KR20230081057A - System and method for managing cargo storing and rehandling processes using artificial intellegience - Google Patents

Abstract

The present disclosure provides a cargo device and a rehandling management method using artificial intelligence performed by at least one processor. This method includes the steps of registering cargo information, reach stacker information, and equipment head information in a database, and based on the information registered in the database, by an artificial intelligence-based device and rehandling scheduling module, cargo, device location, and reach stacker are determined. Determining a first schedule that matches and includes a route to the device site, and transmitting device and rehandling information according to any one of the first schedule and the second schedule received from the device operator terminal to the reach stacker driver terminal. includes

Description

Cargo device and rehandling management system and method using artificial intelligence {SYSTEM AND METHOD FOR MANAGING CARGO STORING AND REHANDLING PROCESSES USING ARTIFICIAL INTELLEGIENCE}

The present invention relates to a cargo device and a rehandling management system and method using artificial intelligence, and relates to cargo to be loaded and rehandled within a predetermined time based on cargo information, reach stacker and driver information, reach stacker operation information, equipment head information, etc. A system and method for constructing a device and rehandling schedule consisting of an optimal route by matching device location and reach stacker.

In order to carry out container loading and rehandling work in a conventional container yard (CY, container yard) or container shed, a manager of a storage yard operator uses his or her own know-how to match cargo, device location, and reach stacker. and a method for determining loading and rehandling scheduling is used. Here, the management staff of the storage yard operator can refer to the information of carrying in and out of the cargo requested by the shipper, cargo information (container information, shipping line information, etc.), reach stacker and driver information, and storage yard current status information.

However, in the conventional method, when communication between the shipper, the yard operator, and the reach stacker driver goes wrong, there are problems such as going through a reconfirmation procedure of the carry-in/out container through a wired or individual contact network or losing it. In addition, since the scheduling of loading and rehandling is determined only by relying on the know-how of the manager of the storage facility operator, the appropriateness or efficiency of the scheduling may be determined by the individual competency of the manager.

Meanwhile, when a container is installed on top of a carry-out container in a cargo storage yard, reprocessing or rehandling of temporarily moving a container located on top of a carry-out container to another place may be executed. The increase in the number of rehandling not only increases the workload of loading and unloading equipment (eg, reach stacker), but also causes congestion inside the cargo terminal and disruption of the loading and unloading plan of cargo vehicles due to waiting for yard trucks to work. Therefore, there is a need for a method for optimizing rehandling as well as cargo equipment or loading in order to improve the overall productivity of a cargo terminal.

Therefore, in the present invention, cargo information and cargo information (eg, container information, shipping line information, etc.) requested by the shipper, reach stacker and driver information, past, present or scheduled information of reach stacker work, equipment yard status information, etc. Based on this, we propose a system and method for constructing a device and rehandling schedule consisting of an optimal route by matching the cargo to be loaded and rehandled within a predetermined time, the location of the device, and the reach stacker.

The present disclosure provides a cargo device and a rehandling management method using artificial intelligence, a computer program and a device (system) stored in a recording medium to solve the above problems.

The present disclosure may be implemented in a variety of ways, including a method, apparatus (system) or computer program stored on a readable storage medium.

According to an embodiment of the present disclosure, a cargo device and rehandling management method using artificial intelligence performed by at least one processor includes registering cargo information, reach stacker information, and equipment head information in a database, Based on the information, matching cargo, device location, and reach stacker with an artificial intelligence-based device and rehandling scheduling module, and determining a first schedule including a route to the device facility, and the first schedule and device operator terminal and transmitting device and rehandling information according to any one of the second schedules received from the Reach Stacker driver terminal.

According to another embodiment of the present disclosure, a method for learning a cargo device and rehandling using artificial intelligence performed by at least one processor includes registering the device and rehandling order information in a database, the information registered in the database Based on this, using a reinforcement learning-based device and rehandling scheduling algorithm, matching cargo, device location, and reach stacker and determining a first schedule including a route to the device field, from the first schedule and device operator terminal Transmitting device and rehandling information according to any one of the received second schedules to the reach stacker driver terminal, receiving a device and rehandling execution result executed according to the device and rehandling information from the driver terminal, Updating the device and rehandling information and the device and rehandling execution result to the database, and learning the device and rehandling scheduling algorithm based on the device and rehandling information updated to the database and the device and rehandling execution result. can include

According to another embodiment of the present disclosure, a computer program stored in a computer readable recording medium is provided to execute a cargo device and a rehandling management method using artificial intelligence in a computer.

According to another embodiment of the present disclosure, a computer program stored in a computer readable recording medium is provided to execute a learning method of a cargo device and rehandling using artificial intelligence in a computer.

According to another embodiment of the present disclosure, a cargo device and a rehandling management system using artificial intelligence include a communication module, a memory, and at least one computer-readable program connected to the memory and configured to execute at least one computer-readable program included in the memory. Including a processor, at least one program registers cargo information, reach stacker information, and device head information in a database, and based on the information registered in the database, by an artificial intelligence-based device and a rehandling scheduling module, cargo and the device location and reach stacker are matched, and a first schedule including a path to the device field is determined, and the device and rehandling information according to any one of the first schedule and the second schedule received from the device field operator terminal are operated by the reach stacker One or more commands to be transmitted to the driver's terminal may be included.

According to some embodiments of the present invention, based on cargo information, reach stacker and driver information, reach stacker job information, equipment head information, etc., an optimal route is matched with cargo to be loaded and rehandled within a predetermined time, equipment location, and reach stacker. A device consisting of and a rehandling schedule can be established.

In addition, according to some embodiments of the present invention, it is possible to improve scheduling inefficiency and unreasonable problems that may occur while determining loading and rehandling scheduling depending only on the know-how of a manager of a conventional equipment yard operator.

The effects of the present disclosure are not limited to the effects mentioned above, and other effects not mentioned are clear to those skilled in the art (referred to as “ordinary technicians”) from the description of the claims. will be understandable.

Embodiments of the present disclosure will be described with reference to the accompanying drawings described below, wherein like reference numbers indicate like elements, but are not limited thereto.
1 is a diagram showing the configuration of a cargo device and a rehandling management system according to an embodiment of the present disclosure.
2 is a schematic diagram illustrating a configuration in which an information processing system according to an embodiment of the present disclosure is communicatively connected with a plurality of user terminals.
3 is a block diagram showing internal configurations of a user terminal and an information processing system according to an embodiment of the present disclosure.
4 is a diagram showing the configuration of a processor of an information processing system according to an embodiment of the present disclosure.
5 is a diagram showing the configuration of a database of a cargo device and a rehandling management system according to an embodiment of the present disclosure.
6 is a diagram illustrating an example of a cargo device and rehandling management process according to one embodiment of the present disclosure.
7 is a diagram illustrating an example of a learning process of automated cargo equipment and rehandling scheduling according to an embodiment of the present disclosure.
8 is a diagram illustrating an example of a reinforcement learning process for automation of automated cargo equipment and rehandling scheduling according to an embodiment of the present disclosure.
9 is a diagram illustrating an example of an automated cargo device and a rehandling management method according to an embodiment of the present disclosure.

Hereinafter, specific details for the implementation of the present disclosure will be described in detail with reference to the accompanying drawings. However, in the following description, if there is a risk of unnecessarily obscuring the gist of the present disclosure, detailed descriptions of well-known functions or configurations will be omitted.

In the accompanying drawings, identical or corresponding elements are given the same reference numerals. In addition, in the description of the following embodiments, overlapping descriptions of the same or corresponding components may be omitted. However, omission of a description of a component does not intend that such a component is not included in an embodiment.

Advantages and features of the disclosed embodiments, and methods of achieving them, will become apparent with reference to the following embodiments in conjunction with the accompanying drawings. However, the present disclosure is not limited to the embodiments disclosed below and may be implemented in various different forms, but only the present embodiments make the present disclosure complete, and the present disclosure does not extend the scope of the invention to those skilled in the art. It is provided only for complete information.

Terms used in this specification will be briefly described, and the disclosed embodiments will be described in detail. The terms used in this specification have been selected from general terms that are currently widely used as much as possible while considering the functions in the present disclosure, but they may vary according to the intention of a person skilled in the related field, a precedent, or the emergence of new technology. In addition, in a specific case, there is also a term arbitrarily selected by the applicant, and in this case, the meaning will be described in detail in the description of the invention. Therefore, the terms used in the present disclosure should be defined based on the meaning of the terms and the general content of the present disclosure, not simply the names of the terms.

Expressions in the singular number in this specification include plural expressions unless the context clearly dictates that they are singular. Also, plural expressions include singular expressions unless the context clearly specifies that they are plural. When it is said that a certain part includes a certain component in the entire specification, this means that it may further include other components without excluding other components unless otherwise stated.

Also, the term 'module' or 'unit' used in the specification means a software or hardware component, and the 'module' or 'unit' performs certain roles. However, 'module' or 'unit' is not meant to be limited to software or hardware. A 'module' or 'unit' may be configured to reside in an addressable storage medium and may be configured to reproduce one or more processors. Thus, as an example, a 'module' or 'unit' includes components such as software components, object-oriented software components, class components, and task components, processes, functions, and attributes. , procedures, subroutines, segments of program code, drivers, firmware, microcode, circuitry, data, databases, data structures, tables, arrays, or variables. Functions provided within components and 'modules' or 'units' may be combined into a smaller number of components and 'modules' or 'units', or further components and 'modules' or 'units'. can be further separated.

According to one embodiment of the present disclosure, a 'module' or 'unit' may be implemented with a processor and a memory. 'Processor' should be interpreted broadly to include general-purpose processors, central processing units (CPUs), microprocessors, digital signal processors (DSPs), controllers, microcontrollers, state machines, and the like. In some circumstances, 'processor' may refer to an application specific integrated circuit (ASIC), programmable logic device (PLD), field programmable gate array (FPGA), or the like. 'Processor' refers to a combination of processing devices, such as, for example, a combination of a DSP and a microprocessor, a combination of a plurality of microprocessors, a combination of one or more microprocessors in conjunction with a DSP core, or a combination of any other such configurations. You may. Also, 'memory' should be interpreted broadly to include any electronic component capable of storing electronic information. 'Memory' includes random access memory (RAM), read-only memory (ROM), non-volatile random access memory (NVRAM), programmable read-only memory (PROM), erasable-programmable read-only memory (EPROM), It may also refer to various types of processor-readable media, such as electrically erasable PROM (EEPROM), flash memory, magnetic or optical data storage, registers, and the like. A memory is said to be in electronic communication with the processor if the processor can read information from and/or write information to the memory. Memory integrated with the processor is in electronic communication with the processor.

In the present disclosure, a 'system' may include at least one of a server device and a cloud device, but is not limited thereto. For example, a system may consist of one or more server devices. As another example, a system may consist of one or more cloud devices. As another example, the system may be operated by configuring a server device and a cloud device together.

In the present disclosure, a 'container stacking area' may refer to a place or storage area where cargo containers carried in by cargo transport vehicles are temporarily stored until the time of unloading. In the present disclosure, a storage yard can be used interchangeably with a container yard.

In the present disclosure, a 'reach stacker' is a vehicle-type mobile device and may refer to equipment or a vehicle that transfers or unloads containers from a chassis in a container yard or yard and loads them into the container yard.

In the present disclosure, 'equipment and rehandling schedule' refers to matching information of cargo and reach stackers to be loaded or rehandled at a container yard or storage yard, location, route, time or order for the matched reach stacker to be loaded or rehandled within the yard. It may include information indicating a .

In the present disclosure, 'each of a plurality of A' or 'each of a plurality of A' may refer to each of all components included in a plurality of A's, or each of some components included in a plurality of A's. .

1 is a diagram showing the configuration of a cargo device and a rehandling management system according to an embodiment of the present disclosure. As shown, the cargo device and rehandling management system 100 includes one or more shipper terminals 110, one or more reach stacker driver terminals 140, and via a network 150 with these terminals 110, 140. It may include one or more integrated device management servers 130 connected thereto. In addition, the cargo device and rehandling management system 100 may further include a device manager terminal 120 connected to each of the integrated device management servers 130 .

The shipper terminal 110 transmits cargo information (eg, ship information and container information) or carry-in/out information (eg, storage order information) to be loaded into the container yard or storage yard through the network 150 to the integrated device management server ( 130). Meanwhile, the reach stacker driver terminal 140 may transmit reach stacker driver information to the integrated device management server 130 through the network 150 through, for example, the mobile app 142 . In addition, the equipment yard operator terminal 120 provides equipment yard status information, know-how information of management personnel of the equipment yard operator (eg, matching of equipment and cargo to be rehandled with reach stackers, know-how information on equipment and rehandling scheduling, etc.) may be transmitted to the integrated device management server 130 through the network 150 .

The integrated device management server 130 includes cargo information or carry-in/out information received from the shipper terminal 110, reach stacker driver information received from the reach stacker driver terminal 140, and equipment storage received from the equipment manager terminal 120. Current status information, management staff know-how information, etc. may be stored and registered in a database (not shown). In addition, the integrated device management server 130 may additionally store and register information on the past, present, and scheduled tasks of the reach stacker in the database.

In addition, the integrated device management server 130, based on various information registered in the database, uses an artificial intelligence-based device and rehandling scheduling algorithm to determine the location of the device (or load) and the cargo to be rehandled and the device within a predetermined time. and the reach stacker may be matched to determine a device composed of an optimal route and a rehandling schedule. In one embodiment, the integrated device management server 130 schedules device and rehandling using a device and rehandling scheduling algorithm based on artificial intelligence-based reinforcement learning based on various information registered in the database. (hereinafter 'first device and rehandling schedule') may be determined.

The integrated device management server 130 may allocate device and rehandling information according to a device and rehandling schedule to each reachstacker driver terminal 140 . In addition, the integrated device management server 130 may provide cargo information or carry-in/out information, reach stacker driver information, equipment yard current status information, and past, present, and scheduled information of reach stacker jobs to the yard operator terminal 120 . Accordingly, the manager of the equipment manager operates a device and rehandling schedule (hereinafter referred to as 'second device and rehandling schedule') according to the know-how of the manager, based on the information provided through the equipment manager terminal 120. can decide

The manager of the device manager selects one of the first device and rehandling schedule and the second device and rehandling schedule, and sends the selected device and rehandling schedule to the integrated device management server through the device manager terminal 120. (130). Accordingly, the integrated device management server 130 may transmit the selected device and rehandling schedule information to the reach stacker driver terminal 140 . According to the device and rehandling schedule provided through the reach stacker driver terminal 140, the reach stacker driver loads the cargo matched to the corresponding reach stacker in a predetermined order or time at a predetermined location in the equipment yard or performs rehandling. can run

According to the configuration and operation of such a cargo device and rehandling management system, the location and reach of cargo and devices to be loaded and rehandled within a predetermined time based on cargo information, reach stacker and driver information, reach stacker operation information, equipment head information, etc. It is possible to build a device and rehandling schedule consisting of an optimal route by matching stackers. In addition, it is possible to improve scheduling inefficiency and irrational problems that may occur while determining loading and rehandling scheduling based on only the know-how of a manager of a conventional equipment yard operator.

2 is a schematic diagram illustrating a configuration in which an information processing system 230 according to an embodiment of the present disclosure is connected to communicate with a plurality of user terminals 210_1, 210_2, and 210_3. As shown, a plurality of user terminals (210_1, 210_2, 210_3) can be connected to the information processing system 230 that can provide a cargo device and rehandling management service through the network (220). Here, the plurality of user terminals 210_1, 210_2, and 210_3 may include a cargo device and a reach stacker driver's terminal (eg, a terminal executing a mobile app) to which a result of rehandling scheduling is provided. In one embodiment, the information processing system 230 is one or more server devices and/or servers capable of storing, providing, and executing computer-executable programs (eg, downloadable applications) and data related to cargo devices and rehandling management services, and the like. or a database, or one or more distributed computing devices and/or distributed databases based on cloud computing services.

The cargo device and rehandling management service provided by the information processing system 230 is provided to the user through a mobile app, web browser, or web browser extension program installed in each of the plurality of user terminals 210_1, 210_2, and 210_3. It can be. For example, the information processing system 230 provides information corresponding to a request for cargo/vehicle matching or scheduling information received from the user terminals 210_1, 210_2, and 210_3 through a mobile app or the like, or performs a corresponding process. can do.

A plurality of user terminals 210_1 , 210_2 , and 210_3 may communicate with the information processing system 230 through the network 220 . The network 220 may be configured to enable communication between the plurality of user terminals 210_1, 210_2, and 210_3 and the information processing system 230. Depending on the installation environment, the network 220 may be, for example, a wired network such as Ethernet, a wired home network (Power Line Communication), a telephone line communication device and RS-serial communication, a mobile communication network, a wireless LAN (WLAN), It may consist of a wireless network such as Wi-Fi, Bluetooth, and ZigBee, or a combination thereof. The communication method is not limited, and the user terminals (210_1, 210_2, 210_3) as well as a communication method utilizing a communication network (eg, mobile communication network, wired Internet, wireless Internet, broadcasting network, satellite network, etc.) that the network 220 may include ), short-range wireless communication between them may also be included.

In FIG. 2, a mobile phone terminal 210_1, a tablet terminal 210_2, and a PC terminal 210_3 are illustrated as examples of user terminals, but are not limited thereto, and the user terminals 210_1, 210_2, and 210_3 may perform wired and/or wireless communication. It can be any computing device that is capable of this and can have a mobile app, web browser or web browser extension installed and executed. For example, user terminals include AI speakers, smartphones, mobile phones, navigation devices, computers, laptop computers, digital broadcasting terminals, personal digital assistants (PDA), portable multimedia players (PMPs), tablet PCs, game consoles, It may include a wearable device, an internet of things (IoT) device, a virtual reality (VR) device, an augmented reality (AR) device, a set-top box, and the like. In addition, although three user terminals 210_1, 210_2, and 210_3 are shown in FIG. 2 to communicate with the information processing system 230 through the network 220, it is not limited thereto, and a different number of user terminals may be connected to the network ( 220) to communicate with the information processing system 230.

According to an embodiment, the information processing system 230 may receive a request for registration of reach stacker job information and reach stacker driver information from the plurality of user terminals 210_1, 210_2, and 210_3. Then, the information processing system 230 generates cargo and vehicle matching and scheduling information based on the information received in this way, and the plurality of user terminals 210_1, 210_2, and 210_3 (eg, reach stacker driver terminal) can be provided.

Figure 3 is a block diagram showing the internal configuration of the user terminal 210 and the information processing system 230 according to an embodiment of the present disclosure. The user terminal 210 may refer to any computing device capable of executing a mobile app, web browser, or web browser extension program and capable of wired/wireless communication, for example, the mobile phone terminal 210_1 of FIG. 2, It may include a tablet terminal 210_2, a PC terminal 210_3, and the like. As shown, the user terminal 210 may include a memory 312 , a processor 314 , a communication module 316 and an input/output interface 318 . Similarly, the information processing system 230 may include a memory 332 , a processor 334 , a communication module 336 and an input/output interface 338 . As shown in FIG. 3, the user terminal 210 and the information processing system 230 are configured to communicate information and/or data through the network 220 using respective communication modules 316 and 336. It can be. In addition, the input/output device 320 may be configured to input information and/or data to the user terminal 210 through the input/output interface 318 or output information and/or data generated from the user terminal 210.

The memories 312 and 332 may include any non-transitory computer readable media. According to one embodiment, the memories 312 and 332 are non-perishable mass storage devices such as random access memory (RAM), read only memory (ROM), disk drives, solid state drives (SSDs), flash memory, and the like. (permanent mass storage device) may be included. As another example, a non-perishable mass storage device such as a ROM, SSD, flash memory, or disk drive may be included in the user terminal 210 or the information processing system 230 as a separate permanent storage device separate from memory. In addition, the memories 312 and 332 may store an operating system and at least one program code (eg, a code for a cargo device installed and driven in the user terminal 210 and a rehandling management service).

These software components may be loaded from a computer readable recording medium separate from the memories 312 and 332 . A recording medium readable by such a separate computer may include a recording medium directly connectable to the user terminal 210 and the information processing system 230, for example, a floppy drive, a disk, a tape, a DVD/CD- It may include a computer-readable recording medium such as a ROM drive and a memory card. As another example, software components may be loaded into the memories 312 and 332 through a communication module rather than a computer-readable recording medium. For example, at least one program is loaded into the memories 312 and 332 based on a computer program installed by files provided by developers or a file distribution system that distributes application installation files through the network 220 . It can be.

The processors 314 and 334 may be configured to process instructions of a computer program by performing basic arithmetic, logic, and input/output operations. Instructions may be provided to processors 314 and 334 by memories 312 and 332 or communication modules 316 and 336 . For example, processors 314 and 334 may be configured to execute instructions received according to program codes stored in a recording device such as memory 312 and 332 .

The communication modules 316 and 336 may provide configurations or functions for the user terminal 210 and the information processing system 230 to communicate with each other through the network 220, and the user terminal 210 and/or information processing. System 230 may provide configurations or functions for communicating with other user terminals or other systems (eg, separate cloud systems, etc.). For example, requests or data generated by the processor 314 of the user terminal 210 according to program codes stored in a recording device such as the memory 312 (eg, cargo and reach stacker driver information registration requests, cargo and Vehicle matching and scheduling requests, etc.) may be transferred to the information processing system 230 through the network 220 under the control of the communication module 316 . Conversely, a control signal or command provided under the control of the processor 334 of the information processing system 230 passes through the communication module 336 and the network 220 and through the communication module 316 of the user terminal 210. It may be received by the user terminal 210 . For example, the user terminal 210 may receive matching information of cargo and reach stacker vehicles, device and rehandling scheduling information, etc. from the information processing system 230 through the communication module 316 .

The input/output interface 318 may be a means for interface with the input/output device 320 . As an example, the input device may include a device such as a camera, keyboard, microphone, mouse, etc. including an audio sensor and/or image sensor, and the output device may include a device such as a display, speaker, haptic feedback device, or the like. can As another example, the input/output interface 318 may be a means for interface with a device in which a configuration or function for performing input and output is integrated into one, such as a touch screen. For example, when the processor 314 of the user terminal 210 processes a command of a computer program loaded into the memory 312, information and/or data provided by the information processing system 230 or other user terminals are used. A service screen or the like configured as described above may be displayed on the display through the input/output interface 318 . Although the input/output device 320 is not included in the user terminal 210 in FIG. 3 , it is not limited thereto, and the user terminal 210 and the user terminal 210 may be configured as one device. In addition, the input/output interface 338 of the information processing system 230 is connected to the information processing system 230 or means for interface with a device (not shown) for input or output that the information processing system 230 may include. can be In FIG. 3, the input/output interfaces 318 and 338 are shown as separate elements from the processors 314 and 334, but are not limited thereto, and the input/output interfaces 318 and 338 may be included in the processors 314 and 334. there is.

The user terminal 210 and the information processing system 230 may include more components than those shown in FIG. 3 . However, there is no need to clearly show most of the prior art components. According to one embodiment, the user terminal 210 may be implemented to include at least some of the aforementioned input/output devices 320 . In addition, the user terminal 210 may further include other components such as a transceiver, a global positioning system (GPS) module, a camera, various sensors, and a database. For example, when the user terminal 210 is a smart phone, it may include components that are generally included in a smart phone, for example, an acceleration sensor, a gyro sensor, a camera module, various physical buttons, and a touch screen. Various components such as a button using a panel, an input/output port, and a vibrator for vibration may be implemented to be further included in the user terminal 210 . According to one embodiment, the processor 314 of the user terminal 210 may be configured to operate an application providing a cargo device and rehandling management service. At this time, codes related to the application and/or program may be loaded into the memory 312 of the user terminal 210 .

While programs for cargo devices and applications providing rehandling management services are running, the processor 314 connects the input/output interface 318 with a touch screen, a keyboard, a camera including an audio sensor and/or an image sensor, a microphone, etc. It is possible to receive text, image, video, voice and / or action input or selected through the input device of, and store the received text, image, video, voice and / or action in the memory 312 or communication module It can be provided to the information processing system 230 through 316 and the network 220. For example, the processor 314 receives an input for selecting cargo and reach stacker driver information registration, a cargo and reach stacker vehicle matching request, a device and rehandling scheduling request, and the like, and the communication module 316 and the network It can be provided to the information processing system 230 through 220.

The processor 314 of the user terminal 210 manages, processes, and/or stores information and/or data received from the input device 320, other user terminals, the information processing system 230, and/or a plurality of external systems. can be configured to Information and/or data processed by processor 314 may be provided to information processing system 230 via communication module 316 and network 220 . The processor 314 of the user terminal 210 may transmit and output information and/or data to the input/output device 320 through the input/output interface 318 . For example, the processor 314 may display the received information and/or data on the screen of the user terminal.

The processor 334 of the information processing system 230 may be configured to manage, process, and/or store information and/or data received from a plurality of user terminals 210 and/or a plurality of external systems. Information and/or data processed by the processor 334 may be provided to the user terminal 210 through the communication module 336 and the network 220 . In one embodiment, the processor 334 of the information processing system 230 converts cargo and reach stacker information to blockchain-based transportation information based on a request for registering cargo and reach stacker driver information from a plurality of user terminals 210. It is possible to register for a shared channel, and to provide the user terminal 210 with matching information of cargo and reach stacker vehicles generated based on the registered information, device and rehandling scheduling information, and the like.

The processor 334 of the information processing system 230 uses the output device 320 such as a display output capable device (eg, a touch screen, a display, etc.) or an audio output capable device (eg, a speaker) of the user terminal 210. It may be configured to output processed information and/or data. For example, the processor 334 of the information processing system 230 transmits cargo and reach stacker vehicle matching information, device and rehandling scheduling information, etc. to the user terminal 210 through the communication module 336 and the network 220. ), and may be configured to output the corresponding information through a display output capable device of the user terminal 210.

4 is a diagram showing the configuration of a processor of an information processing system according to an embodiment of the present disclosure. As shown, the processor 314 includes a shipper management module 410, a yard operator management module 420, a reach stacker management module 430, a yard management module 440, a device/rehandling scheduling module 450 , may include an order management module 460. In addition, the processor 314 is connected to the database 470, cargo information to be loaded in the container yard or storage yard (eg, ship information and container information) or carry-in/out information, reach stacker driver information, storage yard status information, storage yard The know-how information of management personnel of the operator (for example, equipment and cargo to be rehandled, equipment location, reach stacker matching, equipment and rehandling scheduling, etc.) is stored in the database 470 or stored in the database 470. ) can be extracted and processed.

The shipper management module 410 may store and manage cargo information received from the shipper terminal 110 , shipping company information related to the cargo, and carry-in/out information in the database 470 . In addition, the order management module 480 may store and manage cargo device and rehandling order information generated based on the carry-in/out information in the database 470 .

Each of the equipment manager management module 420 and equipment manager management module 440 may store and manage know-how information and equipment equipment status information received from the equipment manager terminal 120 in the database 470.

The reach stacker management module 430 may store and manage the reach stacker driver information received from the reach stacker driver terminal 140 in the database 470 .

The device/rehandling scheduling module 450 uses an artificial intelligence-based device and rehandling scheduling algorithm based on various information registered in the database 470, and the device (or load) and the cargo to be rehandled within a predetermined time. It is possible to determine a device composed of an optimal route and a rehandling schedule by matching the device location and reach stacker with the device location. In one embodiment, the device/rehandling scheduling module 450 uses a device and rehandling scheduling algorithm based on artificial intelligence-based reinforcement learning based on various information registered in the database 470. Devices and rehandling schedules can be determined.

5 is a diagram showing the configuration of a database of a cargo device and a rehandling management system according to an embodiment of the present disclosure. As shown, the database 470 includes a equipment manager information database 510, an order information database 520, a reach stacker information database 530, a shipper information database 540, and an automatic equipment/rehandling information database 550. , and may include a device head operator information database 560 .

The equipment manager information database 510 may store and manage equipment manager code, equipment manager status, warehouse cargo information, export cargo information, equipment equipment information, and person in charge information. The order information database 520 includes application date and time, order code, storage date, delivery date, number of storage days, customer name, person in charge of request, warehousing vehicle number, application type, approval status, delivery vehicle, delivery vehicle contact information, delivery shuttle request, Information on the date and time of gate-in, date and time of gate-out, information on the cargo information of the requested cargo, such as sealing number, weight, loading classification, size, container number, etc., and information on the shipping line related to the requested cargo, such as shipping line code, booking number, dock board It is possible to store and manage information about the location, mother ship voyage, docking date, etc. The reach stacker information database 530 may store and manage information on vehicle number, vehicle type, work status, vehicle manager, and the like. The shipper information database 540 may store and manage information about company information, order summary, and requirements. The automatic device/rehandling information database 550 may store and manage information about device request information, rehandling request information, device history, rehandling history, scheduling history, and the like. The equipment management company information database 560 may store and manage information about a company name, equipment management address, requirements, and the like.

6 is a diagram illustrating an example of a cargo device and rehandling management process according to one embodiment of the present disclosure. As shown, the cargo device and rehandling management process may begin with registering device and rehandling order information in a database (S610). In one embodiment, referring to FIGS. 1 to 5 , the integrated device management server 130 provides cargo information (e.g., container information, shipping line information) to be loaded in a container yard or storage yard received by the shipper terminal 110. etc.), or device and rehandling order information may be created in the database 470 and registered in the database 470 based on the carry-in/out information.

Next, device and rehandling scheduling may be executed (S620). That is, a device or cargo requiring rehandling may be matched with a device location and a reach stacker, and a route for moving the corresponding reach stacker to the device or a location to be rehandled may be determined. In one embodiment, referring to FIGS. 1 to 5 , the integrated device management server 130 uses an artificial intelligence-based device and rehandling scheduling algorithm based on various information registered in the database 470 to determine a predetermined It is possible to determine a device and rehandling schedule consisting of an optimal route by matching the device (or load), cargo to be rehandled, device location, and reach stacker within time. For example, the integrated device management server 130 uses a device and rehandling scheduling algorithm based on artificial intelligence-based reinforcement learning based on various information registered in a database to schedule device and rehandling ( Hereinafter, 'first device and rehandling schedule') may be determined.

In addition, a device and rehandling schedule may be recommended (S630). In one embodiment, referring to FIGS. 1 to 5 , the integrated device management server 130 may transmit a first device and a rehandling schedule to the device manager terminal 120 in order to recommend the manager of the device manager. there is. In addition, the integrated device management server 130 may provide cargo information or carry-in/out information, reach stacker driver information, device yard current status information, and past, present, and scheduled information of reach stacker jobs to the device yard operator terminal 120. . Accordingly, the manager of the equipment manager operates a device and rehandling schedule (hereinafter referred to as 'second device and rehandling schedule') according to the know-how of the manager, based on the information provided through the equipment manager terminal 120. can decide

Next, the equipment and rehandling schedule are determined, and the corresponding schedule information can be shared with management staff and reach stackers of the equipment store operator (S640). In one embodiment, referring to FIGS. 1 to 5 , the manager of the device manager selects one of the first device and rehandling schedule and the second device and rehandling schedule, and the device manager terminal 120 Through, the selected device and rehandling schedule may be transmitted to the integrated device management server 130 . Accordingly, the integrated device management server 130 may transmit the selected device and rehandling schedule information to the reach stacker driver terminal 140 . According to the device and rehandling schedule provided through the reach stacker driver terminal 140, the reach stacker driver loads the cargo matched to the corresponding reach stacker in a predetermined order or time at a predetermined location in the equipment yard or performs rehandling. can run

As described above, after the steps S610 to S640 are executed, device and rehandling information for the cargo remaining after the device and rehandling may be registered (S650). In addition, steps S620 to S640 may be additionally executed based on the device and rehandling information for the remaining cargo.

7 is a diagram illustrating an example of a learning process of automated cargo equipment and rehandling scheduling according to an embodiment of the present disclosure. As shown, the process of learning the autoloader and rehandling scheduling may begin with step 710 of registering the device and rehandling order information in a database. In one embodiment, referring to FIGS. 1 to 5 , the integrated device management server 130 provides cargo information (e.g., container information, shipping line information) to be loaded in a container yard or storage yard received by the shipper terminal 110. etc.), or device and rehandling order information may be created in the database 470 and registered in the database 470 based on the carry-in/out information.

Next, optimization learning of devices and rehandling schedules may be executed (720). For example, by using a reinforcement learning-based device and rehandling scheduling algorithm, a device or cargo requiring rehandling is matched with a device location and a reach stacker, and a route for moving the reach stacker to the device or a location to be rehandled is determined. can be determined In one embodiment, referring to FIGS. 1 to 5 , the integrated device management server 130 uses a reinforcement learning-based device and rehandling scheduling algorithm based on various information registered in the database 470 to determine a predetermined It is possible to determine a device and rehandling schedule consisting of an optimal route by matching the device (or load), cargo to be rehandled, device location, and reach stacker within time.

In addition, devices and rehandling schedules may be recommended (730). In one embodiment, referring to FIGS. 1 to 5 , the integrated device management server 130 may transmit a first device and a rehandling schedule to the device manager terminal 120 in order to recommend the manager of the device manager. there is. In addition, the integrated device management server 130 may provide cargo information or carry-in/out information, reach stacker driver information, device yard current status information, and past, present, and scheduled information of reach stacker jobs to the device yard operator terminal 120. . Accordingly, the manager of the equipment manager operates a device and rehandling schedule (hereinafter referred to as 'second device and rehandling schedule') according to the know-how of the manager, based on the information provided through the equipment manager terminal 120. can decide

Next, the device and rehandling schedule may be confirmed and executed (740). In one embodiment, referring to FIGS. 1 to 5 , the manager of the device manager selects one of the first device and rehandling schedule and the second device and rehandling schedule, and the device manager terminal 120 Through, the selected device and rehandling schedule may be transmitted to the integrated device management server 130 . Accordingly, the integrated device management server 130 may transmit the selected device and rehandling schedule information to the reach stacker driver terminal 140 . According to the device and rehandling schedule provided through the reach stacker driver terminal 140, the reach stacker driver loads the cargo matched to the corresponding reach stacker in a predetermined order or time at a predetermined location in the equipment yard or performs rehandling. can run

As described above, after steps 710 to 740 are executed, the determined device, rehandling schedule, and execution result may be updated in the database (710). The device and rehandling schedule updated in the database and the execution result may be used for optimization learning of the device and rehandling schedule (720). Steps 710 to 740 described above may be repeatedly executed multiple times so that the integrated device management server 130 may learn to further optimize the device and rehandling schedule.

8 is a diagram illustrating an example of a reinforcement learning process for automation of automated cargo equipment and rehandling scheduling according to an embodiment of the present disclosure. 8 illustrates a deep Q-network (DQN) learning process combining a Q-learning algorithm and a deep neural network (DNN) as an example of a reinforcement learning process for automation of automated cargo equipment and rehandling scheduling.

In the illustrated DQN learning process, a virtual agent 810 (e.g., a device rehandling scheduling module) interacts with a virtual device and a rehandling simulator (environment) 820 (e.g., a database) to: State ( s _t ) 812 can be observed. In addition, the agent 810 determines the policy ( π _θ (s, a)) 816 by applying the artificial neural network 814 to the observed state ( s _t ) 812, and according to the policy 816 Execute the action ( a _t ) to maximize the reward ( r _t ). Based on the result of such an action, the parameter θ of the artificial neural network 814 may be updated.

In the process shown in FIG. 8, the state s _t is information representing situation information (eg, cargo information, reach stacker and driver information, reach stacker job information, equipment head information, etc.) at time t and can affect the next state ( s _t+1 ). Also, the action a _t may be an action performed by the agent 810 at time t . For example, the agent 810 may execute an action for determining a device and a rehandling schedule consisting of an optimal route by matching a cargo to be loaded and rehandled with a device location and a reach stacker. Meanwhile, the policy ( π _θ (s, a)) 816 may mean a criterion used by the agent 810 at time t to execute device and rehandling scheduling. In addition, the reward ( r _t ) may represent a reward received by the agent 810 when the action ( a _t ) is executed at time ( t ).

Various reinforcement learning algorithms may be applied to the reinforcement learning process for automating automated cargo equipment and rehandling scheduling according to the present disclosure. 8 illustrates the use of DQN as an example of a reinforcement learning algorithm, but is not limited thereto, and TRPO (Trust Region Policy Optimization), DDPG (Deep Deterministic Policy Gradient), A3C (Asynchronous Advantage Actor Critic), PPO (Proximal Any one of various reinforcement learning algorithms such as Policy Optimization) may be used.

9 is a diagram illustrating an example of an automated cargo device and a rehandling management method according to an embodiment of the present disclosure. As shown, the automated cargo device and rehandling management method 900 may start with registering cargo information, reach stacker information, and device head information in a database (S910). In one embodiment, referring to FIGS. 1 to 8 , the integrated device management server 130 includes cargo information or carry-in/out information received from the shipper terminal 110, and the reach stacker received from the driver terminal 140. Information about the driver and current status information received from the device manager terminal 120 may be stored and registered in the database 470 .

Next, based on the information registered in the database, an artificial intelligence-based device/rehandling scheduling module may determine a first schedule that matches cargo, device location, and reach stacker, and includes a route to the device site ( S920). In one embodiment, referring to FIGS. 1 to 8 , the integrated device management server 130 uses an artificial intelligence-based device and rehandling scheduling algorithm based on various information registered in the database 470 to determine a predetermined It is possible to determine a device and rehandling schedule consisting of an optimal route by matching the device (or load), cargo to be rehandled, device location, and reach stacker within time. For example, the integrated device management server 130 uses a device learned according to the reinforcement learning process shown in FIG. 8 and a rehandling scheduling algorithm based on various information registered in the database 470, A handling schedule (first schedule) may be determined.

In addition, device/rehandling information according to any one of the first schedule and the second schedule received from the manager terminal may be transmitted to the reach stacker driver terminal (S930). In one embodiment, referring to FIGS. 1 to 8 , the integrated device management server 130 may allocate device and rehandling information according to the device and rehandling schedule to each reach stacker driver terminal 140. . In addition, the integrated device management server 130 may provide cargo information or carry-in/out information, reach stacker driver information, equipment yard current status information, and past, present, and scheduled information of reach stacker jobs to the yard operator terminal 120 . Accordingly, the manager of the equipment manager may determine the device and rehandling schedule (second schedule) based on the know-how of the manager based on the information provided through the equipment manager terminal 120 .

In one embodiment, the manager of the device manager selects one of the first schedule and the second schedule, and sends the selected device and rehandling schedule to the integrated device management server 130 through the device manager terminal 120. can be sent to Accordingly, the integrated device management server 130 may transmit the selected schedule information to the reach stacker driver terminal 140 . According to the schedule provided through the reach stacker driver terminal 140, the reach stacker driver may load or rehandle the cargo matched to the corresponding reach stacker in a predetermined order or time at a predetermined location in the equipment yard.

The above-described automated cargo device and rehandling management method or automated cargo device and rehandling scheduling learning process may be provided as a computer program stored in a computer readable recording medium to be executed by a computer. The medium may continuously store programs executable by a computer or temporarily store them for execution or download. In addition, the medium may be various recording means or storage means in the form of a single or combined hardware, but is not limited to a medium directly connected to a certain computer system, and may be distributed on a network. Examples of the medium include magnetic media such as hard disks, floppy disks and magnetic tapes, optical recording media such as CD-ROM and DVD, magneto-optical media such as floptical disks, and ROM, RAM, flash memory, etc. configured to store program instructions. In addition, examples of other media include recording media or storage media managed by an app store that distributes applications, a site that supplies or distributes various other software, and a server.

The methods, acts or techniques of this disclosure may be implemented by various means. For example, these techniques may be implemented in hardware, firmware, software, or combinations thereof. Those skilled in the art will appreciate that the various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the disclosure herein may be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate this interchange of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends on the particular application and design requirements imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementations should not be interpreted as causing a departure from the scope of the present disclosure.

In a hardware implementation, the processing units used to perform the techniques may include one or more ASICs, DSPs, digital signal processing devices (DSPDs), programmable logic devices (PLDs) ), field programmable gate arrays (FPGAs), processors, controllers, microcontrollers, microprocessors, electronic devices, and other electronic units designed to perform the functions described in this disclosure. , a computer, or a combination thereof.

Accordingly, the various illustrative logical blocks, modules, and circuits described in connection with this disclosure may be incorporated into a general-purpose processor, DSP, ASIC, FPGA or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or may be implemented or performed in any combination of those designed to perform the functions described in A general purpose processor may be a microprocessor, but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices, eg, a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other configuration.

In firmware and/or software implementation, the techniques include random access memory (RAM), read-only memory (ROM), non-volatile random access memory (NVRAM), PROM ( on a computer readable medium, such as programmable read-only memory (EPROM), erasable programmable read-only memory (EPROM), electrically erasable PROM (EEPROM), flash memory, compact disc (CD), magnetic or optical data storage device, or the like. It can also be implemented as stored instructions. Instructions may be executable by one or more processors and may cause the processor(s) to perform certain aspects of the functionality described in this disclosure.

Although the embodiments described above have been described as utilizing aspects of the presently-disclosed subject matter in one or more stand-alone computer systems, the disclosure is not limited thereto and may be implemented in conjunction with any computing environment, such as a network or distributed computing environment. . Further, aspects of the subject matter in this disclosure may be implemented in a plurality of processing chips or devices, and storage may be similarly affected across multiple devices. These devices may include PCs, network servers, and portable devices.

Although the present disclosure has been described in relation to some embodiments in this specification, various modifications and changes may be made without departing from the scope of the present disclosure that can be understood by those skilled in the art. Moreover, such modifications and variations are intended to fall within the scope of the claims appended hereto.

100: cargo device and rehandling management system
110: shipper terminal
120: device manager terminal
130: integrated device management server
140: reach stacker driver terminal
150: network

Claims (20)

A cargo device and rehandling management method using artificial intelligence performed by at least one processor, comprising:
Registering cargo information, reach stacker information, and equipment head information in a database;
Based on the information registered in the database, determining a first schedule including a route to a device site by matching a cargo, device location, and reach stacker with an AI-based device and a rehandling scheduling module; and
And transmitting device and rehandling information according to any one of the first schedule and the second schedule received from the device head operator terminal to the reach stacker driver terminal. According to claim 1,
Based on the information registered in the database, determining a first schedule including a route to a device site by matching cargo, device location, and reach stacker with an artificial intelligence-based device and rehandling scheduling module,
A method comprising: matching a cargo with a device location and a reach stacker using a reinforcement learning-based device and rehandling scheduling algorithm and determining a first schedule including a route to a device field. According to claim 1,
transmitting the cargo information, the reach stacker information, the equipment manager information, and the first schedule to a equipment manager terminal; and
The method further comprising receiving the second schedule determined based on the cargo information, the reach stacker information, and the equipment head information from the equipment manager terminal. According to claim 3,
The step of transmitting device and rehandling information according to any one of the first schedule and the second schedule received from the device head operator terminal to the reach stacker driver terminal,
and transmitting device and rehandling information according to a schedule selected by a manager of the equipment manager from among the first schedule and the second schedule to a reach stacker driver terminal through the equipment manager terminal. According to claim 1,
The method further comprising the step of registering device and rehandling information for cargo remaining after execution of device and rehandling according to the device and rehandling information in the database. A learning method of cargo device and rehandling using artificial intelligence performed by at least one processor, comprising:
registering device and rehandling order information in a database;
Based on the information registered in the database, by using an unsupervised learning or reinforcement learning-based device and rehandling scheduling algorithm, a first schedule including a route to a device field is determined by matching cargo with device location and reach stacker doing;
Transmitting device and rehandling information according to any one of the first schedule and the second schedule received from the device head operator terminal to a reach stacker driver terminal;
receiving a device executed according to the device and rehandling information and a rehandling execution result from the driver terminal;
updating the device and rehandling information and the device and rehandling execution result to the database; and
Learning the device and rehandling scheduling algorithm based on the device and rehandling information updated in the database and the device and rehandling execution result;
Including, method. According to claim 6,
Learning the device and rehandling scheduling algorithm based on the device and rehandling information updated in the database and the device and rehandling execution result,
interacting, by an agent, with the database to observe a state;
determining, by the agent, a policy using an artificial intelligence-based reinforcement learning algorithm based on the observed state;
executing, by the agent, an action according to the policy and receiving a reward according to the action; and
based on the result of the action and the reward, updating parameters of the artificial intelligence-based reinforcement learning algorithm to maximize the reward. According to claim 7,
The status includes current cargo information, reach stacker information, and equipment head information,
The action includes an operation of matching a cargo to be loaded and rehandled with a device location and a reach stacker to determine a device and a rehandling schedule consisting of an optimal route,
The method of claim 1 , wherein the policy includes criteria used to determine the device and rehandling schedule. As a cargo device and rehandling management system using artificial intelligence,
communication module;
Memory; and
at least one processor connected to the memory and configured to execute at least one computer readable program contained in the memory
including,
The at least one program,
Register cargo information, reach stacker information, and equipment yard information in the database,
Based on the information registered in the database, an artificial intelligence-based device and rehandling scheduling module determines a first schedule that matches cargo, device location, and reach stacker, and includes a route to the device site;
And one or more commands for transmitting device and rehandling information according to any one of the first schedule and the second schedule received from the device head operator terminal to the reach stacker driver terminal. According to claim 9,
Based on the information registered in the database, the AI-based device and rehandling scheduling module determines a first schedule that matches cargo, device location, and reach stacker and includes a route to the device site,
A system comprising matching a cargo with a device location and a reach stacker and determining a first schedule including a route to a device site using a reinforcement learning-based device and rehandling scheduling algorithm. According to claim 9,
The at least one program,
Transmitting the cargo information, the reach stacker information, the equipment manager information, and the first schedule to a equipment manager terminal;
The system further includes one or more commands for receiving the second schedule determined based on the cargo information, the reach stacker information, and the equipment head information from the equipment manager terminal. According to claim 11,
Transmitting the device and rehandling information according to any one of the first schedule and the second schedule received from the device head operator terminal to the reach stacker driver terminal,
and transmitting device and rehandling information according to a schedule selected by a manager of the equipment manager from among the first schedule and the second schedule to a reach stacker driver terminal through the equipment manager terminal. According to claim 9,
The at least one program,
The system further comprises one or more instructions for registering equipment and rehandling information for cargo remaining after execution of equipment and rehandling according to the equipment and rehandling information in the database. A computer program stored in a computer-readable recording medium to execute a cargo device and rehandling management method using artificial intelligence,
Registering cargo information, reach stacker information, and equipment head information in a database;
Based on the information registered in the database, determining a first schedule including a route to a device site by matching a cargo, device location, and reach stacker with an AI-based device and a rehandling scheduling module; and
A computer readable recording medium comprising one or more instructions for executing the step of transmitting the device and rehandling information according to any one of the first schedule and the second schedule received from the device head operator terminal to the reach stacker driver terminal. A computer program stored in . According to claim 14,
Based on the information registered in the database, determining a first schedule including a route to a device site by matching cargo, device location, and reach stacker with an artificial intelligence-based device and rehandling scheduling module,
Using a reinforcement learning-based device and rehandling scheduling algorithm, matching cargo, device location, and reach stacker and determining a first schedule including a route to the device field, a computer stored in a computer readable recording medium. program. According to claim 14,
transmitting the cargo information, the reach stacker information, the equipment manager information, and the first schedule to a equipment manager terminal; and
A computer stored in a computer readable recording medium further comprising one or more instructions for executing the step of receiving the second schedule determined based on the cargo information, the reach stacker information, and the equipment head information from the equipment manager terminal. program. According to claim 16,
The step of transmitting device and rehandling information according to any one of the first schedule and the second schedule received from the device head operator terminal to the reach stacker driver terminal,
Transmitting device and rehandling information according to a schedule selected by a manager of the equipment manager from among the first schedule and the second schedule to the reach stacker driver terminal through the equipment manager terminal, A computer program stored on a recordable medium. According to claim 14,
A computer stored in a computer readable recording medium further comprising one or more instructions for executing the step of registering the device and rehandling information for the remaining cargo after executing the device and rehandling information according to the device and rehandling information in the database. program. A computer program stored in a computer-readable recording medium to execute a learning method of cargo device and rehandling using artificial intelligence on a computer,
registering device and rehandling order information in a database;
Based on the information registered in the database, by using an unsupervised learning or reinforcement learning-based device and rehandling scheduling algorithm, a first schedule including a route to a device field is determined by matching cargo with device location and reach stacker doing;
Transmitting device and rehandling information according to any one of the first schedule and the second schedule received from the device head operator terminal to a reach stacker driver terminal;
receiving a device executed according to the device and rehandling information and a rehandling execution result from the driver terminal;
updating the device and rehandling information and the device and rehandling execution result to the database; and
A computer-readable recording medium comprising one or more instructions for executing the step of learning the device and rehandling scheduling algorithm based on the device and rehandling information updated in the database and a result of executing the device and rehandling. A computer program stored in . According to claim 19,
Learning the device and rehandling scheduling algorithm based on the device and rehandling information updated in the database and the device and rehandling execution result,
interacting, by an agent, with the database to observe a state;
determining, by the agent, a policy using an artificial intelligence-based reinforcement learning algorithm based on the observed state;
executing, by the agent, an action according to the policy and receiving a reward according to the action; and
Based on the result of the action and the reward, updating parameters of the artificial intelligence-based reinforcement learning algorithm to maximize the reward.

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