JP7637837B2 - Work content analysis device, work content analysis method, program, and sensor - Google Patents

Description

Embodiments of the present invention relate to a work content analysis device, a work content analysis method, a program, and a sensor for the work content analysis device, for analyzing the work content of workers in a factory or the like based on the state of the workers.

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.

However, the measurement of work time required for such conventional work procedure analysis is done by a measurer using a stopwatch, which results in additional labor costs.

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

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

As such, 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 equipment, making it difficult to implement.

As a result, there is not enough capacity to implement measures to improve work efficiency at all of the factories involved, and the current situation is that each factory is left to decide on its own how to improve work efficiency.

Patent No. 4218669 Patent No. 5198981 Patent No. 5107206 Patent No. 6520029 Patent No. 6385613 Patent No. 5159263

The problem that this invention aims to solve is to provide a work content analysis device, a work content analysis method, and a program that analyze the work content of workers in a factory or the like, without incurring extra labor costs or requiring major modifications to the equipment. It is also to provide a sensor that can be used for the work content analysis device.

The work content analysis device of the embodiment is a work content analysis device that analyzes the work content of a worker based on the state of the worker, and includes a first database that stores status information indicating the state of one or more workers in association with time information and identification information of the worker, an estimation unit that estimates the work content performed by the worker from at least two pieces of status information associated with the same time among the status information stored in the first database, an identification unit that identifies the work time spent on the estimated work content from the status information stored in the first database and the time information associated with the status information, and an analysis unit that analyzes the work content based on the estimated work content and the identified work time. The status information includes at least one of the following: position information of the worker, movement information indicating whether the worker's hands are moving, and information indicating the operating state of equipment around the worker.

FIG. 1 is a block diagram showing an example of an electronic circuit configuration of a task content analysis device to which a task content analysis method according to a first embodiment is applied. FIG. 2A is a conceptual diagram showing an example of a connection relationship (including a camera) between the work content analysis device and other devices. FIG. 2B is a conceptual diagram showing an example of a connection relationship between the work content analysis device and other devices (including facilities). FIG. 2C is a block diagram showing an example of an electronic circuit configuration of the sensor. FIG. 3A is an example showing, in time series, status information (sensing information indicating the operating status of equipment) conventionally stored in a status information database. FIG. 3B is another example showing, in time series, status information (sensing information indicating the operating status of the equipment) stored in the status information database. FIG. 4A is a diagram showing an example of a time-series change in sensing information indicating an area number as position information. FIG. 4B is a diagram showing an example of state information determined by performing arithmetic processing on the sensing information shown in FIG. 4A. FIG. 5 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. FIG. 6 is an example of a graphic display showing the time required for each task. FIG. 7 is a flowchart for explaining the flow of business improvement. FIG. 8 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. FIG. 9 is an example of a graphic display showing the frequency and duration of visits to each area of a factory by all workers in a group. FIG. 10 is an example of a graphic display showing the actual work time performance broken down by work content.

Each embodiment of the present invention will be described below with reference to the drawings.

First Embodiment
A work content analysis device to which the work content analysis method of the first embodiment is applied will be described.

Figure 1 is a block diagram showing an example of the electronic circuit configuration of a work content analysis device to which the work content analysis method of the first embodiment is applied.

Figure 2A is a conceptual diagram showing an example of the connection relationship between the work content analysis device and other devices (including a camera).

The work content analysis device 10 is a device that analyzes the work content performed by workers in a factory, etc.

As shown in FIG. 1, the electronic circuit 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 of which are connected to each other by a bus 11.

The memory 20 stores a processing module 21, an estimation module 22, an identification module 23, a work time comparison module 24, an analysis module 25, a display control module 26, and a judgment module 27 as programs that realize the work content analysis device 10.

These program modules 21 to 27 may be pre-stored in the memory 20, or may be read from an external recording medium 13 such as a memory card via the recording medium reading unit 14 and stored in the memory 20. These program modules 21 to 27 are designed not to be rewritten.

In addition to these areas that cannot be rewritten by the user, the memory 20 also has a writable data area 29 that stores rewritable data.

The CPU 12 is an example of one or more processors capable of executing each of the program modules 21 to 27, and controls the operation of each part of the circuit in accordance with each of the program modules 21 to 27.

The storage device 30 includes a worker database 31, a status information database 32, and a reference time database 33.

The worker database 31 stores, for example, the worker ID (e.g., employee number, etc.) of each worker 100 in a factory.

The status information database 32 stores the sensing information from each sensor 110 attached to each worker 100 as status information representing the status of each worker 100, in association with time information, worker ID, and sensor ID.

The status information database 32 can also store sensing information from sensors 110, such as cameras, installed near or around the workers 100 as status information for each worker 100, in association with time information, worker ID, and sensor ID. In this case, however, the sensor 110 is stored in association with the worker ID of a worker 100 who is not attached to the worker 100 but is in the vicinity of the sensor 110, such as a worker 100 who is within a predetermined distance.

The status information can include position information of the worker 100, movement information indicating whether the hands of the worker 100 are moving, information indicating the operating status of equipment around the worker 100, movement information indicating whether the worker 100 is moving, vital information of the worker 100, audio information of the worker 100, ambient sound information of the worker 100, and video information around the worker 100.

In addition, the status information database 32 can store not only sensing information (hereinafter also referred to as "first status information") acquired from the sensor 110 (including the camera 115) as status information, but also status information (hereinafter also referred to as "second status information") that has been subjected to mathematical calculation processing from the sensing information (first status information), in association with time information, worker ID, and sensor ID as status information representing the status of each worker 100. The status information is not limited to sensing information (first status information) from the sensor 110, but can also include image information from the camera 115 and, for example, a signal from a patrol lamp.

The status information can be obtained not only as sensing information transmitted from each sensor 110 attached to each worker 100 or a camera 115 installed at a fixed point and controlled by a microcontroller 120, but also based on sensing information, such as the second status information obtained from the first status information described above.

Each worker 100 can be fitted with multiple types of sensors 110, not just one.

These sensors, including sensor 110 and camera 115, are IoT devices for visualizing the movements of the worker, and specific types include, but are not limited to, a position sensor that measures the position of worker 100 via GPS function, WiFi function, Bluetooth (registered trademark) function, or beacon 125, an acceleration sensor that detects the hand movements of worker 100, a vital sensor that measures vital information such as the pulse of worker 100, a microphone that detects the voice of worker 100, a sound sensor that detects sounds around worker 100, and an imaging sensor such as a camera that is attached to the worker itself and captures an image in the line of sight of worker 100.

These sensors are attached to the front of the worker's 100 body, the left and right sides of the waist, arms, wrists, neck, shoulders, ears, head, etc. so that they can easily receive radio waves from the beacon 125. In particular, the sensor 110 that acquires movement information, such as the acceleration sensor described above, is attached to the dominant arm or hand of the worker 100.

A wearable device including a smartphone can also be used as the position sensor. In this case, the smartphone is placed in a waist holder and secured with a belt, or attached by hooking the hook into a belt loop. If the smartphone is attached on the back, it becomes difficult for the communication unit 15 to receive signals from the smartphone, so it is preferable to attach the smartphone to the front of the worker 100 or to the left or right of the waist. The smartphone may also be carried in the worker's 100 trouser pocket or breast pocket.

The device on the wristband can be used as the vital sensor. In this case, since wearing the device on both arms or hands of the worker 100 may cause problems in the work, it is preferable to wear the vital sensor on the arm or wrist of the dominant arm or hand of the worker 100 so that the movements and work content of the worker 100 can be captured in detail with a minimum configuration.

A headset type device can be used as the sound sensor. In this case, it can be worn around the neck or on the shoulder of the worker 100.

An earphone-type device can be used as the sound sensor. In this case, it can be attached to the ear of the worker 100.

A smart glasses-type device can be used as the image sensor. In this case, it can be attached to the head of the worker 100.

Sensors also include, but are not limited to, cameras 115 that are not attached to the worker 100 but are installed at fixed points.

Figure 2B is a conceptual diagram showing an example of the connection relationship between the work content analysis device and other devices (including equipment).

Using Figure 2B, we will explain how to obtain operation information of equipment around the worker 100 from the status information.

The connection relationship shown in FIG. 2B differs from that shown in FIG. 2A in that equipment 116 is provided instead of camera 115, but the other configurations are the same. Therefore, the following will explain the differences from FIG. 2A.

The equipment 116 is controlled by the microcontroller 120 to transmit operation information of the equipment 116 to the communication unit 15. In addition, sensors (not shown) for acquiring operation information can be installed in the equipment 116, and the operation information acquired from these sensors can be transmitted to the communication unit 15.

In this way, the work content analysis device 10 can obtain operation information of the equipment 116 around the worker 100, which is one piece of status information.

Figure 2C is a block diagram showing an example of the electronic circuit configuration of the sensor.

The block diagram illustrated in FIG. 2C is a typical example of the configuration of the sensor 110, which may be of many types, as described above.

The sensor 110 typically includes a CPU 1102, a sensing unit 1103, a transmitting unit 1104, a receiving unit 1105, a memory 1110, and a storage device 1120, all connected together by a bus 1101.

The CPU 12 is a processor that controls the operation of each part of the above circuits that are connected to each other via the bus 1101.

The sensing unit 1103 is a part that acquires sensing information of the worker 100. For example, if the sensor 110 is an acceleration sensor, the sensing unit 1103 corresponds to a detection unit that detects acceleration, if the sensor 110 is a vital sensor, a detection unit that detects vital values such as pulse rate, if the sensor 110 is a sound sensor, a sound collection unit that detects sound, if the sensor 110 is a camera, an image sensor, or a GPS function, WiFi function, or Bluetooth function if the sensor 110 is a position sensor.

The sensor 110 can also function as a position sensor that measures the position of the worker 100 via the beacon 125. In this case, the receiver 1105 receives a signal from the beacon 125 as sensing information. The beacon 125 can change the intensity of the radio waves it transmits.

When the sensing unit 1103 and the receiving unit 1105 acquire the sensing information in this manner, they output the sensing information to the storage device 1120. The storage device 1120 stores a sensing information database 1-1-21 that stores the sensing information, and stores the sensing information output from the sensing unit 1103 and the receiving unit 1105.

The storage device 1120 may be, for example, a solid state drive (SSD) or a hard disk drive (HDD).

The memory 1110 stores a communication determination module 1111 as a program provided in the sensor 110.

The communication determination module 1111 determines whether the sensor 110 is capable of communicating with the work content analysis device 10.

If the communication determination module 1111 does not determine that communication between the sensor 110 and the work content analysis device 10 is possible (i.e., if the communication determination module 1111 determines that communication between the sensor 110 and the work content analysis device 10 is not possible), the sensing information continues to be stored in the sensing information database 1-1-21 .

When the communication determination module 1111 determines that communication between the sensor 110 and the work content analysis device 10 is possible, the transmission unit 1104 transmits the sensing information stored in the sensing information database 1-1-21 to the work content analysis device 10 via the communication network 70 at predetermined intervals (e.g., every 5 seconds or every 10 seconds), regardless of whether new sensing information has been acquired.

Each sensor 110 is assigned a sensor ID, which is identification information for the sensor, and is associated with the worker ID of the worker 100 to which the sensor is attached. When transmitting sensing information as status information to the work content analysis device 10, the transmission unit 1104 transmits the sensing information together with the sensor ID and worker ID.

When the sensor 110 is a camera 115 in particular, the transmission unit 1104 transmits the sensing information, that is, the image information, together with the sensor ID to the work content analysis device 10.

As explained again in FIG. 1, the communication unit 15 of the work content analysis device 10 is connected to the communication network 70, receives the sensor ID, worker ID, and sensing information transmitted from each sensor 110 via the communication network 70, and sends the received sensor ID, worker ID, and sensing information to the processing module 21. Similarly, it receives the sensor ID and image information, which is the sensing information, transmitted from the camera 115, and sends the received sensor ID and image information, which is the sensing information, to the processing module 21.

The communication unit 15 can also output data required for graph display to the external terminal 130 via the communication network 70, as shown in Figures 2A and 2B, in order to display graphs and the like displayed on the display unit 16 by the display control module 26 from the external terminal 130, as described below.

In this way, the sensing information transmitted as status information from the sensor 110 includes position information of the worker 100, movement information related to the hand movements of the worker 100, movement information related to the movement of the worker 100, vital information of the worker 100, voice information of the worker 100, ambient sound information of the worker 100, video information in the line of sight of the worker 100, and video information of the worker 100. As described below, the processing module 21 can also obtain movement information and position information of the worker 100 from the video information from the camera 115.

The status information database 32 is a database for storing status information, and can use time information measured by an internal clock (not shown) of the work content analysis device 10 as time information. It can also use time information synchronized with a clock (timing means) of an external system to which the work content analysis device 10 is connected.

In addition, if synchronization with the timing means of the connected system is not possible, the time of the work content analysis device 10 itself can be used as the reference time to prevent time discrepancies from occurring between sensors, and each sensor 110 can use the time synchronized with the work content analysis device 10.

The reference time database 33 stores a reference time that is determined in advance for each task. The reference time is not limited to, but may be, for example, a target time that is determined in advance for each task, or the time required by a highly skilled person to complete the task.

The storage device 30 that stores these databases 31 to 33 is also composed of, for example, an SSD (Solid State Drive) or an HDD (Hard Disk Drive), just like the storage device 1120.

The processing module 21 receives the sensing information sent from the communication unit 15 together with the sensor ID and, if present, the worker ID, and determines whether or not processing of the sensing information is required. Then, for sensing information that requires processing, the processing module 21 performs the necessary processing, acquires status information, and sends the status information together with the sensor ID and worker ID to the status information database 32. On the other hand, for sensing information that does not require processing, the processing module 21 does not process the sensing information, and sends the sensing information as status information together with the sensor ID and worker ID to the status information database 32.

An example of sensing information that requires processing is video information from the camera 115. The processing of this video information involves, for example, using AI technology to identify the worker 100 being photographed from the video information, identifying the worker ID of the worker 100, detecting movement information and position information of the identified worker 100, and using the detected movement information and position information as status information. In this way, when processing video information, the processing module 21 functions in particular as a video processing module. In addition, sensing information from an acceleration sensor may also be processed or altered using, for example, AI technology. Note that sensing information that requires processing is not limited to these.

When status information is sent along with the sensor ID and worker ID, the status information database 32 stores the status information, the sensor ID, and the worker ID in association with time information.

In this way, the work content analysis device 10 can accumulate sensing information transmitted from the sensor 110 (including the camera 115) as status information. However, even if the work content analysis device 10 is normal, there are cases where sensing information is not transmitted and status information cannot be acquired due to, for example, a communication error from the sensor 110 (including the camera 115). Conversely, there are cases where, even if there is no problem with communication from the sensor 110 (including the camera 115), there are cases where, for example, some kind of trouble occurs in the work content analysis device 10, and sensing information cannot be received and status information cannot be acquired.

The determination module 27 determines whether the work content analysis device 10 is operating normally. The determination module 27 can determine whether the work content analysis device 10 is operating normally, for example, by monitoring the operation of the CPU 12 and the communication unit 15.

If the status information cannot be acquired, the processing module 21 stores first fixed data (e.g., "0 (zero)") as the status information in the status information database 32 if the judgment module 27 judges that the work content analysis device 10 is normal, and stores second fixed data (e.g., "-1") as the status information in the status information database 32 if the judgment module 27 judges that the work content analysis device 10 is not normal. Note that instead of "0 (zero)", a character string such as "NULL" may also be used.

By storing such fixed data in the state information database 32, the following becomes possible:

For example, a case will be described in which the status information is sensing information indicating the operating status of the equipment 116. Conventionally, when sensing information indicating a "1" indicating an operating status is received from a sensor monitoring the equipment 116, the status information database 32 stores "1" as the status information. This allows the work content analysis device 10 to determine that the equipment 116 is operating.

However, this means that if sensing information is not received, status information will not be stored. Therefore, even if the work content analysis device 10 is normal, for example, if sensing information cannot be received due to a communication error from the sensor monitoring the equipment 116, or conversely, if there is no problem with communication from the sensor but sensing information cannot be received due to a malfunction of the work content analysis device 10, status information will not be stored.

Figure 3A is an example showing a time series of status information (sensing information indicating the operating status of a certain piece of equipment) that was previously stored in the status information database 32 in such a case.

For example, for the first four times, no status information was stored, so the status information is shown as (none). Then for the next three consecutive times, status information was received, so a "1" was stored in the status information database 32, followed by (none), followed by a "1" and then (none).

In this way, if no status information is stored in the status database 32, it is completely unclear whether the cause is a problem with the work content analysis device 10 or a problem with something other than the work content analysis device 10.

In contrast, in this embodiment, if the determination module 27 determines that the work content analysis device 10 is normal but the status information cannot be acquired, the processing module 21 stores "0 (zero)" as the status information in the status information database 32. On the other hand, if the determination module 21 determines that the work content analysis device 10 is not normal but the status information cannot be acquired, the processing module 21 stores "-1" as the status information in the status information database 32. Note that instead of "0 (zero)," a character string such as "NULL" may also be used.

Figure 3B is an example showing a time series of status information (sensing information indicating the operating status of equipment) stored in the status information database in this embodiment.

As shown in Fig. 3B, in this embodiment, there is no such thing as (none). Moreover, when comparing Fig. 3B with Fig. 3A, the first two (none)s shown in Fig. 3A are shown as "-1" in Fig. 3B. This shows that the work content analysis device 10 is not normal and therefore unable to obtain status information. Moreover, the other four (none)s shown in Fig. 3A are shown as " 0 " in Fig. 3B. This shows that the work content analysis device 10 is normal but unable to obtain status information.

As another example of processing, the processing module 21 obtains second state information by performing mathematical arithmetic processing on one or more pieces of first state information among the multiple pieces of state information. Then, the first state information and the second state information are stored in the state information database 32. In this way, it is possible to derive one piece of state information from another piece of state information.

For example, averaging or majority voting can be used as the mathematical calculation process. Below, we will explain an example of using averaging or majority voting as the mathematical calculation process, where the mathematical calculation process is applied to sensing information that indicates position information.

Figure 4A shows an example of time-series changes in sensing information that indicates an area number as location information.

In the example shown in FIG. 4A, ten pieces of sensing information indicating position information detected every second by the sensor 110 attached to the worker 100 are arranged in chronological order. The numbers indicate the area numbers. That is, FIG. 4A shows the area numbers in which the worker 100 was present over a ten-second period, every second. According to this example, the worker 100 is in area 1 for the first second, moves to area 2 after one second, and returns to area 1 after another second, and thereafter moves rapidly between areas 1, 2, and 3 every second.

However, it is known that erroneous detection often occurs in position detection. Furthermore, realistically, it is unlikely that the position of the worker 100 will move between the three areas frequently and instantaneously in a short period of time. Therefore, when handling sensing information indicating position information as status information, it is desirable to determine the status information by performing calculation processing such as averaging the sensing information or taking a majority vote within an arbitrary fixed period of time, such as 10 seconds, rather than using the raw data as is.

Figure 4B shows an example of state information determined by performing calculation processing on the sensing information shown in Figure 4A.

In the 10-second period shown in the example of Figure 4A, the location information most often indicates area 1, with 6 occurrences. Therefore, in a case such as 1 → 2 → 1, the previous and next areas 1 are judged to be correct and are confirmed. On the other hand, the intermediate area 2 is unrealistic and it would be appropriate to consider it to be area 1, so it is corrected to 1 → 1 → 1. Also, in a case such as 1 → 2 → 3 → 1, the first and last area 1 are judged to be correct and are confirmed. Then, the intermediate areas 2 and 3 are also unrealistic and it would be appropriate to consider them to be area 1, so they are considered to be area 1 and are corrected to 1 → 1 → 1 → 1.

Through this type of calculation processing, time-series status information such as that shown in Figure 4B can be obtained from sensing information showing a time series such as that shown in Figure 4A.

Although not shown, similar to FIG. 4A, under conditions where location information most often indicates area 1, if the location information indicates a time sequence such as 1→2→2→1, the first and last area 1 is determined to be correct and confirmed, but if area 1 and area 2 are close to each other, it is possible for the person to move from area 1 to area 2, stay there for 2 seconds, and then return to area 1, so the order can simply be 1→2→2→1.

As will be explained again by returning to FIG. 1, the estimation module 22 estimates the work content performed by the worker 100 from at least two pieces of status information associated with the same time among the status information of the worker 100 stored in the status information database 32. Note that in this specification, the work content includes not only useful work and incidental work, such as manual work and moving work such as moving a cart, but also non-useful work such as simply walking, standing still in place, and taking a break.

For example, when estimating the work content of the worker 100 (#1), the estimation module 22 targets the status information stored in the status information database 32, which is associated with the worker ID of the worker 100 (#1). Furthermore, based on the time information associated with the status information and the sensor ID associated with the status information, the estimation module 22 focuses on at least two pieces of status information associated with the same time, and estimates the work content performed by the worker 100 (#1). Note that the at least two pieces of status information associated with the same time can be not only status information of one worker, such as the worker 100 (#1) in this example, but also status information of multiple workers, such as the worker 100 (#1) in this example and another worker 100 (#2).

As an example of a case where the at least two pieces of status information are two pieces of status information from one worker (e.g., worker 100 (#1)), a case where the pieces of status information are position information from a position sensor of one worker and acceleration information from an acceleration sensor is described. In this case, if the position information indicates within the work area and the acceleration information indicates a value greater than a predetermined value (a state in which the hands are moving due to manual work), the estimation module 22 can estimate that the work content of this worker 100 (#1) is manual work.

On the other hand, if the position information does not change at all and the acceleration information also indicates a state in which the acceleration is not greater than a predetermined value (a state in which the hands are barely moving), the estimation module 22 can estimate that the worker 100 (#1) is stationary, i.e., not doing any work. In this case, by further taking into account the time information, if it is within the break time of the worker 100 (#1), it can be estimated that the worker 100 (#1) is taking a break, and if it is not within the break time, it can be estimated that the worker 100 (#1) is waiting.

As another example of a case where at least two pieces of status information are from one worker (e.g., worker 100 (#1)), if the position information changes over time and the acceleration information does not change, the estimation module 22 can estimate that the worker 100 (#1) was moving. However, this does not allow the estimation module 22 to distinguish whether the worker 100 (#1) was moving due to work such as pushing a cart, or whether the worker was simply walking without working. In such a case, the estimation module 22 also focuses on the sound from the microphone as status information, and can distinguish whether the worker 100 (#1) was moving a cart or simply walking depending on whether the sound generated by the movement of the cart is detected.

As an example of a case where at least two pieces of status information are from multiple workers (for example, two workers, worker 100 (#1) and worker 100 (#2)), the case where the position information from the position sensor and the voice information from the microphone of worker 100 (#1) and the position information from the position sensor and the voice information from the microphone of worker 100 (#2) will be described. In this case, if the position information of worker 100 (#1) and the position information of worker 100 (#2) indicate that they are in the same work area, and the voice information of worker 100 (#1) and the voice information of worker 100 (#2) indicate that worker 100 (#1) and worker 100 (#2) are having a conversation about work, the estimation module 22 can estimate that worker 100 (#1) and worker 100 (#2) have been working together, and can further estimate the work content based on the content of the conversation.

As another example of a case where the at least two pieces of status information are from multiple workers (e.g., two workers, worker 100 (#1) and worker 100 (#2)), the pieces of status information can be at least one of position information of worker 100, movement information indicating whether worker 100's hands are moving, and information indicating the operating status of equipment around worker 100.

By using both location and movement data, it is possible to estimate the work content on a work site basis.

By using two pieces of data, location information and equipment information, it is possible to estimate the behavior of the worker 100 according to the operating status.

By using two pieces of data, movement information and equipment information, it is possible to estimate the work of the worker 100 according to the operating status.

Furthermore, for the above three cases, it is possible to estimate the work of a group by using data from a group consisting of multiple workers 100.

Note that the above is merely one example, and the estimation module 22 can estimate various work contents from various other combinations of state information. Then, the estimated work contents are associated with the corresponding state information and stored in the state information database 32.

The identification module 23 identifies the work time spent on the work content estimated by the estimation module 22 from the status information of the worker 100 stored in the status information database 32 and the time information associated with the status information. For example, if the estimation module 22 estimates that manual work was being performed, the identification module 23 identifies the time period from the time corresponding to the time information corresponding to the start timing of the manual work to the time corresponding to the time information corresponding to the end timing of the manual work as the work time spent on the manual work. The identification module 23 then associates the work time with the corresponding status information and stores it in the status information database 32.

The work time comparison module 24 compares the work time identified by the identification module 23 for the work content estimated by the estimation module 22 with the reference time stored in the reference time database 33. The comparison result can be the difference between the work time and the corresponding reference time. For example, if the estimation module 22 estimates manual work as the work content, the identification module 23 identifies this manual work as having a work time of 30 minutes, and the reference time database 33 stores 33 minutes as the reference time for manual work, the work time comparison module 24 obtains a comparison result of this manual work being the reference time + 3 minutes. Alternatively, the comparison result can be a percentage of the reference time (in this case, the reference time + 10%) rather than a difference.

The analysis module 25 analyzes the work content of the worker 100 from various perspectives using the comparison results from the work time comparison module 24 and the information stored in the status information database 32 and the reference time database 33.

The display control module 26 causes graphs and the like showing the results of various analyses performed by the analysis module 25 to be displayed on the display unit 16, and also causes the external terminal 130 to display them via the communication network 70. These graphs include, for example, the results of the comparison of work times obtained by the work time comparison module 24, and work performance charts created based on the time periods identified by the identification module 23.

Specific examples of the analysis performed by the analysis module 25 and the analysis results displayed by the display control module 26 will be described later.

Next, we will explain an example of the operation of a work content analysis device to which the work content analysis method of the first embodiment configured as described above is applied.

Figure 5 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.

To analyze the work content of the workers 100, it is necessary to attach a sensor 110 to each worker 100. It is possible to attach multiple types of sensors 110 to each worker 100, not just one type.

These sensors 110 are IoT devices for visualizing the movements of the worker 100, and examples thereof include, but are not limited to, a position sensor that measures the position of the worker 100 via a GPS function, a WiFi function, a Bluetooth function, or a beacon 125, an acceleration sensor that detects the hand movements of the worker 100, a vital sensor that measures vital information such as the pulse rate of the worker 100, a microphone that detects the voice of the worker 100, a sound sensor that detects sounds around the worker 100, and a camera that captures an image in the line of sight of the worker 100.

In addition, video information from a camera 115 installed at a fixed point and sensing information from a sensor attached to equipment 116 can be used to analyze the work content of the worker 100.

Each sensor 110 is assigned a unique sensor ID and is also associated with the worker ID of the worker 100 to which it is attached. Each camera 115 is assigned a unique sensor ID.

Sensing information measured by each sensor attached to the worker 100 or the equipment 116 is transmitted from the sensor 110 to the work content analysis device 10 via the communication network 70 together with the sensor ID and the worker ID. The image information, which is the sensing information measured by the camera 115, is transmitted to the work content analysis device 10 via the communication network 70 together with the sensor ID. (S1).

The sensing information sent to the work content analysis device 10 is received by the communication unit 15 together with the sensor ID and, if present, the worker ID, and is sent from the communication unit 15 to the processing module 21. The processing module 21 determines whether or not processing of the sensing information is required (S2).

If the sensing information requires processing (S2: Yes), the processing module 21 performs the necessary processing on the sensing information and obtains status information from the sensing information (S3).

In step S2, an example of sensing information that requires processing is video information from the camera 115. In processing this video information, the processing module 21, for example using AI technology, identifies the worker 100 being imaged from the video information, identifies the worker ID of the worker 100, detects movement information and position information of the identified worker 100, and treats the detected movement information and position information as status information.

In this way, when processing video information, the processing module 21 can function specifically as a video processing module.

The processing module 21 can also process or modify sensing information from the acceleration sensor using, for example, AI technology. Note that the sensing information that needs to be processed by the processing module 21 is not limited to these.

This status information is sent from the processing module 21 to the status information database 32 together with the sensor ID and the worker ID (S4).

Examples of sensing information that requires processing include video information from the camera 115 and sensing information from an acceleration sensor.

When processing is performed on the video information from the camera 115, the processing module 21 functions as a video processing module. Then, in the processing module 21, for example, using AI technology, the worker 100 being imaged is identified from the video information, the worker ID of the worker 100 is identified, movement information and position information of the identified worker 100 are detected, and the detected movement information and position information are treated as status information.

In addition, when processing is performed on sensing information from the acceleration sensor, the processing module 21 acquires status information by performing processing or manipulation using, for example, AI technology.

On the other hand, if the sensing information does not require processing (S2: No), the processing module 21 does not process the sensing information, and the sensing information is sent as status information to the status information database 32 together with the sensor ID and worker ID (S5).

In the status information database 32, the status information, sensor ID, and worker ID sent in steps S4 and S5 are stored in association with time information (S6).

The judgment module 27 constantly monitors the operation of the CPU 12 and the communication unit 15, and constantly judges whether the work content analysis device 10 is operating normally. In response to this, if the judgment module 27 judges that the work content analysis device 10 is operating normally but cannot acquire status information, the processing module 21 stores "0" as status information in the status information database 32. On the other hand, if the judgment module 27 judges that the work content analysis device 10 is not operating normally but cannot acquire status information, the processing module 21 stores "-1" as status information in the status information database 32, as exemplified in FIG. 3B.

Furthermore, as described with reference to Figures 4A and 4B, if necessary, the processing module 21 performs calculations and determines the status information.

Then, the estimation module 22 estimates the work content performed by the worker 100 from at least two pieces of state information associated with the same time among the state information of the worker 100 stored in the state information database 32 (S7). The estimation result is associated with the corresponding state information and stored in the state information database 32.

In the identification module 23, the work time spent on the work content estimated by the estimation module 22 is identified from the status information of the worker 100 stored in the status information database 32 and the time information associated with the status information (S8).

In the work time comparison module 24, for the work content estimated by the estimation module 22, the work time identified by the identification module 23 is compared with the reference time stored in the reference time database 33 (S9). The result of this comparison can be the difference between the work time and the corresponding reference time, or the ratio (percentage) of the work time to the reference time. For example, if the estimation module 22 estimates manual work as the work content, the identification module 23 identifies this manual work as having a work time of 30 minutes, and the reference time database 33 stores 33 minutes as the reference time for manual work, the work time comparison module 24 obtains a comparison result of this manual work being the reference time + 3 minutes, or the reference time + 10%.

The analysis module 25 uses the results of this comparison and the information stored in the status information database 32 and the reference time database 33 to analyze the work content of the worker 100 from various perspectives (S10).

The results of the various analyses performed by the analysis module 25 are displayed, for example, in the form of graphs, on the display unit 16 by the display control module 26 and/or on the external terminal 130 (S11). These graphs include, for example, not only the results of the work time comparison obtained by the work time comparison module 24 and work performance charts created based on the time periods identified by the identification module 23, but also graphic displays such as those described below.

Figure 6 is an example of a graphic display showing the time required for each task.

As an example, the reference time database 33 stores the reference times for main tasks, auxiliary tasks, and other tasks (150 minutes for main tasks, 90 minutes for auxiliary tasks, and 60 minutes for other tasks).

The status information database 32 stores the status information of the worker 100 in association with the work content (main work, auxiliary work, etc.) estimated by the estimation module 22 and the work time (120 minutes, 150 minutes, 120 minutes) for each work content identified by the identification module 23.

Figure 6 (a) shows the cumulative time (in minutes) for each task, divided into base time and actual time.

As mentioned above, the reference time database 33 stores 150 minutes, 90 minutes, and 60 minutes as the reference times for main tasks, auxiliary tasks, and other tasks. By referring to the reference time database 33, the analysis module 25 creates a graph in which the reference times are accumulated by task type, as shown in the reference time graph a1.

Similarly, the status information database 32 stores the status information of a certain worker 100 in association with the work content (main work, auxiliary work, etc.) and the work time for each work content (120 minutes, 150 minutes, 120 minutes), so that the analysis module 25 refers to the status information database 32 to create a graph in which the work time for each work content for the worker 100 is accumulated, as shown in the actual time graph a2. In addition, next to each work content in the actual time graph a2, the difference time (in minutes) from the same work content in the reference time graph a1 is shown.

In this example, the worker 100's actual time for the main task is 30 minutes shorter than the reference time, for the ancillary task it is 60 minutes longer than the reference time, and for the other tasks it is 90 minutes longer than the reference time.

On the other hand, Figure 6(b) shows the time proportion for each task as a percentage.

The reference time graph b1 shows the reference time graph a1, and the performance time graph b2 shows the performance time graph a2, with the total time set to 100% and the time ratio of each task displayed as a percentage.

From the graphic displays in Figures 6(a) and 6(b), it can be seen that compared to the reference time, the actual time for main tasks is shorter and the percentage of the total work is low, while conversely, the time for ancillary tasks and other tasks is not only longer than the reference time, but also a higher percentage of the total work than the reference time. Therefore, it is possible to clarify the specific countermeasures for improving work efficiency: more time should be spent on main tasks and less time should be spent on ancillary tasks and other tasks.

This type of graphic display makes it possible to visualize the work content and identify hidden waste inherent in the work. This makes it possible to clarify specific improvements that can be made to improve work efficiency, making it possible to efficiently formulate improvement proposals.

Furthermore, if this improvement leads to increased productivity, it can also help reduce production and procurement costs, which can lead to cost savings.

Figure 7 is a flowchart explaining the process of business improvement.

As described above, according to the work content analysis device to which the work content analysis method of the first embodiment is applied, as shown in the flowchart of FIG. 7, sensing information is acquired from sensors 110 attached to the worker 100 or equipment 116 and cameras 115 installed at fixed points (S21), and various status information related to the work is collected (S22), stored in a status information database 32 (S23), and various analyses are performed (S24). Based on the results of this analysis, the work content is visualized and hidden waste inherent in the work is identified, making it possible to devise specific and effective improvement plans to improve productivity (S25).

In this way, the work content analysis device 10 can analyze work content without requiring personnel to acquire sensing information and without requiring modifications to the factory's equipment.

If the implementation of the improvement plan (S26) is effective in measuring an improvement in productivity (S27), it can also lead to a reduction in production and procurement costs, which can be expected to result in cost reductions.

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

There is a risk that analyzing the work content of individual workers may lead to personal attacks. If this happens, the individual may lose motivation to be productive, and it may be difficult for them to readily accept the results of the work content analysis, which could make it difficult to improve productivity.

On the other hand, if the evaluation is done as a group rather than as an individual, the results of the work analysis are more likely to be accepted, as it will not be seen as a personal attack. This will also increase the sense of cooperation within the group, which is expected to lead to increased productivity.

A group may include multiple workers organized as a work team, multiple workers performing the same task such as inspection or assembly work, or multiple workers differentiated by role, such as team leader or inspector.

For this reason, the work content analysis device to which the work content analysis method of the second embodiment is applied performs work content analysis in groups, such as work teams.

Therefore, in this embodiment, each worker belongs to one of the groups that have been pre-classified, and unlike the first embodiment, the analysis of work content is performed on a group basis, rather than on a worker basis.

The configuration of the work content analysis device to which the work content analysis method of the second embodiment is applied can be explained using Figure 1, so below, we will refer to Figure 1 and explain the differences from the first embodiment without repeating the explanation.

In this embodiment, each worker 100 is classified into one of the groups. A group can be, for example, a work group in which workers perform work together.

Accordingly, the worker ID of each worker 100 includes not only identification information that identifies the worker 100, but also group information that specifies the group to which the worker 100 belongs. For example, if the group information is a group number that is assigned to the group in advance, the worker ID can be numeric information that serially combines the employee number and the group number.

Therefore, the worker database 31 stores, for each worker 100, a worker ID including identification information for identifying the worker and identification information for specifying the group to which the worker belongs. Similarly, the status information stored in the status information database 32 together with the time information is also stored in association with such a worker ID.

The estimation module 22 estimates the work content performed by the group from at least two pieces of status information associated with the same time among the status information of multiple workers 100 belonging to the same group stored in the status information database 32.

For example, as an example of a case where at least two pieces of status information are from two workers 100 (#1) and 100 (#2) belonging to the same group, a case will be described in which the position information from the position sensor and the voice information from the microphone of the worker 100 (#1) and the position information from the position sensor and the voice information from the microphone of the worker 100 (#2) are included. In this case, if the position information of the worker 100 (#1) and the position information of the worker 100 (#2) both indicate that they are within the work area, and the voice information of the worker 100 (#1) and the voice information of the worker 100 (#2) indicate that the worker 100 (#1) and the worker 100 (#2) are having a conversation about work, the estimation module 22 can estimate that the group was working, and can further estimate the work content based on the content of the conversation.

Note that the above is merely an example, and the estimation module 22 can estimate various work contents performed by the group from a combination of various other status information from multiple workers belonging to the same group. The estimation result is then associated with the status information of the corresponding worker 100 and stored in the status information database 32.

The identification module 23 identifies the work time corresponding to the work content estimated by the estimation module 22 from the status information of multiple workers 100 belonging to the same group stored in the status information database 32. For example, if the estimation module 22 estimates that manual work was being performed in the group, the identification module 23 identifies the time period from the time corresponding to the time information corresponding to the start timing of the manual work to the time corresponding to the time information corresponding to the end timing of the manual work as the work time spent on the manual work. The identification module 23 then associates the work time with the status information of the corresponding worker 100 and stores it in the status information database 32.

As in the first embodiment, the work time comparison module 24 compares the work time identified by the identification module 23 for the work content estimated by the estimation module 22 with the reference time stored in the reference time database 33.

The analysis module 25 analyzes the work content of the group based on the work content estimated by the estimation module 22 and the work time identified by the identification module 23. Alternatively, the analysis module 25 analyzes the work content of each worker 100 belonging to the group based on the work content estimated by the estimation module 22 and the work time identified by the identification module 23, and further analyzes the work content of the group as a whole based on the results of this analysis.

Next, we will explain an example of the operation of a work content analysis device to which the work content analysis method of the second embodiment configured as described above is applied.

Figure 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 second embodiment is applied.

In Figure 8, the same processes as in Figure 5 are given the same step numbers, and only the differences are explained to avoid duplication.

As in the first embodiment, each worker 100 is equipped with one or more types of sensors 110. Also, as in the first embodiment, a camera 115 is installed at a fixed point.

Each sensor 110 is assigned a sensor ID and associated with a worker ID, which includes not only the identification information of the worker 100 but also the identification number of the group to which the worker 100 belongs.

Therefore, in the following explanation of Figure 5, the worker ID will be assumed to include not only the identification information of the worker 100, but also the identification number of the group to which the worker 100 belongs.

In the estimation module 22, the work content performed by the group is estimated from at least two pieces of status information associated with the same time among the status information of multiple workers 100 belonging to the same group stored in the status information database 32 (S7a).

The identification module 23 identifies the work time spent on the work content estimated by the estimation module 22 from the status information of multiple workers 100 belonging to the same group stored in the status information database 32 (S8a).

As in the first embodiment, the work time comparison module 24 compares the work time identified by the identification module 23 for the work content estimated by the estimation module 22 with the reference time stored in the reference time database 33 (S9a).

In the analysis module 25, the work content of the group is analyzed based on the work content estimated by the estimation module 22 and the work time identified by the identification module 23. Alternatively, the work content of each worker 100 belonging to the group is analyzed based on the work content estimated by the estimation module 22 and the work time identified by the identification module 23, and further, the work content of the group as a whole is analyzed based on the results of this analysis (S10a).

As in the first embodiment, this analysis can be performed from various perspectives using the information stored in the state information database 32 and the reference time database 33.

Graphs showing the results of various analyses performed by the analysis module 25 for the group are displayed on the display unit 16 or the external terminal 130 by the display control module 26 (S11a).

Below are examples of graphic displays that are displayed on the display unit 16 and/or the external terminal 130 based on the analysis results by the analysis module 25.

Figure 9 is an example of a graphic display showing the frequency and duration of visits to each area of the factory by all workers in a group.

Figure 9(a) shows the cumulative time that a worker 100 spent in each area A to J performing manual work in one day (or one week). In Figure 9(a), the vertical axis represents the cumulative time, and the horizontal axis represents the area.

From FIG. 9(a), it can be seen that the workers belonging to the group stayed in Area D for a cumulative 6 hours and 15 minutes for manual work. Furthermore, from FIG. 9(b), it can be seen that the breakdown of the 6 hours and 15 minutes is that Worker X stayed there for 3 hours and 33 minutes, Worker Y stayed there for 1 hour and 24 minutes, and Worker Z stayed there for 1 hour and 18 minutes.

Figure 9(c) shows the results of three classifications based on the ratio of time spent in each area to the total cumulative time in all areas shown in Figure 9(a) (area concentration). In Figure 9(c), each area is displayed diagrammatically according to its positional relationship in the actual layout of the factory. According to the example in Figure 9(c), only area D has an area concentration of 21% or more due to manual work, only area I has an area concentration of 4% or less, and the other areas have area concentration of 5-20%.

In addition, as shown in the legend in Figure 9(a), it is also possible to display the display shown in Figure 9 according to the type of work being performed by the worker 100, such as not only manual work but also cart work, walking, and stationary work.

This type of display makes it possible to visualize the actual on-site behavior of the entire group, such as which areas workers tend to concentrate on which tasks, and which personnel have a heavy or light burden.

Figure 10 shows another example of a comparison of the work performance of people with high and low levels of skill.

Figure 10 shows an example of a graphic display that shows actual work hours broken down by type of work.

In this example, the work is classified into main work, ancillary work, and others.

In Figure 10(a), the vertical axis represents cumulative time, and in Figure 10(b), the vertical axis represents time percentage (%).

Figures 10(a) and 10(b), which compare the actual working hours of a highly skilled person, such as an experienced worker, and a less skilled person, such as a beginner, show that compared to experienced worker X, novice worker Y has a longer total working time, a lower proportion of main tasks, and a higher proportion of incidental tasks and other tasks. Therefore, it can be specifically clarified that the area requiring improvement to improve the work efficiency of beginners is to reduce the proportion of incidental tasks and other tasks and increase the proportion of main tasks, thereby shortening the total working hours.

As described above, the work content analysis device to which the work content analysis method of the second embodiment is applied can perform the work content analysis on a group basis, unlike the work content analysis performed on an individual worker basis in the first embodiment.

As a result, workers will no longer be criticized individually, and each worker will be more likely to accept the analysis results and the items for improvement that are derived from them. This will inevitably encourage each worker to proactively work on improving their work, and we can expect to foster a sense of cooperation within the group. This can be expected to revitalize the group, for example by increasing communication within the group and encouraging workers to proactively work on necessary work improvements, such as changing work procedures or layouts.

And ultimately, it is expected that this will lead to cost reduction effects such as improved productivity and reduced production and procurement costs.

In the above, we have explained that in order to improve work efficiency, we should pay attention to the work time and the breakdown of work time of experienced workers. However, it is also possible to pay attention to the locations of experienced workers (for example, the experienced worker went to a certain location, but the novice worker did not) or the routes they took (the experienced worker took this route, but the novice worker took a different route) and use these to improve work efficiency.

Although several 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 gist of the invention. These embodiments and their modifications are within the scope of the invention and its equivalents as set forth in the claims, as well as the scope and gist of the invention.

10: Work content analysis device, 11: Bus, 13: External recording medium, 14: Recording medium reading unit, 15: Communication unit, 16: Display unit, 20: Memory, 21: Processing module, 22: Estimation module, 23: Identification module, 24: Work time comparison module, 25: Analysis module, 26: Display control module, 27: Judgment module, 29: Writable data area, 30: Storage device, 31: Worker database, 32: Behavioral information database , 33: Reference time database, 70: Communication network, 100: Worker, 110: Sensor, 115: Camera, 116: Equipment, 120: Microcontroller, 125: Beacon, 130: External terminal, 1101: Bus, 1102: CPU, 1103: Sensing unit, 1104: Transmitter, 1105: Receiver, 1110: Memory, 1111: Communication determination module, 1120: Storage device, 1121: Sensing information database

Claims (21)

A work content analysis device that analyzes work content of a worker based on a state of the worker,
a first database that stores status information representing the status of one or more of the workers in association with time information and identification information of the workers, the status information including location information of the workers, movement information indicating whether the workers' hands are moving, and information indicating the operating status of equipment around the workers;
an estimation unit that estimates the work content performed by the worker based on state information associated with the same time and stored in the first database;
an identification unit that identifies a task time spent on the estimated task content from the state information stored in the first database and time information associated with the state information;
an analysis unit that analyzes the work content based on the estimated work content and the specified work time ;
a determination unit that determines whether the task content analysis device is operating normally;
a processing unit that stores, in the first database, first fixed data as the status information if the determining unit determines that the work content analysis device is normal when the status information cannot be acquired, and stores, in the first database, second fixed data as the status information if the determining unit determines that the work content analysis device is not normal.
A work content analysis device comprising: The task analysis device according to claim 1, further comprising a processing unit that obtains second status information by performing mathematical arithmetic processing on one or more pieces of first status information among the plurality of pieces of status information, and stores the first status information and the second status information in the first database. The work content analysis device according to claim 1, wherein the status information is acquired by a sensor attached to the worker or a sensor installed around the worker, and is stored in the first database. 4. The work content analysis device according to claim 3, wherein the first database stores the sensor in association with identification information of a worker to whom the sensor is attached, or identification information of a worker within a predetermined distance from a location where the sensor is installed. A second database that stores a reference time that is determined in advance for each of the work contents;
The task content analyzing device according to claim 1 , further comprising a task time comparing unit that compares the identified task time with the corresponding reference time for the estimated task content. The task content analysis device according to claim 5 , wherein the comparison result includes a difference or a ratio between the task time and the corresponding reference time. The task content analysis device according to claim 6 , further comprising a display control unit that displays the difference or the ratio for each of the tasks. The task content analysis device according to claim 6 , wherein the analysis unit further analyzes the task content of the worker based on the difference or the ratio. The task content analysis device according to claim 1, further comprising a display control unit that displays the analysis results by the analysis unit. The identification unit further identifies a time period corresponding to the estimated work content from the state information stored in the first database,
The work content analysis device according to claim 9 , wherein the display control unit displays, as the analysis result, a work performance diagram in which the work content is displayed on a time axis based on the specified time period. A work content analysis device that analyzes work content of a worker based on a state of the worker,
a first database that stores status information representing the status of one or more of the workers in association with time information and identification information of the workers, the status information including location information of the workers, movement information indicating whether the workers' hands are moving, and information indicating the operating status of equipment around the workers;
an estimation unit that estimates the work content performed by the worker based on state information associated with the same time and stored in the first database;
an identification unit that identifies a task time spent on the estimated task content from the state information stored in the first database and time information associated with the state information;
an analysis unit that analyzes the work content based on the estimated work content and the specified work time,
The worker belongs to any one of the groups previously classified,
the identification information includes group information specifying a group to which the worker belongs,
the estimation unit estimates the work content performed by the group based on status information stored in the first database that is associated with the same time among status information of workers belonging to the same group, and
The identification unit identifies a task time spent on the estimated task content from status information of workers belonging to the same group stored in the first database, and
The analysis unit analyzes the work content of the group based on the estimated work content and the specified work time. The work content analysis device according to claim 1, wherein the status information further includes at least one of movement information indicating whether the worker is moving, vital sign information of the worker, voice information of the worker, ambient sound information of the worker, and video information of the surroundings of the worker. The work content analysis device according to claim 1, wherein the time information stored in the first database is synchronized with the timing means of a system to which the work content analysis device itself is connected. The work content analysis device according to claim 1, wherein the work content estimated by the estimation unit includes a worker's break. A work content analysis method implemented by a work content analysis device for analyzing work content of a worker based on a state of the worker, comprising:
The work content analysis device includes:
storing status information representing the status of one or more of the workers in a database in association with time information and identification information of the workers, the status information including position information of the workers, movement information indicating whether the workers' hands are moving, and information indicating the operating status of equipment around the workers;
estimating the work content performed by the worker based on state information associated with the same time and stored in the database;
Identifying a task time spent on the estimated task content from the state information stored in the database and time information associated with the state information;
Analyzing the work content based on the estimated work content and the specified work time ;
Determining whether the task content analysis device is operating normally;
When the status information cannot be obtained, if it is determined that the work content analysis device is normal, first fixed data is stored in the database as the status information, and if it is determined that the work content analysis device is not normal, second fixed data is stored in the database as the status information . A program applied to a work content analysis device that analyzes work content of a worker based on the worker's state,
a function of storing status information representing the status of one or more of the workers in a database in association with time information and identification information of the workers, the status information including position information of the workers, movement information indicating whether the hands of the workers are moving, and information indicating the operating status of equipment around the workers;
A function of estimating the work content performed by the worker based on state information associated with the same time and stored in the database;
a function of identifying a task time spent on the estimated task content from the state information stored in the database and time information associated with the state information;
A function of analyzing the work content based on the estimated work content and the specified work time;
A function of determining whether the task content analysis device is operating normally;
a function of storing, in the database, first fixed data as the status information if it is determined that the work content analysis device is normal when the status information cannot be acquired, and storing, in the database, second fixed data as the status information if it is determined that the work content analysis device is not normal.
A program to make the above happen on a computer. A sensor for acquiring the state information to be transmitted to the task content analysis device according to claim 3 ,
A sensing unit that acquires status information of the worker;
a state information database that stores the acquired state information;
a communication determination unit that determines whether communication with the work content analysis device is possible;
a transmission unit that transmits, when the communication determination unit determines that communication is possible, the status information stored in the status information database to the work content analysis device in predetermined units. The sensor according to claim 17 , further comprising a radio wave receiving unit that receives radio waves from a radio wave transmitter whose radio wave intensity is variable. 19. The sensor of claim 17 or 18 , attached to the worker or located in the vicinity of the worker. The sensor of claim 19 , wherein when attached to the worker, the sensor is attached to any one of the front of the worker's body, the left or right side of the waist, the arm, the wrist, the neck, the shoulder, the ear, and the head. The sensor of claim 20 , wherein the sensor is attached to the worker's dominant arm or hand when acquiring the movement information.