Optimized predictive retrieval of expected flight path of aviation vehicle

Abstract

The invention discloses an optimized predictive retrieval of an expected flight path of an aviation vehicle. In accordance with an embodiment of the present disclosure, a method performed by a computing system of one or more computing devices includes performing a predictive update process of an aviation vehicle (AV) during flight. The prediction update process includes: determining, in three dimensions, an expected flight path of the AV through an operating environment between a current location and a target destination location based on flight plan data; identifying a set of cells within a three-dimensional array of cells forming a spatial representation of the operating environment based on the expected flight path; and for each unit in the unit set: determining a unit-specific prediction time of the unit based on a time-based position profile of the AV defined by the expected flight path, retrieving prediction data of the unit corresponding to the unit-specific prediction time, and outputting the prediction data retrieved for the unit.

Description

Technical Field

[0001] The subject matter of the present disclosure generally relates to retrieving prediction data for an intended flight path of an aircraft vehicle. Background Art

[0002] Personnel operating airplanes and other aircraft vehicles rely on predictions of weather and aviation activities for navigation. During flight, prediction data can be downloaded to an on-board computing device via a wireless communication link. As an example, satellite-based communication can be used to wirelessly request and receive prediction data during the flight of an aircraft vehicle. In at least some instances, the wireless communication link used to retrieve prediction data during flight may be expensive, billed per data unit, and may be bandwidth-limited compared to a ground-based communication link. Summary of the Invention

[0003] According to an embodiment of the present disclosure, a method performed 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, in three dimensions, an expected flight path of the AV through an operating environment between a current location and a target destination location based on flight plan data. The expected flight path includes an altitude profile of the AV above a geographic region of the operating environment and a time-based position profile of the AV along the expected 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 expected flight path. For each cell in 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 retrieved prediction data for the cell.

[0004] This summary is provided to introduce in a simplified form some concepts that will be further described in the detailed description below. This summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter. Additionally, the claimed subject matter is not limited to implementations that solve any or all disadvantages noted in any part of this disclosure. Brief Description of the Drawings

[0005] Figure 1 An operating environment of an example aircraft vehicle is described.

[0006] Figure 2 Is a flowchart depicting an example method that can be performed by a computing system of one or more computing devices to retrieve prediction data.

[0007] Figure 3FIG. is a flowchart depicting another example method that may be executed by a computing system of one or more computing devices to retrieve prediction data.

[0008] Figure 4 FIG. is a diagram depicting the relationship between altitude, geographic distance, and cell-specific prediction time of an example expected flight path of an AV.

[0009] Figure 5 Schematically shows an example graphical user interface through which prediction data is output.

[0010] Figure 6 FIG. depicts that it can be executed Figure 2 and Figure 3 FIG. is a schematic diagram of an example computing system for the method.

[0011] Figure 7 FIG. depicts Figure 6 FIG. is a schematic diagram of additional aspects of the computing system. DETAILED DESCRIPTION

[0012] As briefly introduced above, personnel operating an aircraft rely on predictions of weather and aviation activities for navigation purposes. During flight, prediction data can be downloaded from a remote source to an on-board computing device via a wireless communication link. As an example, satellite-based communication can be used to wirelessly request and receive prediction data during an aircraft's flight. In at least some examples, the wireless communication link used to retrieve prediction data during flight may be expensive on a per-data-unit basis and may have limited bandwidth compared to a ground-based communication link.

[0013] In at least some examples, the amount of predicted data downloaded via a wireless communication link during an aircraft flight can be reduced by selectively retrieving predicted data for the expected flight path of the aircraft within a limited area. According to an example, during flight, a prediction update process can be performed by a computing system for the aircraft. The prediction update process includes determining, based on flight plan data, an expected flight path of the AV through an operating environment between a current location and a target destination location in three dimensions. The expected flight path includes an altitude profile of the AV above a geographical area of the operating environment and a time-based position profile of the AV along the expected 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 expected flight path. For each cell in 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 the predicted data for the cell corresponding to the cell-specific prediction time, and outputting the retrieved predicted data for the cell. In at least some examples, a proximity threshold and other settings can be used to define a prediction update interval, geographical extent, and resolution of the retrieved predicted data related to the expected flight path.

[0014] Figure 1 An operating environment 100 of an example aircraft (AV) 110 is described. In this example, the AV 110 takes the form of a commercial airliner. The AV 110 can take other forms, including other types of aircraft.

[0015] In this example, the AV 110 travels along a flight path 120 from an initial location 122 (e.g., a first airport) towards a destination location 124 (e.g., a second airport). At Figure 1 the current position 126 of the AV 110 along the flight path 120 is schematically depicted. The flight path 120 spans a geographical area 130, and the AV 110 travels through the geographical area within the operating environment 100 at an altitude 132. The flight path 120 has an altitude profile that varies over the geographical area 130 at the altitude 132.

[0016] A portion of the flight path 120 that the AV 110 has not yet traveled between the current position (e.g., 126) and the destination position (e.g., 124) of the AV can be referred to as the expected flight path 134. Additionally, a destination position (e.g., 124) that the AV 110 has not yet reached while traveling along the flight path 120 can be referred to as the target destination position 136. As described in Figure 2 more detail, the expected flight path and / or target destination position of the AV can change during flight based on various factors. As an example, the expected flight path and target destination position can be determined based on flight plan data.

[0017] The flight path 120 can be defined by an initial position 122, a current position 126, a destination position 124, and one or more intermediate waypoints (such as example waypoints 140 and 142) located between the initial position and the destination position. In this example, waypoint 140 defines a point along the flight path 120 at which the climb altitude of the AV 110 transitions from the initial position 122 to the cruise altitude, and waypoint 142 defines a point along the flight path 120 at which the AV transitions from the cruise altitude to a descending altitude. It will be understood that the flight path (e.g., 120) can be defined by any suitable number of waypoints. The initial position 122, the current position 126, the destination position 124, and the intermediate waypoints (e.g., 140, 142) can each be defined by corresponding longitude, latitude, and altitude within the operating environment 100. Similarly, the prospective flight path 134 can be defined by the current position 126, the target destination position 136, and one or more intermediate waypoints (e.g., 140, 142) located between the current position and the target destination position.

[0018] In Figure 1 a portion of a three-dimensional cell array 150 that forms a spatial representation 152 of the operating environment 100 is schematically depicted. The cell array 150 includes a plurality of three-dimensional cells, each three-dimensional cell having a corresponding position and size within a longitude dimension 154, a latitude dimension 156, and an altitude dimension 158. In Figure 1 an example cell 160 among the plurality of cells is identified. As an illustrative example, each cell in the cell array 150 can be defined as having a size of 1 mile in the longitude dimension 154, 1 mile in the latitude dimension 156, and 1,000 feet in the altitude dimension 158. It will be understood that the cells of the cell array (e.g., 150) can have other suitable dimensions to provide a desired level of spatial resolution.

[0019] The flight path 120 and the prospective flight path 134 can be described as passing through a set of cells of the cell array 150. Thus, for a given flight path or prospective flight path, the set of cells through which the flight path or prospective flight path passes can be identified within the cell array 150. Similarly, the AV 110 traveling along or expected to travel along the flight path or prospective flight path can be described as passing through a set of cells of the cell array 150 such that the set of cells through which the AV passes or is expected to pass is identified within the cell array 150. As will be described in further detail in Figure 2 for example, predictive data can be retrieved and output via a computing system for a particular cell in the cell array (e.g., 150), the particular cell being identified based on the prospective flight path of the AV.

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

[0021] Figure 2 is a flowchart depicting an example method 200 that can be executed by a computing system of one or more computing devices to retrieve prediction data. As an example, Figure 6 and Figure 7 the computing system 600 of can execute method 200.

[0022] The method includes, at 210, retrieving an initial set of prediction data for an aircraft vehicle (AV) during a pre-flight period. As an example, the initial set of prediction data can be retrieved during the pre-flight period via a communication network, which can include a wireless or wired communication network. The initial set of prediction data retrieved at 210 can include the operating environment of the AV, which includes the pre-flight position of the AV, the target destination position of the AV, and the expected flight path between the pre-flight position and the target destination. As an example, the operating environment of the AV, the pre-flight position, the target destination, and the expected flight path can be defined by flight plan data.

[0023] In at least some examples, the initial set of prediction data retrieved at 210 can include initial prediction data for each cell in 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 in the three-dimensional cell array can have a corresponding longitude position and / or range within the longitude dimension, a latitude position and / or range within the latitude dimension, and a height position and / or range within the height dimension.

[0024] As an example, the initial set of prediction data can include weather forecast data and / or aviation forecast data. In the context of the above three-dimensional cell array, the initial prediction data for each cell in the set of cells of the array can include weather forecast data and / or aviation forecast data for that cell. The weather forecast data for each cell can include one or more of wind speed forecast (e.g., wind speed and direction), air temperature forecast, precipitation forecast (e.g., type and / or magnitude), visibility forecast (e.g., distance), cloud cover forecast (e.g., type and / or magnitude), turbulence forecast, and / or other suitable forms of weather forecast data. The aviation forecast data for each cell can include predictions of other AVs expected to be in that cell.

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

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

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

[0028] As indicated at 216, the expected flight path includes an altitude profile 217 of the AV over the geographic region of the operating environment and a time-based position profile 219 of the AV along the expected flight path. As an example, the time-based position profile identifies the estimated time for the AV to reach the target destination and / or one or more waypoints along the expected flight path.

[0029] In at least some examples, the expected flight path may include an initial approach and landing procedure for the AV at the target destination. As described in more detail with reference to operation 230, one or more alternative flight paths may be determined for the AV relative to the expected flight path. As an example, the alternative flight paths may include alternative approach and landing procedures for the AV at the target destination position and / or a diverted flight path to the target destination position that is different from the expected flight path. As another example, the alternative flight paths may include a diverted flight path to an alternative target destination position that is different from the target destination position.

[0030] The method includes, at 218, determining whether a prediction update time interval 221 has ended. As an example, the prediction update time interval defines a duration of the last retrieval from prediction data, which can occur during a pre-flight period at 210 or during the flight of the AV, as described in more detail at 236. The prediction update time interval can be stored within the computing system as prediction update interval data and can take the form of settings that can be user-defined. As an illustrative example, the prediction update time interval can define a duration in hours (e.g., 2 hours, 4 hours, etc.) and / or minutes (e.g., 30 minutes). Additionally or alternatively, the prediction update time interval can take the form of a qualitative setting (e.g., high, medium, low) corresponding to a predefined duration.

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

[0032] The method includes, at 222, performing a prediction update process for the AV during flight. As a first example, the prediction update process can be performed at 222 in response to determining at 218 that the prediction update interval has ended. In this example, at the end of the prediction update interval, the prediction update process can be performed at 222. As a second example, the prediction update process can be performed at 222 in response to determining at 220 that the expected flight path of the AV has changed. In this example, when it is determined that the expected flight path of the AV has changed during flight, the prediction update process can be performed at 222.

[0033] As part of performing 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 expected flight path. As an example, each cell in the three-dimensional cell array can have a corresponding longitude position and / or range within the longitude dimension, a latitude position and / or range within the latitude dimension, and a height position and / or range within the height dimension.

[0034] During the flight of the AV, the predicted data for the set of cells identified at operation 224 within the three-dimensional cell array can be retrieved without retrieving the predicted data for all cells of the three-dimensional cell array. For example, compared to retrieving predicted data for the set of cells identified at operation 224 during the flight of the AV, the initial predicted data retrieved during the pre-flight period at operation 210 can include a wider range of cells of the three-dimensional cell array. The method can limit the amount of predicted data retrieved during the flight of the AV based on the expected flight path, thereby reducing the amount of predicted data transmitted to the AV over a wireless network during the flight.

[0035] The set of cells identified within the three-dimensional cell array at 224 can include cells located along the expected flight path. For example, the method can include, at 226, identifying cells located along the expected flight path as part of the set of cells identified at 224. In this example, the cells identified at 226 as part of the set of cells can include cells that the AV is expected to pass through when traveling along the expected flight path.

[0036] In at least some examples, the set of cells identified within the three-dimensional cell array at 224 can further include cells located within a threshold proximity of the expected flight path. For example, the method can include, at 228, identifying cells located within a threshold proximity of the expected flight path as part of the set of cells identified at 224. In this example, one or more cells located within a threshold proximity of the expected flight path and extending outward from the cells identified at 226 in each direction (e.g., longitude, latitude, and altitude directions) can be identified at 228 as being included in the set of cells. The additional cells identified at 228 can provide an expanded range of cells from the cells identified at 226, thereby surrounding a limited area around the expected travel path.

[0037] The threshold proximity applied at operation 228 can be stored as data within the computing system and can take the form of a user-definable setting. In at least some examples, the threshold proximity can be defined based on each dimension such that a first threshold proximity is defined in the longitude and latitude dimensions and a second threshold proximity is defined in the altitude dimension. As an illustrative example, the first threshold proximity can be defined in the longitude and latitude dimensions as a first distance (e.g., 10 miles or kilometers) or a first number of cells (e.g., 1, 5, 10, 20, etc.) of a given cell dimension, and the second threshold proximity can be defined in the altitude dimension as a second distance (e.g., one thousand feet or meters, three thousand feet or meters, etc.) or a second number of cells.

[0038] As part of operation 222, the method can further include, at 230, performing a supplementary flight planning process to identify supplementary cells within the three-dimensional cell array, as referencedFigure 3 is described in more detail. As an example, as part of operation 230, a supplemental unit may be identified for an alternative flight path and / or alternative approach and landing procedures.

[0039] As part of operation 222, the method includes, at 232, for each unit in the set of units identified at 224, determining a unit-specific prediction time 233 for the unit based on a time-based position profile of the AV as indicated at 234. In at least some examples, the unit-specific prediction time for a unit corresponds to the expected time for the AV to reach the unit. As another example, the unit-specific prediction time for a unit corresponds to the expected time for the AV to reach within a threshold proximity of the unit.

[0040] The method includes, at 236, retrieving prediction data for the unit corresponding to the unit-specific prediction time. See Figure 6 Examples of retrieving prediction data are described in more detail. Briefly, for each unit identified in operation 224 and / or operation 230, the prediction data for the unit is retrieved by one or more computing devices on the AV from one or more remote computing devices via a wireless communication link. Retrieving the prediction data may include one or more computing devices on the AV sending one or more requests to one or more remote computing devices via a wireless communication link.

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

[0042] Figure 3 is a flowchart depicting another example method 300 that may be performed by a computing system of one or more computing devices to retrieve prediction data. Method 300 corresponds to the supplemental flight plan process described in operation 230 of method 200 previously seen Figure 2 and may be performed to determine one or more alternative flight paths and associated units for prediction data retrieval, which is different from the expected flight path identified at operation 214 of method 200.

[0043] The method includes, at 310, determining whether one or more alternative flight paths are to 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 the computing system, it may be determined that the AV requires one or more alternative flight paths. When it is determined at 310 that one or more alternative flight paths are to be determined for the AV, the method may proceed to one or more of operations 312 and / or 320.

[0044] An alternative flight path can be determined for a target destination location that is the same as the expected flight path. For example, the method includes, at 312, determining, in three dimensions, an alternative flight path of the AV through the operating environment between the current location and the target destination location, at 313, based on one or more of alternative flight plan data, the current operating state of the AV, and / or event data identifying an event. Examples of events can include weather events or air traffic events along or within a threshold proximity of the expected flight path, events occurring on board the AV (e.g., AV failure events, medical events, etc.), changes to the approach and landing procedures at the target destination location, and the like.

[0045] At 312, as previously described at 214, an alternative flight path can be determined for the expected flight path using alternative flight plan data, the current operating state of the AV, and event data. In at least some examples, the alternative flight plan data can identify or otherwise define the alternative flight path and can include information similar to the flight plan data described with respect to Figure 2 method 200 previously seen.

[0046] As indicated at 314, the alternative flight path can be used to recommend or command the AV to divert from the expected flight path to the same target destination location as the expected flight path. As an example, an alternative flight path can be determined for alternative approach and landing procedures at the target destination location, as indicated at 316. As another example, as indicated at 318, an alternative flight path can be determined for a temporary deviation from the expected flight path due to weather, air traffic, or other conditions identified by the event data.

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

[0048] At 320, as previously described at 214, an alternative flight path can be determined for the expected flight path using alternative flight plan data, the current operating state of the AV, and event data. As an example, an alternative flight path can be determined for the approach and landing procedures at the alternative target destination location as indicated at 322. As previously described with reference to operation 312, an alternative flight path can be determined for a deviation from the expected flight path due to weather, air traffic, or other conditions identified by the event data.

[0049] At 324, the method includes forming a set of supplementary cells within a three-dimensional cell array that spatially represents the operating environment based on an alternative flight path identification. Operation 324 can be performed to identify the set of supplementary cells for the alternative flight path, as described for operation 224 of method 200 with respect to the expected flight path previously seen Figure 2 The set of supplementary cells for the alternative flight path can be used at operation 232 to retrieve prediction data for each supplementary cell identified at 324, the prediction data corresponding to a cell-specific prediction time determined for the cell.

[0050] Figure 4 Diagram 400 is a diagram depicting the relationship between the altitude, geographical distance, and cell-specific prediction time of an exemplary expected flight path of an AV.

[0051] Diagram 400 depicts the altitude of the AV along the vertical axis. Diagram 400 further depicts the geographical distance and time along the horizontal axis. The geographical distance within diagram 400 can include a longitude dimension component and a latitude dimension component. In this example, the cells of the three-dimensional cell array that spatially represents 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 geographical distance dimension of the horizontal axis. The cell dimension sizes of diagram 400 are provided for illustrative purposes, and it will be understood that the cells can have other suitable dimension sizes to provide the desired level of spatial resolution.

[0052] The current position 410 of the AV is depicted in diagram 400 along line 412, which includes an actual flight path component 414 representing the actual flight path of the AV and an expected flight path component 416 representing the expected 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). Additionally, in this example, the expected 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 position 420 (e.g., a second airport) at a future time (e.g., +5.5 hours).

[0053] Line 412 represents the altitude profile of the AV over a geographic area related to the geographic distance the AV travels 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 position 420 has a destination altitude (e.g., 0 feet). Additionally, in this example, the altitude profile of the actual flight path component 414 represents the climb of the AV altitude from the initial position 418 to the cruise altitude (e.g., 29,000), and the expected flight path component 416 represents the descent of the AV altitude from the cruise altitude to the destination altitude.

[0054] Additionally, line 412 represents the time-based position profile of the AV along the actual flight path and the expected flight path. For example, each position of the AV along line 412 in three-dimensional space (as defined in the altitude dimension and the geographic distance dimension) has a defined time at which the AV is at that position on the actual flight path or is expected to reach that position on the expected flight path.

[0055] Within illustration 400, as represented by the expected flight path component 416 of line 410, the expected flight path of the AV from the current position 410 to the target destination position 420 passes through the shaded area 422. This shaded area 422 identifies a set of cells 430 where predictive data can be retrieved at an update interval during the AV flight. As previously seen Figure 2 in method 200 described, the predictive data retrieved for the set of cells at an update interval includes cell-specific predictive data corresponding to the cell-specific prediction time for each cell. As an example, each cell identified by the shaded area 422 has a corresponding cell-specific prediction time that can correspond to the expected time for the AV to reach that cell or a position within a threshold proximity of that cell.

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

[0057] As previously seen Figure 2As described in method 200, the set of identified cells for which predictive data is retrieved may include cells located within a threshold proximity to the expected flight path. In the example of illustration 400, the set of cells 430 identified by the shaded region 422 includes cells in the altitude dimension that are within a threshold proximity (e.g., 4000 feet) to the expected flight path. For example, cells 4000 feet above and below the expected flight path are included in the set of cells 430 identified by the shaded region 422. The set of cells 430 identified by the shaded region 422 also includes cells in the geographic dimension that are within a threshold proximity to the expected flight path. As previously described, the geographic dimension may have a longitude component and a latitude component. It will be understood that other suitable threshold proximities may be used in the altitude dimension, longitude dimension, and latitude dimension from those depicted in illustration 400.

[0058] By including cells within a threshold proximity to the expected flight path for predictive data retrieval, predictive data can be retrieved and used during flight for the actual or potential deviation of the AV from the expected flight path. For example, an operator of the AV may determine based on the retrieved predictive data whether an altitude change should be performed based on the predictive data retrieved for the expected altitude and altitudes above and below the expected altitude. The threshold proximity can be defined in the altitude, longitude, and latitude dimensions to provide a suitable spatial buffer around the expected flight path while also excluding cells outside the threshold proximity from the predictive data retrieval. For example, during a predictive update interval for retrieving predictive data for the set of cells 430 identified by the shaded region 422, predictive data for cells outside the shaded region 422 within illustration 400 is not retrieved.

[0059] Figure 5 An example graphical user interface (GUI) 500 through which predictive data may be output is schematically illustrated. As an example, the GUI 500 may be output via a computing device on the AV.

[0060] In this example, the GUI 500 includes a geographic representation 510 of a geographic area. The geographic representation 510 provides a view of some or all of the longitude dimension and latitude dimension in the operating environment of the AV. The geographic representation 510 includes a line 512 representing the expected flight path of the AV. The geographic representation 510 also includes a plurality of predictive data items along the expected flight path, examples of which include the predictive data item 514. Each predictive data item may include a graphical representation of the cell-specific predictive data for each cell, which corresponds to the cell-specific prediction time for that cell.

[0061] In addition, in this example, the GUI 500 includes a height representation 520. The height representation 520 provides a view of some or all of the operating environment of the AV in the height dimension and the geographical distance dimension. As previously seen Figure 4 As described, the geographical distance dimension may include a longitude dimension component and a latitude dimension component. The height representation 520 includes a line 522 representing the expected flight path of the AV. The height representation 520 further includes a plurality of prediction data items along the flight path, an example of which is the prediction data item 524. As previously mentioned, each prediction data item may include a graphical representation of the unit-specific prediction data for the corresponding unit, and the unit-specific prediction data for the corresponding unit corresponds to the unit-specific prediction time for that unit.

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

[0063] Figure 6 is a schematic diagram of an example computing system 600 depicting the method 200 that can be executed Figure 2 and Figure 3 the method 300.

[0064] The computing system 600 includes a computing device 610 located on Figure 1 the example AV 110. As an example, the computing device 610 may take the form of a personal computing device (e.g., a tablet computer, a handheld computer, a laptop computer, or other mobile computer) operated by an on-board flight crew of the AV 110. Alternatively, the computing device 610 may be integrated with the AV 110.

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

[0066] The sensors 612 may include sensors integrated with or located on the AV 110, and the sensors output sensor data from which the current operating state of the AV can be identified, as described in operation 212 of the method 200 previously referenced Figure 2 Other computing devices 618 may include computing devices integrated with and / or located on the AV 110.

[0067] The first communication interface 614 uses a first wireless communication protocol and network infrastructure to enable communication between the computing device 610 and one or more remote computing devices 620 of the computing system 600. As an example, when the AV 110 is on the ground, the first communication interface 614 can be used by the computing device 610 to communicate with the remote computing device 620. For example, the first communication interface 614 can enable the computing device 610 to retrieve an initial set of prediction data for the AV 110 from the remote computing device 620 during the pre-flight period. Alternatively, the first communication interface 614 can be integrated with the computing device 610 and form a part of the computing device 610.

[0068] The second communication interface 616 uses a second wireless communication protocol and network infrastructure different from the first communication interface 614 to enable communication between the computing device 610 and the remote computing device 620. 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 the flight of the AV 110. For example, the second communication interface 616 can enable the computing device 610 to retrieve prediction data for the AV 110 from the remote computing device 620 during the flight, as described in operation 222 previously referenced Figure 2 . Alternatively, the second communication interface 616 can be integrated with the computing device 610 and form a part of the computing device 610.

[0069] In at least some examples, the second communication interface 616 takes the form of a satellite communication interface for communicating with orbital satellites, and the first communication interface 614 takes the form of a terrestrial communication interface for communicating with ground-based access points. In this example or other examples, communication via the second communication interface 616 may be more expensive, more resource-intensive, and / or have a lower bandwidth capacity compared to the first communication interface 614. Figure 6 The communication network 630 is schematically depicted, and wireless communication between the computing device 610 (or other computing device 618) located on the AV 110 and the remote computing device 120 is carried out through the communication network.

[0070] The computing device 610 includes an application 640 and application data 642 stored thereon. The application 640 can be executed by the computing device 610 to perform various operations and methods described herein with respect to the computing device. The application 640 includes a user interface 644 through which a user (e.g., an AV personnel) can interact with the application. As an example, the user interface 644 can take the form of a GUI (e.g., Figure 5 the GUI 500) and / or other suitable user interfaces (e.g., an audio interface). The application 640 can retrieve, update, and store various data within the application data 642.

[0071] As an example, the application data 642 may include one or more of a client identifier 646, flight plan data 648, retrieved prediction data 650, setting data 652, a spatial representation 654 of the operating environment, and / or other data 656. The client identifier 646 may identify one or more of the user account of the user of the application 640, the AV 110, and / or the application 640 that enables multiple clients to distinguish themselves from one another via the remote computing device 120. The flight plan data 648 may include the flight plan data described in the method 200 previously referenced Figure 2 and the supplementary flight plan data described in the method 300 previously referenced Figure 3 . The retrieved prediction data 650 may include the prediction data retrieved in operation 236 of the method 200 in Figure 2 . As an example, the setting data 652 may define one or more of the following: (1) the resolution of the spatial representation 654, including the size of each cell in the longitude dimension, the latitude dimension, and the altitude dimension; (2) the 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 may define a three-dimensional cell array, such as the array 150 previously referenced Figure 1 and described in operation 224 of the method 200 in Figure 2 .

[0072] As an example, the remote computing device 620 may include a ground-based server computing device that forms a server system. The remote computing device 620 includes a service program 660 and service data 662 stored thereon. The service program 660 may be executed by the remote computing device 620 to perform the various operations and methods described herein with respect to the remote computing device. The service program 660 may retrieve, update, and store various data within the service data 662.

[0073] The service data 662 may include flight plan data 663 of the AV 110. The service data 662 at the remote computing device 620 may refer to an instance of the flight plan data 648 at the computing device 610.

[0074] The service data 662 may include prediction data 664, which includes retrieved prediction data 666 for the computing device 610 and unretrieved prediction data 668 that has not been retrieved for the computing device 610. The retrieved prediction data 666 at the remote computing device 620 may refer to an instance of the retrieved prediction data 650 at the computing device 610.

[0075] Service data 662 may include settings data 670 that may be associated with a client identifier 646 within the service data 662 such that a remote computing device 620 can implement settings on behalf of the client identified by the client identifier 646. The settings data 670 at the remote computing device 620 may refer to an instance of the settings data 652 at the computing device 610.

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

[0077] As described in the following examples, depending on the implementation, Figure 2 method 200 and Figure 3 method 300 may be performed by the computing system 600 in various ways.

[0078] As a first implementation, methods 200 and 300 may be mainly performed by the computing device 110 by executing the application 640. As an example, the application 640 may identify the current operating state of the AV at operation 212, determine the expected flight path at operation 214, determine alternative flight paths at operations 312 and 320, identify a set of units at operation 224, identify a set of supplementary units at operation 324, determine the unit-specific prediction time at operation 234, retrieve prediction data at operation 236, and output the prediction data at operation 238.

[0079] In this example, at operation 236, the computing device 110 may send one or more requests for prediction data to the remote computing device 620 to retrieve the prediction data. The one or more requests may identify the set of units and / or the set of supplementary units for which the prediction data to be provided by the remote computing device 620 to the computing device 610 is targeted. For each unit, the one or more requests may further identify the unit-specific prediction time of the unit. 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 to the application 640, the one or more responses including unit-specific prediction data corresponding to the unit-specific prediction time.

[0080] As a second implementation, methods 200 and 300 may be performed 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.

[0081] As a first example of a second implementation, the application 640 may subscribe to prediction updates (e.g., at a prediction update interval) from the service program 660 by sending a subscription request that includes the client identifier 646. The subscription request may also include one or more of the flight plan data 648 and the setting data 652. In some examples, the flight plan data and the setting data may be predefined and stored at the remote computing device 620 in association with the client identifier 646 without the flight plan data and the setting data accompanying the subscription request. In response to the subscription request, the service program 660 may periodically (e.g., at a prediction update interval) retrieve and send the retrieved prediction data to the application 640. Additionally, the service program 660 may identify the current operating state of the AV (e.g., from radar and / or other third-party data sources) at operation 212, determine the expected flight path at operation 214, determine alternative flight paths at operations 312 and 320, identify a set of units at operation 224, identify a set of supplementary units at 324, determine the unit-specific prediction time at operation 234, retrieve the prediction data at operation 236, and output the prediction data by sending the prediction data as one or more responses to the application 640 at operation 238.

[0082] As a second example of a second implementation, the application 640 may send one or more requests for prediction data (e.g., at a prediction update interval) to the service program 662. The one or more requests may include the client identifier 646. In response to the one or more requests, the service program 660 may process and send one or more responses to the application 640 that include the prediction data as described above for the first example of the second implementation.

[0083] As a third example of a second implementation, the application 640 may send one or more requests for prediction data (e.g., at a prediction update interval) to the service program 662, where the one or more requests include preprocessed data that includes one or more of the client identifier 646, the current state of the AV, the expected flight path, alternative flight paths, and / or the setting data 652. In response to the one or more requests, the service program 660 may utilize the preprocessed data and / or process data accompanying the request to identify the units for which the service program 660 will retrieve prediction data. The service program 660 may send one or more responses to the application 640 that include the prediction data retrieved by the service program.

[0084] As previously described, the methods and operations described herein may be incorporated into the computing systems of one or more computing devices. Specifically, such methods and operations may be implemented as computer applications or services, application programming interfaces (APIs), libraries, and / or other computer program products.

[0085] Figure 7 Schematically illustrates a system that can perform one or more of the methods and operations described herein. Figure 6 An example of a computing system 600 is provided. Figure 7 6. Computing system 600 is shown in simplified form in . 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 (eg, smart phones), and / or other computing devices.

[0086] The 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 instructions 716 stored in the storage machine 712. For example, the logic machine may be configured to execute instructions as part of one or more applications, services, programs, routines, libraries, objects, components, data structures, or other logical constructs. Such instructions may be implemented to perform a task, implement a data type, transform the state of one or more components, implement a technical effect, or otherwise obtain a desired result.

[0087] The logic machine may include one or more processor devices configured to execute software instructions. As an example, the instructions 716 may include an application 640 and a service program 660. Additionally or alternatively, the logic machine may include one or more hardware or firmware logic machines configured to execute hardware or firmware instructions. The processor of the logic machine may be single-core or multi-core, and the instructions executed thereon may be configured for sequential, parallel and / or distributed processing. The various components of the logic machine may optionally be distributed in two or more separate devices, which may be remotely located and / or configured for collaborative processing. Various aspects of the logic machine may be virtualized and executed by a remotely accessible networked computing device configured in a cloud computing configuration.

[0088] The storage machine 712 includes one or more physical devices that are configured to store instructions 716 and other data 718 that can be executed by the logic machine to implement the methods and operations described herein. When implementing such methods and operations, the state of the storage machine 712 can be transformed, for example, to store different data. The data 718 may include Figure 6 application data 642 and service data 662 .

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

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

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

[0092] The terms "module", "program", and "engine" may be used to describe aspects of the computing system 600 implemented to perform a specific function. In some cases, a module, program, or engine may be instantiated by the logic machine 710 executing instructions saved by the storage machine 712. It will be understood that different 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 different applications, services, code blocks, objects, routines, APIs, functions, etc. The terms "module", "program", and "engine" may encompass single or groups of executable files, data files, libraries, drivers, scripts, database records, etc.

[0093] It should be recognized that, as used herein, a "service" is a program executable across multiple client sessions. A service is available to one or more system components, programs, and / or other services. In some implementations, a service may run on one or more server computing devices.

[0094] The input / output subsystem 714 includes one or more input devices and one or more output devices. For example, the input / output subsystem 714 may include or be coupled to a display device for presenting a visual representation of data maintained by the storage machine 712. The visual representation may take the form of a graphical user interface. Since the methods and operations described herein change the data maintained by the storage machine and thus transform the state of the storage machine, the state of the display device may also be changed to visually represent the changes in the underlying data. In some embodiments, the input / output subsystem 714 may include or interface with one or more other input devices such as a keyboard, mouse, touch screen, or game controller.

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

[0096] Furthermore, the present disclosure includes configurations according to the following items.

[0097] Item 1. A method performed by a computing system of one or more computing devices, the method comprising: performing a prediction update process of an aerial vehicle (AV) during flight, the prediction update process including: determining, in three dimensions, an expected flight path of the AV through an operating environment between a current position and a target destination position based on flight plan data; wherein the expected flight path includes an altitude profile over a geographical area of the operating environment through which the AV passes and a time-based position profile of the AV along the expected flight path; identifying a set of cells within a three-dimensional cell array forming 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 of the cell based on the time-based position profile of the AV, retrieving prediction data corresponding to the cell-specific prediction time for retrieval, and outputting the prediction data retrieved for the cell.

[0098] Item 2. The method according to item 1, wherein the prediction update process of the AV is performed when the AV travels along an actual flight path towards the target destination position.

[0099] Item 3. The method according to any one of items 1-2, wherein the prediction update process of the AV is performed at a prediction update time interval when the AV travels along an actual flight path towards the target destination position.

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

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

[0102] Item 6. The method according to item 5, wherein, for each unit in the set of units, retrieving the prediction data of the unit corresponding to the unit-specific prediction time includes: sending one or more requests from the computing device located on the AV to the remote computing device via a wireless communication link, and receiving, at the computing device located on the AV via the wireless communication link, one or more responses from the remote computing device including the prediction data of the unit.

[0103] Item 7. The method according to any one of items 1 to 6, wherein the unit-specific prediction time of the unit is determined based on the expected time for the AV to reach each unit.

[0104] Item 8. The method according to any one of items 1 to 7, wherein the unit-specific prediction time is determined for each unit based on the expected time for the AV to reach a position within a threshold proximity of the unit.

[0105] Item 9. The method according to any one of items 1 to 8, wherein the identified set of units includes a plurality of units located along the expected flight path.

[0106] Item 10. The method according to item 9, wherein the identified set of units further includes units located within a threshold proximity of the expected flight path; and wherein the set of units does not include units located outside the threshold proximity of the expected flight path.

[0107] Item 11. The method according to any one of items 1 - 10, further comprising: determining, in three dimensions, an alternative flight path of the AV through the operating environment between the current position and the target destination position or an alternative target destination position; identifying a set of supplementary units within a three-dimensional array of units forming a spatial representation of the operating environment based on the alternative flight path; and for each unit in the set of supplementary units: determining the unit-specific prediction time of the unit, retrieving the prediction data corresponding to the unit-specific prediction time for the unit, and outputting the prediction data retrieved for the unit.

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

[0109] Item 13. A computing system of one or more computing devices, comprising: a logic machine; and a storage machine having instructions stored thereon that are executable by the logic machine to: perform a predictive update process of an aerial vehicle (AV) during flight, the predictive update process including: determining, in three dimensions, an expected flight path of the AV through an operating environment between a current position and a target destination position based on flight plan data; wherein the expected flight path includes an altitude profile of the AV over a geographical area of the operating environment and a time-based position profile of the AV along the expected flight path; identifying a set of cells within 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 corresponding to the cell-specific prediction time for the cell, and outputting the retrieved prediction data for the cell.

[0110] Item 14. The computing system according to item 13, wherein the predictive update process of the AV is performed at a predictive update time interval.

[0111] Item 15. The computing system according to any one of items 13 to 14, wherein the cell-specific prediction time is determined based on the expected time for the AV to reach each cell.

[0112] Item 16. The computing system according to any one of items 13 to 15, wherein the cell-specific prediction time is determined for each cell based on the expected time for the AV to reach a position within a threshold proximity of the cell.

[0113] Item 17. The computing system according to any one of items 13 to 16, wherein the identified set of cells includes a plurality of cells located along the expected flight path and a plurality of cells located within a threshold proximity of the expected flight path; and wherein the set of cells does not include cells located outside the threshold proximity of the expected flight path.

[0114] Item 18. The computing system according to any one of items 13 to 17, wherein the instructions are further executable by the logic machine to: determine, in three dimensions, an alternative flight path of the AV through an operating environment between a current position and a target destination position or an alternative target destination position; identify a set of supplementary cells within 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 corresponding to the cell-specific prediction time for the cell, and outputting the retrieved prediction data for the cell.

[0115] Item 19. The computing system according to any one of items 13 to 18, wherein the prediction data includes weather forecast data and / or aviation forecast data.

[0116] Item 20. An article, comprising: a computer-readable storage medium having instructions stored thereon, the instructions executable by a computing system to: perform a predictive update process of an aircraft vehicle (AV) during flight, the predictive update process comprising: determining, in three dimensions, an expected flight path of the AV through an operating environment between a current position and a target destination position based on flight plan data; wherein the expected flight path includes an altitude profile of the AV over a geographical area of the operating environment and a time-based position profile of the AV along the expected flight path; identifying a set of cells within 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:

[0117] determine a cell-specific prediction time for the cell based on the time-based position profile of the AV, retrieve prediction data corresponding to the cell-specific prediction time for the cell, and output the retrieved prediction data for the cell.

[0118] It will be understood that the configurations and / or methods described herein are exemplary in nature and that these specific embodiments or instances should not be considered to have a limiting significance since many variations are possible. The particular routines or methods described herein may represent one or more of any number of processing strategies. Accordingly, the various acts illustrated and / or described may be performed in the order illustrated and / or described, in other orders, in parallel, or omitted. Similarly, the order of the above processes may be changed.

[0119] The subject matter of the present disclosure includes all novel and non-obvious combinations and sub-combinations of the various processes, systems, and configurations, as well as other features, functions, acts, and / or properties disclosed herein, and any and all equivalents thereof.

Claims

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

2. The method (200, 300) according to claim 1, wherein: The prediction update process (222) of the air vehicle (110) is performed as the air vehicle (110) travels along an actual flight path (414) toward the target destination location (124).

3. The method (200, 300) according to claim 2, wherein: The prediction update process (222) of the air vehicle (110) is performed at prediction update time intervals (221) as the air vehicle (110) travels along an actual flight path (414) toward the target destination location (124).

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

5. The method (200, 300) according to claim 1, wherein: The computing device (610) of the computing system (600) is mounted on the aviation vehicle (110).

6. The method (200, 300) according to claim 5, wherein: For each cell (160) in the set of cells (430), retrieving the prediction data (650, 666) for the cell (160) corresponding to the cell-specific prediction time (233) comprises: sending one or more requests from the computing device (610) on the aerospace vehicle (110) to a remote computing device (620) via a wireless communication link (630), and One or more responses including the predicted data (650, 666) of the unit (160) are received from the remote computing device (620) via the wireless communication link (630) at the computing device (610) on the aerospace vehicle (110).

7. The method (200, 300) according to claim 1, wherein: The unit-specific predicted time (233) for each unit (160) is determined based on the expected arrival time of the air vehicle (110) at each unit (160).

8. The method (200, 300) according to claim 1, wherein: The unit-specific predicted time (233) for each unit (160) is determined based on an expected arrival time of the air vehicle (110) at a location within a threshold proximity to each unit (160).

9. The method (200, 300) according to claim 1, wherein: The identified set of cells (430) includes a plurality of cells located along the expected flight path (134).

10. The method (200, 300) according to claim 9, wherein: The identified set of cells (430) further includes cells located within a threshold proximity to the expected flight path (134); and in, The set of cells (430) does not include cells located outside a threshold proximity to the expected flight path (134).

11. The method (200, 300) according to claim 1, further comprising: determining in three dimensions an alternative flight path (313) of the aerial vehicle (110) through the operating environment (100) between the current location (126) and the target destination location (124) or an alternative target destination location; identifying a set of supplemental cells within a three-dimensional array of cells (150) forming a spatial representation (152) of the operating environment (100) based on the alternative flight path (313); as well as For each cell (160) in the set of supplementary cells: determining a unit-specific prediction time (233) for the unit (160), retrieving prediction data (650, 666) for the unit (160) corresponding to the unit-specific prediction time (233), and The prediction data (650, 666) retrieved for the unit (160) is output.

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

13. A computing system (600) of one or more computing devices (610, 620), comprising: Logic Machine (710); as well as A storage machine (712) storing instructions (716) executable by the logic machine (710) to: A prediction update process (222) is performed for an aviation vehicle (110) during flight, the prediction update process (222) comprising: determining in three dimensions an expected flight path (134) of the aerial vehicle (110) through an operating environment (100) between a current location (126) and a target destination location (124) based on the flight plan data (648, 663); wherein the expected flight path (134) includes an altitude profile (217) of the aerial vehicle (110) over a geographic area (130) passing through the operating environment (100) and a time-based position profile (219) of the aerial vehicle (110) along the expected flight path (134); identifying a set (430) of cells within a three-dimensional array (150) of cells that form a spatial representation (152) of the operating environment (100) based on the expected flight path (134); and For each cell (160) in the cell set (430): determining a unit-specific predicted time (233) for the unit (160) based on the time-based position profile (219) of the air vehicle (110), retrieving prediction data (650, 666) for the unit (160) corresponding to the unit-specific prediction time (233), and The prediction data (650, 666) retrieved for the unit (160) is output.

14. The computing system (600) of claim 13, wherein: The prediction update process (222) of the aviation vehicle (110) is performed at a prediction update time interval (221).

15. The computing system (600) of claim 13, wherein: The unit-specific predicted time (233) for each unit (160) is determined based on the expected arrival time of the air vehicle (110) at each unit (160).

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

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

18. The computing system (600) of claim 13, wherein: The instructions (716) are further executable by the logic machine (710) to: determining in three dimensions an alternative flight path (313) of the aerial vehicle (110) through the operating environment (100) between the current location (126) and the target destination location (124) or an alternative target destination location; identifying a set of supplemental cells within a three-dimensional array of cells (150) forming a spatial representation (152) of the operating environment (100) based on the alternative flight path (313); as well as For each cell (160) in the set of supplementary cells: determining a unit-specific prediction time (233) for the unit (160), retrieving prediction data (650, 666) for the unit (160) corresponding to the unit-specific prediction time (233), and The prediction data (650, 666) retrieved for the unit (160) is output.

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

20. An article of manufacture comprising: A computer readable storage device (712) storing instructions (716) executable by the computing system (600) to: A prediction update process (222) is performed for an aviation vehicle (110) during flight, the prediction update process comprising: determining in three dimensions an expected flight path (134) of the aerial vehicle (110) through an operating environment (100) between a current location (126) and a target destination location (124) based on the flight plan data (648, 663); wherein the expected flight path (134) includes an altitude profile (217) of the aerial vehicle (110) over a geographic area (130) passing through the operating environment (100) and a time-based position profile (219) of the aerial vehicle (110) along the expected flight path (134); identifying a set (430) of cells within a three-dimensional array (150) of cells that form a spatial representation (152) of the operating environment (100) based on the expected flight path (134); and For each cell (160) in the cell set (430): determining a unit-specific predicted time (233) for the unit (160) based on the time-based position profile (219) of the air vehicle (110), retrieving prediction data (650, 666) for the unit (160) corresponding to the unit-specific prediction time (233), and The prediction data (650, 666) retrieved for the unit (160) is output.

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