Forklifts, estimation systems and estimation programs

Abstract

To provide a fork lift which can adjust resistance values (weights) of a lever to appropriate resistance values for each operator.SOLUTION: A fork lift 1 includes a lever 14 used in operation of the fork lift 1, a resistance value estimation part, and a resistance value changing part. The lever 14 may have a lift lever, and a tilt lever. The resistance value estimation part estimates a resistance value (weight) of an appropriate lever 14 corresponding to the operator O, on the basis of a physical feature of the operator O. The resistance value changing part changes the resistance value of the lever 14, on the basis of the resistance value of the lever 14 estimated by the resistance value estimation part.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to a forklift, and an estimation system and an estimation program related to the forklift. [Background technology]

[0002] There are two types of forklifts: reach forklifts and counter-load forklifts. The number and functions of forklift levers vary depending on the type of forklift.

[0003] As disclosed in Patent Document 1, a reach forklift is equipped with a lift lever for raising and lowering the forks, a tilt lever for changing the vertical angle of the forks, a reach lever for moving the lifting device forward and backward, and an accelerator lever for driving the forklift. Also, as disclosed in Patent Document 2, a counter-load forklift is equipped with a tilt lever and an accelerator lever.

[0004] As disclosed in Patent Document 3, levers are generally configured to swing forward or backward from a neutral position and are automatically returned to the neutral position by a spring when not in operation. However, the resistance of the lever due to this spring, in other words the weight of the lever, causes fatigue to the operator. Therefore, in order to reduce operator fatigue, the invention disclosed in Patent Document 3 assists lever operation by an actuator when the amount of operation of the operating lever exceeds a predetermined value. In addition to Patent Document 3, other documents that disclose technology for assisting lever operation of a vehicle include, for example, Patent Document 4.

[0005] However, there is another problem with operator fatigue caused by lever operation. Unlike ordinary passenger vehicles, forklifts require frequent lever operation, so it is desirable for the resistance value of the lever (e.g., spring resistance) to be an appropriate value for each operator. This appropriate value depends on the gender, muscle strength, and other factors of each operator. Therefore, if a unique forklift is assigned to each operator, one possible method would be to adjust the resistance value of each forklift's lever to an appropriate resistance value for each operator. However, unlike passenger vehicles, a single forklift may be operated by multiple operators, which poses a problem when adopting this method. [Prior art documents] [Patent documents]

[0006] [Patent Document 1] JP 2016-47745 A [Patent Document 2] Japanese Patent Application Laid-Open No. 2017-88390 [Patent Document 3] Japanese Patent Application Laid-Open No. 2002-370898 [Patent Document 4] Japanese Patent Application Laid-Open No. 2003-301942 Summary of the Invention [Problem to be solved by the invention]

[0007] Therefore, an object of the present invention is to provide a forklift truck that can adjust the resistance value of the lever, in other words, the weight of the lever, to an appropriate resistance value for each operator. [Means for solving the problem]

[0008] In order to solve the above problems, the forklift according to the present invention includes a lever used to operate the forklift, a resistance value estimation unit that estimates an appropriate lever resistance value for a driver based on the driver's physical characteristics, and a resistance value change unit that changes the resistance value of the lever based on the lever resistance value estimated by the resistance value estimation unit.

[0009] The forklift preferably further includes a camera that photographs the driver, and a first trained model that uses images of multiple people and the weights of the people corresponding to each image as training data and that has been trained to output the driver's weight when an image of the driver generated by the camera is input, and the resistance value estimation unit estimates an appropriate lever resistance value based on the driver's weight output by the first trained model.

[0010] The forklift preferably further includes a camera that photographs the driver, and a second trained model that uses images of multiple people and the muscle mass corresponding to each person's image as training data and that has been trained to output the driver's muscle mass when an image of the driver generated by the camera is input, and the resistance value estimation unit estimates an appropriate lever resistance value based on the driver's muscle mass output by the second trained model.

[0011] The forklift preferably further includes a camera that captures the driver's face, and a third trained model that uses multiple facial images and the age of the person corresponding to each facial image as training data and is trained to output the driver's age when the driver's facial image generated by the camera is input, and the resistance value estimation unit estimates an appropriate lever resistance value based on the driver's age output by the third trained model.

[0012] In the forklift, the camera is preferably configured to capture an image of the driver wearing predetermined clothing, and the training data for the image of a person is an image of a person wearing the predetermined clothing.

[0013] In the forklift, the camera is preferably configured to capture an image of the driver wearing a helmet, and the training data of the facial image is an image of the face of the person wearing the helmet.

[0014] In the forklift, the camera is preferably configured to detect a person wearing predetermined clothing as the driver and automatically photograph the person.

[0015] The forklift preferably further includes a characteristic memory unit that stores the physical characteristics of the driver in advance, and the resistance value estimation unit estimates an appropriate lever resistance value for the driver based on the physical characteristics of the driver stored in the characteristic memory unit.

[0016] The forklift preferably further includes a feature memory unit that pre-stores the physical features of multiple drivers along with corresponding facial images, a camera that photographs the faces of the drivers, and a driver identification unit that identifies the drivers stored in the feature memory unit based on the facial images generated by the camera, and the resistance value estimation unit estimates an appropriate lever resistance value for the driver based on the physical features corresponding to the identified driver.

[0017] In order to solve the above problem, the estimation system of the present invention includes a lever, a resistance value estimation unit that estimates an appropriate lever resistance value for a driver based on the driver's physical characteristics, and a resistance value change unit that changes the resistance value of the lever based on the estimated appropriate lever resistance value.

[0018] In order to solve the above problem, the estimation program of the present invention is a program used in a forklift having a lever, a resistance value changing unit that changes the resistance value of the lever based on an estimated appropriate resistance value of the lever, and a computer, and is executed by the computer as a resistance value estimating unit that estimates an appropriate lever resistance value for a driver based on the driver's physical characteristics. [Effects of the Invention]

[0019] The forklift according to the present invention allows the resistance value of the lever to be adjusted to an appropriate resistance value for each operator. [Brief explanation of the drawings]

[0020] [Figure 1] 1 is a front view of a forklift according to an embodiment of the present invention. [Figure 2] FIG. 2 is a diagram showing a driver photographed by a camera. [Figure 3] FIG. 2 is a functional block diagram of a control unit. [Figure 4] FIG. 10 is a functional block diagram showing a modified example of the control unit. DETAILED DESCRIPTION OF THE INVENTION

[0021] Hereinafter, an embodiment of a forklift, an estimation system, and an estimation program according to the present invention will be described with reference to the accompanying drawings.

[0022] Fig. 1 is a front view of a forklift 1 according to this embodiment. The forklift 1 is a battery-powered counter-load forklift. As shown in Fig. 1, the forklift 1 includes a plurality of wheels 10, a vehicle body 11, a driver's seat 12, a head guard 13, a lever 14, left and right masts 15, left and right forks 16, a camera 17, a resistance value change unit 18 (see Fig. 3), and a control unit 20 (see Fig. 3).

[0023] A plurality of wheels 10 are provided on all four sides of a vehicle body 11. A driver's seat 12 is provided on the vehicle body 11, and a head guard 13 is provided above the driver's seat 12.

[0024] The lever 14 has a lift lever and a tilt lever, and is provided in front of the driver's seat 12. The lever 14 may also have a slide lever for sliding the fork 16 left and right.

[0025] The left and right forks 16 are configured to be able to be raised and lowered via the left and right masts 15, and the driver O operates a lift lever to raise and lower the forks 16 to perform cargo handling work.

[0026] The camera 17 is fixed to the head guard 13 and configured to capture the entire body of the driver O standing at the side of the vehicle body 11. As shown in FIGS. 1 and 2, the driver O is photographed at least from the front and side at the side of the forklift 1 while wearing a helmet H and a uniform U. The uniform U corresponds to the "predetermined clothing" of the present invention. The camera 17 may be a video camera. The camera 17 may also be configured to detect a person wearing the uniform U (i.e., the driver O) as the driver O, and automatically photograph the person when the driver O approaches the forklift 1. The camera 17 may also be configured to include multiple cameras 17.

[0027] The camera 17 also captures the face of the driver O wearing the helmet H and generates a facial image. The whole-body image and facial image generated by the camera 17 are transmitted to the control unit 20.

[0028] When capturing an image with the camera 17, the forklift 1 may issue a voice message to the driver O, such as "Please look forward" or "Please look to the side."

[0029] Resistance value changing unit 18 changes the resistance value of lever 14 when operated by driver O, based on the resistance value of lever 14 estimated by resistance value estimating unit 24, which will be described later. Resistance value changing unit 18 is configured by, for example, a device that can adjust the resistance value by hydraulic pressure, or a device that can adjust the resistance value by a spring.

[0030] Furthermore, for example, the resistance value changing unit 18 may be an assisting device that assists in operating the lever 14 against resistance. In this case, the resistance value changing unit 18 may adjust the assist force based on the resistance value of the lever 14 estimated by the resistance value estimating unit 24, and may substantially change the resistance value of the lever 14 when the driver O operates it.

[0031] Alternatively, the resistance value changing unit 18 may be configured to change the magnitude of the power of each device responsive to the amount of operation of the lever 14, based on the resistance value of the lever 14 estimated by the resistance value estimating unit 24. In this way, the resistance value changing unit 18 effectively changes the resistance value of the lever 14.

[0032] The control unit 20 is configured by a computer arranged in the vehicle body 11, and has an arithmetic unit, a storage device, and a memory. The storage device stores an estimation program that causes the computer to function as a resistance value estimation unit 24, which will be described later.

[0033] 3 is a functional block diagram of the forklift 1. As shown in FIG. 3, the control unit 20 has a weight estimation unit 21, a muscle mass estimation unit 22, an age estimation unit 23, and a resistance value estimation unit 24.

[0034] The weight estimation unit 21 corresponds to the "first trained model" of the present invention. The weight estimation unit 21 is trained in advance by a neural network using deep learning, using images of multiple people and the weights of the people corresponding to each image as training data, so that when an image of the driver O captured by the camera 17 is input, the weight estimation unit 21 outputs the weight of the driver O. As a result, when the image captured by the camera 17 is input, the weight estimation unit 21 estimates the weight of the driver O and transmits the estimated weight to the resistance value estimation unit 24.

[0035] The muscle mass estimation unit 22 corresponds to the "second trained model" of the present invention. The muscle mass estimation unit 22 is trained in advance by a neural network using deep learning, using images of multiple people and the muscle mass corresponding to each image as training data, so that when an image of the driver O captured by the camera 17 is input, the muscle mass estimation unit 22 outputs the muscle mass of the driver O. As a result, when an image captured by the camera 17 is input, the muscle mass estimation unit 22 estimates the muscle mass of the driver O and transmits the estimated muscle mass to the resistance value estimation unit 24.

[0036] The images of people in the training data used by the weight estimation unit 21 and the muscle mass estimation unit 22 include facial images of both men and women. This allows the weight estimation unit 21 and the muscle mass estimation unit 22 to more appropriately estimate the weight and muscle mass of the driver O, taking into account the gender.

[0037] The image of a person in the training data used for learning by the weight estimation unit 21 and the muscle mass estimation unit 22 may be, for example, an image of a person wearing a uniform U. The uniform U may also be processed to make it easier to estimate the weight and muscle mass. This allows the weight estimation unit 21 and the muscle mass estimation unit 22 to more appropriately estimate the weight and muscle mass of the driver O, respectively.

[0038] The age estimation unit 23 corresponds to the "third trained model" of the present invention. The age estimation unit 23 is trained in advance by a neural network using deep learning, using multiple facial images and the ages of people corresponding to each facial image as training data, to output the age of the driver O when a facial image of the driver O generated by the camera 17 is input. As a result, when a facial image generated by the camera 17 is input, the age estimation unit 23 estimates the age of the driver O and transmits the estimated age to the resistance value estimation unit 24.

[0039] The facial images of both men and women are included in the training data used by the age estimation unit 23. This allows the age estimation unit 23 to more appropriately estimate the age of the driver O by taking the gender into consideration.

[0040] The facial image among the training data used for learning by the age estimation unit 23 may be, for example, a facial image of a person wearing a helmet H. This allows the age estimation unit 23 to more appropriately estimate the age of the driver O.

[0041] The resistance value estimation unit 24 estimates an appropriate resistance value of the lever 14 based on the weight, muscle mass, and age of the driver O estimated by the weight estimation unit 21, muscle mass estimation unit 22, and age estimation unit 23.

[0042] For example, the resistance value estimation unit 24 may estimate a stronger resistance value of the lever 14 the heavier the weight estimated by the weight estimation unit 21. Also, the resistance value estimation unit 24 may estimate a stronger resistance value of the lever 14 the greater the muscle mass estimated by the muscle mass estimation unit 22. Furthermore, the resistance value estimation unit 24 may estimate a stronger resistance value of the lever 14 the younger the age estimated by the age estimation unit 23.

[0043] Furthermore, the resistance value estimation unit 24 may weaken the resistance value of the lever 14 to be estimated if the estimated weight is heavy but the estimated muscle mass is less than a certain amount, or may weaken the resistance value of the lever 14 to be estimated depending on the estimated age even if the estimated muscle mass is large.

[0044] In this way, there are no limitations on the weighting of body weight, muscle mass, and age for the resistance value of the lever 14 estimated by the resistance value estimation unit 24. Furthermore, the resistance value of the lever 14 appropriate for each driver O estimated by the resistance value estimation unit 24 does not have to be the resistance value of all levers of the lever 14. In other words, the resistance value estimation unit 24 only needs to estimate the resistance value of the lift lever or tilt lever.

[0045] The resistance value changing unit 18 changes the resistance value of each lever based on the appropriate resistance value estimated by the resistance value estimating unit 24.

[0046] The forklift 1 according to this embodiment is configured as described above, and therefore can provide the driver O with an appropriate resistance value of the lever 14.

[0047] Although one embodiment of the forklift, estimation system, and estimation program according to the present invention has been described above, the present invention is not limited to the above embodiment. The forklift, estimation system, and estimation program according to the present invention may be implemented, for example, in each of the following modified examples or in a combination of the modified examples.

[0048] <Modification> The forklift 1 may be a reach forklift. In this case, the lever 14 may include an accelerator lever. The forklift 1 may also be an engine-powered forklift.

[0049] For example, if the weight estimation unit 21 and the muscle mass estimation unit 22 can estimate the weight and muscle mass of the driver O from an image of a part of the driver O's body, the camera 17 may be configured to capture only a part of the driver O's body.

[0050] In the above embodiment, the resistance value estimation unit 24 estimated the appropriate resistance value of the lever 14 for the driver O by referring to the driver's weight, muscle mass, and age. However, the resistance value estimation unit 24 may estimate the appropriate resistance value of the lever 14 by referring to, for example, the driver's weight, muscle mass, or age.

[0051] The control unit 20 may have a characteristic storage unit 25 that stores the physical characteristics (weight, muscle mass, age) of each driver O in advance, instead of the weight estimation unit 21, muscle mass estimation unit 22, and age estimation unit 23.

[0052] In this case, the forklift 1 may further include an input unit that accepts input of the name or identifier corresponding to each driver O, or input of a tool that identifies each driver O, such as an ID card, and the resistance value estimation unit 24 may identify the driver O based on the input name or identifier, or input of the above-mentioned tool, and estimate the appropriate resistance value of the lever 14 by referring to the physical characteristics of the driver O.

[0053] Furthermore, when the control unit 20 has a feature memory unit 25, the forklift 1 is provided with a face camera (camera 17) for capturing an image of the face of the driver O, for example, in the driver's seat 12, and as shown in FIG. 4, the control unit 20 further has a driver identification unit 26 for identifying the driver O stored in the feature memory unit 25 based on the face image generated by the face camera, and the resistance value estimation unit 24 may estimate an appropriate resistance value of the lever 14 based on the physical features of the identified driver O.

[0054] Furthermore, in addition to the case where the control unit 20 has a feature memory unit 25, if the helmet H has a tag, QR code (registered trademark), etc. that identifies each driver O, each driver O can be identified using a tool that detects them, and the resistance value estimation unit 24 can estimate the appropriate resistance value of the lever 14 based on the physical features of the identified driver O.

[0055] The control unit 20 may be configured, for example, by a server computer provided on the cloud, and the forklift 1 may estimate an appropriate resistance value of the lever 14 corresponding to the driver O by communicating with this server computer. [Explanation of symbols]

[0056] O Driver H helmet U uniform 1 forklift 10 wheels 11 Body 12 Driver's seat 13 Head guard 14 Lever 15 Mast 16 forks 17 Camera 18 Resistance value change section 20 Control Unit 21 Weight Estimation Section 22 Muscle mass estimation section 23 Age Estimation Department 24 Resistance estimation section 25 Feature memory section 26 Driver Identification Department

Claims

1. A lever used to operate a forklift; a resistance value estimation unit that estimates an appropriate resistance value of the lever for the driver based on the driver's weight; a resistance value changing unit that changes the resistance value of the lever based on the resistance value of the lever estimated by the resistance value estimating unit; a camera for photographing the driver; a first trained model that uses images of a plurality of people and weights of the people corresponding to the images of the people as training data, and that has trained to output the weight of the driver when an image of the driver generated by the camera is input; The resistance value estimation unit estimates an appropriate resistance value of the lever based on the weight of the driver output by the first learned model.

2. A lever used to operate a forklift; a resistance value estimation unit that estimates an appropriate resistance value of the lever for the driver based on the muscle mass of the driver; a resistance value changing unit that changes the resistance value of the lever based on the resistance value of the lever estimated by the resistance value estimating unit; a camera for photographing the driver; a second trained model that uses images of a plurality of people and muscle masses corresponding to the images of the people as training data, and that has trained to output the muscle mass of the driver when an image of the driver generated by the camera is input; The resistance value estimation unit estimates an appropriate resistance value of the lever based on the muscle mass of the driver output by the second trained model.

3. A lever used to operate a forklift; a resistance value estimation unit that estimates an appropriate resistance value of the lever for the driver based on the age of the driver; a resistance value changing unit that changes the resistance value of the lever based on the resistance value of the lever estimated by the resistance value estimating unit; a camera that photographs the driver's face; a third trained model that uses a plurality of facial images and the ages of people corresponding to each of the facial images as training data, and that has been trained to output the age of the driver when a facial image of the driver generated by the camera is input; The resistance value estimation unit estimates an appropriate resistance value of the lever based on the age of the driver output by the third trained model.

4. A lever used to operate a forklift; a resistance value estimation unit that estimates an appropriate resistance value of the lever for the driver based on the driver's weight, muscle mass, and age; a resistance value changing unit that changes the resistance value of the lever based on the resistance value of the lever estimated by the resistance value estimating unit; A camera that photographs the driver, including their face; a first trained model that uses images of a plurality of people and weights of the people corresponding to the images of the people as training data, and that has been trained to output the weight of the driver when an image of the driver generated by the camera is input; a second trained model that uses images of a plurality of people and muscle masses corresponding to the images of the people as training data, and that has been trained to output the muscle mass of the driver when an image of the driver generated by the camera is input; a third trained model that uses a plurality of facial images and the ages of people corresponding to each of the facial images as training data, and that has been trained to output the age of the driver when a facial image of the driver generated by the camera is input; The resistance value estimation unit estimates an appropriate resistance value of the lever based on the weight, muscle mass, and age of the driver output by the first, second, and third trained models.

5. The camera is configured to capture an image of the driver wearing predetermined clothing, 3. The forklift truck according to claim 1, wherein the training data of the human image is an image of a person wearing the predetermined clothing.

6. the camera is configured to capture an image of the driver wearing a helmet; 4. The forklift according to claim 3, wherein the training data of the facial image is a facial image of a person wearing the helmet.

7. The forklift according to claim 5, wherein the camera is configured to detect a person wearing the predetermined clothing as the driver and automatically photograph the person.

8. A lever used to operate a forklift; a resistance value estimation unit that estimates an appropriate resistance value of the lever for the driver based on the driver's weight, muscle mass, and / or age; a resistance value changing unit that changes the resistance value of the lever based on the resistance value of the lever estimated by the resistance value estimating unit; a characteristic storage unit that stores in advance the weight, muscle mass, and / or age of the driver; The resistance value estimation unit estimates an appropriate resistance value of the lever for the driver based on the driver's weight, muscle mass, and / or age stored in the characteristic memory unit.

9. a feature storage unit that stores in advance weights, muscle masses, and / or ages of a plurality of drivers together with corresponding facial images; a camera that photographs the driver's face; a driver identification unit that identifies the driver stored in the feature storage unit based on a face image generated by the camera, The forklift according to claim 8 , wherein the resistance value estimation unit estimates an appropriate resistance value of the lever for the identified driver based on a weight, a muscle mass, and / or an age corresponding to the identified driver.

10. Lever and a resistance value estimation unit that estimates an appropriate resistance value of the lever for the driver based on the driver's weight, muscle mass, and / or age; a resistance value changing unit that changes the resistance value of the lever based on the estimated appropriate resistance value of the lever; a characteristic storage unit that stores in advance the driver's weight, muscle mass, and / or age, The resistance value estimation unit is an estimation system that estimates an appropriate resistance value of the lever for the driver based on the driver's weight, muscle mass, and / or age stored in the feature memory unit.

11. Lever and a resistance value changing unit that changes the resistance value of the lever based on the estimated appropriate resistance value of the lever; A computer that stores in advance the weight, muscle mass, and / or age of a driver, and a program used in a forklift having the computer, The computer An estimation program to be executed as a resistance value estimation unit that estimates an appropriate resistance value of the lever for the driver based on the driver's stored weight, muscle mass, and / or age.

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