JP7546207B2 - Work data analysis device, work management system, work data analysis method and program - Google Patents

Description

The present disclosure relates to an activity data analysis device, an activity management system, an activity data analysis method, and a program, and more particularly to an activity data analysis device, an activity management system, an activity data analysis method, and a program that support improvement of added value in work.

Patent Document 1 describes a system that identifies and analyzes the work content based on data from multiple acceleration sensors and other devices attached to different parts of each worker's body, and displays the analysis results (a screen that allows comparison of the time required for work by day and night and by day of the week).

International Publication No. 2011/111316

When multiple tasks are performed at a workplace, if some of the tasks are inefficient, the other tasks will be affected, and the efficiency of the entire process may decrease. In particular, in the case of line work, where multiple tasks are performed simultaneously in multiple locations, the tasks in one cycle cannot begin until all of the tasks in the next cycle are completed. Therefore, if one task is delayed, the entire process will be delayed, resulting in a decrease in the added value created by the process.

In the system described in Patent Document 1, when trying to make inefficient tasks more efficient and increase added value, the user must refer to the displayed analysis results, for example, identify a time period (day of the week and day or night) when a task takes a long time, and then visit the work site during that time period to observe multiple tasks to identify the tasks. For this reason, in the system described in Patent Document 1, it was not easy to improve the efficiency of multiple tasks and thereby increase added value.

An object of the present disclosure is to provide an operation data analysis device, an operation management system, an operation data analysis method, and a program that can support the improvement of added value in a plurality of operations.

An activity data analysis device according to an aspect of the present disclosure includes a processing unit, an output unit, and a receiving unit. The processing unit, based on both imaging data capturing an image of the activity performed by a plurality of activity subjects and management data including at least one of instruction data for the activity subject to start the activity and operation data indicating completion of the activity by the activity subject, specifies a required time for each activity from activity data recording the status of a series of activities performed by a plurality of activity subjects, and classifies the activity into a category according to the degree of added value of the activity content. The output unit outputs information including the required time and the category of each activity in a graph. The receiving unit receives a designation of one category from the graph, and the output unit outputs imaging data capturing an image of the activity status corresponding to the designated category from among the imaging data. The instruction data includes image data instructing the activity subject to start the activity.
An activity data analysis device according to an aspect of the present disclosure includes a processing unit, an output unit, and a receiving unit. The processing unit, based on both imaging data capturing an image of the activity performed by a plurality of activity subjects and management data including at least one of instruction data for the activity subject to start the activity and operation data indicating completion of the activity by the activity subject, specifies a required time for each activity from activity data recording the status of a series of activities performed by a plurality of activity subjects, and classifies the activity into a category according to the level of added value of the activity content. The output unit outputs information including the required time and the category of each activity in a graph. The receiving unit receives a designation of one category from the graph, and the output unit outputs imaging data capturing an image of the activity status corresponding to the designated category from among the imaging data. The operation data includes time information when the activity subject inputs an operation indicating completion of the activity into a terminal device.
An activity data analysis device according to an aspect of the present disclosure includes a processing unit, an output unit, and a receiving unit. The processing unit, based on both image data capturing an image of the activity performed by a plurality of activity subjects and management data including at least one of instruction data for the activity subject to start the activity and operation data indicating completion of the activity by the activity subject, specifies a required time for each activity from activity data recording the status of a series of activities performed by a plurality of activity subjects, and classifies the activity into a category according to the degree of added value of the activity content. The output unit outputs information including the required time and the category of each activity in a graph. The receiving unit receives a designation of one category from the graph, and the output unit outputs imaging data capturing an activity status corresponding to the designated category from among the imaging data. The imaging data includes images obtained from a plurality of cameras installed in a plurality of activity locations, and information on the plurality of activity locations where the images were captured. When the receiving unit receives a designation of a combination of one category and a work location, the output unit outputs imaging data from the imaging data that captures the designated work location and the work situation corresponding to the designated category.
An operation data analysis device according to an aspect of the present disclosure includes a processing unit, an output unit, and a receiving unit. The processing unit, based on both image data capturing an image of the operation status of a series of operations performed by a plurality of operation subjects and management data including at least one of instruction data for the operation subject to start the operation and operation data indicating completion of the operation by the operation subject, specifies a required time for each operation constituting the series of operations, and classifies the operation into a category according to the degree of added value of the operation content. The output unit outputs information including the required time and the category of each operation in a graph. The receiving unit receives a designation of one category from the graph, and the output unit outputs imaging data capturing an image of the operation status corresponding to the designated category from the imaging data. Each operation constituting the series of operations is an operation performed within one cycle time when a plurality of operations are simultaneously and periodically performed on the same line. The imaging data includes images obtained from a plurality of cameras installed at a plurality of work locations on the same line, and information on the plurality of work locations where the images were captured. When the receiving unit receives a designation of a combination of one category and a work location, the output unit outputs imaging data from the imaging data that captures the work situation corresponding to the designated work location and the designated category.

A task management system according to an aspect of the present disclosure is a task management system for managing a plurality of tasks, and includes a camera and a task data analysis device. The task data analysis device includes a processing unit, an output unit, and a receiving unit. The processing unit, based on both image data capturing an image of the task status of the task performed by the task subject and management data including at least one of instruction data for the task subject to start the task and operation data indicating completion of the task by the task subject, specifies a required time for each task from task data that records the status of a series of tasks performed by a plurality of task subjects, and classifies the task into a category according to the degree of added value of the task content. The output unit outputs information including the required time and the category of each task in a graph. The receiving unit receives a designation of one category from the graph, and the output unit outputs image data capturing an image of the task status corresponding to the designated category from among the image data. The instruction data includes image data instructing the task subject to start the task.

In an operation data analysis method according to one aspect of the present disclosure, a computer identifies the time required for each operation constituting a series of operations from operation data recording the status of a series of operations performed by a plurality of operation subjects, based on both imaging data capturing the status of the operation performed by the operation subjects and management data including at least one of instruction data for the operation subjects to start the operation and operation data indicating completion of the operation by the operation subjects, and classifies the operation into a category according to the level of added value of the operation content. The computer then outputs information including the required time and the category of each operation in the form of a graph, and upon receiving a designation of one category from the graph, outputs imaging data capturing the status of the operation corresponding to the designated category from among the imaging data. The instruction data includes image data instructing the operation subjects to start the operation.

A program according to one aspect of the present disclosure causes a computer to specify a required time for each task constituting a series of tasks based on both imaging data capturing an image of the task status of the task performed by a plurality of task subjects and management data including at least one of instruction data for the task subject to start the task and operation data indicating completion of the task by the task subject, and classify the task into a category according to the degree of added value of the task content. The program further causes the computer to output information including the required time and the category of each task in a graph, and upon receiving a designation of one category from the graph, output imaging data capturing an image of the task status corresponding to the designated category from among the imaging data. The instruction data includes image data instructing the task subject to start the task.

One embodiment of the present disclosure can help improve added value in multiple tasks.

FIG. 1 is a block diagram of an operation management system including an operation data analysis device according to an embodiment of the present disclosure. FIG. 2A is a diagram showing one cycle that constitutes line work at a work site where the above-mentioned work management system is used, FIG. 2B is a diagram showing another cycle that constitutes the above-mentioned line work, and FIG. 2C is a diagram showing another cycle that constitutes the above-mentioned line work. FIG. 3A is a diagram illustrating an example of a plurality of categories used to classify tasks in the above-mentioned activity data analysis device, and the relationship between a plurality of task times corresponding to the plurality of categories. FIG. 3B is a diagram illustrating an example of another plurality of categories used for classification, and the relationship between a plurality of task times corresponding to the other plurality of categories. FIG. 4 is a diagram for explaining the relationship between multiple work times corresponding to multiple categories at each of multiple locations arranged along the line for each of multiple cycles that make up the above-mentioned line work. FIG. 5 is a flowchart showing the operation of the task data analysis device. FIG. 6 is a diagram showing an example of output (time comparison screen) of the operation data analysis device. FIG. 7 is a diagram showing another output example (cycle comparison screen) of the operation data analysis device. FIG. 8 is a diagram showing another example of output (place comparison screen) of the above-mentioned task data analysis device. FIG. 9 is a diagram showing yet another output example (place section captured image) of the activity data analysis device. FIG. 10 is a diagram showing another output example (value comparison screen) of the above operation data analysis device.

The configurations described in the following embodiments are merely examples of the present disclosure. The present disclosure is not limited to the following embodiments, and various modifications are possible depending on the design, etc., as long as the effects of the present disclosure can be achieved.

(1) Overview (1-1) Work Management System The work management system 100 according to an embodiment of the present disclosure manages a plurality of works. The plurality of works may be performed at the same location or at different locations. In addition, the plurality of works may be performed during the same period or during different periods.

In this embodiment, the multiple tasks are performed, for example, at the same location over different time periods, but they may also be performed at different locations over the same time period, or at different locations over different time periods.

The work management system 100 includes a work data analysis device 1.

The work management system 100 further includes, for example, one or more cameras 2 (in this embodiment, five cameras 21-25), a management device 3, and multiple terminal devices 4 (in this embodiment, five terminal devices 41-45), as shown in FIG. 1.

The work data analysis device 1 is communicatively connected to one or more cameras 2, a management device 3, and each of a plurality of terminal devices 4 via a network 110. The network 110 is, for example, a LAN or the Internet. However, the communication may be, for example, a connection via a communication cable or a short-range wireless connection such as Bluetooth (registered trademark).

The work data analysis device 1 includes a processor and a memory. Programs and various types of information are stored in the memory, and the processor operates based on the programs and various types of information in the memory to realize the functions of the work data analysis device 1 (added-value improvement functions). The programs and various types of information will be described later. In addition, the processor and memory that realize such added-value improvement functions, or a device that includes them, may be referred to as a "computer."

The work data analysis device 1 also typically has input devices such as a touch panel or keyboard, output devices such as a display or printer, and a communication module for communicating via the network 110.

Each of the management device 3, the multiple terminal devices 4, and the one or more cameras 2 also has a processor, memory, a communication module, etc. The processor and memory of the work data analysis device 1 may cooperate with the processor and memory of the management device 3, etc., to realize a value-added improvement function.

(1-1-1) Activity Data Analysis Device An activity data analysis device 1 according to an embodiment of the present disclosure includes a receiving unit 11, a processing unit 12, and an output unit 13, as shown in FIG.

The reception unit 11 receives various types of information and various types of instructions. The various types of information are, for example, work data, and the various operations are, for example, category designation operations. The work data and category designation operations will be described later.

The reception by the reception unit 11 is usually the reception of information via a communication module and the reception of operations via an input device. However, reception may also include, for example, the reception of information read from a recording medium, and the reception (reception) of information transmitted from other elements constituting the work data analysis device 1.

The processing unit 12 executes various types of processing. For example, various types of processing are processing by the identification unit 121, which will be described later. The processing unit 12 also performs various types of judgments, which will be described in the flowcharts.

The output unit 13 displays various information. Examples of the various information include comparison images and section capture images. The comparison images and the like will be described later.

The output from the output unit 13 is usually a display on a display. However, the output may also include, for example, transmission via a communication module, printing out by a printer, recording on a recording medium, transmission of information to other elements, etc.

(1-1-2) Camera The camera 2 is installed near a location where multiple tasks are performed, captures images of the space in which the task subject (usually a person, but may be a machine) and the task target (e.g., a product, a container in which the product is stored, or other object) are present, and obtains the captured images. Note that the captured images are usually moving images, but may also be still images obtained intermittently.

In this embodiment, for example, as shown in Figures 2A-2C, line work (described later) is performed using a line 400 (conveyor), so multiple cameras 2 (here, five cameras 21-25) are installed along the line 400.

The five cameras 21-25 are each directed toward five locations 401-405 defined along the line 400, capture images of the line work being performed at the five locations 401-405, and obtain five captured images corresponding to the five locations 401-405.

However, the multiple tasks are not limited to line tasks (see Application Example 3), but may be sequential tasks performed sequentially at one location (see Application Example 1), or distributed tasks performed at multiple locations distributed two-dimensionally (see Application Example 2).

A plurality of tasks (e.g., line work or distributed work) performed at different locations may be captured by one camera 2. In this case, a plurality of captured images (five partial captured images) corresponding to a plurality of locations (five locations 401-405) and a plurality (five) of location identification information corresponding to the plurality of captured images are obtained from a single captured image by one camera 2, for example, by image processing in the management device 3. Note that a plurality of tasks (e.g., sequential tasks) performed at a single location are usually captured by one camera 2, but may be captured from different angles by multiple cameras 2.

Each of the one or more cameras 2 transmits the captured images to at least one of the work data analysis device 1 and the management device 3.

In this embodiment, camera 2 holds location-specific information. Location-specific information is information that identifies the location where work is being performed. Note that, as in the example of FIG. 2, when multiple locations (four locations 401-405) and multiple cameras 2 (five cameras 21-25) correspond one-to-one, the location-specific information is also information that identifies camera 2 itself (camera-specific information). Location-specific information is, for example, the location name, ID, etc.

Each of the one or more cameras 2 transmits the captured image in association with the location-specific information it holds. However, if there is only one camera 2, the location-specific information does not need to be transmitted.

(1-1-3) Management Device The management device 3 stores management data. The management data is data for managing a plurality of tasks. The management data includes the captured images described above.

The management device 3 receives the captured images and location-specific information transmitted from the camera 2, and stores the received captured images in association with the received location-specific information and time-specific information.

Time-specific information is information that specifies a time (e.g., date, time zone, etc.). The time-specific information is obtained, for example, based on the current time information from the built-in clock of the processor of the management device 3 or an NTP (Network Time Protocol) server. Details of the time-specific information will be described later.

The management device 3, for example, receives captured images and location-specific information from each of the multiple cameras 2 (here, five cameras 21-25), and stores in memory pairs (hereinafter, group information) of the received captured images and location-specific information and time-specific information obtained based on current time information from a built-in clock or the like. As a result, the memory of the management device 3 stores the same number of group information as the number of cameras 2 (here, five).

In this embodiment, the management data further includes instruction data and operation data. Instruction data is data related to instructions (instructions to start work) given to the person performing the work (the worker). Operation data is data related to operations (completion operations) performed by the worker. Details of the instruction data and operation data will be described later.

The management device 3 further stores instruction data and operation data. In this embodiment, the management device 3 stores instruction data and operation data in association with a pair of location-specific information and time information.

The location information associated with the instruction data is information that identifies the location where the instruction was made (one of the five locations 401-405), and the time information associated with the instruction data is information that indicates the time when the instruction was made. Similarly, the location information associated with the operation data is information that identifies the location where the operation was accepted (one of the five locations 401-405), and the time information associated with the operation data is information that indicates the time when the operation was accepted. Note that the time information is obtained based on the current time information at the time when the management device 3 issued an instruction or accepted an operation via the terminal device 4.

(1-1-4) Terminal Device The terminal device 4 is installed at a location where a plurality of tasks are performed, outputs instructions (task start instructions) to the person performing the task (worker), and accepts operations (completion operations) by the worker.

In this embodiment, as shown in FIG. 2, five terminal devices 41-45 are installed at five locations 401-405 where line work is performed.

The terminal device 4 (41-45) has lamps (41A-45A) and buttons (41B-45B). Instructions to the worker are given via the lamps (41A-45A), and operations by the worker are accepted via the buttons (41B-45B).

The instruction to start work is to turn on all five lamps 41A-45A at once, and the completion operation is to press each of the five buttons 41B-45B individually. Each of the five terminal devices 41-45 turns on a lamp in response to a work start instruction from the management device 3, and notifies the management device 3 of the acceptance of the completion operation in response to pressing a button.

As described above, the work data analysis device 1 shows the user a comparison image that allows comparison of the difference in the time required between categories (for example, the proportion of the time required by each category in the total time required: occupancy rate), prompts the user to specify a category, and shows the section image of the section corresponding to the specified category. Based on the section image, the user can identify which task in which category is an impediment to adding value improvement and take measures. In this way, the work management system 100 can support the improvement of added value in multiple tasks.

The work data analysis device 1 can also identify the period and category for multiple tasks and obtain work-specific information.

Furthermore, by using captured images, the work data analysis device 1 can improve the accuracy of identifying periods and categories.

(2) Details More specifically, as shown in FIG. 1 , the output unit 13 includes a first output unit 131 and a second output unit 132 .

The processing unit 12 typically includes a determination unit 121, as shown in FIG. 1. However, the determination unit 121 will be described later.

Preferably, as shown in FIG. 1, the processing unit 12 further includes a performance value acquisition unit 122 and an optimum value acquisition unit 123, and the output unit 13 further includes a third output unit 133. However, the performance value acquisition unit 122 and the like will be described later.

The first output unit 131 outputs a comparison image for each of the performed tasks based on multiple pieces of task-specific information.

The first output unit 131 typically displays the comparison image on a display of the work data analysis device 1, but may also transmit it to another device (such as the terminal device 4).

(2-1) Plural Operations The plural operations include, for example, picking. Picking here refers to the operation of removing products from a storage container and storing them in a delivery container.

The products in this embodiment are liquor bottles and paper cartons. The storage container is a container for storing the products, and in this embodiment, is a cardboard box (hereinafter, simply referred to as a "box"). The delivery container is a container for delivering the products to retail stores, and in this embodiment, is a foldable container (hereinafter, simply referred to as a "container").

The multiple tasks also include incidental tasks. An incidental task is a task that is incidental to a main task. In this embodiment, picking is the main task, and the incidental tasks are various tasks that are incidental to picking (e.g., folding boxes, organizing products in containers, etc.).

(2-1-1) Plurality of Work Specific Information Work specific information is information that identifies each of a plurality of works. Each of the plurality of work specific information (hereinafter simply referred to as "work specific information") includes period specific information and category specific information.

The period specification information is the result of specifying the period during which the work was performed. The period specification information is, for example, information indicating the start time and end time, but it may also be information indicating the start time and required time.

Category identification information is the result of identifying the category to which a task belongs among multiple categories (hereinafter simply referred to as "multiple categories") that are predefined according to the degree of added value of the work content of multiple tasks.

The category information is, for example, a category name (such as "value-added work", "non-value-added work", etc.), but it may also be an ID associated with the category name (such as "1" for "value-added work" and "2" for "non-value-added work"). Alternatively, the category information may be expressed as a color (such as blue, orange, gray, etc.) or pattern (such as dark shading, light shading, etc.) associated with the category name.

In this embodiment, the work-specific information further includes the location-specific information described above (e.g., location name, ID, etc.).

In this embodiment, the multiple pieces of task specific information are acquired by an identification unit 121 (described later) that constitutes the task data analysis device 1. However, the multiple pieces of task specific information may also be acquired by another device (e.g., the management device 3 or an external device) and transmitted to the task data analysis device 1.

(2-1-2) Multiple Categories The multiple categories include, for example, value-added work and non-value-added work. Value-added work is work that creates added value. Value-added work is usually a main work, and in this embodiment, it is picking.

Non-value added work is work that does not create added value. Non-value added work is usually work other than the main work.

In this way, the task data analysis device 1 can divide multiple tasks into value-added tasks and non-value-added tasks, and allow the user to recognize the difference in the time required between them (for example, the proportion of the time required for value-added tasks in the total time required for multiple tasks). By taking measures to reduce the proportion of non-value-added tasks, the user can improve added value.

The multiple categories further include, for example, ancillary work and non-ancillary work. Ancillary work and non-ancillary work belong to non-value-added work. In other words, value-added work and non-value-added work are higher-level categories, and ancillary work and non-ancillary work are lower-level categories of non-ancillary work.

As mentioned above, accessory work is work that is incidental to the main work. Accessory work does not usually create added value, but is necessary for the performance of the main work. Examples of accessory work include folding boxes and organizing products in a container.

In this way, the task data analysis device 1 can separate non-value-added tasks into incidental tasks that are incidental to value-added tasks and non-incidental tasks that are tasks other than incidental tasks, and allow the user to recognize the difference in the required time between them. By taking measures to reduce the proportion of non-incidental tasks, the user can improve added value.

The multiple categories further include, for example, non-routine work and non-work. Non-routine work and non-work belong to non-ancillary work. In other words, non-routine work and non-work are subcategories of non-value-added work.

Non-routine work is work that occurs non-regularly. Non-routine work is usually work that occurs suddenly. Non-routine work is, for example, non-ancillary work other than waiting (no work). In other words, non-routine work may be work that is not classified as value-added work, ancillary work, or no work. In this case, value-added work and ancillary work belong to routine work.

Non-task is an action that does not fall under work. Non-task is usually waiting. In this embodiment, waiting is a state in which the main task has been completed, and any ancillary or non-routine tasks have also been completed, and the person is waiting for the start of the next main task.

In this way, the work data analysis device 1 can separate non-ancillary work into non-routine work and non-task, and allow the user to recognize the difference in the time required between them. By taking measures to further reduce the proportion of non-task, the user can increase added value.

Thus, in this embodiment, the multiple categories include the higher-level categories of incidental work and non-incidental work, the lower-level categories (middle-level categories) of non-incidental work, incidental work and non-incidental work, and the lower-level categories of non-incidental work, non-routine work and non-work, and each of the multiple tasks is classified into one of these multiple categories.

(2-1-3) Division of Working Time Corresponding to Multiple Categories When such multiple categories are used for line work (see Application Example 3), the working time T0 corresponding to one cycle (the time from the work start instruction to the start of conveyor movement) is divided as shown in FIG. 3A.

In other words, the work time T0 is divided into value-added work (main work) time T1 and non-value-added work time T2. Value-added work (main work) time T1 is the time required for value-added work (main work), and non-value-added work time T2 is the time required for value-added work.

The non-value-added work time T2 is divided into ancillary work time T21 and non-ancillary work time T22. The ancillary work time T21 is the time required for ancillary work, and the non-ancillary work time T22 is the time required for non-ancillary work.

Furthermore, non-ancillary task time T22 is divided into non-routine task time T221 and non-task (waiting) time T222. Non-routine task time T221 is the time required for non-routine tasks, and non-task (waiting) time T222 is the time when no work is being done, i.e., waiting.

Alternatively, as shown in FIG. 3B, the operation time T0 may be divided into a main operation time T1, a longest main operation waiting time T2x, and a waiting time after all main operations are completed T2y. The longest main operation waiting time T2x is the time from the completion of one's own main operation to the completion of the longest main operation among the main operations other than one's own. The waiting time after all main operations are completed T2y is the time from the completion of all main operations to the start of conveyor movement.

(2-1-4) Modified examples of multiple categories The multiple categories include five categories in total: routine work, non-routine work, and waiting, which are higher-level categories, and main work and ancillary work, which are lower-level categories of routine work. Each of the multiple tasks may be classified into one of these five categories.

Alternatively, the multiple categories may include four categories in total: main work and ancillary work, which are higher-level categories, and travel and other, which are lower-level categories of ancillary work, and each of the multiple tasks may be classified into one of these four categories.

Alternatively, the multiple categories may include three: ancillary work, non-ancillary work, and waiting, and each of the multiple tasks may be classified into one of these three categories.

(2-1-5) Comparison Image A comparison image is an image that shows the time required for a task in a manner that allows comparison between multiple categories.

The required time is the difference between the start time and end time of an operation. The required time to be compared is, for example, the average time obtained by averaging the times corresponding to multiple operations belonging to the same category, but it may also be the total time obtained by adding up the multiple times.

Showing in a comparative manner usually means displaying multiple required times corresponding to multiple categories side by side on one screen (simultaneous display). However, showing in a comparative manner may also mean, for example, displaying multiple required times corresponding to multiple categories sequentially.

The comparison image in this embodiment is a graph (bar graph, pie chart, radar chart, etc.).

The comparison image is, for example, a single screen in which multiple bar graphs corresponding to multiple categories are arranged in association with multiple category identification information, and the length of each of the multiple bar graphs corresponds to the required time. In this embodiment, the multiple bar graphs are arranged in a straight line (in other words, a single bar graph is divided into multiple rectangular sections), but they may also be arranged parallel to each other.

Alternatively, the comparison image may be, for example, a pie chart divided into multiple sectors corresponding to multiple categories, with the central angle of each sector being a size corresponding to the required time.

In addition, the comparison image described above may be output in association with each of the multiple location-specific information.

Alternatively, the comparison image as described above may be output in association with each of a plurality of pieces of time-specific information. Time-specific information is information that specifies a time, as described above. A time is a period of time that is longer than a cycle. A time is, for example, a day, a week, a month, or a year. Alternatively, a time may be a period of time that is shorter than a day, such as morning/afternoon/night, or between 10:00 and 11:00. Time-specific information is associated with the captured image.

(2-1-6) Category Designation Operation The reception unit 11 receives a category designation operation. Note that the reception unit 11 usually receives the category designation operation via an input device such as a keyboard that the activity data analysis device 1 has, but may receive the operation from another device (such as the terminal device 4).

The category designation operation is an operation of designating one of a plurality of categories based on the comparison image output by the first output unit 131. The category designation operation is, for example, an operation of selecting one bar graph out of a plurality of bar graphs corresponding to a plurality of categories (or one rectangular section out of a plurality of rectangular sections constituting one bar graph) in the comparison image in the form of the bar graph described above.

(2-1-7) Section Captured Image The second output unit 132 outputs the section captured image. Note that the second output unit 132 usually displays the section captured image on a display of the activity data analysis device 1, but may transmit the section captured image to another device (such as the terminal device 4).

A section image is an image of a section among one or more captured images of multiple tasks that corresponds to a category specified by a category specification operation.

(2-1-8) Captured Image A captured image is an image captured by the camera 2. In this embodiment, a captured image is acquired by capturing an image of a space in which a work subject and a work object exist, using the camera 2. The work subject is usually a person (a worker, etc.), but may also be a machine (a robot, etc.). The work object is, for example, an object such as a product (a liquor bottle, etc.), a container in which the product is stored (a box, a container, etc.), etc.

The one or more captured images may be captured simultaneously by a single camera 2, or may be captured individually by multiple cameras 2.

In this embodiment, as shown in FIG. 2, five cameras 21-25 are arranged along a line 400, and five captured images are obtained by these five cameras 21-25.

The five captured images from the five cameras 21-25 are stored in the management device 3. The second output unit 132 acquires the five captured images from the management device 3 and outputs the section captured image corresponding to the specified category from among the five acquired captured images.

As described above, the work data analysis device 1 outputs a comparison image that allows a comparison of the required time between categories based on multiple pieces of work specification information that are the results of specifying the period and category for multiple performed tasks. The work data analysis device 1 then accepts a category designation operation that designates one category, and outputs a section image that is an image of a section that corresponds to the designated category from among the captured images of the multiple tasks.

In other words, the task data analysis device 1 shows the user images that enable comparison of differences in task time depending on the category (for example, the proportion of the time required for each category in the total time required for multiple tasks: occupancy rate, etc.), prompts the user to specify a category, and shows the user captured images of the section corresponding to the specified category. This allows the user to identify which task in which category among multiple tasks is an impediment to improving added value, and to take measures.

In this way, the task data analysis device 1 can help improve added value in multiple tasks.

(2-1-9) Acquisition of Task Identification Information The activity data analysis device 1 further includes an identification unit 121. The identification unit 121 identifies periods and categories for multiple tasks based on the activity data, and acquires multiple pieces of task identification information including pairs of period identification information and category identification information.

The identification unit 121 may further identify the location for multiple tasks and obtain multiple task identification information that further includes period identification information.

In this embodiment, task data is stored in the memory of the management device 3, and the identification unit 121 receives the task data from the management device 3, identifies the location, period, and category based on the task data, and acquires multiple pieces of task identification information including a set of location identification information, period identification information, and category identification information.

(2-1-9a) Work Data Work data is information related to multiple works. Work data is usually the image data described above. That is, the work data includes, for example, one or more captured images captured by one or more cameras 2. The work data in this embodiment includes five captured images captured by five cameras 21 to 25.

Note that the work data also includes management data, which will be described later.

(2-1-9b) Procedure for Acquiring Task Identifying Information from Task Data The identifying unit 121 acquires multiple pieces of task identifying information, for example, using an algorithm that receives task data as input and outputs multiple pieces of task identifying information.

As mentioned above, the work data includes one or more captured images. It is preferable that the work data further includes management data (described below).

The algorithm is, for example, an algorithm that uses a function, a model, etc. In detail, a plurality of pieces of feature data (model: a set of pairs of category-specific information and feature data) corresponding to a plurality of categories (value-added work, non-value-added work, etc.) are stored in the memory of the task data analysis device 1, and the identification unit 121 performs pattern matching with the plurality of pieces of feature data for one or more captured images to perform section division and category classification, and obtain a plurality of pieces of task specific information.

When dividing sections and classifying into categories, the accuracy of identification can be improved by referring to the management data. For example, if a worker's movement is detected from the captured image after it is determined from the management data that a task has been completed, the identification unit 121 may determine that the movement is not a main task (it is a non-value-added movement).

The algorithm may also be, for example, an algorithm that uses machine learning. Such an algorithm includes, for example, a task identification algorithm. The task identification algorithm is an algorithm that acquires multiple pieces of task identification information using a learning model that receives task data as input and outputs multiple pieces of task identification information.

In this example, the learning model is generated by the work data analysis device 1, but it may also be generated by an external device, transmitted to the work data analysis device 1, and stored in the memory of the work data analysis device 1.

The identification unit 121 executes a task identification algorithm using a learning model and obtains multiple pieces of task identification information.

The above algorithms further include a learning algorithm. The learning algorithm is a program that generates the above model by machine learning similar to the machine learning (deep learning) used by the task identification algorithm.

The identification unit 121 generates a learning model by executing a learning algorithm using, for example, a learning dataset that includes task data and multiple pieces of task identification information.

The multiple pieces of task-specific information included in the training dataset are pieces of information that have been acquired manually or mechanically. Mechanically acquired information may be, for example, information acquired by the task-specific algorithm described above. Artificially acquired information may be, for example, information acquired by the task-specific algorithm that has been manually corrected (i.e., training data).

(2-1-9c) Associating Category-Specific Information with a Captured Image The second output unit 132 divides each of the one or more captured images into a plurality of sections using a plurality of pieces of task-specific information, and associates each of the plurality of sections with one of the plurality of pieces of category-specific information.

In this embodiment, the second output unit 132 divides each of the five captured images included in the work data into a plurality of sections using the plurality of pieces of work specific information acquired by the identification unit 121 based on the work data, and associates the plurality of sections with a plurality of pieces of category specific information extracted from the plurality of pieces of work specific information.

In this way, by dividing the captured image into multiple sections using multiple pieces of task-specific information and associating category-specific information with each of the multiple sections, the task data analysis device 1 can easily output the captured image for a section that corresponds to a specified category.

(2-2) Management Data As described above, the task data further includes management data. The management data includes one or more types of data that can contribute to identifying at least one of the start time and end time of a period.

In this embodiment, the subject of the work is a person (worker), and the management data includes instruction data for the person and operation data by the person. Such management data contributes to identifying both the start time and end time, and can also contribute to identifying the category.

However, the management data may include only one of the instruction data and the operation data. The management data including the instruction data contributes to identifying the start time and may also contribute to identifying the category. The management data including the operation data contributes to identifying the end time and may also contribute to identifying the category.

(2-2-1) Instruction Data The instruction data in this embodiment is data for instructing a person to start work. The instruction to start work is given by, for example, turning on a lamp.

The work that is the subject of instructions is usually the main work (value-added work), and in many cases, non-value-added work such as ancillary work is performed after the main work is completed. However, the order in which the various tasks are performed may be changed as appropriate.

In the case of the line work shown in Figures 2A-2C, the management device 3 instructs the start of picking by simultaneously turning on the lamps 41A-45A corresponding to the terminal devices 41-45 based on instruction data. The instruction data is associated with multiple pieces of time information indicating predetermined lighting times. If the actual lighting is delayed or advanced from the predetermined lighting time, the time information included in the instruction data is updated to the actual lighting time.

In this embodiment, the task is picking, and the instruction data further includes quantity information indicating the quantity of products to be picked. The quantity information is displayed on the display of the terminal device 4 before the lamp is turned on.

(2-2-2) Operation Data In this embodiment, the operation data is data related to a completion operation. The completion operation is an operation for reporting the completion of a task, and is, for example, data related to pressing a button (41B-45B) on the terminal device 4 (41-45). The operation data includes time information indicating the time when the button (41B-45B) was pressed.

In the case of the line work shown in Figures 2A-2C, the management device 3 detects the pressing of buttons 41B-45B corresponding to terminal devices 41-45, respectively, and stores the five detected time information in memory in association with the above instruction data.

The identification unit 121 further uses management data when identifying the period and category.

In this embodiment, the identification unit 121 always uses both the captured image and the management data to perform identification. However, the identification unit 121 usually uses only the captured image, and may also use the management data in addition when identification is difficult using only the captured image.

In this way, the work data analysis device 1 can further improve the accuracy of category identification by using management data when identifying categories.

(2-3) Application Example 1: Improving the Added Value of Sequential Work In this example, the multiple tasks are sequential tasks. That is, the multiple tasks are performed sequentially at one location. One camera 2 is installed near the one location, and images captured by the one camera 2 are stored in the management device 3.

A terminal device 4 may be installed at the location. In this case, the management device 3 further stores instruction data and operation data related to the terminal device 4 in association with time information.

The work data analysis device 1 receives captured images from the management device 3. The reception unit 11 constituting the work data analysis device 1 receives the captured images, and the identification unit 121 uses the received captured images to sequentially identify periods and categories for multiple tasks that make up the work, and obtains multiple pieces of work identification information that include a set of period identification information and category identification information.

The first output unit 131 outputs a comparison image based on the acquired task-specific information for the tasks. The comparison image output here is an image in which a plurality of bar bluffs corresponding to a plurality of categories are arranged on one screen in association with the plurality of category identification information, and the length of each of the bar bluffs corresponds to the required time.

The user performs a category designation operation to designate one of the multiple categories based on the output comparison image. For example, the category (e.g., "value-added work") corresponding to the bar graph with the longest length among the multiple bar graphs is designated.

The reception unit 11 receives a category specification operation.

The second output unit 132, in response to the acquisition of multiple pieces of task specific information by the identification unit 121, associates the captured image section with category specific information based on the acquired multiple pieces of task specific information, and outputs the captured image section corresponding to the category specified by the accepted category specification operation.

In this way, of the multiple tasks that make up the sequential work, the category of tasks that require the longest time is identified, and the section of captured images that corresponds to that category is shown on the display, allowing the user to take measures to shorten the time required for that task and increase the added value of the sequential work.

(2-4) Application Example 2: Improving Added Value in Distributed Work The multiple tasks in this example are distributed tasks. That is, the multiple tasks are performed at multiple locations that are different from one another. The multiple locations (here, five locations 401-405) shown in FIG. 2 are arranged one-dimensionally, but in this example, they are distributed two-dimensionally. Then, five cameras 21-25 are installed facing the five locations 401-405.

Images captured by each of the five cameras 21-25 are associated with location identification information "401"-"405". For convenience, reference symbols are used here as location identification information, but location identification information is, for example, the MAC address, location name, or ID assigned to camera 2.

The reception unit 11 receives a location/category designation operation. A location/category designation operation is an operation for designating a combination of one of a plurality of categories (such as value-added work and non-value-added work) and one of a plurality of locations (for example, five locations 401-405: see FIG. 2).

The location/category designation operation is, for example, an operation of designating a pair of one category and one location based on a location comparison screen (described later) output by the first output unit 131. The category designation operation is, for example, an operation of selecting one rectangular section (for example, a rectangular section with dark hatching) in one comparison image (for example, a bar graph corresponding to location 405: see FIG. 8) out of multiple comparison images (for example, five bar graphs corresponding to five locations 401-405: see FIG. 8) corresponding to multiple pieces of location-specific information included in the location comparison screen.

The second output unit 132 outputs a location section captured image. A location section captured image is an image of a section corresponding to a specified category of a captured image corresponding to a specified location, among a plurality of captured images (e.g., five captured images) corresponding to a plurality of cameras (e.g., five cameras 21-25).

In this way, the captured images are associated with location identification information, and the task data analysis device 1 accepts a location/category designation operation that designates a pair of one category and one location, and outputs a section captured image that corresponds to the designated pair of location and category from among the captured images. This makes it easy to output a location section captured image that corresponds to the designated pair of location and category. By designating a pair of location and category and viewing the corresponding location section image, the user can identify which task in which category at which location is an obstruction and take measures.

(2-5) Application Example 3: Improving Added Value in Line Work The multiple tasks in this example are multiple tasks that are performed simultaneously at multiple locations in each of multiple cycles that make up the line work.

Line work refers to a number of tasks that are performed simultaneously and periodically at multiple locations arranged one-dimensionally, for multiple work objects that are moved along the multiple locations. For example, as shown in FIG. 2, line work refers to a number of tasks that are performed simultaneously and periodically at multiple locations (here, five locations 401-405) arranged along one line 400, for multiple work objects (here, 12 containers 301a-301l) that are moved along one line 400.

The line 400 is usually a conveyor. A cycle in line work is, for example, the period from when multiple tasks are started simultaneously (a work start instruction is issued), through the completion of the last task, the movement of the multiple work objects to the next location (conveyor drive), and until the multiple tasks are started simultaneously again.

In this embodiment, a cycle is the period from when a work start command is issued (when the lamp turns on) to when multiple work objects start to move (when the conveyor starts to drive). The length of the cycle is usually not constant (it depends on the slowest work).

The work in this embodiment is picking, as described above. The work object in this case is, for example, a delivery container. However, the work object may also be a product to be picked from a storage container to a delivery container. The storage container in which the product was stored may also be included in the work object.

The multiple work objects are, for example, 12 containers 301a-301l to be delivered to 12 retail stores (store a-store l).

In this embodiment, the movement of the multiple work objects is intermittent (repeated movement and stopping). In other words, the conveyor is driven intermittently, and the multiple work objects repeatedly move and stop.

When multiple tasks are performed "simultaneously," it usually means that they start at the same time but do not end at the same time, but they can also end at the same time. In other words, among the multiple cycles that make up line work, in many cycles, multiple tasks start at the same time and end at different times, but there can be cycles that end at the same time and cycles that start differently.

When multiple tasks are performed "cyclically," it usually means that the length of the cycle is not constant, but expands or contracts depending on the progress of the slowest task among the multiple tasks. However, the length of the cycle can also be constant.

In this way, the work data analysis device 1 shows the user a comparison image that allows them to compare the difference in the time required between categories for each of multiple locations arranged along a line (the proportion of the time required for each category in the total work time at each location), prompts the user to specify a pair of locations and categories, and shows the location section image that corresponds to the specified pair. This allows the user to identify which task in which location and category is an impediment to value improvement and take measures.

In this way, the work data analysis device 1 can help improve added value in line work.

(2-5-1) Time Comparison Screen The first output unit 131 outputs a time comparison screen. The time comparison screen is a screen including a plurality of comparison images corresponding to a plurality of times. As described above, a time is a time longer than a cycle, and in this example, it is a day.

The time comparison screen includes, for example, seven bar graphs corresponding to the seven days from June 8 to June 15, 2021, as shown in FIG. 6.

In this time comparison screen, the horizontal axis is defined at the bottom, the first vertical axis is defined on the left, and the second vertical axis is defined on the right. The horizontal axis represents time-specific information "2021/06/08"-"2021/06/15", the first vertical axis represents work time "0"-"80" (seconds), and the second vertical axis represents the total number of tasks "0"-"4000" (items).

The rectangular sections that make up each of the seven bar graphs are hatched (colored) in a shade that corresponds to the category. As with the cycle comparison screen described above, there are three categories here: main task time "T1", longest main task wait time "T2x", and wait time after completion of all main tasks "T2y". The rectangular section that corresponds to "T1" is darkly hatched (colored blue), the rectangular section that corresponds to "T2x" is slightly darkly hatched (colored orange), and the rectangular section that corresponds to "T2y" is lightly hatched (colored gray).

Note that in the time comparison screen of FIG. 6, no numerical values indicating the working time are written in each of the multiple rectangular sections, but they may be written.

In addition, in each comparison screen of Figures 6 to 8, the numbers on the vertical axis (first vertical axis) indicate the work time itself, but may also indicate the proportion (occupancy rate, etc.) of work time in one bar graph (one period, one cycle, one location).

(2-5-2) Time Designation Operation The reception unit 11 receives a time designation operation. The time designation operation is an operation of designating one of a plurality of time periods. The time designation operation in this example is an operation of designating one section from one comparison image from a plurality of comparison images corresponding to a plurality of time periods on the time comparison screen (see FIG. 6).

In this example, the reception unit 11 receives a time designation operation after the first output unit 131 outputs the time comparison screen. For example, on the time comparison screen in FIG. 6, a time designation operation is performed to designate one of the seven times (here, 2021/06/14), and the reception unit 11 receives the time designation operation.

(2-5-3) Cycle Comparison Screen The first output unit 131 outputs a cycle comparison screen. The cycle comparison screen is a screen including a plurality of comparison images corresponding to a plurality of cycles. For example, as shown in FIG. 7, the cycle comparison screen includes 30 bar graphs corresponding to 30 cycles #1 to #30.

In this cycle comparison screen, the horizontal axis is defined at the bottom and the vertical axis is defined at the left, with cycle specific information "#1"-"#30" arranged on the horizontal axis and task time per cycle "0"-"80" (seconds/cycle) arranged on the vertical axis. In addition, the rectangular sections constituting each of the 30 bar graphs are hatched (colored) with a thickness corresponding to the category. There are two categories here: "T1+T2x", the sum of the main task time "T1" and the longest main task wait time "T2x", and "T2y", the wait time after all main tasks are completed. The rectangular section corresponding to "T1+T2x" is hatched with a thick color (colored blue), and the rectangular section corresponding to "T2y" is hatched with a thin color (colored gray).

In the cycle comparison screen in Figure 7, the standard time (31 seconds) is shown by a dotted line. By displaying the dotted line indicating the standard time together with a bar graph showing the cycle length (time), the user can recognize the size of the gap between the cycle length and the standard time (and thus whether there is room for added value improvement, whether there is urgency, etc.).

The cycle comparison screen in this example is a screen that includes multiple comparison images corresponding to multiple cycles that belong to a certain period specified by the time designation operation. The cycle comparison screen in FIG. 7 includes 30 cycles #1 to #30 that belong to a certain period (e.g., 2021/06/14) specified by the time designation operation described above on the period comparison screen in FIG. 6.

In this way, in this example, the first output unit 131 outputs a cycle comparison screen in response to the time designation operation accepted by the acceptance unit 11.

In this way, the work data analysis device 1 outputs a period comparison screen including multiple comparison images corresponding to multiple periods, allows the user to specify a period, and further outputs a cycle comparison screen including multiple comparison images corresponding to multiple cycles belonging to the specified period. In this way, by showing the period comparison screen to the user and then further showing the cycle comparison screen in response to the period specification operation, the user can gradually narrow down the period of the work that is hindering the improvement of added value and take measures.

(2-5-4) Cycle Designation Operation The reception unit 11 receives a cycle designation operation. The cycle designation operation is an operation of designating one of a plurality of cycles. In this example, the cycle designation operation is an operation of designating one section from one comparison image from a plurality of comparison images corresponding to a plurality of cycles on the cycle comparison screen.

In this example, the reception unit 11 receives a cycle designation operation after the first output unit 131 outputs the cycle comparison screen (see FIG. 7). For example, on the cycle comparison screen in FIG. 7, a cycle designation operation is performed to designate one cycle (here, cycle #23) out of 30 cycles, and the reception unit 11 receives the cycle designation operation.

(2-5-5) Location Comparison Screen The first output unit 131 outputs a location comparison screen. The location comparison screen is a screen including a plurality of comparison images corresponding to a plurality of locations. For example, as shown in FIG. 8, the location comparison screen includes five bar graphs corresponding to five locations 401-405.

In this location comparison screen, the horizontal axis is defined at the bottom and the vertical axis is defined on the left, with location-specific information "401"-"405" on the horizontal axis and operation time "0.0"-"60.0" (seconds) on the vertical axis.

The rectangular sections that make up each of the five bar graphs are hatched (colored) in a shade that corresponds to the category. There are three categories here: main task time "T1", longest main task wait time "T2x", and wait time after completion of all main tasks "T2y" (see Figure 3B). The rectangular section that corresponds to "T1" is hatched darkly (colored blue), the rectangular section that corresponds to "T2x" is hatched slightly darkly (colored orange), and the rectangular section that corresponds to "T2y" is hatched lightly (colored gray). A numerical value indicating the task time, such as "58.4", is written in each of the rectangular sections.

The location comparison screen in this example is a screen that includes multiple comparison images corresponding to multiple tasks performed at multiple locations in one cycle specified by the cycle specification operation.

The location comparison screen in FIG. 8 is a screen that includes five comparison images corresponding to the five locations 401-405 taken in one cycle (e.g., cycle #23) specified in the cycle specification operation described above on the cycle comparison screen in FIG. 7.

In this way, in response to the specification of one cycle, the work data analysis device 1 outputs a location comparison screen that includes multiple comparison images corresponding to multiple locations in the specified one cycle.

In this way, by further displaying a location comparison screen for the specified cycle, the user can identify not only the period but also the location of the work that is hindering the improvement of added value, and take measures to address the issue.

In this example, the location section captured image (see FIG. 8) described above is output in response to the location/category designation operation described above after the location comparison screen is output.

This allows users to identify obstacles to improving added value and take measures based on location section images.

(2-6) Evaluation of Added Value Improvement The task data analysis device 1 in this example further includes a performance value acquisition unit 122, an optimal value acquisition unit 123, and a third output unit 133.

The performance value acquisition unit 122 acquires performance values for the above-mentioned added value-related parameters based on the multiple pieces of work identification information acquired by the identification unit 121.

Parameters related to added value include, for example, labor hours, number of people, and required time. The performance value is calculated based on the period information and category information contained in each of the multiple pieces of acquired work-specific information.

The optimal value acquisition unit 123 acquires optimal values for the above parameters based on the multiple pieces of work-specific information acquired by the identification unit 121, the performance values acquired by the performance value acquisition unit 122, and predetermined standard values.

The standard value is, for example, a standard labor hour, a standard number of people, a standard time required, etc. The optimal value is obtained, for example, by using an algorithm (function, model, etc.) that takes multiple pieces of task-specific information, actual values, and standard values as input, and outputs an optimal value.

The algorithm in this example includes an optimization algorithm. The optimization algorithm is, for example, an algorithm that obtains an optimal value using a model that takes multiple pieces of task-specific information, actual results, and standard values as inputs and outputs an optimal value. Note that, while the model is generated by the work data analysis device 1 in this example, it may also be generated by an external device, sent to the work data analysis device 1, and stored in the memory of the work data analysis device 1.

The optimal value acquisition unit 123 executes an optimization algorithm using such a model to obtain optimal parameter values.

The algorithm further includes a learning algorithm. The learning algorithm is a program that generates the above-mentioned model by machine learning such as deep learning.

The optimal value acquisition unit 123 generates a model by executing a machine learning learning algorithm using a learning dataset that includes, for example, multiple pieces of task-specific information, actual values, standard values, and optimal values.

The optimal values included in the training data set are values that are obtained artificially or mechanically. The mechanically obtained optimal values may be values obtained by the optimization algorithm described above, for example. The artificially obtained optimal values may be values (training data) that are obtained by manually correcting values obtained by the optimization algorithm, for example.

The third output unit 133 outputs each of the actual values acquired by the actual value acquisition unit 122 and the optimal values acquired by the optimal value acquisition unit 123, or the changes in each of them, in a comparable manner.

Here, the aspect in which each can be compared may be, for example, a configuration in which two bar graphs corresponding to the actual value and the optimal value are arranged on one screen, with the length of each of the two bar graphs being proportional to the value (not shown).

An example of a mode in which each change can be compared is an image in which two line graphs, each showing a change in the actual value and a change in the optimal value, are arranged on a single screen, as shown in FIG. 10.

In this way, for parameters related to improving the added value of a task, the task data analysis device 1 obtains actual results based on multiple pieces of task specific information, obtains optimal values based on the multiple pieces of task specific information obtained as described above, the actual results obtained here, and a predetermined standard value, and outputs the changes in the actual results and optimal values in a comparable manner.

This allows the user to determine the amount of room for added value improvement based on a comparison of the actual value and the optimal value. For example, if the deviation of the actual value from the optimal value is large or is expanding, the user can have the work data analysis device 1 output various comparison images as described above, as well as section image images or location section image images, identify impeding factors, and take measures.

In addition, in application example 3, after the output of the third output unit 133, the reception unit 11 may receive an operation to output a comparison image (a comparison image output operation), and the first output unit 131 may output the comparison image.

In addition, in application example 3, the work data analysis device 1 may further include a planned value acquisition unit (not shown). The planned value acquisition unit acquires a planned value for a parameter. The planned value is a planned value. The planned value is, for example, a value acquired (artificially or mechanically calculated) based on actual values over a certain period of time in the past (for example, the most recent year), and is stored in advance in the memory of the management device 3.

(2-7) Operation of the activity data analysis device The activity data analysis device 1 executes, for example, processing according to the flowchart in Fig. 5. Note that operations that have already been explained in detail are omitted or simplified as appropriate. The processing in Fig. 5 starts when the power of the activity data analysis device 1 is turned on, and ends when the power is turned off.

The processing unit 12 determines whether the receiving unit 11 has received the work data (step S1). For example, the management device 3 transmits work data to the work data analysis device 1 in response to a request from the work data analysis device 1, and the receiving unit 11 receives the transmitted work data. If it is determined that the receiving unit 11 has received the work data, the process proceeds to step S2. If it is determined that the work data has not been received, the process proceeds to step S3.

The identification unit 121 acquires multiple pieces of work identification information based on the work data received in step S1 (step S2). Then, the process returns to step S1.

The processing unit 12 determines whether the reception unit 11 has received a comparison image output operation (step S3). For example, the user performs a comparison image output operation via an input device of the work data analysis device 1, and the reception unit 11 receives this. If it is determined that the reception unit 11 has received the comparison image output operation, the process proceeds to step S4. If it is determined that the comparison image output operation has not been received, the process returns to step S1.

The first output unit 131 outputs a comparison image based on the multiple pieces of task-specific information received in step S3 (step S4). As a result, for example, a comparison image (a bar graph made up of multiple rectangular sections corresponding to multiple categories) like those shown in Figures 6 to 8 is displayed on the display of the task data analysis device 1.

The processing unit 12 determines whether the receiving unit 11 has received a category designation operation (step S5). For example, the user performs a category designation operation (an operation to select one of the rectangular sections that make up the bar graph) via an input device of the work data analysis device 1, and the receiving unit 11 receives this. If it is determined that the receiving unit 11 has received a category designation operation, the process proceeds to step S6. If it is determined that the category designation operation has not been received, the process returns to step S1.

The second output unit 132 outputs the section image corresponding to the category specified by the category specification operation accepted in step S5 (the category that pairs with the specified rectangular section) (step S6). As a result, for example, the section image shown in FIG. 9 is displayed on the display of the work data analysis device 1. Then, the process returns to step S1.

(2-8) Specific Example This example is a specific example of Application Example 3. The configuration of the work management system 100 in this example is that shown in Fig. 1, and the multiple works are the line works shown in Fig. 2.

For example, in cycle #5, at location 401, picking work is performed to remove product 302 from box 303 and store it in case 301e for store e. Also, at location 402, picking work is performed to remove product 302 from box 303 and store it in case 301d for store d. Furthermore, similar work is performed at other locations 402-405.

In the next cycle #6, at location 401, picking work is performed to remove product 302 from box 303 and store it in case 301f for store f. Also, at location 402, picking work is performed to remove product 302 from box 303 and store it in case 301e for store e. Furthermore, similar work is performed at other locations 402-405.

Furthermore, for example, in cycle #12, at location 401, picking work is performed in which product 302 is removed from box 303 and stored in case 301l for store l. Also, at location 402, picking work is performed in which product 302 is removed from box 303 and stored in case 301k for store k. Furthermore, similar work is performed at other locations 402-405.

In this example, each of the multiple tasks performed at each location (401-404) in each cycle (#5, #6, #12, etc.) is classified into one of three categories: "main task," "longest main task waiting time," and "waiting after all tasks are completed" (corresponding to the main task time T1, the longest main task waiting time T2x, and the waiting time after all tasks are completed T2y, respectively, shown in FIG. 3B).

The work time at each location (401-404) in each cycle (#5, #6, #12, etc.) is divided into main work time T1, longest main work waiting time T2x, and waiting time after all work is completed T2y, as shown in Figure 4.

The multiple pieces of work-specific information correspond to the categories in FIG. 4, and the identification unit 121 acquires multiple pieces of work-specific information corresponding to the categories in FIG. 4 based on five captured images from the five cameras 21-25 and five "sets of instruction data and operation data" from the five terminal devices 41-45.

The first output unit 131 outputs a time comparison screen as shown in FIG. 6 based on the above-mentioned multiple pieces of work specification information acquired by the specification unit 121. The user performs a time specification operation to specify, for example, the time period "2021/06/14" with the longest work time.

The reception unit 11 receives the time designation operation, and the first output unit 131 outputs a cycle comparison screen for the 30 cycles #1-#30 that belong to the designated time period "2021/06/14", as shown in FIG. 7. The user performs a cycle designation operation to designate, for example, cycle "#30", which has the longest working time.

The reception unit 11 receives the cycle designation operation, and the first output unit 131 outputs a location comparison screen for the five locations 401-405 in the designated cycle "#23" as shown in FIG. 8. The user performs a location designation operation to designate, for example, the location 405 with the longest main task time.

The reception unit 11 receives the location specification operation, and the second output unit 132 outputs a location section image corresponding to the pair of cycle "#23" and location "405" as shown in FIG. 8.

By looking at the location section image, the user can see that it is taking time to organize the products in the case.

(2-9) Added-value improvement method and program Note that functions similar to those of the activity data analysis device 1 according to this embodiment may be embodied in an added-value improvement method, a (computer) program, or a non-temporary recording medium on which a program is recorded, etc. Note that the added-value improvement method is a method that includes at least step S4 (first output step), step S5 (reception step), and step S6 (second output step) among the various steps described above. Also, the program is a program for causing one or more processors to execute the above added-value improvement method. The one or more processors may be the processor of the activity data analysis device 1 alone, or may include a processor of the management device 3, or may further include a processor of the terminal device 4 and a processor of the camera 2.

(3) Summary The activity data analysis device (1) according to the first aspect includes a first output unit (131), a receiving unit (11), and a second output unit (132). The first output unit (131) outputs a comparison image based on a plurality of pieces of activity identification information. Each of the plurality of pieces of activity identification information includes period identification information and category identification information. The period identification information is a result of identifying a period during which each of the plurality of activities was performed. The category identification information is a result of identifying a category to which each of the plurality of activities belongs, among a plurality of categories that are predetermined according to the degree of added value of the activity content. The comparison image is an image that shows the required time for each of the plurality of activities in a manner that allows comparison between the plurality of categories. The receiving unit (11) receives a category designation operation. The category designation operation is an operation of designating one of the plurality of categories based on the comparison image. The second output unit (132) outputs a section image. The section image is an image of a section corresponding to a category designated by the category designation operation, among one or more captured images of the plurality of activities.

According to this aspect, the user is shown images that allow comparison of differences in the time required for tasks based on category (e.g., the proportion of the time required for each category in the total time required for multiple tasks: occupancy rate, etc.), is asked to specify one category, and is shown a captured image of the section corresponding to the specified category. This allows the user to identify which task in which category among multiple tasks is an impediment to adding value, and to take measures. In this way, the work management system (100) can support adding value to multiple tasks.

The activity data analysis device (1) according to the second aspect is the first aspect, and further includes an identification unit (121). The identification unit (121) identifies a period and a category for each of the plurality of activities based on the activity data, and acquires the plurality of activity identification information. The activity data is information relating to the plurality of activities.

According to this aspect, it is possible to identify the period and category for multiple tasks and obtain task specific information.

In the task data analysis device (1) according to the third aspect, in the second aspect, the task data includes the one or more captured images.

According to this aspect, by using captured images, it is possible to improve the accuracy of identifying periods and categories.

In the task data analysis device (1) according to the fourth aspect, in the third aspect, the second output unit (132) divides each of the one or more captured images into a plurality of sections using the plurality of task-specific information, and associates each of the plurality of sections with one of the plurality of category-specific information.

This aspect makes it easy to output section capture images that correspond to a specified category.

In the task data analysis device (1) according to the fifth aspect, in the third or fourth aspect, the task data further includes management data. The management data is data for managing the plurality of tasks, and can contribute to identifying at least one of the start time and end time of a period. The identification unit (121) further uses the management data when identifying the period and category.

According to this aspect, by further using management data, it is possible to further improve the accuracy of identification.

In the task data analysis device (1) according to the sixth aspect, in any one of the third to fifth aspects, the tasks are performed at a plurality of different locations (401-405). A camera (2) is installed at each of the locations (401-405). Location identification information is associated with images captured by each of the plurality of cameras (21-25) corresponding to the plurality of locations (401-405). The location identification information is information that identifies a location. The reception unit (11) receives a location/category designation operation. The location/category designation operation is an operation for designating a combination of one category among the plurality of categories and one location among the plurality of locations. The second output unit (132) outputs a location section image. The location section image is an image of a section corresponding to a designated category of an image corresponding to a designated location among a plurality of images corresponding to the plurality of cameras (21-25).

According to this aspect, it is possible to easily output a location section image corresponding to a specified combination of location and category. By specifying a combination of location and category and viewing the corresponding location section image, the user can identify which task in which category at which location is causing an obstruction and take measures.

In the task data analysis device (1) according to the seventh aspect, in any of the first to sixth aspects, the multiple categories include value-added tasks and non-value-added tasks. Value-added tasks are tasks that produce added value, and non-value-added tasks are tasks that do not produce added value.

According to this aspect, multiple tasks are divided into value-added tasks and non-value-added tasks, and the user can be made aware of the difference in the time required between them (for example, the proportion of the time required for value-added tasks in the total time required for multiple tasks). By taking measures to reduce the proportion of non-value-added tasks, the user can improve added value.

In the task data analysis device (1) according to the eighth aspect, in the seventh aspect, the multiple categories further include ancillary tasks and non-ancillary tasks. The ancillary tasks and non-ancillary tasks belong to the non-value-added tasks.

According to this aspect, non-value-added work can be divided into incidental work that is incidental to the value-added work and non-incidental work that is work other than the incidental work, and the user can be made aware of the difference in the required time between them. By taking measures to reduce the proportion of non-incidental work, the user can improve the added value.

In the task data analysis device (1) according to the ninth aspect, in the eighth aspect, the multiple categories further include non-routine tasks and non-tasks. The non-routine tasks and non-tasks belong to the non-accessory tasks.

According to this aspect, non-ancillary work can be divided into non-routine work and non-task, and the user can be made aware of the difference in the time required between them. By taking measures to further reduce the proportion of non-task, the user can increase added value.

In the task data analysis device (1) according to the tenth aspect, in the sixth aspect, the multiple tasks are multiple tasks that are performed simultaneously at multiple locations (401-405) in each of multiple cycles that make up the line task in a line task. In the line task, multiple tasks are performed simultaneously and periodically at multiple locations (401-405) that are arranged one-dimensionally, for multiple work objects (301a-301l) that are moved along the multiple locations (401-405).

According to this aspect, the user is shown a comparison image that allows them to compare the difference in the time required between categories (the proportion of the time required for each category in the total work time at each location) for each of multiple locations arranged in a one-dimensional manner, and is prompted to specify a pair of locations and categories. By showing the user a location section image that corresponds to the specified pair, the user can identify which task in which category at which location is an impediment to value improvement and take measures. In this way, it is possible to support the improvement of added value in line work.

In the task data analysis device (1) according to the eleventh aspect, in the tenth aspect, the first output unit (131) outputs a location comparison screen. The location comparison screen is a screen including a plurality of comparison images corresponding to the plurality of locations.

According to this embodiment, by showing the location comparison screen to the user, the user can identify which location on the line is causing an impediment to added value improvement and take measures.

In the work data analysis device (1) according to the twelfth aspect, in the eleventh aspect, the first output unit (131) outputs a cycle comparison screen. The cycle comparison screen is a screen including a plurality of comparison images corresponding to the plurality of cycles.

According to this embodiment, by showing the cycle comparison screen to the user, the user can identify which period of line work is hindering the improvement of added value and take measures.

In the work data analysis device (1) according to the thirteenth aspect, in the twelfth aspect, the first output unit (131) outputs a period comparison screen. The period comparison screen includes a plurality of comparison images corresponding to the plurality of periods. The reception unit (11) receives a period designation operation. The period designation operation is an operation for designating one of the plurality of periods. The first output unit (131) outputs a cycle comparison screen. The cycle comparison screen includes a plurality of comparison images corresponding to a plurality of cycles belonging to the one designated period.

According to this aspect, by showing the user a time comparison screen and then showing the cycle comparison screen in response to a time specification operation, the user can gradually narrow down the duration of the work that is hindering the improvement of added value and take measures.

In the task data analysis device (1) according to the fourteenth aspect, in the thirteenth aspect, the reception unit (11) receives a cycle designation operation. The cycle designation operation is an operation for designating one of the multiple cycles. The first output unit (131) outputs a location comparison screen. The location comparison screen includes multiple comparison images corresponding to the multiple locations in the one designated cycle.

According to this aspect, by displaying a location comparison screen for the specified cycle in response to the cycle specification operation, the user can identify the location as well as the period of the work that is hindering the improvement of added value, and take measures.

The task data analysis device (1) according to the fifteenth aspect, in any one of the first to fourteenth aspects, further comprises an actual value acquisition unit (122), an optimal value acquisition unit (123), and a third output unit (133). The actual value acquisition unit (122) acquires actual values for the parameters related to added value based on the plurality of task specific information. The optimal value acquisition unit (123) acquires optimal values for the parameters based on the plurality of task specific information, the actual values, and a predetermined standard value. The third output unit (133) outputs each of the actual values and the optimal values, or each change, in a comparable manner.

According to this aspect, the user can determine the amount of room for added value improvement by comparing the actual value with the optimal value. For example, if the deviation of the actual value from the optimal value is large or is expanding, the user can have the work management system output a comparison image, identify the inhibiting factors, and take measures.

The work management system (100) according to the sixteenth aspect is a work management system (100) that manages a plurality of tasks, and includes a work data analysis device (1) according to any one of the first to fifteenth aspects.

The added value improvement method according to the seventeenth aspect includes a first output step (S4), a receiving step (S5), and a second output step (S6). In the first output step (S4), a comparison image is output based on a plurality of pieces of work-specific information. Each of the plurality of pieces of work-specific information includes period-specific information and category-specific information. The period-specific information is a result of identifying a period during which each of the plurality of works was performed. The category-specific information is a result of identifying a category to which each of the plurality of works belongs, among a plurality of categories that are predetermined according to the degree of added value of the work content. The comparison image is an image that shows the required time of each of the plurality of works so that it can be compared between the plurality of categories. In the receiving step (S5), a category designation operation is received. The category designation operation is an operation of designating one of the plurality of categories based on the comparison image. In the second output step (S6), a section image is output. The section image is an image of a section corresponding to the category designated by the category designation operation, among one or more captured images of the plurality of works.

The program according to the eighteenth aspect is a program for causing one or more processors to execute the added value improvement method according to the seventeenth aspect.

REFERENCE SIGNS LIST 100 Work management system 1 Work data analysis device 11 Reception unit 121 Identification unit 122 Performance value acquisition unit 123 Optimum value acquisition unit 131 First output unit 132 Second output unit 133 Third output unit 2, 21-25 Camera 3 Management device 4, 41-45 Terminal device 41A-45A Lamp 41B-45B Button 110 Network 301a-301l Container 302 Product 303 Box

Claims (16)

a processing unit that, for each task constituting a series of tasks, specifies a required time for the task based on both image data capturing an image of the task being performed by the task subject and management data including at least one of instruction data for the task subject to start the task and operation data showing completion of the task by the task subject, from task data recording the status of a series of tasks performed by a plurality of task subjects, and classifies the task into a category according to the level of added value of the task content;
an output unit that outputs information including the required time and the category of each of the tasks in a graph;
a reception unit for receiving a designation of one category from the graph,
The output unit outputs imaging data obtained by imaging a status of a task corresponding to the specified category from among the imaging data;
The instruction data includes image data for instructing the task subject to start the task.
Work data analysis equipment. a processing unit that, for each task constituting a series of tasks, specifies a required time for the task based on both image data capturing an image of the task being performed by the task subject and management data including at least one of instruction data for the task subject to start the task and operation data showing completion of the task by the task subject, from task data recording the status of a series of tasks performed by a plurality of task subjects, and classifies the task into a category according to the level of added value of the task content;
an output unit that outputs information including the required time and the category of each of the tasks in a graph;
a reception unit for receiving a designation of one category from the graph,
The output unit outputs imaging data capturing an image of a work situation corresponding to the specified category from among the imaging data;
The operation data includes time information when the task subject inputs an operation indicating completion of the task into the terminal device.
Work data analysis equipment. When a movement of the work subject is detected in the imaging data after a time determined to be a work completion time in the operation data, the processing unit determines that the movement is a non-value-added movement and classifies the movement into a non-value-added work category.
The task data analysis device according to claim 2 . Each of the operations constituting the series of operations is an operation that is performed within one cycle time among a plurality of operations that are simultaneously and periodically performed on the same line.
The task data analysis device according to claim 1 or 2. a processing unit that, for each task constituting a series of tasks, specifies a required time for the task based on both image data capturing an image of the task being performed by the task subject and management data including at least one of instruction data for the task subject to start the task and operation data showing completion of the task by the task subject, from task data recording the status of a series of tasks performed by a plurality of task subjects, and classifies the task into a category according to the level of added value of the task content;
an output unit that outputs information including the required time and the category of each of the tasks in a graph;
a reception unit for receiving a designation of one category from the graph,
The output unit outputs imaging data obtained by imaging a status of a task corresponding to the specified category from among the imaging data;
The imaging data includes captured images obtained from a plurality of cameras installed in a plurality of work locations, and information on the plurality of work locations where the captured images were captured;
When the reception unit receives the designation of the combination of the one category and the work place, the output unit outputs, from the imaging data, imaging data capturing an image of a work situation corresponding to the designated work place and the designated category.
Work data analysis equipment. The categories include value-added tasks, which are primary tasks, and non-value-added tasks;
The task data analysis device according to any one of claims 1 to 5. The categories include routine work, non-routine work, and no work;
The task data analysis device according to any one of claims 1 to 5. The categories include value-added work, which is the main work, and auxiliary work,
The ancillary work is work necessary for the performance of the value-added work,
The task data analysis device according to any one of claims 1 to 5. a processing unit that, for each task constituting a series of tasks, specifies a required time for the task based on both image data capturing an image of the task being performed by the task subject and management data including at least one of instruction data for the task subject to start the task and operation data showing completion of the task by the task subject, from task data recording the status of a series of tasks performed by a plurality of task subjects, and classifies the task into a category according to the level of added value of the task content;
an output unit that outputs information including the required time and the category of each of the tasks in a graph;
a reception unit for receiving a designation of one category from the graph,
The output unit outputs imaging data obtained by imaging a status of a task corresponding to the specified category from among the imaging data;
Each of the operations constituting the series of operations is performed within one cycle time among a plurality of operations simultaneously and periodically performed on the same line,
The imaging data includes images obtained from a plurality of cameras installed at a plurality of work locations on the same line, and information on the plurality of work locations at which the images were taken,
When the reception unit receives a designation of a combination of the one category and a work place, the output unit outputs imaging data of an image of the work situation corresponding to the designated work place and the designated category from among the imaging data.
Work data analysis equipment. The output unit outputs each cycle time for a plurality of cycles in a comparable manner.
The task data analysis device according to claim 9. The reception unit receives an operation for designating a date or a time period from a predetermined period,
The output unit outputs the time of one cycle on the specified date or time period in a comparable manner.
The task data analysis device according to claim 10. The reception unit receives an operation to designate one of the plurality of cycles,
The output unit outputs images corresponding to the plurality of work locations in the specified cycle.
The task data analysis device according to claim 10. The processing unit calculates a performance value of a required time by category for each of the tasks, and calculates an optimal value based on a preset standard value and the performance value;
The output unit outputs the actual value and the optimal value in a comparable manner.
10. The task data analysis device according to claim 1, 2, 5, or 9. A work management system for managing a plurality of works,
A camera and
An operation data analysis device,
The task data analysis device includes:
a processing unit that, based on work data obtained from the camera and recording the status of a series of work performed by a plurality of work subjects, identifies a required time for each of the work tasks constituting the series of work tasks based on both image data capturing an image of the status of the work performed by the work subjects and management data including at least one of instruction data for the work subjects to start the work and operation data showing completion of the work by the work subjects, and classifies the work tasks into categories according to the level of added value of the work content;
an output unit that outputs information including the required time and the category of each of the tasks in a graph;
a reception unit for receiving a designation of one category from the graph,
The output unit outputs imaging data obtained by imaging a status of a task corresponding to the specified category from among the imaging data;
The instruction data includes image data for instructing the task subject to start the task.
Work management system. The computer
From work data that records the status of a series of work performed by a plurality of work subjects, for each of the work constituting the series of work, a required time for the work is specified based on both imaging data that captures the status of the work performed by the work subjects and management data that includes at least one of instruction data for the work subjects to start the work and operation data indicating completion of the work by the work subjects, and the work is classified into categories according to the level of added value of the work content;
outputting information including the required time and the category of each of the tasks in a graph;
When a category is designated from the graph, image data capturing an image of a task corresponding to the designated category is output from among the image data.
The instruction data includes image data for instructing the task subject to start the task.
Methods for analyzing work data. From work data that records the status of a series of work performed by a plurality of work subjects, for each of the work constituting the series of work, a required time for the work is specified based on both imaging data that captures the status of the work performed by the work subjects and management data that includes at least one of instruction data for the work subjects to start the work and operation data indicating completion of the work by the work subjects, and the work is classified into categories according to the level of added value of the work content;
outputting information including the required time and the category of each of the tasks in a graph;
when a designation of one category is accepted from the graph, outputting imaging data of an operation situation corresponding to the designated category from among the imaging data;
The instruction data includes image data for instructing the task subject to start the task.
Program.

Images