WO2024009467A1 - Conveyor control device, production system, and conveyor control program - Google Patents
Conveyor control device, production system, and conveyor control program Download PDFInfo
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- 230000000977 initiatory effect Effects 0.000 claims description 9
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- 238000003384 imaging method Methods 0.000 description 16
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- 239000011265 semifinished product Substances 0.000 description 3
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
Definitions
- the present disclosure relates to a conveyor control device, a production system, and a conveyor control program.
- production systems equipped with conveyors that transport workpieces are known as equipment for manufacturing products.
- the term "work” is a concept that includes semi-finished products, which are products in the process of being manufactured, and parts that constitute products.
- processing refers to an operation performed on a workpiece in order to make the workpiece resemble a finished product.
- processing refers to the operation of assembling the rest of the parts that make up the product onto a workpiece that is a basic part or semi-finished product.
- a conveyor control device that controls the speed at which a workpiece is conveyed by a conveyor (hereinafter referred to as conveyor feeding speed) according to the processing efficiency of an operator. .
- the conveyor control device according to Patent Document 1 measures the required processing time, which is the time required for a worker to process a workpiece, and controls the conveyor feeding speed according to the measurement result. do.
- the conveyor control device includes a first receiving section and a second receiving section fixed to the side of the conveyor. At the moment when the transmitting section worn on the worker's arm approaches the first receiving section, a start time, which is the starting point of the required processing time, is detected. Furthermore, the end time, which is the end point of the required processing time, is detected at the moment the transmitter approaches the second receiver. The processing time is measured as the time span from the start time to the end time.
- An object of the present disclosure is to provide a conveyor control device, a production system, and a conveyor control program that can improve the accuracy of measuring the processing time required compared to conventional methods.
- the conveyor control device includes: an observation data acquisition unit that acquires observation data representing a time series of processing operations of a worker who performs predetermined processing on a workpiece being transported by a conveyor; Using the observed data, the worker performs an initial action period, which is a period in which the worker performs a series of initial actions at the start of the process, and a series of final actions at the end of the process.
- a specific part that specifies a final operating period, which is a period in which the a processing time calculation unit that calculates a processing time that is a time width from a representative start time belonging to the initial operation period to a representative end time belonging to the final operation period; a feed rate control unit that controls a feed rate that is a speed at which the workpiece is conveyed by the conveyor based on a calculation result of the processing time calculation unit; Equipped with
- the initial operation period and the final operation period each having a time width are specified, and the required processing time is calculated using the specified results. Therefore, the accuracy of measuring the time required for processing is improved compared to the conventional method of directly detecting the times at only two points on the time axis.
- a production system 400 includes a conveyor 100 that constitutes a part of a production line that manufactures products in an assembly line.
- the conveyor 100 conveys the workpiece 500 at a low speed that the worker 600 can follow by walking.
- the work 500 is a semi-finished product or a part.
- the worker 600 performs predetermined processing on the workpiece 500 being transported by the conveyor 100, such as assembling other parts, machining, marking, inspection, and packaging.
- the worker 600 performs the above processing on the workpiece 500 while walking along the conveyor 100 following the workpiece 500, or in some cases, while standing still.
- the works 500 are conveyed one after another toward the worker 600 by the conveyor 100.
- the operator 600 performs the above processing on the workpiece 500 every time the workpiece 500 is transported. In this way, the above process is repeatedly performed by the worker 600.
- the worker 600 If the efficiency of the above-mentioned processing performed by the worker 600 is high relative to the speed at which the workpiece 500 is transported by the conveyor 100, that is, the feed speed of the conveyor 100, the worker 600 has a waiting time for waiting for the arrival of the next workpiece 500. It will happen. On the other hand, if the feed speed of the conveyor 100 is too fast for the efficiency of the processing performed by the worker 600, the burden on the worker 600 may increase or the processing may not be completed.
- the production system 400 further includes an imaging device 200 and a conveyor control device 300 in order to control the feed speed of the conveyor 100 according to the processing efficiency of the worker 600.
- the imaging device 200 images the worker 600 beside the conveyor 100.
- the imaging area IA captured by the imaging device 200 includes a movable area in which the operator 600 can move for one process.
- the imaging device 200 generates observation data DT representing a time series of the above processing operations performed by the worker 600 by imaging the worker 600. That is, the imaging device 200 is an example of an observation data generation device according to the present disclosure.
- the observation data DT is time-series data in which each observation result is associated with the time at which the observation result was obtained.
- the observation data DT is a moving image in which each still image (hereinafter also referred to as a frame) taken in the imaging area IA is associated with the time at which the still image was taken, as an observation result. It is data.
- the conveyor control device 300 uses the observation data DT generated by the imaging device 200 to determine the processing time required, which is an index representing the efficiency of the above processing by the worker 600. Then, the conveyor control device 300 controls the feed speed of the conveyor 100 according to the determined processing time.
- the configuration of the conveyor control device 300 will be specifically described below.
- the conveyor control device 300 includes a communication device 310, which is hardware necessary for communication.
- the communication device 310 plays the role of taking in observation data DT from the imaging device 200 and the role of outputting a control command to the conveyor 100 for controlling the feed speed of the conveyor 100.
- the conveyor control device 300 includes a storage device 320 that stores a conveyor control program 321 that defines a procedure for controlling the feed speed of the conveyor 100.
- the storage device 320 also stores a learned model 322 used to control the feed rate of the conveyor 100 and learning data 323 necessary to generate the learned model 322.
- the conveyor control device 300 includes a processor 330 that executes a conveyor control program 321.
- the functions realized by the processor 330 executing the conveyor control program 321 will be described below.
- the conveyor control device 300 has the function of a control section 331 that controls the feed speed of the conveyor 100, and the function of a learning section 332 that generates a learned model 322 necessary for the control.
- the control unit 331 uses the observation data acquisition unit 331a that acquires the observation data DT from the imaging device 200 and the observation data DT to specify the time necessary for calculating the index representing the efficiency of the above processing of the worker 600. It has a specific part 331b.
- FIG. 4 shows a time axis along the time series represented by the observation data DT.
- the observation data DT has a data structure in which frames representing one still image are arranged along the time axis.
- the specifying unit 331b uses the observation data DT to specify an operation period that is a period in which the worker 600 actually operated for the above processing.
- the operation period represents the period of one cycle of the above-mentioned processing that is periodically repeated by the worker 600.
- the presence or absence of motion can be determined by analyzing each frame arranged in time series. Therefore, the operation period can be specified by the observation data DT.
- the specifying unit 331b uses the observation data DT to specify the initial operating period TA that constitutes the beginning of the operating period.
- the initial operation period TA is a period during which the operator 600 performed a series of initial operations at the start of the process.
- the series of initial operations are operations that follow a predetermined procedure. Therefore, the initial operation period TA can be specified based on the time series of frames represented by the observation data DT.
- the specifying unit 331b uses the observation data DT to specify the representative start time t1 belonging to the initial operation period TA.
- the representative start time t1 is the time at the moment when the worker 600 performs a specific action (hereinafter referred to as representative start action) among the above-mentioned initial actions. Since the representative start motion is a predetermined specific motion, the representative start time t1 can be specified in the time series of frames represented by the observation data DT.
- the worker 600 uses a tool to perform the above process on the workpiece 500.
- the period from when the operator 600 prepares the tool until he starts operating the tool can be defined as the initial operation period TA.
- the action of the worker 600 applying the tool to the workpiece 500 can be defined as a representative start action, and the moment when the representative start action is performed can be defined as the representative start time t1.
- the specifying unit 331b uses the observation data DT to specify the final operating period TB that constitutes the final part of the operating period.
- the final action period TB is a period during which the worker 600 performed a series of final actions at the end of the process.
- the series of final operations is an operation that follows a predetermined procedure, and is clearly different from the series of initial operations described above. Therefore, the final operating period TB can be specified based on the time series of frames represented by the observation data DT.
- the specifying unit 331b uses the observation data DT to specify the representative end time t2 belonging to the final operation period TB.
- the representative end time t2 is the time at the moment when the worker 600 performs a specific action (hereinafter referred to as representative end action) among the final actions. Since the representative end action is a predetermined specific action, the representative end time t2 can be specified based on the time series of frames represented by the observation data DT.
- the period from when the operator 600 finishes operating the tool until the tool is returned to a predetermined position can be defined as the final operation period TB.
- the action of the operator 600 separating the tool from the workpiece 500 can be defined as a representative end action, and the moment when the representative end action is performed can be defined as the representative end time t2.
- the time difference between the representative start time t1 and the representative end time t2 identified as described above will be referred to as the required processing time.
- the processing time required represents the time actually required by the operator 600 to perform the above processing once. In other words, the processing time is an index representing the efficiency of the above-mentioned processing by the worker 600.
- the processing time required is the time difference between the representative start time t1 and the representative end time t2, but is not limited to this. That is, in the present embodiment, the processing time refers to the period between the initial operation period TA and the final operation period TB, and for example, the time required for processing is the period between the initial operation period TA and the final operation period TB.
- the processing time is the time after a predetermined period has elapsed from the start time of the initial operation period TA (for example, the time after 10% of the period length of the initial operation period TA has elapsed from the start time of the initial operation period TA), It may be a difference from the time after a predetermined period has elapsed from the start time of the final operation period TB (for example, the time after 90% of the period length of the final operation period TB has elapsed from the start time of the final operation period TB).
- the processing time is the difference between the time obtained based on the initial operation period TA and the time obtained based on the final operation period TB.
- the representative start time t1 which is one point on the time axis, without specifying the initial movement period TA, the representative start time t1, which is one point on the time axis, may be There is a high possibility that the representative start time t1 may be erroneously identified as the time when an action different from the action was performed, or that the representative start time t1 is failed to be identified.
- the initial operation period TA is first specified. Since the initial movement period TA has a time width, unlike the case of directly specifying one point on the time axis, even if the operator 600 performs an unexpected movement, highly accurate identification is possible. be.
- the representative start time t1 exists within the initial operation period TA, once the initial operation period TA is specified, the range in which the representative start time t1 is searched can be limited to the initial operation period TA. Therefore, it is possible to reduce the possibility of failing to specify the representative start time t1 or erroneously specifying a different representative start time t1. The same applies to specifying the representative end time t2.
- the specifying unit 331b uses the learned model 322 to specify the above-described initial operation period TA, representative start time t1, final operation period TB, and representative end time t2.
- the trained model 322 detects, from the observation data DT, first partial data representing the series of initial operations in the initial operation period TA, and second partial data representing the series of final operations in the final operation period TB. At the same time, machine learning is performed to detect the representative start time t1 using the detected first partial data and to detect the representative end time t2 using the detected second partial data.
- the identification unit 331b inputs the observation data DT acquired by the observation data acquisition unit 331a into the learned model 322. Then, the specifying unit 331b obtains outputs of the detection results of the first partial data, the representative start time t1, the second partial data, and the representative end time t2 from the learned model 322.
- observation data DT is time-series data
- detecting the first partial data from the observation data DT is equivalent to specifying the initial operation period TA.
- detecting the second partial data from the observation data DT is equivalent to specifying the final operating period TB.
- the specifying unit 331b specifies the initial operation period TA, representative start time t1, final operation period TB, and representative end time t2 based on the output of the learned model 322 into which the observation data DT has been input.
- control unit 331 includes a processing time calculation unit 331c that calculates the processing time required, which is the time span from the representative start time t1 to the representative end time t2 specified by the identification unit 331b.
- the processing time required is the time difference between the representative end time t2 and the representative start time t1.
- the processing time is an index representing the efficiency of the above-mentioned processing by the worker 600.
- control unit 331 includes a feed rate control unit 331d that controls the feed rate of the conveyor 100 based on the required processing time that is the calculation result of the required processing time calculation unit 331c.
- the conveyor 100 includes a conveyance belt on which the workpiece 500 is placed, a roller on which the conveyance belt is hung, and a motor that rotates the roller.
- the motor has a configuration that allows its rotational speed to be controlled. Control of the feed speed of the conveyor 100 is realized by controlling the rotation speed of the motor of the conveyor 100, specifically, by controlling an inverter.
- the feed rate control unit 331d determines that the longer the processing time required by the worker 600, the lower the feed speed of the conveyor 100, and the shorter the processing time required by the worker 600, the higher the feed speed of the conveyor 100.
- the feed speed of the conveyor 100 is controlled based on the conditions. As a result, in the next process described above, the worker 600 is less likely to have a waiting time, and moreover, the worker 600 is less likely to be overloaded.
- the learning section 332 includes a learning data acquisition section 332a that acquires the learning data 323.
- the learning data 323 includes the actual measured values of the first partial data, representative start time t1, second partial data, and representative end time t2, the first partial data, representative start time t1, second partial data, and The observation data DT for which the representative end time t2 was actually measured is used.
- actual measurement means correctly specifying by means other than the learned model 322. That is, the "actual measured value of the first partial data” means the first partial data correctly specified by means other than the learned model 322. The same applies to the “actual measurement value of the second partial data”. Furthermore, the “actual measured value of the representative start time t1” means the representative start time t1 correctly specified by means other than the learned model 322. The same applies to the “actual measurement value of representative start time t2”.
- actual measurement can be performed by a person who creates the learning data 323 (hereinafter referred to as the creator).
- a video represented by the observation data DT is played back, and the creator visually identifies the initial operation period TA, representative start time t1, final operation period TB, and representative start time t2 in the video.
- the actual measured values of the first partial data, the representative start time t1, the second partial data, and the representative end time t2 can be obtained.
- the video may be played back frame by frame or played back in slow motion.
- the creator directly visually confirms the work of the worker 600 at the site, and determines the times corresponding to both end points of the initial operation period TA, the representative start time t1, and the final operation period TB.
- the times corresponding to both end points of and the representative start time t2 may be recorded respectively.
- By comparing each recorded time with the time information represented by the observation data DT it is possible to obtain the actual measured values of the first partial data, representative start time t1, second partial data, and representative end time t2. can.
- the learning unit 332 includes a generation unit 332b that generates the learned model 322 using the learning data 323 acquired by the learning data acquisition unit 332a.
- the generation unit 332b uses the learning data 323 to learn a policy for detecting the first partial data, the representative start time t1, the second partial data, and the representative end time t2 from the observation data DT. Through such machine learning, a trained model 322 is generated.
- the representative start time t1 is defined as a time that is uniquely determined from the length of the initial operation period TA (for example, the time after 10% of the length of the initial operation period TA has elapsed from the start time of the initial operation period TA).
- the learning data 323 does not necessarily need to include the actual measured value of the representative start time t1.
- the representative start time t1 can be specified from the length of the initial operation period TA in the specifying unit 331b or the processing time calculation unit 331c without using the learned model 322 to specify the representative start time t1. can.
- the identification unit 331b of the conveyor control device 300 uses the observation data DT to identify a representative start time t1 belonging to the initial operation period TA and a representative end time t2 belonging to the final operation period TB. Further, the processing time calculation unit 331c of the conveyor control device 300 calculates the processing time, which is the time difference between the representative start time t1 and the representative end time t2 (step S5).
- step S1 the process returns to step S1 again, and the unprocessed workpiece 500 arrives at the operator 600 at the controlled feed speed. In this way, steps S1 to S6 are repeated.
- step S5 in order to specify the representative start time t1, an initial operation period TA with a time width is once specified, and in order to specify the representative end time t2, a final operation period TB with a time width is once specified. be done.
- the range in which the representative start time t1 is searched can be limited to the initial operation period TA
- the range in which the representative end time t2 is searched can be limited to the final operation period TB.
- the accuracy of measuring the processing time is less likely to be influenced by the characteristics of the operator's 600 movements. In other words, the accuracy of measuring the time required for processing is improved compared to the conventional method.
- supervised learning using the learning data 323 as teacher data is illustrated as a learning algorithm for generating the trained model 322.
- the learning unit 332 may have a function of updating the learned model 322 by reinforcement learning. A specific example will be described below.
- the remuneration calculation unit 332c uses the observation data DT to calculate the waiting time or insufficient time. When waiting time or insufficient time occurs, the feed rate control performed last time is not necessarily appropriate. Therefore, the remuneration calculation unit 332c calculates the remuneration under the conditions that the longer the waiting time or the shortage time is, the more the remuneration is reduced, and the closer the waiting time or the shortage time is to zero, the more the reward is increased.
- the feed speed of the conveyor 100 is controlled according to the efficiency of the processing by the operator 600.
- the feed speed of the conveyor 100 may be controlled by further taking into consideration the tendency of change in the efficiency of the above-mentioned processing by the worker 600. A specific example will be described below.
- control unit 331 further includes a change amount calculation unit 331e that calculates a difference in the processing required time calculated by the processing required time calculation unit 331c.
- the change amount calculation unit 331e calculates the processing time calculated by the processing time calculation unit 331c for processing the workpiece 500 in the current turn and the processing time required for processing the workpiece 500 in the previous turn.
- the difference between the required processing time calculated by the calculation unit 331c (hereinafter referred to as the amount of change in the required processing time) is calculated.
- the amount of change in processing time is a physical quantity that also includes positive and negative signs.
- a positive value of the amount of change in processing time indicates a tendency that the processing time is increasing.
- One of the factors for this is a decrease in efficiency due to fatigue of the worker 600.
- a negative value of the amount of change in processing time indicates a tendency for processing time to be shortened.
- One of the factors for this is improvement in efficiency as the worker 600 becomes accustomed to the work.
- the feed rate control unit 331d calculates not only the calculation results of the processing time calculation unit 331c, that is, the processing required time calculated for processing the workpiece 500 in the current turn, but also the processing that is the calculation result of the change amount calculation unit 331e.
- the feed speed of the conveyor 100 is also controlled based on the amount of change in required time.
- the feed speed of the conveyor 100 is controlled taking into account not only the processing time but also the amount of change in the processing time, so the feed speed of the conveyor 100 can be controlled to a more appropriate value.
- Other configurations and effects are similar to those in the first embodiment.
- the feed speed of the conveyor 100 is controlled according to the processing time required. Furthermore, the feed speed of the conveyor 100 may be controlled in consideration of the length of the intermediate operation period between the initial operation period TA and the final operation period TB. A specific example will be described below.
- FIG. 8 shows the position of the intermediate operation period TC on the time axis.
- the identifying unit 331b uses observation data DT to identify not only the initial operating period TA and the final operating period TB, but also the intermediate operating period TC between the initial operating period TA and the final operating period TB. do.
- the intermediate motion period TC is a period in which the worker 600 performs a specific series of intermediate motions that constitute a part of the motion of the worker 600 from the end of the initial motion period TA to the start of the final motion period TB. be.
- the series of intermediate operations are operations that follow a predetermined procedure, and are different from the series of initial operations and the series of final operations described above. Therefore, the intermediate operation period TC can be specified based on the time series of frames represented by the observation data DT.
- the length of the intermediate operation period TC varies depending on the degree of fatigue of the worker 600 and the degree of familiarity with the work. On the other hand, a change in the length of the intermediate operation period TC may be difficult to manifest as a change in the required processing time. This is because even if the period length of the intermediate operation period TC changes, the operator 600 may unconsciously adjust the efficiency of the remaining operation.
- the feed speed of the conveyor 100 is controlled taking into consideration not only the processing time but also the length of the intermediate operation period TC, so the feed speed of the conveyor 100 can be controlled to a more appropriate value. Can be done.
- measuring the period length of the intermediate operation period TC is equivalent to specifying the intermediate operation period TC.
- the intermediate operating period TC can also be specified using the learned model 322 in the same manner as the initial operating period TA and the final operating period TB.
- the learning data 323 used to generate the learned model 322 it is preferable to use observation data DT in which the third partial data representing the series of intermediate operations described above is correctly identified in advance. Other configurations and effects are similar to those in the first embodiment.
- the learned model 322 is used not only to specify the initial operation period TA and the final operation period TB, but also to specify the representative start time t1 and representative end time t2.
- the learned model 322 does not necessarily need to be used to specify the representative start time t1 and the representative end time t2.
- the initial operation period TA is specified in order to specify the representative start time t1
- the final operation period TB is specified in order to specify the representative end time t2
- the representative start time t1 may be a time uniquely determined from the initial operation period TA
- the representative end time t2 may be a time uniquely determined from the final operation period TB.
- the identifying unit 331b or the processing time calculating unit 331c determines the time at which the time width of the initial operation period TA is internally divided by a predetermined ratio, specifically, the midpoint of the time width. The time corresponding to t is calculated as the representative start time t1. Similarly, the specifying unit 331b or the processing time calculating unit 331c determines the time at which the time width of the final operating period TB is internally divided by a predetermined ratio, specifically, the time at the midpoint of the time width. , is calculated as the representative end time t2.
- the learned model 322 does not estimate the representative start time t1 and the representative end time t2. Therefore, the learning data 323 as teacher data does not include the actual measured values of the representative start time t1 and the representative end time t2. That is, the learning data 323 uses the actual measured values of the first partial data and the second partial data described above, and the observation data DT in which the first partial data and the second partial data were actually measured.
- the midpoint is exemplified as the point for internally dividing the time width of the initial operation period TA, but the point for internally dividing does not necessarily have to be the midpoint.
- the concept of "a point that internally divides a time width" includes a start end point and an end end point that are both end points of the time width.
- Embodiments 1-5 have been described above. The following variations are also possible.
- observation data DT representing the chronological sequence of the actions of the worker 600.
- the movement of the worker 600 can also be expressed by a change over time in the distance between the reference location and the worker 600, or a change over time in the intensity of the sound that occurs along with the movement of the worker 600. That is, the observation data DT may be time-series data in which observation results such as distance and sound intensity are arranged in chronological order.
- the trained model 322 may be generated by unsupervised learning.
- the learning data 323 may use past observation data DT in which the first partial data, representative start time t1, second partial data, and representative end time t2 are not correctly specified.
- the learned model 322 does not necessarily need to be used to specify the initial operating period TA and final operating period TB.
- the identification unit 331b searches the time series of observation results included in the observation data DT, and extracts observation results representing a specific action of the worker 600 from the observation data DT by pattern recognition. , an initial operating period TA and a final operating period TB may be specified.
- the feed speed control section 331d controls the feed speed of the conveyor 100 based on the calculation result of the processing time calculation section 331c.
- "based on the calculation result of the processing time calculation unit 331c” refers not only to controlling the feed speed of the conveyor 100 based on the processing time that is the calculation result of the processing time calculation unit 331c; This term also includes controlling the feed speed of the conveyor 100 using a physical quantity that depends on the processing time.
- the feed speed control unit 331d calculates the target value of the feed speed of the conveyor 100 by dividing the known length by the required processing time as a physical quantity that depends on the required processing time, and adjusts the feed speed of the conveyor 100 by dividing the known length by the required processing time. Control may be performed to bring the speed closer to the target value.
- the conveyor control device 300 can be realized using an existing computer. That is, by installing the conveyor control program 321 shown in FIG. 2 into a computer, the computer can function as the conveyor control device 300.
- the conveyor control program 321 may be distributed via a communication network, or may be stored in a computer-readable, non-transitory recording medium and then distributed.
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Abstract
In a conveyor control device (300), an observation data acquisition unit (331a) acquires observation data representing a time-series of a processing operation by a worker performing predetermined processing on a workpiece being conveyed by a conveyor. An identification unit (331b) uses the observation data to identify an initial operation period, which is a period in which the worker performs a series of initial operations at the start of the processing, and a final operation period, which is a period in which the worker performs a series of final operations at the end of the processing. A required processing time calculation unit (331c) calculates a required processing time, which is the time span from a representative start time belonging to the initial operation period to a representative end time belonging to the final operation period. A feeding speed control unit (331d) controls the feeding speed, which is the speed at which the conveyor coneys the workpiece, on the basis of the calculation result of the required processing time calculation unit (331c).
Description
本開示は、コンベア制御装置、生産システム、及びコンベア制御プログラムに関する。
The present disclosure relates to a conveyor control device, a production system, and a conveyor control program.
製品を製造するための設備として、ワーク(workpiece)を搬送するコンベア(conveyor)を備えた生産システムが知られている。本明細書において“ワーク”とは、製造途中の製品である半製品、及び製品を構成する部品を含む概念とする。
Production systems equipped with conveyors that transport workpieces are known as equipment for manufacturing products. In this specification, the term "work" is a concept that includes semi-finished products, which are products in the process of being manufactured, and parts that constitute products.
コンベアの脇には、作業者が配置される。作業者は、コンベアによって搬送されるワークに対し、予め定められた処理を施す。本明細書において“処理”とは、ワークを完成品である製品に近づけるために、ワークに対して施す操作を意味する。典型的には“処理”とは、基礎となる1つの部品又は半製品であるワークに対し、製品を構成する残りの部品を組付ける操作を指す。
A worker is placed next to the conveyor. An operator performs predetermined processing on the workpiece being conveyed by the conveyor. In this specification, "processing" refers to an operation performed on a workpiece in order to make the workpiece resemble a finished product. Typically, "processing" refers to the operation of assembling the rest of the parts that make up the product onto a workpiece that is a basic part or semi-finished product.
特許文献1に開示されているように、コンベアによってワークが搬送される速度(以下、コンベアの送り速度という。)を、作業者の処理の能率に応じて制御するコンベア制御装置が知られている。具体的には、特許文献1に係るコンベア制御装置は、作業者がワークに対して処理を施すのに要する時間である処理所要時間を計測し、その計測結果に応じてコンベアの送り速度を制御する。
As disclosed in Patent Document 1, a conveyor control device is known that controls the speed at which a workpiece is conveyed by a conveyor (hereinafter referred to as conveyor feeding speed) according to the processing efficiency of an operator. . Specifically, the conveyor control device according to Patent Document 1 measures the required processing time, which is the time required for a worker to process a workpiece, and controls the conveyor feeding speed according to the measurement result. do.
特許文献1に係るコンベア制御装置は、コンベアの脇に固定された第1受信部及び第2受信部を備える。作業者の腕に装着された送信部が第1受信部に近接した瞬間に、処理所要時間の始点となる開始時刻が検知される。また、その送信部が第2受信部に近接した瞬間に、処理所要時間の終点となる終了時刻が検知される。処理所要時間は、開始時刻から終了時刻までの時間幅として計測される。
The conveyor control device according to Patent Document 1 includes a first receiving section and a second receiving section fixed to the side of the conveyor. At the moment when the transmitting section worn on the worker's arm approaches the first receiving section, a start time, which is the starting point of the required processing time, is detected. Furthermore, the end time, which is the end point of the required processing time, is detected at the moment the transmitter approaches the second receiver. The processing time is measured as the time span from the start time to the end time.
しかし、このように開始時刻及び終了時刻という、時間軸上の2点の時刻だけを検知する手法では、作業者の動作によっては開始時刻又は終了時刻を検出し損ねたり、第1受信部又は第2受信部へのノイズの入力を開始時刻又は終了時刻の到来と誤検出したりする可能性がある。即ち、特許文献1に係る手法では、処理所要時間の計測の正確性が充分であるとは言い難い。
However, with this method of detecting only two points on the time axis, the start time and the end time, depending on the operator's actions, the start time or end time may not be detected, or the first or end time may not be detected. 2. There is a possibility that the input of noise to the receiving section may be mistakenly detected as the arrival of the start time or end time. That is, the method according to Patent Document 1 cannot be said to have sufficient accuracy in measuring the time required for processing.
本開示の目的は、処理所要時間の計測の正確性が従来よりも高められるコンベア制御装置、生産システム、及びコンベア制御プログラムを提供することである。
An object of the present disclosure is to provide a conveyor control device, a production system, and a conveyor control program that can improve the accuracy of measuring the processing time required compared to conventional methods.
本開示に係るコンベア制御装置は、
コンベアによって搬送中のワークに対して予め定められた処理を施す作業者の、前記処理の動作の時系列を表す観測データを取得する観測データ取得部と、
前記観測データを用いて、前記処理の開始の際における一連の初期動作を前記作業者が行った期間である初期動作期間と、前記処理の終了の際における一連の終期動作を前記作業者が行った期間である終期動作期間とを特定する特定部と、
前記初期動作期間に属する代表開始時刻から、前記終期動作期間に属する代表終了時刻までの時間幅である処理所要時間を算出する処理所要時間算出部と、
前記処理所要時間算出部の算出結果に基づいて、前記コンベアによって前記ワークが搬送される速度である送り速度を制御する送り速度制御部と、
を備える。 The conveyor control device according to the present disclosure includes:
an observation data acquisition unit that acquires observation data representing a time series of processing operations of a worker who performs predetermined processing on a workpiece being transported by a conveyor;
Using the observed data, the worker performs an initial action period, which is a period in which the worker performs a series of initial actions at the start of the process, and a series of final actions at the end of the process. a specific part that specifies a final operating period, which is a period in which the
a processing time calculation unit that calculates a processing time that is a time width from a representative start time belonging to the initial operation period to a representative end time belonging to the final operation period;
a feed rate control unit that controls a feed rate that is a speed at which the workpiece is conveyed by the conveyor based on a calculation result of the processing time calculation unit;
Equipped with
コンベアによって搬送中のワークに対して予め定められた処理を施す作業者の、前記処理の動作の時系列を表す観測データを取得する観測データ取得部と、
前記観測データを用いて、前記処理の開始の際における一連の初期動作を前記作業者が行った期間である初期動作期間と、前記処理の終了の際における一連の終期動作を前記作業者が行った期間である終期動作期間とを特定する特定部と、
前記初期動作期間に属する代表開始時刻から、前記終期動作期間に属する代表終了時刻までの時間幅である処理所要時間を算出する処理所要時間算出部と、
前記処理所要時間算出部の算出結果に基づいて、前記コンベアによって前記ワークが搬送される速度である送り速度を制御する送り速度制御部と、
を備える。 The conveyor control device according to the present disclosure includes:
an observation data acquisition unit that acquires observation data representing a time series of processing operations of a worker who performs predetermined processing on a workpiece being transported by a conveyor;
Using the observed data, the worker performs an initial action period, which is a period in which the worker performs a series of initial actions at the start of the process, and a series of final actions at the end of the process. a specific part that specifies a final operating period, which is a period in which the
a processing time calculation unit that calculates a processing time that is a time width from a representative start time belonging to the initial operation period to a representative end time belonging to the final operation period;
a feed rate control unit that controls a feed rate that is a speed at which the workpiece is conveyed by the conveyor based on a calculation result of the processing time calculation unit;
Equipped with
上記構成によれば、各々時間幅をもつ初期動作期間及び終期動作期間が特定され、その特定の結果を用いて処理所要時間が算出される。このため、時間軸上の2点の時刻だけを直接的に検知する従来の手法に比べて、処理所要時間の計測の正確性が高められる。
According to the above configuration, the initial operation period and the final operation period each having a time width are specified, and the required processing time is calculated using the specified results. Therefore, the accuracy of measuring the time required for processing is improved compared to the conventional method of directly detecting the times at only two points on the time axis.
以下、図面を参照し、実施の形態1-4に係る生産システムについて説明する。図中、同一又は対応する部分に同一の符号を付す。
The production system according to Embodiments 1-4 will be described below with reference to the drawings. In the figures, the same or corresponding parts are denoted by the same reference numerals.
[実施の形態1]
図1に示すように、本実施の形態に係る生産システム400は、製品を流れ作業により製造する製造ラインの一部を構成するコンベア100を備える。コンベア100は、作業者600が歩行によって追随できる程度の低速で、ワーク500を搬送する。 [Embodiment 1]
As shown in FIG. 1, a production system 400 according to the present embodiment includes a conveyor 100 that constitutes a part of a production line that manufactures products in an assembly line. The conveyor 100 conveys the workpiece 500 at a low speed that the worker 600 can follow by walking.
図1に示すように、本実施の形態に係る生産システム400は、製品を流れ作業により製造する製造ラインの一部を構成するコンベア100を備える。コンベア100は、作業者600が歩行によって追随できる程度の低速で、ワーク500を搬送する。 [Embodiment 1]
As shown in FIG. 1, a production system 400 according to the present embodiment includes a conveyor 100 that constitutes a part of a production line that manufactures products in an assembly line. The conveyor 100 conveys the workpiece 500 at a low speed that the worker 600 can follow by walking.
ワーク500は、半製品又は部品である。作業者600は、コンベア100によって搬送中のワーク500に対し、他の部品の組み付け、機械加工、マーキング、検査、梱包といった、予め定められた処理を施す。作業者600は、コンベア100に沿ってワーク500に追随して歩行しながら、あるいは、場合によっては立ち止まったままで、上記処理をワーク500に施す。
The work 500 is a semi-finished product or a part. The worker 600 performs predetermined processing on the workpiece 500 being transported by the conveyor 100, such as assembling other parts, machining, marking, inspection, and packaging. The worker 600 performs the above processing on the workpiece 500 while walking along the conveyor 100 following the workpiece 500, or in some cases, while standing still.
ワーク500は、コンベア100によって、次々に作業者600に向かって搬送される。作業者600は、ワーク500が搬送されてくる度に、そのワーク500に対して上記処理を施す。このようにして、作業者600によって上記処理が繰り返し行われる。
The works 500 are conveyed one after another toward the worker 600 by the conveyor 100. The operator 600 performs the above processing on the workpiece 500 every time the workpiece 500 is transported. In this way, the above process is repeatedly performed by the worker 600.
コンベア100によるワーク500の搬送の速度、即ち、コンベア100の送り速度に対して、作業者600が行う上記処理の能率が高いと、作業者600に次のワーク500の到来を待機する待ち時間が発生してしまう。一方、作業者600が行う上記処理の能率に対して、コンベア100の送り速度が速すぎると、作業者600の負担が増大したり、上記処理が未完了になったりする。
If the efficiency of the above-mentioned processing performed by the worker 600 is high relative to the speed at which the workpiece 500 is transported by the conveyor 100, that is, the feed speed of the conveyor 100, the worker 600 has a waiting time for waiting for the arrival of the next workpiece 500. It will happen. On the other hand, if the feed speed of the conveyor 100 is too fast for the efficiency of the processing performed by the worker 600, the burden on the worker 600 may increase or the processing may not be completed.
そこで、本実施の形態に係る生産システム400は、作業者600の処理の能率に応じてコンベア100の送り速度を制御するために、撮像装置200及びコンベア制御装置300をさらに備える。
Therefore, the production system 400 according to the present embodiment further includes an imaging device 200 and a conveyor control device 300 in order to control the feed speed of the conveyor 100 according to the processing efficiency of the worker 600.
撮像装置200は、コンベア100の脇の作業者600を撮像する。撮像装置200によって撮像される撮像領域IAは、作業者600が1回の上記処理のために移動し得る可動領域を内包する。撮像装置200は、作業者600を撮像することにより、作業者600が行う上記処理の動作の時系列を表す観測データDTを生成する。即ち、撮像装置200は、本開示に係る観測データ生成装置の一例である。
The imaging device 200 images the worker 600 beside the conveyor 100. The imaging area IA captured by the imaging device 200 includes a movable area in which the operator 600 can move for one process. The imaging device 200 generates observation data DT representing a time series of the above processing operations performed by the worker 600 by imaging the worker 600. That is, the imaging device 200 is an example of an observation data generation device according to the present disclosure.
観測データDTは、観測結果ごとに、その観測結果が得られた時刻が対応付けられた時系列データである。具体的には、観測データDTは、観測結果としての、上記撮像領域IAを撮像した静止画像(以下、フレームともいう。)ごとに、その静止画像が撮られた時刻が対応付けられている動画データである。
The observation data DT is time-series data in which each observation result is associated with the time at which the observation result was obtained. Specifically, the observation data DT is a moving image in which each still image (hereinafter also referred to as a frame) taken in the imaging area IA is associated with the time at which the still image was taken, as an observation result. It is data.
コンベア制御装置300は、まず、撮像装置200によって生成された観測データDTを用いて、作業者600の上記処理の能率を表す指標である処理所要時間を求める。そして、コンベア制御装置300は、求めた処理所要時間に応じて、コンベア100の送り速度を制御する。以下、コンベア制御装置300の構成を具体的に説明する。
First, the conveyor control device 300 uses the observation data DT generated by the imaging device 200 to determine the processing time required, which is an index representing the efficiency of the above processing by the worker 600. Then, the conveyor control device 300 controls the feed speed of the conveyor 100 according to the determined processing time. The configuration of the conveyor control device 300 will be specifically described below.
図2に示すように、コンベア制御装置300は、通信に必要なハードウエアである通信装置310を備える。通信装置310は、撮像装置200から観測データDTを取り込む役割と、コンベア100の送り速度を制御するための制御指令をコンベア100に出力する役割とを担う。
As shown in FIG. 2, the conveyor control device 300 includes a communication device 310, which is hardware necessary for communication. The communication device 310 plays the role of taking in observation data DT from the imaging device 200 and the role of outputting a control command to the conveyor 100 for controlling the feed speed of the conveyor 100.
また、コンベア制御装置300は、コンベア100の送り速度を制御する手順を規定したコンベア制御プログラム321を記憶している記憶装置320を備える。記憶装置320には、コンベア100の送り速度の制御に用いる学習済モデル322、及び学習済モデル322を生成するのに必要な学習用データ323も記憶されている。
Additionally, the conveyor control device 300 includes a storage device 320 that stores a conveyor control program 321 that defines a procedure for controlling the feed speed of the conveyor 100. The storage device 320 also stores a learned model 322 used to control the feed rate of the conveyor 100 and learning data 323 necessary to generate the learned model 322.
また、コンベア制御装置300は、コンベア制御プログラム321を実行するプロセッサ330を備える。以下、プロセッサ330がコンベア制御プログラム321を実行することにより実現される機能について説明する。
Additionally, the conveyor control device 300 includes a processor 330 that executes a conveyor control program 321. The functions realized by the processor 330 executing the conveyor control program 321 will be described below.
図3に示すように、コンベア制御装置300は、コンベア100の送り速度の制御を行う制御部331の機能と、その制御に必要な学習済モデル322を生成する学習部332の機能とを有する。
As shown in FIG. 3, the conveyor control device 300 has the function of a control section 331 that controls the feed speed of the conveyor 100, and the function of a learning section 332 that generates a learned model 322 necessary for the control.
まず、制御部331について説明する。制御部331は、撮像装置200から観測データDTを取得する観測データ取得部331aと、その観測データDTを用いて、作業者600の上記処理の能率を表す指標の算出に必要な時刻を特定する特定部331bとを有する。
First, the control section 331 will be explained. The control unit 331 uses the observation data acquisition unit 331a that acquires the observation data DT from the imaging device 200 and the observation data DT to specify the time necessary for calculating the index representing the efficiency of the above processing of the worker 600. It has a specific part 331b.
図4を参照し、特定部331bの機能を具体的に説明する。図4には、観測データDTが表す時系列に沿った時間軸を示している。観測データDTは、1つの静止画像を表すフレーム(frame)が時間軸に沿って配列されたデータ構造を有する。
With reference to FIG. 4, the function of the specifying unit 331b will be specifically explained. FIG. 4 shows a time axis along the time series represented by the observation data DT. The observation data DT has a data structure in which frames representing one still image are arranged along the time axis.
特定部331bは、観測データDTを用いて、作業者600が実際に上記処理のために動作した期間である動作期間を特定する。動作期間は、作業者600が周期的に繰り返す上記処理の1周期の期間を表す。動作の有無は、時系列に並ぶ各フレームを解析することで判定できる。このため、観測データDTによって動作期間を特定できる。
The specifying unit 331b uses the observation data DT to specify an operation period that is a period in which the worker 600 actually operated for the above processing. The operation period represents the period of one cycle of the above-mentioned processing that is periodically repeated by the worker 600. The presence or absence of motion can be determined by analyzing each frame arranged in time series. Therefore, the operation period can be specified by the observation data DT.
また、特定部331bは、観測データDTを用いて、動作期間の冒頭部分を構成する初期動作期間TAを特定する。初期動作期間TAは、上記処理の開始の際における一連の初期動作を作業者600が行った期間である。一連の初期動作は、予め定められた手順に従った動作である。このため、観測データDTが表すフレームの時系列で初期動作期間TAを特定できる。
Furthermore, the specifying unit 331b uses the observation data DT to specify the initial operating period TA that constitutes the beginning of the operating period. The initial operation period TA is a period during which the operator 600 performed a series of initial operations at the start of the process. The series of initial operations are operations that follow a predetermined procedure. Therefore, the initial operation period TA can be specified based on the time series of frames represented by the observation data DT.
さらに、特定部331bは、観測データDTを用いて、初期動作期間TAに属する代表開始時刻t1を特定する。本実施形態では、代表開始時刻t1は、作業者600が上記初期動作のうちの特定の動作(以下、代表開始動作という。)を行った瞬間の時刻とする。代表開始動作は予め定められた特定の動作であるため、観測データDTが表すフレームの時系列で代表開始時刻t1を特定できる。
Further, the specifying unit 331b uses the observation data DT to specify the representative start time t1 belonging to the initial operation period TA. In this embodiment, the representative start time t1 is the time at the moment when the worker 600 performs a specific action (hereinafter referred to as representative start action) among the above-mentioned initial actions. Since the representative start motion is a predetermined specific motion, the representative start time t1 can be specified in the time series of frames represented by the observation data DT.
1つの具体例として、作業者600が工具を用いてワーク500に上記処理を施す場合を考える。この場合、作業者600が工具を準備してから、その工具を操作し始めるまでの期間を初期動作期間TAと定めることができる。また、作業者600がその工具をワーク500にあてがう動作を代表開始動作と定め、その代表開始動作が行われた瞬間を代表開始時刻t1と定めることができる。
As one specific example, consider a case where the worker 600 uses a tool to perform the above process on the workpiece 500. In this case, the period from when the operator 600 prepares the tool until he starts operating the tool can be defined as the initial operation period TA. Further, the action of the worker 600 applying the tool to the workpiece 500 can be defined as a representative start action, and the moment when the representative start action is performed can be defined as the representative start time t1.
また、特定部331bは、観測データDTを用いて、動作期間の末尾部分を構成する終期動作期間TBを特定する。終期動作期間TBは、上記処理の終了の際における一連の終期動作を作業者600が行った期間である。一連の終期動作は、予め定められた手順に従った動作であり、かつ上述した一連の初期動作とは明確に異なる。このため、観測データDTが表すフレームの時系列で終期動作期間TBを特定できる。
Furthermore, the specifying unit 331b uses the observation data DT to specify the final operating period TB that constitutes the final part of the operating period. The final action period TB is a period during which the worker 600 performed a series of final actions at the end of the process. The series of final operations is an operation that follows a predetermined procedure, and is clearly different from the series of initial operations described above. Therefore, the final operating period TB can be specified based on the time series of frames represented by the observation data DT.
さらに、特定部331bは、観測データDTを用いて、終期動作期間TBに属する代表終了時刻t2を特定する。本実施形態では、代表終了時刻t2は、作業者600が上記終期動作のうちの特定の動作(以下、代表終了動作という。)を行った瞬間の時刻とする。代表終了動作は予め定められた特定の動作であるため、観測データDTが表すフレームの時系列で代表終了時刻t2を特定できる。
Furthermore, the specifying unit 331b uses the observation data DT to specify the representative end time t2 belonging to the final operation period TB. In this embodiment, the representative end time t2 is the time at the moment when the worker 600 performs a specific action (hereinafter referred to as representative end action) among the final actions. Since the representative end action is a predetermined specific action, the representative end time t2 can be specified based on the time series of frames represented by the observation data DT.
上述した具体例について言えば、作業者600が工具の操作を終えてから、その工具を予め定められた位置に戻すまでの期間を終期動作期間TBと定めることができる。また、作業者600がその工具をワーク500から離す動作を代表終了動作と定め、その代表終了動作が行われた瞬間を代表終了時刻t2と定めることができる。
Regarding the specific example described above, the period from when the operator 600 finishes operating the tool until the tool is returned to a predetermined position can be defined as the final operation period TB. Further, the action of the operator 600 separating the tool from the workpiece 500 can be defined as a representative end action, and the moment when the representative end action is performed can be defined as the representative end time t2.
以上のようにして特定される代表開始時刻t1と代表終了時刻t2との時間差を、処理所要時間と呼ぶことにする。処理所要時間は、1回の上記処理に作業者600が実質的に要した時間を表す。つまり、処理所要時間は、作業者600の上記処理の能率を表す指標である。
The time difference between the representative start time t1 and the representative end time t2 identified as described above will be referred to as the required processing time. The processing time required represents the time actually required by the operator 600 to perform the above processing once. In other words, the processing time is an index representing the efficiency of the above-mentioned processing by the worker 600.
ここで処理所要時間は、代表例として、代表開始時刻t1と代表終了時刻t2との時間差となるが、これに限らない。すなわち、本実施形態において、処理所要時間は、初期動作期間TAと終期動作期間TBとの間の期間を意味しており、例えば、初期動作期間TAの中間時刻と終期動作期間TBの中間時刻との差であってもよい。また、処理所要時間は、初期動作期間TAの開始時刻から所定期間経過後の時刻(例えば、初期動作期間TAの開始時刻から、初期動作期間TAの期間長の10%経過後の時刻)と、終期動作期間TBの開始時刻から所定期間経過後の時刻(例えば、終期動作期間TBの開始時刻から、終期動作期間TBの期間長の90%経過後の時刻)との差であってもよい。換言すると、処理所要時間は、初期動作期間TAに基づいて得られた時刻と、終期動作期間TBに基づいて得られた時刻との差を言う。
Here, as a typical example, the processing time required is the time difference between the representative start time t1 and the representative end time t2, but is not limited to this. That is, in the present embodiment, the processing time refers to the period between the initial operation period TA and the final operation period TB, and for example, the time required for processing is the period between the initial operation period TA and the final operation period TB. It may be the difference between In addition, the processing time is the time after a predetermined period has elapsed from the start time of the initial operation period TA (for example, the time after 10% of the period length of the initial operation period TA has elapsed from the start time of the initial operation period TA), It may be a difference from the time after a predetermined period has elapsed from the start time of the final operation period TB (for example, the time after 90% of the period length of the final operation period TB has elapsed from the start time of the final operation period TB). In other words, the processing time is the difference between the time obtained based on the initial operation period TA and the time obtained based on the final operation period TB.
以下、上記処理の能率を表す処理所要時間を求めるために、代表開始時刻t1及び代表終了時刻t2のみならず、初期動作期間TA及び終期動作期間TBも特定する理由を述べる。
Hereinafter, the reason why not only the representative start time t1 and the representative end time t2, but also the initial operation period TA and final operation period TB are specified in order to obtain the processing time that represents the efficiency of the processing described above will be described.
仮に、初期動作期間TAを特定することなく、時間軸上の1点である代表開始時刻t1を直接的に特定しようとすると、特に作業者600が予期せぬ動作を行った場合に、代表開始動作とは異なる動作が行われた時刻を代表開始時刻t1と誤って特定したり、代表開始時刻t1を特定し損ねたりする可能性が高い。
If we try to directly specify the representative start time t1, which is one point on the time axis, without specifying the initial movement period TA, the representative start time t1, which is one point on the time axis, may be There is a high possibility that the representative start time t1 may be erroneously identified as the time when an action different from the action was performed, or that the representative start time t1 is failed to be identified.
つまり、代表開始時刻t1の特定の精度が作業者600の動作の特性に大きく左右されるため、代表開始時刻t1の、正確性の高い特定が困難である。終期動作期間TBを特定することなく、時間軸上の1点である代表終了時刻t2を直接的に特定する場合も同様である。
In other words, since the accuracy of specifying the representative start time t1 largely depends on the characteristics of the movement of the worker 600, it is difficult to specify the representative start time t1 with high accuracy. The same applies to the case where the representative end time t2, which is one point on the time axis, is directly specified without specifying the final operation period TB.
これに対し、本実施の形態では、まず初期動作期間TAを特定する。初期動作期間TAは時間幅を有するので、直接的に時間軸上の1点を特定する場合とは異なり、作業者600が予期せぬ動作を行ったとしても、正確性の高い特定が可能である。
In contrast, in this embodiment, the initial operation period TA is first specified. Since the initial movement period TA has a time width, unlike the case of directly specifying one point on the time axis, even if the operator 600 performs an unexpected movement, highly accurate identification is possible. be.
また、代表開始時刻t1は初期動作期間TAの中に存在するので、ひとたび初期動作期間TAが特定されれば、代表開始時刻t1を探索する範囲を、初期動作期間TAに限ることができる。このため、代表開始時刻t1を特定し損ねたり、異なる代表開始時刻t1を誤って特定したりする可能性を低減することができる。代表終了時刻t2の特定についても同様である。
Furthermore, since the representative start time t1 exists within the initial operation period TA, once the initial operation period TA is specified, the range in which the representative start time t1 is searched can be limited to the initial operation period TA. Therefore, it is possible to reduce the possibility of failing to specify the representative start time t1 or erroneously specifying a different representative start time t1. The same applies to specifying the representative end time t2.
即ち、本実施の形態では、代表開始時刻t1及び代表終了時刻t2、ひいては処理所要時間の特定の正確性を高めるために、代表開始時刻t1及び代表終了時刻t2のみならず、初期動作期間TA及び終期動作期間TBも特定する。
That is, in this embodiment, in order to increase the accuracy of specifying the representative start time t1 and the representative end time t2, and ultimately the required processing time, not only the representative start time t1 and the representative end time t2, but also the initial operation period TA and The final operating period TB is also specified.
図3に戻り、説明を続ける。特定部331bは、学習済モデル322を用いて、上述した初期動作期間TA、代表開始時刻t1、終期動作期間TB、及び代表終了時刻t2を特定する。
Returning to FIG. 3, the explanation will continue. The specifying unit 331b uses the learned model 322 to specify the above-described initial operation period TA, representative start time t1, final operation period TB, and representative end time t2.
学習済モデル322は、観測データDTの中から、初期動作期間TAにおける一連の上記初期動作を表す第1部分データと、終期動作期間TBにおける一連の上記終期動作を表す第2部分データとを検出するとともに、検出された第1部分データを用いて代表開始時刻t1を検出し、かつ検出された第2部分データを用いて代表終了時刻t2を検出するための機械学習を行ったものである。
The trained model 322 detects, from the observation data DT, first partial data representing the series of initial operations in the initial operation period TA, and second partial data representing the series of final operations in the final operation period TB. At the same time, machine learning is performed to detect the representative start time t1 using the detected first partial data and to detect the representative end time t2 using the detected second partial data.
つまり、特定部331bは、観測データ取得部331aによって取得された観測データDTを学習済モデル322に入力する。そして、特定部331bは、学習済モデル322から、第1部分データ、代表開始時刻t1、第2部分データ、及び代表終了時刻t2の検出結果の出力を得る。
In other words, the identification unit 331b inputs the observation data DT acquired by the observation data acquisition unit 331a into the learned model 322. Then, the specifying unit 331b obtains outputs of the detection results of the first partial data, the representative start time t1, the second partial data, and the representative end time t2 from the learned model 322.
なお、観測データDTは時系列データであるため、観測データDTから第1部分データが検出されることは、初期動作期間TAが特定されたことと等価である。また、観測データDTから第2部分データが検出されることは、終期動作期間TBが特定されたことと等価である。
Note that since the observation data DT is time-series data, detecting the first partial data from the observation data DT is equivalent to specifying the initial operation period TA. Further, detecting the second partial data from the observation data DT is equivalent to specifying the final operating period TB.
以上のようにして、特定部331bは、観測データDTが入力された学習済モデル322の出力によって、初期動作期間TA、代表開始時刻t1、終期動作期間TB、及び代表終了時刻t2を特定する。
As described above, the specifying unit 331b specifies the initial operation period TA, representative start time t1, final operation period TB, and representative end time t2 based on the output of the learned model 322 into which the observation data DT has been input.
また、制御部331は、特定部331bによって特定された代表開始時刻t1から代表終了時刻t2までの時間幅である処理所要時間を算出する処理所要時間算出部331cを備える。処理所要時間は、代表終了時刻t2と代表開始時刻t1との時間差である。
Furthermore, the control unit 331 includes a processing time calculation unit 331c that calculates the processing time required, which is the time span from the representative start time t1 to the representative end time t2 specified by the identification unit 331b. The processing time required is the time difference between the representative end time t2 and the representative start time t1.
作業者600の上記処理の能率が低いほど、処理所要時間は長くなる。また、作業者600の上記処理の能率が高いほど、処理所要時間は短くなる。このため、処理所要時間は、作業者600の上記処理の能率を表す指標である。
The lower the efficiency of the above process by the worker 600, the longer the time required for the process. Furthermore, the higher the efficiency of the above processing by the worker 600, the shorter the time required for the processing. Therefore, the processing time is an index representing the efficiency of the above-mentioned processing by the worker 600.
そこで、制御部331は、処理所要時間算出部331cの算出結果である処理所要時間に基づいてコンベア100の送り速度を制御する送り速度制御部331dを備える。
Therefore, the control unit 331 includes a feed rate control unit 331d that controls the feed rate of the conveyor 100 based on the required processing time that is the calculation result of the required processing time calculation unit 331c.
なお、図示しないが、コンベア100は、ワーク500が載置される搬送用ベルトと、その搬送用ベルトが掛けられるローラと、そのローラを回転させるモータとを備える。そのモータは、回転速度を制御することができる構成を有する。コンベア100の送り速度の制御は、コンベア100のモータの回転速度の制御、具体的には、インバータ制御によって実現される。
Although not shown, the conveyor 100 includes a conveyance belt on which the workpiece 500 is placed, a roller on which the conveyance belt is hung, and a motor that rotates the roller. The motor has a configuration that allows its rotational speed to be controlled. Control of the feed speed of the conveyor 100 is realized by controlling the rotation speed of the motor of the conveyor 100, specifically, by controlling an inverter.
具体的には、送り速度制御部331dは、作業者600の処理所要時間が長いほどコンベア100の送り速度が小さくなり、かつ作業者600の処理所要時間が短いほどコンベア100の送り速度が大きくなる条件で、コンベア100の送り速度を制御する。これにより、次の上記処理においては、作業者600に待ち時間が発生しにくく、しかも、作業者600に過負荷がかかりにくい。
Specifically, the feed rate control unit 331d determines that the longer the processing time required by the worker 600, the lower the feed speed of the conveyor 100, and the shorter the processing time required by the worker 600, the higher the feed speed of the conveyor 100. The feed speed of the conveyor 100 is controlled based on the conditions. As a result, in the next process described above, the worker 600 is less likely to have a waiting time, and moreover, the worker 600 is less likely to be overloaded.
次に、学習部332について説明する。学習部332は、学習用データ323を取得する学習用データ取得部332aを有する。学習用データ323には、上述した第1部分データ、代表開始時刻t1、第2部分データ、及び代表終了時刻t2の実測値と、第1部分データ、代表開始時刻t1、第2部分データ、及び代表終了時刻t2が実測された観測データDTとを用いる。
Next, the learning section 332 will be explained. The learning section 332 includes a learning data acquisition section 332a that acquires the learning data 323. The learning data 323 includes the actual measured values of the first partial data, representative start time t1, second partial data, and representative end time t2, the first partial data, representative start time t1, second partial data, and The observation data DT for which the representative end time t2 was actually measured is used.
ここで“実測”とは、学習済モデル322以外の手段によって正しく特定することを意味する。即ち“第1部分データの実測値”とは、学習済モデル322以外の手段によって正しく特定された第1部分データを意味する。“第2部分データの実測値”についても同様である。また、“代表開始時刻t1の実測値”とは、学習済モデル322以外の手段によって正しく特定された代表開始時刻t1を意味する。“代表開始時刻t2の実測値”についても同様である。
Here, "actual measurement" means correctly specifying by means other than the learned model 322. That is, the "actual measured value of the first partial data" means the first partial data correctly specified by means other than the learned model 322. The same applies to the “actual measurement value of the second partial data”. Furthermore, the “actual measured value of the representative start time t1” means the representative start time t1 correctly specified by means other than the learned model 322. The same applies to the “actual measurement value of representative start time t2”.
なお、“実測”は、学習用データ323を作成する者(以下、作成者という。)が行うことができる。一具体例として、観測データDTが表す動画を再生し、作成者がその動画において初期動作期間TA、代表開始時刻t1、終期動作期間TB、及び代表開始時刻t2を、視認により特定する。このようにして、第1部分データ、代表開始時刻t1、第2部分データ、及び代表終了時刻t2の実測値を得ることができる。視認による特定に際して、動画をコマ送りで再生したり、スローで再生したりしてもよい。
Note that "actual measurement" can be performed by a person who creates the learning data 323 (hereinafter referred to as the creator). As a specific example, a video represented by the observation data DT is played back, and the creator visually identifies the initial operation period TA, representative start time t1, final operation period TB, and representative start time t2 in the video. In this way, the actual measured values of the first partial data, the representative start time t1, the second partial data, and the representative end time t2 can be obtained. When specifying by visual recognition, the video may be played back frame by frame or played back in slow motion.
また、他の具体例として、作成者が、現場における作業者600の作業を直接的に目視で確認しつつ、初期動作期間TAの両端点に相当する時刻、代表開始時刻t1、終期動作期間TBの両端点に相当する時刻、及び代表開始時刻t2をそれぞれ記録してもよい。その記録された各時刻と、観測データDTが表す時刻の情報とを対比させることで、第1部分データ、代表開始時刻t1、第2部分データ、及び代表終了時刻t2の実測値を得ることができる。
In addition, as another specific example, the creator directly visually confirms the work of the worker 600 at the site, and determines the times corresponding to both end points of the initial operation period TA, the representative start time t1, and the final operation period TB. The times corresponding to both end points of and the representative start time t2 may be recorded respectively. By comparing each recorded time with the time information represented by the observation data DT, it is possible to obtain the actual measured values of the first partial data, representative start time t1, second partial data, and representative end time t2. can.
また、学習部332は、学習用データ取得部332aによって取得された学習用データ323を用いて学習済モデル322を生成する生成部332bを有する。生成部332bは、学習用データ323を用いて、観測データDTからの、第1部分データ、代表開始時刻t1、第2部分データ、及び代表終了時刻t2の検出の方針を学習する。このような機械学習により、学習済モデル322が生成される。
Furthermore, the learning unit 332 includes a generation unit 332b that generates the learned model 322 using the learning data 323 acquired by the learning data acquisition unit 332a. The generation unit 332b uses the learning data 323 to learn a policy for detecting the first partial data, the representative start time t1, the second partial data, and the representative end time t2 from the observation data DT. Through such machine learning, a trained model 322 is generated.
但し、代表開始時刻t1を、初期動作期間TAの期間長から一意に定まる時刻(例えば、初期動作期間TAの開始時刻から、初期動作期間TAの期間長の10%経過後の時刻)として定義する場合は、必ずしも学習用データ323に代表開始時刻t1の実測値を含める必要はない。この場合は、代表開始時刻t1の特定に学習済モデル322を用いなくても、特定部331b又は処理所要時間算出部331cにおいて、初期動作期間TAの期間長から代表開始時刻t1を特定することができる。
However, the representative start time t1 is defined as a time that is uniquely determined from the length of the initial operation period TA (for example, the time after 10% of the length of the initial operation period TA has elapsed from the start time of the initial operation period TA). In this case, the learning data 323 does not necessarily need to include the actual measured value of the representative start time t1. In this case, the representative start time t1 can be specified from the length of the initial operation period TA in the specifying unit 331b or the processing time calculation unit 331c without using the learned model 322 to specify the representative start time t1. can.
同様に、代表終了時刻t2を、終期動作期間TBの期間長から一意に定まる時刻(例えば、終期動作期間TBの開始時刻から、終期動作期間TBの期間長の90%経過後の時刻)として定義する場合は、必ずしも学習用データ323に代表終了時刻t2の実測値を含める必要はない。この場合は、代表開始時刻t1の特定に学習済モデル322を用いなくても、特定部331b又は処理所要時間算出部331cにおいて、終期動作期間TBの期間長から代表終了時刻t2を特定することができる。
Similarly, the representative end time t2 is defined as a time that is uniquely determined from the length of the final operation period TB (for example, the time after 90% of the length of the final operation period TB has elapsed from the start time of the final operation period TB). In this case, it is not necessarily necessary to include the actual measurement value of the representative end time t2 in the learning data 323. In this case, without using the learned model 322 to specify the representative start time t1, the representative end time t2 can be specified from the length of the final operation period TB in the specifying unit 331b or the processing time calculation unit 331c. can.
以下、図5を参照し、本実施の形態に係る生産システム400で行われるコンベア制御について説明する。
Hereinafter, with reference to FIG. 5, conveyor control performed in the production system 400 according to the present embodiment will be described.
図5に示すように、未処理のワーク500がコンベア100によって作業者600のもとに到来すると(ステップS1)、撮像装置200が観測データDTの生成を開始する(ステップS2)。撮像装置200による観測データDTの生成は、作業者600によるワーク500の処理(ステップS3)と並行して継続される。作業者600によるワーク500の処理の完了の後に、撮像装置200による観測データDTの生成が完了する(ステップS4)。
As shown in FIG. 5, when an unprocessed workpiece 500 arrives at the worker 600 on the conveyor 100 (step S1), the imaging device 200 starts generating observation data DT (step S2). The generation of observation data DT by the imaging device 200 continues in parallel with the processing of the workpiece 500 by the worker 600 (step S3). After the processing of the workpiece 500 by the worker 600 is completed, the generation of observation data DT by the imaging device 200 is completed (step S4).
次に、コンベア制御装置300の特定部331bが、その観測データDTを用いて、初期動作期間TAに属する代表開始時刻t1と、終期動作期間TBに属する代表終了時刻t2とを特定する。また、コンベア制御装置300の処理所要時間算出部331cが、代表開始時刻t1と代表終了時刻t2との時間差である処理所要時間を算出する(ステップS5)。
Next, the identification unit 331b of the conveyor control device 300 uses the observation data DT to identify a representative start time t1 belonging to the initial operation period TA and a representative end time t2 belonging to the final operation period TB. Further, the processing time calculation unit 331c of the conveyor control device 300 calculates the processing time, which is the time difference between the representative start time t1 and the representative end time t2 (step S5).
既述のとおり、処理所要時間は、作業者600の処理の能率を表す指標である。コンベア制御装置300の送り速度制御部331dは、処理所要時間が長いほどコンベア100の送り速度が小さくなり、かつ作業者600の処理所要時間が短いほどコンベア100の送り速度が大きくなる条件で、処理所要時間に基づいてコンベア100の送り速度を制御する(ステップS6)。
As described above, the processing time is an index representing the processing efficiency of the worker 600. The feed speed control unit 331d of the conveyor control device 300 performs processing under the conditions that the longer the processing time required, the lower the feed speed of the conveyor 100, and the shorter the processing time of the worker 600, the higher the feed speed of the conveyor 100. The feed speed of the conveyor 100 is controlled based on the required time (step S6).
その後、再びステップS1に戻り、制御後の送り速度で、未処理のワーク500が作業者600のもとに到来する。このようにしてステップS1からS6が繰り返される。
Thereafter, the process returns to step S1 again, and the unprocessed workpiece 500 arrives at the operator 600 at the controlled feed speed. In this way, steps S1 to S6 are repeated.
本実施の形態によれば、特に次の効果が得られる。
According to this embodiment, in particular, the following effects can be obtained.
上述したステップS5では、代表開始時刻t1の特定のために、時間幅をもつ初期動作期間TAがいったん特定され、代表終了時刻t2の特定のために、時間幅をもつ終期動作期間TBがいったん特定される。これにより、代表開始時刻t1を探索する範囲を初期動作期間TAに限ることができ、代表終了時刻t2を探索する範囲を終期動作期間TBに限ることができる。
In step S5 described above, in order to specify the representative start time t1, an initial operation period TA with a time width is once specified, and in order to specify the representative end time t2, a final operation period TB with a time width is once specified. be done. Thereby, the range in which the representative start time t1 is searched can be limited to the initial operation period TA, and the range in which the representative end time t2 is searched can be limited to the final operation period TB.
このため、初期動作期間TA及び終期動作期間TBを特定することなく、直接的に代表開始時刻t1又は代表終了時刻t2を特定する場合に比べると、代表開始時刻t1又は代表終了時刻t2を特定し損ねたり、代表開始時刻t1又は代表終了時刻t2を誤って特定したりする可能性を低減することができる。この結果、処理所要時間の計測の正確性が、作業者600の動作の特性に左右されにくい。つまり、処理所要時間の計測の正確性が従来よりも高められる。
Therefore, compared to directly specifying the representative start time t1 or the representative end time t2 without specifying the initial operation period TA and the final operation period TB, it is easier to specify the representative start time t1 or the representative end time t2. It is possible to reduce the possibility of erroneously specifying the representative start time t1 or the representative end time t2. As a result, the accuracy of measuring the processing time is less likely to be influenced by the characteristics of the operator's 600 movements. In other words, the accuracy of measuring the time required for processing is improved compared to the conventional method.
上述したステップS6では、処理所要時間に基づいてコンベア100の送り速度が制御されるので、次回の未処理のワーク500の処理においては、作業者600に待ち時間が発生しにくく、しかも、作業者600に過負荷がかかりにくい。
In step S6 described above, the feed speed of the conveyor 100 is controlled based on the processing time required, so that the worker 600 is less likely to have to wait in the next processing of the unprocessed workpiece 500. 600 is less likely to be overloaded.
[実施の形態2]
上記実施の形態1では、学習済モデル322を生成するための学習アルゴリズムとして、学習用データ323を教師データとする教師あり学習を例示した。学習部332は、強化学習によって学習済モデル322を更新する機能を備えてもよい。以下、その具体例を述べる。 [Embodiment 2]
In the first embodiment described above, supervised learning using the learning data 323 as teacher data is illustrated as a learning algorithm for generating the trained model 322. The learning unit 332 may have a function of updating the learned model 322 by reinforcement learning. A specific example will be described below.
上記実施の形態1では、学習済モデル322を生成するための学習アルゴリズムとして、学習用データ323を教師データとする教師あり学習を例示した。学習部332は、強化学習によって学習済モデル322を更新する機能を備えてもよい。以下、その具体例を述べる。 [Embodiment 2]
In the first embodiment described above, supervised learning using the learning data 323 as teacher data is illustrated as a learning algorithm for generating the trained model 322. The learning unit 332 may have a function of updating the learned model 322 by reinforcement learning. A specific example will be described below.
図6に示すように、本実施の形態に係る学習部332は、観測データ取得部331aによって取得された観測データDTを用いて、強化学習に用いるパラメータである報酬(reward)を算出する報酬算出部332cを備える。
As shown in FIG. 6, the learning unit 332 according to the present embodiment uses observation data DT acquired by the observation data acquisition unit 331a to calculate a reward, which is a parameter used for reinforcement learning. A portion 332c is provided.
報酬は、前回のワーク500に対する処理の後になされた送り速度の制御の妥当性を表す。以下では、作業者600によるワーク500の処理の完了後、次のワーク500が到来するまでに時間が余った場合、その余った時間長を待ち時間と呼ぶ。また、作業者600によるワーク500の処理が未完了に終わった場合、処理の遂行に不足した時間長を不足時間と呼ぶ。
The reward represents the validity of the feed rate control performed after the previous processing of the workpiece 500. In the following, when there is time remaining after the worker 600 completes processing of the workpiece 500 until the next workpiece 500 arrives, the remaining time length will be referred to as waiting time. Furthermore, when the processing of the workpiece 500 by the worker 600 is incomplete, the length of time that is insufficient to complete the processing is referred to as insufficient time.
報酬算出部332cは、観測データDTを用いて、待ち時間又は不足時間を求める。待ち時間又は不足時間が発生している場合、前回行われた送り速度の制御が必ずしも妥当とは言えない。そこで、報酬算出部332cは、待ち時間又は不足時間が大きいほど報酬が減額され、かつ待ち時間又は不足時間がゼロに近いほど報酬が増額される条件で、報酬を算出する。
The remuneration calculation unit 332c uses the observation data DT to calculate the waiting time or insufficient time. When waiting time or insufficient time occurs, the feed rate control performed last time is not necessarily appropriate. Therefore, the remuneration calculation unit 332c calculates the remuneration under the conditions that the longer the waiting time or the shortage time is, the more the remuneration is reduced, and the closer the waiting time or the shortage time is to zero, the more the reward is increased.
また、本実施の形態に係る学習部332は、報酬算出部332cによって算出された報酬に応じて、学習済モデル322を更新する更新部332dを備える。具体的には、更新部332dは、学習済モデル322による、観測データDTからの第1部分データ、代表開始時刻t1、第2部分データ、及び代表終了時刻t2の検出の方針(policy)を、上記報酬が最も多く得られる条件で更新する。
Furthermore, the learning unit 332 according to the present embodiment includes an updating unit 332d that updates the learned model 322 according to the reward calculated by the reward calculating unit 332c. Specifically, the update unit 332d sets a policy for detecting the first partial data, representative start time t1, second partial data, and representative end time t2 from the observed data DT by the learned model 322, as follows: Update with the conditions that will give you the most of the above rewards.
本実施の形態によれば、作業者600によってワーク500に対する処理が繰り返されるごとに、コンベア100の送り速度の制御の妥当性が高められ得る。他の構成及び効果は、実施の形態1と同様である。
According to the present embodiment, the validity of controlling the feed speed of the conveyor 100 can be increased each time the worker 600 repeats the process on the workpiece 500. Other configurations and effects are similar to those in the first embodiment.
[実施の形態3]
上記実施の形態1では、作業者600の上記処理の能率に応じてコンベア100の送り速度を制御した。作業者600の上記処理の能率の変化の傾向もさらに加味して、コンベア100の送り速度を制御してもよい。以下、その具体例を述べる。 [Embodiment 3]
In the first embodiment described above, the feed speed of the conveyor 100 is controlled according to the efficiency of the processing by the operator 600. The feed speed of the conveyor 100 may be controlled by further taking into consideration the tendency of change in the efficiency of the above-mentioned processing by the worker 600. A specific example will be described below.
上記実施の形態1では、作業者600の上記処理の能率に応じてコンベア100の送り速度を制御した。作業者600の上記処理の能率の変化の傾向もさらに加味して、コンベア100の送り速度を制御してもよい。以下、その具体例を述べる。 [Embodiment 3]
In the first embodiment described above, the feed speed of the conveyor 100 is controlled according to the efficiency of the processing by the operator 600. The feed speed of the conveyor 100 may be controlled by further taking into consideration the tendency of change in the efficiency of the above-mentioned processing by the worker 600. A specific example will be described below.
図7に示すように、本実施の形態に係る制御部331は、処理所要時間算出部331cによって算出される処理所要時間の差分を算出する変化量算出部331eをさらに備える。
As shown in FIG. 7, the control unit 331 according to the present embodiment further includes a change amount calculation unit 331e that calculates a difference in the processing required time calculated by the processing required time calculation unit 331c.
具体的には、変化量算出部331eは、今回のターンでのワーク500の処理について処理所要時間算出部331cによって算出された処理所要時間と、前回のターンでのワーク500の処理について処理所要時間算出部331cによって算出された処理所要時間と、の差(以下、処理所要時間変化量という。)を算出する。
Specifically, the change amount calculation unit 331e calculates the processing time calculated by the processing time calculation unit 331c for processing the workpiece 500 in the current turn and the processing time required for processing the workpiece 500 in the previous turn. The difference between the required processing time calculated by the calculation unit 331c (hereinafter referred to as the amount of change in the required processing time) is calculated.
処理所要時間変化量は、正負の符号も含む物理量である。処理所要時間変化量が正の値であることは、処理所要時間が延びている傾向を示す。この要因の1として、作業者600の疲労による能率の低下が挙げられる。一方、処理所要時間変化量が負の値であることは、処理所要時間が短縮されている傾向を示す。この要因の1として、作業者600の作業の慣れによる能率の改善が挙げられる。
The amount of change in processing time is a physical quantity that also includes positive and negative signs. A positive value of the amount of change in processing time indicates a tendency that the processing time is increasing. One of the factors for this is a decrease in efficiency due to fatigue of the worker 600. On the other hand, a negative value of the amount of change in processing time indicates a tendency for processing time to be shortened. One of the factors for this is improvement in efficiency as the worker 600 becomes accustomed to the work.
送り速度制御部331dは、処理所要時間算出部331cの算出結果、即ち、今回のターンでのワーク500の処理について算出された処理所要時間のみならず、変化量算出部331eの算出結果である処理所要時間変化量にも基づいて、コンベア100の送り速度を制御する。
The feed rate control unit 331d calculates not only the calculation results of the processing time calculation unit 331c, that is, the processing required time calculated for processing the workpiece 500 in the current turn, but also the processing that is the calculation result of the change amount calculation unit 331e. The feed speed of the conveyor 100 is also controlled based on the amount of change in required time.
具体的には、送り速度制御部331dは、図5のステップS6では、処理所要時間変化量が正の場合は、その処理所要時間変化量の絶対値が大きいほどコンベア100の送り速度が小さくなり、処理所要時間変化量が負の場合は、その処理所要時間変化量の絶対値が大きいほどコンベア100の送り速度が大きくなる条件で、処理所要時間変化量に基づいてコンベア100の送り速度を制御する。
Specifically, in step S6 of FIG. 5, the feed speed control unit 331d determines that if the amount of change in the required processing time is positive, the feed speed of the conveyor 100 becomes smaller as the absolute value of the amount of change in the required processing time becomes larger. , if the amount of change in the required processing time is negative, the feed speed of the conveyor 100 is controlled based on the amount of change in the required processing time, with the condition that the larger the absolute value of the change in the required processing time is, the greater the feed speed of the conveyor 100 is. do.
本実施の形態によれば、処理所要時間のみならず、処理所要時間変化量も加味してコンベア100の送り速度を制御するので、コンベア100の送り速度をより妥当な値に制御することができる。他の構成及び効果は、実施の形態1と同様である。
According to the present embodiment, the feed speed of the conveyor 100 is controlled taking into account not only the processing time but also the amount of change in the processing time, so the feed speed of the conveyor 100 can be controlled to a more appropriate value. . Other configurations and effects are similar to those in the first embodiment.
[実施の形態4]
上記実施の形態1では、処理所要時間に応じてコンベア100の送り速度を制御した。さらに、初期動作期間TAと終期動作期間TBとの間の中間動作期間の期間長も考慮して、コンベア100の送り速度を制御してもよい。以下、その具体例を述べる。 [Embodiment 4]
In the first embodiment described above, the feed speed of the conveyor 100 is controlled according to the processing time required. Furthermore, the feed speed of the conveyor 100 may be controlled in consideration of the length of the intermediate operation period between the initial operation period TA and the final operation period TB. A specific example will be described below.
上記実施の形態1では、処理所要時間に応じてコンベア100の送り速度を制御した。さらに、初期動作期間TAと終期動作期間TBとの間の中間動作期間の期間長も考慮して、コンベア100の送り速度を制御してもよい。以下、その具体例を述べる。 [Embodiment 4]
In the first embodiment described above, the feed speed of the conveyor 100 is controlled according to the processing time required. Furthermore, the feed speed of the conveyor 100 may be controlled in consideration of the length of the intermediate operation period between the initial operation period TA and the final operation period TB. A specific example will be described below.
図8に、中間動作期間TCの時間軸上における位置を示す。本実施の形態に係る特定部331bは、観測データDTを用いて、初期動作期間TA及び終期動作期間TBのみならず、初期動作期間TAと終期動作期間TBとの間の中間動作期間TCも特定する。
FIG. 8 shows the position of the intermediate operation period TC on the time axis. The identifying unit 331b according to the present embodiment uses observation data DT to identify not only the initial operating period TA and the final operating period TB, but also the intermediate operating period TC between the initial operating period TA and the final operating period TB. do.
中間動作期間TCは、初期動作期間TAの終了時点から、終期動作期間TBの開始時点にわたる作業者600の動作の一部を構成する、特定の一連の中間動作を作業者600が行った期間である。
The intermediate motion period TC is a period in which the worker 600 performs a specific series of intermediate motions that constitute a part of the motion of the worker 600 from the end of the initial motion period TA to the start of the final motion period TB. be.
一連の中間動作は、予め定められた手順に従った動作であり、かつ上述した一連の初期動作及び一連の終期動作とは異なる。このため、観測データDTが表すフレームの時系列で中間動作期間TCを特定できる。
The series of intermediate operations are operations that follow a predetermined procedure, and are different from the series of initial operations and the series of final operations described above. Therefore, the intermediate operation period TC can be specified based on the time series of frames represented by the observation data DT.
一連の中間動作は、一連の初期動作及び一連の終期動作よりも、作業者600の疲労の度合い及び作業の慣れの度合いに依存して変動が生じやすい動作であることが好ましい。具体例として、上記処理が、複数の部品をワーク500に組み付ける操作である場合を考える。この場合、それら複数の部品のうち、ワーク500への組付けに比較的手間を要する部品の組付けを中間動作として決めるとよい。
It is preferable that the series of intermediate motions is a motion that is more likely to fluctuate depending on the degree of fatigue of the worker 600 and the degree of familiarity with the work than the series of initial motions and the series of final motions. As a specific example, consider a case where the above process is an operation of assembling a plurality of parts onto the workpiece 500. In this case, among the plurality of parts, it is preferable to determine as an intermediate operation the assembly of a part that requires relatively more effort to assemble to the workpiece 500.
中間動作期間TCの期間長は、作業者600の疲労の度合い及び作業の慣れの度合いに応じて変動する。一方、中間動作期間TCの期間長の変動は、処理所要時間の変動としては現れにくい場合がある。これは、中間動作期間TCの期間長が変動する場合でも、作業者600が残余の動作の能率を無意識的に調整することがあるためである。
The length of the intermediate operation period TC varies depending on the degree of fatigue of the worker 600 and the degree of familiarity with the work. On the other hand, a change in the length of the intermediate operation period TC may be difficult to manifest as a change in the required processing time. This is because even if the period length of the intermediate operation period TC changes, the operator 600 may unconsciously adjust the efficiency of the remaining operation.
そこで、中間動作期間TCの期間長を計測することで、処理所要時間の計測だけでは把握し難い、作業者600の疲労の度合い及び作業の慣れの度合いを把握し得る。特にこの点に、中間動作期間TCの期間長を計測する意義がある。
Therefore, by measuring the period length of the intermediate operation period TC, it is possible to grasp the degree of fatigue and the degree of work familiarity of the worker 600, which is difficult to grasp only by measuring the required processing time. Particularly in this respect, there is significance in measuring the period length of the intermediate operation period TC.
本実施の形態に係る送り速度制御部331dは、処理所要時間のみならず、中間動作期間TCの期間長にも基づいて、コンベア100の送り速度を制御する。即ち、送り速度制御部331dは、中間動作期間TCの期間長が長いほどコンベア100の送り速度が小さくなり、かつ中間動作期間TCの期間長が短いほどコンベア100の送り速度が大きくなる条件で、コンベア100の送り速度を制御する。
The feed rate control unit 331d according to the present embodiment controls the feed rate of the conveyor 100 based not only on the required processing time but also on the length of the intermediate operation period TC. That is, the feed speed control unit 331d is configured such that the longer the intermediate operation period TC is, the lower the feed speed of the conveyor 100 is, and the shorter the intermediate operation period TC is, the higher the feed speed of the conveyor 100 is. The feed speed of the conveyor 100 is controlled.
本実施の形態によれば、処理所要時間のみならず、中間動作期間TCの期間長も考慮してコンベア100の送り速度を制御するので、コンベア100の送り速度をより妥当な値に制御することができる。
According to the present embodiment, the feed speed of the conveyor 100 is controlled taking into consideration not only the processing time but also the length of the intermediate operation period TC, so the feed speed of the conveyor 100 can be controlled to a more appropriate value. Can be done.
なお、中間動作期間TCの期間長を計測することは、中間動作期間TCを特定することと等価である。中間動作期間TCの特定も、初期動作期間TAの特定及び終期動作期間TBの特定と同様に、学習済モデル322を用いて行えることは当業者に理解できるであろう。学習済モデル322の生成に用いる学習用データ323には、上述した一連の中間動作を表す第3部分データが予め正しく特定された観測データDTを用いるとよい。他の構成及び効果は、実施の形態1と同様である。
Note that measuring the period length of the intermediate operation period TC is equivalent to specifying the intermediate operation period TC. Those skilled in the art will understand that the intermediate operating period TC can also be specified using the learned model 322 in the same manner as the initial operating period TA and the final operating period TB. As the learning data 323 used to generate the learned model 322, it is preferable to use observation data DT in which the third partial data representing the series of intermediate operations described above is correctly identified in advance. Other configurations and effects are similar to those in the first embodiment.
[実施の形態5]
上記実施の形態1では、初期動作期間TA及び終期動作期間TBのみならず、代表開始時刻t1及び代表終了時刻t2の特定にも、学習済モデル322が用いられた。代表開始時刻t1及び代表終了時刻t2の特定には、必ずしも学習済モデル322を用いなくてもよい。代表開始時刻t1の特定のために初期動作期間TAを特定し、代表終了時刻t2の特定のために終期動作期間TBを特定しさえすれば、処理所要時間の計測の正確性を従来よりも高めることができる。代表開始時刻t1は、初期動作期間TAから一意に定まる時刻であってもよく、代表終了時刻t2は、終期動作期間TBから一意に定まる時刻であってもよい。 [Embodiment 5]
In the first embodiment, the learned model 322 is used not only to specify the initial operation period TA and the final operation period TB, but also to specify the representative start time t1 and representative end time t2. The learned model 322 does not necessarily need to be used to specify the representative start time t1 and the representative end time t2. As long as the initial operation period TA is specified in order to specify the representative start time t1, and the final operation period TB is specified in order to specify the representative end time t2, the accuracy of measuring the processing time can be improved more than before. be able to. The representative start time t1 may be a time uniquely determined from the initial operation period TA, and the representative end time t2 may be a time uniquely determined from the final operation period TB.
上記実施の形態1では、初期動作期間TA及び終期動作期間TBのみならず、代表開始時刻t1及び代表終了時刻t2の特定にも、学習済モデル322が用いられた。代表開始時刻t1及び代表終了時刻t2の特定には、必ずしも学習済モデル322を用いなくてもよい。代表開始時刻t1の特定のために初期動作期間TAを特定し、代表終了時刻t2の特定のために終期動作期間TBを特定しさえすれば、処理所要時間の計測の正確性を従来よりも高めることができる。代表開始時刻t1は、初期動作期間TAから一意に定まる時刻であってもよく、代表終了時刻t2は、終期動作期間TBから一意に定まる時刻であってもよい。 [Embodiment 5]
In the first embodiment, the learned model 322 is used not only to specify the initial operation period TA and the final operation period TB, but also to specify the representative start time t1 and representative end time t2. The learned model 322 does not necessarily need to be used to specify the representative start time t1 and the representative end time t2. As long as the initial operation period TA is specified in order to specify the representative start time t1, and the final operation period TB is specified in order to specify the representative end time t2, the accuracy of measuring the processing time can be improved more than before. be able to. The representative start time t1 may be a time uniquely determined from the initial operation period TA, and the representative end time t2 may be a time uniquely determined from the final operation period TB.
本実施の形態では、特定部331b又は処理所要時間算出部331cは、初期動作期間TAの時間幅を予め定められた比率で内分する点にあたる時刻、具体的には、その時間幅の中点にあたる時刻を、代表開始時刻t1として算出する。同様に、特定部331b又は処理所要時間算出部331cは、終期動作期間TBの時間幅を予め定められた比率で内分する点にあたる時刻、具体的には、その時間幅の中点にあたる時刻を、代表終了時刻t2として算出する。
In the present embodiment, the identifying unit 331b or the processing time calculating unit 331c determines the time at which the time width of the initial operation period TA is internally divided by a predetermined ratio, specifically, the midpoint of the time width. The time corresponding to t is calculated as the representative start time t1. Similarly, the specifying unit 331b or the processing time calculating unit 331c determines the time at which the time width of the final operating period TB is internally divided by a predetermined ratio, specifically, the time at the midpoint of the time width. , is calculated as the representative end time t2.
本実施の形態では、学習済モデル322は、代表開始時刻t1及び代表終了時刻t2の推定までは行わない。従って、教師データとしての学習用データ323には、代表開始時刻t1及び代表終了時刻t2の実測値は含まれない。即ち、学習用データ323には、上述した第1部分データ及び第2部分データの実測値と、第1部分データ及び第2部分データが実測された観測データDTとが用いられる。
In this embodiment, the learned model 322 does not estimate the representative start time t1 and the representative end time t2. Therefore, the learning data 323 as teacher data does not include the actual measured values of the representative start time t1 and the representative end time t2. That is, the learning data 323 uses the actual measured values of the first partial data and the second partial data described above, and the observation data DT in which the first partial data and the second partial data were actually measured.
なお、本実施の形態では、初期動作期間TAの時間幅を内分する点として中点を例示したが、内分する点は、必ずしも中点でなくてもよい。終期動作期間TBの時間幅を内分する点についても同様である。また、本明細書においては“時間幅を内分する点”の概念には、その時間幅の両端点にあたる開始端点及び終了端点も含まれるものとする。
Note that in the present embodiment, the midpoint is exemplified as the point for internally dividing the time width of the initial operation period TA, but the point for internally dividing does not necessarily have to be the midpoint. The same applies to internally dividing the time width of the final operation period TB. Furthermore, in this specification, the concept of "a point that internally divides a time width" includes a start end point and an end end point that are both end points of the time width.
以上、実施の形態1-5について説明した。以下に述べる変形も可能である。
Embodiments 1-5 have been described above. The following variations are also possible.
(1)上記実施の形態1では、作業者600の動作の時系列を表す観測データDTとして、観測結果としてのフレームが時系列に並んだ動画データを用いた。作業者600の動作は、基準となる場所と作業者600との距離の時間変化、又は作業者600の動作に伴って生じる音響の強さの時間変化によっても表現することができる。即ち、観測データDTには、距離、音響の強さといった観測結果が時系列に並んだ時系列データを用いてもよい。
(1) In the first embodiment described above, video data in which frames as observation results are arranged in chronological order is used as the observation data DT representing the chronological sequence of the actions of the worker 600. The movement of the worker 600 can also be expressed by a change over time in the distance between the reference location and the worker 600, or a change over time in the intensity of the sound that occurs along with the movement of the worker 600. That is, the observation data DT may be time-series data in which observation results such as distance and sound intensity are arranged in chronological order.
(2)学習済モデル322を生成する学習アルゴリズムとして、上記実施の形態1では教師あり学習を例示し、上記実施の形態2では強化学習を例示した。学習アルゴリズムとしては、教師あり学習及び強化学習以外の公知の手法を用いてもよい。具体的には、学習済モデル322は、教師なし学習によって生成してもよい。この場合、学習用データ323には、第1部分データ、代表開始時刻t1、第2部分データ、及び代表終了時刻t2の正しい特定がなされていない、過去の観測データDTを用いてよい。
(2) As a learning algorithm for generating the trained model 322, supervised learning was exemplified in the first embodiment, and reinforcement learning was exemplified in the second embodiment. As the learning algorithm, known methods other than supervised learning and reinforcement learning may be used. Specifically, the trained model 322 may be generated by unsupervised learning. In this case, the learning data 323 may use past observation data DT in which the first partial data, representative start time t1, second partial data, and representative end time t2 are not correctly specified.
(3)初期動作期間TA及び終期動作期間TBの特定には、必ずしも学習済モデル322を用いなくてもよい。特定部331bは、観測データDTに含まれる観測結果の時系列を探索し、観測データDTの中から、作業者600の特定の動作を表す観測結果をパターン認識(pattern recognition)によって抽出することにより、初期動作期間TA及び終期動作期間TBを特定してもよい。
(3) The learned model 322 does not necessarily need to be used to specify the initial operating period TA and final operating period TB. The identification unit 331b searches the time series of observation results included in the observation data DT, and extracts observation results representing a specific action of the worker 600 from the observation data DT by pattern recognition. , an initial operating period TA and a final operating period TB may be specified.
(4)上記実施の形態1で述べたように、送り速度制御部331dは、処理所要時間算出部331cの算出結果に基づいて、コンベア100の送り速度を制御する。本明細書において、“処理所要時間算出部331cの算出結果に基づいて”とは、処理所要時間算出部331cの算出結果である処理所要時間によってコンベア100の送り速度を制御することのみならず、処理所要時間に依存する物理量によってコンベア100の送り速度を制御することも含む意味とする。具体的には、送り速度制御部331dは、処理所要時間に依存する物理量として、既知の長さを処理所要時間で割り算することによりコンベア100の送り速度の目標値を算出し、コンベア100の送り速度をその目標値に近づける制御を行ってもよい。
(4) As described in the first embodiment above, the feed speed control section 331d controls the feed speed of the conveyor 100 based on the calculation result of the processing time calculation section 331c. In this specification, "based on the calculation result of the processing time calculation unit 331c" refers not only to controlling the feed speed of the conveyor 100 based on the processing time that is the calculation result of the processing time calculation unit 331c; This term also includes controlling the feed speed of the conveyor 100 using a physical quantity that depends on the processing time. Specifically, the feed speed control unit 331d calculates the target value of the feed speed of the conveyor 100 by dividing the known length by the required processing time as a physical quantity that depends on the required processing time, and adjusts the feed speed of the conveyor 100 by dividing the known length by the required processing time. Control may be performed to bring the speed closer to the target value.
(5)上記実施の形態1-4に係るコンベア制御装置300は、既存のコンピュータで実現できる。即ち、図2に示すコンベア制御プログラム321をコンピュータにインストールすることで、そのコンピュータをコンベア制御装置300として機能させることができる。コンベア制御プログラム321は通信ネットワークを介して配布してもよいし、コンピュータ読み取り可能な、非一時的な記録媒体に格納したうえで配布してもよい。
(5) The conveyor control device 300 according to Embodiments 1-4 above can be realized using an existing computer. That is, by installing the conveyor control program 321 shown in FIG. 2 into a computer, the computer can function as the conveyor control device 300. The conveyor control program 321 may be distributed via a communication network, or may be stored in a computer-readable, non-transitory recording medium and then distributed.
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。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。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。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。
Various embodiments and modifications of the present disclosure are possible without departing from the broad spirit and scope of the present disclosure. The embodiments described above are for explaining the present disclosure and do not limit the scope of the present disclosure. The scope of the present disclosure is indicated by the claims rather than the embodiments. Various modifications that come within the scope of the claims and the meaning of equivalent disclosures are deemed to be within the scope of the present disclosure.
100 コンベア、200 撮像装置(観測データ生成装置)、300 コンベア制御装置、310 通信装置、320 記憶装置、321 コンベア制御プログラム、322 学習済モデル、323 学習用データ、330 プロセッサ、331 制御部、331a 観測データ取得部、331b 特定部、331c 処理所要時間算出部、331d 送り速度制御部、331e 変化量算出部、332 学習部、332a 学習用データ取得部、332b 生成部、332c 報酬算出部、332d 更新部、400 生産システム、500 ワーク、600 作業者、DT 観測データ、TA 初期動作期間、TB 終期動作期間、TC 中間動作期間、t1 代表開始時刻、t2 代表終了時刻、IA 撮像領域。
100 conveyor, 200 imaging device (observation data generation device), 300 conveyor control device, 310 communication device, 320 storage device, 321 conveyor control program, 322 learned model, 323 learning data, 330 processor, 331 control unit, 331a observation Data acquisition unit, 331b Specification unit, 331c Processing time calculation unit, 331d Feed rate control unit, 331e Change amount calculation unit, 332 Learning unit, 332a Learning data acquisition unit, 332b Generation unit, 332c Reward calculation unit, 332d Update unit , 400 production system, 500 work, 600 worker, DT observation data, TA initial operation period, TB final operation period, TC intermediate operation period, t1 representative start time, t2 representative end time, IA imaging area.
Claims (10)
- コンベアによって搬送中のワークに対して予め定められた処理を施す作業者の、前記処理の動作の時系列を表す観測データを取得する観測データ取得部と、
前記観測データを用いて、前記処理の開始の際における一連の初期動作を前記作業者が行った期間である初期動作期間と、前記処理の終了の際における一連の終期動作を前記作業者が行った期間である終期動作期間とを特定する特定部と、
前記初期動作期間に属する代表開始時刻から、前記終期動作期間に属する代表終了時刻までの時間幅である処理所要時間を算出する処理所要時間算出部と、
前記処理所要時間算出部の算出結果に基づいて、前記コンベアによって前記ワークが搬送される速度である送り速度を制御する送り速度制御部と、
を備える、コンベア制御装置。 an observation data acquisition unit that acquires observation data representing a time series of processing operations of a worker who performs predetermined processing on a workpiece being transported by a conveyor;
Using the observation data, an initial action period is a period during which the worker performs a series of initial actions at the start of the process, and a series of final actions is performed by the worker at the end of the process. a specific part that specifies a final operating period, which is a period in which the
a processing time calculation unit that calculates a processing time that is a time width from a representative start time belonging to the initial operation period to a representative end time belonging to the final operation period;
a feed rate control unit that controls a feed rate that is a speed at which the workpiece is conveyed by the conveyor based on the calculation result of the processing time calculation unit;
A conveyor control device comprising: - 前記特定部は、前記観測データの中から、前記初期動作期間における一連の前記初期動作を表す第1部分データと、前記終期動作期間における一連の前記終期動作を表す第2部分データとを検出するための機械学習を行った学習済モデルを用いて、前記初期動作期間及び前記終期動作期間を特定する、
請求項1に記載のコンベア制御装置。 The identification unit detects first partial data representing the series of initial actions in the initial action period and second partial data representing the series of final actions in the final action period from the observation data. identifying the initial operating period and the final operating period using a trained model that has been subjected to machine learning for
The conveyor control device according to claim 1. - 前記第1部分データ及び前記第2部分データの各々の実測値と、前記第1部分データ及び前記第2部分データが実測された前記観測データとを学習用データとして取得する学習用データ取得部と、
前記学習用データを用いて前記機械学習により前記学習済モデルを生成する生成部と、
をさらに備える、請求項2に記載のコンベア制御装置。 a learning data acquisition unit that acquires, as learning data, actual measured values of each of the first partial data and the second partial data, and the observed data in which the first partial data and the second partial data are actually measured; ,
a generation unit that generates the learned model by the machine learning using the learning data;
The conveyor control device according to claim 2, further comprising:. - 前記機械学習は、さらに、前記観測データの中から検出された前記第1部分データを用いて前記代表開始時刻を検出し、かつ前記観測データの中から検出された前記第2部分データを用いて前記代表終了時刻を検出するためのものであり、
前記特定部は、前記学習済モデルを用いて、前記初期動作期間及び前記終期動作期間のみならず、前記代表開始時刻及び前記代表終了時刻も特定する、
請求項2に記載のコンベア制御装置。 The machine learning further includes detecting the representative start time using the first partial data detected from the observation data, and detecting the representative start time using the second partial data detected from the observation data. It is for detecting the representative end time,
The identifying unit uses the learned model to identify not only the initial operation period and the final operation period, but also the representative start time and the representative end time.
The conveyor control device according to claim 2. - 前記第1部分データ、前記代表開始時刻、前記第2部分データ、及び前記代表終了時刻の各々の実測値と、前記第1部分データ、前記代表開始時刻、前記第2部分データ、及び前記代表終了時刻が実測された前記観測データとを学習用データとして取得する学習用データ取得部と、
前記学習用データを用いて前記機械学習により前記学習済モデルを生成する生成部と、
をさらに備える、請求項4に記載のコンベア制御装置。 Actual values of each of the first partial data, the representative start time, the second partial data, and the representative end time, and the first partial data, the representative start time, the second partial data, and the representative end. a learning data acquisition unit that acquires the observation data whose time is actually measured as learning data;
a generation unit that generates the learned model by the machine learning using the learning data;
The conveyor control device according to claim 4, further comprising:. - 前記作業者によって前記処理が繰り返し行われ、
前記送り速度制御部によってなされた前記送り速度の制御の妥当性を表す報酬を算出する報酬算出部と、
前記報酬算出部によって算出された前記報酬に応じて、前記学習済モデルを更新する更新部と、
をさらに備える、請求項2又は4に記載のコンベア制御装置。 The process is repeatedly performed by the worker,
a remuneration calculation unit that calculates a remuneration representing the validity of the feed rate control performed by the feed rate control unit;
an updating unit that updates the learned model according to the reward calculated by the reward calculation unit;
The conveyor control device according to claim 2 or 4, further comprising: - 前記作業者によって前記処理が繰り返し行われ、
今回の前記処理について前記処理所要時間算出部によって算出された前記処理所要時間と、前回の前記処理について前記処理所要時間算出部によって算出された前記処理所要時間と、の差を算出する変化量算出部、
をさらに備え、
前記送り速度制御部は、前記処理所要時間算出部の算出結果のみならず、前記変化量算出部の算出結果にも基づいて、前記送り速度を制御する、
請求項1から6のいずれか1項に記載のコンベア制御装置。 The process is repeatedly performed by the worker,
Amount of change calculation that calculates the difference between the processing time calculated by the processing time calculation unit for the current process and the processing time calculated by the processing time calculation unit for the previous process. Department,
Furthermore,
The feed rate control unit controls the feed rate based not only on the calculation result of the processing time calculation unit but also on the calculation result of the change amount calculation unit.
A conveyor control device according to any one of claims 1 to 6. - 前記特定部は、前記観測データを用いて、前記初期動作期間及び前記終期動作期間のみならず、前記初期動作期間と前記終期動作期間との間の期間であって、前記初期動作期間の終了時点から前記終期動作期間の開始時点にわたる前記作業者の動作の一部を構成する一連の中間動作を前記作業者が行った期間である中間動作期間も特定し、
前記送り速度制御部は、前記所要時間算出部の算出結果のみならず、前記中間動作期間の期間長にも基づいて、前記送り速度を制御する、
請求項1から7のいずれか1項に記載のコンベア制御装置。 The identification unit uses the observation data to identify not only the initial operating period and the final operating period, but also the period between the initial operating period and the final operating period, at the end of the initial operating period. Also specifying an intermediate movement period that is a period in which the worker performed a series of intermediate movements that constitute a part of the worker's movements from the start of the final movement period,
The feed rate control unit controls the feed rate based not only on the calculation result of the required time calculation unit but also on the length of the intermediate operation period.
A conveyor control device according to any one of claims 1 to 7. - 請求項1から8のいずれか1項に記載のコンベア制御装置と、
前記送り速度制御部によって前記送り速度が制御される前記コンベアと、
前記観測データを生成する観測データ生成装置と、
を備える、生産システム。 A conveyor control device according to any one of claims 1 to 8,
the conveyor, the feed rate of which is controlled by the feed rate controller;
an observation data generation device that generates the observation data;
A production system equipped with - コンピュータに、
コンベアによって搬送中のワークに対して予め定められた処理を施す作業者の、前記処理の動作の時系列を表す観測データを取得する観測データ取得部、
前記観測データを用いて、前記処理の開始の際における一連の初期動作を前記作業者が行った期間である初期動作期間と、前記処理の終了の際における一連の終期動作を前記作業者が行った期間である終期動作期間とを特定する特定部、
前記初期動作期間に属する代表開始時刻から、前記終期動作期間に属する代表終了時刻までの時間幅である処理所要時間を算出する処理所要時間算出部、
前記処理所要時間算出部の算出結果に基づいて、前記コンベアによって前記ワークが搬送される速度である送り速度を制御する送り速度制御部、
としての機能を実現させる、コンベア制御プログラム。 to the computer,
an observation data acquisition unit that acquires observation data representing a time series of processing operations of a worker who performs predetermined processing on a workpiece being transported by a conveyor;
Using the observation data, an initial action period is a period during which the worker performs a series of initial actions at the start of the process, and a series of final actions is performed by the worker at the end of the process. a specific part that specifies a final operating period, which is a period in which the
a processing time calculation unit that calculates a processing time that is a time width from a representative start time belonging to the initial operation period to a representative end time belonging to the final operation period;
a feed rate control unit that controls a feed rate, which is the speed at which the workpiece is conveyed by the conveyor, based on the calculation result of the processing time calculation unit;
A conveyor control program that realizes this function.
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Citations (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
JPH06274501A (en) * | 1993-01-20 | 1994-09-30 | Hitachi Ltd | Line speed planning system and work supplying system |
JPH0756997A (en) * | 1993-06-30 | 1995-03-03 | Hitachi Ltd | Work allocating system |
JP2011248482A (en) * | 2010-05-24 | 2011-12-08 | Panasonic Electric Works Co Ltd | Cell production system |
JP2017151520A (en) * | 2016-02-22 | 2017-08-31 | 株式会社ブロードリーフ | Computer program, operation analysis support method, and operation analysis support device |
JP2019200560A (en) * | 2018-05-16 | 2019-11-21 | パナソニックIpマネジメント株式会社 | Work analyzing device and work analyzing method |
JP2021086219A (en) * | 2019-11-25 | 2021-06-03 | オムロン株式会社 | Cooperative work system, analysis and collection device, and analysis program |
Family Cites Families (1)
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---|---|---|---|---|
JP6274501B2 (en) | 2013-11-26 | 2018-02-07 | キヤノンメディカルシステムズ株式会社 | X-ray diagnostic equipment |
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Patent Citations (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
JPH06274501A (en) * | 1993-01-20 | 1994-09-30 | Hitachi Ltd | Line speed planning system and work supplying system |
JPH0756997A (en) * | 1993-06-30 | 1995-03-03 | Hitachi Ltd | Work allocating system |
JP2011248482A (en) * | 2010-05-24 | 2011-12-08 | Panasonic Electric Works Co Ltd | Cell production system |
JP2017151520A (en) * | 2016-02-22 | 2017-08-31 | 株式会社ブロードリーフ | Computer program, operation analysis support method, and operation analysis support device |
JP2019200560A (en) * | 2018-05-16 | 2019-11-21 | パナソニックIpマネジメント株式会社 | Work analyzing device and work analyzing method |
JP2021086219A (en) * | 2019-11-25 | 2021-06-03 | オムロン株式会社 | Cooperative work system, analysis and collection device, and analysis program |
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