Conveying system

Abstract

To enable prevention of drunken driving of an operator who operates a manned conveyance vehicle using an unmanned flying object in a conveyance system provided with the manned conveyance vehicle and the unmanned flying object.SOLUTION: A conveyance system S comprises: a manned conveyance vehicle 1; and an unmanned flying object 2. The unmanned flying object 2 is provided with a photographing unit 25 that photographs a face of an operator O who is operating the manned conveyance vehicle 1; and a warning unit 26 that issues a warning to the operator O who is operating the manned conveyance vehicle 1 on the basis of the degree of drinking using a drinking determination unit 35 that determines the degree of drinking of the operator O on the basis of a face image of the operator O captured by the photographing unit 25.SELECTED DRAWING: Figure 4

Description

Technical Field

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

Background Art

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

[0003] And there is known a transportation system configured to guide a manned transport vehicle operated by an operator using an unmanned aerial vehicle capable of hovering in the air (see Patent Document 1, etc.).

[0004] In the transportation system, the unmanned aerial vehicle includes a projector that projects a guidance image onto the road surface. The guidance image, for example, shows an arrow indicating a specific direction and is projected onto the road surface in front of the manned transport vehicle. Thereby, an operator operating the manned transport vehicle is configured to be guided to the cargo handling position by checking the guidance image.

[0005] By the way, in the conventional transportation system, there has been a problem that it is impossible to prevent an operator operating the manned transport vehicle from driving under the influence of alcohol.

Prior Art Documents

Patent Documents

[0006]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0007] Therefore, the problem that the present invention aims to solve is to provide a transport system that includes a manned transport vehicle and an unmanned aerial vehicle, and that can prevent the operator of the manned transport vehicle from driving under the influence of alcohol by using the unmanned aerial vehicle. [Means for solving the problem]

[0008] To solve the above problems, the transport system according to the present invention is a transport system comprising a manned transport vehicle and an unmanned aerial vehicle, wherein the unmanned aerial vehicle includes a camera unit that photographs the face of an operator operating the manned transport vehicle, and an alcohol determination unit that determines the degree of alcohol consumption of the operator based on the facial image of the operator taken by the camera unit, and an alarm unit that alerts the operator operating the manned transport vehicle based on the degree of alcohol consumption.

[0009] Preferably, the alcohol detection unit includes: a collection unit that collects feature data relating to the characteristics of a human face and training data based on the relationship between the characteristics of a human face and the degree of alcohol consumption; a learning model generation unit that performs machine learning on the training data collected by the collection unit and generates and stores a learning model by machine learning; an acquisition unit that uses an imaging unit to acquire the current facial feature data of the operator at predetermined intervals; a prediction unit that inputs the current facial feature data of the operator acquired from the acquisition unit into the learning model generated by the learning model generation unit to acquire the degree of alcohol consumption from the learning model; and a decision unit that determines whether to alert the operator based on the degree of alcohol consumption acquired by the prediction unit.

[0010] Preferably, the alarm unit will sound an alarm not only to the operator but also to those around the manned transport vehicle when the level of alcohol consumption is above a predetermined level.

[0011] Preferably, the alarm unit emits an alarm using sound, light, and water, or a combination thereof.

[0012] Preferably, the unmanned aerial vehicle is equipped with a projection unit that projects images onto a walkway, and the alarm unit is configured to project an alarm image from the projection unit to alert the manned transport vehicle.

[0013] Preferably, the unmanned aircraft is configured to travel along a guiding path for guiding the travel of the manned transport vehicle. [Advantages of the Invention]

[0014] In the transport system according to the present invention, in a transport system including a manned transport vehicle and an unmanned aircraft, the unmanned aircraft can effectively prevent the operator of the manned transport vehicle from driving under the influence of alcohol. [Brief Description of the Drawings]

[0015] [Figure 1] Perspective view showing the transport system. [Figure 2] Side view showing the transport system. [Figure 3] Plan view showing the transport system. [Figure 4] Block diagram showing the transport system. [Figure 5] Flowchart showing the control procedure for preventing drunk driving in the first embodiment. [Figure 6] Block diagram showing the configuration of the drunk driving determination unit in the second embodiment. [Figure 7] Flowchart showing the control procedure for preventing drunk driving in the second embodiment. [Modes for Carrying Out the Invention]

[0016] Hereinafter, embodiments of the transport system according to the present invention will be described based on the drawings.

[0017] [First Embodiment] Based on FIGS. 1 to 5, the transport system of the first embodiment will be described.

[0018] As shown in FIGS. 1 to 4, the conveying system S includes a manned transport vehicle 1 on which an operator O rides and operates. The manned transport vehicle 1 is configured to travel and operate when the operator O rides and operates it. In the present embodiment, the manned transport vehicle 1 is a counterbalanced forklift, and is configured to travel the vehicle body and raise and lower the fork when the operator O rides and operates it.

[0019] 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 is configured to be able to store the load L at a predetermined position of the stepped portion. The manned transport vehicle 1 performs a loading and unloading operation by loading and unloading the load L at a predetermined position of the shelf R. The shelves R are arranged at intervals of a predetermined width so that the manned transport vehicle 1 can travel and perform loading and unloading operations, and a passage P is formed between the shelves R (FIGS. 1 and 3).

[0020] The conveying system S includes an unmanned flying body 2 that can be stopped in the air. The unmanned flying body 2 is called a drone, and is configured to fly to a predetermined in-air stop position and hover at the predetermined in-air stop position by the rotation of rotors provided at the tip sides of a plurality of arms.

[0021] The conveying system S includes a management device 3 for controlling the unmanned flying body 2 (FIG. 4). The management device 3 includes a storage unit 30. The storage unit 30 stores a map M constituted by the shelves R and passages P installed in the facility, the loads L arranged in the facility, and the like.

[0022] Furthermore, the storage unit 30 stores the cargo handling tasks T performed by the manned transport vehicle 1 as a cargo handling schedule J. Specifically, the cargo handling schedule J consists of multiple cargo handling tasks T, such as task T1 for retrieving cargo L from a predetermined location on a predetermined shelf R, task T2 for placing cargo L in a predetermined location on a predetermined shelf R, task T3 for placing cargo L in a shipping location, and task T4 for retrieving cargo L from a receiving location, all set up in a predetermined order. The cargo handling tasks T also include location information for cargo L and cargo handling (retrieval or placement) information for cargo L.

[0023] The management device 3 includes a cargo handling instruction unit 34, which is configured to display the cargo handling tasks T of the cargo handling schedule J transmitted from the storage unit 30 on a display unit 11 located in the driver's seat of the manned transport vehicle 1.

[0024] The display unit 11 is, for example, a touch panel display. The cargo handling instruction unit 34 displays the cargo handling task T that the manned transport vehicle 1 should perform on the display unit 11. The operator O operates the manned transport vehicle 1 to perform cargo handling according to the cargo handling task T displayed on the display unit 11. When the cargo handling task T is completed, the operator O presses the end button displayed on the display unit 11, and an end signal is sent to the cargo handling instruction unit 34. Upon receiving the end signal, the cargo handling instruction unit 34 is configured to display the next cargo handling task T that the manned transport vehicle 1 should perform on the display unit 11.

[0025] The manned transport vehicle 1 is equipped with a position detection unit 10. The position detection unit 10 consists of sensors that detect the surrounding environment, such as a laser sensor, GPS sensor, and electromagnetic induction sensor, or a receiver that receives signals from positioning satellites. The position detection unit 10 is configured to detect the vehicle position D1 of the manned transport vehicle 1.

[0026] The management device 3 includes a guideway generation unit 31. Based on the vehicle position D1 information of the manned transport vehicle 1 transmitted from the position detection unit 10, the facility map M transmitted from the storage unit 30, and the cargo handling task T of the cargo handling schedule J transmitted from the storage unit 30, the guideway generation unit 31 generates a virtual guideway 4 between the vehicle position D1 and the cargo handling position D2 of the manned transport vehicle 1. The cargo handling position D2 is the position on the passage P where the manned transport vehicle 1 picks up and places cargo in the cargo handling task T (Figure 3).

[0027] As shown in Figure 3, the guideway generation unit 31 is configured to generate a virtual guideway 4 that connects, for example, a vehicle position D1 and a cargo handling position D2 on a passage P. The guideway 4 is set, for example, to minimize the travel distance of the manned transport vehicle 1. In this embodiment, as shown in Figure 3, the guideway 4 is composed of a first straight section 41, a bend 40, and a second straight section 42.

[0028] The unmanned aerial vehicle 2 is equipped with a position detection unit 20. The position detection unit 20 consists of, for example, a laser sensor that acquires SLAM (Simultaneous Localization and Mapping) data using LiDAR (Light Detection and Ranging), a motion sensor that acquires odometry data, and a receiver that receives signals from positioning satellites, and can detect the position of the unmanned aerial vehicle 2.

[0029] The unmanned aerial vehicle 2 is equipped 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 results 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 aerial stopping position on the taxiway 4 and hover at the aerial stopping position.

[0030] The position determination unit 32 is further configured to determine the aerial hovering position for the unmanned aerial vehicle 2 on the taxiway 4. The position determination unit 32 is configured to hover at a position on the taxiway 4 that is a certain distance away from the vehicle position D1 of the manned transport vehicle 1 in the forward direction of the manned transport vehicle 1. Therefore, as the manned transport vehicle 1 approaches the unmanned aerial vehicle 2, the unmanned aerial vehicle 2 moves away from the manned transport vehicle 1, so that the distance between the manned transport vehicle 1 and the unmanned aerial vehicle 2 remains constant.

[0031] The unmanned aerial vehicle 2 is equipped with a memory unit 22. The memory unit 22 stores guidance images 200. The guidance images 200 consist of, for example, arrows for guiding the manned transport vehicle 1 to the loading / unloading position D2, and the direction of the arrows is configured to differ depending on the loading / unloading position D2 (Figures 1 and 3).

[0032] The unmanned aerial vehicle 2 is equipped with a projection unit 23. The projection unit 23 is composed of, for example, a projector, and can project guidance images 200 stored in the memory unit 22 onto the corridor P of the facility (Figures 1 to 3).

[0033] The control device 3 includes a projection instruction unit 33. The projection instruction unit 33 is configured to determine the guidance image 200 to be projected onto the road surface P according to the taxiway 4 from the taxiway generation unit 31 and the position of the unmanned aircraft 2 determined by the position detection unit 20, and to send a projection instruction to the projection unit 23 of the unmanned aircraft 2.

[0034] Operator O can visually observe the guidance image 200 projected onto the passage P, drive the manned transport vehicle 1 along the guidance image 200 to the loading / unloading position D2, and operate the manned transport vehicle 1 according to the loading / unloading task T displayed on the display unit 11 to perform loading / unloading operations on the cargo L.

[0035] The unmanned aerial vehicle 2 is equipped with an imaging unit 25 having a CCD image sensor, a CMOS image sensor, etc. The imaging unit 25 is configured to continuously photograph the face of the operator O who is driving the manned transport vehicle 1, by driving the imaging direction so that it is always facing the operator O, based on the position of the manned transport vehicle 1 from the position detection unit 10 and the position of the unmanned aerial vehicle 2 from the position detection unit 20.

[0036] Furthermore, the control device 3 includes an alcohol determination unit 35. The alcohol determination unit 35 is configured to determine the degree of alcohol consumption of operator O (for example, levels 1 to 5) based on the facial image data of operator O acquired by the imaging unit 25.

[0037] For example, drinking level 1 means "not drinking at all," drinking level 2 means "drinking a small amount," drinking level 3 means "drinking moderately," drinking level 4 means "drinking," and drinking level 5 means "drinking heavily."

[0038] The memory unit 30 stores and databases the facial image data of operator O who drives the manned transport vehicle 1. The facial image data of operator O stored in the memory unit 30 is the facial image data of operator O who has been determined to be negative, meaning they have not consumed alcohol, through accurate alcohol testing that measures the alcohol concentration in bodily fluids such as breath, blood, and urine. Since alcohol testing is time-consuming and labor-intensive, from the perspective of work efficiency, alcohol testing is performed approximately every year, when changes in operator O's face are minimal, to obtain facial image data of operator O who has not consumed alcohol.

[0039] The alcohol detection unit 35 compares the facial image data stored in the memory unit 30 with the facial image data acquired by the imaging unit 25 to measure the values ​​of the changes in skin, eye, and lip color. The alcohol detection unit 35 associates the operator O's alcohol level (levels 1 to 5) with a numerical parameter obtained by weighting the values ​​of the changes in skin, eye, and lip color using a weighting coefficient. The system is then configured to determine the operator O's alcohol level (levels 1 to 5) from the measured values ​​of the changes.

[0040] The alcohol detection unit 35 determines that there is no alcohol consumption if the operator O is not drinking at all (level 1), and that there is alcohol consumption if the operator O is drinking at levels 2 to 5.

[0041] The unmanned aerial vehicle 2 is equipped with an alarm unit 26. The alarm unit 26 is configured to alert the operator O and workers around the manned transport vehicle 1 based on the degree of alcohol consumption detected by the alcohol detection unit 35.

[0042] The alarm unit 26 may consist of an acoustic device and be configured to emit a sound as an alarm. The volume of the sound may be configured to change according to the degree of alcohol consumption. Furthermore, the alarm unit 26 may consist of a lighting device and be configured to emit light as an alarm. The intensity of the light may be configured to change according to the degree of alcohol consumption. Furthermore, the alarm unit 26 may consist of a water discharge device and be configured to emit water as an alarm. The strength of the water discharge may be adjusted according to the degree of alcohol consumption.

[0043] Based on Figure 5, the control procedure for preventing drunk driving will be explained.

[0044] The camera unit 25 of the unmanned aerial vehicle 2 sequentially photographs the face of operator O of the manned transport vehicle 1, and the alcohol detection unit 35 acquires the face image data (step S1). Based on the face image of operator O, the alcohol detection unit 35 determines the degree of alcohol consumption of operator O and determines whether or not alcohol has been consumed (step S2). If the alcohol detection unit 35 determines that the degree of alcohol consumption is level 1, it is determined that there has been no alcohol consumption. If the alcohol detection unit 35 determines that the degree of alcohol consumption is levels 2 to 5, it is determined that alcohol has been consumed.

[0045] If the alcohol detection unit 35 determines that operator O has not consumed alcohol (level 1), the process returns to step S1, and the imaging unit 25 of the unmanned aerial vehicle 2 sequentially photographs the face of operator O of the manned transport vehicle 1, and the alcohol detection unit 35 acquires the face image data. If the alcohol detection unit 35 determines that operator O has consumed alcohol (levels 2 to 5), it determines which level of alcohol consumption (levels 2 to 5) it is (step S3).

[0046] In cases of Level 2 and 3 intoxication, Operator O is judged to be driving under the influence of alcohol, and since there is a high probability that Operator O will stop driving if an alarm is issued to Operator O, an alarm is issued only to Operator O (Step S4). In cases of Level 4 and 5 intoxication, Operator O is judged to be driving under the influence of alcohol, and since the situation is extremely dangerous, an alarm is issued not only to Operator O but also to the workers around the manned transport vehicle 1 (Step S5).

[0047] If the alarm unit 26 consists of an acoustic device, the acoustic device is equipped with a directional sound generating unit, and when alarming only the operator O, the sound is emitted locally towards the operator O, and when alarming not only the operator O but also workers around the manned transport vehicle 1, the sound is emitted over a wide area towards the operator O.

[0048] Furthermore, if the alarm unit 26 consists of a lighting device, the lighting device is equipped with a directional light-emitting unit. When alarming only the operator O, light is emitted locally towards the operator O. When alarming not only the operator O but also workers around the manned transport vehicle 1, light is emitted over a wide area towards the operator O. The lighting device may also alarm by flashing light.

[0049] Furthermore, if the alarm unit 26 consists of a water spraying device, the water spraying device is equipped with a directional water jet. When an alarm is to be issued only to operator O, water is sprayed locally towards operator O. When an alarm is to be issued to workers around the manned transport vehicle 1 in addition to operator O, water is sprayed over a wide area towards operator O.

[0050] Furthermore, the alarm unit 26 may use the projection unit 25 to display a large alarm image (for example, an alarm message such as "The driver of this forklift is driving under the influence of alcohol! Please be careful!") in the passageway P around the manned transport vehicle 1, thereby alerting workers around the manned transport vehicle 1 that the operator O operating the manned transport vehicle 1 is driving under the influence of alcohol.

[0051] Then, the drunk driving prevention control from steps S1 to S5 is repeatedly performed to sequentially determine and warn whether operator O is driving under the influence of alcohol.

[0052] [Second Embodiment] The transport system of the second embodiment will now be described. Note that, to avoid redundant explanations, the same configuration as in the first embodiment may be omitted.

[0053] As shown in Figure 6, the alcohol detection unit 35 includes a collection unit 350 for collecting training data 356. The training data 356 includes feature data D relating to the features of a human face. In this embodiment, the feature data D relating to various human facial features are skin, eye, and lip color.

[0054] The alcohol detection unit 35 includes a learning model generation unit 351 that performs machine learning on training data 356 collected by the collection unit 350, and generates and stores a learning model through machine learning. In this embodiment, the learning model generation unit 351 performs supervised learning. In supervised learning, a large amount of training data 356, that is, pairs of input data ID and output data OD, is input to the learning model generation unit 351.

[0055] The input data ID includes skin, eye, and lip color. The output data OD represents the degree of alcohol consumption. The input data ID is evaluated as the degree of alcohol consumption of operator O, and a numerical parameter (level) from 1 to 5 is set.

[0056] A high numerical parameter (level) for the degree of alcohol consumption indicates that operator O has a high degree of alcohol consumption. Examples of such cases include when the skin turns red, the eyes turn red, or the lips turn black. The alcohol consumption determination unit 35 determines operator O's degree of alcohol consumption based on the numerical parameters of the above-mentioned changes.

[0057] The degree of alcohol consumption may be defined by a numerical parameter representing a change in skin, eye, or lip color, or by a numerical parameter weighted by a weighting coefficient.

[0058] Furthermore, in practice, the degree of Operator O's alcohol consumption is often easily discernible through the color of Operator O's skin, eyes, and lips. Therefore, it can be inferred that there is a certain relationship, such as a correlation, between changes in skin, eye, and lip color and Operator O's degree of alcohol consumption.

[0059] The learning model generation unit 351 uses a general machine learning algorithm such as a neural network. The learning model generation unit 351 performs machine learning using the correlated input data ID and output data OD as training data 356 to generate a model (learning model) that estimates output from input, that is, a model that outputs the degree of alcohol consumption when input data ID is input.

[0060] The alcohol detection unit 35 includes an acquisition unit 355 that acquires the current input data ID at predetermined intervals. As described above, the input data ID is the color of the skin, eyes, and lips. The acquisition unit 355 is configured to acquire the input data ID based on the facial image data of operator O obtained by the imaging unit 25, using known image analysis techniques. The input data ID is acquired at predetermined intervals (e.g., every second).

[0061] The alcohol determination unit 35 includes a prediction unit 352 that predicts numerical parameters indicating the degree of alcohol consumption of operator O 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.

[0062] The alcohol detection unit 35 includes a decision unit 353, which determines whether to issue an alarm to operator O based on the output data OD predicted by the prediction unit 352, and outputs a decision signal to the alarm unit 26.

[0063] The alcohol detection unit 35 determines that there is no alcohol consumption at level 1, and that there is alcohol consumption at levels 2 to 5.

[0064] Based on Figure 7, the control procedure for preventing drunk driving will be explained.

[0065] The alcohol detection unit 35 collects training data 356 using the collection unit 350 (step S21). Then, the learning model generation unit 351 performs machine learning on the training data 356 collected by the collection unit 350 in step S21, and generates and stores a learning model through machine learning (step S22).

[0066] The alcohol detection unit 35 acquires the image data of operator O's face, captured by the imaging unit 25, at predetermined intervals using the acquisition unit 355 as the current input data ID (step S23).

[0067] The alcohol determination unit 35 predicts the degree of alcohol consumption by operator O by applying the learning model generated in step S22 to the current input data ID acquired in step S23 using the prediction unit 352 (step S24).

[0068] The alcohol determination unit 35 determines the degree of alcohol consumption of operator O based on the output data OD predicted in step S24 by the determination unit 353, and determines whether or not alcohol has been consumed (step S25). If the alcohol determination unit 35 determines that the degree of alcohol consumption is level 1, it is determined that there has been no alcohol consumption. If the alcohol determination unit 35 determines that the degree of alcohol consumption is levels 2 to 5, it is determined that alcohol has been consumed.

[0069] If the determination unit 353 determines that operator O has not been drinking (level 1), the process returns to step S23, where the imaging unit 25 of the unmanned aerial vehicle 2 photographs the face of operator O of the manned transport vehicle 1, and the acquisition unit 355 acquires the face image data. If the determination unit 353 determines that operator O has been drinking (levels 2-5), it determines which level of drinking (levels 2-5) it is (step S26).

[0070] In cases of Level 2 and 3 intoxication, Operator O is judged to be driving under the influence of alcohol, and since there is a high probability that Operator O will stop driving if an alarm is issued to Operator O, an alarm is issued only to Operator O (Step S27). In cases of Level 4 and 5 intoxication, Operator O is judged to be driving under the influence of alcohol, and since this is a very dangerous situation, an alarm is issued not only to Operator O but also to workers around the manned transport vehicle 1 (Step S28).

[0071] Then, the drunk driving prevention control in steps S23 to S28 is repeatedly performed to sequentially determine and warn whether operator O is driving under the influence of alcohol.

[0072] Although preferred embodiments of the present invention have been described above, the configuration of the present invention is not limited to these embodiments. For example, it can be modified as follows.

[0073] In the above embodiment, the unmanned aerial vehicle 2 is configured to travel along a taxiway 4 for guiding the movement of the manned transport vehicle 1. However, it may also be configured to photograph the operator O of the manned transport vehicle 1 at any time and along any route without traveling along the taxiway 4.

[0074] In the above embodiment, the alcohol determination unit 35 determined the degree of alcohol consumption based on changes in the color of operator O's skin, eyes, and lips. However, it may also be configured to determine the degree of alcohol consumption based on the degree of redness of operator O's eyes, the moisture of their lips, head movements, facial expressions, changes in the opening and closing of their eyelids, etc.

[0075] In the above embodiment, the alarm unit 26 is composed of an acoustic device, a lighting device, or a water spraying device, but these may be selectively combined, or all of them may be provided in the unmanned aerial vehicle 2.

[0076] In the above embodiment, the unmanned aerial vehicle 2 is configured to project a guidance image 200 onto the passage P so that the taxiway 4 can be recognized visually by the operator O. However, it may also be equipped with a sound generating unit (not shown) that emits sounds such as voice, buzzer, or chime, so that the taxiway 4 can be recognized auditorily by the operator O. The sound generating unit is configured to emit voices such as, for example, "Turn left 15m ahead," "Your destination is 30m ahead," or "There is an obstacle ahead. Please be careful."

[0077] The effects of the present invention will be explained.

[0078] In a transport system S comprising a manned transport vehicle 1 and an unmanned aerial vehicle 2, the unmanned aerial vehicle 2 includes a camera unit 25 that photographs the face of operator O operating the manned transport vehicle 1, a drinking determination unit 35 that determines the degree of alcohol consumption of operator O based on the facial image of operator O taken by the camera unit 25, and an alarm unit 26 that alerts operator O operating the manned transport vehicle 1 based on the degree of alcohol consumption.

[0079] Therefore, by equipping the unmanned aerial vehicle 2 with a camera unit 25 and taking photographs from outside the manned transport vehicle 1, it is not necessary to equip each manned transport vehicle 1 with a camera unit 25, and the configuration for photographing the operator O can be minimized. In addition, since the operator O does not know when the unmanned aerial vehicle 2 will fly in, it is expected that the operator O will operate the manned transport vehicle 1 with a sense of tension. Furthermore, by having the alcohol detection unit 35 determine the degree of alcohol consumption of the operator O, an alarm can be issued if the operator O is driving under the influence of alcohol, allowing the operator O to stop driving under the influence of alcohol.

[0080] The alcohol detection unit 35 includes a collection unit 350 that collects feature data related to the characteristics of a human face and training data based on the relationship between the degree of alcohol consumption related to the human face; a learning model generation unit 351 that performs machine learning on the training data collected by the collection unit 350 and generates and stores a learning model through machine learning; an acquisition unit 355 that uses an imaging unit 25 to acquire the current facial feature data of operator O at predetermined intervals; a prediction unit 352 that inputs the current facial feature data of operator O acquired from the acquisition unit 355 into the learning model generated by the learning model generation unit 351 to acquire the degree of alcohol consumption from the learning model; and a decision unit 353 that determines whether to issue an alert to operator O based on the degree of alcohol consumption acquired by the prediction unit 352.

[0081] The alcohol detection unit 35 can accurately determine the degree of drowsiness of operator O by using a learning model generated by machine learning.

[0082] Furthermore, it is desirable that the alarm unit 26 alerts the area around the manned transport vehicle 1 in addition to the operator O when the level of alcohol consumption exceeds a predetermined level.

[0083] If operator O's level of intoxication exceeds a predetermined level, operator O may not stop drunk driving even if an alarm is issued to him. Therefore, by issuing an alarm to workers around the manned transport vehicle 1, it is possible to prevent workers from colliding with the manned transport vehicle 1 operated by operator O who is drunk driving.

[0084] Furthermore, the alarm unit 26 is designed to emit sound, light, and / or a combination thereof as an alarm.

[0085] In this way, the alarm unit 26 emits sound, light, and water towards the operator O who is driving under the influence of alcohol, thereby reliably and quickly stopping the operator O from driving under the influence.

[0086] Furthermore, the unmanned aerial vehicle 2 may be equipped with a projection unit 23 that projects an image onto the passage P, and the alarm unit 26 may project an alarm image from the projection unit 23 to alert the area around the manned transport vehicle 1.

[0087] In this way, the projection unit 23 projects an alarm image, which directly and effectively warns workers around the manned transport vehicle 1 that the operator O of the manned transport vehicle 1 is driving under the influence of alcohol.

[0088] Furthermore, the unmanned aerial vehicle 2 is configured to travel along a taxiway 4 that guides the movement of the manned transport vehicle 1.

[0089] Therefore, the unmanned aerial vehicle 2 guiding the manned transport vehicle 1 can sequentially and accurately capture images of operator O's face. Based on this, the alcohol detection unit 35 can determine operator O's level of alcohol consumption, and if operator O is driving under the influence of alcohol, it can quickly issue a warning and stop operator O from driving under the influence. [Explanation of Symbols]

[0090] S Conveyor System O Operator 1 Manned guided vehicle 2 Unmanned aircraft 4 Taxiway 23 Projection section 25 Photography Department 26 Alarm section 35 Drinking Determination Department 350 Collection Department 351 Learning Model Generation Unit 352 Prediction Section 353 Decision Section 355 Acquisition Department 356 training data ID Input Data OD output data

Claims

1. In a transport system equipped with a manned transport vehicle and an unmanned aerial vehicle, A manned transport vehicle operated by an operator, The vehicle has a camera unit for photographing the face of the operator operating the manned transport vehicle, and a projection unit for projecting guidance images onto the road surface, and also has multiple unmanned aircraft capable of hovering in the air. A control device for controlling the aforementioned unmanned aerial vehicle, A guide path generation unit generates a guide path between the vehicle position and the cargo handling position of the manned transport vehicle, A position determination unit that determines the position in which the multiple unmanned aircraft will hover in the air on the aforementioned taxiway, A projection instruction unit determines the guidance image to be projected onto the road surface based on the distance between the unmanned aircraft hovering in the air on the taxiway and sends instructions to the projection unit, A drinking determination unit that determines the degree of alcohol consumption of the operator based on the facial image of the operator taken by the aforementioned imaging unit, The system includes an alarm unit that uses the alcohol detection unit to alert the operator operating the manned transport vehicle based on the degree of alcohol consumption. A transport system characterized by the following features.

2. The alcohol detection unit is, A collection unit that collects feature data related to the characteristics of human faces and training data based on the relationship between the degree of alcohol consumption related to the said human faces, A learning model generation unit performs machine learning on the training data collected by the collection unit, and generates and stores a learning model using the machine learning. The aforementioned imaging unit acquires the facial feature data of the operator at the present time at predetermined intervals, and the acquisition unit A prediction unit obtains the degree of alcohol consumption from the learning model by inputting the current facial feature data of the operator obtained from the acquisition unit into the learning model generated by the learning model generation unit, The system includes a determination unit that determines whether to alert the operator based on the degree of alcohol consumption obtained by the prediction unit. The transport system according to feature 1.

3. The alarm unit, when the level of alcohol consumption is above a predetermined level, will alert not only the operator but also those around the manned transport vehicle. The transport system according to feature 1.

4. The alarm unit emits sound, light, and water, or a combination thereof, as the alarm. The transport system according to feature 1.

5. The alarm unit is configured to project an alarm image from the projection unit to sound an alarm around the manned transport vehicle. The transport system according to feature 3.

Images