Program, map creation device, and learning device

Abstract

Provided is a program for instructing a processor of a map creation device, which communicates with a monitoring device that includes one or more sensors and moves together with a user being monitored, to execute: a process for acquiring an output value of the sensor of the monitoring device and the location of the user of the monitoring device; a process for determining that a prescribed event has occurred to the user of the monitoring device on the basis of information acquired from the monitoring device; a process for creating map information for displaying a map image representing the number or frequency of the events for each place where the prescribed event has occurred; and a process for outputting the map information to another terminal device.

Description

Program, map generation device, and learning device

[0001] The present application claims priority to Japanese Patent Application No. 2023-150238 filed in Japan on September 15, 2023, and Japanese Patent Application No. 2024-123346 filed in Japan on July 30, 2024, the contents of which are incorporated herein by reference.

[0002] At work sites such as factories, various work-related accidents can occur, such as injuries from falls and heatstroke due to high temperatures. Managers need to monitor and take measures to prevent these accidents from occurring.

[0003] Japan Special Table No. 2022-531490 (A)

[0004] However, it can be difficult for managers to constantly monitor the factory and to identify areas in the factory where countermeasures should be taken.

[0005] The present invention has been made in consideration of these circumstances, and one of its objects is to provide a program, a map generation device, and a learning device that can monitor and predict the occurrence of work-related accidents in order to prevent them.

[0006] To solve the above problems, the program, map generation device, and learning device have the following configurations: A first aspect of the present invention is a program that causes a processor of a map generation device that has one or more sensors and communicates with a monitoring device that moves with a user who is being monitored to execute the following processes: acquiring output values ​​of the sensors of the monitoring device and the location of the user of the monitoring device; determining that a predetermined event has occurred to the user of the monitoring device based on information acquired from the monitoring device; generating map information for displaying a map image that indicates the number or frequency of occurrences of the predetermined event for each location; and outputting the map information to another terminal device.

[0007] In the program of aspect 2 of the present invention, the monitoring device is built into or detachably attached to shoes worn by the user.

[0008] The program of the third aspect of the present invention is a program for executing a process in which the predetermined event is a fall, and the number or frequency of falls at each location of the user is generated as map information.

[0009] The program of aspect 4 of the present invention is a program for executing a process in which the predetermined event is the user's slipping, and the program generates map information indicating the number or frequency of times the user has slipped at each location.

[0010] A program according to a fifth aspect of the present invention is a program for executing a process in which the predetermined event is heatstroke, and the number or frequency of occurrence of heatstroke for each location is generated as map information.

[0011] A program according to a sixth aspect of the present invention is a program for executing a process in which the predetermined event is a user carrying a heavy object, and the program generates map information indicating the number or frequency at each location at which the user has carried a heavy object.

[0012] The program of aspect 7 of the present invention is a program for causing a processor of a map generation device to execute the following processes: a process of acquiring a trained model that has been trained to be able to predict the occurrence of a specified event using past performance data of the specified event as input information; a process of generating a prediction map of the occurrence of the specified event using the trained model; and a process of outputting the prediction map.

[0013] The program of aspect 8 of the present invention is a program for causing a processor of a map generation device to execute the following processes: a process of acquiring a trained model that has been trained to be able to predict the occurrence and risk of a specified event using past performance data of the specified event as input information; a process of generating a risk level map of the specified event using the trained model; and a process of outputting the risk level map.

[0014] The program of aspect 9 of the present invention is a program for causing a processor of the map generating device to execute a process of generating map information for displaying the movement trajectory of the user, who is the target of monitoring, superimposed on the map image, the movement trajectory being generated based on the position of the user.

[0015] The map generating device of aspect 10 of the present invention is a map generating device that has one or more sensors and communicates with a monitoring device that moves with the user being monitored, and is equipped with an acquisition unit that acquires the output value of the sensor of the monitoring device and the position of the user of the monitoring device, a determination unit that determines that a specified event has occurred to the user of the monitoring device based on the information acquired from the monitoring device, a map generating unit that generates map information for displaying a map image showing the number of times or frequency that the specified event has occurred, and an output unit that outputs the map information to another terminal device.

[0016] In the map generating device of aspect 11 of the present invention, the monitoring device is built into or detachably attached to shoes worn by the user.

[0017] The learning device of aspect 12 of the present invention is intended to execute an acquisition unit that acquires past records of specified events that have occurred to users of the monitoring device and a model generation unit that generates a trained model that has been trained to be able to predict the occurrence of the specified events.

[0018] According to the present invention, by outputting a map containing information on work-related accidents, it is possible to carry out efficient monitoring and take preventive measures against work-related accidents.

[0019] FIG. 1 is an overall view of a map output system 1. FIG. 2 is a view showing an example of a map image displayed by a second terminal device 400. FIG. 3 is a view showing an example of an occupational accident occurrence prediction map image displayed by a second terminal device 400. FIG. 4 is a view showing an example of an occupational accident risk level map displayed by a second terminal device 400. FIG. 5 is a view showing an example of an occupational accident risk level map including flow line data displayed by a second terminal device 400. FIG. 6 is a view showing an example of a map image including the flow line data in the map image of FIG. 2. FIG. 7 is a view showing an example of processing performed by a model generation unit 620.

[0020] Hereinafter, embodiments of a program, a learning device, and a map generation method of the present invention will be described with reference to the drawings.

[0021] FIG. 1 is an overall view of a map output system 1. The map output system 1 includes, for example, a monitoring device 100 and a map generation device 200. The monitoring device 100 is connected to the map generation device 200 via a first terminal device 300, a second terminal device 400, and a network NW. The network NW includes, for example, the Internet, a local area network (LAN), a wireless base station, a provider device, etc. The monitoring device 100 and the first terminal device 300 are connected via short-range wireless communication such as Bluetooth (registered trademark). A plurality of beacon transmitters 500 are installed, for example, in a factory where a user works.

[0022] The beacon transmitter 500 wirelessly transmits a signal (e.g., an RFID signal) once every few seconds within a radius of several meters to several tens of meters. The beacon transmitters 500 are installed, for example, at regular intervals in the user's workplace. The beacon transmitter 500 includes, for example, a transmitter ID. The monitoring device 100 is assumed to know in advance the installation location of the beacon transmitter 500 corresponding to the transmitter ID.

[0023] The components of each device are described below. The monitoring device 100 includes, for example, a communication unit 110, pressure sensors 120L and 120R, acceleration sensors 130L and 130R, a temperature sensor 140, a heart rate sensor 150, a position detection unit 160, a reporting unit 170, and a beacon receiver 180. The monitoring device 100 is a device that is built into or detachably attached to a shoe, and includes components for a left foot and a right foot. The communication unit 110, the temperature sensor 140, the heart rate sensor 150, the position detection unit 160, the reporting unit 170, and the beacon receiver 180 are provided on either the left foot, the right foot, or both. Hereinafter, when there is no need to distinguish between the sensors for the left foot and the right foot, the symbols L and R indicating whether the sensor is for the left foot or the right foot may be omitted. It is also desirable to have multiple pressure sensors 120L and 120R for each of the left and right feet. While not particularly limited, the number of sensors included in the monitoring device 100 may be one or more, two or more, or five or more. While not particularly limited, the number of sensors included in the monitoring device 100 may be 100 or less, 10 or less, or five or less. The temperature sensor 140, heart rate sensor 150, position detection unit 160, reporting unit 170, and beacon receiver 180 may be used as part of a mobile terminal, a mobile phone, or a smartwatch.

[0024] The monitoring device 100 may be any device that moves with the user being monitored. For example, the monitoring device 100 may be attached to the user's body or clothing, or attached like a wristwatch strap. In this case, the burden of attaching and carrying the device on the user being monitored is reduced. The monitoring device 100 may be incorporated into an accessory or detachably attached. Attached includes being housed. Examples of accessories include safety shoes, helmets, belts, work clothes, and other items worn by workers. Since the user does not forget to wear such accessories, they are less likely to forget to carry the monitoring device 100. Alternatively, the monitoring device 100 may be a shoe insole, a wristwatch, a ring, a necklace, a bracelet, a name tag, or the like. The shoes into which the insole is inserted are those worn by the user when working in a facility such as a factory. In this way, if the insole that is the monitoring device 100 is inserted into the shoes that the user normally wears, the user is more likely to forget to carry the monitoring device 100, which is more preferable.

[0025] The communication unit 110 communicates with the map generation device 200, for example, via the first terminal device 300 and the network NW. Alternatively, the communication unit 110 may communicate with the map generation device 200 by connecting to the network NW without going through the first terminal device 300.

[0026] The beacon receiver 180 receives the signal emitted by the beacon transmitter 500 and acquires the transmitter ID.

[0027] The pressure sensor 120 is a device that measures the pressure of the user's foot using a pressure-sensitive element, converts it into an electrical signal, and outputs it.

[0028] The acceleration sensor 130 is, for example, a three-axis acceleration sensor that converts the acceleration along each of the three axes into an electrical signal and outputs it. The acceleration along each of the three axes detected by the acceleration sensor includes a gravitational acceleration component, which can be used to detect the tilt and vibration of the user's foot. The acceleration sensor 130 may also be, for example, a gyro sensor.

[0029] The temperature sensor 140 measures the temperature of the user's feet and / or the outside air temperature, converts it into an electrical signal, and outputs it.

[0030] The heart rate sensor 150, for example, shines light onto the user's leg, detects the light reflected by the blood vessels, and measures the pulse rate from changes in the amount of light over time. The heart rate sensor 150 is a device that converts the measured pulse rate into an electrical signal and outputs it. Note that the heart rate sensor 150 may also be a microwave sensor.

[0031] The position detection unit 160 detects the position of the monitoring device 100 (i.e., the user's position) based on, for example, the transmitter ID acquired by the beacon receiver 180. The position detection unit 160 can detect the installation location of the beacon transmitter 500 from the transmitter ID, so it may detect the installation location of the detected beacon transmitter 500 as the user's position, or, if multiple transmitter IDs are acquired, it may detect the user's position based on the principle of triangulation taking into account reception strength. Furthermore, the position detection unit 160 may detect the user's position using information from a GPS receiver (not shown) of the first terminal device 300.

[0032] The reporting unit 170 outputs the output values ​​of the various sensors, together with, for example, the position detected by the position detection unit 160, to the map generating device 200 via the communication unit 110 and the first terminal device 300. At this time, the reporting unit 170 adds identification information of the watching device 100 and outputs the output values ​​and the position to the map generating device 200. The output information is acquired and managed by the map generating device 200. For example, the map generating device 200 manages information in which the identification information of the watching device 100, the output values, and the position are associated with each other as management information.

[0033] The map generating device 200 includes, for example, a communication unit 210, an acquisition unit 220, a determination unit 230, a map generating unit 240, and an output unit 250. The components other than the communication unit 210 are implemented by, for example, a hardware processor such as a CPU (Central Processing Unit) executing a program (software). Some or all of these components may be implemented by hardware (including circuitry) such as an LSI (Large Scale Integration), an ASIC (Application Specific Integrated Circuit), an FPGA (Field-Programmable Gate Array), or a GPU (Graphics Processing Unit), or may be implemented by a combination of software and hardware. The program may be stored in advance in a storage device (a storage device with a non-transitory storage medium) such as an HDD (Hard Disk Drive) or flash memory, or may be stored in a removable storage medium (a non-transitory storage medium) such as a DVD or CD-ROM, and installed by inserting the storage medium into a drive device.

[0034] The communication unit 210 is a communication interface for connecting to the network NW. The communication unit 210 communicates with the watching device 100 via the first terminal device 300.

[0035] The acquisition unit 220 acquires, for example, output values ​​of the pressure sensor 120, acceleration sensor 130, temperature sensor 140, and heart rate sensor 150, as well as the current location of the user, from the watching device 100.

[0036] The determination unit 230 determines whether the user has fallen, slipped, suffered from heat stroke, or carried something heavy, based on the information acquired by the acquisition unit 220.

[0037] (1) The determination unit 230 determines whether the user has fallen based on, for example, the output values ​​of the pressure sensor 120 and the acceleration sensor 130. The determination unit 230 may determine whether the user has fallen based only on, for example, the output value of the pressure sensor 120. The determination unit 230 may determine that the user has fallen, for example, when the output values ​​of the pressure sensors 120 of both feet are less than a threshold value. The determination unit 230 may also determine that the user has fallen, for example, when the output values ​​of the pressure sensors 120 of both feet are less than a threshold value and the angle of the acceleration sensor 130 changes rapidly (for example, the resultant acceleration exceeds the threshold value within one second). When the determination unit 230 determines that the user has fallen, it outputs the number of falls and the locations of falls. The determination unit 230 acquires information on the locations of falls from the acquisition unit 220. The number of falls and the locations of falls together constitute fall information.

[0038] (2) The determination unit 230 may determine whether the user has slipped, for example, based on the output value of the acceleration sensor 130. The determination unit 230 determines that the user has slipped if the angle of the acceleration sensor 130 changes suddenly (for example, if the resultant acceleration exceeds a threshold within one second). If the determination unit 230 determines that the user has slipped, it outputs the number of times the user has slipped and the location of the slip. The determination unit 230 acquires the location of the user's slip from the acquisition unit 220. The number of times the user has slipped and the location of the slip are combined to form semi-fall information.

[0039] (3) For example, when the output value of the temperature sensor 140 is equal to or greater than a threshold value, the determination unit 230 may determine that the user has heatstroke or is likely to have heatstroke. For example, the determination unit 230 may determine whether the user has heatstroke or is likely to have heatstroke using the value of an outdoor thermometer (not shown) installed in a factory and the output value of the temperature sensor 140. When the determination unit 230 determines that the user has heatstroke or is likely to have heatstroke, it acquires the location where the heatstroke has been determined from the acquisition unit 220. The acquired information that the user has heatstroke and the location where the heatstroke occurred are regarded as heatstroke information.

[0040] (4) The determination unit 230 may determine whether the user has held a heavy object based on, for example, the output values ​​of the pressure sensors 120. The determination unit 230 determines that a heavy object has been held when the output values ​​of the pressure sensors 120 on both feet exceed a threshold value for a certain period of time. When the determination unit 230 determines that the user has held a heavy object, it acquires the location where the heavy object was held from the acquisition unit 220. The acquired information that a heavy object has been held and the location where the heavy object was held are regarded as weight information.

[0041] The predetermined events are work-related accidents or events that could potentially become work-related accidents. Each time a predetermined event occurs, the determination unit 230 acquires the location where the predetermined event occurred from the acquisition unit 220 and associates each occurrence of the predetermined event with the location.

[0042] The map generating unit 240 generates map information by receiving various types of information from the determining unit 230. The map generating unit 240 may generate map information for each type of information, or may generate one piece of map information for all the information.

[0043] The output unit 250 outputs the map information generated by the map generation unit 240 to the second terminal device 400 and displays it as a map image. The map information may be, for example, an image in JPEG (Joint Photographic Experts Group) format, or may be information such as parameters (such as the number of occurrences of an event associated with a coordinate) for displaying the map image on the second terminal device 400.

[0044] The storage unit 260 is configured by a storage medium, such as a HDD, a flash memory, an EEPROM (Electrically Erasable Programmable Read Only Memory), a RAM (Random Access Read / Write Memory), a ROM (Read Only Memory), or any combination of these storage media. The storage unit 260 stores a trained model 632, which will be described later.

[0045] The first terminal device 300 and the second terminal device 400 are devices that can be connected to the network NW, such as smartphones and tablet devices. The first terminal device 300 is owned by a user, and the second terminal device 400 is owned by the user's administrator. The second terminal device 400, for example, runs a monitoring app (application software) and displays output values ​​of various sensors and a map output from the map generation device 200 as an image.

[0046] FIG. 2 is a diagram showing an example of a map image displayed by the second terminal device 400. Based on various information determined by the determination unit 230, FIG. 2 indicates locations where predetermined events occurred with black circles, and further indicates the frequency of occurrence, indicating how many times each predetermined event occurred during an observation period (e.g., one month). While FIG. 2 shows the number of occurrences per month, for example, all past occurrences may be displayed, or the number of occurrences per year may be displayed. These occurrences, frequencies, or details of the predetermined events may be displayed when an operation is performed on the map image at or near the location where the predetermined event occurred. In other words, a "map image showing the number or frequency of occurrences for each location where the predetermined event occurred" may be displayed after the above operation is performed. This information may be displayed on a separate screen or superimposed on the map image.

[0047] The map generating unit 240 may generate not only the map information that is the basis for the map image shown in Fig. 2 but also a work-related accident prediction map. Fig. 3 is a diagram showing an example of a work-related accident prediction map image displayed by the second terminal device 400. The work-related accident prediction map is a map showing information predicting what types of work-related accidents will occur. The work-related accident prediction map image, for example, indicates locations where work-related accidents are predicted to occur with black circles, and indicates the type of work-related accident that will occur with a level such as high, medium, or low based on the output values ​​of various sensors and the past actual number of specified events. The level may be expressed, for example, as a numerical value.

[0048] For example, the map generator 240 may set a threshold for each predetermined event, and indicate a level as "high" when the output values ​​of various sensors or the past occurrence count of the predetermined event exceed a first threshold, a level as "medium" when between the first threshold and a second threshold (first threshold > second threshold), or a level as "low" when below the threshold. Information indicating these levels may be displayed when an operation is performed on the map image at or near the location where the predetermined event occurred. This information may be displayed on a separate screen or may be superimposed on the map image.

[0049] The map generator 240 may generate not only the occupational accident prediction map shown in FIG. 3 but also an occupational accident risk level map. FIG. 4 shows an example of an occupational accident risk level map displayed by the second terminal device 400. The occupational accident risk level map represents the predicted risk of occupational accidents in map format. The occupational accident risk level is, for example, a numerical value obtained by multiplying the type of occupational accident predicted to occur by the degree of impact. The higher the "occupational accident risk level," the higher the risk. The degree of impact is a predetermined value that takes into account the impact of the occupational accident, such as the aftereffects it may have on the employer and the impact it may have on the business. FIG. 4 shows, for example, a factory divided into 3 m x 3 m sections, with mesh processing applied to display the occupational accident risk level for each section. The risk of occupational accidents is indicated by shades of color, with darker colors indicating higher risk and lighter colors indicating lower risk.

[0050] The map generation unit 240 may identify locations where physically demanding work is performed as locations where work-related accidents are likely to occur, or may determine that the risk of work-related accidents is high. The map generation unit 240 may also acquire locations where physical stress is high by inputting physical stress information into the trained model 632. The process of generating the trained model 632 will be described later. A location where the output value of the pressure sensor 120 is higher than the threshold value for only one foot for a certain period of time, or a location where the output value of the pressure sensor 120 periodically exhibits similar values, may also be identified as a location where work-related accidents are likely to occur, or may determine that the risk of work-related accidents is high. The information indicating the risk level may be displayed in response to a user's operation. For example, when a user performs an operation on a specified area, information indicating the risk level of the area or the vicinity of the area corresponding to the operation may be displayed. This information may be displayed on a separate screen or superimposed on a map image.

[0051] For the purposes of processing by the map generation unit 240, the determination unit 230 determines that the user is performing work that places a high physical strain on the body when, for example, the output value of the heart rate sensor 150 exceeds a threshold value for a certain period of time. The map generation unit 240 may consider work that places a high physical strain on the body as work that is likely to cause an occupational accident, and add this work to the materials used to generate the occupational accident prediction map and the occupational accident risk level map.

[0052] For example, if the output value of the pressure sensor 120 is higher than the threshold value for only one foot for a certain period of time, the determination unit 230 determines that an occupational accident is likely to occur. Examples of occupational accidents that are likely to occur include falls and injuries such as lower back pain. The map generation unit 240 may add information about occupational accidents that are likely to occur to the materials used to generate the occupational accident prediction map and the occupational accident risk level map.

[0053] For example, the determination unit 230 determines that repetitive work is occurring when the output value of the pressure sensor 120 periodically indicates similar values. The map generation unit 240 may consider repetitive work to be prone to injuries such as lower back pain, and add information about locations where repetitive work occurs to the materials used to generate the work accident prediction map and the work accident risk level map.

[0054] The map generating unit 240 may, for example, generate a map by overlaying flow line data on the generated map. Flow line data is a user's movement route acquired from the position detecting unit 160, with points connected by lines. Flow line data is an example of "a movement trajectory of the user that is generated based on the position of the user who is the monitoring target." The map generating unit 240 may generate a map that displays the flow line data alone.

[0055] 5 is a diagram showing an example of an occupational accident risk level map including movement line data displayed by the second terminal device 400. The dotted lines indicate the movement lines of users. The map generator 240 may generate a map including movement line data of multiple users. Furthermore, the map generator 240 may generate a map including multiple movement line data of a target user (e.g., movement line data for work performed on different dates and times), or may generate a map including movement line data of different users.

[0056] The map generator 240 may generate map information that superimposes the flow line data on the map of Figure 2 or 3. In response to a user's operation, a map may be displayed that displays any or all of the following information: the flow line data, the number of occurrences of a predetermined event, the frequency of the predetermined event, and details of the predetermined event. This allows the administrator to easily check the information they want to obtain.

[0057] For example, displaying a map with movement data overlaid on the map generated as described above can sometimes make it possible to identify the cause of a specific event. For example, by reviewing the movement data of a person immediately before the fall, the cause of the fall can sometimes be identified. It can also be useful for determining whether the cause of the specific event is the individual who caused the specific event or the environment. For example, analyzing the behavior of each specific event can reveal that, even if a number of events appear to occur at first glance, a single person is likely causing the specific event in a similar pattern. This clarification can be used to develop preventive measures for industrial accidents. Furthermore, by generating a map that displays movement data alone while having the function to display specific events, analysis using the movement data can be simplified, even for complex movement data that would be difficult to read if displayed overlaid with a specific event. Multiple movement data can also be displayed on a single map. In this case, analyzing multiple movement data for a specific event can easily determine whether the cause is the individual or the environment.

[0058] Note that the flow line data may be included in an occupational accident risk level map as shown in Fig. 5, or may be included in the map image of Fig. 2 or the occupational accident occurrence prediction map of Fig. 3. Fig. 6 is a diagram showing an example of a map image including the flow line data in the map image of Fig. 2. By including the flow line data in the map image, it is possible to easily compare the occurrence of a predetermined event with the flow line data, identify the cause of the occurrence of the predetermined event, and consider plans to prevent the occurrence of the predetermined event.

[0059] The map generating device 200 may display information indicating the flow line data of a specified monitoring device 100 (user) on a map. For example, the map generating device 200 may refer to the management information to identify the flow line data associated with the identification information of the monitoring device 100 used by the specified user, and display a map including the identified flow line data. Furthermore, the identification information of the management information is associated with information indicating whether a specific event has occurred and information such as details of the specific event. More specifically, the identification information of the management information is associated with the location history when the specific event occurred. For example, when the location where the specific event occurred is operated on the map of FIG. 2 or FIG. 6, the map generating device 200 displays the flow line data of the specific event on the map. In this way, the flow line data is displayed, or the flow line data is displayed in association with the specific event, making it easier to investigate the cause of a work-related accident or to analyze whether the specific event was caused by human factors.

[0060] The trained model 632 is generated, for example, by a learning device 600 that is separate from the map generating device 200. The learning device 600 includes, for example, an acquisition unit 610, a model generating unit 620, and a storage unit 630. The components other than the storage unit 630 are realized by, for example, a processor such as a CPU or GPU executing a program (software). Some or all of these functional units may be realized by hardware such as an LSI, ASIC, or FPGA, or may be realized by a combination of software and hardware. The program may be stored in advance in a storage device such as an HDD or flash memory, or may be stored in a removable storage medium such as a DVD or CD-ROM, and installed in the storage device by inserting the storage medium into a drive device.

[0061] The storage unit 630 is configured by a storage medium, such as a HDD, a flash memory, an EEPROM, a RAM, a ROM, or any combination of these storage media. The storage unit 630 stores information such as a trained model 632, a training dataset 634, and model setting information 636.

[0062] [Learning Phase / Phase 1] The functions of each unit of the learning device 600 will be described below, dividing them into the learning phase, the inference phase, and the relearning phase. In the learning phase, the acquisition unit 610 acquires input information from the monitoring device 100, which serves as the original data for the learning dataset 634. The original data includes, for example, fall information, partial fall information, heatstroke information, and weight information, which serve as the basis for the learning data 634A, and past records of specific events in the factory, which serve as the basis for the teacher data 634B. The learning dataset 634 is obtained by associating the learning data 634A with the teacher data 634B. The teacher data 634B may be manually entered past records or may be past records from other factories acquired from an external source.

[0063] The acquisition unit 610 includes a preprocessing unit 612. The preprocessing unit 612 performs preprocessing on the original data to generate a training dataset 634. The preprocessing unit 612 performs, for example, normalization processing.

[0064] The model generation unit 620 generates the trained model 632 based on the training data 634A and the teacher data 634B. As described above, the training data 634A is obtained by performing preprocessing on various information acquired from the monitoring device 100. The teacher data 634B is obtained by performing preprocessing on past performance data of a specific event in the factory. The number of elements in the training data 634A matches the number of input nodes specified by the model setting information 636. The model setting information 636 is information that specifies the number of input nodes, the number of output nodes, the connection mode of intermediate nodes, etc. of the machine learning model that is the basis of the trained model 632.

[0065] 7 is a diagram showing an example of processing performed by the model generation unit 620. The model generation unit 620 learns parameters of the machine learning model using a technique such as backpropagation so that the output of the machine learning model when training data 634A is used as input data approaches teacher data 634B. For example, the machine learning model at the point when the above processing has been executed a specified number of times is finalized as the trained model 632.

[0066] [Inference Stage / Phase 2] The learning device 600 transmits the trained model 632 to, for example, the map generation device 200. The transmitted trained model 632 is stored in the storage unit 260 of the map generation device 200. The map generation unit 240 generates a work accident occurrence prediction map and a work accident risk level map using the trained model 632. The trained model 632 may be installed in the map generation device 200 by an external medium such as a storage medium.

[0067] [Relearning Stage / Phase 3] The acquisition unit 610 acquires fall information, partial fall information, heatstroke information, and weight information from the determination unit 230. The acquisition unit 610 periodically acquires new various pieces of information. The learning device 600 acquires the new various pieces of information, incorporates them as training data 634B to make the data even more accurate, and repeats the processing of Phase 1 to perform relearning.

[0068] The learning device 600 repeats Phase 2 and Phase 3 as feedback to improve the accuracy of the trained model 632 constructed in Phase 1.

[0069] The functions of the learning device 600 may be included in the map generating device 200, for example.

[0070] According to the map output system 1 described above, by outputting a map containing information about a predetermined event, the frequency and location of the predetermined event are visualized, enabling efficient monitoring and the implementation of preventive measures against work-related accidents. Furthermore, by outputting the location of a predetermined event in map format, the map output system 1 allows managers to understand where the predetermined event is occurring. The map output system 1 generates a work-related accident prediction map and a work-related accident risk level map using the trained model 632 generated by the learning device 600, enabling measures and prevention against future work-related accidents. By outputting a map containing flow-line data, the map output system 1 can visualize the flow-line of experienced users, thereby improving work efficiency by referring to the flow-line of experienced users. Furthermore, by outputting a map containing flow-line data, the map output system 1 allows managers to understand entry and exit from restricted areas.

[0071] The above describes the form for carrying out the present invention using an embodiment, but the present invention is not limited to such an embodiment, and various modifications and substitutions can be made within the scope that does not deviate from the gist of the present invention.

[0072] According to the present invention, it is possible to provide a program, a learning device, and a map generation method that are capable of monitoring and predicting the occurrence of work-related accidents in order to prevent them.

[0073] REFERENCE SIGNS LIST 100 Monitoring device 110 Communication unit 120 Pressure sensor 130 Acceleration sensor 140 Temperature sensor 150 Heartbeat sensor 160 Position detection unit 170 Reporting unit 180 Beacon receiver 200 Map generation device 210 Communication unit 220 Acquisition unit 230 Determination unit 240 Map generation unit 250 Output unit 260 Storage unit 300 First terminal device 400 Second terminal device 500 Beacon transmitter 600 Learning device 610 Acquisition unit 612 Preprocessing unit 620 Model generation unit 630 Storage unit 632 Learned model 634 Learning dataset 636 Model setting information

Claims

1. A program for causing a processor of a map generating device that has one or more sensors and communicates with a monitoring device that moves with a user who is being monitored to execute the following processes: acquiring the output value of the sensor of the monitoring device and the position of the user of the monitoring device; determining that a specified event has occurred to the user of the monitoring device based on the information acquired from the monitoring device; generating map information for displaying a map image showing the number or frequency of the specified event occurring for each location; and outputting the map information to another terminal device.

2. The program according to claim 1, wherein the monitoring device is built into or removably attached to shoes worn by the user.

3. The program according to claim 1 or 2, for executing the process in which the specified event is a fall, and a process for generating the number or frequency of falls at each location where the user has fallen as the map information.

4. A program as claimed in claim 1 or 2, for executing the process in which the specified event is the user having a slip, and the process generates the number or frequency of the user having a slip at each location as the map information.

5. A program as claimed in claim 1 or 2, for executing the process of: the specified event being heat stroke; and generating the number or frequency of occurrence of heat stroke for each location as the map information.

6. A program as claimed in claim 1 or 2, for executing the process in which the specified event is the user carrying a heavy object, and a process for generating as the map information the number or frequency at each location that the user has carried a heavy object.

7. A program as claimed in claim 1 or 2, causing a processor of the map generating device to execute the following processes: obtaining a trained model that has been trained to predict the occurrence of the specified event using the past performance of the specified event as input information; generating an occurrence prediction map of the specified event using the trained model; and outputting the occurrence prediction map.

8. A program as claimed in claim 1 or 2, causing a processor of the map generating device to execute the following processes: a process of acquiring a trained model that has been trained to predict the occurrence and risk of the specified event using the past performance of the specified event as input information; a process of generating a risk level map of the specified event using the trained model; and a process of outputting the risk level map.

9. A program as described in claim 1 or 2, for causing a processor of the map generating device to execute a process of generating map information for superimposing and displaying the movement trajectory of the user who is the target of monitoring, generated based on the position of the user, on the map image.

10. A map generating device having one or more sensors and communicating with a monitoring device that moves together with a user to be monitored, comprising: an acquisition unit that acquires an output value of the sensor of the monitoring device and the position of the user of the monitoring device; a determination unit that determines that a specified event has occurred to the user of the monitoring device based on information acquired from the monitoring device; a map generating unit that generates map information for displaying a map image indicating the number of times or frequency at which the specified event has occurred; and an output unit that outputs the map information to another terminal device.

11. The map generating device according to claim 10, wherein the monitoring device is built into or removably attached to shoes worn by the user.

12. The learning device comprises: an acquisition unit that acquires past records of specified events that have occurred to a user of a monitoring device; and a model generation unit that generates a trained model that has been trained to predict the occurrence of the specified events.

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