Patent Description:
Drones (unmanned flying objects) have been used increasingly for inspecting windmills used for wind power generation, and the like. In such a case, a drone captures images of a windmill, and, on the basis of the images, an inspector makes an assessment as to whether or not maintenance is necessary or the like. When a drone is used for such uses, it is necessary to capture high-resolution images, and so it is necessary for the drone to fly and stay in the air stably. However, because drones are small-sized and lightweight, there are problems that they are easily influenced by the wind, and particularly are easily influenced by wind condition changes such as gusts.

To cope with such problems, Patent Literature <NUM> is disclosed. Patent Literature <NUM> discloses a flight route calculation system, a flight route calculation program, and an unmanned aerial vehicle route control method that are aimed to "make it possible for a drone to fly without requiring manual maneuvering, and taking influences of the wind into consideration. An unmanned aerial vehicle flight management system <NUM> includes: a three-dimensional map data storage section <NUM> that stores three-dimensional map data in the horizontal direction, and height direction of a space where there are no ground objects, and an unmanned aerial vehicle <NUM> can fly; a current position acquiring section <NUM> that acquires a current position; a transport instruction acquiring section <NUM> that acquires a destination; a route calculating section <NUM> that calculates a flyable route in the map data from the current position to the destination; a lidar data acquiring section <NUM> that acquires wind condition data; a dangerous wind condition area assessing section <NUM> that calculates, from the wind condition data, an alert area where flights had better be avoided; and a route recalculating section <NUM> that recalculates a route that avoids the alert area in a case that the route calculated by the route calculating section <NUM> is a route that passes through the alert area calculated by the dangerous wind condition area assessing section <NUM>.

Patent Literature <NUM> discloses a wind forecasting system configured to generate forecasted wind conditions for a time interval based on wind data derived from flight log data collected from a plurality of aerial vehicles operating in a first area. The forecasted wind conditions can be used by an aerial route management system to generate a flight plan for an aerial vehicle from a start location to an end location in the first area during the time interval.

Patent Literature <NUM> discloses an apparatus and method for determining link level wind factors and providing routes for drones based on the wind factors. Wind factor values are assigned to a range of altitudes of drone air space above a road link of the road network based on a wind model and stored in a database. The wind model is applied to a location based on wind condition data and three-dimensional (3D) features from 3D map data associated with the location. The route is optimized based on the determined wind factors.

Patent Literature <NUM> discloses a system for taking into account micro wind conditions in a region. The system comprises a plurality of aerial vehicles within the region and a wind speed calculator. Each of the plurality of aerial vehicles has an altitude sensor and a GPS receiver. The wind speed calculator is configured to determine wind vectors within the region using measurements from the plurality of aerial vehicles.

Patent Literature <NUM> relates to the inspection of assets, such as assets having parts that move during operation. Operational data for the asset may be incorporated into planning or adapting the flight plan and/or operational commands may be issued to asset in accordance with the flight plan to facilitate acquisition of the inspection data.

However, whereas Patent Literature <NUM> describes predictions of wind conditions in the near future from measured wind condition data, specific techniques of the predictions are not described.

The present invention has been made in view of such a background, and an object of the present invention is to enable stable flights of an unmanned flying object.

According to the present invention, it is possible to cause an unmanned flying object to fly stably.

Next, embodiments for carrying out the present invention (referred to as "embodiments") are explained in detail with reference to the figures as appropriate. Note that similar constituent elements in the figures are given identical reference characters, and explanations thereof are omitted.

<FIG> is a figure depicting a configuration example of a drone control assistance system <NUM>.

The drone control assistance system <NUM> has a simulation apparatus <NUM>, and a wind condition estimating apparatus <NUM>. Note that the drone control assistance system <NUM> may include an analysis result DB <NUM>.

The simulation apparatus <NUM> acquires past weather data <NUM> from a weather center B1, and acquires terrain profile data <NUM> from a geographic center B2. Then, the simulation apparatus <NUM> performs a simulation (wind condition simulation) of wind conditions in a wind-condition estimation area on the basis of the data. Here, wind conditions are wind speeds, and wind directions. In addition, the wind-condition estimation area is an area where wind conditions are estimated for flying a drone <NUM>. In other words, the wind-condition estimation area is an area for flying the drone <NUM>.

Furthermore, the simulation apparatus <NUM> performs a flow field feature analysis of wind conditions of the wind-condition estimation area obtained as a result of the wind condition simulation. The flow field feature analysis is described later. It is supposed here that, as a result of the flow field feature analysis, analysis result sets <NUM> are generated. The analysis result sets <NUM> are described later. Then, the simulation apparatus <NUM> stores, in the analysis result DB <NUM>, the generated analysis results (here, the analysis result sets <NUM>).

On the basis of weather forecast data <NUM>, and measured wind condition data <NUM>, the wind condition estimating apparatus <NUM> acquires an analysis result from the analysis result DB <NUM>. Then, on the basis of the acquired analysis result (here, an analysis result set <NUM>), the wind condition estimating apparatus <NUM> estimates wind conditions of the wind-condition estimation area of a time after the passage of predetermined time from the current time. Note that the weather forecast data <NUM> is acquired from the weather center B1 or the like. In addition, the measured wind condition data <NUM> is acquired from a wind speed sensor or the like which is not depicted, but is included in a windmill WM.

Then, the wind condition estimating apparatus <NUM> outputs, to a drone control apparatus <NUM>, data (estimated wind condition data <NUM>) of the estimated wind conditions.

On the basis of the estimated wind condition data <NUM> output to the drone control apparatus <NUM>, an operator P1 controls the drone <NUM> by operating the drone control apparatus <NUM>. For example, the estimated wind condition data <NUM> output to the drone control apparatus <NUM> is displayed on a display apparatus which is not depicted, but is included in the drone control apparatus <NUM>. On the basis of the estimated wind conditions displayed on the display apparatus which is not depicted, the operator P1 performs control (maneuvering) of the drone <NUM>.

<FIG> is a functional block diagram of the simulation apparatus <NUM> in the present embodiment. <FIG> is referred to as appropriate.

The simulation apparatus <NUM> includes at least a memory <NUM>, a CPU (Central Processing Unit) <NUM>, and a transmitting/receiving apparatus <NUM>.

The transmitting/receiving apparatus <NUM> receives the past weather data <NUM> from the weather center B1, receives the terrain profile data <NUM> from the geographic center B2, and so on. In addition, the transmitting/receiving apparatus <NUM> transmits an analysis result set <NUM>, and the like to the analysis result DB <NUM>.

In addition, a program stored on a storage apparatus which is not depicted is loaded onto the memory <NUM>, and the loaded program is executed by the CPU <NUM>. Thereby, a data acquiring section <NUM>, a simulation processing section <NUM>, an analysis processing section <NUM>, and a storage processing section <NUM> are realized.

Via the transmitting/receiving apparatus <NUM>, the data acquiring section <NUM> acquires the past weather data <NUM> from the weather center B1, acquires the terrain profile data <NUM> from the geographic center B2, and so on.

On the basis of the acquired past weather data <NUM>, and terrain profile data <NUM>, the simulation processing section <NUM> calculates virtual wind condition data <NUM> (see <FIG>) of time t. In addition, on the basis of the calculated virtual wind condition data <NUM>, the simulation processing section <NUM> performs a simulation (wind condition simulation) of wind conditions of the wind-condition estimation area of time t+<NUM>. The virtual wind condition data <NUM> is described later, and is input data in the wind condition simulation. Here, time t+<NUM> means a time which is predetermined time after time t.

The analysis processing section <NUM> performs a flow field feature analysis process such as principal component analysis (proper orthogonal decomposition) of results of the simulation by the simulation processing section <NUM>.

Via the transmitting/receiving apparatus <NUM>, the storage processing section <NUM> stores the results obtained by the analysis processing section <NUM> in the analysis result DB <NUM> in association with weather conditions, and the like in the virtual wind condition data <NUM>, and the past weather data <NUM>.

<FIG> is a functional block diagram of the wind condition estimating apparatus <NUM> in the present embodiment. <FIG> is referred to as appropriate.

The wind condition estimating apparatus <NUM> includes at least a memory <NUM>, a CPU <NUM>, and a transmitting/receiving apparatus <NUM>.

The transmitting/receiving apparatus <NUM> receives the weather forecast data <NUM> from the weather center B1, receives the measured wind condition data <NUM>, which is data of measured wind conditions, and so on. In addition, the transmitting/receiving apparatus <NUM> acquires an analysis result set <NUM> from the analysis result DB <NUM>, outputs, to the drone control apparatus <NUM>, the estimated wind condition data (the estimated wind condition data <NUM>) of the wind-condition estimation area, and so on.

Then, a program stored on a storage apparatus which is not depicted is loaded onto the memory <NUM>, and the loaded program is executed by the CPU <NUM>. Thereby, a data acquiring section <NUM>, an analysis result acquiring section <NUM>, a wind condition estimating section <NUM>, and an output processing section <NUM> are realized.

Via the transmitting/receiving apparatus <NUM>, the data acquiring section <NUM> acquires the weather forecast data <NUM> from the weather center B1, acquires the measured wind condition data <NUM> from the wind speed sensor (not depicted) included in the windmill WM or the like, and so on.

On the basis of the acquired weather forecast data <NUM>, and measured wind condition data <NUM>, the analysis result acquiring section <NUM> acquires an analysis result set <NUM> from the analysis result DB <NUM>.

On the basis of the acquired analysis result set <NUM>, the wind condition estimating section <NUM> estimates wind conditions of the wind-condition estimation area.

The output processing section <NUM> outputs, to the drone control apparatus <NUM>, data (the estimated wind condition data <NUM>) of the estimated wind conditions.

<FIG> is a flowchart depicting the procedure of processes performed by the simulation apparatus <NUM> in the present embodiment. Details of processes at Steps S101 to S105 are described later. In addition, <FIG> are referred to as appropriate.

First, via the transmitting/receiving apparatus <NUM>, the data acquiring section <NUM> acquires the past weather data <NUM> from the weather center B1, and acquires the terrain profile data <NUM> from the geographic center B2 (S101).

Next, the simulation processing section <NUM> calculates the virtual wind condition data <NUM> (see <FIG>) of a geographical point (S102). The virtual wind condition data <NUM> is described later.

Then, on the basis of the input virtual wind condition data <NUM>, past weather data <NUM>, and terrain profile data <NUM>, the simulation processing section <NUM> performs a simulation (wind condition simulation) of wind conditions of the wind-condition estimation area (S103).

Next, the analysis processing section <NUM> performs a flow field feature analysis process on results of the process at Step S103 (S104). A technique used for the flow field feature analysis process is sy component analysis (proper orthogonal decomposition), Fourier analysis or the like as described before.

Then, the storage processing section <NUM> stores, in the analysis result DB <NUM>, results (analysis results; the analysis result sets <NUM> in the present embodiment) of the flow field feature analysis process (S105).

The simulation apparatus <NUM> performs the processes at Steps S101 to S105 on various pieces of virtual wind condition data <NUM>. Then, the simulation apparatus <NUM> stores, in the analysis result DB <NUM>, the analysis results (analysis result sets <NUM>) in association with a corresponding piece of the virtual wind condition data <NUM>, and weather conditions used for calculations of the piece of the virtual wind condition data <NUM>.

<FIG> is a flowchart depicting the procedure of processes performed by the wind condition estimating apparatus <NUM> in the present embodiment. Details of processes at Steps S201 to S204 are described later. In addition, <FIG> are referred to as appropriate.

The data acquiring section <NUM> acquires the weather forecast data <NUM>, and the measured wind condition data <NUM> via the transmitting/receiving apparatus <NUM> (S201). As described before, the weather forecast data <NUM> is acquired from the weather center B1, and the measured wind condition data <NUM> is acquired from the wind speed sensor (not depicted) included in the windmill WM or the like, for example.

Next, the analysis result acquiring section <NUM> acquires an analysis result set <NUM> matching weather conditions of the acquired measured wind condition data <NUM>, and weather forecast data <NUM> (S202).

Then, on the basis of the acquired analysis result set <NUM>, the wind condition estimating section <NUM> estimates wind conditions of the wind-condition estimation area of a time after the passage of predetermined time from the current time (S203). If the current time is time t, the time which is the predetermined time after the current time here means a time equivalent to time t+<NUM>.

Thereafter, the output processing section <NUM> outputs the estimated wind condition data (estimated wind condition data <NUM>) of the wind-condition estimation area (S204).

Next, with reference to <FIG>, a specific example of each process in <FIG>, and <FIG> is explained.

First, a specific example of the processes at Steps S101 to S105 in <FIG> is explained with reference to <FIG>, and the like.

<FIG> is a figure depicting an example of the past weather data <NUM> acquired at Step S101 in <FIG>.

<FIG> depicts the past weather data <NUM> about Japan, and its surrounding areas.

<FIG> depicts wind conditions in the past weather data <NUM>. That is, reference character <NUM> denotes arrows representing wind directions, and shading represents wind speeds. That is, the darker the shading is, the faster the wind speed is, and the brighter the shading is, the slower the wind speed is. Here, the past weather data <NUM> of an area represented by reference character <NUM> where the target windmill WM of an estimation of wind conditions is installed is depicted in <FIG>, which is described later.

<FIG> depicts the past weather data <NUM> of the area represented by reference character <NUM> in <FIG>.

In <FIG>, reference character <NUM> denotes the contour of the terrain profile, and reference character <NUM> denotes arrows representing wind directions. In addition, the shading in <FIG> represents wind speeds. That is, the darker the shading is, the faster the wind speed is, and the brighter the shading is, the slower the wind speed is.

In addition, reference character <NUM> denotes the wind-condition estimation area.

<FIG> is a figure depicting the virtual wind condition data <NUM> calculated at Step S102 in <FIG>, and results of the wind condition simulation of the wind-condition estimation area performed at Step S103.

In <FIG>, the virtual wind condition data <NUM> is calculated at Step S102 in <FIG>. The virtual wind condition data <NUM> is calculated by the simulation processing section <NUM> on the basis of the past weather data <NUM> like the ones depicted in <FIG>, the terrain profile data <NUM>, data such as temperature or humidity in the past weather data <NUM>, past wind conditions, and the like.

In <FIG>, reference character <NUM> denotes the windmill WM.

Because of the wind equivalent to the virtual wind condition data <NUM> that blows toward the windmill WM denoted by reference character <NUM> at time t, wind conditions <NUM> that are observed at a downwind location (the wind-condition estimation area) of the windmill WM at time t+<NUM> are obtained through the wind condition simulation. In the wind conditions <NUM>, the shading represents wind speeds. The darker the shading is, the faster the wind speed is, and the brighter the shading is, the slower the wind speed is. As represented by the wind conditions <NUM>, there is a disturbance of air such as a vortex of air at the downwind location (the wind-condition estimation area) of the windmill WM. If the drone <NUM> is caught in such a disturbance (vortex) of air, the drone <NUM> loses the balance significantly, and it becomes difficult to perform image-capturing, and the like.

<FIG> is a figure depicting results of the flow field feature analysis process performed at Step S104 in <FIG>.

It is supposed here that principal component analysis (proper orthogonal decomposition) is used as the flow field feature analysis technique. Then, <FIG> depicts an example in which the principal component analysis is applied to the simulation results depicted as the wind conditions <NUM> in <FIG>.

By performing the principal component analysis on the wind conditions <NUM> of the wind-condition estimation area depicted in <FIG>, the first mode to the n-th mode of analysis results are obtained. In <FIG>, reference character <NUM> denotes the first mode of analysis results, and reference character <NUM> denotes the n-th mode of analysis results. Whereas only the first mode, and n-th mode of analysis results are depicted here, actually, there are the second mode, third mode,. (n-<NUM>)-th mode of analysis results between the first mode of analysis results, and the n-th mode of analysis results.

Here, the first mode of analysis results denoted by reference character <NUM> includes a large vortex of air, and the n-th mode of analysis results denoted by reference character <NUM> includes small flow velocity changes.

One set of the first mode (reference character <NUM>), second mode,. (n-<NUM>)-th mode, and n-th mode (reference character <NUM>) of analysis results generated on the basis of the same simulation results is referred to as an analysis result set <NUM>.

In addition, the analysis processing section <NUM> also calculates a system matrix A for reconstructing the wind conditions <NUM> in <FIG> from the analysis result set <NUM>. Note that the calculation of the system matrix A can be performed very simply.

At Step S105 in <FIG>, the storage processing section <NUM> stores the analysis result set <NUM> depicted in <FIG> in the analysis result DB <NUM>. At this time, the storage processing section <NUM> stores, in association with the analysis result set <NUM> and in the analysis result DB <NUM>, the virtual wind condition data <NUM> in <FIG>, the system matrix A, and weather conditions such as a temperature or a humidity obtained from the past weather data <NUM>. Note that by storing the analysis result set <NUM> in the analysis result DB <NUM> as depicted in <FIG>, the volume of data stored in the analysis result DB <NUM> can be reduced.

Next, a specific example of the processes at Steps S201 to <NUM> in <FIG> is explained.

First, at Step S201, the weather forecast data <NUM> is acquired, and the measured wind condition data <NUM> is acquired. The measured wind condition data <NUM> is measured wind condition data equivalent to the virtual wind condition data <NUM> in <FIG>. That is, the measured wind condition data <NUM> is data related to the wind blowing toward the windmill WM.

Then, at Step S203, from the analysis result DB <NUM>, the analysis result acquiring section <NUM> acquires weather conditions such as a temperature or a humidity obtained from the measured wind condition data <NUM>, and the weather forecast data <NUM>, an analysis result set <NUM> associated with similar virtual wind condition data <NUM>, and weather conditions, and the system matrix A. Note that the system matrix A may be calculated at this timing.

Next, at Step S203, on the basis of the acquired analysis result set <NUM>, and system matrix A, the wind condition estimating section <NUM> performs a process according to a reduced order model to thereby reconstruct the wind conditions <NUM> in <FIG>. Thereby, wind conditions of the wind-condition estimation area of a time after the passage of predetermined time from the current time are estimated. The time after the passage of predetermined time here means a time equivalent to time t+<NUM>, supposing that the current time is time t.

At Step S204, the output processing section <NUM> outputs, to the drone control apparatus <NUM>, data (the estimated wind condition data <NUM>) of the wind conditions of the wind-condition estimation area estimated by the recovery.

<FIG> are figures depicting examples of execution by the drone control assistance system <NUM> according to the present embodiment.

<FIG> depicts the flight condition of the drone <NUM> of the current time, and <FIG> depict the flight condition of the drone <NUM> of times each after the passage of predetermined time from the current time.

It is supposed, as depicted in <FIG>, that the drone <NUM> is flying by the windmill WM for capturing images in the maintenance of the windmill WM. It is supposed in <FIG> that a wind which does not influence the flight of the drone <NUM> is blowing from the left side on the paper surface (thin arrows in <FIG>).

Here, it is supposed that a gust like the one represented by thick arrows in <FIG> starts blowing from the left side on the paper surface. Then, a disturbance of the air like the one represented by the wind conditions <NUM> in <FIG> occurs at a downwind location of the windmill WM at a time after the passage of predetermined time from the current time. As a result, as depicted in <FIG>, the flight posture of the drone <NUM> is disturbed undesirably. Thereby, it becomes difficult to capture appropriate images of the windmill WM undesirably, and a situation occurs where it is necessary to wait until the gust stops, it is necessary to stop the maintenance of that day in some cases, and so on.

In contrast to this, according to the present embodiment, as depicted in <FIG>, it is possible to perform, almost in real time, a calculation as to how a disturbance of the air occurs at a downwind location of the windmill WM at a time after the passage of predetermined time from the current time, after the gust represented by the thick arrows starts blowing from the left side on the paper surface. In the present embodiment, analysis result sets <NUM> are stored in the analysis result DB <NUM> in association with various wind conditions, and weather conditions. Then, the wind condition estimating apparatus <NUM> searches for an analysis result set <NUM> in accordance with wind conditions, and weather conditions of an upwind location of the windmill WM of the current time. Furthermore, this is because the wind condition estimating apparatus <NUM> can reconstruct wind conditions of the wind-condition estimation area (e.g. a downwind location of the windmill WM) on the basis of the analysis result set <NUM> found through the search. Because the reconstruction takes little time, substantially, it is required only to search for and acquire a result that has been obtained already through wind condition simulations, and the influence of wind condition changes such as a gust can be output in real time.

Thereby, the operator P1 can recognize in advance a disturbance of the air generated by a gust or the like, and can perform maneuvering of the drone <NUM> according to the gust or the like. As a result, a stable flight of the drone <NUM> can be realized, and stable image-capturing can be performed in the maintenance or the like.

<FIG> is a figure depicting another application example of the drone control assistance system <NUM> according to the present embodiment.

<FIG> is different from <FIG> in that autonomous control of a drone 500a is performed.

In addition, in <FIG>, the wind condition estimating apparatus <NUM> outputs the estimated wind condition data <NUM> to the drone 500a, and a drone monitoring apparatus 400a.

The drone 500a has an optimal control computing section <NUM>, a control section <NUM>, and a posture sensor <NUM>.

The optimal control computing section <NUM> calculates control data of a time after the passage of predetermined time from the current time on the basis of the current posture data obtained by the posture sensor <NUM>, and the estimated wind condition data <NUM> input from the wind condition estimating apparatus <NUM>.

The control section <NUM> of the drone 500a performs posture control of the drone 500a on the basis of the calculated control data of the time after the passage of the predetermined time.

In addition, an observer P2 monitors whether the drone 500a is performing appropriate autonomous control by monitoring the estimated wind condition data <NUM> acquired from the wind condition estimating apparatus <NUM> on the drone monitoring apparatus 400a.

According to the present embodiment, the influence of wind condition changes such as a gust can be output almost in real time, and so it becomes possible for the drone <NUM> to fly and stay in the air stably. Thereby, stable images can be obtained in image-capturing or the like in maintenance.

In addition, because it is difficult to cope with a gust or the like, typically, only experts are permitted to maneuver the drone <NUM> in many cases. According to the present embodiment, the operator P1 can recognize in advance estimated wind conditions of a wind-condition estimation area, and so does not necessarily have to be an expert to maneuver the drone <NUM>.

In addition, by performing the flow field feature analysis (Step S104 in <FIG>), the amount of data of analysis results can be compressed. Thereby, the volume of data stored in the analysis result DB <NUM> can be reduced.

Furthermore, the simulation processing section <NUM> generates the virtual wind condition data <NUM> on the basis of the past weather data <NUM>, and terrain profile data <NUM> of the wind-condition estimation area. Then, the simulation processing section <NUM> performs a wind condition simulation on the basis of the generated virtual wind condition data <NUM>. Thereby, the wind condition simulation can be performed in accordance with conditions close to actual wind conditions, and the precision of the wind condition simulation can be enhanced.

Note that whereas it is supposed in the present embodiment that control assistance of the drone <NUM> around the windmill WM used for wind power generation or the like is performed, this is not the sole example. The drone control assistance system <NUM> according to the present embodiment may be used for control assistance of the drone <NUM> around a structure such as a bridge or a plant.

In addition, whereas it is supposed in the present embodiment that the measured wind condition data <NUM> is acquired from a wind speed center (not depicted) included in the windmill WM, this is not the sole example. The wind speed sensor may be installed on the ground or a building other than the windmill WM, for example, as long as the wind speed sensor is installed near the windmill WM.

In addition, the analysis processing section <NUM> can be omitted. In this case, Step S104 in <FIG> is not executed, and the simulation result (the wind conditions <NUM> in <FIG>) is stored in an analysis result DB <NUM> in association with virtual wind condition data <NUM>, weather conditions, and the like. In addition, the analysis result acquiring section <NUM> acquires a simulation result on the basis of the current wind conditions, and weather conditions. Thereafter, the simulation result acquired by the output processing section <NUM> is output to the drone control apparatus <NUM>, the drone monitoring apparatus 400a, and a drone 400a.

In addition, the configuration, functionalities, processing sections <NUM> to <NUM>, and <NUM> to <NUM>, analysis result DB <NUM>, and the like that are described before may partially or entirely be realized by hardware by being designed on an integrated circuit, and so on, for example. In addition, as depicted in <FIG>, the configuration, functionalities, and the like that are described before may be realized by software by processors such as the CPUs <NUM>, and <NUM> interpreting and executing programs to realize the functionalities. Information such as programs, tables or files that realize the functionalities can be stored on the memory <NUM> or <NUM>, a recording apparatus such as an SSD (Solid State Drive) or a recording medium such as an IC (Integrated Circuit) card, an SD (Secure Digital) card or a DVD (Digital Versatile Disc), other than being stored on an HD.

Claim 1:
An unmanned flying object control assistance system (<NUM>), comprising:
a simulation apparatus (<NUM>), comprising:
a data acquiring section (<NUM>) configured to acquire past weather data (<NUM>) and terrain profile data (<NUM>),
a simulation processing section (<NUM>) configured to perform a wind condition simulation of wind conditions in a wind condition estimation area on the basis of the acquired past weather data (<NUM>) and the acquired terrain profile data (<NUM>),
an analysis processing section (<NUM>) configured to perform a flow field feature analysis of wind conditions of the wind condition estimation area obtained as a result of the wind condition simulation, to generate analysis result sets (<NUM>), and to store the analysis result sets (<NUM>) in an analysis result database (<NUM>),
a wind condition estimating apparatus (<NUM>), comprising:
a data acquiring section (<NUM>) configured to acquire weather forecast data (<NUM>) and measured wind condition data (<NUM>),
an analysis result acquiring section (<NUM>) configured to acquire an analysis result set (<NUM>) from the analysis result database (<NUM>) on the basis of the acquired weather forecast data (<NUM>) and the acquired measured wind condition data (<NUM>),
a wind condition estimating section (<NUM>) configured to estimate wind conditions (<NUM>) of the wind condition estimation area for a time (t+<NUM>) after the passage of a predetermined time from the current time (t),
an output processing section (<NUM>) configured to output the estimated wind conditions (<NUM>) to a drone control apparatus (<NUM>).