Information processing system and information processing method

Abstract

To provide an information processing system and an information processing method, capable of evaluating flight difficulty and economic efficiency as a flight path for each local space region in which a small aircraft can fly.SOLUTION: In order to solve the above problem, an information processing system of the present invention includes: a wind-condition estimation part that estimates wind-condition information in a predetermined space region; and an evaluation part that evaluates flight difficulty and economic efficiency of an aircraft on the basis of the wind-condition information estimated. Also, an information processing method of the present invention includes a wind-condition estimation step of estimating the wind-condition information in the predetermined space region; and an estimation step of estimating flight difficulty and economic efficiency of an aircraft on the basis of the wind-condition information estimated.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] This invention relates to an information processing system and an information processing method for evaluating the flight difficulty and economic feasibility of various spatial regions in which small aircraft, such as drones and eVTOLs, can fly. [Background technology]

[0002] The widespread use of small aircraft, such as drones and eVTOLs (hereinafter simply referred to as "small aircraft"), is predicted in a variety of fields including inspection, cargo transport, and passenger transport. Compared to conventional large aircraft, these types of small aircraft are extremely lightweight and are designed for low-altitude flight, so the information required for stable flight differs significantly from the information required for large aircraft.

[0003] Specifically, conventional large aircraft fly at high altitudes of 400m or more above the ground, so except during takeoff and landing, there is no need to consider the influence of terrain or buildings, and the flight path can be determined mainly by considering the broad wind conditions in the upper atmosphere. On the other hand, small aircraft fly at low altitudes of less than 400m above the ground, so not only do they need to consider the terrain and buildings themselves as obstacles, but they are also extremely lightweight and highly susceptible to wind, so it is necessary to determine a flight path that allows for stable flight by considering the influence of local wind conditions (turbulence, etc.) caused by terrain and buildings.

[0004] As a prior art to improve this problem, the system described in Patent Document 1 is known. For example, claim 1 of the same document states: "A system for considering fine wind conditions within a region, comprising a plurality of aircraft located within the region, a wind speed calculation unit configured to identify wind vectors within the region using measurements from the plurality of aircraft, wherein the plurality of aircraft are a plurality of unmanned aerial vehicles, a three-dimensional wind map creation unit configured to create a three-dimensional wind map using interpolated wind vectors within the region, the interpolated wind vectors are associated with set grid points of a three-dimensional grid, the flight plan creation unit configured to create the flight plan using the three-dimensional wind forecast map of the region at the future point in time, and further comprising a communication system configured to transmit the flight plan to the plurality of aircraft."

[0005] Furthermore, paragraph 0079 of the same document explains, "Figure 9 is a two-dimensional diagram showing the initial flight plan and a revised flight plan considering fine wind conditions, along with the unmanned aerial vehicle, according to an exemplary embodiment. ... Path 908 is the initial planned path. Path 908 can be determined using a preferred method. In some exemplary embodiments, path 908 is the fastest path with no wind. In some exemplary embodiments, path 908 is the shortest path." Paragraph 0080 explains, "Path 910 is the revised flight plan. In this exemplary embodiment, path 910 is created based on wind vectors 912 within region 902. ... If wind vectors 912 are identified in real time, wind vectors 912 are measured directly by the unmanned aerial vehicle."

[0006] Thus, Patent Document 1 discloses a method for planning alternative flight paths that modify the initially planned flight paths (fastest path, shortest path) by taking into account wind vectors within a region directly measured by an unmanned aerial vehicle. [Prior art documents] [Patent Documents]

[0007] [Patent Document 1] Japanese Patent Publication No. 2019-89538 [Overview of the Initiative] [Problems that the invention aims to solve]

[0008] However, the system described in Patent Document 1 does not pre-evaluate whether the modified flight path is superior to the initial flight path in terms of flight difficulty and economic efficiency. Therefore, there was a possibility that the modified path would be inferior to the initial path in terms of flight difficulty and economic efficiency. In addition, if there were multiple flight path candidates, there was a possibility that the best flight path could not be selected.

[0009] Therefore, the present invention aims to provide an information processing system and an information processing method that evaluate the difficulty and economics of flight paths for each local spatial region in which a small aircraft can fly during a flight from a departure point to a destination, and generate the evaluation results in the form of three-dimensional information. [Means for solving the problem]

[0010] To solve the above problems, the information processing system of the present invention comprises a wind condition estimation unit that estimates wind condition information in a predetermined spatial area, and an evaluation unit that evaluates the difficulty of flight or economic feasibility of an aircraft based on the estimated wind condition information. [Effects of the Invention]

[0011] According to the information processing system and information processing method of the present invention, the difficulty and economic efficiency of each local spatial region in which a small aircraft can fly during a flight from a departure point to a destination can be evaluated as a flight path, and the evaluation results can be generated in the form of three-dimensional information. Based on this three-dimensional information, a flight path with good difficulty and economic efficiency can be easily determined. [Brief explanation of the drawing]

[0012] [Figure 1] Functional block diagram of the information processing system in Example 1. [Figure 2] A diagram showing a three-dimensional grid of a local space area. [Figure 3] A diagram showing an example of a situation with low flight difficulty. [Figure 4] A diagram showing an example of a situation with high flight difficulty. [Figure 5] A diagram showing an example of a situation with high economy. [Figure 6] A diagram showing an example of a flight route planned based on the three-dimensional information of Example 1. [Figure 7] Functional block diagram of the information processing system of Example 2. [Figure 8] A diagram showing an example of a flight route planned based on the three-dimensional information of Example 2.

Mode for Carrying Out the Invention

[0013] Hereinafter, embodiments of the information processing system of the present invention will be described in detail with reference to the drawings.

Embodiment

[0014] First, the information processing system 1 according to Embodiment 1 of the present invention will be described using FIGS. 1 to 6.

[0015] FIG. 1 is a functional block diagram of the information processing system 1 of this embodiment. This information processing system 1 refers to the latest wide-area wind condition forecast data (for example, wind condition forecast data at the municipality level) of the area that a small aircraft can pass through during flight from the departure point to the destination, evaluates the flight difficulty and economy for each local space area S where flight is assumed, and then aggregates the evaluation results in the form of three-dimensional information and provides it to an external route planning system. As shown in FIG. 1, it includes a wind condition database 2, a local wind condition acquisition unit 3, a local wind condition learning unit 4, a local wind condition estimation unit 5, an evaluation unit 6, an aircraft database 7, a three-dimensional information generation unit 8, and a map database 9.

[0016] Specifically, the information processing system 1 is a computer equipped with hardware such as a CPU or other computing device, a memory device such as semiconductor memory, and a communication device, or a server on the cloud. The computing device executes a desired program while referring to various databases to realize each functional unit such as the local wind condition acquisition unit 3. In the following, the functions of each unit for generating 3D information in the information processing system 1 will be explained, while appropriately omitting such well-known technologies.

[0017] Wind Condition Database 2 is a database that stores topographic data D21, obstacle data D22, historical wind condition forecast data D23, and historical wind condition measurement data D24. Topographic data D21 is 3D data showing the location and shape of various terrains, such as mountains and rivers. Obstacle data D22 is 3D data showing the location and shape of various man-made objects, such as buildings and houses. Historical wind condition forecast data D23 is a data set that accumulates wide-area wind condition forecast data acquired in the past. Historical wind condition measurement data D24 is a data set that accumulates wind condition measurement data acquired in the past.

[0018] The local wind condition acquisition unit 3 acquires terrain data D21, obstacle data D22, past wind condition forecast data D23, and past wind condition measurement data D24 as needed, related to the local spatial area S where the flight of a small aircraft is expected, and generates a group of local wind condition data D3 that shows the wind direction and wind speed in three-dimensional space based on the acquired data.

[0019] As for the method of generating the local wind condition data set D3, one method may be adopted in which the local wind condition data set D3 is generated by simulating the wind conditions of each local spatial region S based on the acquired topographic data D21 and obstacle data D22, or one method may be adopted in which the acquired wind condition forecast past data D23 or wind condition measurement past data D24 is used directly as the local wind condition data set D3.

[0020] The local wind condition learning unit 4 extracts feature set information D4 in three-dimensional space by performing learning processes such as machine learning on the local wind condition data set D3. Feature set information D4 is a data set that replaces the local wind condition data set D3, as it is lighter in size but possesses characteristics that allow it to be used for the same purposes as the local wind condition data set D3.

[0021] The local wind condition estimation unit 5 estimates local wind condition information D5 using the latest wide-area wind condition forecast data D10 published by weather forecasting agencies 10 such as the Japan Meteorological Agency, and feature group information D4 extracted by the local wind condition learning unit 4. This estimated local wind condition information D5 is a wind condition vector V based on wind speed and wind direction. W This is a set of 3D data, and as shown in Figure 2, each local spatial region S where the flight of a small aircraft is assumed is divided into a 3D grid L (shown as a representative example of 4 grids in the figure) at arbitrary intervals specified by the user, and wind condition vectors V W This information stores (wind speed and wind direction information). Note that although the 3D grid is represented as a cube in Figure 2, other polyhedral shapes may also be used.

[0022] The evaluation unit 6 calculates the estimated local wind condition information D5 (more specifically, the wind condition vector V in the 3D grid L, as illustrated in Figure 2). W Based on the aircraft information D7 obtained from the aircraft database 7, the flight difficulty D61 and economic efficiency D62 are evaluated for each local spatial region S as a flight path. The aircraft information D7 is information reported in flight plans, etc., and registered in the aircraft database 7.

[0023] For example, when evaluating the flight difficulty D61 of a certain local spatial region S, the aircraft information D7 is used as a threshold, and the wind condition vector V of the 3D grid L in the planned flight airspace is used. W The evaluation is performed based on the following. Specifically, as shown in Figure 3, the wind condition vector V of the 3D grid L is relative to the direction of travel of the small aircraft 12. W If there is a headwind, refer to the wind resistance performance contained in aircraft information D7 and use the wind condition vector V against the wind resistance performance. W It determines whether the value falls below the acceptable limit. Then, the wind condition vector V WIf it is below the allowable value, it is determined that the flight difficulty D61 of the local space area S is low.

[0024] Also, as shown in FIG. 4, when referring to a plurality of wind condition vectors V of the three-dimensional grid L within a certain local space area S and it is determined that turbulent airflows T such as vortices are occurring, it is determined that the flight difficulty D61 of the local space area S is extremely high, and the local space area S is set as a flight prohibited area. W When evaluating the economic efficiency D62, appropriate aircraft performance included in the aircraft information D7 is used as a threshold value, and the evaluation is performed based on the wind condition vector V of the three-dimensional grid L in the planned flight airspace. Specifically, as shown in FIG. 5, if the wind condition vector V is a following wind with respect to the traveling direction of the small aircraft 12, it is determined that the economic efficiency D62 of the local space area S is high.

[0025] When evaluating the economic efficiency D62, appropriate aircraft performance included in the aircraft information D7 is used as a threshold value, and the evaluation is performed based on the wind condition vector V of the three-dimensional grid L in the planned flight airspace. W Based on this, as shown in FIG. 5, if the wind condition vector V is a following wind with respect to the traveling direction of the small aircraft 12, it is determined that the economic efficiency D62 of the local space area S is high. W If the wind condition vector V is a following wind with respect to the traveling direction of the small aircraft 12, it is determined that the economic efficiency D62 of the local space area S is high.

[0026] Then, by mapping the above determination results (flight difficulty D61, economic efficiency D62) to each three-dimensional grid L, three-dimensional evaluation information D63 indicating the evaluation results of each local space area S where the flight of the small aircraft 12 is assumed is generated.

[0027] The three-dimensional information generation unit 8 generates three-dimensional information D8 to be provided to a flight route planning system (not shown) by mapping the three-dimensional evaluation information D63 generated by the evaluation unit 6 onto the three-dimensional map D9 acquired from the map database 9.

[0028] Here, we will explain the difference between the 3D map D9 in map database 9 and the terrain data D21 and obstacle data D22 in the wind condition database 2 mentioned above. Both data in wind condition database 2 are used for wind condition simulations in the local wind condition acquisition unit 3, and therefore only record the positional and shape data necessary for wind condition simulations. In contrast, the 3D map D9 includes not only the positional and shape data corresponding to the terrain data D21 and obstacle data D22, but also attribute data for each region. An example of this attribute data is an attribute indicating a recommended flight area (corresponding to the airspace above river areas, etc.) and an attribute indicating a no-fly area (corresponding to the airspace above airports, fire stations, hospitals, schools, etc.).

[0029] Therefore, the flight path planning system, which receives 3D information D8 from the 3D information generation unit 8, can optimize the flight path planning of small aircraft by considering not only the difficulty and economics of each local spatial region S as a flight path, but also the various attributes mentioned above. As a result, the flight path planning system ultimately determines a flight path R that avoids mountainous areas M and buildings B, which, if estimated based on the latest wide-area wind forecast data D10, is considered to allow for stable flight, as shown in Figure 6.

[0030] As described above, the information processing system of this embodiment can evaluate the difficulty and economic efficiency of each local spatial region of a flight path that a small aircraft can fly during a flight from a departure point to a destination, and generate the evaluation results as 3D information. Based on this 3D information, the best flight path in terms of difficulty and economic efficiency can be easily determined. [Examples]

[0031] Next, using Figures 7 and 8, we will describe the information processing system 1 according to Embodiment 2 of the present invention. Note that we will omit redundant explanations of points common to Embodiment 1.

[0032] In Example 1, before the small aircraft 12 took off, estimated local wind information D5 was estimated based on the latest wide-area wind forecast data D10 published by the weather forecasting agency 10, and the flight path R was planned using this estimated local wind information D5. Therefore, if the departure point and destination of the small aircraft 12 were far apart, the flight path R planned before takeoff could be inappropriate for the weather changes in each area. Also, because the announcement interval of the wide-area wind forecast data D10 by the weather forecasting agency 10 is relatively long, when estimating estimated local wind information D5 based on the latest wide-area wind forecast data D10, but after a considerable amount of time has passed since its publication, it was not possible to estimate sudden weather changes (so-called guerrilla downpours, etc.) that could not be known from the old wide-area wind forecast data D10, and it was possible that an appropriate flight path R could not be planned. The information processing system 1 of this example is intended to improve these problems and is configured as follows.

[0033] Figure 7 is a functional block diagram of the information processing system 1 of this embodiment. As shown here, the information processing system 1 of this embodiment estimates local wind condition information D5 using wind condition measurement data D11 measured by a weather sensor 11, instead of the wide-area wind condition forecast data D10 of Embodiment 1. Since this weather sensor 11 is installed in each region from the departure point to the destination of the small aircraft 12, the information processing system 1 of this embodiment can generate 3D information D8 that corresponds to sudden weather changes in each region, and the route planning system that receives the 3D information D8 of this embodiment can plan the optimal flight path R' that also takes into account sudden weather changes in each region.

[0034] In this embodiment as well, the information processing system 1 first acquires local wind condition data group D3 in the local wind condition acquisition unit 3, and then extracts feature group information D4 in the local wind condition learning unit 4. These processes can be omitted if pre-generated local wind condition data group D3 and feature group information D4 exist, by reusing them.

[0035] Next, the information processing system 1, in the local wind condition estimation unit 5, estimates local wind condition information D5 using the current wind condition measurement data D11 measured by the weather sensor 11 installed in the local spatial area S where the flight of the small aircraft 12 is expected, and the aforementioned feature group information D4. The wind condition measurement data D11, which is the output of the weather sensor 11, is assumed to contain the same type of information as the wide-area wind condition forecast data D10 published by the weather forecasting agency 10.

[0036] When the same processing as in Example 1 is applied to the estimated local wind condition information D5 of this embodiment, the 3D information D8 of this embodiment will reflect the current wind condition measurement data D11 measured by the weather sensors 11 in each region. Therefore, the flight path R' planned by the route planning system based on the 3D information D8 of this embodiment will be an optimized path that takes into account not only economic efficiency D61, flight difficulty D62, and obstacles such as terrain and buildings, which were also considered in the flight path R of Example 1, but also the current local wind condition measurement data D11.

[0037] For example, consider a scenario where the latest regional wind forecast data D10 for a small aircraft 12 at the time of flight path planning indicates a regional wind direction W of 20° north relative to the east, as illustrated in Figure 6, and the optimal flight path based on this is a flight path R that passes between a mountainous area M and a building B. However, the current regional wind direction W' is 40° north relative to the east, as illustrated in Figure 8, and turbulence T is occurring between the mountainous area M and the building B.

[0038] In this case, if the 3D information D8 of Example 1 is used, the flight path R in Figure 6, which is superior in terms of flight difficulty and economics under the environment shown by the wide-area wind forecast data D10, is planned as the optimal path. As a result, the small aircraft 12 would end up taking an inappropriate path that passes through the suddenly occurring turbulence T. However, using the 3D information D8 of this embodiment, the flight path R' in Figure 8, which avoids the turbulence T, can be determined to be optimal, allowing the small aircraft 12 to continue flying safely.

[0039] It should be noted that the present invention is not limited to the embodiments described above, and various modifications are included. For example, the embodiments described above are described in detail to make the present invention easier to understand, and are not necessarily limited to those having all the configurations described. Furthermore, it is possible to replace parts of the configuration of one embodiment with the configuration of another embodiment, and it is also possible to add configurations from other embodiments to the configuration of one embodiment. In addition, it is possible to add, delete, or replace parts of the configuration of each embodiment with other configurations. [Explanation of Symbols]

[0040] 1. Information Processing System 2. Wind Condition Database D21 Terrain Data D22 Obstacle Data D23 Wind Condition Forecast Past Data D24 Wind Condition Measurement Past Data 3. Local wind condition acquisition section D3 Local wind condition data group 4. Local Wind Conditions Learning Department D4 Feature Group Information 5. Local wind condition estimation unit D5 Estimated Local Wind Condition Information 6. Evaluation Department D61 Flight difficulty level D62 Economy D63 3D Evaluation Information 7. Aircraft Database D7 Aircraft Information 8 3D information generation section D8 3D Information 9 Map Database D9 3D Map 10 Weather Forecasting Agencies D10 Wide-area wind forecast data 11 Weather Sensors D11 Wind Condition Measurement Data 12 Small aircraft S Local spatial region L 3D lattice V W Wind vector T turbulence Building B M Mountainous region R, R' Flight path W, W' Regional wind direction

Claims

1. A wind condition estimation unit that estimates wind condition information in a predetermined spatial area, An evaluation unit that evaluates the difficulty or economics of aircraft flight based on the estimated wind condition information, and generates three-dimensional evaluation information of the difficulty or economics of aircraft flight in each spatial region, A 3D information generation unit generates 3D information by adding the aforementioned 3D evaluation information, including position data, shape data, and attribute data, to a 3D map space. It has, The airspace above river areas is assigned attribute data indicating recommended flight areas. An information processing system characterized by assigning attribute data indicating no-fly zones to the airspace above airports, fire stations, hospitals, and schools.

2. In the information processing system described in claim 1, The evaluation unit is characterized by acquiring the aircraft's airframe performance as a threshold and evaluating the flight difficulty or economic efficiency based on a comparison between the estimated wind speed and wind direction obtained from the wind condition information and the aircraft's performance.

3. In the information processing system described in claim 1, The information processing system is characterized in that the three-dimensional information generation unit transmits the three-dimensional information to a route planning system that performs optimization calculations for the flight path of the aircraft.

4. In the information processing system according to claim 1 or claim 2, A wind condition acquisition unit generates a set of wind condition data for each spatial region based on past wind condition data, topographic data, and obstacle data. It includes a wind condition learning unit that learns feature group information based on the aforementioned wind condition data set, The wind condition estimation unit is an information processing system characterized by estimating wind condition information based on the latest wind condition forecast data obtained from a weather forecasting agency and the feature group information.

5. In the information processing system according to claim 1 or claim 2, A wind condition acquisition unit generates a set of wind condition data for each spatial region based on past wind condition data, topographic data, and obstacle data. It includes a wind condition learning unit that learns feature group information based on the aforementioned wind condition data set, The wind condition estimation unit is an information processing system characterized by estimating wind condition information based on current wind condition measurement data obtained from a weather sensor and the feature group information.

6. A wind condition estimation step for estimating wind condition information in a predetermined spatial area, An evaluation step in which the difficulty or economics of flying an aircraft is evaluated based on the estimated wind condition information, and the difficulty or economics of flying in each spatial region is generated as three-dimensional evaluation information, A three-dimensional information generation step involves generating three-dimensional information by applying the aforementioned three-dimensional evaluation information to a three-dimensional map space that includes position data, shape data, and attribute data. It has, The airspace above river areas is assigned attribute data indicating recommended flight areas. An information processing method characterized by assigning attribute data indicating no-fly zones to the airspace above airports, fire stations, hospitals, and schools.

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