables will in practice be discretized. For example the distance Ô can be discretized in the form (center, peripheral, external) to reflect the fact that the trajectory intersects the cumulonimbus at its center, at its periphery, or does not cut it. Similarly, the direction of the cumulonimbus can be discretized into eight values N, NE, E, SE, S, SO, 0, NO. Learning the Bayesian network can be done as previously from the likelihood method when the database is complete or from the EM method when it is not. Fig. 7 represents in the form of a flow chart a method for viewing weather risks according to a first mode of operation of the invention. This method is implemented to visualize the weather risks incurred by a vehicle in a predetermined area, typically in an angular sector whose summit is located at the vehicle position. In a first step, 710, meteorological information is received for each point of said zone. This meteorological information may relate to an observed weather situation or a predicted weather situation for the considered point. In a second step, 720, it is determined from the current position of the vehicle and for each point of the zone considered, the time required for the vehicle to reach this point from its current position, ie the future time at which it would rally this point, given its navigation settings.
    [0014] In a third step, 730, it is estimated for each point of the zone considered, from the meteorological information at this point and the corresponding future instant, the meteorological risk incurred by the vehicle at this point and at the future time. The estimation is obtained by means of a probabilistic model, advantageously implemented by an expert system using a Bayesian network as previously described.
    [0015] In a fourth step, 740, a cartography of the meteorological risks in the zone considered is represented. From this mapping, the driver of the vehicle can then decide, with a high degree of reliability, if his planned route presents a meteorological risk and, if so, look for an alternative route not presenting the risk in question. This method is advantageous insofar as the risk represented in each point of the zone is not the current risk but that which is incurred by the vehicle when it reaches this point. Risk mapping is therefore directly interpretable for the pilot. Fig. 8 gives an example of meteorological hazard mapping obtained by the visualization method according to the invention. The risk represented here is that of encountering a stormy situation. The current position of the aircraft is in the center of the figure. The gray level of a pixel is proportional to the level of risk. It should be noted that the route / can be validated despite the current storm situation in Cbi and Cb2, as the storms will have dissipated before the aircraft arrives on site. The weather hazard display system according to the invention can also operate in a second mode of operation, complementary to the first, as described below in relation to FIG. 9.
    [0016] This operating mode enables the pilot to visualize the meteorological conditions in a given zone, relative to a time t that he can freely vary in an interval [to -T ', t0 + T], for example by moving a cursor on a time axis of the graphical interface. It will be possible to choose Ti - 0, the representation of the past weather conditions being generally of little interest to the pilot. By moving the cursor, the pilot selects a time t of the interval in question.
    [0017] The meteorological situation predicted in the zone for the time t is then displayed as well as, concomitantly, the shipowner symbol (ownship symbol) in the new position of the aircraft provided for the time t. Conversely, when the route is displayed in the area, the pilot can move the aircraft along that route. The movement of the aircraft along the route correlatively causes a displacement of the cursor on the time axis, an instant t on the time axis being associated in a one-to-one way with the position of the aircraft on the route at that time. moment. More precisely, in step 910, a time te [to, to + T] is selected or, conversely, a future position of the aircraft along its route is selected. In step 920, the processor 310 calculates, from the current position A (to) of the aircraft and its navigation parameters, the future position A (t) of the aircraft at the time t selected. Conversely, if a future position A 'is selected along the route of the aircraft, the processor 310 calculates from the positions A (to), A' and navigation parameters the time t necessary for the aircraft to rally. point A '. In step 930, the processor displays the symbol of the aircraft at the position A '= A (t) and the cursor at the position t on the time axis. In step 940, the processor extracts from the mass memory the meteorological information relating to the time t for all the points of the zone concerned and displays the meteorological conditions corresponding to that instant. An automatic scan mode can also be provided. According to this mode, the instant t is scanned in a loop between the terminals to and to + T, and the steps 910 to 940 are iterated. FIG. 10A represents a window 1000 of the graphical interface displaying the current weather conditions in an area at the front of the aircraft. More specifically, the window comprises a first portion 1010 in which these weather conditions are displayed in a horizontal section containing the aircraft and a second portion 1020 displaying these meteorological conditions in a vertical section along the planned route. The current position of the aircraft is designated A (t0) and the route is designated by /. The window 1000 further comprises a third portion 1030 in which is displayed a time axis 1031, advantageously graduated in time, along which a cursor 1032 can be moved (for example in a tactile manner or with the aid of a mouse). The time axis is here vertical but it will be understood that it could be arranged horizontally. Alternatively, the pilot can move, by the same means as before (touch screen or mouse), the aircraft along its path). Moving the cursor 1032 along the time axis 1031 causes the aircraft to move along the route / and vice versa. It can be seen from the figure that, in the situation observed or predicted at time to, the route / does not intersect with storm formation.
    [0018] Fig. 10B represents the same window 1000 at a time t1 posterior 30mns at time t0. Note that the position of the cursor and that of the aircraft have varied accordingly. Here is designated by A (t0) the position of the aircraft at time to and by A (t1) the position of the aircraft at time t1. Thus, by moving the aircraft, or in an equivalent manner, by moving the cursor over the time axis, it is possible to verify the presence or absence of meteorological hazards along the route. In the present case, we note that according to the predicted situation for the time t1, the route crosses a stormy situation in W. However, to ensure the relevance of the risk in W, the pilot can continue moving the symbol d aircraft to this point.
    [0019] This mode of operation can advantageously complement the first mode. Indeed, using the first mode, the pilot can quickly decide an alternative route to avoid a meteorological risk. He can then move the symbol of the aircraft along the route in question, according to the second mode, to ensure that at any point in its trajectory the aircraft is confronted with a significant meteorological risk.
    权利要求:
    Claims (15)
    [0001]
    REVENDICATIONS1. A system for viewing weather risks in a predetermined area, said system being intended to be on board a vehicle and being characterized in that it comprises: communication means (330) for receiving meteorological information at each point of said area; a processor (310) for determining, for each point of said zone, from the current position of the vehicle and said navigation parameters, a future moment at which the vehicle would reach this point; an expert system (340) for estimating, at each point of said zone, from said meteorological information, a meteorological risk at this point at said future instant; a graphical interface (360) for representing, at each point of said zone, the meteorological risk thus estimated by the expert system.
    [0002]
    2. Viewing system according to claim 1, characterized in that the predetermined area is a corridor around a predetermined route of the vehicle.
    [0003]
    3. Viewing system according to claim 1, characterized in that the predetermined area is an angular sector whose apex is located at the current position of the vehicle.
    [0004]
    4. Visualization system according to one of claims 1 to 3, characterized in that the weather information relates to a situation observed at a current time and / or predicted situations for a plurality of future times, said weather information. being further stored in a database (350).
    [0005]
    5. Visualization system according to claim 4, characterized in that the expert system is based on a probabilistic model.
    [0006]
    6. Viewing system according to claim 5, characterized in that the expert system uses a Bayesian network representing said probabilistic model.
    [0007]
    7. Viewing system according to claim 6, characterized in that the vehicle is an aircraft and that the meteorological risk is constituted by the presence of a weather phenomenon delimited by a boundary.
    [0008]
    8. Viewing system according to claim 7, characterized in that the Bayesian network estimates the meteorological risk at a point P of said area and at the future time, t1, at which the aircraft would reach this point, from at least predicted meteorological information in said zone for the moment t1, the distance d between the current position A of the aircraft and the point P, the distance between the line (AP) and the boundary of the meteorological phenomenon, and the age of said predicted meteorological information.
    [0009]
    9. Viewing system according to claim 7, characterized in that the Bayesian network estimates the meteorological risk at a point P of said area and at the future time, t1, at which the aircraft would reach this point, from at least meteorological information observed or predicted in said area for the present time t0, the time required by the aircraft to reach this point, the speed and direction of the meteorological phenomenon, the type of its evolution and the age of said information weather observed or predicted.
    [0010]
    10. Viewing system according to claim 8 or 9, characterized in that the Bayesian network also takes into account the degree of confidence meteorological information when these are predicted.
    [0011]
    11. Display system according to claim 10, characterized in that it comprises means for selecting an instant in a predetermined time range and that, in a second mode of operation, the processor is adapted to calculate aposition of the aircraft corresponding to a selected instant in this range and, conversely to calculate a time for a selected position of the aircraft, the processor being further adapted to display on the graphical interface the calculated or selected position of the aircraft as well as weather conditions at the selected or calculated time.
    [0012]
    A method of visualizing meteorological hazards incurred by a vehicle within a predetermined area, said method comprising: a step of receiving (710) meteorological information at each point of said area; a calculation step (720), for each point of said zone, from the current position of the vehicle and said navigation parameters, of a future instant at which the vehicle would reach this point; an estimation step (730) using an expert system to estimate, at each point of the zone considered, from said meteorological information, a meteorological risk at this point to said future instant; a step of displaying (740), at each point of the zone considered, the meteorological risk thus estimated in the previous step.
    [0013]
    13. Viewing method according to claim 12, characterized in that the predetermined area is a corridor around a predetermined route of the vehicle.
    [0014]
    14. Viewing method according to claim 12, characterized in that the predetermined area is an angular sector whose apex is located at the current position of the vehicle.
    [0015]
    15. Viewing method according to one of claims 12 to 14, characterized in that the weather information is related to a situation observed at a current time and / or predicted situations for a plurality of future times.
    类似技术:
    公开号 | 公开日 | 专利标题
    EP2955481B1|2017-09-13|Method and on-board system for viewing weather risks
    CN106546984B|2021-04-16|Weather radar control system and method of using the same
    JP5973807B2|2016-08-23|Selecting weather data related to aircraft trajectory
    JP6001869B2|2016-10-05|How to select meteorological data to update aircraft flight path
    EP2178065B1|2011-03-09|Device for calculating an aircraft flight plan
    FR3021107A1|2015-11-20|METHOD FOR AIDING NAVIGATION OF AN AIRCRAFT WITH CORRELATION OF DYNAMIC INFORMATION WITH A 4D FLIGHT TRACK
    CA2781873C|2019-03-19|Meteorological data selection along an aircraft trajectory
    US8050864B2|2011-11-01|Vertical situation display of weather information
    CA2257336C|2007-09-25|Method for the lateral avoidance of a mobile zone by a vehicle
    FR2923623A1|2009-05-15|SYSTEM FOR SECURING A FLIGHT PLAN FOR AN AIRCRAFT
    EP1460384A2|2004-09-22|Method and apparatus for constructing a synthetic image of the environment of an aircraft and displaying the image on a display of this aircraft
    FR3001307A1|2014-07-25|METHODS FOR DETERMINING A FLIGHT TRACK
    FR2939558A1|2010-06-11|METEOROLOGICAL MODELING METHOD FOR CALCULATING AN AIRCRAFT FLIGHT PLAN
    EP1991834B1|2017-08-02|Method and device for automatically adjusting an image of an aircraft navigation screen
    FR2926894A1|2009-07-31|METHOD OF ESTIMATING AT ANY POINT OF AN AIRCRAFT AIR DATA PATH
    FR3038999A1|2017-01-20|SYSTEM AND METHOD FOR REFINING TRAJECTORIES FOR AIRCRAFT
    US20180149745A1|2018-05-31|Enhanced weather radar mapping
    WO2000039775A2|2000-07-06|Assistance system for avoiding terrain collision for an aircraft
    WO2016113306A1|2016-07-21|Method and system for displaying information relating to an aircraft, device for producing said information and related computer program product
    FR3038750A1|2017-01-13|METHOD FOR INTEGRATING A NEW NAVIGATION SERVICE IN AN OPEN AIR ARCHITECTURE OPEN ARCHITECTURE SYSTEM OF A CLIENT-SERVER TYPE, IN PARTICULAR A FIM MANUFACTURING SERVICE
    EP3489931A1|2019-05-29|Mission computing system for an aircraft including a mission plate
    FR2928021A1|2009-08-28|METHOD AND DEVICE FOR DETECTION OF A SURROUNDING AIRCRAFT.
    FR3073316B1|2019-11-22|METHOD AND ELECTRONIC DEVICE FOR FILTERING TRAFFIC INFORMATION IN AN AIRPORT DOMAIN, ASSOCIATED COMPUTER PROGRAM
    FR3038751A1|2017-01-13|METHOD FOR INTEGRATING A CONSTRAINED ROAD OPTIMIZATION APPLICATION IN AN OPEN ARCHITECTURE AIRCRAFT SYSTEM OF CLIENT-TYPE SERVER
    FR2941794A1|2010-08-06|Method for managing optional route at initial route of aircraft, involves determining point of vertical divergence on lateral profile in upstream of point of lateral divergence, and displaying point of vertical divergence
    同族专利:
    公开号 | 公开日
    EP2955481B1|2017-09-13|
    US9649935B2|2017-05-16|
    US20150360566A1|2015-12-17|
    FR3022341B1|2017-12-22|
    EP2955481A1|2015-12-16|
    引用文献:
    公开号 | 申请日 | 公开日 | 申请人 | 专利标题
    US6577947B1|2002-03-01|2003-06-10|Rockwell Collins, Inc.|Method and apparatus for identification of hazards along an intended travel route|
    US6744382B1|2002-04-19|2004-06-01|Rockwell Collins|Method and apparatus for guiding an aircraft through a cluster of hazardous areas|
    US20040044445A1|2002-08-30|2004-03-04|David Burdon|Quiet mode operation for cockpit weather displays|
    FR2923008A1|2007-10-26|2009-05-01|Thales Sa|METHOD AND DEVICE FOR DISPLAYING FORECASTS ON A NAVIGATION PLAN|
    US7109913B1|2004-05-04|2006-09-19|Rockwell Collins, Inc.|Airborne weather radar system and radar display|
    FR2947624B1|2009-07-03|2012-11-30|Airbus Operations Sas|METHOD AND DEVICE FOR THE AUTOMATIC DETERMINATION OF METEOROLOGICAL CONDITIONS IN THE VICINITY OF AN AIRCRAFT|
    US9069103B2|2010-12-17|2015-06-30|Microsoft Technology Licensing, Llc|Localized weather prediction through utilization of cameras|
    US9349296B2|2011-03-11|2016-05-24|The Boeing Company|Methods and systems for dynamically providing contextual weather information|
    EP3657473A1|2012-02-28|2020-05-27|Delta Air Lines, Inc.|Weather avoidance tool system|FR2999725B1|2012-12-18|2015-01-23|Thales Sa|METHOD FOR ADJUSTING A CISION / MASKING SECTOR OF AN ENVIRONMENTAL SCRUTING DEVICE, CORRESPONDING ADJUSTER DEVICE AND TERMINAL|
    FR3021107B1|2014-05-16|2018-01-26|Thales|METHOD FOR AIDING NAVIGATION OF AN AIRCRAFT WITH CORRELATION OF DYNAMIC INFORMATION WITH A 4D FLIGHT TRACK|
    US9666084B2|2014-12-19|2017-05-30|Rockwell Collins, Inc.|Display and control of time evolved conditions relative to a vehicle|
    KR101633509B1|2015-08-18|2016-07-11|주식회사 미래기후|Aviation weather forecast chart providing method and device using the same|
    US11112249B1|2018-09-24|2021-09-07|Rockwell Collins, Inc.|Systems and methods for four-dimensional routing around concave polygon avoidances|
    US11054280B2|2018-11-30|2021-07-06|International Business Machines Corporation|Displaying weather-based flight information in vertical profile|
    法律状态:
    2015-04-30| PLFP| Fee payment|Year of fee payment: 2 |
    2015-12-18| PLSC| Publication of the preliminary search report|Effective date: 20151218 |
    2016-03-10| PLFP| Fee payment|Year of fee payment: 3 |
    2017-04-11| PLFP| Fee payment|Year of fee payment: 4 |
    2018-06-29| PLFP| Fee payment|Year of fee payment: 5 |
    2020-06-30| PLFP| Fee payment|Year of fee payment: 7 |
    2021-05-27| PLFP| Fee payment|Year of fee payment: 8 |
    优先权:
    申请号 | 申请日 | 专利标题
    FR1455336A|FR3022341B1|2014-06-12|2014-06-12|METHOD AND SYSTEM FOR VISUALIZING WEATHER RISKS|FR1455336A| FR3022341B1|2014-06-12|2014-06-12|METHOD AND SYSTEM FOR VISUALIZING WEATHER RISKS|
    US14/736,544| US9649935B2|2014-06-12|2015-06-11|Method and on-board system for viewing weather hazards|
    EP15171559.6A| EP2955481B1|2014-06-12|2015-06-11|Method and on-board system for viewing weather risks|
    [返回顶部]