WO2022091571A1 - Work procedure updating device, work procedure updating method, and program - Google Patents

Work procedure updating device, work procedure updating method, and program Download PDF

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Publication number
WO2022091571A1
WO2022091571A1 PCT/JP2021/032074 JP2021032074W WO2022091571A1 WO 2022091571 A1 WO2022091571 A1 WO 2022091571A1 JP 2021032074 W JP2021032074 W JP 2021032074W WO 2022091571 A1 WO2022091571 A1 WO 2022091571A1
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Prior art keywords
work
time
procedure
fastest
actual
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PCT/JP2021/032074
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French (fr)
Japanese (ja)
Inventor
孝忠 長岡
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三菱電機株式会社
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Priority to JP2022558891A priority Critical patent/JP7387025B2/en
Publication of WO2022091571A1 publication Critical patent/WO2022091571A1/en

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    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B19/00Programme-control systems
    • G05B19/02Programme-control systems electric
    • G05B19/418Total 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], computer integrated manufacturing [CIM]
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06QINFORMATION 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
    • G06Q50/00Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/04Manufacturing
    • YGENERAL 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
    • Y02TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02PCLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/30Computing systems specially adapted for manufacturing

Definitions

  • This disclosure relates to a work procedure update device, a work procedure update method, and a program.
  • setup work including parts replacement, mounting board changes, jig or tool replacements, and equipment program changes. Since the market needs in recent years require high-mix low-volume production, shortening the setup work time that occurs when switching production models on a daily basis has become a priority issue for factory reform.
  • Patent Document 1 There is a system that extracts such setup work and calculates the work time required for the setup work.
  • the setup work management system described in Patent Document 1 has a limit number of simultaneous work, which is the maximum number of workers who can efficiently perform setup work at the same time, and an allotted number of workers, which is the number of workers assigned to each setup work. And, based on, the time required for the setup work is calculated. It is explained that this makes it possible to appropriately manage the setup work that occurs corresponding to the component mounting line.
  • the setup work management system described in Patent Document 1 extracts the setup work generated by the setup change based on the mounting data and the production planning information. Then, the simultaneous work limit number of people is calculated based on the extracted setup work and the unit setup work information.
  • Patent Document 1 it is difficult to accurately predict the working time based on the number of people who can work at the same time.
  • the setup change is performed only a few times a day, and the timing of the setup work varies depending on the daily production situation, so it is difficult to specify the timing to analyze the work. For this reason, it was inefficient because it was necessary to assign a production engineer to the site to improve the setup work. That is, it is difficult to evaluate the actual setup work state and reflect it in the work procedure without lowering the production efficiency.
  • This disclosure has been made in view of the above circumstances, and is a work procedure updating device, a work procedure update method, and a work procedure update device capable of updating the fastest work time according to the actual work state to a feasible optimum work procedure.
  • the purpose is to provide a program.
  • the work procedure updating device of the present disclosure acquires the actual work time based on the actual work video of the work, and is the fastest when the actual work time is shorter than the fastest work time stored in the storage unit. It is equipped with the fastest work time update unit that updates the work time to the actual work time. Further, the work procedure update device uses a trained model in which the fastest work time and the appropriateness of work have been learned in advance, and extracts the optimum work procedure based on the updated fastest work time. It is provided with a unit and a work procedure update unit that updates the work procedure stored in the storage unit to the extracted optimum work procedure.
  • the fastest work time is updated based on the video of the work and the optimum work procedure at the fastest work time is extracted, the optimum work that can realize the fastest work time according to the actual work state can be realized. It will be possible to update to the procedure.
  • a block diagram showing a configuration example of a work procedure update system according to an embodiment of the present disclosure.
  • Flowchart of actual work video acquisition process Flowchart of fastest work time update process
  • Optimal work procedure Block diagram showing the learning device of the extraction unit
  • Optimal work procedure Block diagram showing the inference device of the extraction unit
  • Flow chart of inference processing Graph showing the fastest working time and the shortest actual working time
  • a table showing the optimal work procedure
  • a table showing the working time of the optimum work procedure A table showing the working time of the actual work procedure Table showing work improvement plans
  • FIG. 1 is a block diagram showing a configuration example of the work procedure update system 1 according to the embodiment of this disclosure.
  • the work procedure update system 1 is a system that generates and updates a work procedure when the work in the factory is executed at the fastest working time.
  • the work procedure update system 1 generates and updates the work procedure for the setup work that occurs when the production model is changed.
  • the setup work is a work that occurs when a model of a product produced in a factory is switched, and is, for example, replacement of parts, change of a mounting board, replacement of a jig or a tool, and change of a program of equipment.
  • the work procedure update system 1 updates the fastest work time and the optimum work procedure based on the photographing device 100 for photographing the worker performing the setup work and the work moving image photographed by the photographing device 100.
  • a work procedure updating device 200 is provided.
  • the photographing device 100 is an arbitrary photographing device that photographs the setup work of the field worker during the setup work period of the preset analysis target model and during the preset analysis target period.
  • a wearable device that can be attached to the worker's body and photographed is preferable in order to photograph the work contents of the operator at a close distance.
  • smart glasses or HoloLens® may be used.
  • it may be an action camera or a wearable camera mounted next to the worker's helmet.
  • the timing of shooting may be determined by the operation of the operator.
  • the shooting is started by pressing the shooting start button of the shooting device 100. Further, when the operator completes the setup work, the shooting is stopped by pressing the shooting end button of the shooting device 100. As a result, one cycle of setup work is photographed. If the model is not the model to be analyzed or the period is not the analysis target period, the photographing device 100 automatically or manually stops the photographing.
  • the photographing device 100 can be connected to the work procedure updating device 200 by any wired or wireless communication means.
  • the photographing device 100 transmits the captured moving image to the work procedure updating device 200 at an arbitrary timing.
  • the operation including the start or stop of photography of the photographing apparatus 100 may be controlled by the working procedure updating apparatus 200.
  • the work procedure updating device 200 stores a calculation processing unit 210 that executes a process of acquiring or generating various data based on a shooting moving image shot by the shooting device 100, and a data acquired or generated by the calculation processing unit 210. It includes a storage unit 220 and a display unit 230 that displays information acquired or generated by the arithmetic processing unit 210.
  • the arithmetic processing unit 210 is an arbitrary arithmetic processing unit, for example, a CPU (Central Processing Unit).
  • the arithmetic processing unit 210 acquires the actual work video included in the shot video by executing the program stored in the storage unit 220 and saves it in the storage unit 220, based on the actual work video acquisition unit 211 and the actual work time.
  • Fastest work time update unit 212 that updates the fastest work time
  • optimal work procedure extraction unit 213 that extracts the optimum work procedure by artificial intelligence (AI)
  • work procedure update unit 214 that updates the work procedure
  • optimum work It functions as a work time comparison unit 215 that compares the work time of the procedure with the actual work time
  • a work improvement plan output unit 216 that outputs a work improvement plan based on the comparison result of the work time.
  • the storage unit 220 is an arbitrary storage device, and is, for example, a flash memory, a non-volatile semiconductor memory including an EPROM (ErasableProgrammableReadOnlyMemory), or a magnetic disk, a flexible disk, an optical disk, a compact disk, a mini disk, or a DVD ( Digital Versatile Disc).
  • EPROM ErasableProgrammableReadOnlyMemory
  • magnetic disk a magnetic disk
  • a flexible disk an optical disk
  • a compact disk a mini disk
  • DVD Digital Versatile Disc
  • the storage unit 220 has been learned to be used by the actual work database 221 containing the data related to the actual work, the fastest work time database 222 including the data of the fastest work time, and the optimum work procedure extraction unit 213. It has a learned model storage unit 223 for storing a model and a work procedure storage unit 224 for storing a work procedure.
  • the storage unit 220 also stores a program executed by the arithmetic processing unit 210.
  • the function of the arithmetic processing unit 210 and the contents of the storage unit 220 will be described in detail.
  • the actual work video acquisition unit 211 of the arithmetic processing unit 210 acquires the actual work video from the start of the work to the completion of the work for each series of setup work from the shot video shot by the shooting device 100.
  • FIG. 2 is a flowchart of the actual work video acquisition process.
  • the actual work moving image acquisition unit 211 first determines the setup work to be analyzed (step S101), and then determines the work period to be analyzed and the model of the product (step S102). After that, a moving image from the start to the completion of the work is acquired from the photographing device 100 (step S103), and the acquired actual work data is stored in the actual work database 221 of the storage unit 220.
  • the actual work video acquired in step S103 may be recorded by the shooting device 100 in advance before the start of a series of actual work video acquisition processes, or as a part of the actual work video acquisition process, the work procedure update device. It may be taken by the photographing apparatus 100 based on the control signal from 200. The operator may decide the timing of starting and stopping the shooting by the shooting device 100.
  • the shooting device 100 is a moving image shooting device including smart glasses or HoloLens
  • application software for controlling the moving image shooting device is installed in advance in the work procedure updating device 200 in order to acquire the actual work moving image in step S103. Keep it.
  • each moving image file is associated with the identification information of the worker, the work period, and the information of the product model, and is stored as a group.
  • the storage unit 220 of the work procedure update device 200 managed by the manufacturing department includes the actual work database 221.
  • the actual work database 221 is a local database or a cloud type database outside the work procedure update device 200. It may be.
  • the data recorded in the performance work database 221 may be compressed and stored.
  • old data may be automatically deleted to manage the storage capacity of the database.
  • the fastest work time update unit 212 calculates the actual work time per cycle of setup work based on the moving image data acquired from the actual work database 221 of the storage unit 220. More specifically, the fastest work time update unit 212 calculates the time from the start to the completion of the setup work as the actual work time of one cycle. If shooting starts when the work starts and shooting is completed when the work is completed, the shooting time is the actual work time. If the work start time and the shooting start time or the work completion time and the shooting completion time do not match, manual measurement may be performed. Alternatively, the actual working time may be calculated from the captured video image using any analysis software. As the analysis software, for example, Smart Logger (registered trademark), which is a work dynamic analysis system, can be used.
  • FIG. 3 is a flowchart of the fastest working time update process.
  • the fastest work time update unit 212 calculates the actual work time of the setup work of the model to be analyzed performed during one day, and the shortest actual work time is the shortest among them. Is extracted (step S201). The numerical value of the actual work time is continuously managed during the predetermined analysis period, and the fastest work time in the previous period and the shortest actual work time in one day, which are stored in the fastest work time database 222, are compared with each other. Is repeatedly executed (step S202).
  • step S203: Yes If there is a day when the shortest actual work time is shorter than the fastest work time (step S203: Yes), the fastest work time is updated to the shortest actual work time of the day (step S204, fastest work time update step). If multiple actual work times are shorter than the fastest work time during the analysis period, the shortest actual work time within the analysis period is set as the new fastest work time. On the other hand, when all the actual work times are equal to or longer than the fastest work time (step S203: No), the process ends without changing the fastest work time.
  • the optimum working procedure extraction unit 213, which will be described later, may extract the shortest working time of the working procedure with the least waste of operation.
  • the work procedure with the least waste of operation is the work procedure with the largest value-added time ratio indicating the added value per unit time derived based on a predetermined standard.
  • the fastest work time update unit 212 By using the fastest work time update unit 212, it is possible for the setup worker to shoot and analyze the work video at the conventional site and greatly reduce the indirect work time required for the fastest work time. For example, the effect of shortening the analysis time when using a smart logger is 1/10. Further, it is possible to use the fastest working time based on the actual value at the actual production site for the evaluation of the working efficiency instead of the fastest working time obtained from the conventional mathematical formula.
  • the fastest working time updated by the fastest working time updating unit 212 is stored in the fastest working time database 222 of the storage unit 220.
  • the fastest working time database 222 is included in the storage unit 220 of the working procedure updating device 200 managed by the manufacturing department, but the fastest working time database 222 is a local database outside the working procedure updating device 200 or. It may be a cloud-type database.
  • the optimum work procedure extraction unit 213 extracts the optimum work procedure based on the procedure work time corresponding to each work procedure among the updated fastest work time when the fastest work time update unit 212 updates the fastest work time. do. Extraction of the optimum work procedure is performed using artificial intelligence (AI).
  • AI artificial intelligence
  • the artificial intelligence that extracts the optimum work procedure is realized by the optimum work procedure extraction unit 213 of the arithmetic processing unit 210 and the learned model storage unit 223 of the storage unit 220.
  • the optimum work procedure extraction unit 213 includes a learning device 217 and an inference device 218 (see FIGS. 4A and 5A).
  • the configuration in which the learning device 217 and the inference device 218 are included in the work procedure updating device 200 is described.
  • the learning device 217 and the inference device 218 are used to learn the appropriateness of the setup work of the target product including the vacuum cleaner, the dehumidifier, and the dishwasher.
  • the form of the learning device 217 and the inference device 218 is arbitrary, and may be, for example, a device separate from the target product and connected to the target product via a network. Alternatively, it may be built in the target product. Further, the learning device 217 and the inference device 218 may exist on the cloud server together with the learned model storage unit 223.
  • FIG. 4A is a block diagram showing the learning device 217 of the optimum work procedure extraction unit 213, and FIG. 4B is a flowchart of the learning process.
  • the case of learning the setup work of the target product including the vacuum cleaner, the dehumidifier, and the dishwasher will be described.
  • the learning device 217 includes a data acquisition unit 2171 and a model generation unit 2172.
  • the data acquisition unit 2171 acquires the new fastest work time updated by the fastest work time update unit 212 and the appropriateness of the work as learning data.
  • the data of the fastest working time also includes the contents of each working procedure included in the series of setup work and the data of the procedure working time which is the time required for them.
  • the appropriateness of work is an index showing the ease of work or the small number of wastes of work, and the work with the least number of wastes of operations hidden in the work is optimized.
  • the appropriateness of the work may be set by the user, or may be automatically determined based on a predetermined standard.
  • the model generation unit 2172 learns the appropriateness of work based on the learning data including the information of the combination of the new fastest work time and the appropriateness of work acquired by the data acquisition unit 2171. That is, the model generation unit 2172 generates a trained model for inferring the appropriateness of the work from the new fastest work time and the appropriateness of the work related to the setup work of the target product.
  • the learning data in this learning process is data in which the new fastest working time and the appropriateness of the work are associated with each other.
  • the learning algorithm used by the model generation unit 2172 may be a conventional algorithm. For example, supervised learning, unsupervised learning or reinforcement learning can be used.
  • deep learning may be executed to learn the extraction of the feature amount itself, or machine learning may be executed according to genetic programming, functional logic programming, and a support vector machine.
  • the model generation unit 2172 learns the appropriateness of the work for the fastest working time by supervised learning according to the neural network model.
  • supervised learning refers to a method of learning a feature in the learning data by giving a set of input and result (label) data to the learning device, and inferring the result from the input.
  • a neural network is composed of an input layer having a plurality of neurons, an intermediate layer (hidden layer) having a plurality of neurons, and an output layer having a plurality of neurons.
  • the intermediate layer is one layer or two or more layers.
  • the model generation unit 2172 is supervised learning based on the learning data including the information of the combination of the new fastest work time and the appropriateness of the work acquired by the data acquisition unit 2171. , Learn the appropriateness of work for the fastest work time.
  • the model generation unit 2172 adjusts the weight W1 between the input layer intermediate layers and the weight W2 between the intermediate layer output layers, and outputs a result (output from the output layer when a new fastest working time is input to the input layer). Learning is performed by finding the weights W1 and W2 that can approach the appropriateness of the work that is the correct answer). For example, when the work speed calculated from the procedure work time included in the fastest work time is input to the input layer, an index showing less waste of operation is obtained in the intermediate layer, and the appropriateness of the work is shown in the output layer. It is output, and the weights W1 and W2 are determined by comparing this with the appropriateness of the work that is the correct answer.
  • the trained model storage unit 223 stores the trained model output by the model generation unit 2172.
  • the flow of the learning process (model generation step) is as shown in the flowchart of FIG. 4B.
  • the data acquisition unit 2171 of the learning device 217 acquires new data on the fastest working time and the appropriateness of the work (step S301).
  • the new fastest work time and the appropriateness of the work are acquired at the same time, but it is sufficient if the new fastest work time and the appropriateness of the work can be input in association with each other.
  • Data of appropriateness may be acquired at different timings.
  • the model generation unit 2172 performs learning processing based on the combination of the new fastest work time and the appropriateness of the work acquired by the data acquisition unit 2171 (step S302), and generates a trained model. Then, the generated trained model is stored in the trained model storage unit 223 (step S303).
  • FIG. 5A is a block diagram showing the inference device 218 of the optimum work procedure extraction unit 213, and FIG. 5B is a flowchart of the inference process.
  • the inference device 218 includes a data acquisition unit 2181 and an inference unit 2182.
  • the data acquisition unit 2181 acquires a new fastest work time updated by the fastest work time update unit 212 as input data.
  • the inference unit 2182 infers the appropriateness of the work for the fastest work time acquired by the data acquisition unit 2181 by using the trained model stored in the trained model storage unit 223. That is, the inference unit 2182 inputs the fastest work time into the trained model, and outputs the appropriateness of the work inferred from the new fastest work time and the procedure work time included in the new fastest work time.
  • the configuration for outputting the appropriateness of the work using the trained model generated by the model generation unit 2172 of the learning device 217 has been described, but the trained model is acquired from the outside of the work procedure updating device.
  • the appropriateness of the work may be inferred based on this trained model.
  • the flow of inference processing (optimal work procedure extraction step) is as shown in the flowchart of FIG. 5B.
  • the data acquisition unit 2181 of the inference device 218 acquires new data of the fastest working time (step S401).
  • the inference unit 2182 inputs a new fastest working time into the learned model stored in the learned model storage unit 223 (step S402), infers the appropriateness of the work, and outputs it (step S403).
  • the optimum work procedure extraction unit 213 determines and outputs the optimum work procedure with the highest appropriateness for the target model based on the appropriateness of the output work (step S404). Specifically, the optimum work procedure extraction unit 213 is the most wasteful based on the content of each work procedure included in the series of work related to the new fastest work time and the procedure work time which is the time required for them. Extract the optimal procedure with few. When there are a plurality of actual works related to the new fastest work time, the optimum work procedure with the least waste is extracted from the work procedures of the actual work related to the fastest work time.
  • the optimum work procedure is determined at the discretion of the technician, and there is a problem that it may not match the actual situation of the setup worker.
  • the optimum work procedure utilizing artificial intelligence is used.
  • the extraction unit 213 can determine the optimum work procedure based on an objective evaluation.
  • the optimum work procedure extracted by the optimum work procedure extraction unit 213 is output to the work procedure update unit 214 and the work time comparison unit 215.
  • the work procedure update unit 214 updates the work procedure of the setup work stored in the work procedure storage unit 224 to the operator to the optimum work procedure input from the optimum work procedure extraction unit 213 (work procedure). Update step).
  • the work time comparison unit 215 calculates the procedure work time for each procedure in the actual work based on the video acquired by the actual work video acquisition unit 211, and the procedure work time in the optimum work procedure input from the optimum work procedure extraction unit 213. Compare with. Procedure Work time When the actual work time is longer, the work information is output.
  • the actual work data to be compared with the optimum work procedure may be the actual work data of the target worker stored in the actual work database 221, and is the actual work data related to the shortest work time of the target worker. There may be.
  • the work improvement plan output unit 216 creates a work improvement plan in the work time comparison unit 215 indicating that the procedure in which the actual work procedure work time is longer is changed to the procedure included in the optimum work procedure. It is displayed on the display unit 230.
  • the display form of the work improvement plan may be any form, and for example, an improvement plan display sheet in which the extracted difference portion is visualized using a commercially available table comparison tool may be used. By presenting the improvement plan display sheet to the operator, it is possible to obtain the effect of suppressing the variation in the setup work time.
  • FIGS. 6A and 6B are diagrams for explaining the operation of the fastest work time update unit 212 and the optimum work procedure extraction unit 213 for an example of work
  • FIGS. 6A shows the fastest work time and the shortest actual work time per day. It is a graph which shows
  • FIG. 6B is a table which shows the optimum work procedure.
  • the actual work video acquisition unit 211 acquires the actual work video taken by the photographing device 100 and saves it in the actual work database 221.
  • the fastest work time update unit 212 calculates the actual work time based on the actual work video stored in the actual work database 221 and compares the shortest actual work time and the fastest work time in one day. As a result of comparison, when the shortest actual work time per day is shorter than the current fastest work time, the fastest work time is updated.
  • the fastest working time is 25 minutes
  • the shortest actual working time per day changes as shown by black dots
  • the shortest actual working time on October 5 is shorter than the fastest working time.
  • the shortest actual work time on October 10 is shorter than the shortest actual work time on October 5. Therefore, the fastest working time update unit 212 updates the next fastest working time to the shortest actual working time on October 10.
  • the fastest work time update unit 212 stores the updated fastest work time and the procedure work time of each work procedure included in the actual work related to the fastest work time in the fastest work time database 222.
  • the trained model storage unit 223 stores the trained model learned based on the fastest working time in the past and the appropriateness of the work.
  • the optimum work procedure extraction unit 213 extracts the optimum work procedure using the trained model based on the fastest work time and the procedure work time stored in the fastest work time database 222.
  • the optimum work procedure extracted by the optimum work procedure extraction unit 213 is represented by, for example, an identification number (procedure No.) of each work procedure and work contents as shown in FIG. 6B. If there are multiple actual works with the same fastest work time within the analysis period, the optimum work procedure extraction unit 213 selects the procedure with less waste of work based on the trained model and performs the optimum work. It is a procedure.
  • FIG. 7A and 7B are diagrams for explaining the operation of the work time comparison unit 215 for an example of work
  • FIG. 7A is a table showing the work time of the optimum work procedure
  • FIG. 7B is the work of the actual work procedure. It is a table showing the time.
  • the work time comparison unit 215 acquires the optimum work procedure extracted by the optimum work procedure extraction unit 213 and the procedure work time which is the time required for each work procedure.
  • FIG. 7A shows the work content and the work time for each work procedure of the optimum work procedure.
  • the work time comparison unit 215 is based on the actual work video of the worker A acquired by the actual work video acquisition unit 211, for each procedure in the actual work in which the work time is the shortest among the setup work of the worker A. Procedure Calculate the working time.
  • FIG. 7B shows the work content and the work time for each work procedure of the actual work.
  • the work time comparison unit 215 compares the optimum work procedure with the work content of the actual work and the work time, and extracts the item whose time is longer in the actual work. In FIG. 7B, items that take longer in actual work are shaded.
  • the field worker can grasp how much the difference is from the optimum work time and which work procedure deviates from the optimum work procedure.
  • the work improvement plan output unit 216 creates a work improvement plan in the work time comparison unit 215 indicating that the procedure in which the actual work procedure work time is longer is changed to the procedure included in the optimum work procedure. Output.
  • FIG. 8 is a table of work improvement plans for the examples shown in FIGS. 7A and 7B.
  • the actual work showed the improvement contents for the work procedures 1, 2 and 10 which took longer.
  • the content of the improvement may be an independent content for each work procedure, or may be a content related to a plurality of work procedures.
  • the improvement contents for the work procedures 1 and 2 are the change of the order of the work procedures 1 and 2 and the change of the flow line for searching the tool of the work procedure 2.
  • the display unit 230 displays at least one of the fastest work time, the optimum work procedure, and the work improvement plan.
  • the fastest working time, the optimum working procedure, and the method of presenting the work improvement plan are arbitrary, and may be displayed on the display unit 230 or distributed as educational materials to the workers.
  • the fastest working time update unit 212 updates the fastest working time, so that the fastest working time that matches the actual situation at that time can be shown.
  • the optimum work procedure extraction unit 213 can extract the optimum work procedure based on the new fastest work time and create a work procedure manual based on the optimum work procedure.
  • the work procedure manual may be created using general-purpose work analysis software from the updated actual work video related to the fastest work time.
  • a general-purpose work analysis software for example, analysis software OTRS (registered trademark) that can create a work procedure manual from a moving image file can be used.
  • the created procedure manual may be displayed on the display unit 230, or may be a paper medium.
  • the work improvement plan output unit 216 can show the improvement contents for the daily actual work by comparing with the optimum work procedure.
  • the work improvement plan output unit 216 must match the conditions of the analysis method in order to compare the work procedure of the actual work with the optimum work procedure. Therefore, the same analysis software as the analysis of the optimum work procedure is used for the work procedure of the actual work analyzed by the work time comparison unit 215.
  • the fastest work time update unit 212 extracts the shortest actual work time from the actual work time calculated based on the actual work video, and the shortest.
  • the fastest work time is updated when the actual work time is shorter than the fastest work time.
  • the optimum work procedure extraction unit 213 extracts the optimum work procedure based on the fastest work time by using the trained model learned in advance.
  • the work time comparison unit 215 compares each procedure work time of the optimum work procedure extracted by the optimum work procedure extraction unit 213 with each procedure work time of the actual work, and the work improvement plan output unit 216 is based on the comparison result. Decided to output a work improvement plan from the actual work. This makes it possible to present the fastest work time, the optimum work procedure, and the work improvement plan that match the actual work state.
  • the trained model used by the optimum work procedure extraction unit 213 is generated by learning the fastest work time and work aptitude for products including a vacuum cleaner, a dehumidifier, and a dishwasher.
  • the learning target may be a plurality of types of products.
  • the learning device 217 may learn about the actual work in one area, or may learn about the actual work performed independently in a plurality of different areas.
  • the training target product of the trained model may be added in the middle, or may be removed from the training target in the middle.
  • an arbitrary trained model may be applied to a trained model of another model, or the other model may be retrained based on the arbitrary trained model.
  • the hardware configuration and flowchart shown in the above embodiment are examples, and can be arbitrarily changed and modified.
  • Each function realized by the arithmetic processing unit 210 and the storage unit 220 can be realized by using a normal computer system without using a dedicated system.
  • a computer-readable CD-ROM Compact Disc Read-Only Memory
  • DVD Digital Versatile Disc
  • MO Magnetic Optical Disc
  • a computer capable of realizing each function may be configured by storing and distributing it in a recording medium of the above and installing a program on the computer.
  • OS Operating System
  • the application or by cooperating with the OS and the application, only the part other than the OS may be stored in the recording medium.
  • 1 work procedure update system 100 shooting device, 200 work procedure update device, 210 arithmetic processing unit, 211 actual work video acquisition unit, 212 fastest work time update unit, 213 optimal work procedure extraction unit, 214 work procedure update unit, 215 work Time comparison unit, 216 work improvement plan output unit, 217 learning device, 218 inference device, 220 storage unit, 221 actual work database, 222 fastest work time database, 223 trained model storage unit, 224 work procedure storage unit, 230 display unit. , 2171 data acquisition unit, 2172 model generation unit, 2181 data acquisition unit, 2182 inference unit.

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Abstract

A fastest work time updating unit (212) acquires actual work time on the basis of a real work video capturing the work, and if the actual work time is shorter than a fastest work time stored in a storage unit (220), then updates the fastest work time to the actual work time. An optimal work procedure extraction unit (213) extracts an optimal work procedure on the basis of the updated fastest work time by using a trained model which has been trained in advance as to the fastest work time and the adequacy of the work. A work procedure updating unit (214) updates a work procedure stored in the storage unit (220) to the extracted optimal work procedure.

Description

作業手順更新装置、作業手順更新方法及びプログラムWork procedure update device, work procedure update method and program
 本開示は、作業手順更新装置、作業手順更新方法及びプログラムに関する。 This disclosure relates to a work procedure update device, a work procedure update method, and a program.
 工場を含む生産現場において、生産機種の変更時には、部品の交換、実装基板の変更、治具又は工具の交換、及び、設備のプログラム変更を含む、いわゆる段取り作業が必要となる。近年の市場ニーズは、多品種少量生産が求められているため、日々の生産機種切り替え時に発生する段取り作業時間の短縮が工場改革の優先課題となっている。このような段取り作業を抽出し、段取り作業に要する作業時間を算出するシステムが存在する(例えば、特許文献1)。 At production sites including factories, when changing production models, so-called setup work is required, including parts replacement, mounting board changes, jig or tool replacements, and equipment program changes. Since the market needs in recent years require high-mix low-volume production, shortening the setup work time that occurs when switching production models on a daily basis has become a priority issue for factory reform. There is a system that extracts such setup work and calculates the work time required for the setup work (for example, Patent Document 1).
 特許文献1に記載の段取り作業管理システムは、段取り作業を同時に効率的に作業できる限界の作業者の人数である同時作業限界人数と、各段取り作業に割り当てられた作業者の人数である割り当て人数と、に基づいて、段取り作業に要する所要時間を算出する。これにより、部品実装ラインに対応して発生する段取り作業を適切に管理することができると説明されている。 The setup work management system described in Patent Document 1 has a limit number of simultaneous work, which is the maximum number of workers who can efficiently perform setup work at the same time, and an allotted number of workers, which is the number of workers assigned to each setup work. And, based on, the time required for the setup work is calculated. It is explained that this makes it possible to appropriately manage the setup work that occurs corresponding to the component mounting line.
特開2018-92971号公報Japanese Unexamined Patent Publication No. 2018-92971
 特許文献1に記載の段取り作業管理システムは、実装データ及び生産計画情報に基づき、段取り替えに伴って発生する段取り作業を抽出する。そして、抽出された段取り作業と単位段取り作業情報とに基づいて、同時作業限界人数を算出する。 The setup work management system described in Patent Document 1 extracts the setup work generated by the setup change based on the mounting data and the production planning information. Then, the simultaneous work limit number of people is calculated based on the extracted setup work and the unit setup work information.
 しかし、実際の生産現場では日々作業者が入れ替わっており、各作業者の習熟度の違いにより、段取り作業時間、手順及び作業状況は作業者によってばらつきがある。このため特許文献1のように、同時作業限界人数に基づいて正確な作業時間を予測することは困難であった。 However, at the actual production site, the workers are replaced every day, and the setup work time, procedure, and work status vary depending on the worker due to the difference in the proficiency level of each worker. Therefore, as in Patent Document 1, it is difficult to accurately predict the working time based on the number of people who can work at the same time.
 また、段取り替えは1日に数回しか行われず、日々の生産状況によって段取り作業のタイミングにばらつきが発生するため、作業を分析するタイミングを特定するのも難しい。このため、段取り作業改善のための生産技術者を現場に配置する必要があり非効率であった。つまり、生産効率を低下させずに、実際の段取り作業状態を評価して作業手順に反映させることが困難であった。 Also, the setup change is performed only a few times a day, and the timing of the setup work varies depending on the daily production situation, so it is difficult to specify the timing to analyze the work. For this reason, it was inefficient because it was necessary to assign a production engineer to the site to improve the setup work. That is, it is difficult to evaluate the actual setup work state and reflect it in the work procedure without lowering the production efficiency.
 本開示は、上述のような事情に鑑みてなされたものであり、実作業状態に即した最速作業時間を実現可能な最適作業手順に更新することができる作業手順更新装置、作業手順更新方法及びプログラムを提供することを目的とする。 This disclosure has been made in view of the above circumstances, and is a work procedure updating device, a work procedure update method, and a work procedure update device capable of updating the fastest work time according to the actual work state to a feasible optimum work procedure. The purpose is to provide a program.
 上記目的を達成するため、本開示の作業手順更新装置は、作業を撮影した実作業動画に基づいて実績作業時間を取得し、記憶部に記憶されている最速作業時間よりも短い場合に、最速作業時間を実績作業時間に更新する最速作業時間更新部を備える。更に、作業手順更新装置は、予め、最速作業時間と作業の適正度について学習しておいた学習済モデルを用いて、更新された最速作業時間に基づいて最適作業手順を抽出する最適作業手順抽出部と、記憶部に記憶されている作業手順を、抽出した最適作業手順に更新する作業手順更新部と、を備える。 In order to achieve the above object, the work procedure updating device of the present disclosure acquires the actual work time based on the actual work video of the work, and is the fastest when the actual work time is shorter than the fastest work time stored in the storage unit. It is equipped with the fastest work time update unit that updates the work time to the actual work time. Further, the work procedure update device uses a trained model in which the fastest work time and the appropriateness of work have been learned in advance, and extracts the optimum work procedure based on the updated fastest work time. It is provided with a unit and a work procedure update unit that updates the work procedure stored in the storage unit to the extracted optimum work procedure.
 本開示によれば、作業を撮影した動画に基づいて最速作業時間を更新し、最速作業時間での最適な作業手順を抽出するため、実作業状態に即した最速作業時間を実現可能な最適作業手順に更新することが可能となる。 According to the present disclosure, since the fastest work time is updated based on the video of the work and the optimum work procedure at the fastest work time is extracted, the optimum work that can realize the fastest work time according to the actual work state can be realized. It will be possible to update to the procedure.
本開示の実施の形態に係る作業手順更新システムの構成例を示すブロック図A block diagram showing a configuration example of a work procedure update system according to an embodiment of the present disclosure. 実作業動画取得処理のフローチャートFlowchart of actual work video acquisition process 最速作業時間更新処理のフローチャートFlowchart of fastest work time update process 最適作業手順抽出部の学習装置を示すブロック図Optimal work procedure Block diagram showing the learning device of the extraction unit 学習処理のフローチャートFlow chart of learning process 最適作業手順抽出部の推論装置を示すブロック図Optimal work procedure Block diagram showing the inference device of the extraction unit 推論処理のフローチャートFlow chart of inference processing 最速作業時間と最短実績作業時間を示すグラフGraph showing the fastest working time and the shortest actual working time 最適作業手順を示す表A table showing the optimal work procedure 最適作業手順の作業時間を示す表A table showing the working time of the optimum work procedure 実作業手順の作業時間を示す表A table showing the working time of the actual work procedure 作業改善案を示す表Table showing work improvement plans
(実施の形態)
 以下に、本開示を実施するための形態について図面を参照して詳細に説明する。
(Embodiment)
Hereinafter, embodiments for carrying out the present disclosure will be described in detail with reference to the drawings.
 図1は、この開示の実施の形態に係る作業手順更新システム1の構成例を示すブロック図である。作業手順更新システム1は、工場における作業を最速作業時間で実行するときの作業手順を生成し更新するシステムである。本実施の形態においては、作業手順更新システム1は、生産機種の変更時に発生する段取り作業について作業手順を生成し更新する。段取り作業は、工場で生産する製品の機種の切り替えに伴って発生する作業であり、例えば、部品の交換、実装基板の変更、治具又は工具の交換、及び、設備のプログラム変更である。 FIG. 1 is a block diagram showing a configuration example of the work procedure update system 1 according to the embodiment of this disclosure. The work procedure update system 1 is a system that generates and updates a work procedure when the work in the factory is executed at the fastest working time. In the present embodiment, the work procedure update system 1 generates and updates the work procedure for the setup work that occurs when the production model is changed. The setup work is a work that occurs when a model of a product produced in a factory is switched, and is, for example, replacement of parts, change of a mounting board, replacement of a jig or a tool, and change of a program of equipment.
 作業手順更新システム1は、図1に示すように、段取り作業を実施する作業者を撮影する撮影装置100と、撮影装置100が撮影した作業動画に基づいて最速作業時間及び最適作業手順を更新する作業手順更新装置200と、を備える。 As shown in FIG. 1, the work procedure update system 1 updates the fastest work time and the optimum work procedure based on the photographing device 100 for photographing the worker performing the setup work and the work moving image photographed by the photographing device 100. A work procedure updating device 200 is provided.
 撮影装置100は、予め設定した分析対象機種の段取り作業期間中、かつ、予め設定した分析対象期間中に、現場作業者の段取り作業を撮影する任意の撮影装置である。作業者の作業内容を至近距離で撮影するために、作業者の体に装着して撮影できるウェアラブル機器が好ましい。例えば、スマートグラス又はHoloLens(登録商標)でもよい。あるいは、作業者のヘルメットの横に装着したアクションカメラ又はウェアラブルカメラであってもよい。 The photographing device 100 is an arbitrary photographing device that photographs the setup work of the field worker during the setup work period of the preset analysis target model and during the preset analysis target period. A wearable device that can be attached to the worker's body and photographed is preferable in order to photograph the work contents of the operator at a close distance. For example, smart glasses or HoloLens® may be used. Alternatively, it may be an action camera or a wearable camera mounted next to the worker's helmet.
 撮影のタイミングは、作業者の操作により決定してもよい。作業者が段取り作業に着手するタイミングで、撮影装置100の撮影開始ボタンを押すことにより撮影が開始される。また、作業者が段取り作業を完了したタイミングで、撮影装置100の撮影終了ボタンを押すことにより撮影が停止される。これにより1サイクルの段取り作業を撮影する。分析対象機種でない、又は、分析対象期間でない場合は、撮影装置100は自動又は手動で撮影を停止する。 The timing of shooting may be determined by the operation of the operator. When the operator starts the setup work, the shooting is started by pressing the shooting start button of the shooting device 100. Further, when the operator completes the setup work, the shooting is stopped by pressing the shooting end button of the shooting device 100. As a result, one cycle of setup work is photographed. If the model is not the model to be analyzed or the period is not the analysis target period, the photographing device 100 automatically or manually stops the photographing.
 撮影装置100は、作業手順更新装置200と有線又は無線の任意の通信手段により接続することができる。撮影装置100は、作業手順更新装置200に、撮影した動画を任意のタイミングで送信する。撮影装置100の撮影開始又は停止を含む動作は、作業手順更新装置200が制御してもよい。 The photographing device 100 can be connected to the work procedure updating device 200 by any wired or wireless communication means. The photographing device 100 transmits the captured moving image to the work procedure updating device 200 at an arbitrary timing. The operation including the start or stop of photography of the photographing apparatus 100 may be controlled by the working procedure updating apparatus 200.
 作業手順更新装置200は、撮影装置100が撮影した撮影動画に基づいて各種データを取得し又は生成する処理を実行する演算処理部210と、演算処理部210が取得し又は生成したデータを記憶する記憶部220と、演算処理部210が取得し又は生成した情報を表示する表示部230と、を備える。 The work procedure updating device 200 stores a calculation processing unit 210 that executes a process of acquiring or generating various data based on a shooting moving image shot by the shooting device 100, and a data acquired or generated by the calculation processing unit 210. It includes a storage unit 220 and a display unit 230 that displays information acquired or generated by the arithmetic processing unit 210.
 演算処理部210は、任意の演算処理装置であり、例えば、CPU(Central Processing Unit)である。演算処理部210は、記憶部220に格納されるプログラムを実行することにより、撮影動画に含まれる実作業動画を取得して記憶部220に保存する実作業動画取得部211、実績作業時間に基づいて最速作業時間を更新する最速作業時間更新部212と、人工知能(Artificial Intelligence:AI)により最適作業手順を抽出する最適作業手順抽出部213、作業手順を更新する作業手順更新部214、最適作業手順の作業時間と実績作業時間とを比較する作業時間比較部215、及び、作業時間の比較結果に基づいて作業改善案を出力する作業改善案出力部216、として機能する。 The arithmetic processing unit 210 is an arbitrary arithmetic processing unit, for example, a CPU (Central Processing Unit). The arithmetic processing unit 210 acquires the actual work video included in the shot video by executing the program stored in the storage unit 220 and saves it in the storage unit 220, based on the actual work video acquisition unit 211 and the actual work time. Fastest work time update unit 212 that updates the fastest work time, optimal work procedure extraction unit 213 that extracts the optimum work procedure by artificial intelligence (AI), work procedure update unit 214 that updates the work procedure, optimum work It functions as a work time comparison unit 215 that compares the work time of the procedure with the actual work time, and a work improvement plan output unit 216 that outputs a work improvement plan based on the comparison result of the work time.
 記憶部220は、任意の記憶装置であり、例えば、フラッシュメモリ、EPROM(Erasable Programmable Read Only Memory)を含む不揮発性半導体メモリ、又は、磁気ディスク、フレキシブルディスク、光ディスク、コンパクトディスク、ミニディスク、DVD(Digital Versatile Disc)である。 The storage unit 220 is an arbitrary storage device, and is, for example, a flash memory, a non-volatile semiconductor memory including an EPROM (ErasableProgrammableReadOnlyMemory), or a magnetic disk, a flexible disk, an optical disk, a compact disk, a mini disk, or a DVD ( Digital Versatile Disc).
 記憶部220は、図1に示すように、実作業に係るデータを含む実績作業データベース221と、最速作業時間のデータを含む最速作業時間データベース222と、最適作業手順抽出部213が使用する学習済モデルを記憶する学習済モデル記憶部223と、作業手順を記憶する作業手順記憶部224と、を有する。記憶部220には、さらに演算処理部210が実行するプログラムも格納されている。 As shown in FIG. 1, the storage unit 220 has been learned to be used by the actual work database 221 containing the data related to the actual work, the fastest work time database 222 including the data of the fastest work time, and the optimum work procedure extraction unit 213. It has a learned model storage unit 223 for storing a model and a work procedure storage unit 224 for storing a work procedure. The storage unit 220 also stores a program executed by the arithmetic processing unit 210.
 演算処理部210の機能と、記憶部220の内容について、詳細に述べる。演算処理部210の実作業動画取得部211は、撮影装置100が撮影した撮影動画から、一連の段取り作業ごとの作業着手から作業完了までの実作業動画を取得する。 The function of the arithmetic processing unit 210 and the contents of the storage unit 220 will be described in detail. The actual work video acquisition unit 211 of the arithmetic processing unit 210 acquires the actual work video from the start of the work to the completion of the work for each series of setup work from the shot video shot by the shooting device 100.
 図2は、実作業動画取得処理のフローチャートである。実作業動画取得部211は、図2に示すように、まず、分析対象の段取り作業を決定し(ステップS101)、分析対象の作業期間と製品の機種を決定する(ステップS102)。その後、撮影装置100より、作業の着手から完了までの動画を取得し(ステップS103)、取得した実作業データを記憶部220の実績作業データベース221に保存する。 FIG. 2 is a flowchart of the actual work video acquisition process. As shown in FIG. 2, the actual work moving image acquisition unit 211 first determines the setup work to be analyzed (step S101), and then determines the work period to be analyzed and the model of the product (step S102). After that, a moving image from the start to the completion of the work is acquired from the photographing device 100 (step S103), and the acquired actual work data is stored in the actual work database 221 of the storage unit 220.
 ステップS103で取得する実作業動画は、一連の実作業動画取得処理の開始前に、事前に撮影装置100が録画したものでもよく、又は、実作業動画取得処理の一部として、作業手順更新装置200からの制御信号に基づき撮影装置100が撮影したものであってもよい。撮影装置100による撮影の開始と停止のタイミングは、操作者が決めてもよい。 The actual work video acquired in step S103 may be recorded by the shooting device 100 in advance before the start of a series of actual work video acquisition processes, or as a part of the actual work video acquisition process, the work procedure update device. It may be taken by the photographing apparatus 100 based on the control signal from 200. The operator may decide the timing of starting and stopping the shooting by the shooting device 100.
 撮影装置100がスマートグラス又はHoloLensを含む動画撮影装置の場合は、ステップS103で実作業動画を取得するために、作業手順更新装置200には、当該動画撮影装置を制御するアプリケーションソフトを予めインストールしておく。 When the shooting device 100 is a moving image shooting device including smart glasses or HoloLens, application software for controlling the moving image shooting device is installed in advance in the work procedure updating device 200 in order to acquire the actual work moving image in step S103. Keep it.
 撮影された実作業の動画は、撮影装置100又は作業手順更新装置200に内蔵された時計により計測された時刻とともに記録されている。実績作業データベース221において、各動画ファイルは、作業者の識別情報、作業期間、製品機種の情報に対応づけられており、グループ化して保存されている。 The video of the actual work taken is recorded together with the time measured by the clock built in the shooting device 100 or the work procedure updating device 200. In the actual work database 221, each moving image file is associated with the identification information of the worker, the work period, and the information of the product model, and is stored as a group.
 なお、ここでは、製造部門が管理する作業手順更新装置200の記憶部220が実績作業データベース221を含むとしたが、実績作業データベース221は、作業手順更新装置200の外部のローカルデータベースあるいはクラウド型データベースであってもよい。実績作業データベース221に記録されるデータは、圧縮されて保存されてもよい。また、各現場で決められている期間を過ぎれば、古いデータを自動削除してデータベースの保管容量を管理してもよい。 Here, it is assumed that the storage unit 220 of the work procedure update device 200 managed by the manufacturing department includes the actual work database 221. However, the actual work database 221 is a local database or a cloud type database outside the work procedure update device 200. It may be. The data recorded in the performance work database 221 may be compressed and stored. In addition, after a period determined at each site, old data may be automatically deleted to manage the storage capacity of the database.
 最速作業時間更新部212は、記憶部220の実績作業データベース221から取得した動画データを基に段取り作業1サイクル当たりの実績作業時間を算出する。より具体的には、最速作業時間更新部212は、段取り作業の着手から完了までの時間を1サイクルの実績作業時間として算出する。作業の着手時に撮影開始し、作業の完了時に撮影完了する場合は、撮影時間が実績作業時間となる。作業開始と撮影開始の時刻、又は、作業完了と撮影完了の時刻が一致していない場合は、手作業による計測を行ってもよい。あるいは、撮影したビデオ映像から任意の分析ソフトを使用して実績作業時間を算出してもよい。分析ソフトとして、例えば、作業動態分析システムであるスマートロガー(登録商標)が使用可能である。 The fastest work time update unit 212 calculates the actual work time per cycle of setup work based on the moving image data acquired from the actual work database 221 of the storage unit 220. More specifically, the fastest work time update unit 212 calculates the time from the start to the completion of the setup work as the actual work time of one cycle. If shooting starts when the work starts and shooting is completed when the work is completed, the shooting time is the actual work time. If the work start time and the shooting start time or the work completion time and the shooting completion time do not match, manual measurement may be performed. Alternatively, the actual working time may be calculated from the captured video image using any analysis software. As the analysis software, for example, Smart Logger (registered trademark), which is a work dynamic analysis system, can be used.
 図3は、最速作業時間更新処理のフローチャートである。最速作業時間更新部212は、図3に示すように、1日の間に実施された分析対象機種の段取り作業の実績作業時間を算出して、その中で最も短時間である最短実績作業時間を抽出する(ステップS201)。実績作業時間の数値について、予め定めた分析期間中に継続管理を行い、最速作業時間データベース222に記憶されている、前の期間における最速作業時間と、1日の最短実績作業時間と、の比較を繰り返し実行する(ステップS202)。 FIG. 3 is a flowchart of the fastest working time update process. As shown in FIG. 3, the fastest work time update unit 212 calculates the actual work time of the setup work of the model to be analyzed performed during one day, and the shortest actual work time is the shortest among them. Is extracted (step S201). The numerical value of the actual work time is continuously managed during the predetermined analysis period, and the fastest work time in the previous period and the shortest actual work time in one day, which are stored in the fastest work time database 222, are compared with each other. Is repeatedly executed (step S202).
 最短実績作業時間が最速作業時間より短くなった日があれば(ステップS203:Yes)、最速作業時間をその日の最短実績作業時間に更新する(ステップS204、最速作業時間更新ステップ)。分析期間中に複数の実績作業時間が最速作業時間より短かった場合は、分析期間内で最短となる実績作業時間を新たな最速作業時間にする。一方、全ての実績作業時間が最速作業時間以上であった場合には(ステップS203:No)、最速作業時間を変更せずに処理を終了する。 If there is a day when the shortest actual work time is shorter than the fastest work time (step S203: Yes), the fastest work time is updated to the shortest actual work time of the day (step S204, fastest work time update step). If multiple actual work times are shorter than the fastest work time during the analysis period, the shortest actual work time within the analysis period is set as the new fastest work time. On the other hand, when all the actual work times are equal to or longer than the fastest work time (step S203: No), the process ends without changing the fastest work time.
 なお、ステップS203において、分析期間中に複数の同じ最短作業時間を抽出した場合は、後述する最適作業手順抽出部213が動作の無駄が最も少ない作業手順の最短作業時間を抽出してもよい。動作の無駄が最も少ない作業手順は、予め定めた基準に基づいて導出した単位時間あたりの付加価値を示す付加価値時間比率が最も大きい作業手順である。 When a plurality of the same shortest working times are extracted during the analysis period in step S203, the optimum working procedure extraction unit 213, which will be described later, may extract the shortest working time of the working procedure with the least waste of operation. The work procedure with the least waste of operation is the work procedure with the largest value-added time ratio indicating the added value per unit time derived based on a predetermined standard.
 最速作業時間更新部212を用いることで、従来の現場において、段取り作業者が作業動画を撮影して分析し、最速作業時間を求めていた間接作業時間を大きく削減することが可能となる。例えば、スマートロガーを活用した場合の分析時間短縮効果は1/10になる。また、従来の数式から求めた最速作業時間ではなく、実際の生産現場での実績値に基づいた最速作業時間を作業効率の評価に用いることが可能となる。 By using the fastest work time update unit 212, it is possible for the setup worker to shoot and analyze the work video at the conventional site and greatly reduce the indirect work time required for the fastest work time. For example, the effect of shortening the analysis time when using a smart logger is 1/10. Further, it is possible to use the fastest working time based on the actual value at the actual production site for the evaluation of the working efficiency instead of the fastest working time obtained from the conventional mathematical formula.
 最速作業時間更新部212が更新した最速作業時間は、記憶部220の最速作業時間データベース222に記憶される。ここで、最速作業時間データベース222は、製造部門が管理する作業手順更新装置200が有する記憶部220に含まれるとしたが、最速作業時間データベース222は、作業手順更新装置200の外部のローカルデータベースあるいはクラウド型データベースであってもよい。 The fastest working time updated by the fastest working time updating unit 212 is stored in the fastest working time database 222 of the storage unit 220. Here, the fastest working time database 222 is included in the storage unit 220 of the working procedure updating device 200 managed by the manufacturing department, but the fastest working time database 222 is a local database outside the working procedure updating device 200 or. It may be a cloud-type database.
 最適作業手順抽出部213は、最速作業時間更新部212が最速作業時間を更新した際に、更新された最速作業時間のうち、各作業手順に対応する手順作業時間に基づいて最適作業手順を抽出する。最適作業手順の抽出は人工知能(Artificial Intelligence:AI)を用いて行う。 The optimum work procedure extraction unit 213 extracts the optimum work procedure based on the procedure work time corresponding to each work procedure among the updated fastest work time when the fastest work time update unit 212 updates the fastest work time. do. Extraction of the optimum work procedure is performed using artificial intelligence (AI).
 最適作業手順の抽出を行う人工知能は、図1において、演算処理部210の最適作業手順抽出部213と、記憶部220の学習済モデル記憶部223と、により実現する。最適作業手順抽出部213は、学習装置217と推論装置218とを含む(図4A,図5A参照)。 In FIG. 1, the artificial intelligence that extracts the optimum work procedure is realized by the optimum work procedure extraction unit 213 of the arithmetic processing unit 210 and the learned model storage unit 223 of the storage unit 220. The optimum work procedure extraction unit 213 includes a learning device 217 and an inference device 218 (see FIGS. 4A and 5A).
 なお、本実施の形態では、学習装置217及び推論装置218が、作業手順更新装置200に含まれる構成について説明している。学習装置217及び推論装置218は、掃除機、除湿機、食器洗い乾燥機を含む対象製品の段取り作業の適正度を学習するために使用される。学習装置217及び推論装置218の形態は任意であり、例えば、対象製品とは別個の装置であって、ネットワークを介して対象製品に接続される形態であってもよい。あるいは、対象製品に内蔵されてもよい。さらに、学習装置217及び推論装置218は、学習済モデル記憶部223とともにクラウドサーバ上に存在していてもよい。 In the present embodiment, the configuration in which the learning device 217 and the inference device 218 are included in the work procedure updating device 200 is described. The learning device 217 and the inference device 218 are used to learn the appropriateness of the setup work of the target product including the vacuum cleaner, the dehumidifier, and the dishwasher. The form of the learning device 217 and the inference device 218 is arbitrary, and may be, for example, a device separate from the target product and connected to the target product via a network. Alternatively, it may be built in the target product. Further, the learning device 217 and the inference device 218 may exist on the cloud server together with the learned model storage unit 223.
 まず、学習処理について説明する。図4Aは、最適作業手順抽出部213の学習装置217を示すブロック図であり、図4Bは、学習処理のフローチャートである。ここでは、掃除機、除湿機、食器洗い乾燥機を含む対象製品の段取り作業について学習する場合について説明する。 First, the learning process will be explained. FIG. 4A is a block diagram showing the learning device 217 of the optimum work procedure extraction unit 213, and FIG. 4B is a flowchart of the learning process. Here, the case of learning the setup work of the target product including the vacuum cleaner, the dehumidifier, and the dishwasher will be described.
 学習装置217は、図4Aに示すように、データ取得部2171とモデル生成部2172と、を備える。データ取得部2171は、最速作業時間更新部212が更新した新たな最速作業時間と、作業の適正度を学習用データとして取得する。ここで、最速作業時間のデータは、一連の段取り作業に含まれる各作業手順の内容とそれらに要した時間である手順作業時間のデータも含む。作業の適正度は、作業のしやすさ又は作業の無駄の数の少なさを示す指標であり、作業内に潜む動作の無駄の数が最も少ない作業を最適とする。作業の適正度は、ユーザが設定してもよく、又は、予め定めた基準に基づいて自動判定してもよい。 As shown in FIG. 4A, the learning device 217 includes a data acquisition unit 2171 and a model generation unit 2172. The data acquisition unit 2171 acquires the new fastest work time updated by the fastest work time update unit 212 and the appropriateness of the work as learning data. Here, the data of the fastest working time also includes the contents of each working procedure included in the series of setup work and the data of the procedure working time which is the time required for them. The appropriateness of work is an index showing the ease of work or the small number of wastes of work, and the work with the least number of wastes of operations hidden in the work is optimized. The appropriateness of the work may be set by the user, or may be automatically determined based on a predetermined standard.
 モデル生成部2172は、データ取得部2171が取得した新たな最速作業時間及び作業の適正度の組合せの情報を含む学習用データに基づいて、作業の適正度を学習する。すなわち、モデル生成部2172は、対象製品の段取り作業に係る新たな最速作業時間と作業の適正度から、作業の適正度を推論するための学習済モデルを生成する。この学習処理における学習用データは、新たな最速作業時間及び作業の適正度を互いに関連付けたデータである。 The model generation unit 2172 learns the appropriateness of work based on the learning data including the information of the combination of the new fastest work time and the appropriateness of work acquired by the data acquisition unit 2171. That is, the model generation unit 2172 generates a trained model for inferring the appropriateness of the work from the new fastest work time and the appropriateness of the work related to the setup work of the target product. The learning data in this learning process is data in which the new fastest working time and the appropriateness of the work are associated with each other.
 モデル生成部2172が用いる学習アルゴリズムは、従来のアルゴリズムでよい。例えば、教師あり学習、教師なし学習又は強化学習を用いることができる。 The learning algorithm used by the model generation unit 2172 may be a conventional algorithm. For example, supervised learning, unsupervised learning or reinforcement learning can be used.
 また、特徴量そのものの抽出を学習する深層学習(Deep Learning)を実行してもよく、又は遺伝的プログラミング、機能論理プログラミング、サポートベクターマシンに従って機械学習を実行してもよい。 Further, deep learning may be executed to learn the extraction of the feature amount itself, or machine learning may be executed according to genetic programming, functional logic programming, and a support vector machine.
 本実施の形態では、一例として、ニューラルネットワークを適用した場合について説明する。この場合、モデル生成部2172は、ニューラルネットワークモデルに従って、教師あり学習により、最速作業時間に対する作業の適正度を学習する。ここで、教師あり学習とは、入力と結果(ラベル)のデータの組を学習装置に与えることで、それらの学習用データにある特徴を学習し、入力から結果を推論する手法をいう。 In this embodiment, a case where a neural network is applied will be described as an example. In this case, the model generation unit 2172 learns the appropriateness of the work for the fastest working time by supervised learning according to the neural network model. Here, supervised learning refers to a method of learning a feature in the learning data by giving a set of input and result (label) data to the learning device, and inferring the result from the input.
 ニューラルネットワークは、複数のニューロンを有する入力層、複数のニューロンを有する中間層(隠れ層)及び複数のニューロンを有する出力層で構成される。中間層は、1層又は2層以上である。 A neural network is composed of an input layer having a plurality of neurons, an intermediate layer (hidden layer) having a plurality of neurons, and an output layer having a plurality of neurons. The intermediate layer is one layer or two or more layers.
 本実施の形態におけるニューラルネットワークでは、データ取得部2171が取得する新たな最速作業時間と作業の適正度との組合せの情報を含む学習用データに基づいて、モデル生成部2172が、教師あり学習により、最速作業時間に対する作業の適正度を学習する。 In the neural network according to the present embodiment, the model generation unit 2172 is supervised learning based on the learning data including the information of the combination of the new fastest work time and the appropriateness of the work acquired by the data acquisition unit 2171. , Learn the appropriateness of work for the fastest work time.
 すなわち、モデル生成部2172は、入力層中間層間の重みW1と中間層出力層間の重みW2を調整して、入力層に新たな最速作業時間を入力したときの出力層からの出力を、結果(正解)である作業の適正度に近づけることのできる重みW1,W2を求めることで学習する。例えば、入力層に、最速作業時間に含まれる手順作業時間から算出した作業速度が入力されたとき、中間層で動作の無駄の少なさを示す指標が得られ、出力層に作業の適正度が出力され、これを正解である作業の適正度と比較して重みW1、W2を決定する。 That is, the model generation unit 2172 adjusts the weight W1 between the input layer intermediate layers and the weight W2 between the intermediate layer output layers, and outputs a result (output from the output layer when a new fastest working time is input to the input layer). Learning is performed by finding the weights W1 and W2 that can approach the appropriateness of the work that is the correct answer). For example, when the work speed calculated from the procedure work time included in the fastest work time is input to the input layer, an index showing less waste of operation is obtained in the intermediate layer, and the appropriateness of the work is shown in the output layer. It is output, and the weights W1 and W2 are determined by comparing this with the appropriateness of the work that is the correct answer.
 学習済モデル記憶部223は、モデル生成部2172が出力した学習済モデルを記憶する。 The trained model storage unit 223 stores the trained model output by the model generation unit 2172.
 学習処理(モデル生成ステップ)の流れは、図4Bのフローチャートに示したとおりである。まず、学習装置217のデータ取得部2171が、新たな最速作業時間と作業の適正度のデータを取得する(ステップS301)。ここでは新たな最速作業時間及び作業の適正度を同時に取得しているが、新たな最速作業時間と作業の適正度を関連づけて入力できればよく、データ取得部2171は、新たな最速作業時間と作業の適正度のデータを互いに異なるタイミングで取得してもよい。 The flow of the learning process (model generation step) is as shown in the flowchart of FIG. 4B. First, the data acquisition unit 2171 of the learning device 217 acquires new data on the fastest working time and the appropriateness of the work (step S301). Here, the new fastest work time and the appropriateness of the work are acquired at the same time, but it is sufficient if the new fastest work time and the appropriateness of the work can be input in association with each other. Data of appropriateness may be acquired at different timings.
 その後、モデル生成部2172は、データ取得部2171が取得した新たな最速作業時間及び作業の適正度の組合せに基づいて学習処理して(ステップS302)、学習済モデルを生成する。そして、生成された学習済モデルを学習済モデル記憶部223に保存する(ステップS303)。 After that, the model generation unit 2172 performs learning processing based on the combination of the new fastest work time and the appropriateness of the work acquired by the data acquisition unit 2171 (step S302), and generates a trained model. Then, the generated trained model is stored in the trained model storage unit 223 (step S303).
 次に、推論処理について説明する。図5Aは、最適作業手順抽出部213の推論装置218を示すブロック図であり、図5Bは、推論処理のフローチャートである。 Next, the inference process will be explained. FIG. 5A is a block diagram showing the inference device 218 of the optimum work procedure extraction unit 213, and FIG. 5B is a flowchart of the inference process.
 ここでは、掃除機、除湿機、食器洗い乾燥機を含む製品の段取り作業について推論する場合について説明する。推論装置218は、図5Aに示すように、データ取得部2181と推論部2182と、を備える。データ取得部2181は、入力データとして、最速作業時間更新部212が更新した新たな最速作業時間を取得する。 Here, we will explain the case of inferring the setup work of products including vacuum cleaners, dehumidifiers, and dishwashers. As shown in FIG. 5A, the inference device 218 includes a data acquisition unit 2181 and an inference unit 2182. The data acquisition unit 2181 acquires a new fastest work time updated by the fastest work time update unit 212 as input data.
 推論部2182は、学習済モデル記憶部223に記憶されている学習済モデルを用いて、データ取得部2181が取得した最速作業時間に対する作業の適正度を推論する。つまり、推論部2182は、学習済モデルに最速作業時間を入力することで、新たな最速作業時間とそれに含まれる手順作業時間から推論される作業の適正度を出力する。 The inference unit 2182 infers the appropriateness of the work for the fastest work time acquired by the data acquisition unit 2181 by using the trained model stored in the trained model storage unit 223. That is, the inference unit 2182 inputs the fastest work time into the trained model, and outputs the appropriateness of the work inferred from the new fastest work time and the procedure work time included in the new fastest work time.
 なお、本実施の形態では、学習装置217のモデル生成部2172が生成した学習済モデルを用いて作業の適正度を出力する構成について説明したが、作業手順更新装置の外部から学習済モデルを取得し、この学習済モデルに基づいて作業の適正度を推論してもよい。 In the present embodiment, the configuration for outputting the appropriateness of the work using the trained model generated by the model generation unit 2172 of the learning device 217 has been described, but the trained model is acquired from the outside of the work procedure updating device. However, the appropriateness of the work may be inferred based on this trained model.
 推論処理(最適作業手順抽出ステップ)の流れは、図5Bのフローチャートに示したとおりである。まず、推論装置218のデータ取得部2181が、新たな最速作業時間のデータを取得する(ステップS401)。次に、推論部2182が学習済モデル記憶部223に記憶された学習済モデルに新たな最速作業時間を入力し(ステップS402)、作業の適正度を推論し出力する(ステップS403)。 The flow of inference processing (optimal work procedure extraction step) is as shown in the flowchart of FIG. 5B. First, the data acquisition unit 2181 of the inference device 218 acquires new data of the fastest working time (step S401). Next, the inference unit 2182 inputs a new fastest working time into the learned model stored in the learned model storage unit 223 (step S402), infers the appropriateness of the work, and outputs it (step S403).
 最適作業手順抽出部213は、出力された作業の適正度に基づいて、対象機種について適正度の最も高い最適作業手順を決定して出力する(ステップS404)。具体的には、最適作業手順抽出部213は、新たな最速作業時間に係る一連の作業に含まれる各作業手順の内容と、それらに要した時間である手順作業時間と、に基づいて最も無駄の少ない最適の手順を抽出する。新たな最速作業時間に係る実作業が複数あった場合には、最速作業時間に係る実作業の作業手順のうち、最も無駄の少ない最適の作業手順を抽出する。 The optimum work procedure extraction unit 213 determines and outputs the optimum work procedure with the highest appropriateness for the target model based on the appropriateness of the output work (step S404). Specifically, the optimum work procedure extraction unit 213 is the most wasteful based on the content of each work procedure included in the series of work related to the new fastest work time and the procedure work time which is the time required for them. Extract the optimal procedure with few. When there are a plurality of actual works related to the new fastest work time, the optimum work procedure with the least waste is extracted from the work procedures of the actual work related to the fastest work time.
 従来の最適作業手順の決定は技術者の裁量でなされており、段取り作業者の実情に合っていない場合があるという問題があったが、本実施の形態では、人工知能を活用した最適作業手順抽出部213により、客観的な評価に基づいて最適作業手順を決定することができる。 Conventionally, the optimum work procedure is determined at the discretion of the technician, and there is a problem that it may not match the actual situation of the setup worker. However, in this embodiment, the optimum work procedure utilizing artificial intelligence is used. The extraction unit 213 can determine the optimum work procedure based on an objective evaluation.
 最適作業手順抽出部213が抽出した最適作業手順は、作業手順更新部214及び作業時間比較部215に対して出力される。作業手順更新部214は、作業手順記憶部224に記憶されている作業者に提示するための段取り作業の作業手順を、最適作業手順抽出部213から入力された最適作業手順に更新する(作業手順更新ステップ)。 The optimum work procedure extracted by the optimum work procedure extraction unit 213 is output to the work procedure update unit 214 and the work time comparison unit 215. The work procedure update unit 214 updates the work procedure of the setup work stored in the work procedure storage unit 224 to the operator to the optimum work procedure input from the optimum work procedure extraction unit 213 (work procedure). Update step).
 作業時間比較部215は、実作業動画取得部211が取得した動画に基づいて実作業における手順毎の手順作業時間を算出し、最適作業手順抽出部213から入力された最適作業手順における手順作業時間と比較する。手順作業時間について実作業の時間の方が長い場合に、その作業の情報を出力する。ここで、最適作業手順と比較する実作業のデータは、実績作業データベース221に格納されている対象作業者の実績作業データであってもよく、対象作業者の最短作業時間に係る実績作業データであってもよい。 The work time comparison unit 215 calculates the procedure work time for each procedure in the actual work based on the video acquired by the actual work video acquisition unit 211, and the procedure work time in the optimum work procedure input from the optimum work procedure extraction unit 213. Compare with. Procedure Work time When the actual work time is longer, the work information is output. Here, the actual work data to be compared with the optimum work procedure may be the actual work data of the target worker stored in the actual work database 221, and is the actual work data related to the shortest work time of the target worker. There may be.
 作業改善案出力部216は、作業時間比較部215において、実作業の手順作業時間の方が長かった手順を、最適作業手順に含まれる手順に変更することを示した作業改善案を作成して表示部230に表示する。作業改善案の表示形態は任意の形態でよく、例えば、抽出された差異部分を市販の表比較ツールを用いて可視化した改善案表示シートでもよい。改善案表示シートを作業者に提示する事で、段取り作業時間のばらつきを抑制する効果を得ることができる。 The work improvement plan output unit 216 creates a work improvement plan in the work time comparison unit 215 indicating that the procedure in which the actual work procedure work time is longer is changed to the procedure included in the optimum work procedure. It is displayed on the display unit 230. The display form of the work improvement plan may be any form, and for example, an improvement plan display sheet in which the extracted difference portion is visualized using a commercially available table comparison tool may be used. By presenting the improvement plan display sheet to the operator, it is possible to obtain the effect of suppressing the variation in the setup work time.
 以上のように構成された作業手順更新システム1の動作について、図6A-8に示した例を用いて説明する。 The operation of the work procedure update system 1 configured as described above will be described with reference to the example shown in FIG. 6A-8.
 図6A,Bは、作業の一例について、最速作業時間更新部212と最適作業手順抽出部213の動作について説明するための図であり、図6Aは最速作業時間と1日の最短実績作業時間を示すグラフであり、図6Bは最適作業手順を示す表である。 6A and 6B are diagrams for explaining the operation of the fastest work time update unit 212 and the optimum work procedure extraction unit 213 for an example of work, and FIGS. 6A shows the fastest work time and the shortest actual work time per day. It is a graph which shows, and FIG. 6B is a table which shows the optimum work procedure.
 まず、実作業動画取得部211が撮影装置100で撮影した実作業の動画を取得して実績作業データベース221に保存する。最速作業時間更新部212は、実績作業データベース221に保存された実作業動画に基づいて実績作業時間を算出し、1日の最短実績作業時間と最速作業時間とを比較する。比較した結果、1日の最短実績作業時間が現時点の最速作業時間より短い場合に、最速作業時間を更新する。 First, the actual work video acquisition unit 211 acquires the actual work video taken by the photographing device 100 and saves it in the actual work database 221. The fastest work time update unit 212 calculates the actual work time based on the actual work video stored in the actual work database 221 and compares the shortest actual work time and the fastest work time in one day. As a result of comparison, when the shortest actual work time per day is shorter than the current fastest work time, the fastest work time is updated.
 図6Aの例において、最速作業時間が25分なのに対し、1日の最短実績作業時間は黒点で示すように変化し、10月5日の最短実績作業時間が最速作業時間より短くなっており、さらに10月10日の最短実績作業時間が10月5日の最短実績作業時間より短くなっている。このため、最速作業時間更新部212は、次期の最速作業時間を、10月10日の最短実績作業時間に更新する。 In the example of FIG. 6A, while the fastest working time is 25 minutes, the shortest actual working time per day changes as shown by black dots, and the shortest actual working time on October 5 is shorter than the fastest working time. Furthermore, the shortest actual work time on October 10 is shorter than the shortest actual work time on October 5. Therefore, the fastest working time update unit 212 updates the next fastest working time to the shortest actual working time on October 10.
 最速作業時間更新部212は、更新された最速作業時間とともに、その最速作業時間に係る実作業に含まれる各作業手順の手順作業時間を、最速作業時間データベース222に保存する。 The fastest work time update unit 212 stores the updated fastest work time and the procedure work time of each work procedure included in the actual work related to the fastest work time in the fastest work time database 222.
 学習済モデル記憶部223には、過去の最速作業時間と作業の適正度に基づいて学習した学習済モデルが記憶されている。最適作業手順抽出部213は、最速作業時間データベース222に保存された最速作業時間と手順作業時間に基づいて、学習済モデルを用いて最適作業手順を抽出する。最適作業手順抽出部213が抽出する最適作業手順は、例えば、図6Bに示すように各作業手順の識別番号(手順No)と作業内容で表される。なお、分析期間内に最速作業時間が同じ実作業が複数あった場合には、最適作業手順抽出部213は、学習済モデルに基づいて、作業の無駄の少ない手順の方を選択して最適作業手順とする。 The trained model storage unit 223 stores the trained model learned based on the fastest working time in the past and the appropriateness of the work. The optimum work procedure extraction unit 213 extracts the optimum work procedure using the trained model based on the fastest work time and the procedure work time stored in the fastest work time database 222. The optimum work procedure extracted by the optimum work procedure extraction unit 213 is represented by, for example, an identification number (procedure No.) of each work procedure and work contents as shown in FIG. 6B. If there are multiple actual works with the same fastest work time within the analysis period, the optimum work procedure extraction unit 213 selects the procedure with less waste of work based on the trained model and performs the optimum work. It is a procedure.
 図7A,Bは、作業の一例について、作業時間比較部215の動作について説明するための図であり、図7Aは最適作業手順の作業時間を示す表であり、図7Bは実作業手順の作業時間を示す表である。まず、作業時間比較部215は、最適作業手順抽出部213が抽出した最適作業手順と、各作業手順に要する時間である手順作業時間と、を取得する。図7Aは、最適作業手順の各作業手順について作業内容と作業時間を示している。 7A and 7B are diagrams for explaining the operation of the work time comparison unit 215 for an example of work, FIG. 7A is a table showing the work time of the optimum work procedure, and FIG. 7B is the work of the actual work procedure. It is a table showing the time. First, the work time comparison unit 215 acquires the optimum work procedure extracted by the optimum work procedure extraction unit 213 and the procedure work time which is the time required for each work procedure. FIG. 7A shows the work content and the work time for each work procedure of the optimum work procedure.
 また、作業時間比較部215は、実作業動画取得部211が取得した作業者Aの実作業動画に基づいて、作業者Aの段取り作業のうち、作業時間が最短であった実作業における手順毎の手順作業時間を算出する。図7Bは、実作業の各作業手順について作業内容と作業時間を示している。作業時間比較部215は、最適作業手順と実作業の作業内容と作業時間を比較し、実作業の方が、時間が長い項目を抽出する。図7Bにおいて、実作業の方が、時間が長い項目を網掛けで示している。 Further, the work time comparison unit 215 is based on the actual work video of the worker A acquired by the actual work video acquisition unit 211, for each procedure in the actual work in which the work time is the shortest among the setup work of the worker A. Procedure Calculate the working time. FIG. 7B shows the work content and the work time for each work procedure of the actual work. The work time comparison unit 215 compares the optimum work procedure with the work content of the actual work and the work time, and extracts the item whose time is longer in the actual work. In FIG. 7B, items that take longer in actual work are shaded.
 図7Bに示すように差異を可視化する事で、現場作業者は最適作業時間とどのくらい差異があり、どの作業手順が最適作業手順と乖離しているかを把握することができる。 By visualizing the difference as shown in FIG. 7B, the field worker can grasp how much the difference is from the optimum work time and which work procedure deviates from the optimum work procedure.
 作業改善案出力部216は、作業時間比較部215において、実作業の手順作業時間の方が長かった手順を、最適作業手順に含まれる手順に変更することを示した作業改善案を作成して出力する。図8は、図7A,Bに示した例に対する作業改善案の表である。 The work improvement plan output unit 216 creates a work improvement plan in the work time comparison unit 215 indicating that the procedure in which the actual work procedure work time is longer is changed to the procedure included in the optimum work procedure. Output. FIG. 8 is a table of work improvement plans for the examples shown in FIGS. 7A and 7B.
 実作業の方が、時間が長かった作業手順1,2,10について、改善内容を示している。改善内容は、各作業手順に対する独立した内容でもよく、又は、複数の作業手順に係る内容でもよい。図8の例において、作業手順1,2についての改善内容は、作業手順1と2の順番の入れ替えと、作業手順2の工具を探す動線の変更である。 The actual work showed the improvement contents for the work procedures 1, 2 and 10 which took longer. The content of the improvement may be an independent content for each work procedure, or may be a content related to a plurality of work procedures. In the example of FIG. 8, the improvement contents for the work procedures 1 and 2 are the change of the order of the work procedures 1 and 2 and the change of the flow line for searching the tool of the work procedure 2.
 表示部230は、最速作業時間、最適作業手順及び作業改善案の少なくともいずれか1つを表示する。最速作業時間、最適作業手順及び作業改善案の提示方法は任意であり、表示部230に表示し又は作業者への教育資料として配布してもよい。 The display unit 230 displays at least one of the fastest work time, the optimum work procedure, and the work improvement plan. The fastest working time, the optimum working procedure, and the method of presenting the work improvement plan are arbitrary, and may be displayed on the display unit 230 or distributed as educational materials to the workers.
 このようにして、最速作業時間更新部212が最速作業時間を更新することにより、その時点の実情に合致した最速作業時間を示すことができる。また、最適作業手順抽出部213が、新たな最速作業時間に基づいた最適作業手順を抽出し、それに基づいて作業手順書を作成することができる。 In this way, the fastest working time update unit 212 updates the fastest working time, so that the fastest working time that matches the actual situation at that time can be shown. Further, the optimum work procedure extraction unit 213 can extract the optimum work procedure based on the new fastest work time and create a work procedure manual based on the optimum work procedure.
 作業手順書の作成は、更新した最速作業時間に係る実作業動画から汎用の作業分析ソフトを用いて作成してもよい。汎用の作業分析ソフトは、例えば、動画ファイルから作業手順書作成が可能な分析ソフトOTRS(登録商標)が利用可能である。作成した手順書は、表示部230に表示してもよく、又は紙媒体にしてもよい。作業改善案出力部216は、最適作業手順との比較により、日々の実作業に対して改善内容を示すことができる。作業改善案出力部216は、実作業の作業手順と最適作業手順との比較を行うため、分析方法の条件を一致させなければならない。よって、作業時間比較部215が分析する実作業の作業手順も最適作業手順の分析と同じ分析ソフトを用いる。 The work procedure manual may be created using general-purpose work analysis software from the updated actual work video related to the fastest work time. As a general-purpose work analysis software, for example, analysis software OTRS (registered trademark) that can create a work procedure manual from a moving image file can be used. The created procedure manual may be displayed on the display unit 230, or may be a paper medium. The work improvement plan output unit 216 can show the improvement contents for the daily actual work by comparing with the optimum work procedure. The work improvement plan output unit 216 must match the conditions of the analysis method in order to compare the work procedure of the actual work with the optimum work procedure. Therefore, the same analysis software as the analysis of the optimum work procedure is used for the work procedure of the actual work analyzed by the work time comparison unit 215.
 以上説明したように本実施の形態に係る作業手順更新システム1は、最速作業時間更新部212が、実作業動画に基づいて算出した実績作業時間から最短である最短実績作業時間を抽出し、最短実績作業時間が最速作業時間より短かった場合に最速作業時間を更新する。最適作業手順抽出部213は、予め学習しておいた学習済モデルを用いて、最速作業時間に基づいて最適作業手順を抽出する。また、作業時間比較部215は、最適作業手順抽出部213が抽出した最適作業手順の各手順作業時間と実作業の各手順作業時間とを比較し、比較結果に基づいて作業改善案出力部216が実作業からの作業改善案を出力することとした。これにより、実作業状態に合致した最速作業時間、最適作業手順及び作業改善案を提示することが可能となる。 As described above, in the work procedure update system 1 according to the present embodiment, the fastest work time update unit 212 extracts the shortest actual work time from the actual work time calculated based on the actual work video, and the shortest. The fastest work time is updated when the actual work time is shorter than the fastest work time. The optimum work procedure extraction unit 213 extracts the optimum work procedure based on the fastest work time by using the trained model learned in advance. Further, the work time comparison unit 215 compares each procedure work time of the optimum work procedure extracted by the optimum work procedure extraction unit 213 with each procedure work time of the actual work, and the work improvement plan output unit 216 is based on the comparison result. Decided to output a work improvement plan from the actual work. This makes it possible to present the fastest work time, the optimum work procedure, and the work improvement plan that match the actual work state.
 なお、上記実施の形態において、最適作業手順抽出部213が用いる学習済モデルは、掃除機、除湿機、食器洗い乾燥機を含む製品について最速作業時間と作業の適性度を学習して生成されるが、学習対象は、複数種類の製品であってもよい。また、学習装置217は、1つのエリア内での実作業について学習してもよいし、互いに異なる複数のエリアで独立して行われる実作業について学習してもよい。 In the above embodiment, the trained model used by the optimum work procedure extraction unit 213 is generated by learning the fastest work time and work aptitude for products including a vacuum cleaner, a dehumidifier, and a dishwasher. , The learning target may be a plurality of types of products. Further, the learning device 217 may learn about the actual work in one area, or may learn about the actual work performed independently in a plurality of different areas.
 また、学習済モデルの学習対象の製品を途中で追加してもよく、又は途中で学習対象から除去してもよい。さらに、任意の学習済モデルを、他の機種の学習済モデルに適用してもよく、当該任意の学習済モデルを元に他の機種について再学習させてもよい。 Further, the training target product of the trained model may be added in the middle, or may be removed from the training target in the middle. Further, an arbitrary trained model may be applied to a trained model of another model, or the other model may be retrained based on the arbitrary trained model.
 上記実施の形態に示したハードウェア構成及びフローチャートは一例であり、任意に変更及び修正が可能である。演算処理部210及び記憶部220で実現する各機能は、専用のシステムによらず、通常のコンピュータシステムを用いて実現可能である。 The hardware configuration and flowchart shown in the above embodiment are examples, and can be arbitrarily changed and modified. Each function realized by the arithmetic processing unit 210 and the storage unit 220 can be realized by using a normal computer system without using a dedicated system.
 例えば、上記実施の形態の動作を実行するためのプログラムを、コンピュータが読み取り可能なCD-ROM(Compact Disc Read-Only Memory)、DVD(Digital Versatile Disc)、MO(Magneto Optical Disc)、メモリカード等の記録媒体に格納して配布し、プログラムをコンピュータにインストールすることにより、各機能を実現することができるコンピュータを構成してもよい。そして、各機能をOS(Operating System)とアプリケーションとの分担、又はOSとアプリケーションとの協同により実現する場合には、OS以外の部分のみを記録媒体に格納してもよい。 For example, a computer-readable CD-ROM (Compact Disc Read-Only Memory), DVD (Digital Versatile Disc), MO (Magneto Optical Disc), memory card, etc. can be used as a program for executing the operation of the above embodiment. A computer capable of realizing each function may be configured by storing and distributing it in a recording medium of the above and installing a program on the computer. When each function is realized by sharing the OS (Operating System) and the application or by cooperating with the OS and the application, only the part other than the OS may be stored in the recording medium.
 本開示は、本開示の広義の精神と範囲を逸脱することなく、様々な実施の形態及び変形が可能とされるものである。また、上述した実施の形態は、本開示を説明するためのものであり、本開示の範囲を限定するものではない。すなわち、本開示の範囲は、実施の形態ではなく、請求の範囲によって示される。そして、請求の範囲内及びそれと同等の開示の意義の範囲内で施される様々な変形が、本開示の範囲内とみなされる。 The present disclosure allows for various embodiments and variations without departing from the broad spirit and scope of the present disclosure. Moreover, the above-described embodiment is for explaining the present disclosure, and does not limit the scope of the present disclosure. That is, the scope of the present disclosure is shown by the claims, not by the embodiments. And, various modifications made within the scope of the claims and within the scope of the equivalent disclosure are considered to be within the scope of the present disclosure.
 本出願は、2020年11月2日に出願された、日本国特許出願特願2020-183842号に基づく。本明細書中に日本国特許出願特願2020-183842号の明細書、特許請求の範囲、図面全体を参照として取り込むものとする。 This application is based on Japanese Patent Application No. 2020-183842 filed on November 2, 2020. The specification, claims, and the entire drawing of Japanese Patent Application No. 2020-183842 shall be incorporated into this specification as a reference.
 1 作業手順更新システム、100 撮影装置、200 作業手順更新装置、210 演算処理部、211 実作業動画取得部、212 最速作業時間更新部、213 最適作業手順抽出部、214 作業手順更新部、215 作業時間比較部、216 作業改善案出力部、217 学習装置、218 推論装置、220 記憶部、221 実績作業データベース、222 最速作業時間データベース、223 学習済モデル記憶部、224 作業手順記憶部、230 表示部、2171 データ取得部、2172 モデル生成部、2181 データ取得部、2182 推論部。 1 work procedure update system, 100 shooting device, 200 work procedure update device, 210 arithmetic processing unit, 211 actual work video acquisition unit, 212 fastest work time update unit, 213 optimal work procedure extraction unit, 214 work procedure update unit, 215 work Time comparison unit, 216 work improvement plan output unit, 217 learning device, 218 inference device, 220 storage unit, 221 actual work database, 222 fastest work time database, 223 trained model storage unit, 224 work procedure storage unit, 230 display unit. , 2171 data acquisition unit, 2172 model generation unit, 2181 data acquisition unit, 2182 inference unit.

Claims (10)

  1.  作業を撮影した実作業動画に基づいて実績作業時間を取得し、記憶部に記憶されている最速作業時間よりも短い場合に、前記最速作業時間を前記実績作業時間に更新する最速作業時間更新部と、
     予め、前記最速作業時間と作業の適正度について学習しておいた学習済モデルを用いて、更新された前記最速作業時間に基づいて最適作業手順を抽出する最適作業手順抽出部と、
     前記記憶部に記憶されている作業手順を、抽出した前記最適作業手順に更新する作業手順更新部と、
    を備える、
     作業手順更新装置。
    The fastest work time update unit that acquires the actual work time based on the actual work video of the work and updates the fastest work time to the actual work time when it is shorter than the fastest work time stored in the storage unit. When,
    The optimum work procedure extraction unit that extracts the optimum work procedure based on the updated fastest work time using the trained model in which the fastest work time and the appropriateness of the work have been learned in advance, and the optimum work procedure extraction unit.
    A work procedure update unit that updates the work procedure stored in the storage unit to the extracted optimum work procedure, and a work procedure update unit.
    To prepare
    Work procedure updater.
  2.  前記実績作業時間は、作業者の体に装着した撮影装置が撮影した実作業動画に基づいて算出する、
     請求項1に記載の作業手順更新装置。
    The actual work time is calculated based on an actual work video taken by a shooting device attached to the worker's body.
    The work procedure updating device according to claim 1.
  3.  前記最速作業時間更新部は、前記実績作業時間を計測し、予め定めた期間の中で最も短い最短実績作業時間を取得し、前記最短実績作業時間が前記記憶部に記憶されている前記最速作業時間よりも短い場合に、前記最速作業時間を前記最短実績作業時間に更新する、
     請求項1又は2に記載の作業手順更新装置。
    The fastest work time update unit measures the actual work time, acquires the shortest actual work time in a predetermined period, and the shortest actual work time is stored in the storage unit. When the time is shorter than the time, the fastest working time is updated to the shortest actual working time.
    The work procedure updating device according to claim 1 or 2.
  4.  前記最速作業時間更新部は、前記実績作業時間を計測し、予め定めた期間の中で最も短い最短実績作業時間が2以上あって、前記最短実績作業時間が前記記憶部に記憶されている前記最速作業時間よりも短い場合に、前記最短実績作業時間に係る作業の動作の無駄の少なさを示す付加価値時間比率を取得し、前記付加価値時間比率が高い作業に係る前記最短実績作業時間を選択し、前記最速作業時間を選択した前記最短実績作業時間に更新する、
     請求項1から3のいずれか1項に記載の作業手順更新装置。
    The fastest working time updating unit measures the actual working time, has the shortest shortest actual working time of 2 or more in a predetermined period, and the shortest actual working time is stored in the storage unit. When the work time is shorter than the fastest work time, the value-added time ratio indicating that the work operation related to the shortest actual work time is less wasted is acquired, and the shortest actual work time related to the work having a high value-added time ratio is obtained. Select and update the fastest working time to the shortest actual working time selected.
    The work procedure updating device according to any one of claims 1 to 3.
  5.  前記実作業動画は、製品の生産機種を変更する際に発生する、部品の交換、実装基板の変更、治具及び工具の交換、並びに設備のプログラム変更を含む段取り作業を撮影した動画である、
     請求項1から4のいずれか1項に記載の作業手順更新装置。
    The actual work video is a video of the setup work including parts replacement, mounting board change, jig and tool replacement, and equipment program change that occur when the production model of the product is changed.
    The work procedure updating device according to any one of claims 1 to 4.
  6.  前記最適作業手順抽出部は、
     予め取得した前記最速作業時間に含まれる各作業手順に要する手順作業時間と、作業の適正度と、を含む学習用データに基づいて、学習済モデルを生成する学習装置と、
     前記最速作業時間更新部が更新した前記最速作業時間に含まれる前記手順作業時間に基づいて、前記学習済モデルを用いて、作業の前記適正度を推論して、前記適正度が最も高い前記最適作業手順を抽出する推論装置と、
     を有する、
     請求項1から5のいずれか1項に記載の作業手順更新装置。
    The optimum work procedure extraction unit is
    A learning device that generates a trained model based on learning data including the procedure work time required for each work procedure included in the fastest work time acquired in advance and the appropriateness of the work.
    Based on the procedure work time included in the fastest work time updated by the fastest work time update unit, the trained model is used to infer the appropriateness of the work, and the optimum with the highest appropriateness. An inference device that extracts work procedures and
    Have,
    The work procedure updating device according to any one of claims 1 to 5.
  7.  前記適正度は、作業のしやすさ又は作業の無駄の数の少なさを示す指標である、
     請求項6に記載の作業手順更新装置。
    The appropriateness is an index indicating the ease of work or the small number of wasteful work.
    The work procedure updating device according to claim 6.
  8.  前記実績作業時間に含まれる各手順作業時間と、前記最適作業手順の各手順作業時間と、を比較する作業時間比較部と、
     前記作業時間比較部の比較結果に基づいて、前記実績作業時間に係る作業に対する作業改善案を出力する作業改善案出力部と、をさらに備える、
     請求項1から7のいずれか1項に記載の作業手順更新装置。
    A work time comparison unit that compares each procedure work time included in the actual work time with each procedure work time of the optimum work procedure.
    A work improvement plan output unit that outputs a work improvement plan for the work related to the actual work time based on the comparison result of the work time comparison unit is further provided.
    The work procedure updating device according to any one of claims 1 to 7.
  9.  実績作業時間を取得し、前に記憶されている最速作業時間よりも短い場合に、前記最速作業時間を前記実績作業時間に更新する最速作業時間更新ステップと、
     前記最速作業時間と作業の適正度について学習して学習済モデルを生成するモデル生成ステップと、
     前記学習済モデルを用いて、前記最速作業時間更新ステップで更新された前記最速作業時間に基づいて最適作業手順を抽出する最適作業手順抽出ステップと、
     前に記憶されている作業手順を、前記最適作業手順抽出ステップで抽出した前記最適作業手順に更新する作業手順更新ステップと、
    を備える、
     作業手順更新方法。
    The fastest work time update step, which acquires the actual work time and updates the fastest work time to the actual work time when it is shorter than the fastest work time stored previously,
    The model generation step of learning about the fastest work time and the appropriateness of work and generating a trained model,
    Using the trained model, the optimum work procedure extraction step for extracting the optimum work procedure based on the fastest work time updated in the fastest work time update step, and the optimum work procedure extraction step.
    A work procedure update step for updating a previously stored work procedure to the optimum work procedure extracted in the optimum work procedure extraction step, and a work procedure update step.
    To prepare
    How to update the work procedure.
  10.  コンピュータを、
     実績作業時間を取得し、前に記憶されている最速作業時間よりも短い場合に、前記最速作業時間を前記実績作業時間に更新する最速作業時間更新部、
     前記最速作業時間と作業の適正度について学習して学習済モデルを生成するモデル生成部、
     前記学習済モデルを用いて、前記最速作業時間更新部が更新した前記最速作業時間に基づいて最適作業手順を抽出する最適作業手順抽出部、
     前に記憶されている作業手順を、前記最適作業手順抽出部が抽出した前記最適作業手順に更新する作業手順更新部、
     として機能させるプログラム。
    Computer,
    The fastest work time update unit, which acquires the actual work time and updates the fastest work time to the actual work time when it is shorter than the fastest work time stored previously.
    A model generation unit that learns about the fastest work time and the appropriateness of work and generates a trained model.
    Optimal work procedure extraction unit that extracts the optimum work procedure based on the fastest work time updated by the fastest work time update unit using the trained model.
    A work procedure update unit that updates a previously stored work procedure to the optimum work procedure extracted by the optimum work procedure extraction unit.
    A program that functions as.
PCT/JP2021/032074 2020-11-02 2021-09-01 Work procedure updating device, work procedure updating method, and program WO2022091571A1 (en)

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