WO2021166716A1 - 作業稼働率測定装置及び作業稼働率測定方法 - Google Patents

作業稼働率測定装置及び作業稼働率測定方法 Download PDF

Info

Publication number
WO2021166716A1
WO2021166716A1 PCT/JP2021/004573 JP2021004573W WO2021166716A1 WO 2021166716 A1 WO2021166716 A1 WO 2021166716A1 JP 2021004573 W JP2021004573 W JP 2021004573W WO 2021166716 A1 WO2021166716 A1 WO 2021166716A1
Authority
WO
WIPO (PCT)
Prior art keywords
work
data
operation rate
hand
work frame
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
PCT/JP2021/004573
Other languages
English (en)
French (fr)
Japanese (ja)
Inventor
松林　豊
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
NEC Platforms Ltd
Original Assignee
NEC Platforms Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by NEC Platforms Ltd filed Critical NEC Platforms Ltd
Priority to US17/796,335 priority Critical patent/US20230068757A1/en
Priority to CN202180014694.2A priority patent/CN115104113A/zh
Priority to EP21757397.1A priority patent/EP4109362A4/en
Publication of WO2021166716A1 publication Critical patent/WO2021166716A1/ja
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/04—Manufacturing
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
    • G06Q10/06398—Performance of employee with respect to a job function
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/764—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/20—Movements or behaviour, e.g. gesture recognition
    • G06V40/28—Recognition of hand or arm movements, e.g. recognition of deaf sign language
    • 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
    • Y02P90/30—Computing systems specially adapted for manufacturing

Definitions

  • the present invention relates to a work operation rate measuring device for measuring an operation rate when performing manual work within a work frame of a production line such as a factory, and a work operation rate measurement method.
  • the line manager in the factory only conducts hearings for the workers to confirm the work. It is difficult to grasp the situation where the worker is not actually operating, for example, the situation where the hand is not moving just by standing at the work place. In the hearing for confirming the work at this time, it was necessary to visually confirm the actual work of the plurality of processes and to measure the time of the work with a stopwatch.
  • Patent Documents 1 to 3 have been proposed in order to alleviate such a problem.
  • the measured value from the start to the end of the operation is obtained, and the relationship between the content of the operation and the time is defined by using the obtained measured value and the specified operation content.
  • the motion identification part that builds the model is used.
  • This operation specifying unit acquires image data with depth from the depth sensor as the first position information, and acquires image data from the digital camera as the second position information. Based on the measured value of the position information acquisition unit, the operator Identify the position of the hand. Further, in this motion specifying unit, the motion content performed by the worker is specified based on the position of the identified worker's hand, and a model is constructed using the specified motion content and the acquired measured value. Or execute the update.
  • a distance sensor composed of a camera or the like capable of generating a color or monochrome image, and a plurality of time-series distance images taken while a worker performs a series of operations on a work table.
  • a processor that detects the operator's hand is used.
  • the hand region detector of the processor can detect the hand region using a classifier trained in advance to detect the hand on the image, and Histograms (Histograms) extracted from the region of interest of the image. By inputting of Oriented Gradients) into the classifier, it is possible to determine whether or not the region of interest includes the hand region.
  • the analysis unit in the control unit that controls the server includes a work time measurement unit and a residence time measurement unit.
  • the work time measurement unit measures the time when the worker is actually working in the place
  • the residence time measurement unit measures the time when the worker is in the place.
  • Patent Documents 1 to 3 include a technique for defining and modeling the relationship between the operation content and time, and a technique for measuring the amount of work performed by the worker and displaying the measured data. It is shown. However, these Patent Documents 1 to 3 only show each of these techniques individually, and do not show a concrete measure as to how to relate these techniques.
  • An example of the object of the present invention is a work operation rate measuring device and a work operation rate measurement capable of efficiently analyzing and quantifying the state of work performed on a work table by a new method that has never existed before. To provide a method.
  • the first aspect of the present invention is a work operation rate measuring device for measuring an operation rate when performing manual work within predetermined work frames provided in each of a plurality of processes, and is installed in a plurality of processes.
  • a model creation means that takes a picture of the inside of the work frame with a camera, machine-learns the position of the worker's hand held over the work frame based on the shooting data, and creates a machine learning model for each camera, and actual work.
  • the machine learning model created by the model creation means it is analyzed whether or not the position of the worker's hand is included in the work frame, and the analysis data is displayed in time series.
  • It has a data analysis storage means for storing and an operation rate calculation means for obtaining an operation rate in each work frame using the analysis data stored in the data analysis storage means.
  • the second aspect of the present invention is a work operation rate measuring method for measuring an operation rate when performing manual work within predetermined work frames provided in each of a plurality of steps, and is installed in the plurality of steps.
  • a model creation process that takes a picture of the inside of the work frame with a camera, machine-learns the position of the worker's hand held over the work frame based on the shooting data, and creates a machine learning model for each camera, and the actual work.
  • the configuration of the work operating rate measuring device 10 will be described with reference to FIG.
  • the work operation rate measuring device 10 includes a model creation means (model creation unit) 1, a data analysis storage unit (data analysis storage unit) 2, and an operation rate calculation means (operation rate calculation unit) 3.
  • model creation unit model creation unit
  • data analysis storage unit data analysis storage unit
  • operation rate calculation unit operation rate calculation unit
  • the model creation means 1 takes a picture of the inside of the work frame with cameras installed in a plurality of processes, and machine-learns the position of the worker's hand held over the work frame based on the shooting data, and machine learning for each camera. Create a model.
  • the data analysis storage means 2 uses the machine learning model created by the model creation means 1 for the image in which the actual work is being performed, and whether or not the position of the worker's hand is included in the work frame. And save the analysis data in chronological order.
  • the operating rate calculation means 3 obtains the operating rate in each work frame using the analysis data saved in the data analysis saving means 2.
  • the inside of the work frame is photographed by cameras installed in a plurality of processes, and the worker holding the work frame in the work frame based on the photographed data.
  • Machine learning the position of the hand and create a machine learning model for each camera.
  • whether or not the position of the worker's hand is included in the work frame is analyzed using the machine learning model created by the model creation means 1, and the analysis data is analyzed.
  • the operating rate in each work frame can be calculated using the saved analysis data.
  • the operation rate of a plurality of processes can be obtained by targeting only the hands of the operator, so that the overall control operation becomes lighter and the operation rate can be detected in real time. ..
  • the work operation rate measuring device 10 by setting a machine learning model for each camera of the work frame of a plurality of processes and including pre-learning, it is possible to accurately detect hands in various environments, and a plurality of them. It is possible to efficiently grasp the operating rate of the process.
  • FIG. 2 is an overall configuration diagram of the work operating rate measuring device 100 according to the embodiment.
  • the work operation rate measuring device 100 has an operation control unit 11, a data processing unit 12, and a photographing unit 13 directly connected to the network N.
  • the photographing area photographed by the photographing unit 13 is indicated by reference numeral EA.
  • the network N on the photographing unit 13 side is connected to the operation control unit 11 and the network N on the data processing unit 12 side via the hub 14.
  • these components are installed in the factory.
  • the operation control unit 11 is a client terminal (PC) that controls the operation of the entire network N in the work operation rate measuring device 100, and has a model creation means 11A, a data analysis storage means 11B, and an operation rate calculation means 11C.
  • a hardware processor such as a CPU (Central Processing Unit) of a client terminal (PC) executes a program (software). It may be realized by.
  • the program may be stored in a storage medium.
  • the model creating means 11A photographs the inside of the shooting area EA with cameras (indicated by reference numeral CA) (described later) installed in a plurality of steps, and based on the shooting data, a work frame (indicated by reference numeral FL) (described later). ) Is machine-learned about the position of the worker's hand held inside, and a machine learning model for each camera CA is created.
  • the data analysis storage means 11B uses the machine learning model created by the model creation means 11A for the image in which the actual work is being performed, and whether or not the position of the worker's hand is included in the work frame FL.
  • the analysis data is analyzed and the analysis data is saved in chronological order.
  • the operating rate calculation means 11C obtains the operating rate in each work frame FL using the analysis data saved in the data analysis saving means 11B. Specific processing performed by the model creating means 11A, the data analysis saving means 11B, and the operating rate calculation means 11C will be described later.
  • the client terminal (PC) constituting the motion control unit 11 has a screen that can be displayed by GUI (Graphical User Interface), and machine learns the movement of the worker's hand in the factory. It is connected to a server 22 (described later) to be operated and a camera CA that captures an image as an input for its machine learning via a network.
  • GUI Graphic User Interface
  • the data processing unit 12 designates a VMS (Video Management System) server 20 for factories, a recording storage 21 for storing the shooting data of the camera CA supplied through the VMS server 20, and a holder for the shooting data stored in the recording storage 21. It is composed of an image analysis / WEB (World Wide Web) server 22 or the like that is saved as a log (log data) at any time.
  • the camera shooting data and the occasional log (log data) stored in the data processing unit 12 are defined as analysis data.
  • the photographing unit 13 includes a plurality of cameras CA (cameras C1, C2, C3, C4 ...) That photograph the production line 30.
  • the photographing unit 13 photographs the work table of each worker by these cameras CA.
  • a plurality of work frame FLs are set in the shooting area EA on the work table, which is photographed by the cameras CA (cameras C1, C2, C3, C4), respectively, as shown in FIG.
  • FIG. 2 shows, as an example, an example (settings A to D) in which four work frames FL are set in the shooting area EA on each work table.
  • one production line 30 is shown in FIG. 2, a similar photographing area EA may be provided in a plurality of production lines 30.
  • the model creation means 11A of the work operation rate measuring device 100 as described above is used for each camera CA (cameras C1, C2, C3, C4 ...) In the factory before actually detecting the worker's hand. , The worker moves his / her hand in front of the camera CA according to the instruction from the client terminal (PC) to create an optimum machine learning model according to the environment (described later with reference to FIGS. 3 to 4).
  • the data analysis storage means 11B of the work operation rate measuring device 100 detects a hand using a machine learning model optimized for each camera CA, and the detected hand is included in the area of the camera CA set in advance.
  • the log and the moving image of the detected hand displayed in the frame are stored in the server 22 as data for each time (described later with reference to FIG.
  • the operating rate calculation means 11C of the work operating rate measuring device 100 allows the line administrator to confirm the daily operating rate status on the client terminal (PC) by utilizing the log data and the moving image. It can be done (described later with reference to FIG. 6).
  • Step S1 In step S1, in a state where the cameras CA are installed in a plurality of processes of the factory, an instruction is given to the operator to hold his / her hand in the shooting area EA in front of the camera CA (see the part (A) in FIG. 3). In step S1 and the following steps, processing is executed for each of the cameras C1, C2, C3, and C4 constituting the camera CA.
  • Step S2 a general-purpose hand machine learning model is used to recognize the hand photographed by the camera CA (cameras C1, C2, C3, C4), and a frame of the size of the hand (a frame of the size that the hand fits in). ) Is displayed on the client terminal (PC) as the work frame FL. At this time, in the client terminal (PC), the size of the work frame FL for learning the hand in the camera CA (cameras C1, C2, C3, C4) is determined (see the part (A) in FIG. 3).
  • Step S3 In step S3, based on the instruction from the client terminal (PC), the worker places his / her hand in the work frame FL and holds his / her hand over (see the part (A) in FIG. 3).
  • step S4 based on the instruction from the client terminal (PC), the operator is made to perform operations such as front / back / rotation of the hands in the work frame FL for a specified time (see part (A) in FIG. 3).
  • step S5 the image data for each work frame FL is automatically labeled for machine learning at the size of a hand, so that each camera CA (cameras C1, C2, C3, C4) is subjected to labeling processing.
  • Machine learning is performed on the hand according to the environment (brightness / angle of view / type of hand / reflected background, etc.) (see part (A) in FIG. 3).
  • Step S6 in the shooting area EA shot by each camera C1, C2, C3, C4, the area divided into equal parts, for example, nine locations shown in the part (B) of FIG. 3 (indicated by reference numerals M1 to M9).
  • the machine learning is carried out while the operator holds his / her hand in the same order as before and operates as instructed (see part (B) of FIG. 3).
  • step S7 the learning model is updated when the nine locations set in step S6 are executed.
  • the machine learning model for each camera CA stored in the recording storage 21 via the image analysis / WEB server 22 becomes the optimum one according to the camera environment (see part (C) in FIG. 3). ).
  • step S8 in each of the cameras C1, C2, C3, and C4, the part to be actually confirmed as a work process is set as a square work frame FL by the GUI on the client terminal (PC). At this time, the vertical and horizontal sizes of the work frame FL and their coordinate positions can be changed (see the part (A) in FIG. 4).
  • step S9 if there are four work frame FLs for each of the cameras C1, C2, C3, and C4, the size and coordinate position of each of the four work frame FLs are set in the same manner ( (See part (B) and part (C) of FIG. 4).
  • step S10 using the machine learning model for each camera CA (cameras C1, C2, C3, C4) learned earlier, the worker's hand is in the work frame for the image of the line process in which the actual work is being performed. It is determined whether or not it is in the FL, and it is saved as ON-OFF data (ON: 1 / OFF: 0) in chronological order on the image analysis / WEB server 22 (parts (A) and (B) of FIG. 5). reference).
  • the part (A) of FIG. 5 the state in which the worker's hand is in the work frame FL (ON) is shown by a solid line, and the state in which the worker's hand is not in the work frame FL (OFF) is shown by a broken line.
  • the part (B) of FIG. 5 shows an image of ON-OFF data stored in the recording storage 21 through the VMS server 20 and the image data thereof.
  • step S11 the image data to which the work frame FL of the hand when the hand is detected is added to the image data analyzed at the same time is also saved. This is later saved to confirm how the hand was detected (part (B) in FIG. 5).
  • step S12 the operation rates of the plurality of processes are displayed on the client terminal (PC) using the log data and the image data saved earlier (see part (A) of FIG. 6).
  • part (A) of FIG. 6 an example is shown in which the operating rates for each work frame FL of a plurality of processes are displayed using a bar graph.
  • step S13 when the bar graph displayed in the part (A) of FIG. 6 is selected (when an operation such as clicking is performed), the operation rate of a plurality of processes for each time is displayed in the part (B) of FIG. Display. In the part (B) of FIG. 6, the operating rate for each time is shown in the part (A) of FIG. 6 when "setting D in the shooting area EA of the camera C1" is selected as an example.
  • Step S14 when the arrows e1 to e3 of the event are selected (clicked), the moving image at that time is displayed so that the details of what happened at that time can be understood.
  • the work operating rate measuring device 100 according to the present embodiment can be expected to have the following effects. That is, in the work operation rate measuring device 100 of the present embodiment, the control becomes lighter by setting the detection target only by the operator's hand, and the detection can be performed in real time.
  • the work operation rate measuring device 100 of the present embodiment has a machine learning model for each camera CA (cameras C1, C2, C3, C4), and by including pre-learning, hands in various environments can be accurately performed. It can be detected, and the actual operating rates of a plurality of processes can be efficiently grasped. Further, in the work operation rate measuring device 100 of the present embodiment, when the detection portion of the hand is a different object (for example, a tire), if the hand is replaced with a different object from the pre-learning stage, the hand can be used. It also has expandability in that it is possible to grasp the time when a non-object is in the work frame FL and to confirm the stagnation status of the product on the line.
  • the image data obtained by the camera CA is input, the hands according to the worker's environment are pre-learned in an interactive manner, and the worker's hands are placed in the work frame FL set in advance with the fixed point image. It can be used outside the factory as it only determines if there is a hand.
  • it is possible to collect data such as whether the learning proficiency is proportional to the time taken for notes for classrooms and cram schools, and it can be used as a new learning scale. can do.
  • the embodiment of the present invention can be applied to the field of a craftsman who actually performs manual work in a place where a camera can be installed indoors such as a beautician or a cook.
  • FIG. 2 (Modification 2)
  • four work frame FLs as shown in FIG. 2 are set in the shooting area EA of the cameras CA (cameras C1, C2, C3, C4), and in nine areas as shown in FIG. I tried to do machine learning.
  • the number of these work frame FLs and machine learning areas may be the same, and the administrator can freely set them.
  • the worker's hand in the image data processed in the embodiment means the part beyond the worker's wrist.
  • the worker is wearing gloves, or when the worker is holding some tools, jigs, stationery, etc. in his / her hand in the shooting area, he / she is wearing gloves, or , Tools, jigs, stationery, etc. may be treated as a "hand" and the image data may be analyzed.
  • the present invention relates to a work operation rate measuring device for measuring an operation rate when performing manual work within a work frame of a production line such as a factory, and a work operation rate measurement method.
  • Model creation means 2 Data analysis storage means 3 Operation rate calculation means 10 Work operation rate measurement device 11 Operation control unit 11A Model creation means 11B Data analysis storage means 11C Operation rate calculation means 12 Data processing unit 13 Imaging unit 14 Hub 20 VMS server 21 Recording storage 22 Image analysis / WEB server 30 Production line 100 Work utilization rate measuring device CA camera C1 camera C2 camera C3 camera C4 camera EA Shooting area FL Work frame N network

Landscapes

  • Engineering & Computer Science (AREA)
  • Business, Economics & Management (AREA)
  • Human Resources & Organizations (AREA)
  • Theoretical Computer Science (AREA)
  • Strategic Management (AREA)
  • Economics (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Development Economics (AREA)
  • Educational Administration (AREA)
  • General Business, Economics & Management (AREA)
  • Tourism & Hospitality (AREA)
  • Marketing (AREA)
  • Health & Medical Sciences (AREA)
  • General Health & Medical Sciences (AREA)
  • Quality & Reliability (AREA)
  • Operations Research (AREA)
  • Game Theory and Decision Science (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Multimedia (AREA)
  • Artificial Intelligence (AREA)
  • Evolutionary Computation (AREA)
  • Computing Systems (AREA)
  • Medical Informatics (AREA)
  • Software Systems (AREA)
  • Databases & Information Systems (AREA)
  • Psychiatry (AREA)
  • Social Psychology (AREA)
  • Human Computer Interaction (AREA)
  • Manufacturing & Machinery (AREA)
  • Primary Health Care (AREA)
  • General Factory Administration (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)
PCT/JP2021/004573 2020-02-18 2021-02-08 作業稼働率測定装置及び作業稼働率測定方法 Ceased WO2021166716A1 (ja)

Priority Applications (3)

Application Number Priority Date Filing Date Title
US17/796,335 US20230068757A1 (en) 2020-02-18 2021-02-08 Work rate measurement device and work rate measurement method
CN202180014694.2A CN115104113A (zh) 2020-02-18 2021-02-08 作业率测量设备和作业率测量方法
EP21757397.1A EP4109362A4 (en) 2020-02-18 2021-02-08 DEVICE FOR MEASURING WORKING SPEED AND METHOD FOR MEASURING WORKING SPEED

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
JP2020-025497 2020-02-18
JP2020025497A JP7180886B2 (ja) 2020-02-18 2020-02-18 作業稼働率測定装置及び作業稼働率測定方法

Publications (1)

Publication Number Publication Date
WO2021166716A1 true WO2021166716A1 (ja) 2021-08-26

Family

ID=77391143

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/JP2021/004573 Ceased WO2021166716A1 (ja) 2020-02-18 2021-02-08 作業稼働率測定装置及び作業稼働率測定方法

Country Status (5)

Country Link
US (1) US20230068757A1 (https=)
EP (1) EP4109362A4 (https=)
JP (2) JP7180886B2 (https=)
CN (1) CN115104113A (https=)
WO (1) WO2021166716A1 (https=)

Families Citing this family (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2024005533A (ja) * 2022-06-30 2024-01-17 アキュイティー株式会社 作業管理装置及び作業管理方法
CN116341281B (zh) * 2023-05-12 2023-08-15 中国恩菲工程技术有限公司 作业率的确定方法及系统、存储介质、终端

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2000180162A (ja) * 1998-12-11 2000-06-30 Hitachi Plant Eng & Constr Co Ltd 作業分析装置
WO2017222070A1 (ja) 2016-06-23 2017-12-28 Ｎｅｃソリューションイノベータ株式会社 作業分析装置、作業分析方法、及びコンピュータ読み取り可能な記録媒体
JP2019086827A (ja) * 2017-11-01 2019-06-06 キヤノン株式会社 情報処理装置、情報処理方法
JP2019120577A (ja) 2018-01-04 2019-07-22 富士通株式会社 位置推定装置、位置推定方法及び位置推定用コンピュータプログラム
JP2019200560A (ja) 2018-05-16 2019-11-21 パナソニックＩｐマネジメント株式会社 作業分析装置および作業分析方法
JP2020025497A (ja) 2018-08-10 2020-02-20 国立大学法人東京工業大学 再構成膜、再構成膜の作成方法、光酸化反応駆動方法、および、メタノール製造方法

Family Cites Families (14)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP5195465B2 (ja) * 2009-01-28 2013-05-08 トヨタ自動車株式会社 ロボット制御装置及び方法
JP2014035712A (ja) * 2012-08-10 2014-02-24 Hitachi High-Technologies Corp 業務状況の計測方法及び計測装置
JP5529949B2 (ja) * 2012-11-20 2014-06-25 株式会社小松製作所 作業機械及び作業管理システム
US20150193424A1 (en) * 2014-01-07 2015-07-09 Samsung Electronics Co., Ltd. Method of changing dynamic screen layout and electronic device
WO2016073363A1 (en) * 2014-11-03 2016-05-12 Motion Insight LLC Motion tracking wearable element and system
JP6367166B2 (ja) * 2015-09-01 2018-08-01 株式会社東芝 電子機器及び方法
EP3438915A4 (en) * 2016-03-31 2019-09-25 Sumitomo Heavy Industries, Ltd. WORK MANAGEMENT SYSTEM FOR BUILDING MACHINES AND CONSTRUCTION MACHINES
JP6608890B2 (ja) * 2017-09-12 2019-11-20 ファナック株式会社 機械学習装置、ロボットシステム及び機械学習方法
US20190139441A1 (en) * 2017-11-03 2019-05-09 Drishti Technologies, Inc. Contextual training systems and methods
CN108564279A (zh) 2018-04-12 2018-09-21 同济大学 一种考虑认知的生产线手工工位人因复杂性测度方法
JP2019191117A (ja) * 2018-04-27 2019-10-31 キヤノン株式会社 画像処理装置、画像処理方法及びプログラム
EP3788542A1 (en) * 2018-05-03 2021-03-10 3M Innovative Properties Company Personal protective equipment system with augmented reality for safety event detection and visualization
WO2020039559A1 (ja) * 2018-08-23 2020-02-27 ソニー株式会社 情報処理装置、情報処理方法及び作業評価システム
CN109409289A (zh) * 2018-10-26 2019-03-01 国网四川省电力公司电力科学研究院 一种电力作业安全监督机器人安全作业识别方法及系统

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2000180162A (ja) * 1998-12-11 2000-06-30 Hitachi Plant Eng & Constr Co Ltd 作業分析装置
WO2017222070A1 (ja) 2016-06-23 2017-12-28 Ｎｅｃソリューションイノベータ株式会社 作業分析装置、作業分析方法、及びコンピュータ読み取り可能な記録媒体
JP2019086827A (ja) * 2017-11-01 2019-06-06 キヤノン株式会社 情報処理装置、情報処理方法
JP2019120577A (ja) 2018-01-04 2019-07-22 富士通株式会社 位置推定装置、位置推定方法及び位置推定用コンピュータプログラム
JP2019200560A (ja) 2018-05-16 2019-11-21 パナソニックＩｐマネジメント株式会社 作業分析装置および作業分析方法
JP2020025497A (ja) 2018-08-10 2020-02-20 国立大学法人東京工業大学 再構成膜、再構成膜の作成方法、光酸化反応駆動方法、および、メタノール製造方法

Also Published As

Publication number Publication date
EP4109362A4 (en) 2023-07-26
JP2022164831A (ja) 2022-10-27
JP7486751B2 (ja) 2024-05-20
CN115104113A (zh) 2022-09-23
JP7180886B2 (ja) 2022-11-30
EP4109362A1 (en) 2022-12-28
US20230068757A1 (en) 2023-03-02
JP2021131626A (ja) 2021-09-09

Similar Documents

Publication Publication Date Title
US11842511B2 (en) Work analyzing system and work analyzing method
CN111308925B (zh) 信息处理装置、生产指示辅助方法
CN114846514A (zh) 作业分析装置和作业分析方法
JP7486751B2 (ja) 作業稼働率測定装置及び作業稼働率測定方法
US11138805B2 (en) Quantitative quality assurance for mixed reality
JP2023018016A (ja) 管理システムおよび原因分析システム
KR20240064037A (ko) 피로 테스트를 통해 물체의 표면 결함 및 브레이크 디스크의 크랙을 인공 지능에 의해 식별 및 특성화하는 방법
JP2023019009A (ja) 作業状況分析システム及び作業状況分析方法
JP2024019350A5 (ja) 情報処理装置、サーバ装置、情報処理装置の制御方法、サーバ装置の制御方法、及びプログラム
CN110235129A (zh) 复合部件铺叠工具上基于增强现实的铺层铺叠
DE112022004981T5 (de) Anzeigedaten-Erzeugungsprogramm, Anzeigedaten-Erzeugungsgerät und Anzeigedaten-Erzeugungsverfahren
CN112130572A (zh) 一种基于穿戴式设备的机电设备养护单兵辅助方法及系统
HK40078068A (en) Work rate measurement device and work rate measurement method
WO2023286393A1 (ja) 情報処理システム及び情報処理方法
EP4513430A2 (en) System and method for locating and visualizing camera images in relation to a large-scale manufacturing product
Piontek et al. Integration of smart hand tools and digital assistance systems
US20240282068A1 (en) Augmented reality visualization of detected defects
CN114708252B (zh) 设备面板的巡检方法及系统
TW202445509A (zh) 圖像處理方法、程式、圖像處理系統、圖像處理裝置及檢測裝置
WO2023171382A1 (ja) 情報処理システム、情報処理装置及びデータ生成方法
JP2023140036A (ja) 作業分析装置および作業分析方法
JP2024007178A (ja) 画像処理装置、画像処理方法、およびプログラム
CN116051445A (zh) 电路板检测方法、装置、设备、存储介质及产品
Wilson et al. The Use of Digital Image Processing in the Modelling of a Roller Compaction Milling Operation
JP2025127689A (ja) 画像検品装置、画像検品方法および画像検品プログラム

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 21757397

Country of ref document: EP

Kind code of ref document: A1

WWE Wipo information: entry into national phase

Ref document number: 202217046479

Country of ref document: IN

NENP Non-entry into the national phase

Ref country code: DE

ENP Entry into the national phase

Ref document number: 2021757397

Country of ref document: EP

Effective date: 20220919

WWW Wipo information: withdrawn in national office

Ref document number: 2021757397

Country of ref document: EP