WO2023218498A1 - リスク評価装置、リスク評価方法、及び、記録媒体 - Google Patents

リスク評価装置、リスク評価方法、及び、記録媒体 Download PDF

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Publication number
WO2023218498A1
WO2023218498A1 PCT/JP2022/019658 JP2022019658W WO2023218498A1 WO 2023218498 A1 WO2023218498 A1 WO 2023218498A1 JP 2022019658 W JP2022019658 W JP 2022019658W WO 2023218498 A1 WO2023218498 A1 WO 2023218498A1
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Prior art keywords
risk
information
risk evaluation
flying object
aircraft
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PCT/JP2022/019658
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English (en)
French (fr)
Japanese (ja)
Inventor
慎二 中台
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NEC Corp
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NEC Corp
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Priority to JP2024520096A priority Critical patent/JP7831590B2/ja
Priority to PCT/JP2022/019658 priority patent/WO2023218498A1/ja
Priority to US18/854,092 priority patent/US20250246085A1/en
Publication of WO2023218498A1 publication Critical patent/WO2023218498A1/ja
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

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    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G5/00—Traffic control systems for aircraft
    • G08G5/70—Arrangements for monitoring traffic-related situations or conditions
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G5/00—Traffic control systems for aircraft

Definitions

  • the present disclosure relates to the evaluation of risks associated with the flight of an aircraft.
  • Patent Document 1 proposes a device for collecting information that can be used to calculate the risk when an unmanned flying object such as a drone crashes.
  • One objective of the present disclosure is to provide a risk evaluation device that can evaluate risks in advance when flying an aircraft such as a drone.
  • the risk assessment device includes: acquisition means for acquiring input information including position information indicating the position of the aircraft, body information of the aircraft, weather information, and environmental information; Risk evaluation means for evaluating the risk when the flying object is at the position based on the input information using a trained risk evaluation model, and outputting a risk evaluation result; Equipped with
  • the risk assessment method includes: Obtaining input information including position information indicating the position of the aircraft, body information of the aircraft, weather information, and environmental information; Based on the input information, a trained risk evaluation model is used to evaluate the risk when the flying object is at the position, and a risk evaluation result is output.
  • the recording medium includes: Obtaining input information including position information indicating the position of the aircraft, body information of the aircraft, weather information, and environmental information; Based on the input information, a trained risk assessment model is used to evaluate the risk when the flying object is at the position, and a program is recorded that causes a computer to execute a process of outputting a risk assessment result.
  • FIG. 1 shows a risk evaluation device according to a first embodiment.
  • FIG. 2 is a block diagram showing the hardware configuration of a risk evaluation device.
  • FIG. 2 is a block diagram showing a functional configuration for model training of the risk assessment device according to the first embodiment.
  • An example of simulating the behavior of a drone is shown.
  • An example of a simulation result in which a drone crashes is shown.
  • An example of how the risk evaluation department evaluates risks is shown below.
  • An example of a risk assessment model is shown below.
  • It is a flowchart of the training process of a risk evaluation model.
  • FIG. 2 is a block diagram showing a functional configuration for risk evaluation of the risk evaluation device according to the first embodiment. It is a flowchart of risk evaluation processing.
  • It is a block diagram showing the functional composition of the risk assessment device of a 2nd embodiment.
  • FIG. 1 shows a risk evaluation device according to a first embodiment.
  • the risk evaluation device 100 is configured by, for example, a computer such as a personal computer (PC).
  • the risk evaluation device 100 evaluates risks that may occur when a flying object such as a drone is flown along a planned route.
  • a flying object such as a drone
  • the flying object in the present disclosure is not limited to a flying object generally called a drone, but also includes various unmanned flying objects that fly under external control. include.
  • the risk assessment device 100 receives position information, aircraft information, weather information, and environmental information as input information.
  • the risk assessment device 100 includes a previously trained risk assessment model.
  • the risk evaluation device 100 uses a risk evaluation model to evaluate risk based on input information, and outputs a risk evaluation value as a risk evaluation result. By evaluating the risks in advance using the risk evaluation device 100, it is possible to predict in advance the risks that may occur when the drone is flown along the planned route.
  • FIG. 2 is a block diagram showing the hardware configuration of the risk assessment device 100.
  • the risk assessment device 100 includes an interface (I/F) 11, a processor 12, a memory 13, a recording medium 14, a database (DB) 15, a display section 16, an input section 17, Equipped with
  • the I/F 11 inputs and outputs data to and from external devices. Specifically, input information such as location information and aircraft information is input to the risk evaluation device 100 through the I/F 11. Further, the risk evaluation value generated by the risk evaluation device 100 is outputted to an external device via the I/F 11 as necessary.
  • the processor 12 is a computer such as a CPU (Central Processing Unit), and controls the entire risk assessment device 100 by executing a program prepared in advance.
  • the processor 12 may be a GPU (Graphics Processing Unit), a TPU (Tensor Processing Unit), a quantum processor, or an FPGA (Field-Programmable Gate Array).
  • the processor 12 executes a risk assessment model training process and a risk assessment process using a trained risk assessment model, as will be described later.
  • the memory 13 is composed of ROM (Read Only Memory), RAM (Random Access Memory), and the like.
  • the memory 13 is also used as a working memory while the processor 12 executes various processes.
  • the recording medium 14 is a non-volatile, non-temporary recording medium such as a disk-shaped recording medium or a semiconductor memory, and is configured to be detachable from the risk assessment device 100.
  • the recording medium 14 records various programs executed by the processor 12. When the risk evaluation device 100 executes various processes, a program recorded on the recording medium 14 is loaded into the memory 13 and executed by the processor 12.
  • the DB 15 stores data used and generated data by the risk assessment device 100. Specifically, the DB 15 stores location information, aircraft information, weather information, environmental information, etc. input as input information. Further, the DB 15 stores data used in training of a risk evaluation model, which will be described later, such as risk definition data and simulation results of drone behavior. Furthermore, information regarding the risk assessment model obtained through training is stored in the DB 15. Note that if the capacity of the memory 13 is sufficient, the above data may be stored in the memory 13.
  • the display unit 16 is, for example, a liquid crystal display device, and displays the risk evaluation value generated by the risk evaluation device 100.
  • the input unit 17 is, for example, a mouse, a keyboard, etc., and is used by the user to give instructions and input necessary during risk assessment training and risk assessment processing using a risk assessment model.
  • FIG. 3 is a block diagram showing a functional configuration for model training of the risk assessment device 100 according to the first embodiment.
  • the risk evaluation device 100 includes a simulation section 31, a risk evaluation section 32, and a model training section 33 as configurations for model training.
  • the simulation unit 31 simulates the behavior of the drone based on the input information, and outputs a simulation result indicating the predicted behavior of the drone. Specifically, the simulation unit 31 receives position information, aircraft information, weather information, and environmental information as input information. The input information is used as simulation conditions.
  • the position information indicates the three-dimensional position of the drone, and can use, for example, latitude, longitude, altitude, etc. in real geographical space. Note that in simulating the behavior of the drone, a virtual geographical space may be used instead of the actual geographical space. In this case, the position information indicates a three-dimensional position in the virtual geographical space.
  • the aircraft information is information about the drone itself, and includes various information such as the size, shape, type, engine type, and number of wings of the drone. Further, as the aircraft information, movement information of the drone, for example, the movement direction, movement speed, acceleration, etc. of the drone may be used.
  • Weather information is information indicating the weather conditions in the area where the drone is flying, and includes, for example, weather, temperature, humidity, wind direction, wind speed, etc.
  • the environmental information is information indicating the geographical environment in the area in which the drone flies, and includes ground topographic information.
  • map information can be used as the topographical information.
  • the topographical information includes information indicating ground conditions, such as classifications such as sea, river, land, mountainous area, road, and urban area.
  • the environmental information includes information such as whether the area has many buildings or not, and whether the area has many people.
  • the simulation unit 31 uses the input location information, aircraft information, weather information, and environmental information to simulate the behavior of the drone when a certain trouble or malfunction (hereinafter simply referred to as "trouble") occurs in the drone. do.
  • Figure 4 shows an example of simulating the behavior of a drone.
  • the simulation unit 31 sets a simulation space 50 having the position of the drone D as its origin based on the input position information.
  • the simulation space 50 is a geographical space within a predetermined range from the position of the drone D.
  • the simulation unit 31 uses the aircraft information and the weather information and environment information in the simulation space 50 to simulate the behavior of which drone D when a certain trouble occurs in the drone D. Note that the simulation unit 31 sets the probability of occurrence of the trouble based on the input information and performs the simulation.
  • FIG. 5A shows an example of a simulation result in which the drone D crashes.
  • the simulation result S1 indicates that the drone D crashes to the earth's surface along a trajectory indicated by the symbol S1.
  • the simulation unit 31 performs a plurality of simulations by keeping the position and body information of the drone D the same and changing the movement information, weather information, etc. of the drone D.
  • simulation results S2 to S5 that are different from the simulation result S1 are obtained.
  • the simulation unit 31 performs multiple simulations while changing the location information, aircraft information, weather information, and environmental information regarding the area where the drone is scheduled to fly. Moreover, the simulation unit 31 changes the type of trouble that occurs in the drone and performs simulation many times. Examples of problems that can occur with drones include engine problems, communication equipment problems, propeller problems, being blown by strong winds, and contact with birds or other drones. This makes it possible to obtain simulation results of the behavior of various types of drones when various troubles occur under various weather conditions in the area where the drones are scheduled to fly.
  • the simulation section 31 outputs the obtained simulation results to the risk evaluation section 32. Note that various methods such as Monte Carlo simulation using random numbers can be used as the simulation.
  • the risk evaluation unit 32 evaluates the risk corresponding to the behavior of the drone obtained as a simulation result using the risk definition data.
  • the risk definition data is data that defines risks mainly caused by a drone crash, depending on the ground situation indicated by the map information. There are multiple risks that could occur if a drone crashes, including the risk of the drone hitting people and the risk of environmental pollution.
  • the risk definition data defines a risk value for each risk.
  • FIG. 6 shows an example in which the risk evaluation unit 32 evaluates risks using risk definition data.
  • risk A we consider the risk of a crashed drone hitting a person.
  • the risk definition data specifies that the risk value is "0" if the drone crashes into the sea, and the risk value is "1" if the drone crashes on land.
  • simulation results S1 to S5 are obtained regarding the behavior of the drone.
  • the risk value is "1” because the drone D crashes on land.
  • the simulation results S3 to S5 the risk value is "0” because the drone D crashes into the sea. Therefore, the average risk of simulations S1 to S5 for risk A is "0.4". In this way, the risk evaluation unit 32 can calculate the risk value when the drone D is at a certain point.
  • the risk definition data for risk A sets the risk value to "1" if the drone crashes on land, but for example, if the land is in a crowded area such as a city
  • the risk value may be divided into areas where there are no crowds of people and areas where there are no crowds of people, and the risk value in areas where there are crowds is "2", the risk value in areas where there are no crowds is "1", etc.
  • the risk evaluation unit 32 calculates the total risk R when the drone is at a certain point using the following equation (1).
  • “tranjectory i” indicates the trajectory of the drone obtained as a simulation result by the simulation unit 31.
  • the simulation unit 31 simulates the behavior of the drone when a plurality of troubles t occur.
  • the trajectory of one obtained simulation result is indicated by “tranjectory i ”.
  • "i” is an identifier for each simulation, and "S t " indicates the number of simulations.
  • risk j () is a function indicating a risk value. As described above, the risk definition data defines risk values for multiple risks. “j” indicates the type of risk. Therefore, “risk j (tranjectory i )” indicates the risk value of risk j caused by trajectory i obtained as a simulation result.
  • weight j indicates the weight set for each risk.
  • multiple risks have different degrees of importance. For example, the risk of a drone hitting a person should be given a higher importance level than the risk of a drone falling into the ocean. For this reason, a weight is set for each risk, and the total risk R is calculated by taking into account the degree of importance of each risk.
  • the weighted sum of the risk values for each of the multiple simulation results tranjectory i is averaged over the number of simulations S t , thereby calculating the average risk value across multiple simulations. is being calculated.
  • the total risk R is calculated for a plurality of possible troubles t, taking into consideration the probability of occurrence of each trouble.
  • the total risk R when a drone is at a certain point is calculated based on the results of multiple simulations, taking into account multiple risks, the weight of each risk, multiple troubles, and the probability of occurrence of each trouble. It is calculated as follows.
  • the risk evaluation unit 32 outputs the total risk R calculated in this way to the model training unit 33.
  • the model training unit 33 trains a risk evaluation model using the input information input to the simulation unit 31 and the total risk output from the risk evaluation unit 32. Specifically, the model training unit 33 uses input information including location information, aircraft information, etc. as input data, performs supervised learning using total risk as correct data, and trains a risk evaluation model.
  • the risk assessment model obtained through training outputs an evaluation value of the risk caused by the drone under those conditions when inputted with location information, aircraft information, weather information, and environmental information about a drone existing at a certain point. .
  • the output evaluation value is a value indicating the total risk considering multiple troubles and multiple risks that may occur under the conditions.
  • Figure 7 shows an example of a risk assessment model.
  • the risk assessment model is constructed using a neural network.
  • aircraft information As inputs to the risk assessment model, aircraft information, the moving direction and acceleration of the drone, weather information (wind speed), and the risk value in the relative position to the drone are input.
  • the moving direction and acceleration of the drone are part of the aircraft information.
  • the wind speed is given to each cell (voxel) when the X, Y, and Z directions in the simulation space 50 shown in FIG. 4 are each divided into a plurality of cells.
  • a risk value at a relative position to the drone is also given for each cell in the simulation space 50 and for each type of risk. Note that this risk value is obtained based on environmental information and risk definition data.
  • FIG. 8 is a flowchart of the risk assessment model training process. This processing is realized by the processor 12 shown in FIG. 2 executing a program prepared in advance and operating as the element shown in FIG. 3.
  • the simulation unit 31 receives position information, aircraft information, weather information, and environmental information as input information (step S10), and simulates the behavior of the drone regarding a plurality of troubles (step S11).
  • the risk evaluation unit 32 refers to the risk definition data and calculates the total risk considering a plurality of risks based on the trajectory of the drone indicated by the plurality of simulation results (step S12). Specifically, the risk evaluation unit 32 calculates the total risk R using the above-mentioned formula (1) based on a plurality of simulation results.
  • the model training unit 33 trains a risk evaluation model using the input information input in step S10 and the total risk R obtained in step S12 (step S13). Then, the model training process ends.
  • FIG. 9 is a block diagram showing a functional configuration for risk evaluation of the risk evaluation device 100 according to the first embodiment.
  • the risk evaluation device 100 includes a position information input section 41, an aircraft information input section 42, a weather information input section 43, an environmental information input section 44, a risk evaluation section 45, and a risk output section as components for risk evaluation. 46.
  • the position information input unit 41 receives position information indicating the position on the planned flight route of the drone that is subject to risk assessment.
  • the aircraft information input unit 42 receives aircraft information regarding the drone targeted for risk assessment.
  • Weather information indicating weather conditions that are subject to risk assessment is input to the weather information input section 43 .
  • the environmental information input unit 44 receives input of the environment targeted for risk assessment, specifically, topographical information of the region corresponding to the position of the drone.
  • the risk evaluation unit 45 performs risk evaluation using the trained risk evaluation model obtained through the above-described model training process. Specifically, the risk evaluation unit 45 inputs the above input information into a risk evaluation model, and obtains a risk evaluation value as its output. Then, the risk evaluation section 45 outputs the obtained risk evaluation value to the risk output section 46. The risk output unit 46 presents a risk value to the user as a risk evaluation result. For example, the risk output unit 46 displays the input risk value on the display unit 16.
  • the risk evaluation value presented to the user in this way is a comprehensive evaluation that takes into account multiple risks that may occur in the event that various troubles occur with the drone under the conditions specified by the information entered in each input section above. This is a risk value. Therefore, those planning to fly a drone can evaluate the risk in advance by inputting information such as the location where the drone will be flown, information about the drone's body, weather conditions at that time, and environmental information of the area where it will be flown. can.
  • FIG. 10 is a flowchart of the risk evaluation process. This processing is realized by the processor 12 shown in FIG. 2 executing a program prepared in advance and operating as the element shown in FIG. 9.
  • the location information input unit 41, aircraft information input unit 42, weather information input unit 43, and environment information input unit 44 each receive location information, aircraft information, weather information, and environment information (step S21).
  • the risk evaluation unit 45 uses the trained risk evaluation model to perform risk evaluation based on the input information (step S22), and outputs a risk evaluation value as the evaluation result (step S23). For example, the risk evaluation section 45 displays the risk evaluation value on the display section 16. Then, the process ends.
  • the above risk evaluation value indicates the total risk when the drone is located at the location indicated by the input location information. Therefore, the user can check the risks in the planned flight route or the entire region by performing risk evaluation processing on a plurality of points on the route or region where the drone is planned to fly.
  • the risk evaluation device 100 may display the risk value on a map showing the position of the drone.
  • the risk assessment device 100 displays a map on the display unit 16.
  • the position information input unit 41 acquires position information of a point specified by the user using a mouse or the like on the displayed map.
  • the risk output unit 46 displays the calculated risk value in a superimposed manner near the point on the map. Thereby, the user can view the point designated as the drone's position in association with the risk value at that point.
  • FIG. 11 is a block diagram showing the functional configuration of the risk evaluation device 70 of the second embodiment.
  • the risk evaluation device 70 includes an acquisition means 71 and a risk evaluation means 72.
  • FIG. 12 is a flowchart of processing by the risk evaluation device 70 of the second embodiment.
  • the acquisition means 71 acquires input information including position information indicating the position of the aircraft, body information of the aircraft, weather information, and environmental information (step S71).
  • the risk evaluation means 72 uses a trained risk evaluation model to evaluate the risk when the aircraft is at that position, and outputs the risk evaluation result (step S72).
  • the risk evaluation device 70 of the second embodiment it is possible to evaluate in advance risks that may occur due to the flight of an aircraft.
  • acquisition means for acquiring input information including position information indicating the position of the aircraft, body information of the aircraft, weather information, and environmental information;
  • Risk evaluation means for evaluating the risk when the flying object is at the position based on the input information using a trained risk evaluation model, and outputting a risk evaluation result;
  • a risk assessment device equipped with
  • Appendix 6 The risk evaluation device according to appendix 5, wherein the risk value corresponding to the behavior of the flying object is calculated using probabilities of occurrence of a plurality of troubles related to the flying object.
  • Appendix 8 Obtaining input information including position information indicating the position of the aircraft, body information of the aircraft, weather information, and environmental information; A risk evaluation method that uses a trained risk evaluation model to evaluate the risk when the flying object is at the position based on the input information, and outputs a risk evaluation result.
  • Appendix 9 Obtaining input information including position information indicating the position of the aircraft, body information of the aircraft, weather information, and environmental information;
  • a recording medium recording a program that causes a computer to execute a process of evaluating the risk when the flying object is in the position based on the input information using a trained risk evaluation model and outputting a risk evaluation result.

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PCT/JP2022/019658 2022-05-09 2022-05-09 リスク評価装置、リスク評価方法、及び、記録媒体 Ceased WO2023218498A1 (ja)

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JP2024520096A JP7831590B2 (ja) 2022-05-09 2022-05-09 リスク評価装置、リスク評価方法、及び、プログラム
PCT/JP2022/019658 WO2023218498A1 (ja) 2022-05-09 2022-05-09 リスク評価装置、リスク評価方法、及び、記録媒体
US18/854,092 US20250246085A1 (en) 2022-05-09 2022-05-09 Risk evaluation device, risk evaluation method, and recording medium

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Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2018110634A1 (ja) * 2016-12-14 2018-06-21 株式会社自律制御システム研究所 無人航空機の飛行管理システム、及び飛行管理方法
JP2020112574A (ja) * 2020-04-03 2020-07-27 東京電力ホールディングス株式会社 無人飛翔体情報収集装置、無人飛翔体情報収集方法、およびプログラム
WO2021157034A1 (ja) * 2020-02-06 2021-08-12 Ａｎａホールディングス株式会社 航空機危険予測装置および航空機危険予測システム

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2018110634A1 (ja) * 2016-12-14 2018-06-21 株式会社自律制御システム研究所 無人航空機の飛行管理システム、及び飛行管理方法
WO2021157034A1 (ja) * 2020-02-06 2021-08-12 Ａｎａホールディングス株式会社 航空機危険予測装置および航空機危険予測システム
JP2020112574A (ja) * 2020-04-03 2020-07-27 東京電力ホールディングス株式会社 無人飛翔体情報収集装置、無人飛翔体情報収集方法、およびプログラム

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