Optimized forecast retrieval for anticipated flight path of aeronautical vehicle

Abstract

To retrieve anticipation data of an anticipated flight path of an aeronautical vehicle.SOLUTION: A forecast update process includes: determining an anticipated flight path (134) for an AV (110) in three dimensions through an operating environment (100) between a current location (126) and a target destination location (124) based on flight plan data; identifying, based on the anticipated flight path (134), a set of cells within a three-dimensional cell array (150) forming a spatial representation (152) of the operating environment (100); for each cell (160) of the set of cells, determining a cell-specific forecast time for the cell (160) based on a time-based location profile of the AV (110) defined by the anticipated flight path (134); retrieving forecast data for the cell (160) corresponding to the cell-specific forecast time; and outputting the forecast data retrieved for the cell (160).SELECTED DRAWING: Figure 1

Description

Technical Field

[0001]

[0001] The subject matter of the present disclosure broadly relates to retrieving prediction data for a predicted flight path of an aircraft vehicle.

Background Art

[0002]

[0002] Personnel operating aircraft and other aircraft vehicles rely on weather and aviation activity predictions for navigation purposes. During flight, prediction data can be downloaded to a computing device mounted via a wireless communication link. As an example, satellite communication can be used to wirelessly request and receive prediction data during the flight of an aircraft vehicle. In at least some examples, the wireless communication link used to retrieve prediction data during flight may have a higher cost per data unit and limited bandwidth compared to a ground-based communication link.

Summary of the Invention

[0003]

[0003] According to one embodiment of the present disclosure, a method executed by a computing system of one or more computing devices includes performing a prediction update process for an aircraft vehicle (AV) during flight. The prediction update process includes determining a three-dimensional predicted flight path of the AV through an operating environment between a current position and a target destination based on flight plan data. The predicted flight path includes an altitude profile of the AV above a geographic area through the operating environment and a time-based position profile of the AV along the predicted flight path. The prediction update process further includes identifying a set of cells within a three-dimensional cell array that forms a spatial representation of the operating environment based on the predicted flight path. For each cell of the set of cells, the prediction update process further includes determining a cell-specific prediction time for the cell based on the time-based position profile of the AV, retrieving prediction data for the cell corresponding to the cell-specific prediction time, and outputting the prediction data retrieved for the cell.

[0004]

[0004] This summary is provided to introduce selected ones of the concepts in a simplified form that will be further described in the following detailed description. This summary is not intended to identify the key features or essential features of the claimed subject matter, nor is it intended to limit the scope of the claimed subject matter. Further, the claimed subject matter is not limited to embodiments that solve any or all of the disadvantages described in any part of this disclosure.

Brief Description of the Drawings

[0005]

Figure 1

[0005] An exemplary operating environment of an aerial vehicle is shown.

Figure 2

[0006] It is a flowchart showing an exemplary method that can be executed by a computing system of one or more computing devices to search for prediction data.

Figure 3

[0007] It is a flowchart showing another exemplary method that can be executed by a computing system of one or more computing devices to search for prediction data.

Figure 4

[0008] It is a graph showing the relationship between the altitude, geographical distance, and cell-specific prediction time of an exemplary predicted flight path of an AV.

Figure 5

[0009] It schematically shows an exemplary graphical user interface where prediction data can be output.

Figure 6

[0010] It is a schematic diagram showing an exemplary computing system that can execute the methods of FIGS. 2 and 3.

Figure 7

[0011] It is a schematic diagram showing further multiple aspects of the computing system of FIG. 6.

Modes for Carrying Out the Invention

[0006]

[0012] As briefly described above, the personnel operating the aerial vehicle rely on weather and aviation activity predictions for the purpose of navigation. During flight, prediction data can be downloaded from a remote source to a computing device onboard via a wireless communication link. As an example, satellite communication can be used to wirelessly request and receive prediction data during the flight of the aerial vehicle. In at least some examples, the wireless communication link used to retrieve prediction data during flight may have a higher cost per data unit and limited bandwidth compared to a ground-based communication link.

[0007]

[0013] In at least some embodiments, the amount of prediction data downloaded via a wireless communication link during the flight of an aerial vehicle can be reduced by selectively retrieving prediction data for the predicted flight path of the aerial vehicle within a limited area. According to one embodiment, a prediction update process can be executed by a computing system of the aerial vehicle during flight. The prediction update process includes determining a three-dimensional predicted flight path of the AV through the operating environment between the current position and the target destination based on flight plan data. The predicted flight path includes an altitude profile of the AV above the geographical area through the operating environment and a time-based position profile of the AV along the predicted flight path. The prediction update process further includes identifying a set of cells within a three-dimensional cell array that forms a spatial representation of the operating environment based on the predicted flight path. For each cell of the set of cells, the prediction update process further includes determining a cell-specific prediction time for the cell based on the time-based position profile of the AV, retrieving prediction data for the cell corresponding to the cell-specific prediction time, and outputting the prediction data retrieved for the cell. In at least some embodiments, threshold proximity and other multiple settings can be used to define the prediction update interval, geographical range, and resolution of the search prediction data regarding the predicted flight path.

[0008]

[0014] Figure 1 shows the operating environment 100 of an exemplary aerial vehicle (AV) 110. In this embodiment, the AV 110 takes the form of a commercial airliner. The AV 110 can take other forms including other types of aircraft.

[0009]

[0015] In this embodiment, the AV 110 is moving along a flight path 120 from an initial position 122 (e.g., a first airport) towards a destination 124 (e.g., a second airport). The current position 126 of the AV 110 along the flight path 120 is schematically shown in FIG. 1. The flight path 120 spans a geographical area 130. The AV 110 moves at an altitude 132 above the geographical area 130 within the operating environment 100. The flight path 120 has an altitude profile. The altitude 132 varies within the altitude profile above the geographical area 130.

[0010]

[0016] A portion of the flight path 120 along which the AV has not yet moved between the current position (e.g., 126) and the destination (e.g., 124) of the AV can be referred to as a predicted flight path 134. Further, while the AV 110 is moving along the flight path 120, a destination (e.g., 124) that the AV 110 has not yet reached can be referred to as a target destination 136. As will be described in more detail with reference to FIG. 2, the predicted flight path and / or target destination of the AV can change during flight based on various factors. The predicted flight path and target destination can be determined, for example, based on flight plan data.

[0011]

[0017] Flight path 120 can be defined by an initial position 122, a current position 126, a destination 124, and one or more intermediate waypoints (such as exemplary waypoints 140 and 142) disposed between the initial position and the destination. In this embodiment, waypoint 140 defines a point along flight path 120 that transitions from the altitude increase by AV110 from the initial position 122 to the cruise altitude, and waypoint 142 defines a point along flight path 120 that transitions from the cruise altitude to the altitude decrease by the AV. It will be understood that the flight path (e.g., 120) can be defined by any suitable amount of waypoints. The initial position 122, the current position 126, the destination 124, and the intermediate waypoints (e.g., 140, 142) can each be defined by their respective longitude, latitude, and altitude within the operating environment 100. Similarly, the predicted flight path 134 can be defined by the current position 126, the target destination 136, and one or more intermediate waypoints (e.g., 140, 142) disposed between the current position and the target destination.

[0012]

[0018] In FIG. 1, a portion of the three-dimensional cell array 150 that forms the spatial representation 152 of the operating environment 100 is schematically shown. The cell array 150 includes a plurality of three-dimensional cells. Each cell has its respective position and size within the longitude dimension 154, the latitude dimension 156, and the altitude dimension 158. An exemplary one of the plurality of cells 160 is identified in FIG. 1. As an exemplary embodiment, each cell of the cell array 150 can be defined to have a size of 1 mile within the longitude dimension 154, 1 mile within the latitude dimension 156, and 1000 feet within the altitude dimension 158. It will be understood that the plurality of cells of the cell array (e.g., 150) can have other suitable sizes in order to provide a desired level of spatial resolution.

[0013]

[0019] Flight path 120 and predicted flight path 134 can be described as passing through a set of cells of cell array 150. Thus, for a given flight path or predicted flight path, the set of cells through which the flight path or predicted flight path passes can be identified within cell array 150. Similarly, AV 110 moving or predicted to move along a flight path or predicted flight path can be described as passing through a set of cells of cell array 150, enabling the set of cells through which the AV passes or is predicted to pass to be identified within cell array 150. As will be described in more detail with reference to FIG. 2, for example, prediction data can be retrieved and output via a computing system for a particular plurality of cells of a cell array (e.g., 150) identified based on the predicted flight path of the AV.

[0014]

[0020] Flight path 120 and predicted flight path 134 can be further defined by the time-based position profile of AV 110 along the flight path or predicted flight path. This time-based position profile can specify the actual or predicted time of arrival of the AV at any position along the flight path or predicted flight path, including initial position 122, destination 124, target destination 136, current position 126, and intermediate waypoints (e.g., 140, 142).

[0015]

[0021] FIG. 2 is a flowchart showing an exemplary method 200 that can be performed by a computing system of one or more computing devices to retrieve prediction data. As one example, the computing system 600 of FIGS. 6 and 7 can perform method 200.

[0016]

[0022] At 210, the method includes retrieving an initial set of prediction data for an aerial vehicle (AV) preflight. As an example, the initial set of prediction data can be retrieved via a communication network preflight. This communication network can include a wireless or wired communication network. The initial set of prediction data retrieved at 210 can encompass the operating environment of the AV, including the preflight position of the AV, the target destination of the AV, and the predicted flight path between the preflight position and the target destination. The operating environment of the AV, the preflight position, the target destination, and the predicted flight path can, as an example, be defined by flight plan data.

[0017]

[0023] In at least some embodiments, the initial set of prediction data retrieved at 210 can include initial prediction data for each cell of a set of cells within a three-dimensional cell array that forms a spatial representation of the operating environment of the AV. For example, each cell of the three-dimensional cell array can have a longitude position and / or range within a longitude dimension, a latitude position and / or range within a latitude dimension, and an altitude position and / or range within an altitude dimension, respectively.

[0018]

[0024] The initial set of prediction data can, as a plurality of examples, include weather prediction data and / or aviation prediction data. Within the context of the three-dimensional cell array described above, the initial prediction data for each cell of a set of cells of the array can include weather prediction data and / or aviation prediction data for that cell. The weather prediction data for each cell can include one or more of wind speed prediction (e.g., wind speed and direction), temperature prediction, precipitation prediction (e.g., type and / or magnitude), visibility prediction (e.g., distance), cloud cover prediction (e.g., type and / or magnitude), turbulence prediction, and / or other suitable forms of weather prediction data. The aviation prediction data for each cell can include predictions of other AVs predicted to be present in the cell.

[0019]

[0025] At 212, the method includes identifying the current operating state 211 of the in-flight AV. The current operating state may include, as a plurality of examples, the current position (e.g., longitude, latitude, and altitude), trajectory, and speed of the AV (e.g., measured as the ground speed). The current operating state may be identified based on sensor data received from sensors mounted on the AV in one example. Additionally or alternatively, the current operating state may be identified based on user input received via a user interface of the computing system.

[0020]

[0026] At 214, the method includes determining a three-dimensional predicted flight path 215 of the AV through the operating environment between the current position of the AV and the AV's target destination. The predicted flight path may be determined at 214 based on flight plan data 209. Additionally or alternatively, the predicted flight path may be determined based on the current operating state 211 of the AV identified at 212.

[0021]

[0027] In at least some examples, the predicted flight path may be defined by a function that defines a three-dimensional path of movement between the current position and the target destination. The three-dimensional path of the movement function may be defined by one or more waypoints along the predicted flight path between the current position and the target destination. The current position, target destination, and each waypoint may be represented by respective positions in three-dimensional space. In one example, each position in three-dimensional space may be represented by respective longitude values, latitude values, and altitude values.

[0022]

[0028] As shown at 216, the predicted flight path includes an altitude profile 217 of the AV over a geographic area through the operating environment and a time-based position profile 219 of the AV along the predicted flight path. In one example, the time-based position profile identifies the estimated times of arrival of the AV at the target destination and / or one or more waypoints along the predicted flight path.

[0023]

[0029] In at least some embodiments, the predicted flight path may include the primary approach and landing procedures of the AV at the target destination. As will be described in more detail with reference to operation 230, one or more alternative flight paths may be determined for the AV with respect to the predicted flight path. As an example, an alternative flight path may include alternative approach and landing procedures of the AV at the target destination and / or a detour flight path to the target destination that is different from the predicted flight path. As another example, an alternative flight path may include a detour flight path to an alternative target destination that is different from the target destination.

[0024]

[0030] At 218, the method includes determining whether a prediction update time interval 221 has elapsed. As an example, the prediction update time interval defines the duration since the last retrieval of prediction data. This may occur prior to flight at 210 or during flight of the AV as will be described in more detail at 236. The prediction update time interval may be stored in the computing system as prediction update interval data and may take the form of a setting that can be defined by the user. As exemplary embodiments, the prediction update time interval may define the duration in units of time (e.g., 2 hours, 4 hours, etc.) and / or minutes (e.g., 30 minutes). Additionally or alternatively, the prediction update time interval may take the form of a qualitative setting (e.g., high, medium, low) corresponding to a predefined duration.

[0025]

[0031] At 220, the method includes determining whether the predicted flight path determined at 214 has changed. As an example, the current operating state determined at 212 can be compared, for example, with the predicted flight path defined by the flight plan, to determine whether the AV has deviated from the predicted flight path. The deviation of the AV from the predicted flight path can include a spatial deviation (e.g., latitude, longitude, and / or altitude) and / or a temporal deviation (e.g., based on the speed and / or current position of the AV relative to a time-based position profile). As another example, the flight plan data can be updated by a process implemented on a user or other computer to identify different predicted flight paths, and the change in the predicted flight path can be determined at 220 based on the update of the flight plan data. Operation 220 can return to operation 212 as part of a process loop.

[0026]

[0032] At 222, the method includes executing a prediction update process for the AV during flight. As a first example, the prediction update process can be executed at 222 in response to determining that the prediction update interval has expired at 218. In this example, when the prediction update interval expires, the prediction update process can be executed at 222. As a second example, the prediction update process can be executed at 222 in response to determining that the predicted flight path of the AV has changed at 220. In this example, when it is determined that the predicted flight path of the AV has changed during flight, the prediction update process can be executed at 222.

[0027]

[0033] As part of executing the prediction update process at 222, the method includes, at 224, identifying a set of cells 225 within a three-dimensional cell array that forms a spatial representation of the operating environment based on the predicted flight path. As an example, each cell of the three-dimensional cell array can have a longitude position and / or range within a longitude dimension, a latitude position and / or range within a latitude dimension, and an altitude position and / or range within an altitude dimension, respectively.

[0028]

[0034] During the flight of the AV, prediction data can be retrieved for a set of cells identified in operation 224 within the three-dimensional cell array without retrieving prediction data for all cells of the three-dimensional cell array. For example, the initial prediction data retrieved prior to flight in operation 210 may include a wider range of cells of the three-dimensional cell array compared to a set of cells identified in operation 224 for the retrieval of prediction data during the flight of the AV. This approach may limit the amount of prediction data retrieved during the flight of the AV based on the predicted flight path, thereby reducing the amount of prediction data transferred to the AV via the wireless network during flight.

[0029]

[0035] A set of cells identified within the three-dimensional cell array in 224 may include a plurality of cells arranged along the predicted flight path. For example, in 226, the method may include identifying a plurality of cells arranged along the predicted flight path as part of identifying a set of cells in 224. In this example, the plurality of cells identified in 226 as part of the set of cells may include a plurality of cells that the AV is expected to pass through while moving along the predicted flight path.

[0030]

[0036] In at least some embodiments, a set of cells identified within the three-dimensional cell array in 224 may further include a plurality of cells disposed within a threshold proximity to the predicted flight path. For example, in 228, the method may include identifying a plurality of cells disposed within a threshold proximity to the predicted flight path as part of identifying a set of cells in 224. In this example, one or more cells that are within a threshold proximity to the predicted flight path and extend outwardly in each direction (e.g., longitude, latitude, and altitude directions) from the cells identified in 226 may be identified in 228 to be included in the set of cells. The additional cells identified in 228 may provide a wider range of cells than the cells identified in 226, thereby encompassing a limited region surrounding the predicted path of movement.

[0031]

[0037] The proximity threshold applied in operation 228 can be stored as data within the computing system and can take the form of settings that can be defined by a user. In at least some embodiments, the proximity threshold can be defined on a per dimension basis. Thereby, a first proximity threshold is defined in the longitude and latitude dimensions, and a second proximity threshold is defined in the altitude dimension. As an exemplary embodiment, the first proximity threshold can be defined in the longitude and latitude dimensions as a first distance (e.g., 10 miles or kilometers) or a first cell quantity (e.g., 1, 5, 10, 20, etc.) of a given cell size, and the second proximity threshold can be defined in the altitude dimension as a second distance (e.g., a thousand feet or meters, three thousand feet or meters, etc.) or a second cell quantity.

[0032]

[0038] As part of operation 222, the method may further include, at 230, executing a supplemental flight plan process to identify a plurality of supplemental cells within the three-dimensional cell array, as will be described in more detail with reference to FIG. 3. The plurality of supplemental cells can be identified, as an example, as part of operation 230 for alternative flight paths and / or alternative approach and landing procedures.

[0033]

[0039] As part of operation 222, the method includes, at 232, for each cell of the set of cells identified at 224, determining, as shown at 234, a cell-specific predicted time 233 for the cell based on the time-based position profile of the AV. In at least some embodiments, the cell-specific predicted time for a cell corresponds to the predicted time of arrival of the AV at that cell. As another example, the cell-specific predicted time for a cell corresponds to the predicted time of arrival of the AV within the proximity threshold of that cell.

[0034]

[0040] At 236, the method includes retrieving prediction data for a cell corresponding to a cell-specific prediction time. Multiple examples of retrieving prediction data are described in more detail with reference to FIG. 6. Briefly, for each cell identified in operation 224 and / or operation 230, the prediction data for that cell is retrieved by one or more computing devices mounted on the AV from one or more remote computing devices via a wireless network. Retrieving the prediction data may include one or more computing devices mounted on the AV sending one or more requests to one or more remote computing devices via a wireless communication link.

[0035]

[0041] At 238, the method includes outputting the prediction data retrieved for a cell for each cell of a set of cells identified in operation 224 and / or for each cell of a set of supplementary cells identified in operation 230. As an example, the prediction data for each cell may be output via a graphical user interface presented on a display device.

[0036]

[0042] FIG. 3 is a flowchart showing another exemplary method 300 that may be performed by a computing system of one or more computing devices to retrieve prediction data. Method 300 corresponds to a supplementary flight plan process previously described with reference to operation 230 of method 200 in FIG. 2. This supplementary flight plan process may be performed to determine one or more alternative flight paths and associated cells for prediction data retrieval that are different from the predicted flight path identified in operation 214 of method 200.

[0037]

[0043] At 310, the method includes determining whether one or more alternative flight paths should be determined for the AV. As an example, in response to user input, a reroute recommendation, a reroute command, or an indication of an event received by a computing system, it may be determined that one or more alternative flight paths are required for the AV. At 310, if it is determined that one or more alternative flight paths should be determined for the AV, the method may proceed to one or more of operations 312 and / or 320.

[0038]

[0044] An alternative flight path may be determined for the same target destination as the predicted flight path. For example, at 312, the method includes determining a three-dimensional alternative flight path 313 for the AV through the operating environment between the current position and the target destination based on one or more of alternative flight plan data, the current operating state of the AV, and / or event data identifying an event. A plurality of examples of events may include weather events or air traffic events located along or within a threshold proximity of the predicted flight path, events occurring on the AV (e.g., an AV failure event, a medical event, etc.), a change in the approach and landing procedures at the target destination, and the like.

[0039]

[0045] An alternative flight path may be determined at 312 for the predicted flight path as previously described in 214 using alternative flight plan data, the current operating state of the AV, and event data. In at least some embodiments, the alternative flight plan data may identify or otherwise define an alternative flight path and may include information similar to the flight plan data previously described with reference to method 200 of FIG. 2.

[0040]

[0046] As shown at 314, an alternative flight path can be determined for a recommended or commanded deviation of the AV from a predicted flight path to the same target destination as the predicted flight path. As one example, an alternative flight path can be determined for an alternative approach and landing procedure to the target destination, as shown at 316. As another example, as shown at 318, an alternative flight path can be determined for a temporary deviation from the predicted flight path due to weather, air traffic, or other conditions identified by event data.

[0041]

[0047] Additionally or alternatively, an alternative flight path can be determined for an alternative target destination that is different from the target destination of the predicted flight path. For example, at 320, the method includes determining a three-dimensional alternative flight path 321 of the AV through the operating environment between the current location and the target destination based on one or more of alternative flight plan data, the current operating state of the AV, and / or event data that identifies an event.

[0042]

[0048] An alternative flight path can be determined at 320, as previously described at 214 for the predicted flight path, using alternative flight plan data, the current operating state of the AV, and event data. As one example, an alternative flight path can be determined for an approach and landing procedure to the alternative target destination, as shown at 322. As previously described while referring to operation 312, an alternative flight path can be determined for a deviation from the predicted flight path due to weather, air traffic, or other conditions identified by event data.

[0043]

[0049] In 324, the method includes identifying a set of supplementary cells within a three-dimensional cell array that forms a spatial representation of the operating environment based on an alternative flight path. Operation 324 may be performed to identify a set of supplementary cells for the alternative flight path as previously described while referring to operation 224 of method 200 of FIG. 2 for the predicted flight path. The set of supplementary cells for the alternative flight path may be used in operation 232 to retrieve prediction data for each supplementary cell identified in 324. This prediction data corresponds to the cell-specific predicted time determined for that cell.

[0044]

[0050] FIG. 4 is a graph 400 showing the relationship between altitude, geographic distance, and cell-specific predicted time for an exemplary predicted flight path of an AV.

[0045]

[0051] Graph 400 shows the altitude of the AV along the vertical axis. Graph 400 further shows geographic distance and time along the horizontal axis. The geographic distance within graph 400 may include components of the longitude dimension and the latitude dimension. In this example, a plurality of cells of the three-dimensional cell array that forms the spatial representation of the operating environment have a size of 1000 feet in the altitude dimension of the vertical axis and a size of 500 miles in the geographic distance dimension of the horizontal axis. The cell dimensions of graph 400 are provided for illustrative purposes, and it will be understood that the plurality of cells may have other suitable dimensions to provide a desired level of spatial resolution.

[0046]

[0052] The current position 410 of the AV is shown within graph 400 along line 412. Line 412 includes an actual flight path component 414 representing the actual flight path of the AV and a predicted flight path component 416 representing the predicted flight path of the AV. In this example, the actual flight path component 414 of line 412 extends from an initial position 418 (e.g., a first airport) at an earlier time (e.g., -5 hours) to the current position 410 at the current time (e.g., 0 hours). Further, in this example, the predicted flight path component 416 of line 412 extends from the current position 410 at the current time (e.g., 0 hours) to a target destination 420 (e.g., a second airport) at a future time (e.g., +5.5 hours).

[0047]

[0053] Line 412 represents the altitude profile of the AV over a geographic area related to the geographic distance traveled by the AV through the operating environment. In this example, the initial position 418 of the AV has an initial altitude (e.g., 0 feet), the current position 410 of the AV has a current altitude (e.g., 29,000 feet), and the target destination 420 has a destination altitude (e.g., 0 feet). Further, in this example, the altitude profile of the actual flight path component 414 represents the ascent of the AV's altitude from the initial position 418 to the cruising altitude (e.g., 29,000 feet), and the predicted flight path component 416 represents the descent of the AV's altitude from the cruising altitude to the destination altitude.

[0048]

[0054] Further, line 412 represents the time-based position profile of the AV along the actual and predicted flight paths. For example, each position of the AV in the three-dimensional space along line 412 has a defined time, as defined in the altitude dimension and the geographic distance dimension. At the defined time, the AV is at its position for the actual flight path or is expected to arrive at its position for the predicted flight path.

[0049]

[0055] Within graph 400, as represented by component 416 of the predicted flight path of line 412, the predicted flight path of the AV from the current position 410 to the target destination 420 passes through the shaded region 422. This shaded region 422 identifies a set of cells 430. Prediction data for the set of cells 430 can be retrieved at the update intervals during the flight of the AV. As previously described with reference to method 200 of FIG. 2, the prediction data retrieved at the update intervals for the set of cells includes cell-specific prediction data corresponding to the cell-specific prediction times of each cell. As an example, each cell identified by the shaded region 422 may have a respective cell-specific prediction time corresponding to the expected time of arrival of the AV at or within threshold proximity to the position in that cell.

[0050]

[0056] In one example of graph 400, at some geographic distance in front of the AV, cells 432 corresponding to an altitude range of 29,000 feet to 30,000 feet may have a cell-specific prediction time of +2 hours in the future. As another example, at some further geographic distance in front of the AV, cells 434 corresponding to an altitude range of 16,000 feet to 17,000 feet may have a cell-specific prediction time of +5 hours in the future. Thus, in these examples, the cell-specific prediction data retrieved for cell 432 is for a first time (e.g., +2 hours), while the cell-specific prediction data retrieved for cell 434 is for a second time different from the first time (e.g., +5 hours).

[0051]

[0057] Referring to method 200 of FIG. 2, as previously described, the set of cells identified for predictive data search can include a plurality of cells disposed within a threshold proximity to the predicted flight path. In one embodiment of graph 400, a set of cells 430 identified by the shaded region 422 includes a plurality of cells disposed within a threshold proximity (e.g., 4000 feet) to the predicted flight path in the altitude dimension. For example, a plurality of cells disposed 4000 feet above and below the predicted flight path are included within the set of cells 430 identified by the shaded region 422. The set of cells 430 identified by the shaded region 422 also includes a plurality of cells disposed within a threshold proximity to the predicted flight path in the geographic dimension. As previously described, the geographic dimension can have a longitude component and a latitude component. It will be understood that other suitable threshold proximities can be used in the altitude dimension, longitude dimension, and latitude dimension from those shown by graph 400.

[0052]

[0058] By including a plurality of cells disposed within a threshold proximity to the predicted flight path for predictive data search, predictive data can be searched for actual or potential deviations from the predicted flight path by the AV and made available during flight. For example, an AV operator can determine whether altitude changes should be performed based on the searched predictive data for the predicted altitude and for altitudes disposed above and below the predicted altitude. The threshold proximity can be defined in the altitude, longitude, and latitude dimensions to provide an appropriate spatial buffer around the predicted flight path, while also excluding a plurality of cells disposed beyond the threshold proximity from the predictive data search. For example, predictive data is not searched for a plurality of cells disposed outside the shaded region 422 in graph 400 at the predictive update interval at which predictive data is searched for the set of cells 430 identified by the shaded region 422.

[0053]

[0059] FIG. 5 schematically shows an exemplary graphical user interface (GUI) 500 through which predictive data can be output. As an example, the GUI 500 can be output via a computing device mounted in an AV.

[0054]

[0060] In this example, the GUI 500 includes a geographical representation 510 of a geographical area. The geographical representation 510 provides a map of part or all of the operating environment of the AV in longitude and latitude dimensions. The geographical representation 510 includes a line 512 representing the predicted flight path of the AV. The geographical representation 510 further includes a plurality of predictive data items along the predicted flight path. One example of this item further includes a predictive data item 514. Each predictive data item can include a graphical representation of cell-specific predictive data for each cell. The predictive data corresponds to the cell-specific prediction time for that cell.

[0055]

[0061] Further, in this example, the GUI 500 includes an altitude representation 520. The altitude representation 520 provides a map of part or all of the operating environment of the AV in altitude and geographical distance dimensions. As previously described with reference to FIG. 4, the geographical distance dimension can include components of the longitude dimension and the latitude dimension. The altitude representation 520 includes a line 522 representing the predicted flight path of the AV. The altitude representation 520 further includes a plurality of predictive data items along the flight path. One example of this item includes a predictive data item 524. As previously described, each predictive data item can include a graphical representation of cell-specific predictive data for each cell. The predictive data corresponds to the cell-specific prediction time for that cell.

[0056]

[0062] It will be understood that the GUI 500 is provided as an exemplary example of the predictive data to be output, and other suitable graphical representations and / or other non-visual forms of output can be used to present the predictive data.

[0057]

[0063] FIG. 6 is a schematic diagram showing an exemplary computing system 600 that can execute the method 200 of FIG. 2 and the method 300 of FIG. 3.

[0058]

[0064] The computing system 600 includes a computing device 610 mounted on the exemplary AV 110 of FIG. 1. As an example, the computing device 610 can take the form of a personal computing device (e.g., a tablet computer, a laptop computer, or other mobile computer) operated by a flight attendant on board the AV 110. Alternatively, the computing device 610 can be integrated with the AV 110.

[0059]

[0065] The AV 110 may further include an integrated sensor 612, a first communication interface 614 that enables wireless communication via a first wireless link, a second communication interface 616 that enables wireless communication via a second wireless link different from the first wireless link, and one or more other computing devices 618.

[0060]

[0066] The sensor 612 can include sensors integrated with or mounted on the AV 110 that output sensor data. As previously described with reference to operation 212 of the method 200 of FIG. 2, the current operating state of the AV can be determined from this sensor data. The one or more other computing devices 618 can include computing devices integrated with and / or mounted on the AV 110.

[0061]

[0067] The first communication interface 614 enables communication between the computing device 610 and one or more remote computing devices 620 of the computing system 600 using a first wireless communication protocol and a network infrastructure. As an example, the first communication interface 614 can be used by the computing device 610 to communicate with the remote computing device 620 while the AV110 is located on the ground. For example, the first communication interface 614 can enable the computing device 610 to retrieve an initial set of prediction data about the AV110 from the remote computing device 620 prior to flight. Alternatively, the first communication interface 614 can be integrated with the computing device 610 and form part of the computing device 610.

[0062]

[0068] The second communication interface 616 enables communication between the computing device 610 and the remote computing device 620 using a second wireless communication protocol and a network infrastructure different from the first communication interface 614. As an example, the second communication interface 616 can be used by the computing device 610 to communicate with the remote computing device 620 during flight of the AV110. For example, the second communication interface 616 can enable the computing device 610 to retrieve prediction data about the AV110 from the remote computing device 620 during flight, as previously described with reference to operation 222 of FIG. 2. Alternatively, the second communication interface 616 can be integrated with the computing device 610 and form part of the computing device 610.

[0063]

[0069] In at least some embodiments, the second communication interface 616 takes the form of a satellite communication interface for communicating with an orbiting satellite, and the first communication interface 614 takes the form of a terrestrial communication interface for communicating with a terrestrial-based access point. In this embodiment or in multiple other embodiments, communication via the second communication interface 616 may be more costly, consume more resources, and / or have a lower bandwidth capacity compared to the first communication interface 614. The communication network 630 is schematically shown in FIG. 6. Wireless communication flows between the computing device 610 (or other computing device 618) mounted on the AV110 and the remote computing device 120 via the communication network 630.

[0064]

[0070] The computing device 610 includes an application program 640 and application data 642 stored therein. The application program 640 is executable by the computing device 610 to perform various operations and methods described herein with respect to the computing device. The application program 640 includes a user interface 644. The user interface 644 enables a user (e.g., an AV personnel) to interact with the application program. As an example, the user interface 644 may take the form of a GUI (e.g., the GUI 500 of FIG. 5) and / or other suitable user interface (e.g., an audio interface). The application program 640 can search, update, and store various data within the application data 642.

[0065]

[0071] As an example, the application data 642 may include one or more of a client identifier 646, flight plan data 648, search prediction data 650, setting data 652, a spatial representation 654 of the operating environment, and / or other data 656. The client identifier 646 can identify one or more of a user account of a user of the application program 640, the AV 110, and / or the application program 640, and can distinguish multiple clients from each other by the remote computing device 120. The flight plan data 648 may include the flight plan data previously described with reference to the method 200 of FIG. 2 and the supplementary flight plan data previously described with reference to the method 300 of FIG. 3. The search prediction data 650 may include the prediction data retrieved in operation 236 of the method 200 of FIG. 2. The setting data 652, as multiple examples, can define one or more of (1) the resolution of the spatial representation 654, including the size of each cell in the longitude dimension, latitude dimension, and altitude dimension, (2) various threshold proximities described herein for identifying cells for a given flight path or supplementary flight path and for determining cell-specific prediction times, and (3) the prediction update interval. The spatial representation 654 can define a three-dimensional cell array, such as that previously described with reference to the array 150 of FIG. 1 and operation 224 of the method 200 of FIG. 2.

[0066]

[0072] The remote computing device 620 may include, as an example, a ground-based server computing device that forms a server system. The remote computing device 620 includes a service program 660 and application data 662 stored therein. The service program 660 is executable by the remote computing device 620 to perform various operations and methods described herein with respect to the remote computing device. The service program 660 can search, update, and store various data within the service data 662.

[0067]

[0073] Service data 662 may include the flight plan data 663 of AV110. The service data 662 in the remote computing device 620 may refer to an instance of the flight plan data 648 in the computing device 610.

[0068]

[0074] Service data 662 may include prediction data 664, which includes search prediction data 666 of the computing device 610 and non-search prediction data 668 that has not been searched for the computing device 610. The search prediction data 666 in the remote computing device 620 may refer to an instance of the search prediction data 650 in the computing device 610.

[0069]

[0075] Service data 662 may include setting data 670 that may be associated with the client identifier 646 in the service data 662 to enable the remote computing device 620 to execute settings on behalf of the client identified by the client identifier 646. The setting data 670 in the remote computing device 620 may refer to an instance of the setting data 652 in the computing device 610.

[0070]

[0076] Service data 662 may include a spatial representation 672 of the operating environment. The spatial representation 672 in the remote computing device 620 may refer to an instance of the spatial representation 654 in the computing device 610. Service data 662 may include other data 674.

[0071]

[0077] As will be described by the following multiple embodiments, the method 200 of FIG. 2 and the method 300 of FIG. 3 may be executed by the computing system 600 in various ways according to the implementation manners.

[0072]

[0078] As a first embodiment, methods 200 and 300 may be mainly executed by computing device 110 through the execution of application program 640. As an example, application program 640 may identify the current operating state of the AV in operation 212, determine a predicted flight path in operation 214, determine alternative flight paths in operations 312 and 320, identify a set of cells in operation 224, identify a set of supplementary cells in operation 324, determine cell-specific prediction times in operation 234, search for prediction data in operation 236, and output the prediction data in operation 238.

[0073]

[0079] In this example, prediction data may be searched for in operation 236 by the computing device 110 sending one or more requests for the prediction data to the remote computing device 620. The one or more requests may identify a set of cells and / or a set of supplementary cells. For the set of cells and / or the set of supplementary cells, the prediction data is provided by the remote computing device 620 to the computing device 610. For each cell, the one or more requests may further identify the cell-specific prediction time for that cell. The one or more requests may further identify the client identifier 646. In response to the one or more requests, the service program 660 of the remote computing device 620 may send one or more responses including cell-specific prediction data corresponding to the cell-specific prediction times to the application program 640.

[0074]

[0080] As a second embodiment, methods 200 and 300 may be executed by a combination of the computing device 110 and the remote computing device 620 in a manner that transfers one or more operations of methods 200 and 300 from the computing device 110 to the remote computing device 620.

[0075]

[0081] As a first example of the second embodiment, the application program 640 may subscribe to prediction updates from the service program 660 (e.g., at a prediction update interval) by sending a subscription request that includes a client identifier 646. The subscription request may further include one or more of flight plan data 648 and setting data 652. In some examples, the flight plan data and the setting data may be predefined and stored in the remote computing device 620 in relation to the client identifier 646 without the flight plan data and the setting data being attached to the subscription request. In response to the subscription request, the service program 660 may periodically (e.g., at a prediction update interval) search for prediction data and send the retrieved prediction data to the application program 640. Further, the service program 660 may identify the current operating state of the AV in operation 212 (e.g., from a radar and / or other third-party data source), determine a predicted flight path in operation 214, determine alternative flight paths in operations 312 and 320, identify a set of cells in operation 224, identify a set of supplementary cells in operation 324, determine cell-specific prediction times in operation 234, search for prediction data in operation 236, and output the prediction data in operation 238 by sending the prediction data to the application program 640 as one or more responses.

[0076]

[0082] As a second example of the second embodiment, the application program 640 may send one or more requests for prediction data to the service program 660 (e.g., at a prediction update interval). The one or more requests may include a client identifier 646. In response to the one or more requests, the service program 660 may process and send one or more responses that include prediction data to the application program 640 as previously described above for the first example of the second embodiment.

[0077]

[0083] As a third example of the second embodiment, the application program 640 may send one or more requests for prediction data to the service program 660 (e.g., at the prediction update interval). In that case, the one or more requests include pre - processed data including one or more of the client identifier 646, the current state of the AV, the expected flight path, alternative flight paths, and / or the configuration data 652. In response to the one or more requests, the service program 660 may utilize the pre - processed data and / or process data associated with the request to identify the cells where the prediction data should be retrieved by the service program 660. The service program 660 may send one or more responses including the prediction data retrieved by the service program to the application program 640.

[0078]

[0084] As described above, the methods and operations described herein may be associated with a computing system of one or more computing devices. In particular, such methods and operations may be implemented as a computer application program or service, an application programming interface (API), a library, and / or other computer program products.

[0079]

[0085] FIG. 7 schematically shows an example of the computing system 600 of FIG. 6 that may execute one or more of the methods and operations described herein. The computing system 600 is shown in a simplified form in FIG. 7. The computing system 600 may take the form of one or more personal computers, server computers, tablet computers, network computing devices, mobile computing devices, mobile communication devices (e.g., smartphones), and / or other computing devices.

[0080]

[0086] Computing system 600 includes a logic machine 710, a computer-readable storage machine 712, and an input / output subsystem 714. The logic machine 710 includes one or more physical devices configured to execute a plurality of instructions 716 stored within the storage machine 712. For example, the logic machine may be configured to execute a plurality of instructions. The plurality of instructions may be part of one or more applications, services, programs, routines, libraries, objects, components, data structures, or other logical constructs. Such a plurality of instructions may be implemented to perform work, implement data types, transform the state of one or more components, achieve a technical effect, or otherwise reach a desired result.

[0081]

[0087] The logic machine may include one or more processor devices configured to execute a plurality of software instructions. The plurality of instructions 716 may include, as a plurality of examples, an application program 640 and a service program 660. Further or alternatively, the logic machine may include one or more hardware or firmware logic machines configured to execute a plurality of hardware or firmware instructions. The processor of the logic machine may be single-core or multi-core, and the plurality of instructions executed by the processor may be configured to be processed sequentially, in parallel, and / or distributively. The individual components of the logic machine may optionally be distributed across two or more separate devices. These devices may be located remotely and / or configured for coordinated processing. The plurality of aspects of the logic machine may be virtualized and executed by a remotely accessible network computing device configured as a cloud computing configuration.

[0082]

[0088] Storage machine 712 includes one or more physical devices configured to hold a plurality of instructions 716 and other data 718 executable by a logic machine for implementing the methods and operations described herein. When such methods and operations are implemented, storage machine 712 may be transformed, for example, to hold various data. Data 718 may include application data 642 and service data 662 of FIG. 6.

[0083]

[0089] The storage machine may include removable and / or built-in devices. The storage machine may include, among other things, optical memory (e.g., CD, DVD, HD-DVD, Blu-Ray Disc, etc.), semiconductor memory (e.g., RAM, EPROM, EEPROM, etc.), and / or magnetic memory (e.g., hard disk drive, floppy disk, tape drive, MRAM, etc.). The storage subsystem may include volatile, non-volatile, dynamic, static, read / write, read-only, random access, sequential access, location-addressable, file-addressable, and / or content-addressable devices.

[0084]

[0090] It will be appreciated that the storage machine includes one or more physical devices. However, alternative aspects of the plurality of instructions described herein may be propagated by a communication medium (e.g., electromagnetic signal, optical signal, etc.) that is not held by a physical device for a finite duration.

[0085]

[0091] Aspects of logic machine 710 and storage machine 712 may be integrated within one or more hardware logic components. Such hardware logic components may include, for example, field programmable gate arrays (FPGA), application specific integrated circuits (ASIC), application specific standard products (ASSP), system on chips (SOC), and complex programmable logic devices (CPLD).

[0086]

[0092] The terms "module", "program", and "engine" may be used to describe multiple aspects of a computing system 600 implemented to perform a particular function. In some cases, a module, program, or engine may be instantiated via a logic machine 710 that executes a plurality of instructions held by a storage machine 712. It will be understood that various modules, programs, and / or engines may be instantiated from the same application, service, code block, object, library, routine, API, function, etc. Similarly, the same module, program, and / or engine may be instantiated by various applications, services, code blocks, objects, routines, APIs, functions, etc. The terms "module", "program", and "engine" may encompass individual ones or groups such as executable files, data files, libraries, drivers, scripts, database records, etc.

[0087]

[0093] As used herein, it will be understood that a "service" is an application program executable over multiple user sessions. A service may be available to one or more system components, programs, and / or other services. In some embodiments, a service may be executed on one or more server computing devices.

[0088]

[0094] The input / output subsystem 714 includes one or more input devices and one or more output devices. As an example, the input / output subsystem 714 may include or be connected to a display device used to present a visual representation of data held by the storage machine 712. This visual representation may take the form of a graphical user interface. When the methods and operations described herein change the data held by the storage machine and thus transform the state of the storage machine, the state of the display device may similarly be transformed to visually represent the underlying data change. The input / output subsystem 714 may include or interact with one or more other input devices such as a keyboard, mouse, touch screen, or game controller. In some embodiments,

[0089]

[0095] the input / output subsystem 714 may include a communication subsystem that communicatively couples the computing system 600 to one or more other computing devices. The communication subsystem may include wired and / or wireless communication devices compatible with one or more various communication protocols. As examples, the communication subsystem may be configured for communication via a wired or wireless local or wide area network. In some examples, the communication subsystem may enable the computing system 600 to send and / or receive messages to and from other devices via a network such as the Internet.

[0090]

[0096] Further, the present disclosure includes a plurality of configurations according to the following clauses.

[0091]

[0097] Clause 1. A method executed by a computing system of one or more computing devices, comprising executing a prediction update process of an aerial vehicle (AV) during flight, wherein the prediction update process determines a three-dimensional predicted flight path of the AV through an operating environment between a current position and a target destination based on flight plan data, the predicted flight path including an altitude profile of the AV above a geographical area through the operating environment and a time-based position profile of the AV along the predicted flight path, determining the predicted flight path, identifying a set of cells in a three-dimensional cell array forming a spatial representation of the operating environment based on the predicted flight path, and for each cell of the set of cells, determining a cell-specific prediction time for the cell based on the time-based position profile of the AV, searching for prediction data for the cell corresponding to the cell-specific prediction time, and outputting the prediction data retrieved for the cell.

[0092]

[0098] Clause 2. The method according to clause 1, wherein the prediction update process for the AV is executed while the AV is moving along an actual flight path towards the target destination.

[0093]

[0099] Clause 3. The method according to clause 1 or 2, wherein the prediction update process for the AV is executed at prediction update time intervals while the AV is moving along an actual flight path towards the target destination.

[0094]

[0100] Clause 4. The method according to clause 3, wherein the prediction update time interval is defined by a user as a setting stored in the computing system.

[0095]

[0101] Clause 5. The method according to any one of clauses 1 to 4, wherein the computing device of the computing system is mounted on the AV.

[0096]

[0102] Clause 6. For each cell of the set of cells, retrieving the prediction data for the cell corresponding to the prediction time specific to the cell comprises transmitting one or more requests from the computing device mounted on the AV to a remote computing device via a wireless communication link, and receiving, at the computing device mounted on the AV via the wireless communication link, one or more responses from the remote computing device that include the prediction data for the cell, the method according to clause 5.

[0097]

[0103] Clause 7. The prediction time specific to the cell is determined for each cell based on the predicted time of arrival of the AV at the cell, the method according to any one of clauses 1 to 6.

[0098]

[0104] Clause 8. The prediction time specific to the cell is determined for each cell based on the predicted time of arrival of the AV at a position within a threshold proximity to the cell, the method according to any one of clauses 1 to 7.

[0099]

[0105] Clause 9. The set of cells identified includes a plurality of cells arranged along the predicted flight path, the method according to any one of clauses 1 to 8.

[0100]

[0106] Clause 10. The set of cells identified further includes a plurality of cells arranged within a threshold proximity to the predicted flight path, and the set of cells does not include a plurality of cells arranged beyond the threshold proximity to the predicted flight path, the method according to clause 9.

[0101]

[0107] Clause 11. Determining an alternative flight path of the AV in three dimensions through the operating environment between the current position and the target destination or an alternative target destination; identifying a set of supplementary cells in a three-dimensional cell array that forms a spatial representation of the operating environment based on the alternative flight path; and for each cell in the set of supplementary cells, determining a cell-specific prediction time for the cell, retrieving prediction data for the cell corresponding to the cell-specific prediction time, and outputting the retrieved prediction data for the cell, the method according to any one of clauses 1 to 10.

[0102]

[0108] Clause 12. The prediction data includes weather prediction data and / or aviation prediction data, the method according to any one of clauses 1 to 11.

[0103]

[0109] Clause 13. A computing system of one or more computing devices, comprising a logic machine and a storage machine storing a plurality of instructions, the plurality of instructions being executable by the logic machine to perform a prediction update process of an aerial vehicle (AV) during flight, the prediction update process determining an expected flight path of the AV in three dimensions through the operating environment between the current position and the target destination based on flight plan data, the expected flight path including an altitude profile of the AV over a geographical area through the operating environment and a time-based position profile of the AV along the expected flight path, determining the expected flight path, identifying a set of cells in a three-dimensional cell array that forms a spatial representation of the operating environment based on the expected flight path, and for each cell in the set of cells, determining a cell-specific prediction time for the cell based on the time-based position profile of the AV, retrieving prediction data for the cell corresponding to the cell-specific prediction time, and outputting the retrieved prediction data for the cell.

[0104]

[0110] Article 14 The AV's prediction update process is executed at a prediction update time interval by the computing system described in Article 13.

[0105]

[0111] Article 15 For each cell, the cell-specific prediction time is determined based on the predicted arrival time of the AV at the cell by the computing system described in Article 13 or 14.

[0106]

[0112] Article 16 For each cell, the cell-specific prediction time is determined based on the predicted arrival time of the AV at a position within a threshold proximity to the cell by the computing system according to any one of Articles 13 to 15.

[0107]

[0113] Article 17 The identified set of cells includes a plurality of cells arranged along the predicted flight path and a plurality of cells arranged within a threshold proximity to the predicted flight path, and does not include a plurality of cells arranged beyond the threshold proximity to the predicted flight path, by the computing system according to any one of Articles 13 to 16.

[0108]

[0114] Article 18 The plurality of instructions are further executable by the logic machine to determine an alternative flight path of the three-dimensional AV through the operating environment between the current position and the target destination or an alternative target destination, identify a set of supplementary cells in a three-dimensional cell array that forms a spatial representation of the operating environment based on the alternative flight path, and for each cell in the set of supplementary cells, determine a cell-specific prediction time for the cell, search for prediction data for the cell corresponding to the cell-specific prediction time, and output the prediction data retrieved for the cell, by the computing system according to any one of Articles 13 to 17.

[0109]

[0115] Article 19. The prediction data includes weather prediction data and / or aviation prediction data, and is a computing system according to any one of Articles 13 to 18.

[0110]

[0116] Article 20. A manufactured product comprising a computer-readable storage machine storing a plurality of instructions, the plurality of instructions being executable by a computing system to perform a prediction update process of an aircraft vehicle (AV) during flight, the prediction update process comprising determining a three-dimensional predicted flight path of the AV through an operating environment between a current position and a target destination based on flight plan data, the predicted flight path including an altitude profile of the AV over a geographical area through the operating environment and a time-based position profile of the AV along the predicted flight path; determining a set of cells in a three-dimensional cell array forming a spatial representation of the operating environment based on the predicted flight path; and for each cell of the set of cells, determining a cell-specific prediction time for the cell based on the time-based position profile of the AV, searching for prediction data for the cell corresponding to the cell-specific prediction time, and outputting the prediction data retrieved for the cell.

[0111]

[0117] The configurations and / or approaches described herein are exemplary in nature, and it should be understood that these specific embodiments or examples should not be considered in a limiting sense. This is because numerous modifications are possible. The specific routines or methods described herein may represent one or more of any number of processing strategies. In that way, the various operations illustrated and / or described may be performed in other orders, concurrently, or omitted, in the order illustrated and / or described. Similarly, the order of the processes described above may be changed.

[0112]

[0118] The subject matter of the present disclosure includes all novel and non-obvious combinations and sub-combinations of the various processes, systems, and configurations disclosed herein, as well as all other features, functions, operations, and / or characteristics, and any and all equivalents thereof.

Explanation of Signs

[0113] 100 Operating environment 110 AV 120 Flight path 122 Initial position 124 Destination 126 Current position 130 Geographic area 132 Altitude 134 Predicted flight path 136 Target destination 140, 142 Exemplary waypoints 144 Waypoint 150 Three-dimensional cell array 152 Spatial representation 154 Longitude dimension 156 Latitude dimension 158 Altitude dimension 160 Exemplary cell 200 Method 209 Flight plan data 211 Current operating state 215 Predicted flight path 217 Altitude profile 219 Time-based position profile 221 Prediction update time interval 225 Set of cells 230 Operation 233 Cell-specific prediction time 300 Method 313 Alternative flight path 321 Alternative flight path 400 Graph 410 Current position 412 Line 414 Components of the actual flight path 416 Components of the predicted flight path 418 Initial position 420 Target destination 422 Diagonal area 500 GUI 510 Geographical representation 512 Line 514 Predicted data item 520 Altitude representation 522 Line 524 Predicted data item 600 Computing system 610 Computing device 612 Integrated sensor 614 First communication interface 616 Second communication interface 618 Other computing devices 620 Remote computing device 630 Communication network 640 Application program 642 Application data 644 User interface 646 Client identifier 648 Flight plan data 650 Search prediction data 652 Configuration data 654 Spatial representation 656 Other data 660 Service program 662 Service data 663 Flight plan data 664 Prediction data 666 Search prediction data 668 Non-search prediction data 670 Configuration data 672 Spatial representation 674 Other data 710 Logic machine 712 Computer-readable storage machine 714 Input / output subsystem 716 Multiple instructions 718 Other data

Claims

1. A method (200, 300) performed by a computing system (600) of one or more computing devices (610, 620), comprising: performing a predictive update process (222) for an air vehicle (AV) (110) during flight, the predictive update process (222) comprising: determining a projected flight path (134) for the AV (110) in three dimensions through an operating environment (100) between a current position (126) and a target destination (124) based on flight plan data (648, 663), determining a predicted flight path (134), the predicted flight path (134) including an altitude profile (217) of the AV (110) over a geographic region (130) through the operating environment (100) and a time-based position profile (219) of the AV (110) along the predicted flight path (134); identifying a set of cells (430) within a three-dimensional cell array (150) that forms a spatial representation (152) of the operating environment (100) based on the predicted flight path (134); and For each cell (160) of said set of cells (430): determining a cell-specific predicted time (233) for the cell (160) based on the time-based location profile (219) of the AV (110); retrieving forecast data (650, 666) for said cell (160) corresponding to said cell-specific forecast time (233); and outputting the predicted data (650, 666) retrieved for the cell (160).

2. 2. The method of claim 1, wherein the predictive update process for the AV is performed while the AV is traveling along an actual flight path toward the target destination.

3. 3. The method (200, 300) of claim 2, wherein the predictive update process (222) for the AV (110) is performed at predictive update time intervals (221) while the AV (110) is traveling along an actual flight path (414) toward the target destination (124).

4. The method (200, 300) of claim 3, wherein the predictive update time interval (221) is defined by a user as a setting (652) stored in the computing system (600).

5. The method (200, 300) of claim 1, wherein the computing device (610) of the computing system (600) is mounted on the AV (110).

6. retrieving, for each cell (160) of the set of cells (430), the forecast data (650, 666) for the cell (160) corresponding to the cell-specific forecast time (233), comprising: transmitting one or more requests from the computing device (610) mounted on the AV (110) to a remote computing device (620) via a wireless communication link (630); and 6. The method (200, 300) of claim 5, further comprising receiving, at the computing device (610) mounted on the AV (110), one or more responses including the forecast data (650, 666) for the cell (160) from the remote computing device (620) via the wireless communication link (630).

7. 2. The method (200, 300) of claim 1, wherein the cell-specific predicted time (233) is determined for each cell (160) based on an expected time of arrival of the AV (110) to the cell (160).

8. 2. The method of claim 1, wherein the cell-specific predicted time is determined for each cell based on an expected time of arrival of the AV at a location within a threshold proximity to the cell.

9. 2. The method of claim 1, wherein the identified set of cells comprises a plurality of cells located along the projected flight path.

10. the identified set of cells (430) further includes a plurality of cells located within a threshold proximity to the projected flight path (134); 10. The method (200, 300) of claim 9, wherein the set of cells (430) does not include a plurality of cells located beyond the threshold proximity to the projected flight path (134).

11. determining an alternative flight path (313) for the AV (110) in three dimensions through the operating environment (100) between the current position (126) and the target destination (124) or an alternative target destination (124); identifying a set of supplemental cells within a three-dimensional cell array (150) forming a spatial representation (152) of the operating environment (100) based on the alternative flight paths (313); and For each cell (160) of said set of supplemental cells: determining a cell-specific predicted time (233) for said cell (160); retrieving forecast data (650, 666) for said cell (160) corresponding to said cell-specific forecast time (233); and The method (200, 300) of claim 1, further comprising outputting the retrieved predictive data (650, 666) for the cell (160).

12. The method (200, 300) of claim 1, wherein the forecast data (650, 666) comprises weather forecast data (240) and / or aviation forecast data (242).

13. A computing system (600) of one or more computing devices (610, 620), comprising: A logic machine (710), and a storage machine (712) having a plurality of instructions (716) stored thereon, the plurality of instructions (710) comprising: Executable by the logic machine (710) to perform a predictive update process (222) for an air vehicle (AV) (110) during flight, the predictive update process (222) comprising: determining a projected flight path (134) for the AV (110) in three dimensions through an operating environment (100) between a current position (126) and a target destination (124) based on flight plan data (648, 663), determining a predicted flight path (134), the predicted flight path (134) including an altitude profile (217) of the AV (110) over a geographic region (130) through the operating environment (100) and a time-based position profile (219) of the AV (110) along the predicted flight path (134); identifying a set of cells (430) within a three-dimensional cell array (150) that forms a spatial representation (152) of the operating environment (100) based on the predicted flight path (134); and For each cell (160) of said set of cells (430): determining a cell-specific predicted time (233) for the cell (160) based on the time-based location profile (219) of the AV (110); retrieving forecast data (650, 666) for said cell (160) corresponding to said cell-specific forecast time (233); and and outputting the retrieved predictive data for the cell.

14. 14. The computing system (600) of claim 13, wherein the predictive update process (222) of the AV (110) is performed at predictive update time intervals (221).

15. 14. The computing system (600) of claim 13, wherein the cell-specific predicted time (233) is determined for each cell (160) based on an expected time of arrival of the AV (110) to the cell (160).

16. The computing system (600) of claim 13, wherein the cell-specific predicted time (233) is determined for each cell (160) based on an expected time of arrival of the AV (110) at a location within a threshold proximity to the cell (160).

17. the identified set of cells (430) includes a plurality of cells located along the predicted flight path (134) and a plurality of cells located within a threshold proximity to the predicted flight path (134); 14. The computing system (600) of claim 13, wherein the set of cells (430) does not include a plurality of cells located beyond the threshold proximity to the projected flight path (134).

18. The instructions (716) include: determining an alternative flight path (313) for the AV (110) in three dimensions through the operating environment (100) between the current position (126) and the target destination (124) or an alternative target destination (124); identifying a set of supplemental cells within a three-dimensional cell array (150) forming a spatial representation (152) of the operating environment (100) based on the alternative flight paths (313); and For each cell (160) of said set of supplemental cells: determining a cell-specific predicted time (233) for said cell (160); retrieving forecast data (650, 666) for said cell (160) corresponding to said cell-specific forecast time (233); and 14. The computing system (600) of claim 13, further executable by the logic machine (710) to: output the retrieved predictive data (650, 666) for the cell (160).

19. The computing system (600) of claim 13, wherein the forecast data (650, 666) comprises weather forecast data (240) and / or aviation forecast data (242).

20. An article of manufacture comprising a computer readable storage machine (712) having a plurality of instructions (716) stored thereon, The instructions (716) include: A method for performing a predictive update process (222) for an air vehicle (AV) (110) during flight, the predictive update process (222) being executable by a computing system (600), the predictive update process (222) comprising: determining a projected flight path (134) for the AV (110) in three dimensions through an operating environment (100) between a current position (126) and a target destination (124) based on flight plan data (648, 663), determining a predicted flight path (134), the predicted flight path (134) including an altitude profile (217) of the AV (110) over a geographic region (130) through the operating environment (100) and a time-based position profile (219) of the AV (110) along the predicted flight path (134); identifying a set of cells (430) within a three-dimensional cell array (150) that forms a spatial representation (152) of the operating environment (100) based on the predicted flight path (134); and For each cell (160) of said set of cells (430): determining a cell-specific predicted time (233) for the cell (160) based on the time-based location profile (219) of the AV (110); retrieving forecast data (650, 666) for said cell (160) corresponding to said cell-specific forecast time (233); and outputting the retrieved forecast data (650, 666) for the cell (160).

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