WO2022139326A1 - Système d'exploitation d'usine autonome reposant sur un calcul informatisé en périphérie de réseau - Google Patents

Système d'exploitation d'usine autonome reposant sur un calcul informatisé en périphérie de réseau Download PDF

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Publication number
WO2022139326A1
WO2022139326A1 PCT/KR2021/019195 KR2021019195W WO2022139326A1 WO 2022139326 A1 WO2022139326 A1 WO 2022139326A1 KR 2021019195 W KR2021019195 W KR 2021019195W WO 2022139326 A1 WO2022139326 A1 WO 2022139326A1
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WIPO (PCT)
Prior art keywords
edge computing
optimal control
intelligent
factory
operating system
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PCT/KR2021/019195
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English (en)
Korean (ko)
Inventor
이용관
김수경
김성렬
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한국공학대학교산학협력단
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Publication of WO2022139326A1 publication Critical patent/WO2022139326A1/fr

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    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B19/00—Programme-control systems
    • G05B19/02—Programme-control systems electric
    • G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
    • G05B19/41885—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM] characterised by modeling, simulation of the manufacturing system
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B19/00—Programme-control systems
    • G05B19/02—Programme-control systems electric
    • G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B19/00—Programme-control systems
    • G05B19/02—Programme-control systems electric
    • G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
    • G05B19/41865—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM] characterised by job scheduling, process planning, material flow
    • G05B19/4187—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM] characterised by job scheduling, process planning, material flow by tool management
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]

Definitions

  • the present invention relates to an autonomous factory operating system based on distributed edge computing that enables small-quantity production in response to market demand.
  • the conventional manufacturing process is a manufacturing system suitable for a supplier-oriented market.
  • the supplier understands the market demand, manufactures the product by mass production, and supplies it to an agency or retail store, which is composed of a business model based on sales revenue.
  • a personalized product eg, each individual's body size, weight, waist circumference, taste, etc.
  • the conventional manufacturing system is composed of supplier-oriented product planning-development-manufacturing-logistics-sales, so it is difficult to build a multi-variety small-volume production system in response to the personalized market.
  • the present invention has been derived to solve the above-mentioned problems, and aims to provide an autonomous factory operating system based on distributed edge computing that enables small-quantity production of various types in response to market demand.
  • the autonomous factory operating system based on distributed edge computing can be applied to a factory including a plurality of process facilities.
  • the autonomous factory operating system based on distributed edge computing is connected to each of the plurality of process equipment, collects process basic data related to the process equipment, and is defined based on the collected process basic data.
  • a plurality of intelligent edge computing devices (AI-sons) that generate and apply optimal control conditions to process equipment according to an artificial intelligence algorithm; and each of the plurality of intelligent edge computing devices in a cloud environment to check the optimal control conditions for each of the plurality of process facilities, and according to a predefined artificial intelligence algorithm based on the identified optimal control conditions, the factory and an intelligent center computing device (AI-mother), which generates an optimization condition.
  • AI-mother intelligent center computing device
  • the autonomous factory operating system may further include a database for collecting and storing market demand information for products produced in the factory.
  • the market demand information may include individual demand information for the product.
  • the intelligent center computing device responds to the individual market demand of the product according to a predefined artificial intelligence algorithm based on the identified optimal control condition and the market demand information stored in the database It is possible to create factory optimization conditions for
  • the intelligent center computing device may generate an optimal control condition for each of the plurality of intelligent edge computing devices (AI-son) based on the generated factory optimization condition .
  • the intelligent center computing device provides the generated optimal control condition to each of the plurality of intelligent edge computing devices (AI-son), and the plurality of intelligent edge computing devices (AI-mother) -son) may update the pre-stored optimum control condition to the provided optimum control condition, and apply the updated optimum control condition to each process facility.
  • the intelligent center computing device (AI-mother) generates the optimal control condition for each predefined time unit and provides it to each of the plurality of intelligent edge computing devices (AI-son), and the plurality of Each of the intelligent edge computing devices (AI-son) may update the pre-stored optimum control condition to the provided optimum control condition, and apply the updated optimum control condition to each process facility.
  • the present invention relates to a plurality of intelligent edge computing devices (AI-son) that are connected to each of a plurality of process equipment to generate optimal control conditions according to an artificial intelligence algorithm and apply to the process equipment, and a plurality of intelligent edge computing devices ( It is composed of an intelligent center computing device (AI-mother) that interworks with AI-son) in a cloud environment to create factory optimization conditions according to an artificial intelligence algorithm, but the intelligent center computing device (AI-mother) Based on the optimal control conditions and the collected market demand information, factory optimization conditions are created and operated to provide these to a plurality of intelligent edge computing devices (AI-sons), thereby enabling small-volume production of individual products in response to market demand. do.
  • AI-son intelligent edge computing devices
  • 1 is a reference diagram for explaining a conventional manufacturing process.
  • FIG. 2 is a reference diagram for explaining a manufacturing process according to the present invention.
  • FIG. 3 is a reference diagram for explaining an autonomous factory operating system based on distributed edge computing according to the present invention.
  • FIG. 2 is a reference diagram for explaining a manufacturing process according to the present invention.
  • the conventional manufacturing process consists of supplier-oriented product planning-development-manufacturing-logistics-sales, so it is difficult to establish a multi-variety small-volume production system in response to a personalized market.
  • the present invention has been devised to solve the problems of the conventional manufacturing process, and stores personalized specifications that meet the needs of the market in real time in a database, and utilizes artificial intelligence algorithms based on the stored data to optimize product production. By deriving the process specifications and applying the derived process specifications to each process facility, it is possible to produce small amounts of individually tailored multi-variety in response to market demand.
  • FIG. 3 is a reference diagram for explaining an autonomous factory operating system based on distributed edge computing according to the present invention.
  • the distributed edge computing-based autonomous factory operating system 10 (hereinafter, autonomous factory operating system) according to the present invention includes a plurality of process facilities 100 and a plurality of intelligent edge computing devices 200 and AI- son, hereinafter edge computing device), the intelligent center computing device (300, AI-mother, hereinafter center computing device) and may be configured to include a database (400).
  • autonomous factory operating system includes a plurality of process facilities 100 and a plurality of intelligent edge computing devices 200 and AI- son, hereinafter edge computing device), the intelligent center computing device (300, AI-mother, hereinafter center computing device) and may be configured to include a database (400).
  • the plurality of process facilities 100 are provided in a specific factory and correspond to facilities for performing a defined process, respectively.
  • the first process equipment 100A corresponds to the fabric processing equipment
  • the second process equipment 100B corresponds to the sewing process equipment
  • the third process equipment 100C corresponds to the quality equipment. It may correspond to an inspection facility.
  • these examples are not intended to limit the scope of the present invention, of course, the process equipment according to the present invention may correspond to various process equipment for producing a specific product.
  • each of the plurality of process facilities 100 may be configured to include a control device 110 for performing a defined operation.
  • each of the control devices 110A, 110B, 110C, ... may output a control signal for operating each of the process equipment 100A, 100B, 100C, ..., and the control signal is the process equipment 100A , 100B, 100C, (7) may include operation parameters for each.
  • the edge computing device 200 is a computing device connected to each of the plurality of process facilities 100 .
  • the edge computing devices 200A, 200B, 200C, ... are implemented as separate hardware and disposed or attached to the vicinity of the process facilities 100A, 100B, 100C, ... and interconnected through a wired/wireless network, or A plurality of process equipment (100A, 100B, 100C, ...) may be implemented by being included in the interior.
  • the edge computing devices 200A, 200B, 200C, ... may collect process basic data associated with each of the process facilities 100A, 100B, 100C, ....
  • the process basic data is data related to the operation of the process equipment 100 , and includes control signals, operating parameters, environmental conditions, operating conditions, process amounts, and control signals for each of the process facilities 100A, 100B, 100C, ... It may include failure events and the like.
  • Each of the edge computing devices 200A, 200B, 200C, ... generates optimal control conditions for each of the process facilities 100A, 100B, 100C, ... according to a predefined artificial intelligence algorithm based on the collected process basic data. can do.
  • the optimal control condition may mean a control signal capable of optimizing each process facility.
  • each of the edge computing devices 200A, 200B, 200C, . can be analyzed using a predefined deep learning model to create optimal control conditions that can show the best process efficiency.
  • the artificial intelligence algorithm may correspond to various well-known artificial intelligence models, for example, it may correspond to a deep learning-based learning model.
  • a method for learning a deep learning model using various process basic data as input data and deriving an optimal control condition that is intended output information using the learned deep learning model is a well-known technology and is a core technology of the present invention. Since it is not an idea, a detailed description thereof will be omitted.
  • Each of the edge computing devices 200A, 200B, 200C, ... may apply the generated optimal control condition to each of the process facilities 100A, 100B, 100C, .... That is, when the edge computing device 200 transmits the optimum control condition to the process facility 100 , the process facility 100 applies the optimum control condition to each control device 110 to operate according to the optimum control condition. have.
  • the center computing device 300 may be linked with each of the edge computing devices 200A, 200B, 200C, ... in a cloud environment.
  • the center computing device 300 may be implemented as a cloud server, and may be interconnected with each of the edge computing devices 200A, 200B, 200C, ... through a network to transmit and receive data.
  • the center computing device 300 identifies optimal control conditions for each of the plurality of process facilities 100A, 100B, 100C, ..., and factory optimization conditions according to a predefined artificial intelligence algorithm based on the identified optimal control conditions can create
  • the factory optimization condition may mean a condition for optimizing the entire factory including each process facility.
  • the factory optimization condition may include product production by specification, operation time for each process facility, environmental conditions in the factory, etc., and also the optimum for each of the plurality of process facilities 100A, 100B, 100C, ... Control conditions may be included.
  • the artificial intelligence algorithm may correspond to various well-known artificial intelligence models, for example, it may correspond to a deep learning-based learning model.
  • the method of learning a deep learning model using the optimal control condition as input data and deriving the factory optimization condition, which is the intended output information, using the learned deep learning model is a well-known technology and is a core technical idea of the present invention. Since this is not the case, a detailed description thereof will be omitted.
  • the database 400 may collect and store market demand information for products produced in a corresponding factory.
  • the market demand information may mean information indicating product demand for each individual consumer, and may include individual demand information for each consumer.
  • the market demand information may include clothing order information or an order quantity for each clothing model, size, and period.
  • the database 400 may be connected to an external information providing device that provides market demand information through a network, and for example, the information providing device corresponds to a server of a sales company that sells products produced in a corresponding factory. can do.
  • the market demand information may be processed according to a predefined format and stored in the database 400 .
  • order information for each individual consumer is stored as raw data in the database 400 , or the raw data is processed in the form of an order quantity for each clothing model, size, and period through a quantitative analysis process and stored in the database 400 . it might be
  • the center computing device 300 checks the optimal control conditions for each of the plurality of process facilities 100A, 100B, 100C, ... and the market demand information stored in the database 400, and the identified optimal control Based on the conditions and market demand information, it is possible to create factory optimization conditions according to a predefined artificial intelligence algorithm.
  • the factory optimization condition according to this embodiment is an optimization condition for responding to individual market demand for products produced in the factory, and may include product production by specification according to product demand, operation time for each process facility, environmental conditions in the factory, etc. can
  • the center computing device 300 may generate an optimal control condition for each of the plurality of process facilities 100A, 100B, 100C, ..., based on the generated factory optimization condition.
  • the center computing device 300 may provide the generated optimal control condition to each of the edge computing devices 200A, 200B, 200C, .... Thereafter, each of the edge computing devices 200A, 200B, 200C, ... updates the optimal control conditions provided with the pre-stored optimal control conditions, and updates the updated optimal control conditions to the process facilities 100A, 100B, 100C, ... can be applied to each.
  • the center computing device 300 generates an optimal control condition for each predefined time unit (eg, 2 minutes or 10 minutes, etc.) to provide to each of the edge computing devices 200A, 200B, 200C, ...
  • each of the edge computing devices 200A, 200B, 200C, ... may update pre-stored information immediately upon receiving the optimal control condition.
  • the center computing device (AI-mother) generates factory optimization conditions based on the optimal control conditions for each process facility and the collected market demand information, and uses the It is provided to computing devices (AI-son) so that each process facility operates optimally in response to the individual needs of each consumer, thereby enabling small-volume production of individually tailored multi-products in response to market demand.
  • the operation of the autonomous factory operating system according to the present invention described above may be implemented as computer-readable codes on a computer-readable recording medium.
  • the computer-readable recording medium includes all types of recording devices in which data readable by a computer system is stored. Examples of the computer-readable recording medium include ROM, RAM, CD-ROM, magnetic tape, floppy disk, and optical data storage device.
  • the computer-readable recording medium is distributed in network-connected computer systems, and computer-readable codes can be stored and executed in a distributed manner.

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  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Automation & Control Theory (AREA)
  • General Physics & Mathematics (AREA)
  • Manufacturing & Machinery (AREA)
  • Quality & Reliability (AREA)
  • General Engineering & Computer Science (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Software Systems (AREA)
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Abstract

Un système d'exploitation d'usine autonome reposant sur un calcul informatisé en périphérie de réseau selon la présente invention comprend : une pluralité de dispositifs informatiques périphériques intelligents qui sont connectés à chacune d'une pluralité d'installations de traitement pour collecter des données de processus de base associées aux installations de traitement, pour générer une condition de commande optimale selon un algorithme d'intelligence artificielle prédéfini sur la base des données de processus de base collectées, et pour appliquer cette dernière aux installations de traitement ; et un dispositif informatique central intelligent qui est relié à chacun de la pluralité de dispositifs informatiques périphériques intelligents dans un environnement en nuage pour identifier des conditions de commande optimales pour chacune de la pluralité d'installations de traitement, et génère des conditions d'optimisation d'usine selon un algorithme d'intelligence artificielle prédéfini sur la base des conditions de commande optimales identifiées.
PCT/KR2021/019195 2020-12-24 2021-12-16 Système d'exploitation d'usine autonome reposant sur un calcul informatisé en périphérie de réseau WO2022139326A1 (fr)

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KR10-2020-0183793 2020-12-24
KR1020200183793A KR20220092227A (ko) 2020-12-24 2020-12-24 분산형 엣지 컴퓨팅 기반의 자율화 공장 운영 시스템

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CN111090243A (zh) * 2018-10-23 2020-05-01 宁波方太厨具有限公司 一种实现厨房电器智能互联的方法

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KR102668875B1 (ko) * 2022-09-21 2024-06-26 농업회사법인 주식회사 솔오토메틱 IoT기반의 곡물 세척 공정 자동화 모니터링 및 로봇화 구현 방법 및 시스템

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CN111090243B (zh) * 2018-10-23 2023-04-14 宁波方太厨具有限公司 一种实现厨房电器智能互联的方法

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