Industrial vehicle, notification sound volume estimation system and notification sound volume estimation program

Abstract

To prevent people from being surprised by notification sounds and falling over while exhibiting effect of the notification sound in also response to difference in each person's degree of surprise at sound volume.SOLUTION: An industrial vehicle comprises: a speaker 12; a learned model 20 having a sound volume estimation part 25; a sound volume change part 26 which changes sound volume of the speaker 12; a registration part 16 in which person information corresponding to a plurality of persons is registered; and a person specification part 17 which specifies person information corresponding to a notification object person. The sound volume estimation part 25 previously performs machine learning by teacher data that uses a notification distance from the speaker 12 to the person, person information of the person and sound volume of the notification sound as input data and uses a surprise degree of the person who has been notified as output data. When the person information and the notification distance is inputted to the sound estimation part, the sound estimation part estimates such a sound volume that the person corresponding to the inputted person information becomes a prescribed surprise degree or less. The sound volume change part 26 changes sound volume of the speaker 12 to the estimated sound volume or less.SELECTED DRAWING: Figure 2

Description

[Technical field]

[0001] The present invention relates to a volume estimation system for use in an industrial vehicle, an industrial vehicle including the system, and an announcement sound volume estimation program. [Background technology]

[0002] In recent years, the proportion of workers aged 60 or older among those killed or injured in industrial accidents has been increasing. One of the major workplace accidents involving elderly people is falling while working. Therefore, it is necessary to create a work environment that reduces the number of falls among elderly people.

[0003] Industrial vehicles such as forklifts are equipped with speakers that warn of their approach while driving, and the warning sound helps to avoid contact with pedestrians (see, for example, Patent Documents 1 and 2). However, many elderly people have hearing loss and tend to have difficulty hearing the warning sound. One solution is to make the warning sound louder, but if the warning sound is too loud, it could startle pedestrians and cause them to fall. Also, the degree of surprise caused by the volume of the warning sound (hereinafter referred to as "surprise level") varies from person to person. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Application Publication No. 6-149373 [Patent Document 2] Patent Publication No. 2022-139495 Summary of the Invention [Problem to be solved by the invention]

[0005] Therefore, the problem that the present invention aims to solve is to provide an industrial vehicle that can respond to the differences in the degree of surprise each person has in response to the volume of the sound and prevent people from being startled by the alarm sound and falling over. [Means for solving the problem]

[0006] In order to solve the above problems, the industrial vehicle according to the present invention comprises: A speaker that outputs an alarm sound to a person; A trained model having a volume estimation unit; a volume change unit for changing the volume of a speaker; a registration unit in which personal information corresponding to a plurality of people is registered; a person identification unit that identifies person information corresponding to a person to be notified, the volume estimation unit performs machine learning in advance using teacher data in which the notification distance from the speaker to the person, the person's personal information, and the volume of the notification sound are input data, and the level of surprise of the person when notified is output data; and when the person information and the notification distance are input, the volume estimation unit estimates a volume at which the person corresponding to the input person information will be surprised to a predetermined level or less; The volume change unit changes the volume of the speaker to be equal to or lower than the estimated volume.

[0007] The industrial vehicle preferably has Further comprising a camera for photographing a person and generating a person image, The registration section further registers face images corresponding to each person's information, The person identification unit refers to a face image included in the person image and a face image corresponding to each piece of person information, and identifies person information corresponding to the person to be notified.

[0008] The industrial vehicle preferably has Further comprising a camera for photographing a person and generating a person image, The trained model further has a distance estimation unit, which has undergone machine learning in advance using teacher data in which a human image is input data and the reported distance from the speaker to the person is output data, and when a human image is input, the distance estimation unit estimates the reported distance from the speaker to the person.

[0009] The industrial vehicle preferably has Further comprising a camera for photographing a person and generating a person image, The speaker has directionality, The volume estimation unit identifies the human image of the person closest to the speaker based on the notification distance corresponding to each human image, and outputs the estimated volume corresponding to the human image to the volume change unit.

[0010] The industrial vehicle preferably has Further comprising a camera for photographing a person and generating a person image, The trained model further includes a posture estimation unit, The posture estimation unit performs machine learning in advance using training data in which a human image is input data and the type of human posture is output data, and when a human image is input, the posture estimation unit estimates the type of human posture, When the type of posture of the person is input, the volume estimation unit estimates a volume that will be equal to or lower than a predetermined surprise level based on the type of posture of the person.

[0011] The industrial vehicle preferably has Further comprising a camera for photographing a person and generating a person image, The trained model further includes an orientation estimation unit, The orientation estimation unit performs machine learning in advance using teacher data in which a human image is input data and the orientation of a person's face is output data, and when a human image is input, the unit estimates the orientation of the person's face, When the direction of the person's face is input, the volume estimation unit estimates a volume that will be equal to or lower than a predetermined surprise level based on the direction of the person's face.

[0012] The industrial vehicle preferably has An environmental sound acquisition unit for acquiring environmental sound is further provided, When the environmental sound is input, the volume estimation unit estimates a volume that will be equal to or lower than a predetermined surprise level based on the environmental sound.

[0013] The industrial vehicle preferably has Further comprising a camera for photographing a person and generating a person image, The trained model further includes a surprise level estimation unit, the surprise level estimation unit performs machine learning in advance using teacher data in which the image of the person notified and the personal information of the person notified are input data and the surprise level is output data, and when the image of the person notified by the alarm sound and the personal information of the person notified are input, the surprise level estimation unit estimates a surprise level corresponding to the personal information; The volume estimation unit re-learns the volume that is equal to or less than a predetermined surprise level based on the image of the person before being notified, the personal information of the notified person, the notification distance, the volume of the notification sound when it is notified, and the surprise level of the notified person.

[0014] The industrial vehicle preferably has a tone storage unit storing a plurality of tones; a tone control unit that determines a tone of the speaker for making a notification based on the person information identified by the person identification unit and switches the tone of the speaker to the determined tone, The volume estimation unit has previously performed machine learning for each tone, and is configured to estimate, in correspondence with the determined tone, the volume at which a person corresponding to the input person information will be surprised to a predetermined level or less.

[0015] The industrial vehicle preferably has A position detection unit that detects its own position; and a tone control unit that determines a tone of a speaker for notifying based on the detected position and switches the tone of the speaker to the determined tone, The volume estimation unit has previously undergone machine learning for each tone, and is configured to estimate, for each tone, the volume at which a person corresponding to the input person information will be surprised to a predetermined level or less.

[0016] The industrial vehicle preferably has The stored tones include buzzers, beeps, and melodies.

[0017] In order to solve the above problem, the informative sound volume estimation system according to the present invention comprises: An alarm sound volume estimation system for use in an industrial vehicle having a speaker that outputs an alarm sound to a person, A trained model having a volume estimation unit; A volume change unit; A registration unit in which multiple pieces of person information are registered; a person identification unit that identifies person information corresponding to a person to be notified, the volume estimation unit performs machine learning in advance using teacher data in which the notification distance from the speaker to the person, the person's personal information, and the volume of the notification sound are input data, and the level of surprise of the person when notified is output data; and when the person information and the notification distance are input, the volume estimation unit estimates a volume at which the person corresponding to the input person information will be surprised to a predetermined level or less; The volume change unit changes the volume of the speaker to be equal to or lower than the estimated volume.

[0018] In order to solve the above problem, the informative sound volume estimation program according to the present invention is An informative sound volume estimation program for use in an informative sound volume estimation system, the informative sound volume estimation system being a system for use in an industrial vehicle having a speaker for outputting informative sounds to people, the informative sound volume estimation system comprising a computer, The alarm sound volume estimation program is executed by a computer as a volume estimation unit that performs machine learning in advance using teacher data in which the alarm distance from the speaker to a person, the person's personal information, and the volume of the alarm sound are input as input data, and the volume estimation unit estimates the volume at which the person corresponding to the input personal information will be surprised to a predetermined level or less when personal information and the alarm distance are input. Effect of the Invention

[0019] The industrial vehicle according to the present invention can respond to the difference in the degree of surprise each person has in response to the volume of the alarm sound, and can prevent people from falling over due to being startled by the alarm sound while still achieving its effect. [Brief description of the drawings]

[0020] [Figure 1] 1 is a schematic diagram showing an industrial vehicle according to an embodiment of the present invention and its surroundings. [Diagram 2] FIG. 2 is a block diagram of a control unit of the industrial vehicle. [Diagram 3] A diagram illustrating the operation of each estimation unit of a trained model. [Figure 4] FIG. 1 is a diagram showing examples of human postures. [Diagram 5] FIG. 13 is a diagram illustrating machine learning of a volume estimation unit. [Figure 6] FIG. 11 is a diagram illustrating an operation of a volume estimation unit. [Figure 7] FIG. 11 is a flow chart showing an operation of the industrial vehicle to notify a person of its approach. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0021] Hereinafter, an embodiment of an industrial vehicle, an informative sound volume estimation system, and an informative sound volume estimation program according to the present invention will be described with reference to the accompanying drawings.

[0022] Fig. 1 is a schematic diagram showing an industrial vehicle 1 according to an embodiment of the present invention and its surroundings. As shown in Fig. 1, the industrial vehicle 1 is a forklift truck moving forward along an aisle R. In front of the industrial vehicle 1 are pedestrians H (hereinafter sometimes referred to as "person H") 1, H2, and H3, and next to the aisle R, a worker H4 is working.

[0023] The industrial vehicle 1 is an autonomously traveling unmanned guided vehicle, but this is merely one example, and the industrial vehicle 1 according to the present invention may be a manned industrial vehicle 1. The industrial vehicle 1 does not have to be a forklift, and may be, for example, an in-plant transport vehicle, an in-plant towing vehicle, a straddle carrier, or the like that does not have a fork 11, and is not particularly limited.

[0024] As shown in Fig. 1, the industrial vehicle 1 includes a vehicle body 10 and a fork 11. The fork 11 is configured to be capable of ascending and descending, thereby lifting and lowering a load.

[0025] As shown in FIG. 2, the industrial vehicle 1 includes a speaker 12, a camera 13, and a control unit 15.

[0026] The speaker 12 has directionality and outputs an alarm sound V to the person H. The speaker 12 is composed of a plurality of parametric speakers and is configured to be able to expand and contract the range of directivity according to the distance from the person H. This makes it possible to prevent the speaker 12 from outputting the alarm sound V to anyone other than the target person H. Therefore, it is possible to prevent the alarm sound V from being output to a worker H4 or the like who is not located in the traveling direction of the industrial vehicle 1, and to prevent the worker H4 from being startled. Since the industrial vehicle 1 moves forward and backward, the speaker 12 is configured to be able to output the alarm sound V at least in the forward and backward directions of the industrial vehicle 1. In addition, the number of speakers 12 and the configuration of the directionality of the speakers 12 are not particularly limited.

[0027] There is no particular limitation on the specific sound of the notification sound V. For example, the notification sound V may be a buzzer sound or a beep sound, or may be composed of a specific melody or a human voice.

[0028] The speaker 12 may not output the alarm sound V unless a person H is detected by the industrial vehicle 1. Alternatively, the speaker 12 may be configured to output the alarm sound V at a predetermined volume only at an intersection of the passage R, even if a person H is not detected. In this embodiment, the speaker 12 is configured to output the alarm sound V in the direction of the detected person H when the person H is detected by the camera 13 (i.e., when the person H is photographed). Note that, when multiple people H are detected simultaneously, the speaker 12 is configured to output the alarm sound V from the speaker 12 toward the person H closest to the person H.

[0029] Moreover, the industrial vehicle 1 further includes a melody output unit (not shown) that outputs a melody around the vehicle while it is traveling. The volume of the melody is set to a volume that will not startle the person H.

[0030] The camera 13 is configured to be able to capture an image of the periphery of the vehicle body 10, captures an image of a person H, and generates a person image HI. The camera 13 is made up of a plurality of cameras 13, and is configured to be able to capture images of at least the front and rear directions of the industrial vehicle 1.

[0031] The control unit 15 has a computer having a storage device, a memory, and a calculation device, and the storage device stores an informative sound volume estimation program. The informative sound volume estimation program causes the computer to function as a person identification unit 17, a trained model 20, and a volume change unit 26, which will be described later.

[0032] As shown in FIG. 2, the control unit 15 has, as its functional configuration, a registration unit 16, a person identification unit 17, a trained model 20, and a volume change unit .

[0033] The registration unit 16 registers personal information PI and face images of a plurality of persons H. In this embodiment, the personal information PI is an identifier corresponding to each person H.

[0034] The person identification unit 17 identifies a face image corresponding to a face image included in the person image HI from a plurality of face images registered in the registration unit 16, and identifies person information PI (identifier in this embodiment) corresponding to the identified face image. The person identification unit 17 transmits the person information PI (identifier) ​​corresponding to each person image HI to the volume estimation unit 25.

[0035] <Configuration of trained model> The trained model 20 includes a surprise level estimation unit 21, a distance estimation unit 22, a posture estimation unit 23, a direction estimation unit 24, and a volume estimation unit 25.

[0036] The surprise degree estimation unit 21 is, for example, a neural network, and previously learns the correlation by machine learning using teacher data having the human image HI of the notified person H as input data and the surprise degree Q as output data. The machine learning method may be, for example, a machine learning method that learns the correlation between the difference between the human image HI of the person H before being notified and the human image HI of the person H after being notified and the surprise degree Q. Alternatively, the surprise degree estimation unit 21 may learn by machine learning a face image expressing an emotion such as surprise or impatience as teacher data, and may be configured to estimate the surprise degree Q of the notified person H when the human image HI of the notified person H is input. By such machine learning, the surprise degree estimation unit 21 estimates the surprise degree Q of the notified person H when the human image HI of the person H notified by the alarm sound V is input, as shown in FIG. 3A. The surprise degree Q may be output as a numerical value between 0.01 and 1.0, for example.

[0037] The "predetermined surprise level RQ" in the present invention is a surprise level Q that can prevent a person from falling over, dropping something they are holding, or making a mistake in their work due to being startled. This predetermined surprise level RQ may be a value of the surprise level Q, such as 0.7.

[0038] The distance estimation unit 22 is, for example, a neural network, and has previously learned the correlation by machine learning using teacher data having a human image HI as input data and a notification distance DN from the speaker 12 to the person H as output data. As a result, when a human image HI is input, the distance estimation unit 22 estimates the notification distance DN from the speaker 12 to the person H and the direction of the person H from the speaker 12, as shown in FIG. 3B. The estimated notification distance DN and the direction from the speaker 12 corresponding to each human image HI are output to the volume estimation unit 25. Note that when a human image HI is not input, the distance estimation unit 22 does not output the notification distance DN to the volume estimation unit 25.

[0039] The posture estimation unit 23 is, for example, a neural network, and has previously learned the correlation between these by machine learning using teacher data having the human image HI as input data and the posture type AP of the person H as output data. As a result, when the posture estimation unit 23 receives the human image HI as input data, it estimates the posture type AP of the person H (hereinafter, may be simply referred to as the "posture AP of the person H") as shown in FIG. 3C. The estimated posture AP of the person H corresponding to each human image HI is output to the volume estimation unit 25.

[0040] Fig. 4 is a diagram showing examples of postures AP of a person H. As shown in Fig. 4, types of postures AP of the person H include, for example, a standing posture (see Fig. 4A), a posture carrying a load (see Fig. 4B), a walking posture (see Fig. 4C), and a posture performing work (see Fig. 4D).

[0041] The orientation estimation unit 24 is, for example, a neural network, and performs machine learning in advance using teacher data in which the human image HI is input data and the facial orientation DF of the person H is output data. As a result, when the orientation estimation unit 24 receives the human image HI as input, it estimates the facial orientation DF of the person H, as shown in FIG. 3D. The facial orientation DF of the person H is, for example, the direction of the industrial vehicle 1, the opposite direction to the industrial vehicle 1, the direction of the load that the person H is carrying, the direction of the work tool when working, etc. The estimated facial orientation DF of the person H corresponding to each human image HI is output to the volume estimation unit 25.

[0042] The volume estimation unit 25 is, for example, a neural network, and as shown in Fig. 5, input data include the notification distance DN from the speaker 12 to the person H, the person information PI (identifier) ​​of the person H, the posture AP of the person H, the facial direction DF of the person H, and the volume (hereinafter referred to as "alert volume") db of the alert sound V, and the correlation between these has been machine-learned in advance using teacher data that outputs a surprise level Q. As a result, when the volume estimation unit 25 receives the person information PI (identifier), as well as the notification distance DN, the posture AP of the person H, and the facial direction DF estimated by the distance estimation unit 22, the direction estimation unit 24, and the posture estimation unit 23, as shown in Fig. 6, it estimates an alert volume db that is equal to or less than a predetermined surprise level RQ. Furthermore, the volume estimation unit 25 identifies the human image HI of the person H that is closest to the speaker 12 based on the notification distance DN corresponding to each human image HI, and outputs the estimated notification volume db corresponding to the human image HI and the direction of the person H that is closest to the speaker 12 to the volume change unit 26.

[0043] In this embodiment, the volume estimation unit 25 does not output the notification volume db to the volume change unit 26 unless the notification distance DN is input (i.e., unless the person H is detected). Note that the volume estimation unit 25 may be configured to estimate the notification volume db that is equal to or less than a predetermined surprise level RQ as zero when the notification distance is equal to or less than a predetermined notification distance DN. This makes it possible to prevent the notification sound V from being output when the distance between the industrial vehicle 1 and the person H is too close.

[0044] The volume change unit 26 changes the notification volume db of the speaker 12 to be equal to or lower than the notification volume db estimated by the volume estimation unit 25, and changes the output direction of the speaker 12 to the direction of the person H who is closest to the speaker 12. By making the speaker 12 output the notification sound V at a volume equal to or lower than the estimated notification sound db, the volume change unit 26 can prevent the person H from being startled by the notification sound V and falling over.

[0045] Furthermore, when the alarm sound V is actually output, the volume estimation unit 25 uses the person information PI (identifier), the alarm distance DN, the posture AP of the person H, the facial orientation DF of the person H, the actually output alarm sound volume db, and the surprise level Q estimated by the surprise level estimation unit 21 at that time as teacher data, and re-learns the correlations among them. That is, the volume estimation unit 25 in this embodiment uses the person information PI (identifier), the alarm distance DN, the posture AP of the person H, the facial orientation DF, and the actually output alarm sound volume db estimated by the distance estimation unit 22, the orientation estimation unit 24, and the posture estimation unit 23 as input data, and uses the surprise level Q estimated by the surprise level estimation unit 21 as teacher data, and re-learns the correlations among them, thereby making it possible to make more accurate estimations for each person information PI from the next time.

[0046] A system including the above-mentioned camera 13, the registration unit 16, the person identification unit 17, the trained model 20, and the volume change unit 26 corresponds to the informative sound volume estimation system according to the present invention.

[0047] <Industrial vehicle operation> Next, the operation of the industrial vehicle 1 will be described again with reference to FIG.

[0048] (1) While traveling, the industrial vehicle 1 outputs a melody by the melody output unit (see S1 (step 1) in FIG. 7). In the industrial vehicle 1, the notification volume db is set to zero until the person H is photographed by the camera 13 (see S2 in FIG. 7).

[0049] (2) When the industrial vehicle 1 captures an image of person H with the camera 13 (i.e., detects person H) (Yes in S3 of FIG. 7), the learned model 20 estimates the notification distance DN, the posture AP, and the facial direction DF of person H based on the human image HI (see S4 of FIG. 7). Furthermore, when the industrial vehicle 1 detects person H, the person identification unit 17 identifies an identifier corresponding to the facial image, and identifies which person the detected person H is (see S5 of FIG. 7).

[0050] (3) Next, the industrial vehicle 1 uses the learned model 20 to estimate the alarm volume db at which the identified person H will be below a predetermined surprise level RQ, based on the identifier of the identified person H, the estimated alarm distance DN, the posture AP of the person H, and the facial direction DF (see S6 in Figure 7).

[0051] (4) Next, the industrial vehicle 1 changes the notification volume db of the speaker 12 by the volume changer 26 so that the notification volume is equal to or less than the estimated notification volume db (see S7 in FIG. 7).

[0052] (5) Next, the industrial vehicle 1 outputs the alarm sound V toward the person H (pedestrian H1 in FIG. 2) closest to the speaker 12 (see S8 in FIG. 7).

[0053] 7, the industrial vehicle 1 repeats steps S4 to S8 in Fig. 7, and automatically changes the notification volume db as the notification distance DN, the posture AP of the person H, and the facial direction DF of the person H change. Then, when the camera 13 no longer photographs the person H (i.e., when the person H is no longer detected) (No in S3 in Fig. 7), the notification volume db is changed to zero again (see S2 in Fig. 7).

[0054] The industrial vehicle 1 has the above configuration, and can prevent the person H from falling over due to being startled by the alarm sound V. In addition, the industrial vehicle 1 learns not only the correlation between the alarm distance DN, the alarm volume db, and the surprise level Q, but also the correlation between the posture AP and the facial direction DF of the person H, using the trained model 20, and can therefore prevent the alarm volume db from being too loud for the person H concentrating on work or looking in the opposite direction to the industrial vehicle 1, causing the person H to fall over. Moreover, the industrial vehicle 1 has machine learning for each person information PI, and can estimate the alarm volume db more appropriately. Furthermore, the industrial vehicle 1 estimates the alarm distance DN, the posture AP and the facial direction DF of the person H based on the person image HI, and therefore does not need to add special sensors.

[0055] Although an embodiment of the industrial vehicle, the notification sound volume estimation system, and the notification sound volume estimation program of the present invention has been described above, the present invention is not limited to the above embodiment. For example, the industrial vehicle, the notification sound volume estimation system, and the notification sound volume estimation program of the present invention may be implemented by appropriately combining the following modified examples.

[0056] <Modification> The industrial vehicle 1 may be equipped with a known sensor capable of detecting the direction of the person H and the notification distance DN. In this case, the sound volume estimation unit 25 acquires the notification distance DN from the sensor, and the trained model 20 does not need to include the distance estimation unit 22.

[0057] The human image HI when each component of the trained model 20 undergoes machine learning and the human image HI when each estimation is performed may be input data consisting of a plurality of human images HI. In other words, the human image HI according to the present invention is a concept that includes a moving image.

[0058] The input data of the teacher data when the volume estimation unit 25 performs machine learning may include at least the person information PI, the notification distance DN, and the notification volume db. In this case, when the person information PI and the notification distance DN are input, the volume estimation unit 25 estimates the notification volume db that is equal to or less than a predetermined surprise level RQ.

[0059] The industrial vehicle 1 may further include an environmental sound acquisition unit that acquires environmental sound. In this case, the volume estimation unit 25 may further learn correlations between the environmental sound, the person information PI, the notification distance DN, the posture AP of the person H, the facial direction DF of the person H, and the notification volume db by machine learning in advance using teacher data having the environmental sound, the person information PI, the notification distance DN, the posture AP of the person H, and the facial direction DF as input data, and the surprise level Q as output data, and estimate the notification volume db that is equal to or lower than a predetermined surprise level RQ when the person information PI, the environmental sound, the notification distance DN, the posture AP of the person H, and the facial direction DF are input. In this case, the volume estimation unit 25 may learn correlations between the environmental sound, the person information PI, the notification distance DN, and the notification volume db by machine learning in advance using teacher data having the surprise level Q as input data, and estimate the notification volume db that is equal to or lower than a predetermined surprise level RQ when the environmental sound, the person information PI, and the notification distance DN are input.

[0060] The industrial vehicle 1 may be configured to change the tone of the notification sound V appropriate for each person H to be notified. In this case, the industrial vehicle 1 further includes a tone storage unit that stores a plurality of tones, and a tone control unit. The tone control unit determines the tone of the speaker 12 to be notified based on the person information PI identified by the person identification unit 17, and switches the tone of the speaker 12 to the determined tone. The volume estimation unit 25 may be configured to perform machine learning for each tone in advance, and to estimate the volume at which the person H corresponding to the input person information PI becomes equal to or lower than a predetermined surprise level RQ, corresponding to the determined tone. The tone stored in the tone storage unit may be, for example, a buzzer sound, a beep sound, or a melody, and the tone control unit may determine the tone to be a buzzer sound for young people and a high-pitched beep sound for elderly people with reduced hearing.

[0061] The industrial vehicle 1 may be configured to change the tone for each location in the facility. In this case, the industrial vehicle 1 further includes a position detection unit that detects its own position, and a tone control unit. The tone control unit determines the tone of the speaker 12 that issues the notification based on the detected position information, and switches the tone of the speaker 12 to the determined tone. In this case, the volume estimation unit 25 may be configured to perform machine learning in advance for each tone, and to estimate, for each tone, the volume at which the person H corresponding to the input person information PI becomes equal to or lower than a predetermined surprise level RQ.

[0062] The person information PI may further include gender, age, or other physical characteristics instead of an identifier. In this case, the volume estimation unit 25 estimates an appropriate volume at which the notified person H is surprised to a predetermined surprise level RQ or less not for each person H but for each gender, age, and / or other physical characteristics.

[0063] The alert sound volume estimation system may further include a server computer capable of communicating with the industrial vehicle 1, and the trained model 20 may be configured by the server computer. In this case, the industrial vehicle 1 receives an alert sound volume db that is equal to or lower than a predetermined surprise level RQ by communicating with the server computer, and outputs the alert sound V at this alert sound volume db. [Explanation of symbols]

[0064] 1. Industrial vehicles (forklifts) 10. Body 11. Fork 12 Speaker 13. Camera 15 Control section 16 Registration Department 17 Person identification part 20 Pre-trained models 21 Surprise Level Estimation Unit 22 Distance Estimation Unit 23 Posture estimation section 24 Orientation Estimation Unit 25 Volume Estimation Unit 26 Volume change section Types of AP posture DF Face Direction DN broadcast distance H People (pedestrians, workers) HI People Images PI Person Information Q Surprise Level R aisle RQ Predetermined surprise level

Claims

1. A speaker that outputs an alarm sound to a person; A trained model having a volume estimation unit; a volume change unit for changing the volume of the speaker; a registration unit in which personal information corresponding to a plurality of people is registered; a person identification unit that identifies the person information corresponding to a person to be notified, the volume estimation unit performs machine learning in advance using teacher data in which the notification distance from the speaker to a person, the person information of the person, and the volume of the notification sound are input data, and the surprise level of the person notified is output data, and when the person information and the notification distance are input, the volume estimation unit estimates a volume at which the person corresponding to the input person information will be at or below a predetermined surprise level; The volume change unit changes the volume of the speaker to be equal to or lower than the estimated volume.

2. Further comprising a camera for photographing a person and generating a person image; a face image corresponding to each of the personal information is further registered in the registration unit; The industrial vehicle according to claim 1 , wherein the person identification unit identifies the person information corresponding to a person to be notified by referring to a face image included in the human image and the face image corresponding to each of the person information.

3. Further comprising a camera for photographing a person and generating a person image; The trained model further includes a distance estimation unit, 2. The industrial vehicle according to claim 1, wherein the distance estimation unit performs machine learning in advance using teacher data in which the human image is input data and the reporting distance from the speaker to a person is output data, and when the human image is input, estimates the reporting distance from the speaker to a person.

4. Further comprising a camera for photographing a person and generating a person image, The speaker has directionality, The industrial vehicle described in claim 1, wherein the volume estimation unit identifies the human image of the person closest to the speaker based on the notification distance corresponding to each of the human images, and outputs the estimated volume corresponding to the human image to the volume change unit.

5. Further comprising a camera for photographing a person and generating a person image; The trained model further includes a posture estimation unit, the posture estimation unit performs machine learning in advance using teacher data in which the human image is input data and a type of posture of the person is output data, and when the human image is input, the posture estimation unit estimates a type of posture of the person; The industrial vehicle according to claim 1 , wherein the volume estimating unit, when receiving the type of the person's posture, estimates a volume that will be equal to or lower than a predetermined level of surprise based on the type of the person's posture.

6. Further comprising a camera for photographing a person and generating a person image; The trained model further includes an orientation estimation unit, the orientation estimation unit performs machine learning in advance using teacher data in which the human image is input data and the orientation of the person's face is output data, and when the human image is input, estimates the orientation of the person's face; The industrial vehicle according to claim 1 , wherein the volume estimating unit, upon receiving the direction of the person's face, estimates a volume that will be equal to or lower than a predetermined surprise level based on the direction of the person's face.

7. An environmental sound acquisition unit for acquiring environmental sound is further provided, The industrial vehicle according to any one of claims 1 to 6, wherein the volume estimation unit, when receiving the environmental sound, estimates a volume that will be equal to or lower than a predetermined level of surprise based on the environmental sound.

8. Further comprising a camera for photographing a person and generating a person image; The trained model further includes a surprise level estimation unit, the surprise degree estimation unit performs machine learning in advance using teacher data in which the human image of the notified person and the personal information of the notified person are input data and the surprise degree is output data, and when the human image of the person notified by the alarm sound and the personal information of the notified person are input, the surprise degree estimation unit estimates the surprise degree corresponding to the personal information; The industrial vehicle according to claim 1, wherein the volume estimation unit re-learns a volume that is below a predetermined level of surprise based on the human image of the person before being notified, the personal information of the person who has been notified, the notification distance, the volume of the notification sound at the time of notification, and the level of surprise of the person who has been notified.

9. a tone storage unit storing a plurality of tones; a tone control unit that determines a tone of the speaker for making a notification based on the person information identified by the person identification unit and switches the tone of the speaker to the determined tone, The industrial vehicle according to claim 1, wherein the volume estimation unit has previously performed machine learning for each tone color, and is configured to estimate, in correspondence with the determined tone color, the volume at which a person corresponding to the input personal information will be at or below a predetermined level of surprise.

10. a tone storage unit storing a plurality of tones; A position detection unit that detects its own position; and a tone control unit that determines a tone of the speaker for reporting based on the detected position and switches the tone of the speaker to the determined tone, The industrial vehicle according to claim 1, wherein the volume estimation unit has previously performed machine learning for each tone color and is configured to estimate, for each tone color, the volume at which a person corresponding to the input person information will be at or below a predetermined level of surprise.

11. 11. The industrial vehicle according to claim 9, wherein the stored tones include a buzzer sound, a beep sound, and a melody.

12. An alarm sound volume estimation system for use in an industrial vehicle having a speaker that outputs an alarm sound to a person, A trained model having a volume estimation unit; A volume change unit; A registration unit in which multiple pieces of person information are registered; a person identification unit that identifies the person information corresponding to a person to be notified, the volume estimation unit performs machine learning in advance using teacher data in which the notification distance from the speaker to a person, the person information of the person, and the volume of the notification sound are input data, and the surprise level of the person notified is output data, and when the person information and the notification distance are input, the volume estimation unit estimates a volume at which the person corresponding to the input person information will be at or below a predetermined surprise level; The volume change unit changes the volume of the speaker to be equal to or lower than the estimated volume.

13. An informative sound volume estimation program for use in an informative sound volume estimation system, The notification sound volume estimation system is a system used in an industrial vehicle having a speaker that outputs a notification sound to a person, the system including a computer, The informative sound volume estimation program is configured to: An alarm sound volume estimation program that performs machine learning in advance using teacher data in which the alarm distance from the speaker to a person, the person's personal information, and the volume of the alarm sound are input as data, and the person's level of surprise when notified is output as data, and that executes the program as a volume estimation unit that, when the personal information and the alarm distance are input, estimates the volume at which the person corresponding to the input personal information will be at or below a predetermined level of surprise.

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