Conveyance 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 transport system equipped with a manned transport vehicle and an unmanned air vehicle. [Background technology]

[0002] A manned guided vehicle (e.g., a forklift) used in a facility such as a factory or a warehouse is configured to run and operate when an operator boards and operates the vehicle. The forklift is also configured to perform a loading and unloading operation for picking up and placing cargo using forks.

[0003] A transport system is known that is configured to guide a manned transport vehicle operated by an operator using an unmanned aerial vehicle that can stop in the air (see Patent Document 1, etc.).

[0004] In the transportation system, the unmanned aerial vehicle is equipped with a projector that projects a guidance image onto a road surface. The guidance image, for example, displays an arrow pointing to a specific direction and is projected onto the road surface in front of the manned transport vehicle. This allows an operator operating the manned transport vehicle to be guided to a loading position by checking the guidance image.

[0005] However, conventional transport systems have a problem in that they are unable to prevent operators of manned transport vehicles from drinking and driving. [Prior art documents] [Patent documents]

[0006] [Patent Document 1] JP 2020-52629 A Summary of the Invention [Problem to be solved by the invention]

[0007] Therefore, the problem that the present invention aims to solve is to provide a transportation system that includes a manned guided vehicle and an unmanned aerial vehicle, which can prevent drunk driving by an operator who operates the manned guided vehicle using the unmanned aerial vehicle. [Means for solving the problem]

[0008] In order to solve the above problems, the transportation system of the present invention is a transportation system equipped with a manned transport vehicle and an unmanned aerial vehicle, in which the unmanned aerial vehicle is equipped with a photographing unit that photographs the face of an operator operating the manned transport vehicle, and an alarm unit that alerts the operator operating the manned transport vehicle based on the level of drinking using a drinking judgment unit that determines the operator's level of drinking based on the facial image of the operator captured by the photographing unit.

[0009] Preferably, the drinking judgment unit includes a collection unit that collects feature data related to human facial features and teacher data based on the relationship between the human face and the drinking level, a learning model generation unit that performs machine learning from the teacher data collected in the collection unit and generates and stores a learning model by machine learning, an acquisition unit that acquires the current facial feature data of the operator at predetermined time intervals using the shooting unit, a prediction unit that acquires the drinking level from the learning model by inputting the current facial feature data of the operator acquired from the acquisition unit into the learning model generated by the learning model generation unit, and a decision unit that decides whether to issue an alert to the operator based on the drinking level acquired by the prediction unit.

[0010] Preferably, when the degree of drinking is equal to or higher than a predetermined level, the alarm unit issues an alarm to those around the manned guided vehicle in addition to the operator.

[0011] Preferably, the alarm unit issues any one or a combination of sound, light and water as an alarm.

[0012] Preferably, the unmanned aerial vehicle is equipped with a projection unit that projects an image onto the passageway, and the alarm unit is configured to project an alarm image from the projection unit to issue an alarm around the manned guided vehicle.

[0013] Preferably, the unmanned aerial vehicle is configured to travel along a guideway for guiding the travel of the manned guided vehicle. Effect of the Invention

[0014] The transport system according to the present invention is a transport system equipped with a manned transport vehicle and an unmanned air vehicle, and the unmanned air vehicle can effectively prevent drunk driving by an operator operating the manned transport vehicle. [Brief description of the drawings]

[0015] [Figure 1] FIG. [Diagram 2] FIG. [Diagram 3] FIG. [Figure 4] FIG. 1 is a block diagram showing a transport system. [Diagram 5] FIG. 4 is a flowchart showing a control procedure for preventing drunk driving in the first embodiment. [Figure 6] FIG. 11 is a block diagram showing the configuration of a drinking determination unit according to a second embodiment. [Figure 7] FIG. 11 is a flowchart showing a control procedure for preventing drunk driving in the second embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0016] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, an embodiment of a transport system according to the present invention will be described with reference to the drawings.

[0017] [First embodiment] A transfer system according to a first embodiment will be described with reference to FIGS.

[0018] 1 to 4, the transport system S includes a manned transport vehicle 1 that an operator O boards and operates. The manned transport vehicle 1 is configured to travel and operate when the operator O boards and operates it. In this embodiment, the manned transport vehicle 1 is a counterbalance type forklift, and is configured to be able to travel the vehicle body and raise and lower the forks when the operator O boards and operates it.

[0019] The transport system S comprises a plurality of shelves R installed in a facility such as a factory or warehouse. The shelves R have a plurality of steps in the height direction and are configured so that luggage L can be stored at a predetermined position on the steps. The manned transport vehicle 1 performs loading and unloading by placing and picking up luggage L at a predetermined position on the shelves 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, and an aisle P is formed between each shelf R (FIGS. 1 and 3).

[0020] The transport system S includes an unmanned aerial vehicle 2 capable of stopping in the air. The unmanned aerial vehicle 2 is called a drone, and is configured to fly to a predetermined stopping position in the air by rotating rotors provided at the tip of each of a number of arms, and to hover at the predetermined stopping position in the air.

[0021] The transport system S includes a management device 3 for controlling the unmanned aerial vehicle 2 (FIG. 4). The management device 3 includes a memory unit 30. The memory unit 30 stores a map M including shelves R and aisles P installed within a facility, luggage L placed within the facility, and the like.

[0022] Furthermore, the storage unit 30 stores the loading and unloading tasks T to be performed by the manned guided vehicle 1 as a loading and unloading schedule J. That is, the loading and unloading schedule J has a plurality of loading and unloading tasks T set in a predetermined order, such as task T1 of picking up the luggage L from a predetermined location on a predetermined shelf R, task T2 of placing the luggage L at a predetermined location on a predetermined shelf R, task T3 of placing the luggage L at a shipping location, task T4 of picking up the luggage L from a receiving location, etc. Also, the loading and unloading tasks T include position information of the luggage L and loading and unloading (picking up or placing) information for the luggage L.

[0023] The management device 3 is equipped with a loading / unloading instruction unit 34, which is configured to display the loading / unloading tasks T of the loading / unloading schedule J transmitted from the memory unit 30 on a display unit 11 provided in the driver's seat of the manned guided vehicle 1.

[0024] The display unit 11 is configured, for example, with a touch panel display. The loading / unloading instruction unit 34 displays the loading / unloading task T to be performed by the manned guided vehicle 1 on the display unit 11. The operator O operates the manned guided vehicle 1 to perform loading / unloading in accordance with the loading / unloading task T displayed on the display unit 11. When the loading / unloading task T is completed, the operator O presses an end button displayed on the display unit 11, and an end signal is sent to the loading / unloading instruction unit 34. When the loading / unloading instruction unit 34 receives the end signal, it is configured to display the loading / unloading task T to be performed next by the manned guided vehicle 1 on the display unit 11.

[0025] The manned guided vehicle 1 is equipped with a position detection unit 10. The position detection unit 10 is composed of a sensor for detecting the surrounding environment, such as a laser sensor, a GPS sensor, an electromagnetic induction sensor, or a receiver for receiving a signal from a positioning satellite. The position detection unit 10 is configured to detect the vehicle position D1 of the manned guided vehicle 1.

[0026] The management device 3 includes a taxiway generating unit 31. The taxiway generating unit 31 generates a virtual taxiway 4 between the vehicle position D1 of the manned guided vehicle 1 and a loading position D2 based on information on the vehicle position D1 of the manned guided vehicle 1 transmitted from the position detecting unit 10, the facility map M transmitted from the memory unit 30, and the loading task T of the loading schedule J transmitted from the memory unit 30. The loading position D2 is a position on the passage P where the manned guided vehicle 1 picks up and places a load in the loading task T (FIG. 3).

[0027] 3, the taxiway generating unit 31 is configured to generate a virtual taxiway 4 that connects, for example, a vehicle position D1 and a loading position D2 on a passage P. The taxiway 4 is set, for example, so as to minimize the travel distance of the manned guided vehicle 1. In this embodiment, as shown in FIG. 3, the taxiway 4 is configured with a first straight section 41, a bent section 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 is composed of, for example, a laser sensor that acquires data for SLAM (Simultaneous Localization and Mapping) using LiDAR (Light Detection and Ranging), a motion sensor that acquires data for odometry, a receiver that receives signals from positioning satellites, and the like, 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 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 air stop position on the taxiway 4 and hover so as to stop in the air at the air stop position.

[0030] The location determination unit 32 is further configured to determine a hovering position on the taxiway 4 where the unmanned aerial vehicle 2 will hover. The location determination unit 32 is configured to stop in the air at a position on the taxiway 4 that is a fixed distance away from the vehicle position D1 of the manned guided vehicle 1 in the forward direction of the manned guided vehicle 1. Therefore, when the manned guided vehicle 1 approaches the unmanned aerial vehicle 2, the unmanned aerial vehicle 2 moves away from the manned guided vehicle 1, so that the distance between the manned guided vehicle 1 and the unmanned aerial vehicle 2 becomes constant.

[0031] The unmanned aerial vehicle 2 includes a memory unit 22. The memory unit 22 stores a guidance image 200. The guidance image 200 is configured, for example, with an arrow for guiding the manned guided vehicle 1 to the loading position D2, and is configured so that the direction of the arrow differs depending on the loading position D2 (FIGS. 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 a guidance image 200 stored in a memory unit 22 onto an aisle P of a facility (FIGS. 1 to 3).

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

[0034] The operator O can visually view the guidance image 200 projected onto the aisle P, drive the manned guided vehicle 1 along the guidance image 200 to the loading position D2, and operate the manned guided vehicle 1 in accordance with the loading task T displayed on the display unit 11 to perform loading work on the luggage 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 capture images of the face of the operator O who drives the manned guided vehicle 1 by driving the imaging unit 25 so that the imaging direction is always facing the operator O based on the position of the manned guided vehicle 1 from the position detection unit 10 and the position of the unmanned aerial vehicle 2 from the position detection unit 20.

[0036] The management device 3 also includes a drinking alcohol determination unit 35. The drinking alcohol determination unit 35 is configured to determine the drinking level (for example, levels 1 to 5) of the operator O based on the face image data of the operator O acquired by the imaging unit 25.

[0037] For example, drinking level 1 is "not drinking at all," drinking level 2 is "drinking a little," drinking level 3 is "drinking a little," drinking level 4 is "drinking a lot," and drinking level 5 is "drinking a lot."

[0038] The storage unit 30 stores face image data of the operator O who drives the manned guided vehicle 1 in advance and organizes it into a database. The face image of the operator O stored in the storage unit 30 is face image data of the operator O who has been determined to be negative and not consuming alcohol by an accurate alcohol test that measures the alcohol concentration in body fluids such as breath, blood, and urine. Since the alcohol test requires time and effort, from the viewpoint of work efficiency, the alcohol test is performed approximately once a year when the change in the operator O's face is small, and face image data of the operator O who has not consumed alcohol is obtained.

[0039] The drinking level determination unit 35 compares the face image data stored in the storage unit 30 with the face image data acquired by the photographing unit 25, and measures the value of the amount of change in each of the skin, eye, and lip colors. The drinking level determination unit 35 associates the drinking level (levels 1 to 5) of the operator O with a numerical parameter obtained by weighting the values ​​of the amount of change in each of the skin, eye, and lip colors using a weighting coefficient, and sets the parameter. Then, the drinking level determination unit 35 is configured to determine the drinking level (levels 1 to 5) of the operator O from the measured amount of change.

[0040] The drinking determination unit 35 determines that the operator O has not drunk alcohol at level 1, where the operator O has not drunk alcohol at all, and determines that the operator O has drunk alcohol at levels 2 to 5, where the operator O has drunk alcohol.

[0041] The unmanned aerial vehicle 2 is equipped with an alarm unit 26. The alarm unit 26 is configured to issue an alarm to the operator O and workers around the manned guided vehicle 1 based on the level of drinking from the drinking level determination unit 35.

[0042] The alarm unit 26 may be an audio device configured to emit a sound as an alarm. The volume of the sound may be changed depending on the degree of alcohol consumption. The alarm unit 26 may be configured to emit light as an alarm, with the intensity of the light being variable depending on the level of alcohol consumption. The alarm unit 26 may be configured to emit water as an alarm, the strength of which may vary depending on the level of alcohol consumption.

[0043] The control procedure for preventing drunk driving will be described with reference to FIG.

[0044] The photographing unit 25 of the unmanned aerial vehicle 2 sequentially photographs the face of the operator O of the manned guided vehicle 1, and the drinking judgment unit 35 acquires facial image data (step S1). The drinking judgment unit 35 judges the drinking level of the operator O based on the facial image of the operator O, and judges whether or not the operator O has drunk alcohol (step S2). If the drinking judgment unit 35 judges the drinking level to be level 1, it is judged that the operator has not drunk alcohol, and if the drinking judgment unit 35 judges the drinking level to be levels 2 to 5, it is judged that the operator has drunk alcohol.

[0045] If the drinking alcohol determination unit 35 determines that the operator O has not drunk alcohol (Level 1), the process returns to step S1, and the photographing unit 25 of the unmanned aerial vehicle 2 sequentially photographs the face of the operator O of the manned guided vehicle 1, and the drinking alcohol determination unit 35 acquires facial image data. If the drinking alcohol determination unit 35 determines that the operator O has drunk alcohol (Levels 2 to 5), it determines which of Levels 2 to 5 the drinking level is (Step S3).

[0046] In the case of levels 2 and 3, the operator O is judged as a light drunk driver, and since there is a high possibility that the operator O will stop driving if a warning is issued to the operator O, a warning is issued only to the operator O (step S4). In the case of levels 4 and 5, the operator O is judged as a heavy drunk driver, and since the operator O is in a very dangerous state, a warning is issued to the workers around the manned guided vehicle 1 in addition to the operator O (step S5).

[0047] When the alarm unit 26 consists of an acoustic device, the acoustic device is equipped with a directional sound generating unit, and when warning only the operator O, a sound is emitted locally toward the operator O, and when warning workers around the manned guided vehicle 1 in addition to the operator O, a sound is emitted over a wide area toward the operator O.

[0048] Furthermore, when the alarm unit 26 is composed of a lighting device, the lighting device is provided with a light generating unit having directionality, and when warning only the operator O, light is emitted locally toward the operator O, and when warning workers around the manned guided vehicle 1 in addition to the operator O, light is emitted over a wide range toward the operator O. Furthermore, the lighting device may issue a warning by flashing light.

[0049] In addition, when the alarm unit 26 consists of a water-discharging device, the water-discharging device is equipped with a directional water injection unit, and when warning only the operator O, water is sprayed locally toward the operator O, and when warning workers around the manned guided vehicle 1 in addition to the operator O, water is sprayed over a wide area toward the operator O.

[0050] In addition, the warning unit 26 may use the projection unit 25 to display a large warning image (for example, a warning message such as "The driver of this forklift is driving while drunk! Be careful!") in the aisle P around the manned transport vehicle 1, thereby warning workers around the manned transport vehicle 1 that the operator O operating the manned transport vehicle 1 is driving while drunk.

[0051] Then, the drunk driving prevention control of steps S1 to S5 is repeatedly performed so as to successively determine whether the operator O is driving while drunk and to issue a warning.

[0052] [Second embodiment] A transfer system according to the second embodiment will be described. Note that, in order to avoid duplication, descriptions of the same configuration as in the first embodiment may be omitted.

[0053] 6, the drinking determination unit 35 includes a collection unit 350 that collects teacher data 356. The teacher data 356 includes feature data D related to human facial features. In this embodiment, the feature data D related to various human facial features is the color of skin, eyes, and lips.

[0054] The drinking 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 machine learning. The learning model generation unit 351 of this embodiment performs supervised learning. In supervised learning, a large amount of teacher data 356, i.e., pairs of input data ID and output data OD, are input to the learning model generation unit 351.

[0055] The input data ID includes the color of skin, eyes, and lips. The output data OD is the drinking level. The input data ID is evaluated as the drinking level of the operator O, and a numerical parameter (level) from 1 to 5 is set.

[0056] The drinking level numerical parameter (level) is high, i.e., the drinking level of the operator O is determined to be high, when, for example, the skin turns red, the eyes turn red, or the lips turn black. The drinking level determination unit 35 determines the drinking level of the operator O based on the above-mentioned change numerical parameters.

[0057] The degree of drinking may be set by any of the numerical parameters of the changes in the color of the skin, eyes, and lips, or may be set by a numerical parameter weighted by a weighting coefficient.

[0058] In fact, the drinking level of the operator O can often be easily recognized by the color of the skin, eyes, and lips of the operator O. Therefore, it can be inferred that there is a certain relationship, such as a correlation, between the change in the color of the skin, eyes, and lips and the drinking level of the operator O.

[0059] 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 correlated input data ID and output data OD as teacher data 356 to generate a model (learning model) that estimates an output from an input, that is, a model that outputs a drinking level when an input data ID is input.

[0060] The drinking determination unit 35 includes an acquisition unit 355 that acquires the current input data ID at predetermined time 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 by a known image analysis technique based on the facial image data of the operator O captured by the imaging unit 25. The input data ID is acquired at predetermined time intervals (for example, every second).

[0061] The drinking judgment unit 35 includes a prediction unit 352 that predicts a numerical parameter indicating the drinking level of the 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 drinking alcohol determination section 35 includes a decision section 353, which decides whether to issue a warning to the operator O based on the output data OD predicted by the prediction section 352, and outputs a decision signal to the warning section .

[0063] The drinking determination unit 35 determines that a drinking level of level 1 indicates that the user has not drunk alcohol, and determines that a drinking level of levels 2 to 5 indicates that the user has drunk alcohol.

[0064] The control procedure for preventing drunk driving will be described with reference to FIG.

[0065] The drinking 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 from 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).

[0066] The drinking alcohol determination unit 35 acquires, via the acquisition unit 355, image data of the face of the operator O captured by the image capture unit 25 as the current input data ID at every predetermined time (step S23).

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

[0068] The drinking determination unit 35 determines the drinking level of the operator O based on the output data OD predicted in step S24 by the decision unit 353, and determines whether or not the operator O has drunk alcohol (step S25). If the drinking determination unit 35 determines that the drinking level is level 1, it is determined that the operator has not drunk alcohol, and if the drinking determination unit 35 determines that the drinking level is level 2 to 5, it is determined that the operator has drunk alcohol.

[0069] If the determination unit 353 determines that the operator O has not drunk alcohol (Level 1), the process returns to step S23, where the photographing unit 25 of the unmanned aerial vehicle 2 photographs the face of the operator O of the manned guided vehicle 1, and the acquisition unit 355 acquires the facial image data. If the determination unit 353 determines that the operator O has drunk alcohol (Levels 2 to 5), it determines which of Levels 2 to 5 the degree of drinking is (Step S26).

[0070] In the case of levels 2 and 3, the operator O is judged as a light drunk driver, and since there is a high possibility that the operator O will stop driving if a warning is issued to the operator O, a warning is issued only to the operator O (step S27). In the case of levels 4 and 5, the operator O is judged as a heavy drunk driver, and since the operator O is in a very dangerous state, a warning is issued to the workers around the manned guided vehicle 1 in addition to the operator O (step S28).

[0071] Then, the drunk driving prevention control of steps S23 to S28 is repeatedly performed so as to successively determine whether the operator O is driving while drunk and to issue a warning.

[0072] Although the preferred embodiments of the present invention have been described above, the configuration of the present invention is not limited to these embodiments. For example, the following modifications are possible.

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

[0074] In the above embodiment, the drinking determination unit 35 determined the degree of drinking based on changes in the color of the operator O's skin, eyes, and lips, but it may also be configured to determine the degree of drinking based on the degree of bloodshot eyes, moisture of the lips, head movement, facial expressions, fluctuations in opening and closing of the eyelids, etc.

[0075] In the above embodiment, the alarm unit 26 is composed of either an audio device, a lighting device, or a water-discharging device, but these may be selectively combined, or the unmanned aerial vehicle 2 may be equipped with all of them.

[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 operator O can visually recognize the taxiway 4, but may also be configured to include a voice generating unit (not shown) that emits sounds such as voice, buzzer, and chime so that the operator O can hear the taxiway 4. The voice generating unit is configured to emit sounds such as "Turn left 15 m ahead", "Destination 30 m ahead", "There is an obstacle ahead. Please be careful", etc.

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

[0078] In a transport system S equipped with a manned transport vehicle 1 and an unmanned aerial vehicle 2, the unmanned aerial vehicle 2 is equipped with a photographing unit 25 that photographs the face of an operator O operating the manned transport vehicle 1, a drinking judgment unit 35 that judges the level of drinking of the operator O based on the facial image of the operator O captured by the photographing unit 25, and an alarm unit 26 that issues an alarm to the operator O operating the manned transport vehicle 1 based on the level of drinking.

[0079] Therefore, by providing the unmanned aerial vehicle 2 with the imaging unit 25 and imaging from outside the manned guided vehicle 1, it is not necessary to provide an imaging unit 25 in each manned guided vehicle 1, and the configuration for imaging the operator O can be minimized. In addition, since the operator O does not know when the unmanned aerial vehicle 2 will approach, it is expected that the operator O will operate the manned guided vehicle 1 with a sense of tension. In addition, by the drinking level determination unit 35 determining the drinking level of the operator O, when the operator O drinks and drives, an alarm is issued to stop the operator O from drinking and driving.

[0080] The drinking judgment unit 35 includes a collection unit 350 that collects feature data related to human facial features and teacher data based on the relationship between the human face and the drinking level, 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 machine learning, an acquisition unit 355 that acquires the current facial feature data of the operator O at predetermined time intervals using the shooting unit 25, a prediction unit 352 that acquires the drinking level from the learning model by inputting the current facial feature data of the operator O acquired from the acquisition unit 355 into the learning model generated by the learning model generation unit 351, and a decision unit 353 that decides whether to issue an alert to the operator O based on the drinking level acquired by the prediction unit 352.

[0081] The drinking determination unit 35 can accurately determine the drowsiness level of the operator O by using a learning model generated by machine learning.

[0082] Moreover, it is preferable that the warning unit 26 warns the surroundings of the manned guided vehicle 1 in addition to the operator O when the degree of drinking is equal to or higher than a predetermined level.

[0083] If the operator O's level of drinking is above a predetermined level, even if a warning is given to the operator O, the operator O may not stop drunk driving. Therefore, by giving a warning to workers around the manned transport vehicle 1, it is possible to prevent the workers from colliding with the manned transport vehicle 1 operated by the drunk-driving operator O.

[0084] The alarm unit 26 is adapted to emit any one or a combination of sound, light and water as an alarm.

[0085] In this way, the alarm unit 26 emits sound, light, and water toward the operator O who is driving while drunk, thereby making it possible to stop the operator O from driving while drunk reliably and quickly.

[0086] In addition, 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 issue an alarm to those around the manned guided vehicle 1.

[0087] In this way, by the projection unit 23 projecting a warning image, it is possible to directly and effectively warn workers around the manned guided vehicle 1 that the operator O of the manned guided vehicle 1 is driving under the influence of alcohol.

[0088] In addition, the unmanned aerial vehicle 2 is configured to travel along a taxiway 4 that guides the manned guided vehicle 1.

[0089] Therefore, the unmanned aerial vehicle 2 guiding the manned transport vehicle 1 can sequentially and accurately capture facial images of the operator O, which allows the drinking judgment unit 35 to judge the degree of drinking of the operator O, and if the operator O drinks while driving, an alarm can be quickly issued to stop the operator O from drinking while driving. [Explanation of symbols]

[0090] S Conveyor System O Operator 1 Manned guided vehicle 2 Unmanned Aerial Vehicles 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 Department 353 Decision Section 355 Acquisition Department 356 Teacher Data ID Input Data OD Output Data

Claims

1. In a transportation system including a manned guided vehicle and an unmanned aerial vehicle, The unmanned aerial vehicle is An imaging unit that images a face of an operator operating the manned guided vehicle; and a warning unit that uses a drinking judgment unit that judges the drinking level of the operator based on the face image of the operator captured by the photographing unit to warn the operator who is operating the manned transport vehicle based on the drinking level. A transport system comprising:

2. The drinking determination unit is A collection unit that collects training data based on a relationship between feature data on human facial features and a drinking level on the human face; a learning model generation unit that performs machine learning on the teacher data collected by the collection unit, and generates and stores a learning model by the machine learning; an acquisition unit that acquires current facial feature data of the operator at predetermined time intervals by the photographing unit; a prediction unit that acquires the drinking level from the learning model generated by the learning model generation unit by inputting the facial feature data of the operator at the current time acquired by the acquisition unit into the learning model; a determination unit that determines whether to issue a warning to the operator based on the drinking degree acquired by the prediction unit.

2. The transport system according to claim 1.

3. The warning unit warns the operator and those around the manned transport vehicle when the degree of drinking is equal to or higher than a predetermined level.

2. The transport system according to claim 1.

4. The alarm unit issues the alarm by any one of sound, light, and water, or a combination of these.

2. The transport system according to claim 1.

5. The unmanned aerial vehicle includes a projection unit that projects an image onto the passageway, The warning unit is configured to project a warning image from the projection unit to warn people around the manned guided vehicle.

4. The transport system according to claim 3.

6. The unmanned aerial vehicle is configured to travel along a guideway for guiding the travel of the manned guided vehicle.

2. The transport system according to claim 1.

Images