Crane control device and control method

Abstract

The present disclosure relates to a crane control device and control method, the device comprising: at least one memory including computer program instructions; and at least one processor for executing the computer program instructions, wherein the at least one processor is configured to: on the basis of work data received from a production management system, generate steel plate information regarding at least one steel plate and work instruction information regarding a hoisting work of attracting and lifting the steel plate by using a magnet port mounted on a crane; input the steel plate information and the work instruction information into a pre-trained artificial intelligence model to output a hoisting work condition for performing the hoisting work; and perform hoisting work control for the steel plate by supplying power to the magnet port on the basis of the hoisting work condition.

Description

Crane control device and control method

[0001] The present disclosure relates to a crane control device and a control method, and more specifically, to a crane control device and a control method capable of transporting steel plates using a crane.

[0002] A crane refers to a mechanical device that uses power to perform lifting, lowering, horizontal movement, slewing, and transporting objects. Depending on the support and operating methods, the workpiece, etc., it can be classified into various types, such as fixed and mobile, hydraulic and electric, grapple cranes, and magnet cranes.

[0003] Furthermore, when transporting steel plates using such cranes, there are cases where only a desired number of plates at a specific location must be handled; if this is controlled manually, time variations may occur depending on the operator, and the rework rate may increase.

[0004] In particular, in the case of quantity control using a magnet crane, there was a problem that automation control was difficult because there were various and complex variables depending on the work instructions and work environment, such as information regarding steel plates and magnet ports, as well as stacking information of steel plates and the degree of compression of steel plates before hoisting.

[0005] The present disclosure aims to provide a crane control device and a control method capable of automating the transport control of steel plates.

[0006] In addition, the present disclosure aims to provide a crane control device and a control method capable of lifting and transporting steel plates in a desired quantity.

[0007] In one aspect, the embodiments may provide a crane control device comprising at least one memory containing computer program instructions and at least one processor for executing computer program instructions, wherein the at least one processor generates steel plate information regarding at least one steel plate and work instruction information regarding a lifting operation in which the steel plate is sucked up and lifted using a magnet port mounted on the crane based on work data received from a production management system, inputs the steel plate information and work instruction information into a pre-trained artificial intelligence model to output lifting operation conditions for performing the lifting operation, and supplies power to the magnet port based on the lifting operation conditions to perform lifting operation control for the steel plate.

[0008] In another aspect, the embodiments may provide a crane control method comprising: an information generation step of generating steel plate information regarding at least one steel plate and work instruction information regarding a lifting operation of adsorbing and lifting a steel plate using a magnet port mounted on a crane based on work data received from a production management system; a work condition output step of inputting the steel plate information and work instruction information into a pre-trained artificial intelligence model to output a lifting operation condition for performing the lifting operation; and a work control step of supplying power to the magnet port based on the lifting operation condition to perform a lifting operation control for the steel plate.

[0009] According to the present disclosure, a crane control device and a control method capable of automating the transportation control of steel plates can be provided.

[0010] In addition, according to the present disclosure, a crane control device and a control method capable of lifting and transporting steel plates in a desired number can be provided.

[0011] FIG. 1 is a diagram illustrating the configuration of a crane control system according to the present disclosure in an exemplary manner.

[0012] FIG. 2 is a block diagram relating to an exemplary configuration of a computing system used in the present disclosure.

[0013] FIG. 3 is a block diagram relating to another exemplary configuration of a computing system used in the present disclosure.

[0014] FIG. 4 is a drawing for exemplarily illustrating the interface of a crane control device according to one embodiment.

[0015] FIG. 5 is a drawing illustrating an embodiment of performing a steel plate lifting operation using a crane control device.

[0016] FIG. 6 is a drawing illustrating another embodiment of performing a steel plate lifting operation using a crane control device.

[0017] Figure 7 is a graph showing the change over time of hoisting control according to one embodiment.

[0018] FIG. 8 is a flowchart relating to a crane control method according to the present disclosure.

[0019] FIG. 9 is a flowchart illustrating, by way of example, the process of controlling a magnet port using a crane control method according to one embodiment.

[0020] FIG. 10 is a flowchart illustrating, by way of example, a method for performing purchase control according to one embodiment.

[0021] Hereinafter, some embodiments of the present disclosure will be described in detail with reference to the exemplary drawings. In assigning reference numerals to the components of each drawing, the same components may have the same reference numeral as much as possible, even if they are shown in different drawings. Furthermore, in describing the embodiments, if it is determined that a detailed description of related known components or functions may obscure the essence of the technical concept, such detailed description may be omitted. Where terms such as "comprising," "having," or "consisting of" are used in this specification, other parts may be added unless "only" is used. Where a component is expressed in the singular, it may include a plural unless otherwise specified.

[0022] Additionally, terms such as first, second, A, B, (a), (b), etc., may be used to describe the components of the present disclosure. These terms are used merely to distinguish the components from other components, and the nature, order, sequence, or number of the components are not limited by such terms.

[0023] In describing the positional relationship of components, where it is stated that two or more components are "connected," "combined," or "joined," it should be understood that while the two or more components may be directly "connected," "combined," or "joined," they may also be "connected," "combined," or "joined" with other components "intervened." Here, the other components may be included in one or more of the two or more components that are "connected," "combined," or "joined" with one another.

[0024] In describing the temporal flow relationship regarding components, methods of operation, or methods of production, for example, when the temporal or sequential relationship is described using "after," "following," "next," or "before," it may include cases where the relationship is not continuous unless "immediately" or "directly" is used.

[0025] Meanwhile, where numerical values ​​or corresponding information regarding a component (e.g., levels, etc.) are mentioned, even without separate explicit notation, the numerical values ​​or corresponding information may be interpreted as including a range of error that may occur due to various factors (e.g., process factors, internal or external shocks, noise, etc.).

[0026] FIG. 1 is a diagram illustrating the configuration of a crane control system according to the present disclosure in an exemplary manner.

[0027] Referring to FIG. 1, a crane control system (100) according to the present disclosure may include a production management system (110), a crane control device (120), and a crane device (130). The production management system (110), the crane control device (120), and the crane device (130) may be physically connected to each other or through a communication network.

[0028] The production management system (110) may include a configuration for integrally managing each stage of production activities, and for this purpose, it may transmit and receive various information and work instructions, etc., to other devices or other systems.

[0029] For example, the production management system (110) may include a production planning module, a production progress management module, and a product shipment management module to comprehensively manage all stages of production activities as a PMS (Production Management System).

[0030] For example, the production management system (110) may include a Manufacturing Execution System (MES). In this case, the production management system (110) may include a work instruction module, a material management module, an equipment and quality management module, a production process tracking module, a product history tracking module, etc., for managing actual production activities occurring at the production site, and additionally may include a database for storing and managing data generated or transmitted and received from the MES, and a communication module for transmitting and receiving data.

[0031] For example, the production management system (110) can transmit work instruction information regarding the number of steel plates adsorbed to the crane control device (120) based on a predetermined steel plate production plan, and can receive monitoring information regarding whether work is performed according to the work instruction information, information regarding the results of the work performance, etc., from sensors installed in the crane control device (120) and the crane device (130).

[0032] The crane control device (120) can transmit and receive data with the production management system (110) and the crane device (130), generate certain information based on the received data, output work conditions regarding work using the crane device (130), and control the crane device (130) to perform work according to the work conditions.

[0033] For example, the crane control device (120) can receive work data from a production management system and can generate steel plate information and work instruction information based on the received work data, and can control the output of lifting work conditions by inputting the steel plate information and work instruction information into an artificial intelligence model, and can perform lifting work control for the crane device (130) based on the lifting work conditions.

[0034] In this case, hoisting operation can generally be defined as the operation of lifting an object. However, in the hoisting operation of a magnet crane, even when the magnet port is lifted without adsorbing an object such as a steel plate, the magnet port is still lifted; therefore, this case can also be defined as being included in the hoisting operation.

[0035] The specific configuration of the crane control device (120) described above will be explained in more detail below in FIG. 3.

[0036] The crane device (130) is a device capable of performing actions such as lifting and lowering an object or moving it horizontally through a power supply, and may include a grapple crane and a magnet crane depending on the object of operation.

[0037] For example, when the crane device (130) includes a magnet crane, it may include at least one magnet port, and the magnet ports may be moved up and down or horizontally, and may include a configuration in which power is supplied to each magnet port to generate magnetic force, and the magnet port is used to attract and move a magnetic object. Specifically, in the embodiment of FIG. 1, the crane device (130) may include magnet port A (132), magnet port B (134), and magnet port C (136).

[0038] For example, each magnet port (132, 134, 136) may include an electromagnet capable of generating magnetic force upon power supply, and may be used to perform a lifting operation by adsorbing and lifting magnetic steel plates (142, 144) using such an electromagnet. Specifically, in the embodiment of FIG. 1, a lifting operation can be performed on stacked steel plates A (142) and steel plates B (144) using magnet port A (132), magnet port B (134), and magnet port C (136).

[0039] Depending on the case, the hoisting operation may target one steel plate in a single operation, or two or more steel plates in a single operation. Additionally, when performing a hoisting operation on two or more steel plates, the hoisting operation may be performed on all of the stacked steel plates, or the hoisting operation may be performed on only a predetermined number of steel plates among the stacked steel plates.

[0040] For example, the lifting operation may be performed only on steel plate A (142) using magnet port A (132), magnet port B (134), and magnet port C (136), or the lifting operation may be performed on both steel plate A (142) and steel plate B (144).

[0041] For example, the crane control device (120) may perform a lifting operation using all of the magnet ports (132, 134, 136) mounted on the crane device (130), or in some cases, may perform a lifting operation using only some of the magnet ports (132, 134, 136).

[0042] For example, the crane control device (120) can perform a lifting operation by taking into account the length of the steel plate and using only the magnet ports among all magnet ports that are included in the steel plate length range based on the length direction.

[0043] For example, if the lengths of steel plate A (142) and steel plate B (144) are smaller than the maximum length of steel plate that can be lifted by the crane device (130) as shown in FIG. 1, and are located only below magnet port A (132) and magnet port B (134), the crane control device (120) can perform the lifting operation using only magnet port A (132) and magnet port B (134) by taking into account the lengths of steel plate A (142) and steel plate B (144).

[0044] For example, the crane control device (120) can perform lifting operations by outputting different power supply conditions for each magnet port based on steel plate information.

[0045] For example, the crane control device (120) can output different power supply conditions for each magnet port based on steel plate length information. An example related to this is to be explained by the case where the distance between magnet port A (132) and magnet port B (134), and between magnet port B (134) and magnet port C (136) is 0.8m each, and the distance between magnet port A (132) and magnet port C (136) is 1.6m.

[0046] For example, if the length of the steel plate is 1m, the crane control device (120) can output power supply conditions such that power is supplied only to magnet port A (132) and magnet port B (134), and power is not supplied to magnet port C (136). In this case, power supply conditions of the same size can be output to magnet port A (132) and magnet port B (134), respectively.

[0047] As another example, when the length of the steel plate is 2m, the crane control device (120) can output power supply conditions to supply power to all of the magnet ports A (132), B (134), and C (136). In this case, the magnet ports A (132) and C (136) that lift the ends can output power supply conditions relatively larger, and the magnet port B (134) that does not lift the ends can output power supply conditions relatively smaller. In this way, by outputting different power supply conditions for each magnet port according to the steel plate information and work instruction information, the lifting operation can be performed more safely.

[0048] Meanwhile, the present disclosure may include a crane control device for implementing the aforementioned device and / or method. For example, the crane control device may be implemented as a computing system.

[0049] FIG. 2 is a block diagram relating to an exemplary configuration of a computing system used in the present disclosure. A computing system or computing device may be used to include or implement its components, such as a system or a data processing system.

[0050] A computing system includes a bus or other communication component for transmitting information, and a processor or processing circuit connected to the bus to process information. A computing system may also include one or more processors or processing circuits connected to the bus to process information. A computing system also includes main memory, such as random access memory (RAM) or other dynamic storage devices connected to the bus to store information, and instructions (instructions) to be executed by the processor. The main memory may be or may contain a data store. The main memory may also be used to store location information, temporary variables, or other intermediate information during the execution of instructions by the processor. A computing system may further include ROM or other static storage devices connected to the bus to store static information and instructions for the processor. Storage devices, such as solid-state devices, magnetic disks, or optical disks, may be coupled to the bus to continuously store information and instructions. A storage device may include or be part of a data store.

[0051] For example, a computing system may include at least one computing device. For instance, it may include various computer devices such as smartphones, tablets, laptops, desktops, servers, and clients. In this case, the computing device may be a single stand-alone device, or it may be a configuration including multiple computing devices operating in a distributed environment composed of multiple computing devices that cooperate with each other via a communication network.

[0052] Meanwhile, a computing device may be a quantum computing device rather than a classical computing device. Quantum computing devices perform operations in units of qubits rather than bits. A qubit can have a state in which 0 and 1 are simultaneously superpositioned, and if there are M qubits, 2^M states can be represented simultaneously.

[0053] A quantum computing device can use various types of quantum gates (e.g., Pauli / Rotation / Hadamard / CNOT / SWAP / Toffoli) that receive one or more qubits to perform quantum operations and perform specified operations, and can combine quantum gates to form a quantum circuit with a special function.

[0054] Quantum computing devices can use quantum artificial neural networks (e.g., QCNN, QGRNN) that can perform functions of conventional artificial neural networks (e.g., CNN, RNN) at a faster speed while using fewer parameters.

[0055] In some cases, the computing system may further include a Graphic Processing Unit (GPU). In this case, the GPU can process image data at high speed and may include a configuration specialized for parallel processing of data, floating-point operations, and matrix-based operations for training and inference of artificial intelligence models.

[0056] Data may be stored in the memory, and at least one of volatile memory (e.g., SRAM, DRAM) or non-volatile memory (e.g., NAND Flash) may be included. For example, the memory may include RAM (Random Access Memory) and ROM (Read-Only Memory). In this case, RAM may include both volatile memory that allows reading and writing of data, and ROM may include both non-volatile memory that allows only reading of data but retains data even when the power of the computing system is turned off.

[0057] For example, data can include text and images, executable programs and code, etc. In some cases, data may be stored not only in memory but also on separate high-capacity storage servers.

[0058] For example, memory may be a medium for storing computer-readable software, applications, program modules, routines, instructions, and / or data, etc., which are coded to perform a specific task when executed by a processor. And the processor may read and execute computer-readable software, applications, program modules, routines, instructions, and / or data, etc., stored in memory.

[0059] In some cases, artificial intelligence models may be stored in memory. In this case, the AI ​​models may include models where supervised learning or unsupervised learning is performed. For example, if supervised learning is performed on an AI model, it may further include annotation-based learning or data labeling.

[0060] For example, when a computing system performs various calculations or data classification and generation tasks using an artificial intelligence model, it may utilize an artificial intelligence model stored in the computing system's memory.

[0061] The processor can extract specific data from data stored in memory or generate new data. For example, the processor can perform the task of classifying at least one preset class from multiple images stored in memory. As an example, the processor can execute an artificial intelligence model stored in memory to perform class classification for each image and output the class classification result to generate new data.

[0062] A computing system can be connected to input devices and displays. For example, a computing system can be connected to a display, such as a liquid crystal display or an active matrix display, to display information to a user via a bus, and an input device, such as a keyboard containing alphanumeric and other keys, can be connected to the bus to transmit information and command selections to the processor.

[0063] For example, the input device may include a touch screen display. The input device may also include cursor controls, such as a mouse, trackball, or cursor direction keys, for transmitting direction information and command selection to a processor and controlling cursor movement on the display.

[0064] For example, an input device may include a configuration that allows a user to input a command to the processor to execute a specific task or to input data necessary for the execution of a specific task. For example, the input device may include a physical or virtual keyboard or keypad, key buttons, a mouse, a joystick, a trackball, a touch-sensitive input means, or a microphone.

[0065] For example, the display may be part of a data processing system, a client computing device, or other components. For example, the display may include a visual display device, a printer, a speaker, or a vibrating device.

[0066] The processes, systems, and methods described herein may be implemented by a computing system in response to a processor executing an array of instructions contained in main memory. These instructions may be read into main memory from other computer-readable media, such as storage devices. The execution of the array of instructions contained in main memory causes the computing system to perform the exemplary processes described herein. In a multiprocessing array, one or more processors may also be used to execute instructions contained in main memory. Hardwired circuits may be used in place of or in conjunction with hardware instructions with the systems and methods described herein. The systems and methods described herein are not limited to any specific combination of hardware circuits and software.

[0067] Although exemplary computing systems have been described above, the essence including the operations described herein may be implemented in other types of digital electronic circuits, or in computer software, firmware, or hardware including structures disclosed herein and structural equivalents thereof or combinations of one or more of these.

[0068] "Data processing system," "computing device," "module," "engine," "component," or "computing device" includes various devices, devices, and machines for processing data, including, for example, a programmable processor, a computer, a system on a chip, or a number of such things, or a combination thereof.

[0069] For example, a processor may include special-purpose logic circuits. For example, it may include a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), a digital signal processor (DSP), digital signal processing devices (DSPD), a programmable logic device (PLD), etc.

[0070] In some cases, the processor may further include a separate artificial intelligence semiconductor device for processing tasks using an artificial intelligence model. For example, the artificial intelligence chip may include special-purpose logic circuits such as FPGA, ASIC, DSP, DSPD, PLD, etc., and may be designed to be specialized for learning or inference tasks using an artificial intelligence model.

[0071] For example, the present disclosure may be implemented using an artificial intelligence semiconductor device in which neurons and synapses of a deep neural network are implemented using semiconductor devices. In this case, the semiconductor devices may be currently used semiconductor devices, such as SRAM, DRAM, NAND, etc., or next-generation semiconductor devices, such as RRAM, STT MRAM, PRAM, etc., or a combination thereof. Furthermore, when the present disclosure is implemented using an artificial intelligence semiconductor, the results (weights) of training a deep learning model with software may be transferred to synapse mimic devices arranged in an array, or training may be performed on the artificial intelligence semiconductor device.

[0072] In addition to these hardware configurations, code that creates an execution environment for computer programs may also be included, for example, code that constitutes processor firmware, a protocol stack, a database management system, an operating system, a cross-platform runtime environment, a virtual machine, or a combination of one or more of these. The device and execution environment may realize various different computing model infrastructures, such as web services, distributed computing, and grid computing infrastructures. For example, a content request module, a content rendering module, or a rendered content delivery module may include or share one or more data processing units, systems, computing units, or processors. Components of the system may include or share one or more data processing units, systems, computing units, or processors.

[0073] A computer program (also known as a program, software, software application, app, script, or code) may be written in any form of programming language, including compiled or interpreted languages, declarative or procedural languages, and may be distributed as a standalone program or in any form including modules, components, subroutines, objects, or other units suitable for use in a computing environment. A computer program may or may not correspond to a file in a file system. A computer program may be stored in a file containing other programs or data (e.g., one or more scripts stored in a markup language document), a single file dedicated to that program, or a portion of a file containing multiple coordinated files (e.g., files storing one or more modules, subprograms, or parts of code). A computer program may be distributed to be executed on a single computer or a single site, or on multiple computers distributed across multiple sites and interconnected by a communication network.

[0074] The processes and logic flows described herein may be performed by one or more programmable processors that execute one or more computer programs (e.g., components of a data processing system) to perform actions by operating input data and generating outputs. The processes and logic flows may also be performed by special-purpose logic circuits, e.g., FPGAs, ASICs, DSPs, DSPDs, or PLDs, and devices may also be implemented by special-purpose logic circuits. Devices suitable for storing computer program instructions and data include all forms of non-volatile memory, media, and memory devices, such as semiconductor memory devices like EPROM, EEPROM, and flash memory devices; magnetic disks, e.g., internal hard disks or removable disks; optomagnetic disks; and CD-ROM and DVD-ROM disks. Processors and memory may be complemented or integrated by special-purpose logic circuits.

[0075] FIG. 3 is a block diagram relating to another exemplary configuration of a computing system used in the present disclosure.

[0076] Referring to FIG. 3, a computing system according to the present disclosure may be connected to a server system. For example, a server system may be connected to the computing system according to the present disclosure via a network via wired or wireless means to transmit and receive data and share computing resources.

[0077] For example, a server system can be built in the form of a cloud system. For instance, the server system may include a configuration that allows individual computing devices to connect to the server system via a network and shares computing resources with the connected devices. In this case, individual computing devices can access the cloud system from anywhere as long as they are connected to a network, such as the Internet.

[0078] For example, a cloud system can provide computing resources by flexibly scaling them up or down as needed, and can share these resources with other computing devices connected via a network. Furthermore, depending on the purpose or scope of use, a cloud system can be built based on various service models such as IaaS (Infrastructure as a Service), PaaS (Platform as a Service), and SaaS (Software as a Service).

[0079] For example, a cloud system may include at least one computing device, a storage device, and a network device. Each computing device included in the cloud system may include a processor and memory to enable processing various computing tasks, the storage device may include configurations related to data storage such as an HDD (Hard Disk Drive), SSD (Solid State Drive), NAS (Network Attached Storage), or SAN (Storage Area Network) for storing large amounts of data, and the network device may include configurations related to networking such as a switch, a router, a load balancer, and a firewall.

[0080] For example, when a computing system performs various calculations or data classification and generation tasks using an artificial intelligence model, it may utilize an artificial intelligence model stored in the computing system's memory.

[0081] Alternatively, in some cases, a computing system may share computing resources from a cloud system and utilize artificial intelligence models stored in the cloud system. In this case, the computing system can process related tasks by using the artificial intelligence models provided by the cloud system, even if it does not directly possess the configurations related to the artificial intelligence models itself.

[0082] For example, a crane control device according to the present disclosure comprises at least one memory including computer program instructions and at least one processor for executing computer program instructions, wherein the at least one processor generates steel plate information regarding at least one steel plate and work instruction information regarding a lifting operation in which the steel plate is sucked up and lifted using a magnet port mounted on a crane based on work data received from a production management system, inputs the steel plate information and work instruction information into a pre-trained artificial intelligence model to output lifting operation conditions for performing the lifting operation, and supplies power to the magnet port based on the lifting operation conditions to perform lifting operation control for the steel plate.

[0083] The processor can generate steel plate information regarding at least one steel plate and work instruction information regarding a lifting operation in which the steel plate is sucked up and lifted using a magnet port mounted on a crane, based on work data received from a production management system.

[0084] The steel plate information may include information regarding a steel plate located at a specific pre-set location. Alternatively, depending on the case, it may include information regarding a steel plate detected by a sensor mounted on a crane. Here, the steel plate may be a single sheet stacked, or two or more sheets stacked vertically.

[0085] For example, the steel plates referred to in this disclosure may include all types of steel plates capable of being lifted by a crane. For example, the steel plates may include thick plates with a relatively high thickness, and in some cases, medium plates or thin plates may also be lifted by the crane control device according to this disclosure.

[0086] For example, steel plate information may include information regarding the size, location, type, etc. of each laminated steel plate. For instance, steel plate information may include steel plate location information, steel plate width information, steel plate length information, steel plate thickness information, steel plate weight information, and steel plate type information. Depending on the case, it may further include steel plate surface condition information regarding the flatness or presence of defects, steel plate production information regarding the production date, production location, or manufacturer, and steel plate quality inspection information. This steel plate information can be input into an artificial intelligence model and used to output hoisting operation conditions.

[0087] For example, the steel plate information may include system steel plate information generated based on work data and sensing steel plate information sensed from an image sensor mounted on a crane. In this case, the system steel plate information and the sensing steel plate information may match each other, or differences may appear depending on the circumstances.

[0088] Here, whether there is a match or a difference can be determined by judging that the two pieces of information match if the difference between the system steel plate information and the sensing steel plate information is less than a preset steel plate threshold range, and that a difference occurs if it is greater than or equal to that range. Additionally, depending on the case, the steel plate threshold may be set to a specific value for each type of steel plate information, or it may be set to a specific ratio value so as not to be affected by the type of steel plate information.

[0089] For example, the steel plate width threshold and steel plate length threshold can be set to 100 mm, the steel plate thickness threshold to 1 mm, and the steel plate weight threshold to 0.1 ton, respectively. As another example, the steel plate threshold can be set to 5% based on the system steel plate information.

[0090] In addition, depending on the case, steel plate information may include non-quantified information. For example, steel plate information may include information that is not recorded as quantitative figures, such as steel plate type information, steel plate surface condition information, steel plate production information, and steel plate quality inspection information. In such cases, whether there is a difference between the system steel plate information and the sensed steel plate information can be determined by whether the contents included in each piece of information match.

[0091] The work instruction information may include information regarding the contents of the work instruction when performing a lifting operation on a steel plate using a magnet port. For example, the work instruction information may include information on the steel plate to be lifted, quantity instruction information, interval instruction information, and pressing time instruction information.

[0092] For example, the information on steel plates to be hoisted may include information regarding the steel plates among those included in the steel plate information that are the target of the hoisting operation. For instance, if steel plates are loaded at points a, b, and c of a workstation, and a hoisting operation is to be performed on the steel plates loaded at point a, the steel plate information received from the MES includes information regarding all steel plates loaded at points a, b, and c, but the information on steel plates to be hoisted may include information regarding a work instruction to perform the hoisting operation only on the steel plates loaded at point a.

[0093] For example, the purchase instruction information may include information regarding the number of steel plates to be subject to the lifting operation. For example, if steel plates A, B, and C are loaded at point a, and lifting operations are to be performed only on steel plates A and B, the purchase instruction information may include information regarding a work instruction to perform lifting operations on two steel plates, A and B, at point a.

[0094] For example, the spacing indication information may include information regarding the vertical spacing between the magnet port and the steel plate during the hoisting operation. In this case, the vertical spacing between the magnet port and the steel plate can be sensed using a sensor mounted on the crane. For instance, if the vertical spacing is positive, it can be determined by sensing how high the magnet port is floating above the upper surface of the steel plate using an image sensor mounted on the crane; if the vertical spacing is negative, it can be determined by estimating the degree of pressure by sensing how much the magnet port is pressing against the upper surface of the steel plate using a load cell mounted on the crane, taking into account the type of steel plate and other factors.

[0095] For example, if the spacing instruction information is positive, it may include information regarding a work instruction to vertically move the magnet port so that the magnet port is suspended above the steel plate by a spacing corresponding to the spacing instruction information.

[0096] For example, the load cell can sense the weight of the object being lifted when the magnet port lifts the object, and in this case, the sensed value may appear as a positive number. Additionally, the load cell can sense the degree to which the object is pressed by the magnet port when the magnet port presses the object, and in this case, the sensed value may appear as a negative number, and using this, steel plate pressing control can be performed to control the degree of pressing of the steel plate by the magnet port.

[0097] As another example, when the gap instruction information is negative, the information may include a work instruction for vertically moving the magnet port so that the crane's tension force on the magnet port is controlled so that the magnet port presses against the steel plate, and the magnet port presses the steel plate by the gap corresponding to the gap instruction information.

[0098] For example, the lifting operation of a steel plate using a magnet port may be performed with the magnet port and the steel plate separated by a certain distance, or with them in close contact, and in some cases, with the magnet port pressing the steel plate to a certain depth.

[0099] For example, the pressing time instruction information may include information regarding the time for performing steel plate pressing control when the magnetic port is instructed to perform a lifting operation while pressing the steel plate to a preset pressing depth. For example, if the pressing time is instructed to be relatively long, it may include information regarding a work instruction to have the magnetic port press the steel plate relatively slowly to reach the pressing depth. As another example, if the pressing time is instructed to be relatively short, it may include information regarding a work instruction to have the magnetic port press the steel plate relatively quickly to reach the pressing depth.

[0100] The processor can input steel plate information and work instruction information into a pre-trained artificial intelligence model to output hoisting operation conditions for performing a hoisting operation. For example, the hoisting operation conditions may include a required hoisting weight condition, a power supply condition regarding the power supplied to the magnet port to hoist the steel plate, a vertical spacing condition, and a pressing control condition.

[0101] For example, the required lifting weight condition may include conditions regarding the weight of the steel plate on which the crane will perform the lifting operation using a magnet port. For example, if there is one steel plate, the required lifting weight condition may be output based on the steel plate information and work instruction information of the steel plate. For another example, if there are two or more steel plates, the required lifting weight condition may be output based on the steel plate information and work instruction information of each of the steel plates.

[0102] For example, if there are two steel plates and the information for each steel plate is the same, the required lifting weight condition can be output based on weight information obtained by multiplying the steel plate weight information by 2. For another example, if there are two steel plates and the information for each steel plate is different, the required lifting weight condition can be output based on weight information obtained by summing the weight information of each of the two steel plates.

[0103] For example, power supply conditions may include conditions regarding the power supplied to the magnet port to lift the steel plate. In addition, steel plate information and work instruction information can be input into an artificial intelligence model and used to output lifting operation conditions.

[0104] For example, the steel plate information may include steel plate weight information regarding the weight of the steel plate, and the work instruction information may include quantity instruction information regarding the quantity of steel plates for which a hoisting operation is required; the hoisting operation condition may include power supply conditions regarding the power supplied to the magnet port to hoist the steel plate; and when the steel plate weight information and quantity instruction information are input, the artificial intelligence model may be trained to estimate the magnitude of the power that must be supplied to the magnet port to hoist the quantity of steel plates corresponding to the quantity instruction information and output this as one of the power supply conditions.

[0105] In this case, the power supply conditions may be output differently depending on the number of steel plates to be hoisted. For example, if there is one steel plate, the power supply conditions may be output based on the steel plate information and work instruction information of the steel plate. As another example, if there are two or more steel plates, the power supply conditions may be output based on the steel plate information and work instruction information of each of the steel plates.

[0106] For example, if there are two steel plates and the information for each steel plate is the same, the power supply condition can be output based on weight information obtained by multiplying the weight information of the steel plates by 2. For another example, if there are two steel plates and the information for each steel plate is different, the power supply condition can be output based on weight information obtained by summing the weight information of each of the two steel plates.

[0107] In addition, depending on the case, if two or more steel plates are of different types, power supply conditions may be output by further considering the stacking order. For example, when performing a hoisting operation on a total of two steel plates, Steel Plate A with a width of 1 m, a length of 2 m, a thickness of 20 mm, and a weight of 2 tons, and Steel Plate B with a width of 1 m, a length of 2 m, a thickness of 10 mm, and a weight of 1 ton, the power supply conditions when the plates are stacked in the order of Steel Plate A-Steel Plate B from top to bottom may be output differently from the power supply conditions when the plates are stacked in the order of Steel Plate B-Steel Plate A.

[0108] For example, power supply conditions may include power profile conditions that are output in a form indicating a change over time with respect to the amount of power supplied to the magnet port.

[0109] For example, the power profile conditions may include a hoisting control power profile condition that indicates a change over time with respect to the amount of power supplied to the magnet port during the process of performing hoisting operation control for hoisting a steel plate using the magnet port.

[0110] In some cases, the power profile conditions may include a press control power profile condition that indicates a change over time with respect to the amount of power supplied to the magnet port during the process of performing steel plate press control when steel plate press control is performed.

[0111] For example, a push control power profile condition may include a condition for performing push control by supplying power to a magnet port based on a change in the amount of power supplied over time included in the power profile condition.

[0112] Meanwhile, when outputting power supply conditions, the power source to be targeted may include all parameters that can be controlled when supplying electricity to the crane device, such as voltage, current, and power. Furthermore, in this case, the power source may include cases where electricity is supplied to the crane device to hoist or lower the magnet port, or to change the tensile force on the magnet port, as well as cases where electricity is supplied to generate magnetic force on the magnet port.

[0113] For example, the artificial intelligence model can control the crane device to supply power of a specific size, to allow a current of a specific size to flow, or to generate a voltage of a specific size in order to raise or lower the magnet port. As another example, the artificial intelligence model can control the crane device to supply power of a specific size, to allow a current of a specific size to flow, or to generate a voltage of a specific size in order to change the tensile force on the magnet port. As yet another example, the artificial intelligence model can control the crane device to supply power of a specific size, to allow a current of a specific size to flow, or to generate a voltage of a specific size in order to generate a magnetic force of a specific size in the magnet port.

[0114] For example, the vertical spacing condition may include a condition regarding the vertical spacing between the magnet port and the steel plate at the start of the hoisting operation. For example, the vertical spacing condition may be output based on spacing instruction information. As another example, if it is determined that there is a high probability of failure in count control when performing the hoisting operation at the spacing instruction information when considering the steel plate information and count instruction information, the vertical spacing condition may be output as an interval at which count control is estimated to succeed.

[0115] For example, the pressing control condition may include a condition to ensure that the vertical gap between the magnet port and the steel plate reaches the gap corresponding to the gap indication information when the gap indication information is negative. For example, the pressing control condition may include a pressing control power size condition regarding the size of the power to be supplied to perform steel plate pressing control, and a pressing control power supply time condition regarding the time during which power must be supplied to perform steel plate pressing control.

[0116] The artificial intelligence model can output hoisting operation conditions when steel plate information and work instruction information are input. In this case, the artificial intelligence model may be a pre-trained model that uses steel plate information and work instruction information as training data and hoisting operation conditions as training result data.

[0117] In addition, depending on the case, additional training for the artificial intelligence model may be performed after the hoisting operation is actually carried out, using steel plate information, work instruction information, hoisting operation conditions, and the results of the actual hoisting operation. For example, if the control of the number of steel plates is successful, additional supervised learning can be performed using the steel plate information and work instruction information of that successful case as input data, the hoisting operation conditions as output data when successful, and the actual hoisting operation results as feedback data. As another example, if the control of the number of steel plates fails, additional supervised learning can be performed using the steel plate information and work instruction information of that failure case as input data, the hoisting operation conditions as output data when failed, and the actual hoisting operation results as feedback data.

[0118] For example, an artificial intelligence model may be a pre-trained model that, when steel plate weight information and purchase instruction information are input, estimates the magnitude of the power required to be supplied to the magnet port to lift the steel plate in the quantity corresponding to the purchase instruction information, and outputs power supply conditions including this.

[0119] For example, when steel plate weight information and quantity instruction information are generated and input into an artificial intelligence model to instruct a lifting operation for two steel plates weighing 2 tons and 1 ton each, the artificial intelligence model can estimate and output the amount of power that must be supplied to the magnet port to lift the two steel plates based on the prior learned content.

[0120] For example, the artificial intelligence model may be a model pre-trained to output the power supply conditions required when starting a hoisting operation in a state where the gap between the magnet port and the steel plate corresponds to the gap indication information.

[0121] For example, when information such as spacing instruction +10mm, steel plate weight information of 2 tons and 1 ton, and quantity instruction information of 2 plates is input to an artificial intelligence model, when a hoisting operation is started with the magnet port positioned 10mm above the upper surface of the steel plate relative to the vertical position, it is possible to output power supply conditions required to successfully control the hoisting of 2 steel plates corresponding to a total of 3 tons (2 tons + 1 ton).

[0122] As another example, when information such as spacing indication information of -1mm, steel plate weight information of 2 tons and 1 ton, and quantity indication information of 2 plates is input to an artificial intelligence model, and when a hoisting operation is started while the magnet port is pressing the upper surface of the steel plate by 1mm relative to the vertical position, it is possible to output power supply conditions required to successfully control the hoisting of 2 steel plates corresponding to a total of 3 tons (2 tons + 1 ton).

[0123] For example, the artificial intelligence model may be a model that has undergone missing information filling learning to estimate missing steel plate information based on the remaining steel plate information when at least one of steel plate width information, steel plate length information, steel plate thickness information, steel plate type information, and steel plate weight information is missing.

[0124] Generally, work data received from the Manufacturing Execution System (MES) and steel plate information generated based thereon have high accuracy, but in some cases, some information may be missing. In such cases, the processor can train an artificial intelligence model to estimate the missing information by using the remaining information, excluding the missing information from the steel plate data.

[0125] For example, the artificial intelligence model may be a model trained to output a power profile by reflecting the unloading performance of steel plate products. Depending on the case, the artificial intelligence model may be a linear model or a non-linear model.

[0126] For example, the artificial intelligence model may be a model that takes the width, length, thickness, type and stacking order, stacking position, and degree of indentation of the product as input values, and the size of the power to be supplied to the crane and the magnet port mounted on the crane as output values.

[0127] In addition, depending on the case, the power input time and the degree of indentation of the magnet port can be consistently controlled in controlling the number of adsorptions of the thick plate using the artificial intelligence model described above. Through this, the problem of requiring rework due to variations in the power input size, time, and control position of the magnet port depending on the operator in controlling the number of adsorptions of the thick plate can be reduced.

[0128] For example, the processor can control an artificial intelligence model to perform learning to fill in missing information regarding the weight information of a steel plate by using steel plate information of a steel plate and other steel plates for which steel plate weight information is missing.

[0129] Furthermore, by comparing the steel plate weight information output as a result of this missing information filling learning with the sensing steel plate weight information generated by actually measuring the weight of the steel plate for which weight information is missing, the inference ability of the AI ​​model regarding missing information filling learning can be improved through a continuous process of providing feedback on the learning results and supplementing the AI ​​model.

[0130] For example, if information regarding steel plate A is given, such that the steel plate width information is 1m, the steel plate length information is 2m, the steel plate thickness information is 10mm, the steel plate type information is carbon steel thick plate, and the steel plate weight information is missing, steel plate A can be determined to be a carbon steel thick plate with a width of 1m, a length of 2m, and a thickness of 10mm.

[0131] And when the steel plate width information of steel plate B is 1m, the steel plate length information is 1.8m, the steel plate thickness information is 10mm, the steel plate type information is carbon steel thick plate, and the steel plate weight information of steel plate B is 1.8 tons, the processor can input the steel plate information of steel plate A and steel plate B into an artificial intelligence model and output the steel plate weight information that is missing from the steel plate information of steel plate A.

[0132] For example, an AI model can estimate the density of Steel Plate B by considering that Steel Plate A and Steel Plate B are of the same type, and use this to output the weight information of Steel Plate A. In this case, based on the steel plate information of Steel Plate B, the density of the carbon steel thick plate is 1.8 tons / (1m × 1.8m × 10mm) = 1.8 × 10 3 kg / 1.8×10 -2 m 3 = 10 5 kg / m 3 It can be calculated as, and 10 5 kg / m 3 If the density is applied to Steel Plate A, a carbon steel thick plate with a width of 1m, a length of 2m, and a thickness of 10mm, the weight information of Steel Plate A is 10 5 kg / m 3 × 1m × 2m × 10mm = 2,000kg = 2 tons.

[0133] As another example, the artificial intelligence model can output the weight information of steel plate A using a proportional equation, considering that the types of steel plates A and B are the same. In this case, considering that the width, thickness, and type information of steel plates A and B are the same, and the length information of steel plates is different at 2m and 1.8m, the weight information of steel plate A can be estimated as 2 tons through the process of 2m : 1.8m = x ton : 1.8 ton, x = 2.

[0134] For example, the processor can output a hoisting operation condition based on the sensing steel plate information when the difference between the system steel plate information and the sensing steel plate information is greater than or equal to a preset steel plate threshold.

[0135] For example, the steel plate information includes system steel plate information generated based on work data and sensing steel plate information sensed from an image sensor mounted on a crane, and the processor can determine that a difference exists if the difference between the system steel plate information and the sensing steel plate information is greater than or equal to a preset steel plate threshold.

[0136] Generally, work data received from the Manufacturing Execution System (MES) and the system steel plate information generated based thereon have high accuracy; however, in some cases, information may be missing or contain errors, resulting in data that differs from the information of the steel plates actually loaded. In such cases, since the sensing steel plate information received from sensors mounted on cranes can be relatively more accurate, it is possible to determine whether the system steel plate information is missing or erroneous by comparing it with the sensing steel plate information.

[0137] For example, an image sensor mounted on a crane can be used to sense the width, length, and thickness of the steel plates actually loaded, and based on this, information such as the steel plate width, length, and thickness can be sensed. Then, by comparing this with the system steel plate width, length, and thickness information, respectively, it is possible to determine whether there is a match between the corresponding information.

[0138] In some cases, when steel plate type information, steel plate production information, steel plate quality inspection information, etc., are printed at specific locations on the steel plate or attached in the form of tags, an image sensor mounted on the crane can be used to generate sensing steel plate type information, sensing steel plate production information, and sensing steel plate quality inspection information. Then, this can be compared with system steel plate type information, system steel plate production information, and system steel plate quality inspection information to determine whether they match.

[0139] As another example, a load cell mounted on a crane can be used to sense the weight of a steel plate while lifting it, and based on this, the weight information of the steel plate can be sensed. Then, this can be compared with the system steel plate weight information to determine whether they match.

[0140] In addition, if the processor determines that the information corresponding to each other, which is included in the system steel plate information and the sensing steel plate information respectively as described above, does not match, it can input the sensing steel plate information into an artificial intelligence model to output hoisting operation conditions.

[0141] For example, based on an example where the system steel plate width information is 2m, the system steel plate length information is 1m, the system steel plate thickness information is 6mm, and the system steel plate weight information is 1 ton, the sensing steel plate width information is 2.02m, the sensing steel plate length information is 0.99m, the sensing steel plate thickness information is 6mm, and the sensing steel plate weight information is 2 ton, and the steel plate threshold is 5%, it is possible to determine whether there is a match or difference between the two pieces of information.

[0142] In this case, the processor can determine that the system steel plate width information and the sensing steel plate width information, the system steel plate length information and the sensing steel plate length information, and the system steel plate thickness information and the sensing steel plate thickness information match each other, since the difference between the system steel plate information and the sensing steel plate information is less than the steel plate threshold.

[0143] In contrast, the processor can determine that the system steel plate weight information and the sensed steel plate weight information do not match each other, as the difference between the system steel plate information and the sensed steel plate information is greater than or equal to the steel plate threshold.

[0144] In this case, the processor can control the output of lifting operation conditions by inputting sensing plate weight information, along with system plate width information, system plate length information, and system plate thickness information, into the artificial intelligence model instead of system plate weight information.

[0145] The processor can perform hoisting operation control for steel plates by supplying power to the magnet port based on hoisting operation conditions. For example, the processor can supply power to the magnet port based on power supply conditions output by inputting steel plate information and work instruction information into an artificial intelligence model, and can attract a number of steel plates corresponding to the number instruction information using the magnetic force generated at the magnet port, and can perform hoisting operation control by controlling the crane to lift the steel plates attracted by the magnet port.

[0146] For example, the processor inputs steel plate weight information and quantity instruction information into an artificial intelligence model to output a required lifting weight condition, and when performing a lifting operation, if the difference between the lifting sensing weight sensed using a load cell mounted on the crane and the required lifting weight condition is less than or equal to a preset reference weight difference, the lifting operation control can be determined to be successful. Conversely, if the difference is greater than or equal to the reference weight difference, it is determined that the required lifting weight condition determined based on the steel plate weight information and quantity instruction information has not been satisfied, and thus the lifting operation control can be determined to have failed.

[0147] For example, when the gap indication information is negative, the processor can perform steel plate pressing control to control the crane's tension force on the magnet port so that the magnet port presses the steel plate by a height corresponding to the absolute value of the gap indication information, and then perform lifting operation control.

[0148] For example, when the gap indication information is -1mm, the processor can control the crane's tension force on the magnet port to perform steel plate pressing control so that the magnet port presses the steel plate by 1mm, and can perform lifting operation control while the magnet port is pressing the steel plate by 1mm.

[0149] For example, the processor can perform steel plate pressing control and hoisting operation control by supplying power to the magnet port based on the change in power supply amount over time included in the power profile conditions.

[0150] For example, when the steel plate weight information is 2 tons and 1 ton respectively, the number of plates information is 2 plates, and the spacing information is -1 mm, the power profile conditions for 2 steel plates totaling 2+1 = 3 tons may include: i) a condition of supplying a voltage of 150 V to the crane for 10 seconds to control the tensile force of the crane on the magnet port so that the magnet port presses the upper surface of the steel plate by 1 mm; and ii) a condition of supplying a voltage of 300 V to the magnet port for 15 seconds to generate a magnetic force on the magnet port to adsorb and lift the steel plate while the magnet port is pressing the upper surface of the steel plate by 1 mm. And the processor can perform steel plate pressing control and lifting operation control for the crane device and the magnet port mounted on the crane device based on these power profile conditions.

[0151] FIG. 4 is a drawing for exemplarily illustrating the interface of a crane control device according to one embodiment.

[0152] Referring to FIG. 4, the interface (400) of the crane control device may include a monitoring window (410), a work information window (420), and a work condition window (430).

[0153] For example, the monitoring window (410) may display monitoring results regarding the status of each magnet port. For example, the monitoring window (410) may display power monitoring information and magnetic force monitoring information supplied to the magnet port. Additionally, depending on the case, height information of the magnet port and load information received by the magnet port may be further displayed.

[0154] For example, the power monitoring information displayed in the monitoring window (410) may include voltage monitoring information, current monitoring information, and power monitoring. For example, the magnetic monitoring information displayed in the monitoring window (410) may include magnetic flux monitoring information, magnetic force monitoring information, etc.

[0155] As a specific example, the monitoring window (410) of FIG. 4 displays voltage monitoring information, current monitoring information, and magnetic flux monitoring information for five magnet ports, POT #1, POT #2, POT #3, POT #4, and POT #5, respectively, and shows an embodiment in which the height of the magnet port is 1.248 m and the load received by the magnet port is -8.381 ton.

[0156] For example, the work information window (420) may display steel plate information and work instruction information regarding the lifting operation using a magnet port. For example, the work information window (420) may display steel plate information regarding the width, length, thickness, and weight of the steel plate to be lifted. In some cases, the work information window (420) may further display work instruction information such as information on the steel plate to be lifted, quantity instruction information, interval instruction information, and pressing time instruction information.

[0157] As a specific example, the work information window (420) of FIG. 4 displays the width, length, thickness, and weight of four steel plates, steel plate #1, steel plate #2, steel plate #3, and steel plate #4, respectively, and shows an example where only a significant value is displayed for steel plate #1, so the total thickness of the steel plates is 14 mm and the total weight is 2.839.

[0158] For example, the work condition window (430) may display the hoisting work conditions set for performing a hoisting work on the steel plate. For example, the work condition window (430) may display power supply conditions regarding the power to be supplied to perform the hoisting work, quantity conditions regarding the quantity of steel plates to be hoisted, and spacing conditions regarding the spacing between the magnet port and the steel plate.

[0159] For example, the power supply conditions displayed in the work condition window (430) may include current supply conditions, voltage supply conditions, and power supply conditions. In some cases, only one of the current supply condition, voltage supply condition, and power supply condition may be displayed in the work condition window (430), or two or more may be displayed.

[0160] As a specific example, the working condition window (430) of FIG. 4 shows an embodiment in which the power supply condition is indicated as 3.0 in units of current [A], the number of sheets is indicated as 1 sheet, and the spacing condition is indicated as 0.5m based on the height between the magnet port and the steel plate.

[0161] In addition, depending on the case, spacing instruction information regarding the vertical spacing between the magnet port and the upper surface of the steel plate and quantity instruction information regarding the number of steel plates to be hoisted may be displayed before the start of the hoisting operation.

[0162] FIG. 5 is a drawing illustrating an embodiment of performing a steel plate lifting operation using a crane control device.

[0163] Referring to FIG. 5, the crane control device can perform a lowering control to bring the magnet port mounted on the crane closer to the steel plate by a preset spacing condition, attempt adsorption control by supplying power to the magnet port while it is lowered, and perform a lifting control to lift the magnet port after the adsorption control is completed.

[0164] In addition, depending on the case, the gap between the magnet port and the upper surface of the steel plate can be moved to correspond to a preset gap condition before the steel plate adsorption control is initiated. Furthermore, if the steel plate adsorption control fails, the crane control device can perform hoisting control for the magnet port in a state where the steel plate has not been adsorbed.

[0165] Hereinafter, the embodiment shown in FIG. 5 is described as the first embodiment, wherein the part related to the lowering control in the first embodiment is described as the 1-1 state, the part related to the adsorption control is described as the 1-2 state, and the part related to the lifting control is described as the 1-3 state.

[0166] For example, the crane control device can perform unloading control for the magnet port_1-1 state (510) mounted on the crane_1-1 state (500). In this case, the magnet port A_1-1 state (512), magnet port B_1-1 state (514), and magnet port C_1-1 state (516) included in the magnet port_1-1 state (510) can be controlled to move downwards where the steel plate A_1-1 state (520) and steel plate B_1-2 state (522) are located.

[0167] For example, the crane control device may perform spacing control to position the vertical gap between the magnet port and the steel plate so that it corresponds to a preset spacing condition after performing the lowering control of the 1-1 state and before performing the adsorption control of the 1-2 state. However, FIG. 5 shows an embodiment in which the lowering control is performed until the magnet port is in close contact with the upper surface of the steel plate with the spacing condition set to 0, and thus no separate spacing control is performed.

[0168] For example, the crane control device can perform adsorption control on the magnet port_1st-2nd state (540) mounted on the crane 1st-2nd state (530). In this case, the crane control device can control power to be supplied to the magnet port_1st-2nd state (540) according to preset power supply conditions so that magnetic force is generated, and using this magnetic force, the magnet port A_1st-2nd state (542), magnet port B_1st-2nd state (544), and magnet port C_1st-2nd state (546) included in the magnet port_1st-2nd state (540) can attempt to adsorb steel plate A_1st-2nd state (550) and steel plate B_1st-2nd state (552). However, FIG. 5 shows an example in which adsorption control was performed, but the magnetic force generated in the magnet port_1st-2nd state (540) was not sufficient, so the steel plate A_1st-2nd state (550) and steel plate B_1st-2nd state (552) were not adsorbed to the extent that they were lifted.

[0169] For example, the crane control device can perform lifting control for the magnet port_1-3 state (570) mounted on the crane 1-3 state (560). In this case, the crane control device can control the lifting of the magnet port A_1-2 state (542), magnet port B_1-2 state (544), and magnet port C_1-2 state (546) included in the magnet port_1-3 state (570) by supplying power to the crane 1-3 state (560) according to preset power supply conditions. However, in FIG. 5, since adsorption control failed in the first-2nd state, the crane control device shows an embodiment in which the magnet port_first-3rd state (570) fails to adsorb steel plate A_first-2nd state (550) and steel plate B_first-2nd state (552) and performs lifting control only for the magnet port_first-3rd state (570).

[0170] And if the adsorption and hoisting control of the steel plate by the magnet port fails as in this first embodiment, the crane control device can reset the hoisting operation conditions to perform the unloading control, adsorption control, and hoisting control again. This will be explained in more detail in the second embodiment shown in FIG. 6 below.

[0171] FIG. 6 is a drawing illustrating another embodiment of performing a steel plate lifting operation using a crane control device.

[0172] Referring to FIG. 6, the crane control device can perform a lowering control to bring the magnet port mounted on the crane closer to the steel plate by a preset spacing condition, attempt adsorption control by supplying power to the magnet port while it is lowered, and perform a lifting control to lift the magnet port after the adsorption control is completed.

[0173] In addition, depending on the case, the gap between the magnet port and the upper surface of the steel plate can be moved to correspond to a preset gap condition before the steel plate adsorption control is initiated. Furthermore, if the gap condition is negative, the crane control device can perform steel plate pressing control in which the magnet port presses the steel plate by the gap corresponding to the gap condition, and then perform hoisting control.

[0174] Hereinafter, the embodiment shown in FIG. 6 is described as the second embodiment, wherein the part related to the lowering control in the second embodiment is described as the 2-1 state, the part related to the adsorption control is described as the 2-2 state, and the part related to the lifting control is described as the 2-3 state.

[0175] For example, the crane control device can perform unloading control for the magnet port A_2-1 state (612), magnet port B_2-1 state (614), and magnet port C_2-1 state (616) included in the magnet port_2-1 state (610) mounted on the crane_2-1 state (600). In this case, the magnet port_2-1 state (610) can be controlled to move downwards where the steel plate A_2-1 state (620) and steel plate B_2-2 state (622) are located.

[0176] For example, the crane control device may perform spacing control after performing lowering control in state 2-1 and before performing adsorption control in state 2-2, positioning the vertical gap between the magnet port and the steel plate to correspond to a preset spacing condition. FIG. 6 shows an embodiment in which the spacing condition is set to a negative value, and after performing steel plate pressing so that the magnet port presses the upper surface of the steel plate by the spacing corresponding to the spacing condition, lifting control is performed.

[0177] For example, the crane control device can perform adsorption control for the magnet port A_2-2 state (642), magnet port B_2-2 state (644), and magnet port C_2-2 state (646) included in the magnet port_2-2 state (640) mounted on the crane 2-2 state (630). In this case, the crane control device can control power to be supplied to the magnet port_2-2 state (640) according to preset power supply conditions so that a magnetic force is generated, and can attempt to use this magnetic force to make the magnet port_2-2 state (640) adsorb steel plate A_2-2 state (650) and steel plate B_2-2 state (652).

[0178] FIG. 6 shows an embodiment in which, unlike the case where adsorption control failed in FIG. 5, power supply conditions are reset so that steel plate A_2-2 state (650) and steel plate B_2-2 state (652) are sufficiently adsorbed by the magnetic force generated in the magnet port_2-2 state (640) to be lifted.

[0179] For example, the crane control device can perform lifting control for the magnet port A_2-3 state (672), magnet port B_2-3 state (674), and magnet port C_2-3 state (676) included in the magnet port_2-3 state (670) mounted on the crane 2-3 state (660). In this case, the crane control device can control the lifting of the magnet port_2-3 state (670) by supplying power to the crane 2-3 state (660) according to preset power supply conditions.

[0180] In FIG. 6, unlike the embodiment of FIG. 5, since adsorption control was successful in the second-2nd state, the crane control device shows an embodiment in which the magnet port_second-3rd state (670) adsorbs steel plate A_second-2nd state (650) and steel plate B_second-2nd state (652) and performs lifting control.

[0181] Figure 7 is a graph showing the change over time of hoisting control according to one embodiment.

[0182] Referring to FIG. 7, the crane control device can perform unloading control, suction control, and lifting control, etc., based on preset power supply conditions. In some cases, the power supply conditions may include power profile conditions that are output in a form showing a change over time with respect to the amount of power supplied to the crane device.

[0183] For example, power profile conditions may include current profile conditions, voltage profile conditions, and power profile conditions. In some cases, the crane control device may perform unloading control, adsorption control, and lifting control, etc., based on at least one of current supply conditions, voltage supply conditions, and power supply conditions. For example, the graph of the first embodiment (700) and the graph of the second embodiment (730) may be represented by a horizontal axis based on time and a vertical axis based on weight and power.

[0184] In this case, the vertical axis of the power supply can represent the magnitude of the power supplied to the magnet port, and the graph in FIG. 7 shows an example in which the vertical axis of the power supply is represented based on voltage, and in the first example graph (700), it is represented as the first weight change line (720), and in the second example graph (730), it is represented as the second weight change line (750).

[0185] And the weight vertical axis can represent the degree to which the magnet port lifts an object (weight value is positive) and the degree to which the magnet port presses down on an object (weight value is negative) using a load cell mounted on the crane, and is represented as the first weight change line (720) in the first embodiment graph (700) and the second weight change line (750) in the second embodiment graph (730).

[0186] Below, the first embodiment graph (700) showing a first embodiment in which adsorption control failed and the second embodiment graph (730) showing a second embodiment in which adsorption control succeeded will be explained separately.

[0187] For example, the graph of the first embodiment (700) and the graph of the second embodiment (730) can be described by dividing them into: Section A, which is a waiting section before the control is performed; Section B, which performs a lowering control that moves the vertical position of the magnet port according to the interval condition and an adsorption control that adsorbs the steel plate using the magnetic force generated from the magnet port; and Section C, which performs a lifting control that lifts the magnet port after the adsorption control is performed.

[0188] In the first embodiment_A section (702) of the first embodiment graph (700), prior to physically controlling the crane and the magnet port mounted on the crane using the crane control device, operations such as receiving data necessary to perform such control, generating information, and outputting work conditions can be performed. Accordingly, in the first embodiment_A section (702), no physical control is performed yet, so neither the first power change line (710) nor the first weight change line (720) shows a change from the initial value of 0.

[0189] In the first embodiment_B section (704) of the first embodiment graph (700), lifting control and adsorption control can be performed by using a crane control device to move the vertical position of the magnet port so that the gap between the magnet port and the upper surface of the steel plate corresponds to a preset gap condition. Additionally, depending on the case, steel plate pressing control can be further performed when the gap condition is negative, but the first embodiment does not correspond to a case where the gap condition is negative, so steel plate pressing control is not performed.

[0190] For example, in the first embodiment_B section (704) of the first power change line (710), winding control can be performed on the magnet port according to preset power supply conditions. Accordingly, the change in the supplied power may appear in the part corresponding to the first embodiment_B section (704) of the first power change line (710). In this way, in the first embodiment_B section (704), power can be supplied so that winding control is performed on the magnet port during a preset first interval power input time (712).

[0191] However, in the first embodiment_B section (704) of the first weight change line (720), the magnet port does not press or lift the steel plate, so the sensing value of the load cell may appear not to change from the initial value of 0. That is, the first embodiment_B section (704) of the first weight change line (720) indicates a case where lowering control was performed on the magnet port but steel plate pressing control was not performed, and the adsorption control of the magnet port on the steel plate failed.

[0192] In the first embodiment_C section (706) of the first embodiment graph (700), hoisting control to lift the magnet port can be performed using a crane control device. In this case, hoisting control can be performed after adsorption control is performed. However, as seen in the first embodiment_B section (704), since the first embodiment is a case where the adsorption control for the steel plate of the magnet port fails, the first embodiment_C section (706) represents a case where hoisting control is performed in a form where only the magnet port is lifted without the steel plate being adsorbed.

[0193] For example, in the first embodiment_C section (706) of the first power change line (710), hoisting control can be performed on the magnet port according to preset power supply conditions. Accordingly, the change in the supplied power may appear in the part corresponding to the first embodiment_C section (706) of the first power change line (710). In this way, power can be supplied so that hoisting control is performed on the magnet port in the first embodiment_C section (706).

[0194] However, in the first embodiment_C section (706) of the first weight change line (720), the magnet port does not press or lift the steel plate, so the sensing value of the load cell may appear not to change from the initial value of 0. That is, the first embodiment_C section (706) of the first weight change line (720) represents a case where lifting control is performed on the magnet port, but the lifting control is performed such that only the magnet port is lifted without the magnet port adsorbing the steel plate.

[0195] And if the adsorption and hoisting control of the steel plate by the magnet port fails as in this first embodiment, the crane control device can reset the hoisting operation conditions to perform the down control, adsorption control, and hoisting control again. This will be explained in more detail below in the graph (730) of the second embodiment.

[0196] In the second embodiment_A section (732) of the second embodiment graph (730), operations such as receiving data necessary to perform such control, generating information, and outputting work conditions can be performed prior to physical control, just as in the first embodiment. Accordingly, in the second embodiment_A section (732), no physical control is performed yet, so neither the second power change line (740) nor the second weight change line (750) shows a change from the initial value of 0.

[0197] In the second embodiment_B section (734) of the second embodiment graph (730), a crane control device can be used to perform pipe-down control and adsorption control to move the vertical position of the magnet port so that the gap between the magnet port and the upper surface of the steel plate corresponds to a preset gap condition. Additionally, it shows a case where steel plate pressing control is also performed because the gap condition corresponds to a negative value.

[0198] For example, in the second embodiment_B section (734) of the second power change line (740), control of the magnet port can be performed according to preset power supply conditions. Accordingly, the change in the supplied power may appear in the part corresponding to the second embodiment_B section (734) of the second power change line (740). In this way, in the second embodiment_B section (734), power can be supplied so that control of the magnet port is performed during a preset second interval power input time (742).

[0199] And, in the second embodiment_B section (734) of the second weight change line (750), steel plate pressing control is performed on the magnet port, and as steel plate pressing control is performed during the second interval power input time (742), the weight sensed by the load cell may appear as a negative number. That is, the second embodiment_B section (734) of the second weight change line (750) represents a case where the load control and steel plate pressing control are performed on the magnet port, and the adsorption control on the steel plate of the magnet port is successful.

[0200] In the second embodiment_C section (736) of the second embodiment graph (300), hoisting control to lift the magnet port can be performed using a crane control device. In this case, hoisting control can be performed after adsorption control is performed. And as seen in the second embodiment_B section (734), since the second embodiment is a case where adsorption control of the steel plate of the magnet port is successful, the second embodiment_C section (736) represents a case where hoisting control is performed in a form where both the magnet port and the steel plate are lifted while the steel plate is adsorbed.

[0201] For example, in the second embodiment_C section (736) of the second power change line (740), hoisting control can be performed on the magnet port according to preset power supply conditions. Accordingly, the change in the supplied power may appear in the part corresponding to the second embodiment_C section (736) of the second power change line (740). In this way, power can be supplied so that hoisting control is performed on the magnet port in the second embodiment_C section (736).

[0202] However, in the second embodiment_C section (736) of the second weight change line (750), since the magnetic port has succeeded in steel plate pressing control and adsorption control, the sensing value of the load cell may appear to change from an initial value of 0 to a negative value. That is, the second embodiment_C section (706) of the second weight change line (750) indicates a case where hoisting control is performed in which both the magnetic port and the steel plate adsorbed by it are lifted.

[0203] Below, a crane control method using a crane control device capable of performing all the aforementioned contents of the present disclosure is described. Content that overlaps with the above description may be omitted depending on the circumstances, but all of the following methods may also be applicable.

[0204] FIG. 8 is a flowchart relating to a crane control method according to the present disclosure.

[0205] Referring to FIG. 8, a crane control method according to one embodiment may include an information generation step (S810), a work condition output step (S820), and a work control step (S830).

[0206] For example, a crane control method may include: an information generation step of generating steel plate information regarding at least one steel plate and work instruction information regarding a lifting operation of suctioning and lifting a steel plate using a magnet port mounted on a crane based on work data received from a production management system; a work condition output step of inputting the steel plate information and work instruction information into a pre-trained artificial intelligence model to output a lifting operation condition for performing the lifting operation; and a work control step of supplying power to the magnet port based on the lifting operation condition to perform a lifting operation control for the steel plate.

[0207] The information generation step (S810) may include generating steel plate information regarding at least one steel plate and work instruction information regarding a lifting operation in which a steel plate is sucked up and lifted using a magnet port mounted on a crane, based on work data received from a production management system.

[0208] For example, steel plate information may include information regarding the size, location, type, etc. of each laminated steel plate. For instance, steel plate information may include steel plate location information, steel plate width information, steel plate length information, steel plate thickness information, steel plate weight information, and steel plate type information. Depending on the case, it may further include steel plate surface condition information regarding the flatness or presence of defects, steel plate production information regarding the production date, production location, or manufacturer, and steel plate quality inspection information. This steel plate information can be input into an artificial intelligence model and used to output hoisting operation conditions.

[0209] For example, the steel plate information may include system steel plate information generated based on work data and sensing steel plate information sensed from an image sensor mounted on a crane. In this case, the system steel plate information and the sensing steel plate information may match each other, or differences may appear depending on the circumstances.

[0210] Here, whether there is a match or a difference can be determined by judging that the two pieces of information match if the difference between the system steel plate information and the sensing steel plate information is less than a preset steel plate threshold range, and that a difference occurs if it is greater than or equal to that range. Additionally, depending on the case, the steel plate threshold may be set to a specific value for each type of steel plate information, or it may be set to a specific ratio value so as not to be affected by the type of steel plate information.

[0211] The work instruction information may include information regarding the contents of the work instruction when performing a lifting operation on a steel plate using a magnet port. For example, the work instruction information may include information on the steel plate to be lifted, quantity instruction information, interval instruction information, and pressing time instruction information.

[0212] For example, the spacing indication information may include information regarding the vertical spacing between the magnet port and the steel plate during the hoisting operation. In this case, the vertical spacing between the magnet port and the steel plate can be sensed using a sensor mounted on the crane. For instance, if the vertical spacing is positive, it can be determined by sensing how high the magnet port is floating above the upper surface of the steel plate using an image sensor mounted on the crane; if the vertical spacing is negative, it can be determined by estimating the degree of pressure by sensing how much the magnet port is pressing against the upper surface of the steel plate using a load cell mounted on the crane, taking into account the type of steel plate and other factors.

[0213] The work condition output step (S820) may include inputting steel plate information and work instruction information into a pre-trained artificial intelligence model to output a hoisting work condition for performing a hoisting work.

[0214] For example, hoisting operation conditions may include a required lifting weight condition, a power supply condition regarding the power supplied to the magnet port to lift the steel plate, a vertical spacing condition, and a pressing control condition.

[0215] For example, the required lifting weight condition may include a condition regarding the weight of the steel plate on which the crane will perform the lifting operation using a magnet port.

[0216] For example, power supply conditions may include conditions regarding the power supplied to the magnet port to lift the steel plate. In addition, steel plate information and work instruction information can be input into an artificial intelligence model and used to output lifting operation conditions.

[0217] For example, the steel plate information may include steel plate weight information regarding the weight of the steel plate, and the work instruction information may include quantity instruction information regarding the quantity of steel plates for which a hoisting operation is required; the hoisting operation condition may include power supply conditions regarding the power supplied to the magnet port to hoist the steel plate; and when the steel plate weight information and quantity instruction information are input, the artificial intelligence model may be trained to estimate the magnitude of the power that must be supplied to the magnet port to hoist the quantity of steel plates corresponding to the quantity instruction information and output this as one of the power supply conditions.

[0218] For example, power supply conditions may include power profile conditions that are output in a form indicating a change over time with respect to the amount of power supplied to the magnet port.

[0219] For example, an artificial intelligence model may be a pre-trained model that, when steel plate weight information and purchase instruction information are input, estimates the magnitude of the power required to be supplied to the magnet port to lift the steel plate in the quantity corresponding to the purchase instruction information, and outputs power supply conditions including this.

[0220] For example, the artificial intelligence model may be a model pre-trained to output the power supply conditions required when starting a hoisting operation in a state where the gap between the magnet port and the steel plate corresponds to the gap indication information.

[0221] For example, the artificial intelligence model may be a model that has undergone missing information filling learning to estimate missing steel plate information based on the remaining steel plate information when at least one of steel plate width information, steel plate length information, steel plate thickness information, steel plate type information, and steel plate weight information is missing.

[0222] For example, the work condition output step (S820) may include inputting steel plate weight information and quantity instruction information into an artificial intelligence model to output the required lifting weight condition.

[0223] For example, in the operation condition output step (S820), if the difference between the system steel plate information and the sensing steel plate information is greater than or equal to a preset steel plate threshold, the operation condition may be output based on the sensing steel plate information.

[0224] The work control step (S830) may include supplying power to the magnet port based on the lifting operation conditions to perform lifting operation control for the steel plate. For example, in the work control step (S830), power may be supplied to the magnet port based on the power supply conditions output by inputting steel plate information and work instruction information into an artificial intelligence model, and the number of steel plates corresponding to the number instruction information may be attracted using the magnetic force generated at the magnet port, and lifting operation control may be performed by controlling the crane to lift the steel plate attracted by the magnet port.

[0225] For example, in the work control step (S830), when performing a lifting operation, if the difference between the lifting sensing weight sensed using a load cell mounted on the crane and the lifting required weight condition is less than or equal to a preset standard weight difference, it can be determined that the lifting operation control is successful.

[0226] For example, in the work control step (S830), if the gap indication information is negative, the process may include performing steel plate pressing control to control the tensile force of the crane on the magnet port so that the magnet port presses the steel plate by a height corresponding to the absolute value of the gap indication information, and then performing a hoisting work control.

[0227] For example, the work control step (S830) may include performing steel plate pressing control and hoisting work control by supplying power to the magnet port based on the change in power supply amount over time included in the power profile conditions.

[0228] FIG. 9 is a flowchart illustrating, in an exemplary manner, the process of controlling a magnet port using a crane control method according to one embodiment.

[0229] Referring to FIG. 9, a crane control method according to one embodiment may include a work instruction and input step (S910), a magnet lowering step (S920), a model output step (S930), a power input step (S940), and a magnet lifting step (S950).

[0230] The work instruction and input step (S910) may include generating steel plate information regarding at least one steel plate and work instruction information regarding a lifting operation in which a steel plate is sucked up and lifted using a magnet port mounted on a crane based on work data received from a production management system, and inputting the steel plate information and work instruction information into an artificial intelligence model.

[0231] For example, the work instruction and input step (S910) may include inputting operating conditions, such as conditions regarding product specifications of steel plates and conditions regarding the number of operations, into an artificial intelligence model. In this case, the input of operating conditions may be performed using communication between the computer that issued the operation instruction and the computer equipped with the artificial intelligence model.

[0232] The magnet lowering step (S920) may include performing lowering control to lower the magnet port mounted on the crane downwards to the steel plate. In some cases, the magnet hoisting step (S920) may include moving the magnet port so that the vertical distance between the magnet port and the upper surface of the steel plate corresponds to a preset distance condition.

[0233] The model output step (S930) may include outputting work conditions from an artificial intelligence model into which steel plate information and work instruction information are input. For example, the model output step (S930) may include outputting hoisting work conditions regarding a hoisting operation for lifting a magnet port. Additionally, depending on the case, the model output step (S930) may include outputting a steel plate pressing control condition in which, when the gap condition regarding the gap between the magnet port and the upper surface of the steel plate is negative, the tension force of the crane on the magnet port is controlled before the start of the hoisting operation to vertically move the magnet port so that the magnet port presses the steel plate by the gap corresponding to the gap condition.

[0234] The power input step (S940) may include supplying power required to perform a hoisting operation based on work conditions output from an artificial intelligence model. In some cases, the power input step (S940) may further include supplying power required to perform steel plate pressing control before the start of the hoisting operation.

[0235] In this case, power input may take the form of current or voltage values, and may be based on conditions regarding the number of ports to which power is supplied. Furthermore, such power input can be achieved through communication between a computer equipped with an artificial intelligence model and a computer equipped with a magnetic PLC.

[0236] The magnet hoisting step (S950) may include supplying power to a crane to perform a hoisting operation to lift the magnet port. In this case, if the steel plate adsorption control by the magnet port is successful, the hoisting operation may proceed by lifting both the magnet port and the steel plate adsorbed to the magnet port. Alternatively, if the steel plate adsorption control by the magnet port fails, the hoisting operation may proceed by lifting only the magnet port.

[0237] When the work is completed, work information, work conditions, and data from the work execution process can be stored in a database, and performance data regarding actual work results can be generated based on this. In this case, the performance data may include data regarding operating conditions, magnetic flux, current, load cells, etc.

[0238] FIG. 10 is a flowchart illustrating, by way of example, a method for performing purchase control according to one embodiment.

[0239] Referring to FIG. 10, a purchase control method according to one embodiment may include a work information receiving step (S1010), a power profile modeling step (S1020), a magnet PLC input step (S1030), a magnet port control step (S1040), a purchase control success determination step (S1050), and a model learning step (S1060).

[0240] The work information receiving step (S1010) may include receiving work information necessary for lifting and moving steel plates by adsorbing them using a crane and a magnet port mounted on the crane.

[0241] For example, work information may include steel plate information regarding at least one steel plate and work instruction information regarding a lifting operation in which the steel plate is sucked up and lifted using a magnet port mounted on a crane, based on work data received from a production management system.

[0242] The power profile modeling step (S1020) may include inputting work information into an artificial intelligence model to model it so as to output work conditions. For example, the power profile modeling step (S1020) may include inputting steel plate information and work instruction information into an artificial intelligence model to output hoisting work conditions for performing a hoisting operation.

[0243] As a specific example, the power profile modeling step (S1020) may include inputting spacing instruction information regarding the vertical spacing between the magnet port and the steel plate included in the work instruction information, and quantity instruction information regarding the quantity of steel plates to be hoisted, into an artificial intelligence model to model the hoisting operation conditions.

[0244] And, depending on the case, the power profile modeling step (S1020) may include modeling to output a steel plate pressing control condition that, when the gap indication information is negative, controls the crane's tension force on the magnet port before the start of the hoisting operation to vertically move the magnet port so that the magnet port presses the steel plate by a gap corresponding to the gap condition.

[0245] The magnet PLC input step (S1030) may include inputting work information and work conditions output from an artificial intelligence model into the magnet PLC. Through this, control values ​​required to control the crane and the magnet port mounted on the crane can be input into the magnet PLC to complete the preparation for control.

[0246] The magnet port control step (S1040) may include performing unloading control, steel plate pressing control, steel plate adsorption control, and hoisting control using the crane and the magnet port mounted on the crane through the crane control device. Additionally, depending on the case, current conversion between AC and DC may be performed using a rectifier during the process of controlling the magnet port.

[0247] The purchase control success determination step (S1050) may include determining whether the steel plate purchase control using the magnet port has been successful when the hoisting control through the crane control device is completed. For example, the purchase control success determination step (S1050) may include sensing the weight being lifted by the magnet port using a load cell mounted on the crane, and determining whether the purchase control is successful based on whether the sensed weight differs from the total weight of the steel plates to be hoisted, estimated based on purchase instruction information and steel plate weight information, by a difference greater than a preset steel plate threshold value.

[0248] In this case, if the purchase control is determined to have failed as a result of the judgment in the purchase control success judgment step (S1050), the process can be repeated by returning to the power profile modeling step (S1020), modeling the artificial intelligence model to output new power profile conditions, inputting this into the magnet PLC, and controlling the magnet port to perform the purchase control success judgment step (S1050).

[0249] And if the judgment result in the buy control success judgment step (S1050) indicates that the buy control is successful, the model learning step (S1060) can be performed.

[0250] The model training step (S1060) may include using the work information and work conditions of the successful task, data from the control process, etc., to include in the training data of the artificial intelligence model and performing additional training.

[0251] As explained in the above, the present disclosure can provide a control device and a method capable of automating the transport control of steel plates.

[0252] In addition, the present disclosure may provide a control device and method capable of lifting and transporting steel plates in a desired number.

[0253] Hereinafter, another embodiment of controlling a crane and a magnet port mounted on the crane using a crane control device according to the present disclosure will be described.

[0254] For example, the crane control device can move the crane to a location where the steel plates to be controlled are stacked. Next, it can receive work instructions from the MES. Next, through modeling of an artificial intelligence model, it can output the number of magnet ports and power values ​​required to perform control of the number of steel plates adsorbed. Next, it can perform hoisting control for the magnet ports, and in this case, it can perform steel plate pressing control using the magnet ports based on the output work conditions so that the degree of pressing of the steel plates by the magnet ports is standardized. Next, based on the output work conditions, power can be supplied to the crane and magnet ports for a fixed amount and a fixed amount for a fixed period of time. Next, it can perform hoisting control to lift the magnet ports to a fixed height. Next, while the magnet ports are hoisted, it can determine whether the steel plate adsorption control and hoisting control have been successful by checking whether the weight and magnetic flux sensing values ​​match the weight of the steel plates to be controlled and the magnetic flux generated from the magnet ports accordingly. Next, if it is determined that the adsorption and hoisting controls of the steel plate have been successful, steel plate transfer control can be performed; in this case, maximum voltage can be applied to the magnet port to ensure the stability of the transfer control. Next, performance data regarding the above operations can be stored. Next, the crane can be moved to the transfer position.

[0255]

[0256] The devices, methods, configurations, glyphs, and operations described herein may be implemented in digital electronic circuits, or computer software, firmware, or hardware comprising structures disclosed herein and structural equivalents, or combinations of one or more of these. The glyphs described herein may be implemented as one or more computer programs, for example, as one or more modules of computer program instructions encoded on a computer storage medium to control execution by a data processing device or operation by a data processing device. Program instructions may be encoded in artificially generated propagated signals, for example, mechanically generated electrical, optical, or electromagnetic signals generated to encode information for transmission to a suitable receiver device for execution by a data processing device. The computer storage medium may be or may include a computer-readable storage device, a computer-readable storage substrate, a random or serial access memory array or device, or a combination of one or more of these. Although the computer storage medium is not a propagated signal, the computer storage medium may be a source or destination of computer program instructions encoded in an artificially generated propagated signal. Additionally, a computer storage medium may be one or more individual physical components or media (e.g., multiple CDs, disks, or other storage devices) or may include. The operations described herein may be implemented as operations performed by a data processing device on data stored in one or more computer-readable storage devices or data received from other sources.

[0257] The foregoing description is merely an illustrative explanation of the technical concept of the present disclosure, and those skilled in the art to which the present disclosure pertains may make various modifications and variations within the scope of the essential characteristics of the technical concept. Furthermore, since these embodiments are intended to explain rather than limit the technical concept of the present disclosure, the scope of the technical concept is not limited by these embodiments.

[0258] CROSS-REFERENCE TO RELATED APPLICATION

[0259] This patent application claims priority pursuant to Section 119(a) of the U.S. Patent Act (35 USC § 119(a)) to Korean Patent Application No. 10-2024-0183893 filed on December 11, 2024, the entire contents of which are incorporated by reference into this patent application. Furthermore, this patent application claims priority in countries other than the United States for the same reasons as above, and the entire contents of which are incorporated by reference into this patent application.

Claims

1. In a crane control device, At least one memory containing computer program instructions; and It includes at least one processor that executes the above computer program instructions, The above at least one processor is, Based on work data received from a production management system, steel plate information regarding at least one steel plate and work instruction information regarding a lifting operation for suctioning and lifting the steel plate using a magnet port mounted on a crane are generated. The above steel plate information and the above work instruction information are input into a pre-trained artificial intelligence model to output hoisting work conditions for performing the above hoisting work, and A crane control device that supplies power to the magnet port based on the above hoisting operation conditions to perform hoisting operation control for the steel plate.

2. In Paragraph 1, The above steel plate information includes steel plate weight information regarding the weight of the steel plate, and the above work instruction information includes quantity instruction information regarding the quantity of the steel plate for which the above lifting operation is required, and the above lifting operation condition includes power supply conditions including conditions regarding voltage, current, and power as conditions regarding the power supplied to the magnet port to lift the steel plate. The above artificial intelligence model is, A crane control device characterized by being pre-learned to estimate the magnitude of the power to be supplied to the magnet port to lift the steel plate by the quantity corresponding to the purchase instruction information when the above steel plate weight information and the above purchase instruction information are input, and to output the above power supply conditions.

3. In Paragraph 2, The above processor is, A crane control device that outputs different power supply conditions for each magnet port based on the above steel plate information.

4. In Paragraph 2, The above processor is, Input the steel plate weight information and the purchase instruction information into the above artificial intelligence model to output the required lifting weight conditions, and A crane control device that determines that the lifting operation control is successful when the difference between the lifting sensing weight sensed using a load cell mounted on the crane and the lifting required weight condition is less than or equal to a preset standard weight difference when performing the lifting operation.

5. In Paragraph 2, The above work instruction information is, It includes spacing indication information regarding the vertical spacing between the magnet port and the steel plate, and The above artificial intelligence model is, A crane control device characterized by being pre-learned to output the power supply conditions required when starting the hoisting operation in a state where the gap between the magnet port and the steel plate corresponds to the gap indication information.

6. In Paragraph 5, The above processor is, A crane control device that performs a steel plate pressing control to control the tensile force of the crane on the magnet port so that the magnet port presses the steel plate by a height corresponding to the absolute value of the gap indication information when the gap indication information is negative, and then performs the hoisting operation control.

7. In Paragraph 6, The above power supply conditions are, It includes power profile conditions that are output in a form showing a change over time regarding the amount of power supplied to the above magnet port, and The above processor is, A crane control device that performs steel plate pressing control and hoisting operation control by supplying power to the magnet port based on the change in power supply amount over time included in the power profile conditions above.

8. In Paragraph 1, The above steel plate information is, It includes system steel plate information generated based on the above work data and sensing steel plate information sensed from an image sensor mounted on the crane, The above processor is, A crane control device that outputs the hoisting operation condition based on the sensing plate information when the difference between the system plate information and the sensing plate information is greater than or equal to a preset plate threshold.

9. In Paragraph 1, The above steel plate information is, Includes steel plate width information, steel plate length information, steel plate thickness information, steel plate type information, and steel plate weight information, The above artificial intelligence model is, A crane control device characterized by performing missing information filling learning to estimate missing steel plate information based on the remaining steel plate information when at least one of the above steel plate width information, steel plate length information, steel plate thickness information, steel plate type information, and steel plate weight information is missing.

10. An information generation step of generating steel plate information regarding at least one steel plate and work instruction information regarding a lifting operation of adsorbing and lifting the steel plate using a magnet port mounted on a crane, based on work data received from a production management system; A work condition output step for outputting a hoisting work condition to perform the hoisting work by inputting the steel plate information and the work instruction information into a pre-trained artificial intelligence model; and A crane control method comprising a work control step of supplying power to the magnet port based on the above hoisting work conditions to perform hoisting work control for the steel plate.

11. In Paragraph 10, The above steel plate information includes steel plate weight information regarding the weight of the steel plate, and the above work instruction information includes quantity instruction information regarding the quantity of the steel plate for which the lifting operation is required, respectively. The above hoisting operation conditions include power supply conditions, which include conditions regarding voltage, current, and power, as conditions regarding the power supplied to the magnet port to hoist the steel plate. The above artificial intelligence model is, A crane control method characterized by being pre-learned to estimate the size of the power supply to be supplied to the magnet port to lift the steel plate by the quantity corresponding to the purchase instruction information when the above steel plate weight information and the above purchase instruction information are input, and to output the above power supply conditions.

12. In Paragraph 11, The above work condition output step is, A crane control method comprising outputting different power supply conditions for each magnet port based on the above steel plate information.

13. In Paragraph 11, The above work condition output step is, It includes inputting the steel plate weight information and the purchase instruction information into the above artificial intelligence model to output the required lifting weight condition, The above work control step is, A crane control method comprising determining that the lifting operation control is successful when the difference between the lifting sensing weight sensed using a load cell mounted on the crane and the lifting required weight condition is less than or equal to a preset reference weight difference when performing the lifting operation.

14. In Paragraph 11, The above work instruction information is, It includes spacing indication information regarding the vertical spacing between the magnet port and the steel plate, and The above artificial intelligence model is, A crane control method characterized by being pre-learned to output the power supply conditions required when starting the hoisting operation in a state where the gap between the magnet port and the steel plate corresponds to the gap indication information.

15. In Paragraph 14, The above work control step is, A crane control method comprising, when the above gap indication information is negative, performing a steel plate pressing control to control the tensile force of the crane on the magnet port so that the magnet port presses the steel plate by a height corresponding to the absolute value of the above gap indication information, and then performing the above hoisting operation control.

16. In Paragraph 15, The above power supply conditions are, It includes power profile conditions that are output in a form showing a change over time regarding the amount of power supplied to the above magnet port, and The above work control step is, A crane control method that performs steel plate pressing control and hoisting operation control by supplying power to the magnet port based on a change in the amount of power supplied over time included in the power profile conditions.

17. In Paragraph 10, The above steel plate information is, It includes system steel plate information generated based on the above work data and sensing steel plate information sensed from an image sensor mounted on the crane, The above work condition output step is, A crane control method comprising outputting a hoisting operation condition based on the sensing plate information when the difference between the system plate information and the sensing plate information is greater than or equal to a preset plate threshold.

18. In Paragraph 10, The above steel plate information is, Includes steel plate width information, steel plate length information, steel plate thickness information, steel plate type information, and steel plate weight information, The above artificial intelligence model is, A crane control method characterized by performing missing information filling learning to estimate missing steel plate information based on the remaining steel plate information when at least one of the above steel plate width information, steel plate length information, steel plate thickness information, steel plate type information, and steel plate weight information is missing.

Images