Program, map generation device, and learning device

Abstract

To provide a program capable of monitoring for preventing a labor accident, and of predicting occurrence thereof; and to provide a learning device, and a map generation device therefor.SOLUTION: A program causing a processor for a mag generation device communicating with a watching device that moves with a user as a watching object, the watching device comprising one or more sensors, to execute the processes of: acquiring an output value of each sensor in the watching device, and user's position of the watching device; determining that a predetermined event is generated to the user of the watching device, based on information acquired from the watching device; producing map information for displaying a map image indicating the number of times or frequency for every location where the predetermined event is generated; and outputting the map information to the other terminal device.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to a program, a map generating device, and a learning device. [Background technology]

[0002] In factories and other workplaces, various workplace accidents can occur, such as injuries from falls and heatstroke due to high temperatures. Managers need to monitor and take measures to prevent these workplace accidents from occurring. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Special Publication No. 2022-531490 Summary of the Invention [Problem to be solved by the invention]

[0004] However, it can be difficult for managers to constantly monitor the factory and to discover areas in the factory where measures need to be taken.

[0005] The present invention has been made in consideration of the above 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. [Means for solving the problem]

[0006] In order to solve the above problems, the program, map generating device, and learning device have the following configurations. A first aspect of the present invention is a program for causing a processor of a map generating device, which has one or more sensors and communicates with a monitoring device that moves together with a user who is the subject of monitoring, to execute the following processes: acquiring an output value of the sensor of the monitoring device and the position of the user of the monitoring device, determining that a predetermined 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 occurrence 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 removably attached to shoes worn by the user.

[0008] The program according to a 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 the user's falls for each location is generated as map information.

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

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

[0011] The program of aspect 6 of the present invention is a program for executing a process in which a specified event is a user carrying a heavy object, and the program generates map information indicating the number of times or frequency at each location that 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 generating device to execute the following processes: a process of acquiring a trained model that has been trained to predict the occurrence of a specified event using the past performance of the specified event as input information; a process of generating an occurrence prediction map of the specified event using the trained model; and a process of outputting the occurrence prediction map.

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

[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 together with a user to be 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 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.

[0016] In the map generating device of aspect 11 of the present invention, the monitoring device is built into or removably 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 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. Effect of the Invention

[0018] According to the present invention, by outputting a map including information on work-related accidents, it is possible to take measures for efficient monitoring and prevention of work-related accidents. [Brief description of the drawings]

[0019] [Figure 1] FIG. 1 is an overall view of a map output system 1. [Diagram 2] 13 is a diagram showing an example of a map image displayed by a second terminal device 400. FIG. [Diagram 3] 13 is a diagram showing an example of a work accident occurrence forecast map image displayed by the second terminal device 400. FIG. [Figure 4] 13 is a diagram showing an example of an occupational accident risk level map displayed by the second terminal device 400. FIG. [Diagram 5] 13 is a diagram showing an example of an occupational accident risk level map including flow line data displayed by the second terminal device 400. FIG. [Figure 6] 3 is a diagram showing an example of a map image in which flow line data is included in the map image of FIG. 2; FIG. [Figure 7] FIG. 13 is a diagram showing an example of processing performed by a model generating unit 620. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0020] Hereinafter, embodiments of a program, a learning device, and a map generating 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 generating device 200. The monitoring device 100 is connected to the map generating 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 LAN (Local Area Network), a wireless base station, a provider device, and the like. The monitoring device 100 and the first terminal device 300 are connected by short-range wireless communication, for example, 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 work area. The beacon transmitter 500 includes, for example, a transmitter ID. It is assumed that the monitoring device 100 knows in advance the installation location of the beacon transmitter 500 corresponding to the transmitter ID.

[0023] The components of each device will be 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 heartbeat sensor 150, a position detection unit 160, a report 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 the left foot and the right foot. The communication unit 110, the temperature sensor 140, the heartbeat sensor 150, the position detection unit 160, the report unit 170, and the beacon receiver 180 are provided on either the left foot or the right foot, or on both. Hereinafter, when there is no need to distinguish between the sensor for the left foot and the sensor for the right foot, the symbols L and R indicating whether the sensor is for the left foot or the right foot may be omitted in the description. It is also preferable to provide multiple pressure sensors 120L and 120R for the left and right feet. The temperature sensor 140, the heart rate sensor 150, the position detection unit 160, the report unit 170, and the beacon receiver 180 may be used as part of the monitoring device 100 in the form of a mobile terminal, a mobile phone, or a smart watch.

[0024] The monitoring device 100 may be anything that moves with the user who is the target of monitoring. 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 attachment and carrying of the user who is the target of monitoring is reduced. The monitoring device 100 may be built into an accessory, or detachably attached. Attached also includes being stored. Accessories include, for example, safety shoes, helmets, belts, work clothes, and other items worn by workers. Since the user does not forget to wear such accessories, forgetting to carry the monitoring device 100 is suppressed. In addition, the monitoring device 100 may be an insole (insole) of a shoe, a wristwatch, a ring, a necklace, a bracelet, a name tag, or the like. The shoes into which the insole is inserted are the shoes worn by the user when working in a facility such as a factory. In this way, if the insole, which 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 with him or her, 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 a 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, which is a device that converts the acceleration of each of the three axes into an electric signal and outputs it. Since the acceleration of each of the three axes detected by the acceleration sensor includes a weight acceleration component, the inclination and vibration of the user's foot can be detected by this. The acceleration sensor 130 may be, for example, a gyro sensor.

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

[0030] For example, the heartbeat sensor 150 shines light on the user's leg, detects the light reflected by blood vessels, and measures the pulse from the change over time in the amount of light. The heartbeat sensor 150 is a device that converts the measured pulse into an electrical signal and outputs it. Note that the heartbeat sensor 150 may 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 the transmitter ID acquired by the beacon receiver 180, for example. Since the position detection unit 160 can detect the installation location of the beacon transmitter 500 from the transmitter ID, it may detect the installation location of the detected beacon transmitter 500 as the user's position, or, when multiple transmitter IDs are acquired, may detect the user's position based on the principle of triangulation taking into account the reception strength. In addition, 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 report 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 generation device 200 via the communication unit 110 and the first terminal device 300. At this time, the report unit 170 adds identification information of the watching device 100 and outputs the output values ​​and the position to the map generation device 200. The output information is acquired and managed by the map generation device 200. For example, the map generation 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 realized 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 realized 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 realized by cooperation between software and hardware. The program may be stored in advance in a storage device (a storage device having a non-transient storage medium) such as an HDD (Hard Disk Drive) or a flash memory, or may be stored in a removable storage medium (non-transient storage medium) such as a DVD or a CD-ROM, and may be installed by mounting the storage medium in 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 apparatus 100 via the first terminal apparatus 300.

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

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

[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 determines that the user has fallen when, for example, the output values ​​of the pressure sensors 120 of both feet indicate a value less than a threshold value. The determination unit 230 may also determine that the user has fallen when, for example, the output values ​​of the pressure sensors 120 of both feet indicate a value less than a threshold value and the angle of the acceleration sensor 130 changes suddenly (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 place of falls. As for the place of falls, the determination unit 230 acquires information on the place where the fall occurred from the acquisition unit 220. The number of falls and the place of falls are combined to form fall information.

[0038] (2) The determination unit 230 may determine whether or not the user has slipped, for example, based on the output value of the acceleration sensor 130. When the angle of the acceleration sensor 130 changes suddenly (for example, the resultant acceleration exceeds a threshold within one second), the determination unit 230 determines that the user has slipped. When 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. As for 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 temperature sensor 140 is equal to or greater than a threshold value, determination unit 230 may determine that the user has heat stroke or is likely to have heat stroke. For example, determination unit 230 may determine whether the user has heat stroke or is likely to have heat stroke using the value of an outside temperature gauge (not shown) installed in a factory and the output value of temperature sensor 140. When determination unit 230 determines that the user has heat stroke or is likely to have heat stroke, it acquires the location where heat stroke has been determined from acquisition unit 220. The acquired information that the user has heat stroke and the location where heat stroke occurred are regarded as heat stroke information.

[0040] (4) The determination unit 230 may determine whether or not the user has held a heavy object based on, for example, the output value of the pressure sensor 120. The determination unit 230 determines that a heavy object has been held when the output values ​​of the pressure sensors 120 of 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 above-mentioned predetermined event is an industrial accident or an event that may become an industrial accident. The determination unit 230 acquires the location where the predetermined event occurred from the acquisition unit 220 every time the predetermined event occurs, and associates each occurrence of the predetermined event with the location.

[0042] The map generating unit 240 receives various types of information from the determining unit 230 and generates map information. 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 a Joint Photographic Experts Group (JPEG) format, or may be information such as parameters (such as the number of occurrences of an event associated with coordinates) 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 a network NW, such as a smartphone or a tablet terminal. The first terminal device 300 is carried by a user, and the second terminal device 400 is carried by an administrator of the user. In the second terminal device 400, for example, a monitoring application (application software) runs and displays output values ​​of various sensors and a map output from the map generating 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 shows the location where a predetermined event occurred with a black circle, and further shows the frequency that shows how many times what predetermined event occurred during an observation period (for example, one month). FIG. 2 shows the number of times per month, but for example, all past numbers may be displayed, or the number of times per year may be displayed. These numbers, frequencies, or details of the predetermined event may be displayed when an operation is performed on the map image at the location where the predetermined event occurred or its vicinity. In other words, the "map image showing the number or frequency of each location where the predetermined event occurred" may be displayed after the above operation is performed. These pieces of information may be displayed on a separate screen, or may be displayed superimposed on the map image.

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

[0048] The map generating unit 240 may, for example, determine a threshold value for each predetermined event, and indicate the level as "high" when the output values ​​of various sensors or the past number of occurrences of a predetermined event exceed a first threshold value, the level as "medium" when between the first threshold value and a second threshold value (first threshold value>second threshold value), and the level as "low" when below the threshold value. 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 displayed superimposed on the map image.

[0049] The map generating unit 240 may generate a work accident risk level map in addition to generating the work accident occurrence prediction map shown in FIG. 3. FIG. 4 is a diagram showing an example of a work accident risk level map displayed by the second terminal device 400. The work accident risk level map shows the predicted risk of work accidents predicted to occur in map form. The risk of work accidents is, for example, a numerical value obtained by multiplying the type of work accident predicted to occur by the degree of impact. The higher the numerical value of the "work accident risk", the higher the risk. The degree of impact is a numerical value that is set in advance in consideration of the impact of the work accident occurring, such as the aftereffects on the user, and the impact on the business. In FIG. 4, for example, a factory is divided into sections of 3 [m] x 3 [m], and mesh processing is performed so that the risk of work accidents can be displayed for each section. The risk of work accidents is indicated by the shade of color, and the darker the color, the higher the risk, and the lighter the color, the lower the risk.

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

[0051] For the processing of the map generating unit 240, the determining unit 230 determines that the user is performing work that places a high physical strain on the user when, for example, the output value of the heart rate sensor 150 exceeds a threshold value for a certain period of time. The map generating unit 240 may consider the work that places a high physical strain on the user as work that is likely to cause a work-related accident, and may add the work-related accident occurrence prediction map and the work-related accident risk level map to the materials generated.

[0052] For example, when the output value of the pressure sensor 120 is higher than the threshold value for a certain period of time only for one foot, the determination unit 230 determines that a work accident is likely to occur. Examples of work accidents that are likely to occur include injuries such as falls and back pain. The map generation unit 240 may add information on work accidents that are likely to occur to materials used to generate a work accident prediction map and a work 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 a similar value. The map generation unit 240 may consider repetitive work to be prone to injuries such as back pain, and add information about the 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 generate a map by overlaying the flow line data on the generated map, for example. The flow line data is a movement route of the user acquired from the position detecting unit 160, and points are connected with lines. The flow line data is an example of "a movement trajectory of the user 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 generating unit 240 may generate a map including movement line data of multiple users. Furthermore, the map generating unit 240 may generate a map including multiple movement line data of a target user (for example, movement line data such as work on different dates and times), or may generate a map including movement line data of different users.

[0056] The map generating unit 240 may generate map information that superimposes the flow line data on the map of Fig. 2 or Fig. 3. Depending on the user's operation, a map may be displayed that displays any or all of the information of the flow line data, the number of occurrences of a predetermined event, the frequency of the predetermined event, and the details of the predetermined event. This allows the administrator to easily check the information he or she wants to obtain.

[0057] For example, by displaying a map with the flow line data superimposed on the map generated as described above, it may be possible to identify the cause of a specific event. For example, by checking the flow line data of a person who has fallen just before the person fell, it may be possible to identify the cause of the fall. It is also useful for determining whether the specific event is caused by the person who caused the specific event or the environment. For example, by analyzing the behavior of each specific event, it is possible to clarify that one person causes the specific events in a similar pattern, even if they appear to be many at first glance. The results of this clarification can be used to plan preventive measures for industrial accidents. Furthermore, by generating a map that displays the flow line data alone after having the function of displaying the specific event, it becomes easy to analyze using the flow line data, even if the flow line data is complex and difficult to see when it is displayed superimposed on the specific event. In addition, multiple flow line data can be displayed on one map. In this case, it is possible to easily determine whether the specific event is caused by the person or the environment by analyzing multiple flow line data.

[0058] 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 forecast 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 a plan to prevent the occurrence of the predetermined event.

[0059] The map generating device 200 may display information indicating the flow line data of the specified monitoring device 100 (user) on the 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. In addition, the identification information of the management information is associated with information indicating whether a specific event has occurred, details of the specific event, and the like. More specifically, the identification information of the management information is associated with the history of the location 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 where the specific event occurred 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 accident or to analyze whether a specific event occurred due to a human factor.

[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, for example, by a processor such as a CPU or a GPU executing a program (software). Some or all of these functional units may be realized by hardware such as an LSI, an ASIC, or an FPGA, or may be realized by a combination of software and hardware. The program may be stored in a storage device such as an HDD or a flash memory in advance, or may be stored in a removable storage medium such as a DVD or a CD-ROM, and installed in the storage device by mounting the storage medium in 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 stage / Phase 1] The functions of each unit of the learning device 600 will be described below, divided into a learning stage, an inference stage, and a re-learning stage. In the learning stage, the acquisition unit 610 acquires input information from the monitoring device 100, which is the original data of the learning data set 634. The original data is, for example, fall information, half-fall information, heat stroke information, and weight information that are the original data of the learning data 634A, and past records of a predetermined event in the factory that are the original data of the teacher data 634B. The learning data set 634 is obtained by associating the learning data 634A with the teacher data 634B. The teacher data 634B may be past records manually input, or may be past records of other factories acquired from outside.

[0063] The acquisition unit 610 includes a preprocessing unit 612. The preprocessing unit 612 performs preprocessing on the original data to generate a learning 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 results of a predetermined event in a factory. The number of elements in the training data 634A matches the number of input nodes defined by the model setting information 636. The model setting information 636 is information that defines the number of input nodes, the number of output nodes, the connection mode of intermediate nodes, and the like 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 by a method such as backpropagation so that the output of the machine learning model when the learning data 634A is used as input data approaches the teacher data 634B. For example, the machine learning model at the time when the above processing is executed a specified number of times is determined as the trained model 632.

[0066] [Inference stage / Phase 2] The learning device 600 transmits, for example, the trained model 632 to 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 Phase / Phase 3] The acquisition unit 610 acquires the fall information, partial fall information, heat stroke information, and weight information from the determination unit 230. The acquisition unit 610 periodically acquires new various information. The learning device 600 acquires the new various information, incorporates it as teacher data 634B so as to obtain more accurate data, and repeats the process of phase 1 to perform re-learning.

[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 above-mentioned map output system 1, by outputting a map including information on a predetermined event, the occurrence frequency and the location of the predetermined event are visualized, so that efficient monitoring and preventive measures against industrial accidents can be taken. In addition, the map output system 1 outputs the location where the predetermined event occurred in map format, so that the manager can grasp where the predetermined event is occurring. The map output system 1 generates a work accident occurrence prediction map and a work accident risk level map using the trained model 632 generated by the learning device 600, so that measures and prevention can be taken against future industrial accidents. The map output system 1 outputs a map including movement line data, so that the movement line of a skilled user can be visualized, and work efficiency can be improved by referring to the movement line of a skilled user. In addition, the map output system 1 outputs a map including movement line data, so that the manager can grasp the entry and exit of a prohibited entry and exit point.

[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. [Explanation of symbols]

[0072] 100 Monitoring device 110 Communications Department 120 Pressure Sensor 130 Acceleration Sensor 140 Temperature Sensor 150 Heart Rate Sensor 160 Position detection unit 170 Reporting Department 180 Beacon Receiver 200 Map Generator 210 Communications Department 220 Acquisition Department 230 Judgment section 240 Map Generation Unit 250 Output section 260 Storage section 300 First terminal device 400 Second terminal device 500 Beacon Transmitter 600 Learning Device 610 Acquisition Department 612 Pretreatment section 620 Model Generation Unit 630 Storage section 632 trained models 634 Training Dataset 636 Model setting information

Claims

1. 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 a target of monitoring, A process of acquiring an output value of a sensor of the monitoring device and a position of a user of the monitoring device; A process of determining that a predetermined event has occurred to a user of the monitoring device based on information acquired from the monitoring device; A process of generating map information for displaying a map image showing the number or frequency of occurrence of the predetermined event for each location; outputting the map information to another terminal device; A program for executing the above.

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

3. The predetermined event is a fall, A process of generating the number or frequency of falls at each location of the user as the map information; 3. The program according to claim 1 or 2 for executing the above.

4. The predetermined event is that the user has slipped, A process of generating the number or frequency of the user's slipping at each location as the map information; 3. The program according to claim 1 or 2 for executing the above.

5. The predetermined event is heat stroke, A process of generating the number or frequency of occurrence of heat stroke for each location as the map information; 3. The program according to claim 1 or 2 for executing the above.

6. The predetermined event is the user carrying a heavy object, generating the map information based on the number or frequency of times that the user has carried a heavy object for each location; 3. The program according to claim 1 or 2 for executing the above.

7. A processor of the map generating device, A process of acquiring a trained model trained to predict the occurrence of the specified event using the past record of the specified event as input information; A process of generating an occurrence prediction map of the predetermined event using the trained model; A process of outputting the occurrence prediction map; 3. The program according to claim 1 or 2 for executing the above.

8. A processor of the map generating device, A process of acquiring a trained model that has been trained to predict the occurrence and risk of the specified event using past records of the specified event as input information; A process of generating a risk level map of the predetermined event using the trained model; A process of outputting the risk level map; 3. The program according to claim 1 or 2 for executing the above.

9. A processor of the map generating device, A process of generating map information for superimposing and displaying a movement trajectory of the user, which is generated based on the position of the user who is the target of monitoring, on the map image; 3. The program according to claim 1 or 2 for executing the above.

10. A map generating device that has one or more sensors and communicates with a monitoring device that moves with a user to be monitored, an acquisition unit that acquires an output value of a sensor of the monitoring device and a position of a user of the monitoring device; a determination unit that determines that a predetermined event has occurred to a 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 showing the number or frequency of occurrence of the predetermined event; an output unit that outputs the map information to another terminal device; A map generating device comprising:

11. The monitoring device is built into or detachably attached to shoes worn by the user. The map generating device of claim 10.

12. The learning device is An acquisition unit that acquires past records of predetermined events that have occurred to a user of the monitoring device; A model generation unit that generates a trained model that is trained to predict the occurrence of the predetermined event; Equipped with learning equipment.

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