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

[0006] , , ,

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

Background Art

[0002] Patent Document 1 describes a forklift equipped with a mist device that injects water in a fine mist form to a driver riding on the forklift. By injecting water from the mist device, the driver and the surrounding environment of the driver can be cooled.

[0003] In addition, a mist device is installed in a predetermined location in the environment where the cargo handling work is performed, and the mist device is used to cool the cargo handling worker and the surrounding environment of the cargo handling worker.

[0004] However, in the configuration of Patent Document 1, since the forklift needs to be equipped with a mist device, there is a problem that the introduction cost of the forklift increases. In addition, in the configuration where the mist device is installed in a predetermined location, the larger the environment where the cargo handling work is performed, the more mist devices are required, resulting in a cost increase.

Prior Art Documents

Patent Documents

[0005]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0006] The present invention has been made in view of the above circumstances, and an object thereof is to provide a cooling system capable of cooling a cargo handling worker and the surrounding environment of the cargo handling worker while suppressing an increase in cost.

Means for Solving the Problems

[0007] To solve the above problems, the cooling system of the present invention is characterized by comprising: a data collection unit that collects temperature data and non-temperature data of the environment in which cargo handling operations are performed as training data; a learning model generation unit that generates a learning model by performing machine learning from the training data collected by the data collection unit; an information acquisition unit that acquires environmental information relating to the environment in which cargo handling operations are performed; an environmental temperature prediction unit that predicts the temperature of the environment in which cargo handling operations are 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 aircraft configured to be capable of autonomous flight and that sprays according to the spray plan created by the spray planning unit.

[0008] Furthermore, it is preferable that the unmanned aircraft flies in a manner that follows a manned vehicle carrying cargo handling workers, and sprays while flying according to the spraying plan formulated by the spraying planning unit.

[0009] Furthermore, it is preferable that the information acquisition unit further acquires worker information relating to cargo handling workers, and that the spray planning unit creates a spray plan based on the temperature predicted by the ambient temperature prediction unit and the worker information acquired by the information acquisition unit.

[0010] Furthermore, it is preferable to further include a spray instruction unit that is configured to communicate with a plurality of the aforementioned unmanned aerial vehicles and transmits spray plan information related to the spray plan established by the spray planning unit to the aforementioned unmanned aerial vehicles.

[0011] Furthermore, it is preferable that the unmanned aerial vehicle is equipped with the spray planning unit. [Effects of the Invention]

[0012] According to the present invention, it is possible to provide a cooling system that can cool cargo handling workers and the surrounding environment while keeping costs down. [Brief explanation of the drawing]

[0013] [Figure 1] This is a schematic diagram of a cooling system according to one embodiment of the present invention. [Figure 2] This block diagram shows a schematic configuration of the cooling system according to the same embodiment. [Figure 3] This block diagram shows a schematic configuration of the cooling system according to the same embodiment. [Figure 4] This flowchart shows the cooling flow according to the present embodiment. [Figure 5] This is a block diagram showing the schematic configuration of an unmanned aerial vehicle in a modified form. [Modes for carrying out the invention]

[0014] A cooling system according to one embodiment of the present invention will be described with reference to the drawings. As shown in Figure 1, the cooling system according to this embodiment comprises a plurality of forklifts 1, a plurality of drones 2, a management device 3, and an environmental sensor 4. The cooling system cools the cargo handling worker M operating the forklift 1 and the surrounding environment of the cargo handling worker M using the drones 2.

[0015] Forklift 1 is a manned vehicle operated by cargo handling worker M, and is used for cargo handling operations. Cargo handling worker M operates forklift 1 to perform cargo handling operations, including loading, unloading, and transporting goods.

[0016] Drone 2 is an unmanned aerial vehicle configured for autonomous flight and is a multi-rotor with excellent attitude stability. Drone 2 flies in accordance with the forklift 1 on which cargo handling worker M is riding, and sprays while flying according to the spraying plan described later.

[0017] The management device 3 is configured to communicate with the forklift 1, the drone 2, and the environmental sensor 4, and is a management server that manages various types of information. The management device 3 transmits cargo handling operation information related to the cargo handling operations to be performed by the cargo handling worker M to the forklift 1, and transmits spraying plan information related to the spraying plan 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 cargo handling operation is performed. The environmental information acquired by the environmental sensor 4 is, for example, information related to the temperature, humidity, and air volume inside the warehouse. The environmental sensor 4 transmits the environmental information to the management device 3.

[0019] Referring to FIGS. 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 composed of a display that presents information to the cargo handling worker M, and displays the cargo handling operation information received from the management device 3. The cargo handling worker M performs the designated cargo handling operation by referring to the cargo handling operation information displayed on the display unit 11.

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

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

[0023] A plurality of rotary wings 21 are provided at intervals on concentric circles, and generate lift for the drone 2 to fly. The flight speed and flight direction of the drone 2 are configured to be changeable by controlling the rotary wings 21.

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

[0025] The object recognition unit 23 recognizes objects present around the drone 2, including the forklift 1 to be followed and obstacles that may hinder flight. The object recognition unit 23 is composed of, for example, a laser sensor that acquires SLAM data using LiDAR, or a camera that takes pictures of the surroundings.

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

[0027] The water storage section 25 consists of a tank for storing water for spraying. The spray unit 26 is composed of a spray that atomizes and sprays water, and uses water stored in the water reservoir 25 for spraying.

[0028] The spray control unit 27 consists 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, based on the spraying plan information received from the management device 3, the spray control unit 27 controls the spray unit 26 so that the drone 2 sprays the target person or the surrounding environment of the target person at the predetermined spraying location, timing, and spraying amount.

[0029] Furthermore, as shown in Figure 3, the management device 3 includes an information storage unit 31, a cargo handling operation instruction unit 32, a data collection unit 33, a learning model generation unit 34, an information acquisition unit 35, an ambient 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 composed of 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 information related to the cargo handling work to be performed by cargo handling 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 cargo handling 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, drone 2, and environmental sensor 4.

[0031] The cargo handling instruction unit 32 is configured to communicate with multiple forklifts 1 and instructs cargo handling workers M to perform cargo handling operations via the forklifts 1. Specifically, the cargo handling instruction unit 32 instructs cargo handling operations by transmitting cargo handling operation information stored in the information storage unit 31 to a specific forklift 1 among the multiple forklifts. The specific forklift 1 (i.e., the recipient of the cargo handling operation information) is the forklift 1 on which the cargo handling worker M, who is the recipient of the cargo handling operation instructions, is operating.

[0032] The data collection unit 33 collects temperature data and non-temperature data of the environment where the cargo handling work was performed from the information storage unit 31 as training data. In other words, the data collection unit 33 acquires past environmental information, including the temperature of the environment where the cargo handling work was performed, in order to generate a learning model. Non-temperature data collected as training data include, 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 goods placed on the racks, and work time data showing the time required for the cargo handling work.

[0033] The learning model generation unit 34 generates a learning model by performing machine learning on the training data collected by the data collection unit 33. In other words, 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 from the information storage unit 31 for predicting the ambient temperature and for planning the spraying operation. Specifically, in order to predict the ambient temperature, the information acquisition unit 35 acquires environmental information related to the environment in which the cargo handling operation is performed. This environmental information includes, for example, date and time information related to the date and time the cargo handling operation is performed, temperature trend information, and weather forecast information. The information acquisition unit 35 also acquires worker information related to cargo handling worker M in order to plan the spraying operation. This worker information includes, for example, whether or not spraying is necessary according to the request of cargo handling worker M, the time and place in which cargo handling worker M performs the cargo handling operation, and information related to the forklift 1 on which cargo handling worker M is operating.

[0035] The environmental temperature prediction unit 36 ​​predicts the temperature of the environment in which cargo handling operations are 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. In other words, the environmental temperature prediction unit 36 ​​estimates the temperature of the environment in which cargo handling operations are performed.

[0036] The spraying planning unit 37 creates a spraying plan based on the temperature predicted by the ambient temperature prediction unit 36 ​​and the worker information acquired by the information acquisition unit 35. Specifically, the spraying planning unit 37 determines the spraying locations, spraying timing, and spraying volume to effectively cool the environment inside the warehouse, and also designates the cargo handling workers M who perform cargo handling operations at the spraying locations and who require spraying as targets for spraying. Furthermore, the spraying planning unit 37 determines the spraying drones that will perform the spraying. In this way, the spraying planning unit 37 creates a spraying plan by determining the spraying locations, spraying timing, spraying volume, targets for spraying, and spraying drones.

[0037] The spray instruction unit 38 is configured 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 created by the spray planning unit 37 to a specific drone 2 among the multiple drones. The specific drone 2 (i.e., the recipient of the spray plan information) is a spray drone designated by the spray planning unit 37.

[0038] Referring to Figure 4, the flow of cooling the cargo handling worker M and the environment surrounding cargo handling worker M will be explained. It is assumed that the various types of information mentioned above are pre-stored in the information storage unit 31.

[0039] First, the data acquisition unit 33 collects various data, including temperature data, from the information storage unit 31 as training data (step S1), and the learning model generation unit 34 generates a learning model from the training 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 cargo handling 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 acquires more worker information from the information storage unit 31 (step S5), and the spraying planning unit 37 creates a spraying 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 the spray plan information related to the spray plan established in step S6 to the drone 2 (step S7), and the drone 2, having received the spray plan information, performs spraying according to the spray plan (step S8). In other words, in step S8, the drone 2 sprays according to the spray location, spray timing, and spray amount determined by the spray planning unit 37.

[0043] In this embodiment, the following effects can be obtained. (1) The cooling system comprises a data collection unit 33 for collecting training data, a learning model generation unit 34 for generating a learning model from the training data, an information acquisition unit 35 for acquiring environmental information, an environmental temperature prediction unit 36 ​​for predicting the temperature based on the learning model and environmental information, a spray planning unit 37 for formulating a spray plan based on the predicted temperature, and a drone 2 (unmanned aerial vehicle) for spraying according to the spray plan. With this configuration, machine learning can predict the temperature in the environment where cargo handling operations are performed and create an appropriate spray plan for cooling cargo handling workers M and the environment surrounding them. Furthermore, since the drone 2 sprays according to the spray plan, the forklift 1 does not need to be equipped with a misting device, thus reducing the increase in the introduction cost of the forklift 1. In addition, since the drone 2 can change the spraying location, costs can be reduced compared to a configuration in which many misting devices are installed in predetermined locations. Therefore, it is possible to cool cargo handling workers M and the environment surrounding them while keeping costs down.

[0044] (2) The drone 2 flies in a manner that follows the forklift 1 (manned vehicle) on which the cargo handling worker M is riding, and sprays while flying according to the spraying plan set up by the spraying plan unit 37. With this configuration, the cargo handling worker M and the environment surrounding the cargo handling worker M can be effectively cooled.

[0045] (3) The spraying planning unit 37 creates a spraying plan based on the temperature predicted by the ambient temperature prediction unit 36 ​​and the worker information acquired by the information acquisition unit 35. With this configuration, for example, the need for spraying in response to the request of the cargo handling worker M, the time and place in which the cargo handling worker M performs cargo handling work, and information related to the forklift 1 on which the cargo handling worker M is riding can be used to cool the cargo handling worker M and the surrounding environment of the cargo handling worker M.

[0046] (4) The spray instruction unit 38 is configured to communicate with multiple drones 2 and transmits spray plan information related to the spray plan to the drones 2. With this configuration, each drone 2 does not need to have a spray plan unit 37, so the configuration of the drones 2 can be simplified.

[0047] The present invention is not limited to the embodiments described above, and the above configuration can be modified. For example, it can be implemented with the following modifications, or a combination of the following modifications can be used.

[0048] As shown in Figure 5, the drone 2 may be equipped with a spray planning unit 29. The configuration of the first modified example shown in Figure 5 will be described below. Configurations similar to those of the above embodiment will not be described.

[0049] The first modified drone 2 is equipped with an information acquisition unit 28 and a spray planning unit 29. The information acquisition unit 28 acquires information from the management device 3 for planning the spraying operation. Specifically, the information acquisition unit 28 acquires, for example, information related to the estimated temperature of the environment in which the cargo handling operation is performed from the environmental temperature prediction unit 36, and worker information related to cargo handling worker M from the information storage unit 31.

[0050] The spraying planning unit 29 creates a spraying plan based on the estimated temperature (i.e., the temperature predicted by the ambient temperature prediction unit 36) and worker information acquired by the information acquisition unit 28. Specifically, the spraying planning unit 29 determines the spraying location, spraying timing, and spraying amount, and also designates cargo handling workers M who perform cargo handling work at the spraying location and who require spraying as targets for spraying. In this way, the spraying planning unit 29 creates a spraying plan by determining the spraying location, spraying timing, spraying amount, and targets for spraying.

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

[0052] Furthermore, 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 the predetermined spray location, spray timing, and spray volume, according to the spray plan formulated by the spray planning unit 29.

[0053] The following effects can be obtained in the first modified example. (5) Since the drone 2 is equipped with a spray planning unit 29, the control device 3 does not need to plan the spray, and the burden on the control device 3 in the cooling system can be reduced.

[0054] The configuration of drone 2 may be modified as appropriate. For example, drone 2 may be equipped with a blower unit consisting of a fan that sends out mist. This configuration allows for a wider area to be sprayed.

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

[0056] Drone 2 may spray cargo handling workers who are not in cargo handling vehicles and the surrounding environment of said cargo handling workers. In other words, Drone 2 may fly in a manner that follows cargo handling workers who are moving on foot. In this case, the object recognition unit 23 recognizes the cargo handling worker to be followed based on information relating to the cargo handling worker.

[0057] Drone 2 does not need to follow the cargo handlers and manned vehicles. In other words, the operation of Drone 2 may be modified as appropriate, as long as it can cool the cargo handlers and the environment surrounding them. [Explanation of symbols]

[0058] 1. Forklift (manned vehicle) 2. Drones (unmanned aerial vehicles) 3 Management device 33 Data Acquisition Unit 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 collects temperature data and other data from the environment where cargo handling operations were performed as training data. A learning model generation unit generates a learning model by performing machine learning on training data collected by the aforementioned data collection unit, An information acquisition unit that acquires environmental information related to the environment in which cargo handling operations are performed, An environmental temperature prediction unit predicts the temperature of the environment in which cargo handling operations are 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 spraying plan based on the temperature predicted by the aforementioned ambient temperature prediction unit, The system includes an unmanned aerial vehicle configured to fly autonomously and which sprays according to a spray plan formulated by the spray planning unit. A cooling system characterized by the following features.

2. The unmanned aerial vehicle flies in accordance with the manned vehicle carrying the cargo handling worker, and sprays while flying according to the spraying plan formulated by the spraying planning unit. The cooling system according to feature 1.

3. The aforementioned information acquisition unit further acquires worker information relating to cargo handling workers, The spraying planning unit creates a spraying plan based on the temperature predicted by the ambient temperature prediction unit and the worker information acquired by the information acquisition unit. The cooling system according to claim 1 or 2, characterized in that it is the same as described in claim 1 or 2.

4. The system further comprises a spray instruction unit configured to communicate with multiple unmanned aerial vehicles and to transmit spray plan information related to the spray plan formulated by the spray planning unit to the unmanned aerial vehicles. The cooling system according to claim 1 or 2, characterized in that it is the same as described in claim 1 or 2.

5. The unmanned aerial vehicle is equipped with the spray planning unit. The cooling system according to claim 1 or 2, characterized in that it is the same as described in claim 1 or 2.

Images