Work content analysis apparatus, work content analysis method, and program

Abstract

To provide a work content analysis apparatus and method, and a program for analyzing the state of crowding or close contact of people such as operators in a region to be analyzed such as a factory for each of areas in the region to be analyzed.SOLUTION: A work content analysis apparatus analyzes the state of crowding or close contact of people in a region to be analyzed for each of areas in the region to be analyzed, and comprises: a first database that stores positional information representing the positions of people in the region to be analyzed together with time information in association with identification information on corresponding people; an accumulation unit that obtains accumulation values of predetermined items for each of the areas based on the positional information and time information stored in the first database in association with the identification information on the people; and a classification unit that classifies the areas for the analysis based on the accumulation values obtained by the accumulation unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] An embodiment of the present invention relates to a task content analysis device, a task content analysis method, and a program for analyzing task content of a worker in a factory or the like based on the state of the worker. [Background technology]

[0002] Typically, to improve work efficiency, the time required for actual work is measured, the work procedures are analyzed, and based on the results of this measurement and analysis, the current state of work is visualized, the procedures are standardized, and each worker follows the standardized procedures.

[0003] However, the measurement of the work time required for such a conventional analysis of the work procedure is performed by a measurer using a stopwatch, which requires additional labor costs.

[0004] IoT can also be used to measure work time without incurring extra labor costs. In other words, the idea is to use IoT to replace human-based work.

[0005] However, heavy-duty IoT requires modifications to the factory equipment itself, making it difficult to retrofit equipment in an already operating factory.

[0006] Thus, even if one were to conduct a work content analysis, collecting the data for it would require extra labor costs, and conversely, if one were to try to reduce labor costs, it would require modifying the facilities, making it no easy task.

[0007] As a result, the company does not have the resources to implement measures to improve the work efficiency of its suppliers, and the current situation is that it is up to the suppliers to implement measures to improve work efficiency at their own factories. [Prior art documents] [Patent documents]

[0008] [Patent Document 1] Patent No. 4218669 [Patent Document 2] Patent No. 5198981 [Patent Document 3] Patent No. 5107206 [Patent Document 4] Patent No. 6520029 [Patent Document 5] Patent No. 6385613 Summary of the Invention [Problem to be solved by the invention]

[0009] The problem that the present invention aims to solve is to provide a work content analysis device, a work content analysis method, and a program for analyzing the work content of workers in a factory or the like, without incurring extra labor costs or requiring major modifications to facilities. [Means for solving the problem]

[0010] The work content analysis device of the embodiment is a work content analysis device that analyzes the congestion and closeness of people within an analysis target area for each area within the analysis target area, and is equipped with a first database that stores location information indicating the location of each person within the analysis target area together with time information in association with the identification information of the corresponding person, an accumulation unit that obtains cumulative values ​​of predetermined items for each area based on the location information and time information stored in the first database in association with the identification information of the person, and a classification unit that classifies each area for analysis based on the cumulative values ​​obtained by the accumulation unit. [Brief description of the drawings]

[0011] [Figure 1] FIG. 1 is a block diagram showing an example of an electronic circuit configuration of a work content analysis device to which a work content analysis method according to a first embodiment is applied. [Diagram 2] FIG. 2 is a conceptual diagram showing an example of the connection relationship between the work content analysis device and other devices. [Diagram 3] FIG. 3 is a plan view showing an example of the layout of each area in a factory. [Figure 4] FIG. 4 is a diagram showing an example of a diagram illustrating the area stay history of each worker. [Diagram 5] FIG. 5 is a diagram showing (1) an example in which areas are classified based on the cumulative number of times that all workers have entered each area. [Figure 6] FIG. 6 is a diagram showing an example in which (2) areas are classified based on the cumulative value of the stay time in each area by all workers. [Figure 7] FIG. 7 is a diagram showing an example of (3) correlations between areas determined based on the cumulative number of movements between a plurality of areas by all workers. [Figure 8] FIG. 8 is a flowchart showing an example of the operation of a work content analysis device to which the work content analysis method of the first embodiment is applied. [Figure 9] FIG. 9 is a diagram showing an example in which the number of people staying in each area is displayed along the time axis. [Figure 10] FIG. 10 is a flowchart showing an example of the operation of a task content analysis device to which the task content analysis method of the second embodiment is applied. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0012] Hereinafter, embodiments of the present invention will be described with reference to the drawings.

[0013] (First embodiment) A task content analysis device to which a task content analysis method according to a first embodiment is applied will be described.

[0014] FIG. 1 is a block diagram showing an example of an electronic circuit configuration of a work content analysis device to which a work content analysis method according to a first embodiment is applied.

[0015] The work content analysis device 10 is a device that analyzes the crowding and close contact of people within an analysis target area for each area within the analysis target area.

[0016] In this embodiment, although it is not limited thereto, an example will be described in which the analysis target area is a factory and the people are factory workers.

[0017] As shown in FIG. 1, the electronic circuitry of the work content analysis device 10 includes a CPU 12, a recording medium reading unit 14, a communication unit 15, a display unit 16 (e.g., a display), a memory 20, and a storage device 30, all connected to each other by a bus 11.

[0018] The memory 20 stores a processing module 21, an accumulation module 22, a classification module 23, a result presentation module 24, and a display control module 25 as programs for implementing the work content analysis device 10.

[0019] These program modules 21 to 25 may be stored in advance in the memory 20, or may be read and stored in the memory 20 from an external recording medium 13 such as a memory card via the recording medium reading unit 14. These program modules 21 to 25 are designed not to be rewritable.

[0020] In addition to such a memory area that cannot be rewritten by the user, the memory 20 also has a writable data area 29 secured as a memory area for storing rewritable data.

[0021] The CPU 12 is an example of one or more processors capable of executing each of the program modules 21-25, and controls the operation of each part of the circuit in accordance with each of the program modules 21-25.

[0022] FIG. 2 is a conceptual diagram showing an example of the connection relationship between the work content analysis device and other devices.

[0023] Each worker 100 is fitted with, for example, a wristwatch-type position sensor 110 that is assigned a unique sensor ID.

[0024] The position sensor 110 measures the position of the worker 100 using a GPS function, a WiFi function, or via a beacon 125, and transmits the measured position information together with a sensor ID to the work content analysis device 10 via the communication network 70.

[0025] Further, each worker 100 may be equipped with an image sensor 115 such as a camera instead of or in addition to the position sensor 110. Similarly, the image sensor 115 is assigned a unique sensor ID.

[0026] The image sensor 115 captures an image in the line of sight of each worker 100, and transmits the captured image information together with the sensor ID to the work content analysis device 10 via the communication network 70.

[0027] The storage device 30 includes a worker database 31 and a position information database 32 .

[0028] The worker database 31 stores the worker ID (e.g., employee number) of each worker 100. In addition, the sensor IDs of the position sensor 110 and the image sensor 115 attached to each worker 100 are also stored in association with the worker ID of the corresponding worker 100.

[0029] The position information database 32 is a database for storing the position information of each worker 100 together with time information in association with the worker ID of the worker 100.

[0030] The storage device 30 that stores these databases 31 and 32 is, for example, a solid state drive (SSD) or a hard disk drive (HDD).

[0031] The communication unit 15 is connected to a communication network 70, and receives location information transmitted from the position sensor 110 and video information transmitted from the video sensor 115 along with a sensor ID via the communication network 70, and outputs the received location information or video information along with the sensor ID to the processing module 21.

[0032] FIG. 2 shows, as an example, an example in which position information (and / or image information) from the position sensor 110 (and / or image sensor 115) is received by a communication unit 15 realized by a gateway via a communication network 70.

[0033] The communication unit 15 can also output data necessary for displaying the analysis results, etc., displayed on the display unit 16 by the display control module 25 to the external terminal 130 via the communication network 70, as shown in Figure 2, in order to display the analysis results, etc., displayed on the external terminal 130.

[0034] When the location information and sensor ID are output from the communication unit 15, the processing module 21 obtains the worker ID associated with the sensor ID from the worker database 31, and outputs the worker ID to the location information database 32 together with the location information and the sensor ID.

[0035] Furthermore, when the video information and the sensor ID are output from the communication unit 15, the processing module 21 not only acquires the worker ID associated with the sensor ID from the worker database 31, but also acquires position information from the video information, for example, by AI. Similarly, the processing module 21 then outputs the worker ID to the position information database 32 together with the position information and the sensor ID.

[0036] The position information database 32 stores the position information output from the processing module 21 in association with time information, a worker ID, and a sensor ID. As this time information, the position information database 32 can use time information measured by an internal clock (not shown) of the work content analysis device 10, or time information synchronized with a clock (timing means) of an external system to which the work content analysis device 10 is connected.

[0037] The accumulation module 22 obtains an accumulated value of a predetermined item for each area in the factory based on the location information and time information stored in association with the worker ID in the location information database 32. The accumulated values ​​of the predetermined items include, for example, (1) the accumulated value of the number of times that all workers enter each area, (2) the accumulated value of the time that all workers stay in each area, and (3) the accumulated value of the number of times that all workers move between multiple areas.

[0038] For this purpose, the accumulation module 22 determines the area for each worker from the location information. Furthermore, based on the determination result and time information, it creates an area stay history that chronologically indicates in which area each worker stayed.

[0039] FIG. 3 is a plan view showing an example of the layout of each area in a factory.

[0040] In FIG. 3, A to I indicate the areas located in the factory.

[0041] FIG. 4 is a diagram showing an example of a diagram illustrating the area stay history of each worker.

[0042] In FIG. 4, the vertical axis represents areas A to J, and the horizontal axis represents time.

[0043] According to FIG. 4, for example, worker X stayed in area A for 15 minutes from 8:00 a.m., then immediately moved to area C, stayed in area C for 15 minutes, then immediately moved to area E, stayed in area E for 15 minutes, then moved to area F, it took about 30 minutes to move from area E to area F, and after staying in area F for 15 minutes, he immediately moved to area I.

[0044] Similarly, the area stay histories of workers Y, Z, and W are also shown.

[0045] Such area stay history makes it possible to visualize the actual behavior of each worker, such as how long he or she stayed in which area, which area he or she then moved to, and how long he or she stayed there.

[0046] Based on such area stay history, the accumulation module 22 can obtain (1) the cumulative number of times that all workers entered each area, (2) the cumulative number of times that all workers stayed in each area, and (3) the cumulative number of times that all workers moved between multiple areas.

[0047] A classification module 23 classifies each area for analysis based on the accumulated values ​​obtained by the accumulation module 22 .

[0048] The result display module 24 displays the classification results by the classification module 23 in the corresponding areas on a map showing the factory, which is divided into predetermined areas.

[0049] The display control module 25 causes the map, in which the classification results are displayed in each area by the result display module 24, to be displayed on the display unit 16 and / or the display screen of the external terminal 130. In addition to the map displayed by the result display module 24, the display control module 25 also causes a correlation diagram created by the classification module 23, as described later, to be displayed on the display unit 16 and / or the display screen of the external terminal 130.

[0050] The detailed configurations of the classification module 23, the result presentation module 24, and the display control module 25 will be collectively described below for each of the above examples (1) to (3).

[0051] For example, when the accumulation module 22 obtains (1) the accumulated number of times each area is entered by all workers, the classification module 23 classifies each area based on the accumulated number of times each area is entered.

[0052] Then, the result display module 24 displays the classification results by the classification module 23 in the corresponding areas on the map showing the factory divided into predetermined areas. An example of this is shown in FIG.

[0053] FIG. 5 is a diagram showing (1) an example in which areas are classified based on the cumulative number of times that all workers have entered each area.

[0054] The cumulative value here would be 12, for example, if worker X enters area A twice, worker Y enters it five times, worker Z enters it three times, and worker W enters it twice.

[0055] 5, the classification module 23 classifies areas A to I into five classes according to the cumulative number of visits by all workers. In this example, area G belongs to class 1 with the highest number of visits, areas E and H belong to class 2 with the second highest number of visits, areas A, D and I belong to class 3 with the third highest number of visits, areas C and F belong to class 4 with the fourth highest number of visits, and area B belongs to class 5 with the lowest number of visits.

[0056] As shown in FIG. 5, the classification results by the classification module 23 are displayed by the result display module 24 with different legends for each area on the map showing the factory.

[0057] Then, the display control module 25 causes a map such as that illustrated in FIG. 5 to be displayed on the display unit 16 and / or on the display screen of the external terminal 130 via the communication network 70.

[0058] Furthermore, when the accumulation module 22 obtains (2) the accumulated value of the stay time in each area by all workers, the classification module 23 classifies each area based on the accumulated stay time in each area.

[0059] Then, the result display module 24 displays the classification results by the classification module 23 in the corresponding areas on the map showing the factory divided into predetermined areas. An example is shown in FIG.

[0060] FIG. 6 is a diagram showing an example in which (2) areas are classified based on the cumulative value of the stay time in each area by all workers.

[0061] 6, the classification module 23 classifies areas A to I into five classes according to the cumulative value of the stay time by all workers. In this example, area D belongs to class 1, which has the longest cumulative stay time, areas G and H belong to class 2, which has the second longest cumulative stay time, areas A, E, F, H, and I belong to class 3, which has the third longest stay time, area C belongs to class 4, which has the fourth longest stay time, and area B belongs to class 5, which has the shortest stay time.

[0062] As shown in FIG. 6, the classification results by the classification module 23 are displayed by the result display module 24 with different legends for each area on the map showing the factory.

[0063] Then, the display control module 25 causes a map such as that illustrated in FIG. 6 to be displayed on the display unit 16 and / or on the display screen of the external terminal 130 via the communication network 70.

[0064] In addition, when the accumulation module 22 obtains (3) the accumulated value of the number of movements by all workers between multiple areas, the classification module 23 determines the correlation between each area based on the accumulated number of movements between each area.

[0065] FIG. 7 is a diagram showing an example of (3) correlations between areas determined based on the cumulative number of movements between a plurality of areas by all workers.

[0066] In FIG. 7, solid lines indicate a large amount of movement, and dashed lines indicate a small amount of movement. Among the solid lines, the thicker the solid line, the larger the movement, and among the dashed lines, the thicker the dashed line, the smaller the movement.

[0067] The display control module 25 causes the correlation diagram as exemplified in FIG. 7 to be displayed on the display unit 16 and / or on the display screen of the external terminal 130 via the communication network 70.

[0068] Next, an example of the operation of the work content analysis device to which the work content analysis method of the first embodiment configured as above is applied will be described.

[0069] FIG. 8 is a flowchart showing an example of the operation of a work content analysis device to which the work content analysis method of the first embodiment is applied.

[0070] A position sensor 110 with a unique sensor ID is attached to each worker 100. Also, instead of or in addition to the position sensor 110, an image sensor 115 such as a camera may be attached to each worker 100.

[0071] Position information of the worker 100 measured by the position sensor 110 (sensing information by the position sensor 110) is transmitted from the position sensor 110 together with a sensor ID to the work content analysis device 10. Video information captured by the video sensor 115 (sensing information by the video sensor 115) is also transmitted from the video sensor 115 together with the sensor ID to the work content analysis device 10 via the communication network 70 (S1).

[0072] The sensing information transmitted from the position sensor 110, which is the location information and the sensor ID, and the sensing information transmitted from the video sensor 115, which is the image information and the sensor ID, are received by the communication unit 15 in the work content analysis device 10, and are output by the communication unit 15 to the processing module 21.

[0073] In the processing module 21, when the communication unit 15 outputs the position information and the sensor ID, the processing module 21 obtains the worker ID associated with the sensor ID from the worker database 31. Similarly, in the processing module 21, when the communication unit 15 outputs the video information and the sensor ID, the processing module 21 obtains the worker ID associated with the sensor ID from the worker database 31 (S2).

[0074] Furthermore, in the processing module 21, if the sensing information transmitted in step S1 is location information (S3: Yes), the worker ID acquired in step S2 is output to the location information database 32 together with the location information and the sensor ID, and the location information is associated with the time information, the worker ID, and the sensor ID and stored in the location information database 32 (S5).

[0075] On the other hand, in the processing module 21, if the sensing information transmitted in step S1 is video information (S3: No), position information is acquired from the video information, for example, by AI (S4). After that, the worker ID acquired in step S2 is output to the position information database 32 together with the position information and sensor ID acquired in step S4, and the position information is associated with the time information, worker ID, and sensor ID and stored in the position information database 32 (S5).

[0076] Next, the accumulation module 22 determines the area for all workers from the location information based on the information stored in the location information database 32 (S6). Furthermore, based on the determination result and time information, an area stay history is created that chronologically indicates which areas each worker stayed in, as shown in Fig. 4 for example (S7).

[0077] Based on such area stay history, the accumulation module 22 obtains accumulated values ​​of predetermined items (S8). The accumulated values ​​of predetermined items include, for example, (1) the accumulated value of the number of times that all workers enter each area, (2) the accumulated value of the time that all workers stay in each area, and (3) the accumulated value of the number of times that all workers move between multiple areas.

[0078] In the classification module 23, when the accumulation module 22 obtains (1) the cumulative value of the number of entries by all workers into each area, and (2) the cumulative value of the time spent in each area by all workers (S9: (1), (2)), each area is classified for analysis based on the obtained cumulative values, and this classification result is displayed in the corresponding area on a map by the result display module 24, and the resulting map as shown in Figures 5 and 6 is displayed as the analysis result by the display control module 25 on the display unit 16, or on the display screen of the external terminal 130 via the communication network 70 (S10).

[0079] On the other hand, in the classification module 23, when the accumulation module 22 obtains (3) a cumulative value of the number of movements by all workers between multiple areas (S9:(3)), the correlation between each area is determined based on the accumulated number of movements between each area, and the resulting correlation diagram as shown in FIG. 7 is displayed as the analysis result by the display control module 25 on the display unit 16 or on the display screen of the external terminal 130 via the communication network 70 (S11).

[0080] In this way, according to the work content analysis device to which the work content analysis method of the first embodiment is applied, it is possible to obtain analysis results such as those shown in Figures 5 to 7. By referring to these analysis results, it becomes possible to analyze the crowding and close contact of people such as workers within an analysis target area such as a factory for each area within the analysis target area.

[0081] In particular, by referring to analysis results such as those shown in Figure 5, it is possible to understand the frequency of stays in each area.

[0082] In addition, by referring to analysis results such as those shown in Figure 6, it is possible to understand the length of stay in each area.

[0083] For example, in order to improve the productivity of the entire factory, it is desirable to review the layout by arranging frequently used machines in areas where workers visit frequently, i.e., areas where they visit frequently and for long periods of time, and by arranging machines that are not used very often in areas where workers visit infrequently, i.e., areas where they visit less frequently and / or for short periods of time.

[0084] The analysis results shown in Figures 5 and 6 can be effectively used to consider optimal machine placement decisions as described above. They can also be effectively used when considering deciding the areas to place machines in order to minimize unnecessary crowding or close contact between workers.

[0085] In addition, by referring to analysis results such as those in Figure 7, it is possible to understand the degree of frequency of movement between areas.

[0086] Therefore, by referring to the analysis results in Figure 7 in addition to Figures 5 and 6, it is possible to effectively utilize these results when deciding on the layout, while also reducing overcrowding and close contact when workers move between locations.

[0087] For example, from the viewpoint of productivity, it is efficient to arrange the production lines for major products in a straight line and place frequently used machines on the production lines. By doing so, the area in which each worker stays is naturally limited, which makes it possible to reduce the density of people.

[0088] On the other hand, from the perspective of constraint theory (throughput), productivity can be improved by replacing bottleneck processes with more efficient machines, dividing production processes, or increasing the number of workers.

[0089] Second embodiment A task content analysis device to which a task content analysis method according to the second embodiment is applied will be described.

[0090] The configuration of a work content analysis device to which the work content analysis method of the second embodiment is applied can be explained using FIG. 1. Therefore, in the following, the differences from the first embodiment will be explained without repeating the explanation, while referring to FIG. 1.

[0091] That is, in this embodiment, the accumulation module 22 accumulates the number of people who stayed in each area by hour, based on the location information and time information stored in the location information database 32 in association with the worker.

[0092] The display control module 25 displays the number of people who stayed in each area along the time axis according to the accumulation result by the accumulation module 22. An example is shown in FIG.

[0093] FIG. 9 is a diagram showing an example in which the number of people staying in each area is displayed along the time axis.

[0094] In Fig. 9, the vertical axis represents areas A to J, and the horizontal axis represents time. Fig. 9 shows how many workers (one, two, or three or more) are staying in each area, during what time period, and for how long.

[0095] Next, an example of the operation of the work content analysis device to which the work content analysis method of the second embodiment configured as above is applied will be described.

[0096] FIG. 10 is a flowchart showing an example of the operation of a task content analysis device to which the task content analysis method of the second embodiment is applied.

[0097] In FIG. 10, the same processes as those in FIG. 8 are denoted by the same step numbers, and only the different contents will be described to avoid duplication of explanation.

[0098] That is, in this embodiment, after step S7, the number of people who stayed in each area is accumulated by time by the accumulation module 22 based on the information stored in the position information database 32 (S81).

[0099] In response to this, the display control module 25 causes the accumulation result obtained in step S81 to be displayed on the display screen of the display unit 16 or the external terminal 130, as exemplified in FIG. 9 (S82).

[0100] In FIG. 9, the number of people staying in each area is displayed along the time axis, so you can see how many workers (1, 2, or 3 or more) stayed in each area, for how long, and at what time of day.

[0101] According to the analysis results shown in Figure 9, it is possible to grasp the congestion and closeness of workers by time period for each area from the viewpoint of what time period in each area workers concentrate / do not concentrate and whether the period of concentration is long / short. Such results, together with the analysis results in Figures 5, 6, and 7, can also be effectively utilized for consideration when deciding the optimal layout while suppressing congestion and closeness when workers move between each other.

[0102] Although some embodiments of the present invention have been described, these embodiments are presented as examples and are not intended to limit the scope of the invention. These embodiments can be implemented in various other forms, and various omissions, substitutions, and modifications can be made without departing from the spirit of the invention. These embodiments and their modifications are included in the scope of the invention and its equivalents described in the claims, as well as in the scope and spirit of the invention. [Explanation of symbols]

[0103] 10 ·· Work content analysis device, 11 ·· Bus, 12 ·· CPU, 13 ·· External recording medium, 14 ·· Recording medium reading unit, 15 ·· Communication unit, 16 ·· Display unit, 20 ·· Memory, 21 ·· Processing module, 22 ·· Accumulation module, 23 ·· Classification module, 24 ·· Result display module, 25 ·· Display control module, 29 ·· Writable data area, 30 ·· Storage device, 31 ·· Worker database, 32 ·· Behavior information database, 70 ·· Communication network, 100 ·· Worker, 110 ·· Position sensor, 115 ·· Image sensor, 120 ·· Microcontroller, 125 ·· Beacon, 130 ·· External terminal

Claims

1. A work content analysis device that analyzes the crowding and close contact of people in a factory for each area in the factory, comprising: a first database that stores location information representing the location of each person in the factory in association with identification information of the corresponding person together with time information; an accumulation unit that obtains an accumulated value of a predetermined item for each of the areas based on the position information and time information stored in the first database in association with the person's identification information; a classification unit that classifies each of the areas for the analysis based on the accumulated value obtained by the accumulation unit; a second database that stores the person's identification information and identification information given to the camera attached to the person in association with each other; a result display unit that displays the classification result by the classification unit in a corresponding area on a map showing the factory divided by the areas; a display control unit that displays the map on which the classification results are displayed in each area by the result display unit, The camera captures an image in the line of sight of a person to whom the camera is attached, and outputs the image information together with identification information given to the camera; When the video information and the identification information assigned to the camera are output from the camera, the identification information of a person associated with the identification information assigned to the camera is obtained from the second database, and location information of the person to whom the camera is attached is detected from the video information; The map is utilized to determine the optimal placement and placement area of ​​machines in the factory.

2. A work content analysis method implemented by a work content analysis device that analyzes crowding and close contact conditions of people in a factory for each area in the factory, comprising: The work content analysis device includes: storing location information representing the location of each person in the factory together with time information in a first database in association with the corresponding person; obtaining a cumulative value of a predetermined item for each of the areas based on the position information and time information stored in the first database in association with the person; categorizing each of the areas for the analysis based on the obtained cumulative values; storing the identification information of the person and the identification information given to the camera attached to the person in a second database in association with each other; A classification result is displayed in a corresponding area on a map showing the factory divided by the area; Displaying the map with the classification results clearly indicated for each area; The camera captures an image in the line of sight of a person to whom the camera is attached, and outputs the image information together with identification information given to the camera; The work content analysis device includes: When the video information and the identification information assigned to the camera are output from the camera, the identification information of a person associated with the identification information assigned to the camera is obtained from the second database, and location information of the person to whom the camera is attached is detected from the video information; A work content analysis method in which the map is utilized to determine the optimal placement and placement area of ​​machines in the factory.

3. A program for analyzing the congestion and closeness of people in each area of ​​a factory, comprising: a function of storing location information representing the location of each person in the factory together with time information in a first database in association with the corresponding person; a function of obtaining a cumulative value of a predetermined item for each of the areas based on location information and time information stored in the first database in association with the person; categorizing each of said areas for said analysis based on said obtained cumulative values; a function of storing in a second database, in association with identification information of the person and identification information given to the camera attached to the person; A program for causing a computer to realize the function of, when the camera outputs image information in the line of sight of the person to whom the camera is attached and identification information assigned to the camera, obtaining from the second database the identification information of the person associated with the identification information assigned to the camera, and detecting the location information of the person to whom the camera is attached from the image information.

Images