Work classification device, program, and proposal system

Abstract

A work classification device, program, and proposal system are provided that classify work related to loading and unloading vehicles while reducing the workload on the business operator. [Solution] The work classification server 1 comprises an image receiving unit 11 that acquires images related to loading and unloading operations performed by the forklift via a camera 9 installed on the forklift, a work information acquiring unit 13 that receives forklift work information including the work content and operating status using the forklift based on the images acquired by the image receiving unit 11, and a work classification unit 15 that classifies the work into forklift work operated by the driver, forklift work remotely controlled by an operator, and forklift work operated autonomously without the need for human operation based on the work information received by the work information acquiring unit 13.

Description

[Technical Field]

[0001] The present invention relates to a task classification device, a program, and a suggestion system. [Background technology]

[0002] Conventionally, forklifts are operated by a driver who actually rides on the vehicle, or by remote operation. For example, a cargo handling system including a plurality of remote control devices, a plurality of industrial vehicles that are operated automatically and remotely in a facility, and a management device that manages the correspondence between the plurality of remote control devices and the plurality of industrial vehicles, wherein the plurality of remote control devices, the plurality of industrial vehicles, and the management device communicate with each other wirelessly, wherein each remote control device of the plurality of remote control devices includes an operation command unit that outputs an operation command in response to an operation by an operator, a communication strength measurement unit that measures the communication strength of the wireless communication, and a change command unit that outputs a mode change command when the communication strength is less than a predetermined threshold, and each of the plurality of industrial vehicles includes a driving control unit that performs the remote control driving based on the operation command, a position information output unit that outputs position information regarding the current location of the vehicle, and a driving mode change unit that changes the driving mode between the automatic driving mode and the forward driving mode. and an operation mode setting unit that sets the operation mode to the remotely controlled operation, wherein the management device comprises a memory unit that stores map data of the facility indicating a first area suitable for the automatic operation and a second area suitable for the remotely controlled operation, and an area determination unit that performs area determination to determine whether the current location is the first area or the second area based on the map data and the position information, wherein the operation mode setting unit of the industrial vehicle sets the operation mode to the automatic operation when it receives the mode change command and the industrial vehicle is in the first area, and sets the operation mode to the remotely controlled operation by a remote control device different from the remote control device when it receives the mode change command and the industrial vehicle is in the second area (see, for example, Patent Document 1). [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Patent No. 7372021 Publication Summary of the Invention [Problem to be solved by the invention]

[0004] The technology disclosed in Patent Document 1 performs processing such as switching to automatic operation mode when the strength of wireless communication between a remote control device that remotely controls an industrial vehicle such as a forklift becomes weak, so the vehicle is basically operated remotely by an operator. On the other hand, there are quite a few cargo handling tasks that are better performed by workers on-site. Also, there are various types of tasks that can be performed without a worker on-site, such as tasks that can be performed by autonomously operating forklifts or tasks that can be operated by an operator in a remote location. However, there are cases where businesses that perform cargo handling operations have not yet implemented operational improvements by introducing automated equipment. In addition, when proposing business improvements such as automation to businesses, proposal providers had to check the business's loading and unloading operations on-site.

[0005] An object of the present invention is to provide a task classification device, program, and proposal system that classifies tasks related to cargo handling vehicles while reducing the workload on both the business operator and the proposing company. [Means for solving the problem]

[0006] The present invention relates to a work classification device comprising: an image acquisition means for acquiring images related to the loading and unloading work performed by the loading and unloading vehicle via a camera installed on the loading and unloading vehicle; a work information receiving means for receiving work information of the loading and unloading vehicle including the work content and operating status using the loading and unloading vehicle based on the images acquired by the image acquisition means; and a work classification means for classifying the work of the loading and unloading vehicle into work performed by the driver, work performed by the loading and unloading vehicle remotely operated by an operator, and work performed by the loading and unloading vehicle by autonomous driving without the need for human operation, based on the work information received by the work information receiving means.

[0007] The task classification device may further include an image analysis means for analyzing the image acquired by the image acquisition means, and the task information receiving means may receive the task information obtained from the analysis by the image analysis means.

[0008] In addition, in the work classification device, the image analysis means may analyze at least the shape of the pallet and the work area from the image, and the work information receiving means may receive the work information including at least the shape of the pallet and the work area obtained from the analysis by the image analysis means.

[0009] In the task classification device, the task classification means may further classify tasks performed by the cargo handling vehicle during the autonomous driving period during the nighttime hours.

[0010] In addition, in the work classification device, the image acquisition means may further acquire images of the interior of the warehouse via a camera attached to the warehouse, and may include a map generation means that generates a map of the warehouse from each image acquired by the image acquisition means, and the work classification means may further classify the work of the loading and unloading vehicle based on the map generated by the map generation means.

[0011] The task classification device may also be provided with a cost calculation means for calculating an implementation cost including transition costs for transitioning from driver operation to autonomous driving and / or remote operation and operating costs after the transition based on the classification results by the task classification means, and a report creation means for creating an implementation proposal including the implementation cost calculated by the cost calculation means.

[0012] The task classification device may also include a virtual space generation means for generating a virtual three-dimensional space from the images acquired by the image acquisition means, and a simulation means for simulating the tasks of the loading vehicle classified by the task classification means using the virtual three-dimensional space generated by the virtual space generation means.

[0013] The present invention also relates to a program for causing a computer to function as the above-mentioned task classification device.

[0014] The present invention also relates to a proposal system that includes the above-mentioned work classification device and the camera, in which the camera is sent by the proposing company to the business operator that owns the loading vehicle and can be attached to the loading vehicle operated by the driver. [Effects of the Invention]

[0015] According to the present invention, it is possible to provide a task classification device, program, and proposal system that classify tasks related to loading and unloading vehicles while reducing the workload on both the business operator and the proposing company. [Brief explanation of the drawings]

[0016] [Figure 1] FIG. 10 is a diagram illustrating an example of how the proposal system is used by a business operator according to the present embodiment. [Figure 2] 1 is an overall configuration diagram of a proposal system according to an embodiment of the present invention and a functional block diagram of a task classification server. [Figure 3] FIG. 2 is a diagram illustrating an example of a classification data storage unit of the task classification server according to the present embodiment. [Figure 4] FIG. 2 is a diagram illustrating a business flow of the proposed system according to the present embodiment. [Figure 5] 10 is a flowchart showing a task classification process of the task classification server according to the present embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0017] Hereinafter, embodiments of the present invention will be described with reference to the drawings. However, this is merely an example, and the technical scope of the present invention is not limited to this example. (Embodiment) <Proposed System 100> FIG. 1 is a diagram showing an example of how a proposal system 100 according to this embodiment is used by a business operator. FIG. 2 is a diagram showing the overall configuration of the proposal system 100 according to this embodiment and a functional block diagram of the task classification server 1. FIG. 3 is a diagram showing an example of the classification data storage unit 23 of the task classification server 1 according to this embodiment.

[0018] The proposal system 100 is a system that allows a proposing agent to propose business improvements such as automation to a business operator by separating, for example, work using a forklift (cargo handling vehicle). As shown in Fig. 1, the proposed system 100 grasps from images the current work (loading and unloading operations) being performed in a warehouse W using forklifts 8A and 8B operated by drivers. The images are acquired by cameras 9A and 9B attached to the forklifts 8A and 8B. The images may also include images acquired by a camera 9C installed in the warehouse W. The proposed system 100 then determines whether the forklifts 8A and 8B can perform the current work identified from the image by autonomous driving, which does not require a driver or operator, or by remote control, where the forklifts are operated by an operator from a remote location.

[0019] In addition, based on the judgment results, the proposal system 100 creates a report (implementation proposal) that describes the proposal content, including the implementation costs for automating work using the forklifts 8A and 8B, including autonomous driving and remote operation of the forklifts 8A and 8B. In this way, the proposal system 100 makes proposals relating to the automation of work using the forklifts 8A and 8B from the images. When automating work using forklifts 8A and 8B, a control device is attached to each of the forklifts 8A and 8B, which are operated by a driver on board. This allows each of the forklifts 8A and 8B to be able to flexibly switch between operation modes, including operation by a driver on board, autonomous operation, and remote control, without the need to purchase a new controllable forklift. In the following description, when the individual forklifts 8A and 8B are not specified, they will simply be referred to as forklifts 8. Similarly, cameras 9A, 9B, and 9C will also be simply referred to as cameras 9.

[0020] The proposed system 100 shown in FIG. 2 includes a task classification server 1 (task classification device), a camera 9 provided in a warehouse W, and a gateway T. The gateway T is a relay device that relays data between the camera 9 and the task classification server 1. The task classification server 1 and the gateway T are able to communicate with each other via a data communication network N. The data communication network N is, for example, an internet line. However, the data communication network N is not limited to this, and other communication lines may be used, and it does not matter whether the communication is wired or wireless. The camera 9 and the gateway T are connected via a wireless LAN such as Wi-Fi (registered trademark).

[0021] <Work classification server 1> The work classification server 1 is a server operated by, for example, a proposing company, which proposes business improvements related to work performed by forklifts 8 to the operator of the warehouse W. The work classification server 1 understands the work performed by forklifts 8 in the warehouse W from images and makes proposals related to the automation of work performed by forklifts 8. The task classification server 1 includes a control unit 10, a storage unit 20, and a data communication IF (interface) 29.

[0022] The control unit 10 is a CPU (Central Processing Unit) that controls the entire task classification server 1. The control unit 10 works in cooperation with the above-mentioned hardware to execute various functions by appropriately reading and executing the operating system (OS) and application programs stored in the storage unit 20. The control unit 10 includes an image receiving unit 11 (image acquisition means), an image analysis unit 12 (image analysis means), a work information acquisition unit 13 (work information reception means), a work classification unit 15 (work classification means), a cost-effectiveness calculation unit 17 (cost calculation means), and a report creation unit 18 (report creation means).

[0023] The image receiving unit 11 receives images taken by the cameras 9 attached to each forklift 8 or the cameras 9 installed in the warehouse W. The image receiving unit 11 may, for example, receive all images from the cameras 9 covering a day's worth of work performed by the forklift 8 at once. Alternatively, the image receiving unit 11 may receive images from the cameras 9 at predetermined time intervals, such as every hour. Furthermore, the image receiving unit 11 may receive images from the cameras 9 in real time, for example. Here, the images are assumed to be videos, but are not limited to this and may also be still images taken at unit times, such as every five seconds. The images are associated with the date and time of their capture. The image receiving unit 11 then stores the acquired images in the image storage unit 22.

[0024] The image analysis unit 12 analyzes the images received by the image receiving unit 11. As an example, the image analysis unit 12 analyzes the shape of the pallet and the work area such as the loading area from the image. The image analysis unit 12 also analyzes the work content (whether loading or unloading is being performed, whether the loading area within the work area is being checked, and whether there has been any intrusion into the loading area) and the operation status (the start time and duration of each work, the operating time of each forklift 8, the work speed, etc.) from the image. The image analysis unit 12 can perform a process of analyzing the work content from the image using, for example, AI (artificial intelligence). As an example, the image analysis unit 12 analyzes the shape of the pallet and the size of the work area from the image using a learning model that stores images of the shape of the pallet, the work area, etc. in advance.

[0025] The work information acquisition unit 13 receives work information using the forklift 8 based on the images received by the image receiving unit 11. Here, the work information includes the work content and operating status of the forklift 8. More specifically, the work information acquisition unit 13 receives work information including at least information on the shape of the pallet and the size of the work area obtained from the analysis by the image analysis unit 12.

[0026] The work classification unit 15 classifies the work of the forklift 8 into work performed by the forklift 8 operated by the driver, work performed by the forklift 8 remotely controlled by the operator, and work performed by the forklift 8 autonomously without human operation, based on the work information. The work classification unit 15 classifies the work of the forklift 8 based on, for example, the classification judgments and work examples stored in the classification data storage unit 23. Furthermore, the work classification unit 15 may further classify the work of the forklift 8 by adding work performed by the forklift 8 that is autonomously driven during the nighttime hours.

[0027] The cost-effectiveness calculation unit 17 calculates an introduction cost including a transition cost for transitioning from driver operation to autonomous driving and / or remote operation and an operation cost after the transition, based on the classification result by the task classification unit 15. The cost-effectiveness calculation unit 17 calculates the introduction cost by, for example, referring to the cost storage unit 24. The report creation unit 18 creates a report on the cost-effectiveness information including the introduction cost calculated by the cost-effectiveness calculation unit 17. The report creation unit 18 creates the report using, for example, a template stored in the template storage unit 25.

[0028] The storage unit 20 is a storage area such as a hard disk or semiconductor memory element for storing programs, data, etc. required for the control unit 10 to execute various processes. The storage unit 20 includes a program storage unit 21 , an image storage unit 22 , a classification data storage unit 23 , a cost storage unit 24 , and a template storage unit 25 . The program storage unit 21 is a storage area that stores application programs for executing the various functions performed by the control unit 10 described above. The image storage unit 22 is a storage area for storing images acquired by the camera 9.

[0029] The classification data storage unit 23 is a storage area that stores classification judgments and work examples for each work category. 3 shows an example of the classification data storage unit 23. The classification data storage unit 23 classifies work into four categories: remote, manual, autonomous, and night, and stores each classification judgment in association with a work example. Remote is work performed by remote control by an operator, manual is work performed by a driver, autonomous is work performed by autonomous driving that does not require human operation, and night is autonomous work that can be performed at night. In this example, autonomous tasks that can be performed at night are classified as nighttime. Also, in this example, tasks that cannot be performed autonomously are classified as remote, and tasks that cannot be performed remotely are classified as manual. The classification data storage unit 23 may also store, for example, images of standard pallet shapes as shapes of pallets that are capable of autonomous driving.

[0030] The cost storage unit 24 is a storage area that stores additional costs for implementation and labor costs per unit time as cost savings resulting from replacing work performed by a driver with work performed by autonomous driving or remote control. Examples of additional costs for implementation include the cost of a control device attached to the forklift 8 and the cost of outsourcing remote control or autonomous driving. The labor costs may be, for example, the cost of a standard driver determined through interviews with businesses, or may be a preset cost. The template storage unit 25 is a storage area for storing a template of a report to be presented to a business operator. Here, each storage unit stored in the storage unit 20 is an example. Also, the items are an example, and other items may be present, or some items may not be present.

[0031] The data communication IF 29 is an interface for communicating with the gateway T via the data communication network N. It should be noted that a computer refers to an information processing device equipped with a control unit, a storage device, etc., and the task classification server 1 is an information processing device equipped with a control unit 10, a storage unit 20, etc., and is included in the concept of a computer.

[0032] <Camera 9> The cameras 9 shown in FIGS. 1 and 2 are attached to the forklift 8 and installed in the warehouse W. The camera 9 is sent from the proposing company to the operator of the warehouse W. The sending method may be by mail or home delivery, for example, and the proposing company does not need to visit the warehouse W. The camera 9 has, for example, a mounting portion (not shown) that allows it to be attached to the forklift 8, and is detachable from the mounting portion. Cameras 9A and 9B attached to the forklift 8 constantly capture images of a predetermined area in the driving direction of the forklift 8, like a surveillance camera, for example. Camera 9C installed in the warehouse W captures an overhead view of the warehouse W.

[0033] The camera 9 has a communication function and transmits the captured image to the task classification server 1 via the gateway T. It is not essential that the camera 9 has a communication function. If the camera 9 does not have a communication function, the images may be uploaded from the camera 9 to, for example, a personal computer (PC), and then transmitted from the PC to the task classification server 1 via the gateway T.

[0034] <Gateway T> The gateway T is a relay device (base station) installed inside the warehouse W or in the vicinity of the warehouse W (for example, inside a building close to the warehouse W). The gateway T includes a control unit, a memory unit, a data communication IF, etc., which are not shown.

[0035] <Processing of the proposed system 100> Next, the processing performed by the proposal system 100 will be described. First, the business flow of the proposed system 100 will be explained. FIG. 4 is a diagram illustrating the business flow of the proposal system 100 according to this embodiment. First, the proposing company should explain the business improvement in advance to the operator of warehouse W, for example, by telephone or email. At that time, the proposing company should ask the operator about the number of forklifts 8 that warehouse W has, etc.

[0036] In step S1 of FIG. 4 (hereinafter, "step S" will be simply referred to as "S"), the proposing company packages the camera 9 to be attached to the forklift 8 and the camera 9 to be installed in warehouse W to the operator of warehouse W, and sends them by courier or the like. Note that the camera 9 to be installed in warehouse W is not necessarily required and is optional. In S2, the operator of the warehouse W attaches the delivered camera 9 to the forklift 8 and installs the camera 9 in the warehouse W. The installation position of the camera 9 on the forklift 8 is preferably a position that can capture images of the range visible to the driver of the forklift 8 so that the work content of the forklift 8 can be understood. In S3, the operator of warehouse W performs work using a forklift 8 for a predetermined period of time, and during that time activates the camera 9 to take pictures. Then, the operator of warehouse W (camera 9) transmits the images taken by the camera 9 to the work classification server 1 via gateway T.

[0037] In S4, the proposing contractor (the image receiving unit 11 of the work classification server 1) receives the images sent from the operator of the warehouse W. Then, the proposing contractor (the work classification server 1) analyzes the images and creates a report including cost-effectiveness information. In S5, the proposing company sends the created report to the operator of warehouse W. The report may be sent by mail or to the email address of the operator of warehouse W. In S6, the operator of Warehouse W checks the report and considers ways to improve the business. If necessary, the proposing company explains the contents of the proposal to the operator of Warehouse W.

[0038] In this way, according to the proposal system 100, the proposer can propose improvements to operations related to work using forklifts 8 in warehouse W without actually visiting warehouse W, simply by sending at least the number of cameras 9 equal to the number of forklifts 8 via courier service or the like. In addition, the operator of warehouse W does not need to have the proposing company come to the warehouse; he or she can receive proposals for business improvement from the proposing company simply by attaching the sent camera 9 to at least the forklift 8.

[0039] Next, the process from when the work classification server 1 receives the image to when it creates the report will be described. FIG. 5 is a flowchart showing the task classification process of the task classification server 1 according to this embodiment. 5, the control unit 10 (image analysis unit 12, work information acquisition unit 13) of the work classification server 1 analyzes the images from each camera 9 and acquires work information for each forklift 8. The control unit 10 also stores the acquired images in the image storage unit 22. The control unit 10 analyzes the shape of the pallet and the size of the work area from the image, and also analyzes the work content and operating status from the image. Here, the control unit 10 can analyze the shape of the pallet and the size of the work area from the image, for example, using a learning model in which images of the shape of the pallet, the work area, etc. are stored in advance.

[0040] In S12, the control unit 10 (task classification unit 15) extracts tasks that can be remotely controlled from the acquired task information and calculates the cost. The control unit 10 extracts tasks that can be remotely controlled by, for example, referring to the classification data storage unit 23. The control unit 10 extracts non-standard pallet handling and transport work from, for example, the shape of the pallet and the work content. More specifically, the control unit 10 extracts the work based on the fact that the shape of the pallet is non-standard and that it is a handling and transport work, and the time required for handling and transport. Furthermore, the control unit 10 extracts the shipment sorting work from, for example, the work area and the work content. More specifically, the control unit 10 extracts the work by checking the entry and exit of the shipment sorting area. Then, the control unit 10 (cost-effectiveness calculation unit 17) refers to the cost storage unit 24 and calculates the introduction cost and the reduction cost when the extracted work is performed by remote control.

[0041] In S13, the control unit 10 (task classification unit 15) extracts tasks that must be performed manually from the acquired task information. The control unit 10 extracts tasks that must be performed manually, for example, by referring to the classification data storage unit 23. Loading and unloading tasks are complex tasks that require detailed operations while checking the position and distance from the truck, and therefore cannot be performed remotely or autonomously. Therefore, the control unit 10 extracts loading and unloading tasks, for example, from the task area and task content. More specifically, when the control unit 10 confirms the presence of a truck within a certain distance in the image for a certain period of time or more, it extracts tasks that occurred a certain period before and after that as the task in question. In the case of manual work, the work is the same as the current work, so there is no need to calculate the cost.

[0042] In S14, the control unit 10 (task classification unit 15) extracts tasks that can be performed autonomously at night from the acquired task information and calculates the cost. The control unit 10 extracts tasks that can be performed autonomously at night, for example, by referring to the classification data storage unit 23. For example, from the task time and task content, the control unit 10 extracts tasks that do not need to be stored on the day of arrival but are stored after work hours, and tasks that need to be shipped the following morning but are shipped after work hours the previous day. Then, the control unit 10 (cost-effectiveness calculation unit 17) calculates the introduction cost and cost reduction cost of performing autonomous operation at night, by referring to the cost storage unit 24. Furthermore, some tasks that can be performed by autonomous driving at night may also be performed by autonomous driving or remote control during the day. Therefore, the control unit 10 (cost-effectiveness calculation unit 17) may refer to the cost storage unit 24 to calculate the introduction cost and reduction cost in the case of using autonomous driving or remote control during the day.

[0043] In S15, the control unit 10 (task classification unit 15) extracts the remaining tasks as tasks that can be performed autonomously, and calculates the costs. The control unit 10 extracts tasks such as standard pallet handling and transport work, for example. Then, the control unit 10 (cost-effectiveness calculation unit 17) refers to the cost storage unit 24 to calculate the introduction cost and reduction cost when autonomous driving is performed. Furthermore, tasks that can be performed autonomously can also be performed by remote control. Therefore, the control unit 10 (cost-effectiveness calculation unit 17) may refer to the cost storage unit 24 to calculate the introduction cost and reduction cost in the case of remote control.

[0044] In S16, the control unit 10 (report creation unit 18) creates a report that describes the work content for each of the four work classifications and information on cost-effectiveness, including the cost, of introducing work based on the work content, using, for example, a template stored in the template storage unit 25. Here, the report to be created may be a comprehensive report that classifies the work in the order of nighttime, autonomous, remote, and manual as the best mode, as well as a report that includes cases where nighttime work is changed to autonomous or remote, and cases where autonomous work is changed to remote. Thereafter, the control unit 10 may transmit the created report to a PC (not shown) that is communicatively connected to the work classification server 1. Then, the person in charge at the proposing company may make additions to the report created by the work classification server 1.

[0045] As described above, the proposed system 100 of this embodiment has the following advantages. (1) The work classification server 1 acquires images related to the loading and unloading operations performed by the forklift 8 via the camera 9 installed on the forklift 8, receives work information for the forklift 8 based on the acquired images, including the work content and operating status of the forklift 8, and, based on the received work information, classifies the work of the forklift 8 into work performed by the driver, work performed by the forklift 8 remotely controlled by an operator, and work performed by the forklift 8 by autonomous driving that does not require human operation. Therefore, the work performed by the forklift 8 can be classified using only the images captured by the camera 9 attached to the forklift 8. As a result, the work performed by the forklift 8 can be classified with reduced workload on both the business operator performing the work using the forklift 8 and the proposing contractor.

[0046] (2) The task classification server 1 analyzes the acquired images and receives task information obtained from the analysis. Therefore, work information can be obtained by analyzing the image, and therefore, work information can be obtained without human intervention.

[0047] (3) The work classification server 1 analyzes at least the shape and work area of ​​the pallet from the image, and receives work information including information relating to at least the shape and work area of ​​the pallet obtained from the analysis. Therefore, the shape of the pallet and / or the work area required for classifying the work of the forklift 8 operated by the driver, autonomous operation, or remote operation can be obtained by analyzing the image.

[0048] (4) The work classification server 1 further classifies the work of the forklift 8 by adding work performed by the forklift 8 that is autonomously driven during the nighttime hours. Therefore, the work performed by the forklift 8 can be classified, including the time period of the work.

[0049] (5) Based on the results of the work classification, the work classification server 1 calculates the implementation costs, including the costs associated with transitioning from driver operation to autonomous driving and / or remote control and the operating costs after the transition, and creates a report including the calculated implementation costs. Therefore, a report can be created that includes whether or not work related to autonomous driving and remote operation can be performed, and the implementation costs if it is possible. Therefore, it is convenient to simply propose the created report to the operator of warehouse W.

[0050] (6) The camera 9 was sent by the proposing company to the business operator who owns the forklift 8, and was designed to be able to be attached to the forklift 8 operated by the driver. Therefore, even if the forklift 8 owned by the business operator does not have a camera 9 mounted thereon, the business operator can attach the camera 9 sent by the proposing company to the forklift 8 and obtain images relating to the operation of the forklift 8. This reduces the burden on the business operator.

[0051] Although the embodiments of the present invention have been described above, the present invention is not limited to the above-described embodiments. Furthermore, the effects described in the embodiments are merely a list of the most preferable effects resulting from the present invention, and the effects of the present invention are not limited to those described in the embodiments. Note that the above-described embodiments and the modified embodiments described below can be used in appropriate combinations, but detailed description thereof will be omitted.

[0052] (Variations) (1) Although not specifically mentioned in this embodiment, the control unit (map generation means) of the work classification server may generate a map of the warehouse W from each image, and the control unit (autonomous mode determination means and remote mode determination means) may further determine whether work related to autonomous driving and remote operation of the forklift can be performed based on the generated map. In this way, a map including the state of the cargo can be generated without obtaining a map of warehouse W from the operator, and the map can be used to determine whether or not work related to autonomous driving or remote operation can be performed, allowing for more accurate decisions.

[0053] (2) In the present embodiment, an example has been described in which a report is generated that comprehensively covers all possible patterns (nighttime, autonomous, remote, manual), but this is not limiting. For example, a learning model that has learned the work content and costs may be used to propose work methods in order of optimal cost reduction and shortened work time. Furthermore, when a business operator introduces automation based on the proposed content, a learning model may be trained on the proposed content and the content actually introduced by the business operator, and the learning model may be used to propose methods in order of most frequently requested by the business operator.

[0054] (3) In the present embodiment, the process of obtaining task information by analyzing an image has been described, but the present invention is not limited to this. For example, all or part of the process of obtaining task information from an image may be performed manually. The control unit (virtual space generation means) of the work classification server may also generate a virtual three-dimensional space from an image, and determine whether the work can be performed by autonomous driving or remote control by autonomously driving or remotely controlling a forklift in the generated virtual three-dimensional space. Furthermore, the control unit (simulation means) of the work classification server may simulate a proposed work method that reflects the feasibility of the work by autonomous driving or remote control, and provide the simulated virtual three-dimensional space to the business operator.

[0055] (4) In the present embodiment, the classification of tasks based on images has been described as an example, but this is not limiting. For example, when classifying tasks performed during autonomous driving at night, data (incoming and outgoing data) from a warehouse management system or the like may be used instead of or in addition to images. In this case, for example, the business operator downloads incoming and outgoing data (e.g., data in CSV format) from the warehouse management system, and the task classification server receives the data via email or the like. This allows for more precise classification of tasks that can be performed during autonomous driving at night.

[0056] (5) In the present embodiment, the image transmitted by the camera 9 is received by the task classification server, but this is not limiting. For example, the business operator may return the camera 9 to the proposing company by courier or the like, and the proposing company may acquire the image from the camera 9. [Explanation of symbols]

[0057] 1. Work classification server 8,8A,8B Forklift 9, 9A, 9B, 9C Camera 10 Control Unit 11 Image receiving unit 12 Image Analysis Department 13 Work information acquisition unit 15 Work classification department 17 Cost-effectiveness Calculation Department 18 Report Writing Department 20 Memory section 22 Image storage unit 23 Classification data storage unit 24 Cost Memory Unit 25 Template memory section 100 Proposal System T Gateway W Warehouse

Claims

1. image acquisition means for acquiring images relating to the cargo handling work performed by the cargo handling vehicle via a camera provided on the cargo handling vehicle; a work information receiving means for receiving work information of the cargo handling vehicle, including the work content and operating status of the cargo handling vehicle, based on the images acquired by the image acquisition means; a work classification means for classifying work of the cargo handling vehicle into work performed by a driver, work performed by a remote operator, and work performed by an autonomous driving vehicle that does not require human operation, based on the work information received by the work information receiving means; A work classification device comprising:

2. 2. The task classification device according to claim 1, image analysis means for analyzing the image acquired by the image acquisition means; The task information receiving means receives the task information obtained from the analysis by the image analysis means.

3. 3. The task classification device according to claim 2, the image analysis means analyzes at least the shape and working area of ​​the pallet from the image; The task classification device, wherein the task information receiving means receives the task information including at least the shape of the pallet and the task area obtained from the analysis by the image analysis means.

4. 2. The task classification device according to claim 1, The task classification means further classifies the tasks of the cargo handling vehicle by adding tasks performed by the autonomously driven cargo handling vehicle during nighttime hours.

5. 2. The task classification device according to claim 1, The image acquisition means further acquires the image of the interior of the warehouse via a camera attached to the warehouse, a map generation means for generating a map of the warehouse from each image acquired by the image acquisition means; The task classification means classifies tasks performed by the cargo handling vehicles based further on the map generated by the map generation means.

6. 2. The task classification device according to claim 1, a cost calculation means for calculating an introduction cost including a transition cost for transitioning from driver operation to autonomous driving and / or remote operation and an operation cost after the transition based on the classification result by the task classification means; a report creation means for creating an introduction proposal including the introduction cost calculated by the cost calculation means; A work classification device comprising:

7. 2. The task classification device according to claim 1, a virtual space generating means for generating a virtual three-dimensional space from the image acquired by the image acquiring means; a simulation means for simulating the work of the cargo handling vehicle classified by the work classification means using the virtual three-dimensional space generated by the virtual space generation means; A work classification device comprising:

8. A program for causing a computer to function as the task classification device according to any one of claims 1 to 7.

9. A task classification device according to any one of claims 1 to 7; The camera; In the proposed system, In the proposed system, the camera is sent by the proposing company to the business operator that owns the cargo handling vehicle and can be attached to the cargo handling vehicle operated by the driver.

Images