WO2019087275A1 - 作業分析装置、及び作業分析方法 - Google Patents

作業分析装置、及び作業分析方法 Download PDF

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Publication number
WO2019087275A1
WO2019087275A1 PCT/JP2017/039245 JP2017039245W WO2019087275A1 WO 2019087275 A1 WO2019087275 A1 WO 2019087275A1 JP 2017039245 W JP2017039245 W JP 2017039245W WO 2019087275 A1 WO2019087275 A1 WO 2019087275A1
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Prior art keywords
work
information
particle size
analysis
field
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PCT/JP2017/039245
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English (en)
French (fr)
Japanese (ja)
Inventor
鉄平 井上
晃久 辻部
孝裕 小倉
茂木　俊行
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株式会社日立製作所
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Application filed by 株式会社日立製作所 filed Critical 株式会社日立製作所
Priority to JP2019550025A priority Critical patent/JP6864756B2/ja
Priority to PCT/JP2017/039245 priority patent/WO2019087275A1/ja
Priority to CN201780095513.7A priority patent/CN111164622B/zh
Publication of WO2019087275A1 publication Critical patent/WO2019087275A1/ja

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling

Definitions

  • the present invention relates to a work analysis device and a work analysis method.
  • the manufacturing particle size and manufacturing method differ depending on the product specification, so the control particle size is determined by combining the work content in each process, the product specification, and the worker Is desirable.
  • the management particle size is calculated by determining whether the difference in work time of each attribute is significant. However, when the number of data of work results is small, it is determined whether it is significant. I can not
  • the present invention has been made in view of such circumstances, and it is an object of the present invention to enable a series of processes from determination of appropriate control granularity to extraction of improvement points to be performed without relying on human power. I assume.
  • a work analysis apparatus determines a management particle size determination unit that determines a management particle size for each process based on work performance information in which information on a plurality of executed work is accumulated.
  • the evaluation value calculation unit calculates an evaluation value of each of a plurality of evaluation indices for each work based on the work performance information, and the work is grouped into a management particle size group according to the determined management granularity for each process, A work classification unit that classifies excellent work from the plurality of works belonging to each management granularity group based on the evaluation value of each of the plurality of evaluation indices calculated, and the evaluation value of the excellent work of each management granularity group And a work improvement point extraction unit for extracting a work improvement point of a non-excellent work belonging to each of the control granularity groups.
  • operation refers to a plurality of delimiting units that form a series of operations for manufacturing a product.
  • the work is assumed to have information indicating the process, work content, model, and worker as the attribute information.
  • “granularity” represents a combination of conditions when grouping work into groups in order to analyze work by process. For example, since a series of operations for manufacturing an elevator consists of a plurality of steps, there are different work contents in the same step, different types of machines to be manufactured, and workers are different. It is possible to group into groups based on combinations of processes, work contents, models, and workers.
  • control granularity is only a process, it may be a combination of a process and at least one of a model, work content, and a worker.
  • control granularity when grouping each operation under the least condition, the control granularity may be only the process. Moreover, “grain size is coarse” means that there are few conditions when grouping. On the contrary, “fine-grained” means that there are many conditions for grouping.
  • FIG. 1 is a block diagram showing an example of the configuration of a work analysis system according to an embodiment (hereinafter referred to as the present embodiment) according to the present invention.
  • the work analysis system 100 is configured by connecting a work analysis apparatus 101 to a user terminal 103 and a database 104 via a network 102.
  • the work analysis apparatus 101 analyzes work that has already been performed for each process to extract work improvement points.
  • the work improvement point refers to a point to be improved, for example, a long work time is required in the work that has already been performed.
  • the network 102 is a two-way communication network including, for example, a Local Area Network (LAN), a Wide Area Network (WAN), a Virtual Private Network (VPN), the Internet, and the like.
  • LAN Local Area Network
  • WAN Wide Area Network
  • VPN Virtual Private Network
  • the user terminal 103 comprises, for example, a personal computer, etc., accepts a user's operation of designating a process to be analyzed, newly registering a record of the work improvement point library information 128, editing it, and so forth. , And transmitted to the work analysis apparatus 101 via the network 102. Further, the user terminal 103 displays an output screen 1300 (FIG. 14) or the like representing an analysis result and the like supplied from the work analysis apparatus 101 on a display (not shown) and presents the same to the user.
  • an output screen 1300 FIG. 14
  • the database 104 stores, for example, a system such as a MES (Manufacturing Execution System) or data based thereon. Specifically, the work record information in which the information about the executed work is stored is stored, and among the stored work record information, the work record information on the process designated as the process to be analyzed is analyzed The device 101 is supplied.
  • MES Manufacturing Execution System
  • the work analysis apparatus 101 includes, for example, a personal computer or a server computer, and includes an arithmetic unit 110, a storage unit 120, an input unit 130, and an output unit 140.
  • the calculation unit 110 includes a management particle size determination unit 111, an evaluation value calculation unit 112, a work classification unit 113, and a work improvement point extraction unit 114.
  • the management particle size determination unit 111 Based on the work record information 121 (details described later) supplied from the database 104 and stored in the storage unit 120, the management particle size determination unit 111 performs the work belonging to each process according to a plurality of analysis particle sizes determined in advance. Grouping into analysis particle size groups, calculate the average work time of the work belonging to each analysis particle size group.
  • the analysis particle size is a combination of only the process or at least one of the process, the work content, the model, and the worker. For example, when there are three types of each of the process, the work content, the model, and the worker, if the analysis particle size is only the process, the work belonging to a certain process is grouped into one analysis particle size group Become. Also, if the analysis particle size is a process and a model, operations belonging to a certain process will be grouped into three analysis particle size groups.
  • control particle size determination unit 111 calculates the degree of variation of the working time of the work belonging to each analysis particle size group, and determines an analysis particle size that minimizes the calculated degree of variation as the control particle size in the process. For example, the control particle size determination unit 111 calculates an absolute value error between the operation time of the work belonging to each analysis particle size group and the average operation time, calculates a sum of them, and an analysis in which the sum of the absolute value errors is minimized. The particle size is determined to the control particle size in the process. Furthermore, the management granularity determination unit 111 stores the management granularity in the management granularity information 122 (details will be described later) stored in the storage unit 120.
  • the items of the control particle size and analysis particle size for each process are four items of process, work content, model, and worker, but other items such as materials may be added.
  • the evaluation value calculation unit 112 detects movement line information representing the movement route of the worker in each work from the moving image file obtained by imaging the work, which is included in the work record information 121 (details described later) stored in the storage unit 120. Then, the detection result is stored in flow line information 123 (details will be described later) stored in the storage unit 120. Further, the evaluation value calculation unit 112 calculates, based on the work record information 121 and the flow line information 123, a work time, a flow line distance, and a non-operation ratio, which are evaluation indices of the work. Furthermore, the evaluation value calculation unit 112 stores the calculated evaluation values in the evaluation value information 124 (details will be described later) stored in the storage unit 120.
  • the work classification unit 113 groups the work into a management particle size group according to the management particle size of each process based on the work performance information 121 stored in the storage unit 120, the management particle size information 122, and the evaluation value information 124. Select excellent work from among the above. Also, the work classification unit 113 stores the selected excellent work in the work classification information 125 (details will be described later) stored in the storage unit 120.
  • the work improvement point extraction unit 114 divides the angle of view of a moving image file obtained by imaging a work, which is included in the work result information 121 stored in the storage unit 120, into a plurality of work areas. Further, the work improvement point extraction unit 114 becomes an evaluation index of each work for each divided work area based on the work performance information 121, the management particle size information 122, and the flow line information 123 stored in the storage unit 120. Calculate the evaluation value. Furthermore, the work improvement point extraction unit 114 stores the evaluation value for each work area in the area-by-area evaluation value information 126 stored in the storage unit 120.
  • the work improvement point extraction unit 114 extracts work improvement points based on the work classification information 125 stored in the storage unit 120, the area-by-area evaluation value information 126, and the work improvement point library information 128. Furthermore, the work improvement point extraction unit 114 stores the extracted improvement points in the work improvement point information 127 stored in the storage unit 120.
  • the storage unit 120 is information necessary for work analysis, specifically, work record information 121, management particle size information 122, flow line information 123, evaluation value information 124, work classification information 125, area-by-area evaluation value information 126, work
  • the improvement point information 127 and the work improvement point library information 128 are stored.
  • the input unit 130 receives operation information transmitted from the user terminal 103 via the network 102 and notifies the operation unit 110 of the operation information. Further, the input unit 130 receives the work record information supplied from the database 104 via the network 102, and adds it to the work record information 121 stored in the storage unit 120. Furthermore, according to the operation information of editing to the work improvement point library information 128 among the operation information transmitted from the user terminal 103, the input unit 130 stores the work improvement point library information 128 stored in the storage unit 120. Change
  • the output unit 140 (corresponding to the presentation control unit of the present invention) causes the display of the user terminal 103 to display an output screen 1300 (FIG. 14) representing the analysis result of the work. Further, the output unit 140 causes the display of the user terminal 103 to display the editing screen 1500 (FIG. 16) of the work improvement point library information 128.
  • FIG. 2 shows an example of the data structure of the work record information 121.
  • the work record information 121 stores information on a plurality of already performed works.
  • the work record information 121 is composed of a plurality of records corresponding to each work, and each record is composed of a work ID field 1211, a process field 1212, a work content field 1213, a model field 1214, and a worker field 1215. , A start time field 1216, an end time field 1217, and a moving image file field 1218.
  • the work ID field 1211 stores work ID (Identification) information for identifying each work.
  • Process information is stored in the process field 1212.
  • the process information is information indicating which of a plurality of processes sequentially performed in a series of operations for manufacturing a product.
  • the work content field 1213 stores information representing the work content of the process (hereinafter, referred to as the process) represented by the process information stored in the process field 1212. A plurality of different work contents may exist for the same process.
  • the model field 1214 stores information indicating the model of the product manufactured in the process.
  • the worker field 1215 stores information representing the worker who is in charge of the process.
  • the start time field 1216 stores the work start time.
  • the end time field 1217 stores the end time of the work.
  • the moving image file field 1218 stores a moving image file obtained by capturing an operation.
  • the frame rate of the moving image file may be, for example, about 1 fps (frames per second), but may be a higher frame rate such as 30 fps.
  • FIG. 3 shows an example of the data structure of the control particle size information 122.
  • the control particle size information 122 stores information indicating the control particle size of each process.
  • the control granularity information 122 is composed of a plurality of records, and each record has a process field 1221, a work content field 1222 for representing the control granularity, a model field 1223, and a worker field 1224.
  • the process field 1221 stores process information representing a process.
  • the work content field 1222 information as to whether the work content is adopted or not is stored as the control granularity of the process. Specifically, “ ⁇ ” is stored when the work content is adopted as the management granularity, and “ ⁇ ” is stored when it is not adopted.
  • the model field 1223 stores information as to whether the model is adopted as the control granularity of the process. Specifically, “ ⁇ ” is stored when a model is adopted as the management granularity, and “ ⁇ ” is stored when not adopted.
  • the worker field 1224 information as to whether the worker is adopted or not is stored as the control granularity of the process. Specifically, “ ⁇ ” is stored when a worker is adopted as the control granularity, and “ ⁇ ” is stored when it is not adopted.
  • step 1 As the control granularity of the process 1, none of the work content, the model, and the worker is employed. In this case, the control particle size of step 1 represents that a step is adopted. Thus, all operations belonging to step 1 are grouped into the same control granularity group and analyzed.
  • control granularity of step 2 indicates that a model is adopted. Therefore, the operations belonging to the process 2 are grouped and analyzed in different control granularity groups for each model.
  • FIG. 4 shows an example of the data structure of the flow line information 123.
  • the flow line information 123 stores information on the flow line of the worker in each work.
  • the flow line information 123 is composed of a plurality of records, and each record is composed of a work ID field 1231, a frame field 1232, an X coordinate field 1233, and a Y coordinate field 1234.
  • the work ID field 1231 stores a work ID for identifying each work.
  • the frame field 1232 stores the frame numbers of the frames making up the moving image file.
  • the X coordinate field 1233 and the Y coordinate field 1234 store the X coordinate and the Y coordinate of the barycentric position of the worker in the frame.
  • FIG. 4 represents the X and Y coordinates of the center of gravity of the worker in each frame of the moving image file of the work 1.
  • the X and Y coordinates of the frame 1 are (29, 16).
  • the coordinates represent (25, 10).
  • FIG. 5 shows an example of the data structure of the evaluation value information 124.
  • the evaluation value information 124 stores evaluation values of each of a plurality of evaluation indexes of each work.
  • the evaluation value information 124 is composed of a plurality of records, and each record has a work ID field 1241, a work time field 1242, a flow line distance field 1243, and a non-operation ratio field 1244.
  • the work ID field 1241 stores a work ID for identifying each work.
  • the working time field 1242 stores working time as the evaluation value of the evaluation index.
  • the flow line distance field 1243 stores the flow line distance of the worker as the evaluation value of the evaluation index.
  • the non-operation ratio field 1244 stores the non-operation ratio as the evaluation value of the evaluation index.
  • the evaluation index of work 1 is 30 minutes
  • the working distance is 5 m
  • the non-operation ratio is 10%
  • the evaluation index of work 2 is 50 minutes of work time It represents that the line distance is 7 m and the non-operation ratio is 15%.
  • FIG. 6 shows an example of the data structure of the work classification information 125.
  • the work classification information 125 stores information on excellent work at the control granularity of each process.
  • the work classification information 125 is composed of a plurality of records, and each record has a process field 1251, a work content field 1252, a model field 1253, a worker field 1254, and a work ID field 1255.
  • the process field 1251 stores process information representing a process.
  • the work content field 1252 stores information on the work content among the control granularity of the process. If no work content is adopted for the control granularity of the process, “-” is stored in the work content field 1252.
  • the model field 1253 stores information on the model of the control granularity of the process. If a model is not adopted as the control granularity of the process, “-” is stored in the model field 1253.
  • the worker field 1254 stores information on the worker in the control granularity of the process. Note that “-” is stored in the worker field 1254 when the worker is not employed for the control granularity of the process.
  • the work ID field 1255 stores a work ID representing an excellent work in the control granularity of the process.
  • control granularity of the process 1 is a process
  • the excellent work of the work grouped into the control grain size group of the process 1 is a work 1.
  • control granularity of the process 2 is a model
  • the excellent work of the work grouped into the control granularity group of the process 2 and the model 1 represents the work 3 and the work 5.
  • the excellent work of the work grouped into the control granularity group of the process 2 and the model 2 represents the work 11.
  • FIG. 7 shows an example of the data structure of the evaluation value information 126 classified by area.
  • the area-by-area evaluation value information 126 stores evaluation value information which is an evaluation index of each work collected for each work area.
  • the area-by-area evaluation value information 126 is composed of a plurality of records, and each record includes a work ID field 1261, a work area field 1262, an extraction start time field 1263, an extraction end time field 1264, and a work time field. 12 has a flow distance field 1266 and a non-operating ratio field 1267.
  • the work ID field 1261 stores a work ID for identifying each work.
  • the work area field 1262 stores information representing a work area.
  • the extraction start time field 1263 stores the start time of the work in the work area.
  • An extraction end time field 1264 stores the end time of the work in the work area.
  • the working time field 1265 stores working time in the working area.
  • the movement distance of the worker in the work area is stored in the movement distance field 1266.
  • the non-operation ratio field 1267 stores the non-operation ratio (described in detail later).
  • the working time in the working area 2 of the working 1 is 5 minutes from 9:10 to 9:15 on 2017/4/2, the flow line distance is 2 m, and the non-operating ratio is 10%.
  • FIG. 8 shows an example of the data structure of the work improvement point information 127.
  • the work improvement point information 127 stores information on the work improvement point extracted at the control granularity of each process.
  • the work improvement point information 127 includes a plurality of records, and each record includes a work ID field 1271, a work area field 1272, an improvement point field 1273, an extraction start time field 1274, and an extraction end time field 1275. And.
  • the work ID field 1271 stores the work ID of the work whose improvement point has been extracted.
  • the work area field 1272 stores information indicating the work area targeted for the improvement point.
  • the improvement point field 1273 stores specific contents of the improvement point.
  • the extraction start time field 1274 stores the extraction start time of the improvement point.
  • the extraction end time field 1275 stores the extraction end time of the improvement point.
  • FIG. 9 shows an example of the data structure of the work improvement point library information 128.
  • the work improvement point library information 1228 information to be referred to when extracting improvement points from each process is stored in advance.
  • the work improvement point library information 128 can be newly registered or corrected by the user.
  • the work improvement point library information 128 is composed of a plurality of records, and each record includes a process field 1281, a work content field 1282, a model field 1283, a worker field 1284, an improvement point field 1285, and a work It has a time field 1286, a flow distance field 1287, and a non-operating rate field 1288.
  • the process field 1281 stores process information representing a process.
  • the work content field 1282 stores information on the work content among the control granularity of the process. If no work content is adopted as the control granularity of the process, “-” is stored in the work content field 1282.
  • the model field 1283 stores information on the model of the control granularity of the process. If a model is not adopted as the control granularity of the process, “-” is stored in the model field 1283.
  • the worker field 1284 stores information on the worker in the control granularity of the process. When the worker is not employed for the control granularity of the process, “-” is stored in the worker field 1284.
  • the improvement point field 1285 stores the contents of the improvement point to be extracted.
  • the working time field 1286 stores a threshold value of the difference between working time of excellent work and non-good work, which is referred to when extracting the improvement point.
  • a threshold of the difference between the movement distance between the excellent operation and the non-excellent operation which is referred to when extracting the improvement point, is stored.
  • the non-operation ratio field 1288 a threshold of the difference between non-operation ratio of excellent work and non- excellent operation, which is referred to when extracting the improvement point, is stored.
  • the condition that “Tatsuke at work” is extracted as an improvement point from the process 1 indicates that the difference in working time with the excellent work is 10 minutes or more. Further, the condition that “moving distance excess” is extracted as the improvement point from the process 1 represents that the difference in moving distance from the excellent work is 3 m or more.
  • FIG. 10 is a flowchart illustrating an example of the task analysis process by the task analysis system 100.
  • This work analysis process is premised on the fact that a predetermined number of work record information is recorded in the database 104, and is started, for example, in response to a start command from the user.
  • the user terminal 103 receives an operation input from a user specifying a process to be analyzed, and transmits the operation information to the work analysis apparatus 101 via the network 102 (step S11).
  • the input unit 130 of the work analysis apparatus 101 having received the operation information acquires all the work record information corresponding to the process represented by the operation information from the database 104, and the work stored in the storage unit 120. It stores in the track record information 121 (step S12).
  • the management particle size determination unit 111 of the calculation unit 110 determines the management particle size for the process represented by the operation information transmitted from the user terminal 103 based on the work record information 121 of the storage unit 120, and the storage unit 120 stores It stores in the management particle size information 122 which has been done (step S13).
  • the management particle size determination unit 111 reads a record matching the analysis particle size determined in advance from the work record information 121 stored in the storage unit 120, and calculates the work time of each record from the start time and the end time. For example, when the process to be analyzed is process 1 and the analysis particle size is a process, the record in which process 1 is stored in process field 1212 of work record information 121 is read, and start time field 1216 and end time field of each record The difference between the times stored in 1217 and 1217 is calculated as the working time.
  • the analysis target process is process 2 and the analysis particle size is the process and model
  • the record in which process 2 is stored in process field 1212 of work record information 121 is read, and is further stored in model field 1214
  • the analysis particle size group is grouped for each model ID, and the difference between the times stored in the start time field 1216 and the end time field 1217 of each record is calculated as the operation time for each analysis particle size group.
  • FIG. 11 visualizes the degree of variance of the calculated working time of each record, and in the process 1 and the process 2, the analysis particle size 1 grouped by only the process and the analysis particle size 2 grouped by combining the process and the model Is a scatter plot in which the working time of the work grouped into each analysis particle size group is plotted.
  • the horizontal axis in the figure represents the work day, and the vertical axis represents the work time.
  • thick frame lines in FIG. 11 indicate the appropriate ones as the control particle sizes of the analysis particle size 1 and the analysis particle size 2 in each of the process 1 and the process 2 (the reason will be described later).
  • control particle size determination unit 111 quantifies the degree of variation of the operation time of the work grouped into the analysis particle size group. Specifically, the management particle size determination unit 111 calculates an average operation time for each analysis particle size group.
  • control particle size determination unit 111 removes outliers that may be defective data from the work record information 121.
  • histogram creation and Smirnov-Grabbs test are utilized. First, create a histogram of work time for each work. When creating a histogram, the number of histogram bins is determined according to the Stargest equation, but the number of histogram bins may be determined by other methods. After that, it is determined by the Smirnov-Grabs test whether or not the generated histogram contains an outlier. If no outlier is contained, the average value of the histogram is used as the average operation time. Conversely, if outliers are included, create the histogram again.
  • the records in the range where the frequency of the histogram is the highest and the ranges before and after it are extracted, and a histogram is created again using the extracted data, and the Smirnov-Grabs test is performed. Thereafter, the same processing is repeated until the generated histogram contains no outliers.
  • other ranges may be extracted, such as extracting only the range where the frequency is the highest. You may
  • FIG. 12 shows an example of the calculation result of the average operation time at each analysis particle size after removing the outliers. Similar to FIG. 11, FIG. 12 shows calculation results of the average operation time for the analysis particle size 1 of only the process and the analysis particle size 2 in which the process and the model are combined in the process 1 and the process 2.
  • the average working time at each analysis particle size shown in FIG. 12 is plotted as a dotted line on the scatter plot of FIG.
  • the average work time in analysis particle size 1 of step 1 is 10
  • the average work time of model 1 in analysis particle size 2 of step 1 is 11
  • the average work time of model 2 is 9
  • the average work time of model 3 is 12 .
  • the average working time in analysis particle size 1 of step 2 is 20
  • the average working time of model 1 in analysis particle size 2 of step 2 is 25
  • the average working time of model 2 is 35
  • the average working time of model 3 is 21 It is.
  • the control particle size determination unit 111 determines, as the control particle size, the analysis particle size at which the variation of the operation time is minimized. Specifically, the control particle size determination unit 111 determines, as the control particle size of the process, an analysis particle size that minimizes the total sum of absolute value errors between the calculated average operation time and the operation time of each record.
  • the calculation method of the dispersion degree of working time may be limited to the specific example mentioned above. For example, variance, standard deviation, etc. may be calculated.
  • the control particle size is determined as the one with the larger analysis particle size.
  • FIG. 13 shows the sum of absolute value errors of analysis particle size 1 and analysis particle size 2 in step 1 and step 2.
  • the management granularity determination unit 111 stores the determined management granularity in the management granularity information 122 (FIG. 3) stored in the storage unit 120.
  • “process 1” is stored in the process field 1221 of the control particle size information 122 as a record corresponding to the process 1
  • “-” is stored in the work content field 1222, the machine type field 1223 and the worker field 1224.
  • “process 2” is stored in the process field 1221 of the control granularity information 122 as a record corresponding to the process 2
  • "-" is stored in the work content field 1222 and the worker field 1224
  • "type" is stored in the machine type field 1223.
  • the evaluation value calculation unit 112 calculates and calculates the working time, the flow line distance, and the non-operation ratio, which are the evaluation values of the evaluation index of each work.
  • the obtained evaluation value is stored in the evaluation value information 124 stored in the storage unit 120 (step S14).
  • three items of work time, flow distance and non-operation ratio are adopted as work evaluation index, but at least two of work time, flow distance and non-operation ratio May be adopted. Furthermore, in addition to the above three items, for example, time of each posture (standing, squatting, etc.), smoothness of movement, time of talking, movement of eyes, etc. may be adopted as an evaluation index.
  • the evaluation value calculation unit 112 creates a flow line data by reading a moving image file of each operation from the operation result information 121 stored in the storage unit 120 and performing image analysis. Do. Specifically, the evaluation value calculation unit 112 searches the worker on each frame of the moving image file read from the work record information 121, and acquires the coordinates of the worker's center of gravity. As a method of searching for the worker, for example, a method is employed in which the feature of the worker is learned by machine learning in advance and the learning result and the image of each frame are compared, but other methods may be used.
  • the evaluation value calculation unit 112 stores the created flow line data in the flow line information 123 stored in the storage unit 120. Next, the evaluation value calculation unit 112 calculates the working time, the flow line distance, and the non-operation ratio based on the work record information 121 and the flow line information 123.
  • the work time is calculated by calculating the difference between the start time and the finish time of each work in the work record information 121.
  • the flow line distance of each operation is calculated by adding up the amount of change of the barycentric coordinates of the workers between the frames of the flow line information 123.
  • the flow line information 123 of each work detects the time spent in the previously designated work area (area where work is not performed), and the ratio of the detected time to the work time is the non-operation ratio Calculated as Finally, the evaluation value calculation unit 112 stores the calculated work time, flow line distance and non-operation ratio in the evaluation value information 124 stored in the storage unit 120.
  • step S14 This is the end of the detailed description of the process of step S14. It returns to the description of the work analysis process of FIG.
  • the work classification unit 113 performs work classification to classify excellent work from the work matching the process designated in step S11 (step S15).
  • the work classification unit 113 acquires the management grain size of the process with reference to the management grain size information 122 (FIG. 3), and acquires from the work record information 121 a record that matches the acquired management grain size. Further, the work classification unit 113 refers to the work ID field 1211 of the acquired record to acquire the work ID matching the management granularity of the process, and groups the work ID into the management granularity group. Furthermore, the work classification unit 113 acquires, from the evaluation value information 124 (FIG. 5), a record that matches the work ID belonging to each management granularity group. Furthermore, the work classification unit 113 refers to the records acquired from the evaluation value information 124, and selects candidate records that can be excellent work in each evaluation value of work time, flow distance and non-operation ratio.
  • step S11 when the process designated in step S11 is process 1, it is acquired that the particle size is only the process from the control particle size information 122, and then the work ID matching the process 1 from the work record information 121 (FIG. In the case, operations 1 to 7) are acquired and grouped into management granularity groups. Further, from the evaluation value information 124, records matching the tasks 1 to 7 are obtained.
  • step S11 when the process designated in step S11 is process 2, the particle size is acquired from the management particle size information 122 as the process and the model, and next, the operation record information 121 matches the process 2 and the model 2.
  • a work ID in the case of FIG. 2, work 11
  • a record matching the operation 11 (not shown in FIG. 5) is acquired.
  • the work classification unit 113 calculates the average work time based on the record acquired from the evaluation value information 124. In addition, as a method of calculating the average work time here, the average work time is calculated after removing outliers from the work time, similarly to the processing in the management particle size determination unit 111. Next, the work classification unit 113 selects a record whose work time is equal to or less than the average work time as a candidate for excellent work.
  • the work classification unit 113 similarly selects records having a flow line distance equal to or less than the average flow line distance for evaluation values other than the work time as candidates for excellent work, and the non-operation ratio averages non-operation Select a record that is less than the percentage as a candidate for excellent work.
  • the work classification unit 113 selects a record selected as a candidate for excellent work in all evaluation indexes (work time, flow distance and non-operation ratio) as excellent work. If a plurality of records are candidates for excellent work in all evaluation values, the corresponding multiple records are selected as excellent work.
  • the work classification unit 113 stores the selected excellent work record in the work classification information 125 stored in the storage unit 120.
  • the control particle size is only the process, so the work classification unit 113 stores “process 1” in the process field 1251, and the work content field 1252, the model field 1253, and the worker field 1254. And “work 1” selected as an excellent work in the work ID field 1255. Further, in the case of step 2 of FIG.
  • the work classification unit 113 stores “step 2” in the step field 1251 and stores “model 1” of the model field 1253 , “-” Is stored in the work content field 1252 and the worker field 1254, and “work 3” selected as the excellent work is stored in the work ID field 1255. Further, the work classification unit 113 stores “step 2” in the step field 1251, stores “model 1” in the model field 1253, and stores “ ⁇ ” in the work content field 1252 and the worker field 1254. The "work 5" selected as the excellent work is stored in the work ID field 1255.
  • the work classification unit 113 stores “step 2” in the step field 1251, stores “model 2” in the model field 1253, and stores “ ⁇ ” in the work content field 1252 and the worker field 1254.
  • the "work 11" selected as the excellent work is stored in the work ID field 1255.
  • step S15 This is the end of the detailed description of the process of step S15. It returns to the description of the work analysis process of FIG.
  • the work improvement point extraction unit 114 performs steps based on the work record information 121, the management particle size information 122, the flow line information 123, the work classification information 125, and the work improvement point library information 128 stored in the storage unit 120.
  • the improvement point of the work in the management granularity of the process designated in S11 is extracted, and the extracted improvement point is stored in the work improvement point information 127 stored in the storage unit 120 (step S16).
  • the work improvement point extraction unit 114 refers to the management particle size information 122 to acquire the management particle size of the process specified in step S11, and acquires a record matching the acquired management particle size from the work record information 121 Identify the ID.
  • the management granularity is only the process, and the work improvement point extraction unit 114 acquires a record matching the process 1 from the work result information 121 and acquires the work ID (in the case of FIG. 3). Task ID1 to task ID7) are identified.
  • the work improvement point extraction unit 114 acquires a record that matches the specified work ID from the flow line information 123, and based on the X, Y coordinates of the center of gravity of the worker in the acquired record, the moving image file of the process The angle of view of is divided into multiple work areas.
  • the Starge's equation for determining the number of bins of the histogram is utilized, and from the data of the X coordinate stored in the flow line information 123, the horizontal direction of the work area The number of divisions of the work area in the vertical direction is determined from the Y coordinate data stored in the flow line information. Also, the work area may be divided according to the input from the user using the user terminal 103.
  • the work improvement point extraction unit 114 calculates, for each work area, the work time, the flow distance, and the non-operation ratio, which are evaluation values of the work, in the management granularity of the process.
  • the work improvement point extraction unit 114 acquires, from the management particle size information 122 and the flow line information 123, a record that matches the management particle size of the process. For example, in the case of step 1 of FIG. 3, since the control granularity is only the step, the work improvement point extraction unit 114 acquires a record in which the step information matches the step 1 from the work record information 121 and the flow line information 123. Do.
  • the range of the working area and the barycentric coordinates of the worker stored in the flow line information 123 are compared, and the worker specifies the frame included in the working area.
  • the work time in the work area is calculated from the specified frame and the number of frame rates. Also, the start time and the end time of the work in each work area are acquired from the specified frame.
  • the range of the work area and the coordinates of the center of gravity of the worker stored in the flow line information 123 are compared, and the worker specifies the frame included in the work area Do. After that, the flow line distance in the work area is calculated from the change distance of the flow line between the specified frames.
  • the range of the work area and the coordinates of the barycenter of the worker stored in the flow line information 123 are compared, and the time spent in the non-work area in the work area is compared. Calculate the ratio of work area to work time and the non-operation ratio of each work area.
  • the work improvement point extraction unit 114 stores the calculated work time, flow line distance, and non-operation ratio for each work area, the start time of the work in the work area, and the end time in the evaluation value information 126 for each area. Do.
  • the work improvement point extraction unit 114 based on the work record information 121, the management grain size information 122, the work classification information 125, the evaluation value information by area 126, and the work improvement point library information 128, in the management granularity of the process. Extract improvement points of non-excellent work that were not classified as excellent work. Specifically, first, the work improvement point extraction unit 114 acquires excellent work in the control granularity of the process based on the work classification information 125. For example, in the case of process 1 of FIG. 3, the work improvement point extraction unit 114 acquires the work 1 as the excellent work in the process 1 from the work classification information 125.
  • the work improvement point extraction unit 114 calculates the difference between the evaluation values of the excellent work and the non- excellent work based on the work record information 121 and the evaluation value information by area 126, and the difference between the evaluation values improves the work. It is determined whether it is equal to or more than the threshold of the evaluation value registered in the point library information 128, and an improvement point is extracted.
  • the work improvement point extraction unit 114 calculates each evaluation value of work 1 which is excellent work and work 7 which is non- excellent work from the evaluation value information 126 classified by area (FIG. 7). Calculate the difference.
  • the difference between working time of work 1 which is excellent work and work 7 which is non-good work is calculated as “5 minutes”, the difference of flow line distance is “4 m” and the difference of non-operating ratio is “10%” Be done.
  • the threshold of each evaluation value is acquired from the work improvement point library information 128 (FIG. 8), and it is determined that the difference "4 m" in flow line distance is equal to or more than the threshold "3 m". , “Travel distance exceeded” is selected.
  • the work improvement point extraction unit 114 stores the extracted improvement points in the work improvement point information 127 stored in the storage unit 120.
  • the work improvement point extraction unit 114 stores “work 7” in the work ID field 1271 of the work improvement point information 127, stores “area 2” in the work area field 1272, and improves “Moved distance exceeded” is stored in the point field 1273.
  • the work improvement point extraction unit 114 stores “2017/5/1 13:00” in the extraction start time field 1274 of the work improvement point information 127 and “2017/5/13 13: in the extraction end time field 1275. Store 10 ".
  • step S16 This is the end of the detailed description of the process of step S16. It returns to the description of the work analysis process of FIG.
  • the output unit 140 generates an output screen 1300 (FIG. 14) representing the result of the work analysis based on each information stored in the storage unit 120, and outputs the generated output screen 1300 to the user terminal 103 via the network 102. Further, the output unit 140 updates the output screen 1300 as needed in response to an operation from the user on the output screen 1300 and outputs the updated screen 1300 to the user terminal 103.
  • the user terminal 103 presents it to the user by displaying the output screen 1300 on the display (step S17). Thus, the task analysis process by the task analysis system 100 is completed.
  • FIG. 14 illustrates a display example of the output screen 1300 displayed on the user terminal 103.
  • the output screen 1300 includes a process information selection field 1301, a control particle size display field 1302, an analysis object selection field 1303, an excellent work display field 1304, a work improvement point display field 1305, and a work improvement point library information display field 1306. , Library correction button 1307. Further, the output screen 1300 has an excellent work moving image display field 1308 and an extracted work moving image display field 1309.
  • the control particle size display column 1302 displays the control particle size of the process (the process selected in the process information selection column 1301).
  • the analysis object selection column 1303 the user can select an analysis object of management granularity for displaying the analysis result.
  • the control particle size of the process is a process
  • “-” is displayed in the control particle size display field 1302
  • an analysis object of the control particle size can not be selected in the analysis object selection field 1303.
  • the management particle size of the process is a worker
  • “worker” is displayed in the management particle size display field 1302, and the worker can be selected in the analysis object selection field 1303.
  • the excellent work display column 1304 displays a work ID and a worker as a record of the excellent work in the process.
  • the user can select the displayed record, and the record surrounded by a bold line frame (in this case, work 7) is selected.
  • the library correction button 1307 is a button for starting an editing process for newly registering a record in the work improvement point library information 128 or correcting a registered record, and editing when the library correction button 1307 is pressed. Screen 1500 (FIG. 15) is displayed.
  • a moving picture file of the excellent work in the process is reproduced and displayed.
  • a moving image file of the work selected by the user in the work improvement point display field 1305 is reproduced and displayed.
  • FIG. 15 shows a display example of the editing screen 1500.
  • the edit screen 1500 has a process information selection field 1501, a new registration reception unit 1502, a registration button 1503, a correction reception unit 1504, and a correction button 1505.
  • the user can select the process of the record to be newly registered in the work improvement point library information 128 or the record to be corrected.
  • the user can input a record to be newly registered in the work improvement point library information 128 to the new registration reception unit 1502.
  • a new registration acceptance unit 1502 is used to newly register a record
  • the control granularity of the process selected in the process information selection field 1501 is not set in advance.
  • the user since the user needs to set the work content, the model, and the worker in combination, the user may set the control granularity with respect to the process based on the experience of the expert. If the management granularity set here is inappropriate, the management granularity may be corrected using a correction accepting unit 1504 described later, based on the result of the task analysis by the task analysis device 101.
  • the registration button 1503 can instruct the new registration reception unit 1502 to register the record input in the work improvement point library information 128.
  • the correction receiving unit 1504 can display the existing record in the work improvement point library information 128 so that the user can correct it.
  • the correction button 1505 can reflect the correction input by the correction receiving unit 1504 in the work improvement point library information 128 when the user presses it.
  • FIG. 16 is a flowchart illustrating an example of an editing process that can newly register or correct the work improvement point library information 128. This editing process is started in response to pressing of the library correction button 1307 on the output screen 1300, and the editing screen 1500 is displayed on the user terminal 103.
  • the input unit 130 that receives the transmitted operation information newly registers a record in the work improvement point library information 128 stored in the storage unit 120 based on the received operation information, or corrects an existing record.
  • the result is stored (step S22). Thus, the editing process is ended.
  • the task analysis device 101 includes the management particle size determination unit 111, so that it is possible to determine an appropriate management particle size. Further, since the work analysis device 101 includes the evaluation value calculation unit 112, evaluation values of a plurality of different evaluation indexes can be calculated without relying on human power. Further, since the work analysis device 101 includes the work classification unit 113 and the work improvement point extraction unit 114, the work improvement point can be extracted based on the excellent work and the non- excellent work. Further, since the work improvement point extraction unit 114 extracts the work improvement point with reference to the work improvement point library information 128, the user adjusts the standard of the work improvement point by editing the work improvement point library information 128. be able to.
  • the work analysis apparatus 101 can be configured by hardware or can be realized by software.
  • a program that configures the software is installed in a computer.
  • the computer includes, for example, a general-purpose personal computer that can execute various functions by installing a computer incorporated in dedicated hardware and various programs.
  • FIG. 17 is a block diagram showing an example of a hardware configuration of a computer that realizes the work analysis apparatus 101 by a program.
  • a central processing unit (CPU) 2001 a read only memory (ROM) 2002, and a random access memory (RAM) 2003 are mutually connected by a bus 2004.
  • CPU central processing unit
  • ROM read only memory
  • RAM random access memory
  • an input / output interface 2005 is connected to the bus 2004.
  • An input unit 2006, an output unit 2007, a storage unit 2008, a communication unit 2009, and a drive 2010 are connected to the input / output interface 2005.
  • the input unit 2006 includes a keyboard, a mouse, a microphone and the like.
  • the output unit 2007 includes a display, a speaker, and the like.
  • the storage unit 2008 includes a hard disk, a non-volatile memory, and the like.
  • the communication unit 2009 includes a network interface and the like.
  • the drive 2010 drives removable media 2011 such as a magnetic disk, an optical disk, a magneto-optical disk, or a semiconductor memory.
  • the CPU 2001 loads a program stored in the storage unit 2008 into the RAM 2003 via the input / output interface 2005 and the bus 2004 and executes the task analysis.
  • the arithmetic unit 110, the input unit 130, and the output unit 140 of the device 101 are realized.
  • the storage unit 120 of the work analysis apparatus 101 is realized by the storage unit 2008, the RAM 2003, or the removable medium 2011.
  • the program executed by the computer 2000 can be provided by being recorded on, for example, the removable medium 2011 as a package medium or the like. Also, the program can be provided via a wired or wireless transmission medium such as a local area network, the Internet, or digital satellite broadcasting.
  • the program can be installed in the storage unit 2008 via the input / output interface 2005 by attaching the removable media 2011 to the drive 2010. Also, the program can be received by the communication unit 2009 via a wired or wireless transmission medium and installed in the storage unit 2008. In addition, the program can be installed in advance in the ROM 2002 or the storage unit 2008.
  • the program executed by computer 2000 may be a program that performs processing in chronological order according to the order described in this specification, or in parallel, or when necessary, such as when a call is made.
  • the program may be a program to be processed in
  • the present invention is not limited to the embodiments described above, but includes various modifications.
  • the above-described embodiments are described in detail in order to explain the present invention in an easy-to-understand manner, and the present invention is not necessarily limited to those having all the described components.
  • part of the configuration of one embodiment can be replaced with the configuration of another embodiment, and the configuration of another embodiment can be added to the configuration of one embodiment.
  • each of the configurations, functions, processing units, processing means, etc. described above may be realized by hardware, for example, by designing part or all of them with an integrated circuit.
  • each configuration, function, etc. described above may be realized by software by the processor interpreting and executing a program that realizes each function.
  • Information such as a program, a table, and a file for realizing each function can be placed in a memory, a hard disk, a storage device such as a solid state drive (SSD), or a recording medium such as an IC card, an SD card, or a DVD.
  • control lines and information lines indicate what is considered to be necessary for the description, and not all control lines and information lines in the product are necessarily shown. In practice, almost all configurations may be considered to be mutually connected.
  • the present invention can be provided not only in a work analysis apparatus and a work analysis method, but also in various modes such as a system including a plurality of apparatuses and a computer readable program.
  • 100 ... work analysis system 101 ... work analysis device, 102 ... network, 103 ... user terminal, 104 ... database, 110 ... calculation unit, 111 ... management particle size determination unit , 112: evaluation value calculation unit, 113: work classification unit, 114: work improvement point extraction unit, 120: storage unit, 121: work performance information, 122: management particle size information , 123: flow line information, 124: evaluation value information, 125: work classification information, 126: evaluation value information by area, 127: work improvement point information, 128: work improvement Point library information, 130: input unit, 140: output unit, 1211: work ID field, 1212: process field, 1213: work content field, 1214 ⁇ ⁇ ⁇ Machine type field, 1215 ...
  • worker field 1216 ... start time field, 1217 ... end time field, 1218 ... movie file field, 1221 ... process field, 1222 ... work content Field, 1223 ... model field, 1224 ... worker field, 1231 ... work ID field, 1232 ... frame field, 1233 ... X coordinate field, 1234 ...
  • improvement point field 1274 ... extraction Start time field, 1275 extraction end time field, 1281 step field, 1282 work content field, 1283 type field, 1284 operator field, 1285 improvement point field , 1286 ... at the time of work
  • process information selection field 1302 ... management granularity display field, 1303 ... Analysis target selection column, 1304 ... excellent work display column, 1305 ... work improvement point display column, 1306 ... work improvement point library information display column, 1307 ... library correction button, 1308 ...

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