Carrier system

Abstract

To provide a carrier system capable of recording accident images using an unmanned flying object without equipping a manned carrier vehicle with an onboard camera.SOLUTION: In a carrier system S with a manned carrier vehicle 1 and an unmanned flying object 2, the manned carrier vehicle 1 comprises a position detection part 10 to detect a vehicle position and a sound collection part 12 to collect the sound around the vehicle. The unmanned flying object 2 comprises a photographing part 25 to take the picture around the flying object and a flight control part 21 to control the flight to an accident site when the manned carrier vehicle 1 causes an accident using an accident determination part 35 to determine whether or not the manned carrier vehicle 1 causes the accident based on the collected sound by the sound collection part 12. It is configured that the unmanned flying object 2 takes the picture of the accident site by the photographing part 25.SELECTED DRAWING: Figure 2

Description

Technical Field

[0001] The present invention relates to a transportation system including a manned transporter and an unmanned aerial vehicle.

Background Art

[0002] A manned transporter (for example, a forklift) used in facilities such as factories and warehouses is configured to run and operate when an operator boards and operates it. Further, the forklift is configured to perform a loading / unloading operation for loading and unloading goods using forks.

[0003] By the way, in the facilities where manned transporters are used, there may occur collision accidents where a manned transporter collides with a rack or a wall during running, dropping accidents where a manned transporter drops goods during a loading / unloading operation, personal injury accidents where a manned transporter collides with a person during running or drops goods on a person during a loading / unloading operation, fire accidents where a fire breaks out during a loading / unloading operation of a manned transporter, and the like.

[0004] Therefore, there is a technology in which an in-vehicle camera is mounted on a manned transporter, and dangerous driving is detected based on an image captured by the in-vehicle camera (Patent Document 1, etc.). According to this, dangerous driving can be prevented, and accident images can also be recorded when an accident occurs.

[0005] However, since in-vehicle cameras are expensive, when the number of manned transporters in a facility is large, it is difficult to mount them on all manned transporters. Further, since dangerous driving cannot be completely prevented, when a personal injury accident or a fire accident occurs, it is necessary to take prompt action for appropriate treatment.

Prior Art Documents

Patent Documents

[0006]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0007] Therefore, the problem to be solved by the present invention is to provide a transport system that can record accident images using an unmanned aerial vehicle even when an in-vehicle camera is not mounted on a manned transport vehicle.

Means for Solving the Problems

[0008] To solve the above problems, a transport system according to the present invention is in a transport system including a manned transport vehicle and an unmanned aerial vehicle, the manned transport vehicle includes a position detection unit that detects the position of the vehicle, and a sound collection unit that collects sound around the vehicle, the unmanned aerial vehicle includes a photographing unit that photographs the surroundings of the aircraft body, and a flight control unit that controls to fly to the accident site when the manned transport vehicle has an accident, using an accident determination unit that determines whether or not the manned transport vehicle has an accident based on the sound collected by the sound collection unit, the unmanned aerial vehicle is configured to photograph the accident site with the photographing unit.

[0009] Preferably, the accident determination unit includes a collection unit that collects teacher data based on the relationship between the characteristic data regarding the characteristics of the normal sound when the manned transport vehicle travels and performs loading and unloading operations, and the accident sound when the manned transport vehicle has an accident, a learning model generation unit that performs machine learning from the teacher data collected by the collection unit, and generates and stores a learning model by the machine learning, an acquisition unit that acquires, at predetermined time intervals, the characteristic data of the sound around the manned transport vehicle at the current time by the sound collection unit, a prediction unit that inputs the characteristic data of the sound around the manned transport vehicle at the current time acquired from the acquisition unit into the learning model generated by the learning model generation unit, and predicts whether or not it is an accident sound from the learning model, A determination unit that determines whether to fly a drone to the accident site based on the prediction unit.

[0010] Preferably, The accident determination unit is configured to determine whether the accident is a personal injury accident. When it is determined to be a personal injury accident, a drone equipped with a first aid kit is configured to fly to the accident site.

[0011] Preferably, The accident determination unit is configured to determine whether the accident is a personal injury accident. When it is determined to be a personal injury accident, an ambulance is configured to come to the accident site.

[0012] Preferably, The accident determination unit is configured to determine whether the accident is a fire accident. When it is determined to be a fire accident, a drone equipped with a fire extinguishing set is configured to fly to the accident site.

[0013] Preferably, The accident determination unit is configured to determine whether the accident occurred in a hazardous material handling area. When it is determined that the accident occurred in a hazardous material handling area, a drone equipped with an emergency set for hazardous materials is configured to fly to the accident site.

[0014] Preferably, The drone is provided with a notification unit that notifies the surroundings of the accident site that an accident has occurred.

Advantages of the Invention

[0015] The transportation system according to the present invention is a transportation system including a manned transport vehicle and a drone. Even when the manned transport vehicle is not equipped with an in-vehicle camera, accident images can be recorded using the drone.

Brief Description of the Drawings

[0016]

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Mode for Carrying Out the Invention

[0017] Hereinafter, embodiments of the conveying system according to the present invention will be described with reference to the drawings.

[0018] [First Embodiment] Based on FIGS. 1 to 3, the conveying system of the first embodiment will be described.

[0019] As shown in FIGS. 1 and 2, the conveying system S includes a manned carrier vehicle 1 on which an operator O rides and operates. The manned carrier vehicle 1 is configured to travel and operate when the operator O rides and operates it. In the present embodiment, the manned carrier vehicle 1 is a reach-type forklift, and is configured to be able to travel the vehicle and raise and lower the fork by the operator O riding and operating it.

[0020] The conveying system S includes a plurality of shelves R installed in facilities such as factories and warehouses. The shelf R has a plurality of stepped portions in the height direction and the horizontal direction, and is configured to be able to store the load L at a predetermined position of the stepped portion. The manned carrier vehicle 1 performs loading and unloading by placing and taking the load L at a predetermined position of the shelf R.

[0021] The conveying system S includes an unmanned flying body 2 that can stop in the air. The unmanned flying body 2 is a drone, and is configured to fly to a predetermined air stop position by the rotation of the rotors provided at the tip sides of a plurality of arms, and to be able to hover at the predetermined air stop position.

[0022] The transportation system S includes a management device 3 for controlling the unmanned aerial vehicle 2. The management device 3 includes a storage unit 30. The storage unit 30 stores a map M composed of shelves R installed in the facility, passageways, luggage L arranged in the facility, and the like.

[0023] The storage unit 30 stores the handling task T performed by the manned transport vehicle 1 as a handling schedule J. That is, the handling schedule J includes a plurality of handling tasks T such as a task T1 of picking up luggage L from a predetermined location on a predetermined shelf R, a task T2 of placing luggage L at a predetermined location on a predetermined shelf R, a task T3 of placing luggage L at the shipping location, and a task T4 of picking up luggage L from the receiving location, which are set in a predetermined order. Further, the handling task T includes information on the handling position D2 and handling (picking up or placing) information for the luggage L.

[0024] The management device 3 includes a handling instruction unit 34, and the handling instruction unit 34 is configured to display the handling task T of the handling schedule J transmitted from the storage unit 30 on a display unit 11 provided in the driver's seat of the manned transport vehicle 1.

[0025] The display unit 11 is composed of, for example, a touch panel display. The handling instruction unit 34 displays the handling task T that the manned transport vehicle 1 should perform on the display unit 11. The operator O drives and operates the manned transport vehicle 1 according to the handling task T displayed on the display unit 11 to perform the handling work. When the handling task T is completed, the operator O presses an end button displayed on the display unit 11, and an end signal is transmitted to the handling instruction unit 34. The handling instruction unit 34 is configured to display the next handling task T that the manned transport vehicle 1 should perform on the display unit 11 when it receives the end signal.

[0026] The operator O can drive the manned transport vehicle 1 to the handling position D2 and operate the manned transport vehicle 1 according to the handling task T displayed on the display unit 11 to perform the handling work on the luggage L.

[0027] The manned transport vehicle 1 is provided with a position detection unit 10. The position detection unit 10 is composed of sensors for detecting the surrounding environment such as a laser sensor, a GPS sensor, and an electromagnetic induction sensor, or a receiver for receiving signals from positioning satellites, etc. The position detection unit 10 is configured to detect the vehicle position D1 of the manned transport vehicle 1.

[0028] The manned transport vehicle 1 is further provided with a sound collection unit 12. The sound collection unit 12 is installed at a predetermined position such as a head guard of the vehicle and is configured to collect the sounds around the vehicle. Therefore, when a collision accident occurs where the manned transport vehicle 1 collides with a rack R or a wall during travel, a personal accident occurs where the manned transport vehicle 1 collides with a person during travel or the load L falls on a person during the loading and unloading operation, or a fire accident occurs where a fire breaks out due to the incorrect handling of the manned transport vehicle 1 or the load L, etc., the sound collection unit 12 is configured to be able to collect each accident sound.

[0029] The unmanned aerial vehicle 2 is provided with a position detection unit 20. The position detection unit 20 is composed of, for example, a laser sensor that acquires data for SLAM (Simultaneous Localization and Mapping) by LiDAR (Light Detection and Ranging), a motion sensor that acquires data for odometry, a receiver that receives signals from positioning satellites, etc., and can detect the position of the unmanned aerial vehicle 2.

[0030] The unmanned aerial vehicle 2 is provided with a flight control unit 21. The flight control unit 21 is configured to control the rotation of the rotor blades. Based on the detection result of the position detection unit 20 and the control of the flight control unit 21, the unmanned aerial vehicle 2 can fly to a predetermined hovering position in the air and hover at the hovering position.

[0031] The arrangement determination unit 32 is further configured to determine the hovering position in the air where the unmanned aerial vehicle 2 hovers. When the manned transporter 1 has an accident, the arrangement determination unit 32 designates the vehicle position D1 of the manned transporter 1 as the accident site, and determines that the unmanned aerial vehicle 2 will fly to and be arranged at the accident site under the control of the flight control unit 21.

[0032] The unmanned aerial vehicle 2 is provided with a storage unit 22. The storage unit 22 stores notification sounds and notification images. The notification sound consists of sounds such as voices and siren sounds for notifying the surroundings of the occurrence of an accident, and the notification image consists of notification texts, notification diagrams, etc. projected onto the floor surface of the accident site for notifying the surroundings of the occurrence of an accident.

[0033] The unmanned aerial vehicle 2 is provided with a notification unit 26. The notification unit 26 is composed of, for example, a speaker that generates a notification sound, a projector that projects a notification image, etc.

[0034] The unmanned aerial vehicle 2 is provided with a photographing unit 25 having a CCD image sensor, a CMOS image sensor, etc. The photographing unit 25 is configured to be able to photograph the accident site from above when the manned transporter 1 has an accident.

[0035] The management device 3 is provided with an accident determination unit 35. The accident determination unit 35 is configured to determine whether or not the manned transporter 1 has had an accident based on the sound data around the manned transporter 1 acquired by the sound collection unit 12.

[0036] The storage unit 30 stores the sound data and is organized into a database. The sound data consists of normal sound data when the manned transporter 1 performs normal loading and unloading operations and travels, collision accident sound data when the manned transporter 1 collides with a rack R, a wall, etc. while traveling, dropping accident sound data when the manned transporter 1 drops a load L during loading and unloading operations, personal accident sound data when the manned transporter 1 collides with a person while traveling or drops a load L on a person during loading and unloading operations, and fire accident sound data when a fire occurs due to the manned transporter 1 accidentally handling a load L during loading and unloading operations.

[0037] The accident determination unit 35 compares the sound data stored in the storage unit 30 with the sound data collected by the sound collection unit 12, and measures the differences and consistencies in sound volume, pitch, and timbre. Note that the accident determination unit 35 may be configured to associate and set a numerical parameter obtained by weighted-averaging the values of sound volume, pitch, and timbre in the accident sound data with a weighting coefficient. Then, the accident determination unit 35 is configured to be able to determine an accident sound from the measured differences and consistencies in sound.

[0038] The storage unit 30 further stores image data and is configured as a database. The image data includes normal image data when the manned transporter 1 performs normal cargo handling operations and travels, collision accident image data when the manned transporter 1 collides with a rack R, a wall, etc. during travel, drop accident image data when the manned transporter 1 drops a load L during cargo handling operations, personal accident image data when the manned transporter 1 collides with a person during travel or drops a load L on a person during cargo handling operations, and fire accident image data when a fire occurs due to the manned transporter 1 mishandling a load L during cargo handling operations.

[0039] The accident determination unit 35 further compares the image data stored in the storage unit 30 with the image data acquired by the imaging unit 25 to detect the position of a person, the presence or absence of a fire, etc. The accident determination unit 35 is configured to determine that it is a personal accident when an image of a person in a fallen state or a person sitting on the floor surface is captured, and to determine that it is a fire accident when an image of a fire is captured.

[0040] The storage unit 30 further stores a dangerous goods handling area in the map M. The dangerous goods handling area is an area where the manned transporter 1 handles a load L containing dangerous goods such as gasoline or oil-based paint with a high risk of fire, or a load L containing dangerous goods such as mercury or organic solvents that have an adverse effect on the human body. Then, the accident determination unit 35 further determines whether an accident occurred in the dangerous goods handling area based on the dangerous goods handling area stored in the storage unit 30 and the location of the accident site.

[0041] Based on FIG. 3, the accident handling control procedure will be described.

[0042] The sound collection unit 12 of the manned transport vehicle 1 sequentially collects the sounds generated around the vehicle of the manned transport vehicle 1 to obtain sound data (step S1). The collected sound data is transmitted to the accident determination unit 35 of the management device 3, and the accident determination unit 35 determines the presence or absence of an accident sound based on the transmitted sound data (step S2). When the accident determination unit 35 determines that there is no accident sound, it returns to step S1, and the sound collection unit 12 of the manned transport vehicle 1 sequentially collects the sounds generated around the vehicle of the manned transport vehicle 1 to obtain sound data.

[0043] When the accident determination unit 35 determines that there is an accident sound, taking the position D1 of the manned transport vehicle 1 that caused the accident as the accident site, the flight control unit 21 is controlled so that the unmanned aerial vehicle 2 equipped with the imaging unit 25 flies to the accident site (step S3). When the unmanned aerial vehicle 2 arrives at the accident site, the imaging unit 25 starts imaging the accident site (step S4). The image data captured by the imaging unit 25 is stored in the storage unit 30 for recording as accident images (step S5).

[0044] The captured image data is transmitted to the accident determination unit 35 of the management device 3, and the accident determination unit 35 determines whether it is a personal accident based on the transmitted image data (step S6). When the accident determination unit 35 determines that it is a personal accident, it reports to an ambulance and / or the unmanned aerial vehicle 2 equipped with a first aid set flies to the accident site (step S7).

[0045] The accident determination unit 35 further determines whether it is a fire accident based on the transmitted image data (step S8). When the accident determination unit 35 determines that it is a fire accident, the unmanned aerial vehicle 2 equipped with a fire extinguishing set flies to the accident site (step S9).

[0046] The accident determination unit 35 further determines whether the accident occurred in a hazardous material handling area based on the position of the accident site (step S10). When the accident determination unit 35 determines that the accident occurred in a hazardous material handling area, the unmanned aerial vehicle 2 equipped with an emergency set corresponding to the hazardous material flies to the accident site (step S11).

[0047] Also, if necessary, in order to notify an accident around the manned transport vehicle 1, the notification unit 26 of the unmanned aerial vehicle 2 may generate a notification sound (e.g., siren sound or voice) with a speaker, or generate a notification text (e.g., an alarm text such as "An accident has occurred in the vicinity! Please be careful!") or a notification image (e.g., an accident illustration) with a projector.

[0048] Then, it is sequentially determined whether the manned transport vehicle 1 has an accident, and the accident processing control of steps S1 to S11 is repeatedly performed to record the accident image.

[0049] [Second Embodiment] The transport system of the second embodiment will be described. Note that the description of the same configuration as that of the first embodiment may be omitted to avoid redundant description.

[0050] As shown in FIG. 4, the accident determination unit 35 includes a collection unit 350 that collects teacher data 356. The teacher data 356 includes feature data D regarding the characteristics of sound. In this embodiment, the feature data D regarding various sound characteristics are the sound volume, pitch, and timbre of the sound.

[0051] The accident determination unit 35 includes a learning model generation unit 351 that performs machine learning from the teacher data 356 collected by the collection unit 350 and generates and stores a learning model by the machine learning. The learning model generation unit 351 of this embodiment performs supervised learning. In supervised learning, a large amount of the teacher data 356, that is, a set of input data ID and output data OD, is input to the learning model generation unit 351.

[0052] The input data ID includes the sound volume, pitch, and timbre of the normal sound and the accident sound. The output data OD is the sound volume, pitch, and timbre of the accident sound. The input data ID is evaluated, and the presence or absence of the accident sound is set.

[0053] When it is determined that there is an accident sound, for example, the loudness of the sound has a large difference from the normal sound, the pitch has a large difference from the normal sound, or the timbre has a large difference from the normal sound. The accident determination unit 35 determines the presence or absence of an accident based on the above numerical parameters of the difference degree and the coincidence degree.

[0054] The accident sound may be set by any numerical parameter of the loudness, pitch, or timbre of the sound, or may be set by a numerical parameter weighted by a weighting coefficient.

[0055] In fact, regarding the accident sound, it is often easy to recognize the accident sound because the loudness, pitch, and timbre of the accident sound are different from those of the normal sound. Therefore, it can be inferred that there is a certain relationship such as a correlation relationship between the difference degrees of the loudness, pitch, and timbre of the accident sound with respect to the normal sound and the accident sound.

[0056] The learning model generation unit 351 uses a machine learning algorithm such as a general neural network. The learning model generation unit 351 performs machine learning using the input data ID and the output data OD having a correlation relationship as the teacher data 356, thereby generating a model (learning model) for estimating the output from the input, that is, a model that outputs the presence or absence of an accident sound when the input data ID is input.

[0057] The accident determination unit 35 includes an acquisition unit 355 that acquires the current input data ID at regular intervals. As described above, the input data ID is the loudness, pitch, and timbre in the normal sound and the accident sound. The acquisition unit 355 is configured to acquire the input data ID by a known sound analysis technique based on the sound data by the sound collection unit 12.

[0058] The accident determination unit 35 includes a prediction unit 352 that predicts whether it is an accident sound based on a numerical parameter indicating the difference degree between the normal sound and the accident sound by applying the learning model generated by the learning model generation unit 351 to the current input data ID acquired from the acquisition unit 355.

[0059] The accident determination unit 35 includes a determination unit 353, and the determination unit 353 determines whether to fly the unmanned aerial vehicle 2 to the accident site based on the output data OD predicted by the prediction unit 352.

[0060] Based on FIG. 5, the accident processing control procedure will be described.

[0061] The accident determination unit 35 collects the teacher data 356 by the collection unit 350 (step S21). Then, the learning model generation unit 351 performs machine learning on the teacher data 356 collected by the collection unit 350 in step S21, and generates and stores a learning model by machine learning (step S22).

[0062] The accident determination unit 35 acquires, by the acquisition unit 355, the sound data collected by the sound collection unit 12 as the input data ID at the current time every predetermined time (step S23).

[0063] The accident determination unit 35 predicts whether it is accident sound by applying the learning model generated in step S22 to the input data ID at the current time acquired in step S23 by the prediction unit 352 (step S24).

[0064] The accident determination unit 35 determines the presence or absence of accident sound based on the output data OD predicted in step S24 by the determination unit 353 (step S25).

[0065] When the accident determination unit 35 determines that there is accident sound, it controls the flight control unit 21 so that the unmanned aerial vehicle 2 equipped with the imaging unit 25 flies to the accident site with the position D1 of the manned transport vehicle 1 that caused the accident as the accident site (step S26). When the unmanned aerial vehicle 2 arrives at the accident site, the imaging unit 25 starts imaging the accident site (step S27). The image data captured by the imaging unit 25 is stored in the storage unit 30 for recording as accident images (step S28).

[0066] The captured image data is transmitted to the accident determination unit 35 of the management device 3, and the accident determination unit 35 determines whether it is a personal accident based on the transmitted image data (step S29). When the accident determination unit 35 determines that it is a personal accident, it notifies the ambulance and / or the unmanned aerial vehicle 2 equipped with a first aid kit flies to the accident site (step S30).

[0067] Furthermore, the accident determination unit 35 determines whether it is a fire accident based on the transmitted image data (step S31). When the accident determination unit 35 determines that it is a fire accident, the unmanned aerial vehicle 2 equipped with a fire extinguishing set flies to the accident site (step S32).

[0068] Furthermore, the accident determination unit 35 determines whether the accident occurred in a dangerous goods handling area based on the location of the accident site (step S33). When the accident determination unit 35 determines that the accident occurred in a dangerous goods handling area, the unmanned aerial vehicle 2 equipped with an emergency set corresponding to the dangerous goods flies to the accident site (step S34).

[0069] Also, if necessary, in order to notify an accident around the manned transport vehicle 1, the notification unit 26 of the unmanned aerial vehicle 2 may generate a notification sound with a speaker, or generate a notification text or a notification image with a projector.

[0070] Then, it is sequentially determined whether the manned transport vehicle 1 has an accident, and the accident processing control of steps S21 to S34 is repeatedly performed so as to record accident images.

[0071] As described above, the preferred embodiments of the present invention have been described, but the configuration of the present invention is not limited to these embodiments. For example, it can also be changed as follows.

[0072] In the above embodiment, the accident determination unit 35 determines the presence or absence of an accident sound based on the loudness, pitch, and timbre of the sound. However, it may be configured to determine the presence or absence of an accident sound based on the sound waveform, spectral analysis, frequency band, distribution of frequency components, amount of frequency change, etc.

[0073] Further, the accident determination unit 35 does not determine whether the manned transport vehicle 1 has had an accident based only on the sound data around the manned transport vehicle 1 acquired by the sound collection unit 12. Instead, an ultrasonic sensor is mounted on the manned transport vehicle 1, and information on obstacles detected by the ultrasonic sensor (such as the presence or absence of obstacles and the distance to the obstacles) is taken into account, so that it can be more reliably determined whether the manned transport vehicle 1 has had an accident.

[0074] Further, the accident determination unit 35 does not determine whether the manned transport vehicle 1 has had an accident based only on the sound data around the manned transport vehicle 1 acquired by the sound collection unit 12. Instead, an acceleration sensor is mounted on the manned transport vehicle 1, and information detected by the acceleration sensor (such as the tilt, vibration, and impact of the vehicle) is taken into account, so that it can be more reliably determined whether the manned transport vehicle 1 has had an accident.

[0075] The effects of the present invention will be described.

[0076] In a transport system S including a manned transport vehicle 1 and an unmanned aerial vehicle 2, the manned transport vehicle 1 includes a position detection unit 10 that detects the position of the vehicle and a sound collection unit 12 that collects sound around the vehicle. The unmanned aerial vehicle 2 includes a photographing unit 25 that photographs the surroundings of the aircraft body, and a flight control unit 21 that controls the unmanned aerial vehicle 2 to fly to the accident site when the manned transport vehicle 1 has had an accident, using an accident determination unit 35 that determines whether the manned transport vehicle 1 has had an accident based on the sound collected by the sound collection unit 12. The unmanned aerial vehicle 2 is configured to photograph the accident site with the photographing unit 25.

[0077] Therefore, by providing the sound collection unit 12 in the manned transport vehicle 1, it is not necessary to mount an expensive in-vehicle camera on the manned transport vehicle 1, and the equipment mounted on the manned transport vehicle 1 can be made inexpensive and have a simple configuration. Further, since the unmanned aerial vehicle 2 records the accident site with the photographing unit 25, the entire accident site can be recorded not only from the front but also from above.

[0078] The accident determination unit 35 includes a collection unit 350 that collects teacher data based on the relationship between the characteristic data regarding the characteristics of the normal sound when the manned transporter 1 travels and performs cargo handling operations and the accident sound when the manned transporter 1 has an accident, a learning model generation unit 351 that performs machine learning from the teacher data collected by the collection unit 350 and generates and stores a learning model by the machine learning, an acquisition unit 355 that acquires, at regular intervals, the characteristic data of the sound around the manned transporter 1 at the current time by the sound collection unit, and a prediction unit 352 that inputs the characteristic data of the sound around the manned transporter 1 at the current time acquired from the acquisition unit 355 into the learning model generated by the learning model generation unit 351 to predict from the learning model whether it is an accident sound, and a determination unit 353 that determines whether to fly the unmanned aerial vehicle 2 to the accident site based on the prediction unit 352.

[0079] By using the learning model generated by machine learning, the accident determination unit 35 can accurately determine whether it is an accident sound.

[0080] The accident determination unit 35 is configured to determine whether the accident is a personal accident, and when it is determined to be a personal accident, the unmanned aerial vehicle 2 equipped with a first aid set is configured to fly to the accident site.

[0081] In this way, when there is a personal accident, by flying the unmanned aerial vehicle 2 equipped with a first aid set to the accident site, it is possible to quickly perform first aid treatment on the injured person.

[0082] The accident determination unit 35 is configured to determine whether the accident is a personal accident, and when it is determined to be a personal accident, it is configured such that an ambulance comes to the accident site.

[0083] In this way, when there is a personal accident, by enabling the ambulance to come to the accident site quickly, it is possible to quickly perform first aid treatment on the injured person or transport the injured person to the hospital.

[0084] The accident determination unit 35 is configured to determine whether the accident is a fire accident. When it is determined to be a fire accident, the unmanned aerial vehicle 2 equipped with a fire extinguishing set is configured to fly to the accident scene.

[0085] Thereby, in the case of a fire accident, the unmanned aerial vehicle 2 equipped with a fire extinguishing set flies to the accident scene, so that the fire can be quickly extinguished.

[0086] The accident determination unit 35 is configured to determine whether the accident occurred in a dangerous goods handling area. When it is determined that the accident occurred in a dangerous goods handling area, the unmanned aerial vehicle 2 equipped with an emergency set corresponding to the dangerous goods is configured to fly to the accident scene.

[0087] Thereby, in the case of an accident occurring in a dangerous goods handling area, when the dangerous goods are likely to cause a fire, the unmanned aerial vehicle 2 equipped with a fire extinguishing set flies to the accident scene. When the dangerous goods are likely to have an adverse effect on the human body, the unmanned aerial vehicle 2 equipped with a first aid set flies to the accident scene, so that the treatment corresponding to the dangerous goods can be quickly carried out.

[0088] The unmanned aerial vehicle 2 is provided with a notification unit 26 that notifies the surroundings of the accident scene that an accident has occurred.

[0089] Thereby, it is possible to quickly notify the surroundings of the accident scene that an accident has occurred, and the people around can quickly handle the accident.

Explanation of Reference Numerals

[0090] S Conveying System 1 Occupied Conveying Vehicle 2 Unmanned Aerial Vehicle 10 Position Detection Unit 12 Sound Collection Unit 21 Flight Control Unit 25 Photographing Unit 26 Notification Unit 30 Storage Unit 35 Accident Determination Unit 350 Collection Unit 351 Learning model generation unit 352 Prediction unit 353 Decision unit 355 Acquisition unit 356 Teacher data ID input data OD output data

Claims

1. In a transport system including a manned transport vehicle and an unmanned aerial vehicle, the manned transport vehicle includes a position detection unit that detects the position of the vehicle, and a sound collection unit that collects sound around the vehicle, and the unmanned aerial vehicle includes a photographing unit that photographs the surroundings of the aircraft body, and a flight control unit that controls the unmanned aerial vehicle to fly to the accident site when the manned transport vehicle has an accident, using an accident determination unit that determines whether the manned transport vehicle has an accident based on the sound collected by the sound collection unit, and the unmanned aerial vehicle is configured to photograph the accident site with the photographing unit. A transport system characterized by the above.

2. The accident determination unit includes a collection unit that collects teacher data based on the relationship between the characteristic data regarding the normal sound characteristics when the manned transport vehicle travels and performs loading and unloading operations and the accident sound when the manned transport vehicle has an accident, a learning model generation unit that performs machine learning from the teacher data collected by the collection unit and generates and stores a learning model by the machine learning, an acquisition unit that acquires, at predetermined time intervals, the characteristic data of the sound around the manned transport vehicle at the current time by the sound collection unit, a prediction unit that inputs the characteristic data of the sound around the manned transport vehicle at the current time acquired from the acquisition unit into the learning model generated by the learning model generation unit, and predicts whether it is the accident sound from the learning model, and a determination unit that determines whether to fly the unmanned aerial vehicle to the accident site based on the prediction unit. The transport system according to claim 1, characterized by the above.

3. The accident determination unit is configured to determine whether the accident is a personal accident, and when it is determined to be the personal accident, the unmanned aerial vehicle equipped with a first aid set is configured to fly to the accident site. The conveying system according to claim 1, characterized in that...

4. The accident determination unit is configured to determine whether the accident is a personal accident, and when it is determined to be a personal accident, an ambulance is configured to come to the accident site. The conveying system according to claim 1, characterized in that...

5. The accident determination unit is configured to determine whether the accident is a fire accident, and when it is determined to be a fire accident, the unmanned aerial vehicle equipped with a fire extinguishing set is configured to fly to the accident site. The conveying system according to claim 1, characterized in that...

6. The accident determination unit is configured to determine whether the accident occurred in a hazardous material handling area, and when it is determined that the accident occurred in a hazardous material handling area, the unmanned aerial vehicle equipped with an emergency set corresponding to the hazardous material is configured to fly to the accident site. The conveying system according to claim 1, characterized in that...

7. The unmanned aerial vehicle is provided with a notification unit for notifying the surroundings of the accident site that an accident has occurred. The conveying system according to claim 1, characterized in that...

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