Cooling system

Abstract

To provide a cooling system that can cool a cargo handling worker and an environment around the cargo handling worker while restraining an increase in cost.SOLUTION: A cooling system comprises: a data collection unit 33 for collecting temperature data and data other than temperature in an environment where cargo handling work has been performed, as teacher data; a learning model generation unit 34 for generating a learning model by performing machine learning from the teacher data collected by the data collection unit 33; an information acquisition unit 35 for acquiring environmental information related to an environment where cargo handling work is performed; an environmental temperature prediction unit 36 for predicting temperature in the environment where the cargo handling work is performed, on the basis of the learning model generated by the learning model generation unit 34, and the environmental information acquired by the information acquisition unit 35; a spray plan unit 37 for making a spay plan on the basis of the temperature predicted by the environmental temperature prediction unit 36; and a drone 2 that is an unmanned flying object constituted so as to be capable of autonomously flying, and for spraying in accordance with the spray plan made by the spray plan unit 37.SELECTED DRAWING: Figure 3

Description

[Technical field]

[0001] The present invention relates to a cooling system capable of cooling a cargo worker and the environment surrounding the cargo worker. [Background technology]

[0002] Patent Document 1 describes a forklift equipped with a mist device that sprays water in a fine mist toward the driver riding on the forklift. By spraying water from the mist device, the driver and the environment around the driver can be cooled.

[0003] Also, mist devices are installed in predetermined locations in the environment where loading and unloading work is carried out, and the loading and unloading workers and the environment around the loading and unloading workers are cooled by spraying from the mist devices.

[0004] However, the configuration of Patent Document 1 requires the forklift to be equipped with a mist device, which increases the cost of introducing the forklift. Also, in a configuration in which the mist device is installed in a predetermined location, the larger the environment in which the loading and unloading work is carried out, the more mist devices are required, which increases the cost. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] Patent Publication No. 2021-109760 Summary of the Invention [Problem to be solved by the invention]

[0006] The present invention has been made in consideration of the above circumstances, and an object of the present invention is to provide a cooling system that can cool cargo workers and their surrounding environment while suppressing increases in costs. [Means for solving the problem]

[0007] In order to solve the above problems, the cooling system of the present invention is characterized in that it comprises a data collection unit that collects temperature data and non-temperature data of the environment in which loading and unloading work is performed as teacher data, a learning model generation unit that performs machine learning on the teacher data collected by the data collection unit to generate a learning model, an information acquisition unit that acquires environmental information related to the environment in which loading and unloading work is performed, an environmental temperature prediction unit that predicts the temperature of the environment in which loading and unloading work is performed based on the learning model generated by the learning model generation unit and the environmental information acquired by the information acquisition unit, a spray planning unit that prepares a spray plan based on the temperature predicted by the environmental temperature prediction unit, and an unmanned aerial vehicle that is configured for autonomous flight and sprays in accordance with the spray plan prepared by the spray planning unit.

[0008] In addition, it is preferable that the unmanned aerial vehicle flies in a manner that follows a manned vehicle carrying a loading and unloading worker, and sprays while flying in accordance with the spray plan established by the spray planning unit.

[0009] It is also preferable that the information acquisition unit further acquires worker information related to loading and unloading workers, and the spray planning unit formulates a spray plan based on the temperature predicted by the environmental temperature prediction unit and the worker information acquired by the information acquisition unit.

[0010] It is also preferable that the unmanned aerial vehicle further includes a spray instruction unit configured to be able to communicate with multiple unmanned aerial vehicles and transmits spray plan information relating to the spray plan established by the spray planning unit to the unmanned aerial vehicle.

[0011] It is also preferable that the unmanned aerial vehicle is equipped with the spray planning unit. Effect of the Invention

[0012] According to the present invention, it is possible to provide a cooling system capable of cooling cargo workers and their surrounding environment while suppressing increases in costs. [Brief description of the drawings]

[0013] [Figure 1] 1 is a schematic diagram of a cooling system according to an embodiment of the present invention; [Diagram 2] FIG. 2 is a block diagram showing a schematic configuration of a cooling system according to the embodiment. [Diagram 3] FIG. 2 is a block diagram showing a schematic configuration of a cooling system according to the embodiment. [Figure 4] 4 is a flowchart showing a cooling procedure according to the embodiment; [Diagram 5] A block diagram showing the general configuration of an unmanned aerial vehicle relating to a modified example. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0014] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS A cooling system according to an embodiment of the present invention will be described with reference to the drawings. 1, the cooling system according to this embodiment includes a plurality of forklifts 1, a plurality of drones 2, a management device 3, and an environmental sensor 4. The cooling system cools a cargo worker M operating the forklift 1 and the surrounding environment of the cargo worker M by using the drone 2.

[0015] The forklift 1 is a manned vehicle on which a cargo worker M rides, and is a cargo handling vehicle for performing cargo handling work. The cargo worker M operates the forklift 1 to perform cargo handling work including loading and unloading and transporting cargo.

[0016] The drone 2 is an unmanned aerial vehicle configured to be capable of autonomous flight, and is a multicopter with excellent attitude stability. The drone 2 flies so as to follow the forklift 1 on which the loading and unloading worker M is riding, and sprays while flying according to a spraying plan described later.

[0017] The management device 3 is a management server that is configured to be able to communicate with the forklift 1, the drone 2, and the environmental sensor 4, and manages various types of information. The management device 3 transmits loading / unloading work information related to the loading / unloading work to be performed by the loading / unloading worker M to the forklift 1, and transmits spraying plan information related to a spraying plan to be described later to the drone 2.

[0018] The environmental sensor 4 is a sensor that acquires environmental information related to the environment in which the loading and unloading work is carried out. The environmental information acquired by the environmental sensor 4 is, for example, information related to the temperature, humidity, and air volume in the warehouse. The environmental sensor 4 transmits the environmental information to the management device 3.

[0019] 2 and 3, the schematic configurations of the forklift 1, the drone 2, and the management device 3 will be described. As shown in FIG. 2, the forklift 1 includes a display unit 11 and a position information acquisition unit 12.

[0020] The display unit 11 is configured with a display that presents information to the cargo handling worker M, and displays the cargo handling work information received from the management device 3. The cargo handling worker M refers to the cargo handling work information displayed on the display unit 11 and performs the specified cargo handling work.

[0021] The position information acquisition unit 12 acquires position information of the forklift 1, which is the vehicle itself. The position information acquisition unit 12 is configured, for example, with a sensor that detects the surrounding environment for estimating the self-position of the forklift 1, or a receiver that receives a signal from a positioning satellite. The position information acquisition unit 12 transmits the position information of the forklift 1 to the management device 3.

[0022] As shown in FIG. 2, the drone 2 includes a rotor 21, a position information acquisition unit 22, an object recognition unit 23, a flight control unit 24, a water storage unit 25, a spray unit 26, and a spray control unit 27.

[0023] A plurality of rotors 21 are provided concentrically at intervals from one another, and generate lift for flying the drone 2. The flight speed and flight direction of the drone 2 are configured to be changeable by controlling the rotors 21.

[0024] The position information acquisition unit 22 acquires position information of the drone 2, which is the drone itself. The position information acquisition unit 22 is configured, for example, by a laser sensor that acquires data for SLAM (Simultaneous Localization and Mapping) using LiDAR (Light Detection and Ranging), a motion sensor that acquires data for odometry, a receiver that receives signals from positioning satellites, or a combination of these.

[0025] The object recognition unit 23 recognizes the forklift 1 to be followed and obstacles that impede flight as objects present around the drone 2. The object recognition unit 23 is configured, for example, with a laser sensor that acquires data for SLAM using LiDAR, or a camera that captures images of the surroundings.

[0026] The flight control unit 24 is composed of a controller that controls the flight of the drone 2. The flight control unit 24 controls the rotor 21 so that the drone 2 flies following the forklift 1 based on the information received from the management device 3 (specifically, spraying plan information described later, etc.), the position information acquired by the position information acquisition unit 22, and the position information of the object recognized by the object recognition unit 23. Specifically, the flight control unit 24 controls the rotor 21 so that the drone 2 flies to a location where the spray target is located based on the position information of the spray target person or the position information of the forklift 1 on which the spray target person is riding received from the management device 3 and the position information acquired by the position information acquisition unit 22. Then, when the flight control unit 24 recognizes the spray target person or the forklift 1 on which the spray target person is riding by the object recognition unit 23, it controls the rotor 21 so that the drone 2 flies following the spray target person or the forklift 1.

[0027] The water storage section 25 is composed of a tank that stores water to be sprayed. The spray unit 26 is configured with a spray that sprays water in a mist form, and sprays using the water stored in the water storage unit 25.

[0028] The spray control unit 27 is composed of a control unit that drives the spray unit 26. The spray control unit 27 controls the spray unit 26 based on information received from the management device 3. Specifically, the spray control unit 27 controls the spray unit 26 based on the spray plan information received from the management device 3 so that the drone 2 sprays the spray target or the surrounding environment of the spray target at a specified spray location, spray timing, and spray amount.

[0029] 3, the management device 3 includes an information storage unit 31, a loading / unloading work instruction unit 32, a data collection unit 33, a learning model generation unit 34, an information acquisition unit 35, an environmental temperature prediction unit 36, a spray planning unit 37, and a spray instruction unit 38. Each of the units 31 to 38 constituting the management device 3 is configured by hardware that operates according to a predetermined program.

[0030] The information storage unit 31 stores various types of information. Specifically, the information storage unit 31 stores, for example, cargo handling work information related to the cargo handling work to be performed by the cargo worker M, past environmental information related to the environment in which the cargo handling work was performed, current environmental information related to the environment in which the cargo handling work is performed, and worker information related to the cargo worker M. The information stored in the information storage unit 31 is information input using an input device (not shown) connected to the management device 3, or information acquired through communication with the forklift 1, the drone 2, and the environmental sensor 4.

[0031] The loading / unloading work instruction unit 32 is configured to be able to communicate with multiple forklifts 1, and instructs the loading / unloading worker M to perform the loading / unloading work via the forklift 1. Specifically, the loading / unloading work instruction unit 32 instructs the loading / unloading work by transmitting the loading / unloading work information stored in the information storage unit 31 to a specific one of the multiple forklifts 1. The specific forklift 1 (i.e., the destination of the loading / unloading work information) is the forklift 1 on which the loading / unloading worker M, who is the recipient of the loading / unloading work instructions, is riding.

[0032] The data collection unit 33 collects temperature data and non-temperature data of the environment in which the loading and unloading work was performed as teacher data from the information storage unit 31. That is, the data collection unit 33 acquires past environmental information including the temperature of the environment in which the loading and unloading work was performed in order to generate a learning model. The non-temperature data collected as teacher data includes, for example, date and time data, weather data associated with the date and time data, layout data showing the arrangement of racks and ventilation openings in the warehouse, inventory data showing the amount of cargo placed on the racks, and work time data showing the time required for the loading and unloading work.

[0033] The learning model generation unit 34 generates a learning model by performing machine learning on the teaching data collected by the data collection unit 33. That is, the learning model generation unit 34 generates a learning model for predicting temperature from past environmental information.

[0034] The information acquisition unit 35 acquires information for predicting the environmental temperature and information for making a spraying plan from the information storage unit 31. Specifically, the information acquisition unit 35 acquires environmental information related to the environment in which the loading and unloading work is performed in order to predict the environmental temperature. The environmental information includes, for example, date and time information related to the date and time when the loading and unloading work is performed, temperature transition information, and weather forecast information. Furthermore, the information acquisition unit 35 acquires worker information related to the loading and unloading worker M in order to make a spraying plan. The worker information includes, for example, whether spraying is required according to the request of the loading and unloading worker M, the time and place where the loading and unloading work is performed by the loading and unloading worker M, and information related to the forklift 1 on which the loading and unloading worker M rides.

[0035] The environmental temperature prediction unit 36 ​​predicts the temperature of the environment where the loading and unloading work is performed, based on the learning model generated by the learning model generation unit 34 and the environmental information acquired by the information acquisition unit 35. That is, the environmental temperature prediction unit 36 ​​estimates the temperature of the environment where the loading and unloading work is performed.

[0036] The spray planning unit 37 makes a spray plan based on the temperature predicted by the environmental temperature prediction unit 36 ​​and the worker information acquired by the information acquisition unit 35. Specifically, the spray planning unit 37 determines the spray location, spray timing, and spray amount for effectively cooling the environment in the warehouse, and determines the loading and unloading worker M who performs loading and unloading work at the spray location and requires spraying as the spray target who is the target of spraying. Furthermore, the spray planning unit 37 determines the spray drone that will perform the spray. In this way, the spray planning unit 37 makes a spray plan by determining the spray location, spray timing, spray amount, spray target, and spray drone.

[0037] The spray instruction unit 38 is configured to be able to communicate with multiple drones 2, and instructs the drones 2 to spray. Specifically, the spray instruction unit 38 instructs spraying by transmitting spray plan information related to the spray plan established by the spray planning unit 37 to a specific drone 2 among the multiple drones. The specific drone 2 (i.e., the destination of the spray plan information) is the spray drone determined by the spray planning unit 37.

[0038] 4, a flow of cooling the cargo worker M and the surrounding environment of the cargo worker M will be described. Note that it is assumed that the information storage unit 31 has previously stored therein the various pieces of information described above.

[0039] First, the data collection unit 33 collects various data including temperature data from the information storage unit 31 as teacher data (step S1), and the learning model generation unit 34 generates a learning model from the teacher data collected in step S1 (step S2).

[0040] Next, the information acquisition unit 35 acquires environmental information from the information storage unit 31 (step S3), and the environmental temperature prediction unit 36 ​​predicts the temperature of the environment in which the loading and unloading work is performed based on the learning model generated in step S2 and the environmental information acquired in step S3 (step S4).

[0041] Next, the information acquisition unit 35 further acquires worker information from the information storage unit 31 (step S5), and the spray planning unit 37 creates a spray plan based on the temperature predicted in step S4 and the worker information acquired in step S5 (step S6).

[0042] Then, the spray instruction unit 38 transmits spray plan information related to the spray plan established in step S6 to the drone 2 (step S7), and the drone 2 that receives the spray plan information executes spraying according to the spray plan (step S8). That is, in step S8, the drone 2 sprays at the spray location, spray timing, and spray amount determined by the spray plan unit 37.

[0043] The present embodiment provides the following effects. (1) The cooling system includes a data collection unit 33 that collects teacher data, a learning model generation unit 34 that generates a learning model from the teacher data, an information acquisition unit 35 that acquires environmental information, an environmental temperature prediction unit 36 ​​that predicts the temperature based on the learning model and the environmental information, a spraying plan unit 37 that creates a spraying plan based on the predicted temperature, and a drone 2 (unmanned aerial vehicle) that sprays according to the spraying plan. According to this configuration, the temperature in the environment where the loading and unloading work is performed can be predicted by machine learning, and an appropriate spraying plan for cooling the loading and unloading worker M and the surrounding environment of the loading and unloading worker M can be created. Since the drone 2 sprays according to the spraying plan, the forklift 1 does not need to be equipped with a mist device, and an increase in the introduction cost of the forklift 1 can be suppressed. In addition, since the drone 2 can change the location where it sprays, costs can be reduced compared to a configuration in which many mist devices are installed in predetermined locations. Therefore, it is possible to cool the loading and unloading worker M and the surrounding environment of the loading and unloading worker M while suppressing an increase in costs.

[0044] (2) The drone 2 flies so as to follow the forklift 1 (manned vehicle) on which the cargo worker M is riding, and sprays while flying according to the spray plan made by the spray planner 37. With this configuration, the cargo worker M and the environment around the cargo worker M can be effectively cooled.

[0045] (3) The spray planning unit 37 creates a spray plan based on the temperature predicted by the environmental temperature prediction unit 36 ​​and the worker information acquired by the information acquisition unit 35. With this configuration, it is possible to cool the cargo worker M and the environment around the cargo worker M based on, for example, the necessity of spraying according to the request of the cargo worker M, the time and place where the cargo worker M will perform the cargo handling work, and information related to the forklift 1 on which the cargo worker M is riding.

[0046] (4) The spray instruction unit 38 is configured to be able to communicate with multiple drones 2 and transmits spray plan information related to the spray plan to the drones 2. With this configuration, it is not necessary for each drone 2 to be equipped with the spray plan unit 37, and therefore the configuration of the drone 2 can be simplified.

[0047] The present invention is not limited to the above embodiment, and the above configurations can be modified. For example, the following modifications can be made, or the following modifications can be combined to make the present invention.

[0048] As shown in Fig. 5, the drone 2 may include a spray planning unit 29. The configuration of the first modified example shown in Fig. 5 will be described below. Note that the description of the same configuration as the above embodiment will be omitted.

[0049] The drone 2 of the first variant example is equipped with an information acquisition unit 28 and a spray planning unit 29. The information acquisition unit 28 acquires information for making a spray plan from the management device 3. Specifically, the information acquisition unit 28 acquires, for example, information related to an estimated temperature of the environment where the loading and unloading work is performed from the environmental temperature prediction unit 36, and acquires worker information related to the loading and unloading worker M from the information storage unit 31.

[0050] The spray planning unit 29 makes a spray plan based on the estimated temperature (i.e., the temperature predicted by the environmental temperature prediction unit 36) and the worker information acquired by the information acquisition unit 28. Specifically, the spray planning unit 29 determines the spray location, spray timing, and spray amount, and determines the cargo worker M who performs cargo handling work at the spray location and requires spraying as the person to be sprayed. In this way, the spray planning unit 29 makes a spray plan by determining the spray location, spray timing, spray amount, and the person to be sprayed.

[0051] The flight control unit 24 of the first modified example controls the rotor 21 so that the drone 2 flies to the location where the person to be sprayed is located, based on the position information of the person to be sprayed determined by the spray planning unit 29 or the position information of the forklift 1 on which the person to be sprayed is riding, and the position information acquired by the position information acquisition unit 22.

[0052] In addition, the spray control unit 27 of the first modified example controls the spray unit 26 so that the drone 2 sprays the target person or the surrounding environment of the target person at a specified spray location, spray timing, and spray amount in accordance with the spray plan established by the spray planning unit 29.

[0053] The first modified example provides the following effects. (5) Since the drone 2 is equipped with the spray planning unit 29, the management device 3 does not need to create a spray plan, thereby reducing the burden on the management device 3 in the cooling system.

[0054] The configuration of the drone 2 may be changed as appropriate. For example, the drone 2 may be equipped with a blower unit configured with a fan that blows mist. With this configuration, the area to be sprayed can be expanded.

[0055] The manned vehicle on which the cargo worker M rides may be a cargo handling vehicle other than the forklift 1. That is, the drone 2 may spray, for example, a transport cart, a towing vehicle, or the cargo worker riding on the towing vehicle and the surrounding environment of the cargo worker.

[0056] The drone 2 may spray the cargo worker not on board the cargo handling vehicle and the environment around the cargo worker. In other words, the drone 2 may fly so as to follow the cargo worker who is walking. In this case, the object recognition unit 23 recognizes the cargo worker to be followed based on information related to the cargo worker.

[0057] The drone 2 does not have to follow the cargo workers and the manned vehicle. In other words, the operation of the drone 2 may be changed as appropriate as long as the cargo workers and the surrounding environment of the cargo workers can be cooled. [Explanation of symbols]

[0058] 1 Forklift (manned vehicle) 2. Drones (unmanned aerial vehicles) 3 Management device 33 Data Collection Section 34 Learning model generation unit 35 Information Acquisition Department 36 Environmental temperature prediction section 37 Spray Planning Department 38 Spray instruction section

Claims

1. a data collection unit that collects temperature data and non-temperature data of the environment in which the loading and unloading work is carried out as teacher data; a learning model generation unit that performs machine learning on the teacher data collected by the data collection unit to generate a learning model; an information acquisition unit that acquires environmental information relating to an environment in which loading and unloading work is performed; an environmental temperature prediction unit that predicts a temperature of an environment in which loading and unloading work is performed based on the learning model generated by the learning model generation unit and the environmental information acquired by the information acquisition unit; A spray planning unit that creates a spray plan based on the temperature predicted by the environmental temperature prediction unit; and an unmanned aerial vehicle configured to be capable of autonomous flight and spraying according to the spray plan established by the spray planning unit. A cooling system comprising:

2. The unmanned aerial vehicle flies so as to follow a manned vehicle on which a cargo worker is aboard, and sprays while flying according to a spray plan established by the spray planning unit. The cooling system of claim 1 .

3. The information acquisition unit further acquires worker information related to loading and unloading workers, The spray planning unit creates a spray plan based on the temperature predicted by the environmental temperature prediction unit and the worker information acquired by the information acquisition unit.

3. The cooling system according to claim 1 or 2.

4. The method further includes a spray instruction unit configured to be able to communicate with a plurality of the unmanned aerial vehicles and transmitting spray plan information related to the spray plan established by the spray planning unit to the unmanned aerial vehicles.

3. The cooling system according to claim 1 or 2.

5. The unmanned aerial vehicle is equipped with the spray planning unit.

3. The cooling system according to claim 1 or 2.

Images