Flight body monitoring system

Abstract

[Problem] To provide a flight body monitoring system capable of visualizing the state of unstable flight bodies, so as to easily detect malfunctioning flight bodies. [Solution] According to the embodiment, the flight body monitoring system includes: a receiving unit for receiving flight information of a flight body; a calculation unit for calculating the acceleration of the flight body in each flight direction based on the flight information; a display unit for displaying an acceleration distribution image formed by plotting the calculated acceleration relative to the flight direction; and a determination unit for determining whether the flight body is malfunctioning based on the acceleration distribution image.

Description

Technical Field

[0001] An embodiment of the present invention relates to an airborne object monitoring system for monitoring an airborne object such as a drone. Prior Art

[0002] The use of drones has increased in recent years, whether for personal, commercial or military purposes.

[0003] This is accompanied by the development of UTM (UAV Traffic Management System). UTM is a drone operation management system adopted by ISO 23629-5 UAS traffic management (UTM) - Part 5: UTM functional structure. Therefore, cooperation with UTM will be essential for future drone flights. By cooperating with UTM, registration information can be obtained, providing access to airspace-related information, including officially registered drones.

[0004] The information that can be obtained through UTM includes, for example, flyable airspace, information on manned air traffic control systems, information on drones in flight, operator information, location information of drones in flight, drone events, accident information, weather information, map information, etc.

[0005] In addition to UTM maintenance, drones will be required to be equipped with the ability to transmit remote IDs starting in June 2022. This will allow drones to transmit their remote IDs while flying.

[0006] Information that can be obtained through Remote ID includes, for example, registration number, manufacturing serial number, location information (e.g., latitude and longitude), speed, altitude, and time.

[0007] In addition to UTM, information about drones currently in flight can also be obtained through remote ID. [Prior Art Literature] [Patent Document]

[0008] [Patent Document 1] Japanese Patent Application Laid-Open No. 2023-051403 [Patent Document 2] Japanese Patent Application Laid-Open No. 2021-043598 [Patent Document 3] Japanese Patent Application Laid-Open No. 2022-141240 Summary of the Invention

[0009] [Problems to be solved by the invention] However, the drone information obtained from UTM and remote ID is based on the premise that the drone is not faulty, that is, the drone has no abnormal conditions such as mechanical failure and is flying normally.

[0010] For example, if the drone is damaged, a switch failure, a broken cable or other mechanical failure, communication with the operator is lost, there is an error in the control software, a malfunction of various sensors unrelated to Remote ID transmission, a battery failure such as power failure, or a partial defect in the Remote ID information (for example, inconsistent flight time), the drone will not be able to fly normally, and the information from the UTM or Remote ID will become unreliable.

[0011] However, for drones that have been registered or have a clear remote ID, even if they malfunction or are not operating normally, they are currently considered normal. Therefore, even if a malfunction occurs, it cannot be detected, and there is a risk that even if a crash occurs, it may not be detected in advance.

[0012] Therefore, a health check function is necessary to confirm that the drone is not malfunctioning. However, a drone health check cannot be performed based solely on information such as UTM or remote ID. For example, a drone health check can be performed by displaying the drone's trajectory and confirming that there is no unstable flight. However, when there are many drones, their tracks overlap, making it difficult to identify the tracks, so this method is not always effective.

[0013] Currently, it is not easy to detect a malfunctioning drone.

[0014] In view of this situation, the present invention aims to provide a flying object monitoring system and method, which can visualize the state of an unstable flying object so as to easily detect a malfunctioning flying object. [Methods for solving the problem]

[0015] The flying object monitoring system of the embodiment comprises: a receiving unit for receiving flight information of the flying object, a calculating unit for calculating the acceleration of the flying object in each flight direction based on the flight information, a display unit for displaying an acceleration distribution image formed by plotting the calculated acceleration relative to the flight direction, and a judging unit for judging whether the flying object is faulty based on the acceleration distribution image. Simple diagram description

[0016] [FIG. 1] is a block diagram showing a structural example of a flying object monitoring system according to a first embodiment. FIG2A is a schematic diagram showing several examples of radar chart images. FIG2B is a schematic diagram showing several examples of radar chart images. FIG2C is a schematic diagram showing several examples of radar chart images. FIG. 3A is a schematic diagram showing an example of a radar chart image represented by changing the black density according to the magnitude of acceleration in the vertical direction. FIG. 3B is a schematic diagram showing an example of a radar chart image represented by changing the black density according to the magnitude of acceleration in the vertical direction. [Figure 4] is an explanatory diagram of an example of similarity judgment using AI. FIG. 5 is a display diagram showing an example of a map screen in which a drone icon is arranged superimposed on a fault level display image in the first embodiment. FIG6A is a display diagram showing the color change of the fault level display image when a drone changes from a faulty drone to a suspicious drone. [Figure 6B] is a display diagram showing the color change of the fault level display image when the drone changes from a faulty drone to a suspicious drone. FIG. 7 is a flowchart showing an example of the operation of the flying object monitoring system according to the first embodiment. [Figure 8] is a display diagram showing an example of a map screen configured with an icon of a drone judged to be faulty or suspicious and detailed information in the second embodiment. FIG. 9 is a flowchart showing an example of the operation of the flying object monitoring system according to the second embodiment. Implementation Method

[0017] The following describes various embodiments of the present invention with reference to the drawings. The drawings are schematic or conceptual; the relationship between the thickness and width of various components, the size ratios of various components, and other aspects may not necessarily correspond to actual components. Even when depicting the same components, the dimensions or ratios may differ depending on the drawing. In the present specification and drawings, components identical to those previously described in the prior drawings are denoted by the same reference numerals, and detailed description or repetition will be omitted as appropriate.

[0018] (First embodiment) First, a first embodiment of the present invention will be described.

[0019] FIG1 is a block diagram showing a configuration example of a flying object monitoring system according to a first embodiment.

[0020] The flying object monitoring system 10 of the first embodiment includes a CPU 12, a recording medium reading unit 14, an input unit 15, a display screen 16, a receiving unit 17, an antenna 18, a memory 20, and a storage device 30, which are interconnected via a bus 11.

[0021] In the following embodiments, although the flying object is described as a drone, this is only an example, and any flying object that can be flown by remote control is not necessarily limited to a drone.

[0022] The CPU 12 is a computer that controls the operation of various components within the flying object monitoring system 10 according to various programs stored in the memory 20 .

[0023] The input unit 15 may be, for example, a keyboard, a mouse, a trackpad, etc. The user may input required operation information into the flying object monitoring system 10 via the input unit 15 .

[0024] The display screen 16 is used to display the video information provided by the aircraft monitoring system 10. For example, the display screen 16 is a liquid crystal display (LCD). The LCD may also be a touchscreen LCD. In this case, the touchscreen LCD can function as both the input unit 15 and the display screen 16.

[0025] The receiving unit 17 receives the drone's flight information a from the UTM 40 via a communication network such as the Internet, and outputs the received flight information a to the memory 20. As mentioned above, the drone's flight information a from the UTM 40 includes: flight-permitted airspace, information from the manned air traffic control system, information about the drone in flight, operator information, location information of the drone in flight, drone incidents and accidents, weather information, map information, and the like.

[0026] Antenna 18 receives remote IDs continuously transmitted from the flying drone D and outputs the received remote IDs to memory 20. As mentioned above, the remote ID includes: registration number, manufacturing serial number, location information (e.g., latitude and longitude), speed, altitude, and time.

[0027] This configuration including the receiver 17 and antenna 18 is an example. A flying drone D continuously transmits a remote ID (only "ID" is shown in FIG1 ) to the UTM 40, and the receiver 17 can receive both the flight information a of the flying drone and the remote ID from the UTM 40. In this case, the antenna 18 can be omitted.

[0028] The memory device 30 is composed of, for example, an SSD (Solid State Drive) or an HDD (Hard Disk Drive), and stores a radar chart database 31 described later.

[0029] The memory 20 stores various programs for implementing the flying object monitoring system 10, which are implemented by: a calculation unit 21, a display unit 22, a judgment unit 23, a display control unit 24, and a simulation unit 25.

[0030] Programs for implementing the calculation unit 21, the display unit 22, the judgment unit 23, the display control unit 24, and the simulation unit 25 may be pre-stored in the memory 20 or read from an external recording medium 13 such as a memory card via the recording medium reader 14 and stored in the memory 20. These programs cannot be rewritten.

[0031] In the memory 20, in addition to the area that cannot be written by the user, a writable data area 26 is ensured as an area for storing rewritable data.

[0032] The calculation unit 21 uses the flight information a and the information included in the remote ID to calculate the acceleration b of the drone D in each flight direction.

[0033] The display unit 22 displays the acceleration b of the drone D for each flight direction over the past few seconds, calculated by the calculation unit 21, against the flight direction on the horizontal plane, creating a radar chart image. In other words, the radar chart image is an acceleration distribution image that shows the acceleration distribution.

[0034] 2A to 2C are schematic diagrams showing several examples of radar chart images.

[0035] Figures 2A, 2B, and 2C all show arrows extending in four 90-degree directions, with the horizontal direction of drone D at 0°. Radar chart images c1, c2, and c3 (hereinafter collectively referred to as "radar chart image c") are formed by plotting the acceleration b for each flight direction of drone D, calculated by the calculation unit 21, against the horizontal flight direction. The resulting radar chart image c is displayed on the display screen 16 by the display unit 22, superimposed on the drone D icon.

[0036] Although the arrow indicating the acceleration in the height direction, ie, the vertical direction, is not shown in FIG. 2A to FIG. 2C , the display unit 22 displays the radar chart image c by changing the black density in accordance with the magnitude of the vertical acceleration and displays it on the display screen 16 .

[0037] 3A and 3B are schematic diagrams showing an example of a radar chart image represented by changing the black density according to the magnitude of the acceleration in the vertical direction.

[0038] When drone D is unstable and flying, radar image c1 has a larger horizontal display area, as shown in Figure 2A. Furthermore, as drone D's vertical acceleration decreases, radar image c1 displays a lighter black density, as shown in Figure 3A. As the acceleration increases, radar image c1 displays a darker black density, as shown in Figure 3B.

[0039] When drone D is hovering in the vertical direction, radar image c2 displays a small circular area, as shown in Figure 2B. In this radar image c2, the smaller the vertical acceleration of drone D, the lighter the blackness, while the greater the acceleration, the darker the blackness.

[0040] When drone D has a horizontal linear component, as shown in Figure 2C , radar image c3, for example, is an isosceles triangle with an acute-angle vertex in the linear direction. Similarly, radar image c3 displays a lighter black density as drone D's vertical acceleration decreases, while a darker black density appears as the acceleration increases.

[0041] The determination unit 23 determines whether the drone D is faulty based on the radar image c shown in FIG. 2A to FIG. 2C and FIG. 3A to FIG. 3B .

[0042] The determination unit 23 first determines which drone is faulty based on the density of the black mark in the radar image c. Specifically, if the density of the black mark in the radar image c is darker, the corresponding drone D can be determined to be faulty. This is because greater vertical acceleration indicates a higher risk of falling.

[0043] The vertical acceleration value that serves as the boundary between a fault and a normal state can be arbitrarily determined by the user or empirically. Therefore, based on a radar image c with a darker black density than the density corresponding to the threshold acceleration, the determination unit 23 determines that the drone D is faulty. Conversely, based on a radar image c with a lighter black density than the density corresponding to the threshold acceleration, the determination unit 23 determines that the drone D is not faulty.

[0044] The determination unit 23 can further determine whether the drone D is faulty based on the shape of the radar image c. To make this determination, the determination unit 23 compares the shape of the radar image c displayed by the display unit 22 with the shape of a comparative radar image. The radar image c is used to determine the fault level of the drone D. Therefore, in this manual, the radar image generated by the display unit 22 is also referred to as the fault level display image.

[0045] Comparative radar image C is stored in radar image database 31 stored in memory device 30. Radar image database 31 stores radar images showing faulty drones and radar images showing non-faulty drones as comparative radar image C. Radar image C previously determined by determination unit 23 is also associated with the faulty or non-faulty determination result and stored as comparative radar image C.

[0046] For example, a radar chart image with a large horizontal display area, such as the fault level display image c1, indicates that the movement of drone D is unstable and the flight of drone D is unstable. Such a radar chart image is associated with the fault and stored.

[0047] The determination unit 23 compares the shape of the radar image c, i.e., the shape of the fault level display image c, with the shapes of the radar image C stored in the radar image database 31. If a radar image C with a shape similar to the fault level display image c is associated with a fault, the corresponding drone D is determined to be faulty. If a radar image C with a shape similar to the fault level display image c is associated with no fault, the drone D is determined to be not faulty.

[0048] When the radar chart database 31 does not store a radar chart image C having a shape similar to the shape of the fault level display image c, the determination unit 23 determines whether the drone D has a fault based on the simulation result of the simulation unit 25 .

[0049] The simulation unit 25 uses the flight information a or remote ID of the drone D to simulate the flight path of the drone D. If the simulated flight path shows any unnatural motion, the determination unit 23 may determine that the drone D has malfunctioned. If no unnatural motion is observed, the determination unit 23 may determine that the drone D has not malfunctioned.

[0050] The determination unit 23 determines whether the drone D is faulty or not in the manner described above. Furthermore, the fault level display image c is associated with the determination result and stored as a new radar chart image C for comparison in the radar chart database 31.

[0051] AI may also be added to the judgment unit 23, and the judgment unit 23 may use AI to judge whether the shape of the fault level display image c is similar to the shape of the radar map image C.

[0052] An example of similarity determination using AI will be described using FIG4 .

[0053] As described above, the radar chart database 31 stores radar chart images showing faulty drones or radar chart images showing non-faulty drones and associates them with the judgment results, that is, faulty or non-faulty.

[0054] That is, the radar chart database 31 stores radar chart images c1 of an "unstable" flying drone as shown in FIG2A, radar chart images c2 of a "hovering" drone as shown in FIG2B, and radar chart images c3 of a "straight-flying" drone as shown in FIG2C, etc., and associates them with faults or non-faults.

[0055] The radar chart images stored in the radar chart database 31 in this manner are pre-learned by the AI as learning data.

[0056] When an image displaying an unknown fault level (d) is input to the AI trained in this manner, the AI outputs an image similar to the image displaying the unknown fault level (d). If this similar image represents a faulty drone, the determination unit 23 may determine that the drone D corresponding to the image displaying the unknown fault level (d) is a faulty drone. Conversely, if the similar image represents a normal drone, the determination unit 23 may determine that the drone D corresponding to the image displaying the unknown fault level (d) is a normal drone.

[0057] Next, the display control unit 24 will be described.

[0058] FIG. 5 is a display diagram showing an example of a map screen in which an icon of a drone D is arranged superimposed on a fault level display image c in the first embodiment.

[0059] As shown in FIG5 , the display control unit 24 places the icon of the drone D, on which the fault level display image c, which is blackened with a density corresponding to the vertical acceleration, is superimposed, at a position on the map determined based on the flight information a of the drone D and the remote ID, and displays it on the display screen 16.

[0060] The map M shown in Figure 5 shows icons for eight drones D1 through D8. The locations on map M where the icons for drones D1 through D8 are displayed correspond to the locations determined based on the flight information a and remote ID of each drone D1 through D8. Map M may or may not be an aerial photograph.

[0061] Overlaid on each of the icons of drones D1 to D8 is a fault level display image c for each drone D1 to D8. Each fault level display image c is blackened with a density corresponding to the vertical acceleration.

[0062] For example, the fault level display image c2 shown in FIG2B is superimposed on drones D1 and D5 in a lighter black color. By observing this, the user can recognize that drone D1 is a slowly hovering drone with no faults.

[0063] For example, the fault level display image c3 shown in Figure 2C is overlaid on drones D3, D4, D6, and D7 in a lighter black color. By observing this, the user can identify drones D3, D4, D6, D7, and D8 as healthy drones traveling straight ahead without experiencing significant vertical acceleration.

[0064] For example, the fault level display image c1 shown in Figure 2A is overlaid on drone D2 in a darker black color. By observing this, the user can discern that drone D2 is experiencing a malfunction, with significant vertical acceleration and unstable flight. Furthermore, detailed information E indicating "Fault (Needs Capture)", "Model", "Operator Information", "Speed", and "Altitude" is displayed near the drone D2 icon on map M. This allows the user to obtain detailed information related to drone D2, in addition to the determination that drone D2 is malfunctioning.

[0065] Furthermore, drone D8 enters restricted area R. Restricted area R includes areas with restricted or prohibited access, such as government agencies, private land, military facilities, and airports. Based on flight information a, judgment unit 23 identifies drone D that has entered restricted area R as a suspicious drone that should be captured, regardless of whether it is malfunctioning. Because restricted area R is predetermined, judgment unit 23 can immediately identify drone D that has approached or entered restricted area R as a suspicious drone based on drone D's location information.

[0066] The determination unit 23 also identifies drones that have not yet completed body registration, drones prohibited from flying due to incidents, and drones that have not yet transmitted their Remote IDs as suspicious drones. These drones can be easily detected by verifying UTM or Remote ID information.

[0067] The display control unit 24, through the determination unit 23, changes the color of the fault level display image c of the drone D determined to be suspicious to a bright color, such as red (shown as shaded in Figures 5 and 6A-6B), and displays it on the display screen 16. Furthermore, the display control unit 24 can also emphasize the detailed information E, for example, by using a bright color such as red, or in bold or italic font.

[0068] In this way, the user can immediately identify a suspicious drone by viewing the fault level display image c and / or detailed information E on the display screen 16, for example, changing to red. For example, as shown in FIG6A , the user can immediately identify that drone D has changed from a faulty drone to a suspicious drone by viewing the fault level display image c1 overlapping the icon of drone D, which has been determined to be faulty, on the display screen 16, changing to red, for example, as shown in FIG6B .

[0069] The drone D8 shown in FIG5 is determined to be a suspicious drone because it has entered the prohibited entry area R. Therefore, the fault level display image c3 displayed on the display screen 16 is, for example, red.

[0070] Since all suspicious drones are capture targets, users can immediately prepare to capture them.

[0071] Specific capture methods, such as those disclosed in Patent Document 3, include transmitting noise radio waves, capturing with a net, and using a capture drone (counter drone). Therefore, by incorporating the flying object monitoring system 10 of this embodiment into the system disclosed in Patent Document 3, it is possible to rapidly detect and capture a malfunctioning or suspicious drone.

[0072] As described above, in the aircraft monitoring system 10, the hardware components, such as the bus 11, CPU 12, recording medium reader 14, input unit 15, display screen 16, receiver 17, antenna 18, memory 20, and storage device 30, and the various programs stored in the memory 20, i.e., software, operate in cooperation with each other.

[0073] Next, an operation example of the flying object monitoring system according to the first embodiment having the above configuration will be described.

[0074] FIG7 is a flowchart showing an example of the operation of the flying object monitoring system according to the first embodiment.

[0075] In step S1, data related to drone D, such as drone D's flight information a or remote ID, is received. This is accomplished by having the receiver 17 receive drone D's flight information a from the UTM 40, and the antenna 18 receive the remote ID from the in-flight drone D. When the in-flight drone D transmits the remote ID to the UTM 40, the receiver 17 receives both the in-flight drone's flight information a and the remote ID from the UTM 40.

[0076] In step S2, the calculation unit 21 calculates the acceleration of the drone D. This is done by having the calculation unit 21 use the flight information a and the information included in the remote ID to calculate the acceleration b of the drone D in each flight direction.

[0077] In step S3, the display unit 22 generates a fault level display image c for drone D. This image c is created by plotting the acceleration b of drone D in each flight direction over the past few seconds against the horizontal plane. In other words, the fault level display image c is a radar chart image showing the acceleration distribution of drone D. Furthermore, the fault level display image c displays lighter shades of black as the vertical acceleration of drone D decreases, and darker shades of black as the acceleration increases.

[0078] In step S4, the determination unit 23 determines whether the drone D is faulty or not based on the fault level display image c.

[0079] First, the determination unit 23 determines that the corresponding drone D is faulty when the fault level display image c is darker than the density corresponding to the boundary acceleration at the boundary between faulty and non-faulty states.

[0080] The determination unit 23 further determines whether drone D is faulty or not based on the shape of the fault level display image c. To make this determination, the determination unit 23 compares the shape of the fault level display image c with the shapes of radar chart images C stored in the radar chart database 31. If a radar chart image C with a similar shape to the fault level display image c is associated with a fault, drone D is determined to be faulty. If a radar chart image C with a similar shape to the fault level display image c is associated with not being faulty, drone D is determined to be not faulty. The determination unit 23 may also use AI to determine whether the shape of the fault level display image c is similar to the shape of the radar chart image C.

[0081] When the radar chart database 31 does not store a radar chart image C having a shape similar to the fault level display image c, the determination unit 23 determines whether the drone D is faulty or not based on the simulation result of the simulation unit 25 .

[0082] In step S5 , as shown in FIG5 , the health check result of each flying drone D is visualized and displayed on the display screen 16 .

[0083] By viewing the screen shown in FIG. 5 , the user can easily grasp the status of each drone D in flight.

[0084] In step S6, if the image shown in FIG. 5 shows an unknown fault level display image c (S6: Yes), the process proceeds to step S7; if not (S6: No), the process returns to step S1.

[0085] The judgment unit 23 may also use AI to learn the unknown fault level display image c with AI in step S7. After step S7, the process returns to step S1.

[0086] As described above, the flying object monitoring system 10 of this embodiment can visualize the status of drones D that may be experiencing malfunctions based on publicly available information about the flight of drones D and display it on a map. Furthermore, suspicious drones can be discovered and displayed on the map.

[0087] Furthermore, AI can be used to identify faulty drones, so the accuracy of identifying faulty drones can be improved through the learning effect of AI.

[0088] Thus, users can easily find faulty or suspicious drones. Therefore, by incorporating the flying object monitoring system 10 of this embodiment into the system disclosed in Patent Document 3, it is also possible to quickly carry out the process from finding a faulty or suspicious drone to capturing it.

[0089] (Second embodiment) Next, a second embodiment of the present invention will be described.

[0090] The flying object monitoring system of the second embodiment is a modified example of the flying object monitoring system of the first embodiment, and has a structure as shown in FIG. 1 .

[0091] Therefore, the differences from the first embodiment will be described here, and since the other aspects have been described in the first embodiment, repeated description will be avoided.

[0092] In addition to the first embodiment, in the second embodiment, the determination unit 23 determines whether drone D is faulty based on flight information a. If a fault is determined, the determination unit 23 further infers the cause of the fault. The cause of a faulty drone includes, but is not limited to, a loss of communication with the operator.

[0093] Flight information a from UTM 40 does not contain any information related to the health status of drone D. In other words, flight information a from UTM 40 does not contain any information related to the loss of communication with drone D. Therefore, it is impossible to directly determine the status related to the loss of communication with drone D from flight information a.

[0094] When communication is interrupted, UAV D will not receive instructions from the operator, so even before reaching the pre-designated destination, it can only hover at the location where communication was interrupted, like a lost person wandering around, with its flight speed significantly reduced.

[0095] The UTM 40 determines the location of the drone D based on the remote ID continuously transmitted from the drone D. The UTM 40 then calculates the flight speed of the drone D based on the location information continuously transmitted from the drone D, includes it in the flight information a, and transmits it to the flying object monitoring system 10A.

[0096] This flight information a is received by the receiving unit 17. If the flight information a indicates a significant decrease in flight speed before reaching the destination, the determination unit 23 infers that drone D has experienced a communication interruption. The determination unit 23 can indirectly infer the communication interruption of drone D based on the flight information a. The determination unit 23 then determines that drone D, which has been inferred to have experienced a communication interruption, is a faulty drone.

[0097] As described above, drone D, which experiences a communication interruption, hovers at the location where it was at the time of the interruption, and therefore has no propulsion in its direction of travel. Consequently, drone D could be blown away by the wind and deviate from its planned flight path (airspace).

[0098] The UTM 40 understands the drone's flight path and constantly monitors the location of drone D based on the remote ID transmitted from drone D. Based on the location information continuously transmitted from drone D, if drone D deviates significantly (e.g., by more than 40 meters) from the pre-planned flight path, the UTM 40 includes this information in the flight information a and transmits it to the aircraft monitoring system 10.

[0099] It's impossible to tell whether a drone D that significantly deviates from its pre-planned flight path is a harmless malfunction or a dangerous drone that intentionally deviates from its planned flight path with malicious intent. Therefore, drone D that significantly deviates from its pre-planned flight path must be captured, regardless of whether it has malicious intent or not.

[0100] When the flight information a includes information that the drone D significantly deviates from the flight route, the judgment unit 23 judges the drone D as a suspicious drone that should be captured.

[0101] As described in the first embodiment, any drone D that enters a pre-designated prohibited entry area R should be captured as a suspicious drone D regardless of whether communication is interrupted or not.

[0102] The judgment unit 23 identifies the position of the drone D from the flight information a. If the identified position is within a pre-specified prohibited entry area R, the drone D is judged to be a suspicious drone that should be captured.

[0103] FIG8 is a display diagram showing an example of a map screen configured with icons of drones determined to be faulty or suspicious and detailed information in the second embodiment.

[0104] As described in the first embodiment, the display control unit 24 places the drone D icon, superimposed with the fault level display image c, which is shaded black with a density corresponding to vertical acceleration, at a location on the map determined based on the drone D's flight information a and the remote ID, and displays the image on the display screen 16. In this embodiment, the display control unit 24 further displays detailed information E containing text information indicating the determination result of the display determination unit 23 near the drone D icon on the display screen 16.

[0105] Examples of judgment results include "fault" or "suspicious." Furthermore, detailed information E may include, in addition to the judgment result, specific information indicating the basis for the judgment result, such as "communication interruption." As shown in Figure 5 , detailed information E may also include supplementary information such as model, operator information, speed, and altitude.

[0106] The display control unit 24 displays the text within the detailed information E of the drone D determined by the determination unit 23 as "faulty" or "suspicious" in a bright color such as red, bold, or italic font, and highlights it on the display screen 16. Similarly to the icon of the drone D determined by the determination unit 23 as "faulty" or "suspicious," the display control unit 24 highlights it on the display screen 16 in a bright color such as red, or by flashing it. The display control unit 24 may also display the icon and detailed information of the faulty drone in different colors from the icon and detailed information of the suspicious drone.

[0107] By viewing this map information, users can easily identify the origin and location of drones identified as "faulty" or "suspicious." By adding detailed information such as "communication interruption" to the detailed information E, users can gain detailed information about faulty or suspicious drones. By displaying the icons and detailed information for faulty drones in different colors from those for suspicious drones, faulty drones can be distinguished from suspicious drones.

[0108] Next, an operation example of the flying object monitoring system according to the second embodiment configured as above will be described. Fig. 9 is a flowchart showing an operation example of the flying object monitoring system according to the second embodiment.

[0109] First, similarly to step S1 shown in FIG. 7 , data related to the drone D, such as the flight information a of the drone D or the remote ID, is continuously received ( S11 ).

[0110] The judgment unit 23 identifies the position of the drone D from the flight information a. If the identified position is within the pre-specified prohibited entry area R (S12: Yes), the drone D is judged as a suspicious drone that should be captured (S16).

[0111] The display control unit 24 determines the location of a drone identified as suspicious based on the flight information a and the remote ID, and places an icon representing the drone on a map, which is then displayed on the display screen 16. Furthermore, detailed information E related to the drone is displayed near the drone icon on the display screen 16. The detailed information E is highlighted using a bright color such as red, or in bold or italic font.

[0112] By viewing this map information, users can easily identify the origin and location of "suspicious" drones. Furthermore, by referring to the detailed information E, users can also obtain more specific information related to the suspicious drone.

[0113] In step S12, even if the drone D is determined not to be in the prohibited entry area R (S12: No), when communication is interrupted, it hovers at the position when communication was interrupted, so the flight speed is significantly reduced (S13: Yes).

[0114] When the flight information a shows a significant decrease in flight speed, the judgment unit 23 infers that communication with the drone D is interrupted and determines that the drone is a faulty drone (S14).

[0115] The display control unit 24 also determines the location of a drone that has been determined to be faulty based on the flight information a and the remote ID, and places an icon representing the drone on a map, which is then displayed on the display screen 16. Furthermore, detailed information E related to the drone is also displayed near the drone icon on the display screen 16. The detailed information E is highlighted using a bright color such as red, or in bold or italic font.

[0116] By viewing this map information, users can easily identify the origin and location of the "faulty" drone. Furthermore, by referring to the detailed information E, users can also obtain more specific information about the faulty drone.

[0117] As described above, drone D, which experiences a communication interruption, hovers at the location where it was lost, thus lacking propulsion in its direction of travel. Consequently, drone D could be blown away by the wind and deviate from its pre-planned flight path (airspace). It is difficult to distinguish between drones D that significantly deviate from their flight path (S15: Yes) and are harmless, simply malfunctioning, or dangerous drones that intentionally deviate from their planned flight path with malicious intent. Therefore, drones D that significantly deviate from their pre-planned flight path are treated as suspicious drones and must be captured, regardless of whether they have malicious intent or not.

[0118] That is, when the flight information a includes information that significantly deviates from the flight route, the judgment unit 23 judges the drone D as a suspicious drone (S15: Yes), and the above-mentioned step S16 is performed.

[0119] If the flight speed does not decrease significantly in step S13 (S13: No) and if the flight does not deviate from the route in step S15 (S15: No), the process returns to step S11 and the process from step S12 to step S16 is repeated based on the new flight information a.

[0120] As described above, the flying object monitoring system 10 of this embodiment can identify malfunctioning drones, such as those experiencing communication interruptions. Furthermore, the location of the malfunctioning drone can be clearly indicated on a map, along with detailed information about the malfunctioning drone. Furthermore, the location of suspicious drones, such as those that have intruded into restricted areas or deviated from their flight paths, can also be clearly indicated on a map, along with detailed information. This allows malfunctioning drones to be easily and reliably distinguished from suspicious drones.

[0121] While several embodiments of the present invention have been described, these embodiments are presented merely as examples and are not intended to limit the scope of the invention. These embodiments can be implemented in various other forms, and various omissions, substitutions, and modifications may be made without departing from the spirit of the invention. These embodiments and modifications, as long as they encompass the scope and spirit of the invention, encompass the inventions set forth in the claims and their equivalents.

[0122] 10: Flying object monitoring system 11: Bus 12:CPU 13: External recording media 14: Recording medium reading unit 15: Input 16: Display screen 17: Receiving Department 18: Antenna 20: Memory 21: Computing Department 22: Performance Department 23: Judgment Department 24: Display control unit 25:Simulation Department 26: Writable data area 30: Memory device 31: Radar Chart Database a: Flight Information b: acceleration c: Radar chart image (fault level display image) C: Radar chart image for comparison D: Drone E: Detailed information M: Map R: No entry area

Claims

1. An aircraft monitoring system comprising: a receiving unit for receiving flight information of an aircraft; a calculation unit for calculating the acceleration of the aircraft in each flight direction based on the flight information; a display unit for displaying an acceleration distribution image formed by plotting the calculated acceleration relative to the flight direction; and a determination unit for determining whether the aircraft is malfunctioning based on the acceleration distribution image.

2. The flight body monitoring system as described in request item 1, wherein, Furthermore, it possesses a database containing: acceleration distribution images representing malfunctioning aircraft and acceleration distribution images representing non-malfunctioning aircraft.

3. The flight body monitoring system as described in request item 2, wherein, The aforementioned judgment unit compares the acceleration distribution image shown above with the acceleration distribution image stored in the aforementioned database to determine whether the aforementioned aircraft is malfunctioning.

4. The flight body monitoring system as described in request item 3, wherein, If the aforementioned judgment unit determines that the aircraft is faulty when the acceleration distribution image shown is similar to the acceleration distribution image of the faulty aircraft stored in the aforementioned database, then the aforementioned judgment unit determines that the aircraft is faulty.

5. The flight body monitoring system as described in request item 4, wherein, It further includes: a simulation unit that simulates the trajectory of the aircraft based on the flight information; and a judgment unit that, when the acceleration distribution image shown is not similar to any acceleration distribution image stored in the database, determines whether the aircraft is malfunctioning based on the simulation results of the simulation unit.

6. The flight body monitoring system as described in request item 5, wherein, The aforementioned judgment unit, in response to the judgment result made based on the aforementioned acceleration distribution image according to the aforementioned simulation result, stores it in the aforementioned database as either an acceleration distribution image representing the aforementioned malfunctioning aircraft or an acceleration distribution image representing the aforementioned malfunctioning aircraft.

7. A flight body monitoring system as described in any of requests 1 to 6, wherein, Furthermore, it includes a display control unit, which displays the icon representing the aircraft on a map determined based on the flight information of the aircraft by overlaying the acceleration distribution image shown above.

8. A flight body monitoring system as described in any of requests 1 to 6, wherein, The aforementioned acceleration distribution image is a radar image formed by plotting the calculated acceleration relative to the aforementioned flight direction.

9. An aircraft monitoring system comprising: a receiving unit for receiving flight information of an aircraft; a judgment unit for determining whether the aircraft is malfunctioning based on the flight information; and a display control unit; wherein, when the judgment unit determines that a malfunction has occurred, the display control unit displays an icon representing the aircraft and text information clearly emphasizing the judgment result of the judgment unit near a location on a map determined based on the flight information of the aircraft; wherein, when the flight information indicates that the flight speed of the aircraft has significantly decreased before reaching a predetermined destination, the judgment unit determines that the aircraft has experienced a communication interruption.

10. The flight body monitoring system as described in request item 9, wherein, If the flight information indicates that the aircraft that caused the aforementioned communication interruption has significantly deviated from its pre-planned flight path, the judgment unit determines that the aircraft is a suspicious aircraft that should be captured.

11. The flight body monitoring system as described in request item 9, wherein, If the aforementioned flight information indicates that the aircraft that caused the aforementioned communication interruption has intruded into a pre-designated prohibited area, the aforementioned judgment unit determines that the aforementioned aircraft is a suspicious aircraft that should be captured.

12. An aircraft monitoring system comprising: a receiving unit for receiving flight information of an aircraft; a judgment unit for determining whether the aircraft is malfunctioning based on the flight information; and a display control unit; wherein, when the judgment unit determines that the aircraft is malfunctioning, the display control unit displays an icon representing the aircraft and text information clearly emphasizing the judgment result of the judgment unit near a location on a map determined based on the flight information of the aircraft; the judgment unit determines whether the aircraft is a suspicious aircraft that should be captured based on the flight information; and the display judgment unit displays an icon representing the aircraft and text information clearly indicating that the aircraft is a suspicious aircraft near a location on a map determined based on the flight information of the aircraft.

13. The flight body monitoring system as described in request item 12, wherein, If the flight information indicates that the aircraft deviates from the pre-specified route, the judgment unit determines that it is a suspicious aircraft that should be captured.

Images