system

Abstract

The system according to the embodiment aims to improve safety by predicting dangerous locations at construction sites and monitoring the behavior of workers. [Solution] A system according to an embodiment includes a collection unit, a prediction unit, a monitoring unit, a detection unit, and a warning unit. The collection unit collects past accident data and on-site conditions. The prediction unit analyzes the data collected by the collection unit and predicts dangerous locations. The monitoring unit monitors the movements of workers based on the dangerous locations predicted by the prediction unit. The detection unit detects dangerous behavior based on the movements of workers monitored by the monitoring unit. The warning unit issues a warning based on the dangerous behavior detected by the detection unit.

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology did not adequately predict dangerous areas on construction sites or monitor worker behavior, resulting in a high risk of work-related accidents.

[0005] The system according to the embodiment aims to improve safety by predicting dangerous locations at construction sites and monitoring the behavior of workers. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a prediction unit, a monitoring unit, a detection unit, and a warning unit. The collection unit collects past accident data and on-site conditions. The prediction unit analyzes the data collected by the collection unit and predicts dangerous locations. The monitoring unit monitors the movements of workers based on the dangerous locations predicted by the prediction unit. The detection unit detects dangerous behavior based on the movements of workers monitored by the monitoring unit. The warning unit issues a warning based on the dangerous behavior detected by the detection unit. [Effects of the Invention]

[0007] The system according to the embodiment can predict dangerous locations at a construction site and monitor the behavior of workers to improve safety. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10]1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A safety management system according to an embodiment of the present invention is a system that uses generative AI to analyze past accident data and site conditions, predict dangerous locations, monitor worker behavior, detect dangerous behavior, and issue warnings to improve safety at construction sites. The safety management system collects past accident data and site conditions, and generative AI analyzes the data to predict dangerous locations. Next, it uses AI cameras to monitor worker movements and detect dangerous behavior. Furthermore, it issues warnings based on the detected dangerous behavior. For example, the safety management system collects past accident data and site conditions. For example, the safety management system identifies locations where many accidents have occurred in the past and locations where specific tasks are performed, and evaluates the level of risk. Next, the safety management system uses AI cameras to monitor worker movements. For example, it detects when workers are not using safety harnesses while working at heights or when they are behaving inappropriately near heavy machinery, and issues warnings in real time. This significantly improves safety at construction sites and reduces work-related accidents. Furthermore, automating safety checks and document creation improves work efficiency, and streamlining safety education and training can foster safety-conscious personnel. As a result, the safety management system significantly improves safety at construction sites, leading to a reduction in work-related accidents. In addition, work efficiency is improved by automating safety checks and document creation, and safety education and training can be made more efficient, fostering safety-conscious personnel.

[0029] A safety management system according to an embodiment includes a collection unit, a prediction unit, a monitoring unit, a detection unit, and a warning unit. The collection unit collects past accident data and on-site conditions. The past accident data includes, for example, but is not limited to, the type of accident, the date and time of the accident, and the cause. The on-site conditions include, for example, but are not limited to, the work environment, the state of the equipment, and the deployment of workers. The collection unit, for example, acquires past accident data from a database and collects on-site conditions using sensors and cameras. The prediction unit analyzes the data collected by the collection unit and predicts dangerous areas. The prediction unit predicts dangerous areas, such as falls, based on the collected data, using, for example, a generation AI. For example, the generation AI analyzes past accident data and evaluates the risk level in a specific work area or work procedure. The monitoring unit monitors the movements of workers based on the dangerous areas predicted by the prediction unit. The monitoring unit monitors the movements of workers in real time, for example, using an AI camera. For example, the monitoring unit detects when a worker is not using a safety harness while working at a height or when the worker is behaving inappropriately near heavy machinery. The detection unit detects dangerous behavior based on the movements of the worker monitored by the monitoring unit. The detection unit detects dangerous behavior from the monitored movements of the worker, for example, using a generation AI. For example, the generation AI analyzes patterns of the worker's movements and identifies the dangerous behavior. The warning unit issues a warning based on the dangerous behavior detected by the detection unit. The warning unit issues a warning to the worker, for example, in real time. For example, the warning unit issues a warning to the worker using an audio or visual alert. As a result, the safety management system according to the embodiment improves safety at construction sites and contributes to reducing work-related accidents.

[0030] The safety management system includes a simulation unit that uses VR and AR technology to simulate safe work procedures. The simulation unit uses VR and AR technology to simulate safe work procedures. VR technology, for example, uses a head-mounted display to create a virtual reality space in which workers can experience safe work procedures. For example, the simulation unit can allow workers to experience procedures for working at heights or how to operate heavy machinery in a virtual space. AR technology, for example, uses smart glasses to overlay virtual information on the real work environment, allowing workers to visually confirm safe work procedures. For example, the simulation unit can display safe work procedures at the work site, allowing workers to perform the work according to those procedures. This allows the simulation unit to increase workers' safety awareness and reduce the risk of accidents.

[0031] The safety management system includes a creation unit that creates safety documents to automatically add dangerous points based on past accident data and on-site conditions. The creation unit creates safety documents to automatically add dangerous points based on past accident data and on-site conditions. Safety documents include, but are not limited to, risk assessment sheets and safety procedure manuals. For example, the creation unit analyzes past accident data and automatically generates documents that list points to be careful of in specific tasks and details of accidents that have occurred in the past. For example, the creation unit uses a generation AI to create a risk assessment sheet based on past accident data and provides information to workers to work safely. In this way, the creation unit can provide workers with information to work safely and reduce the risk of accidents.

[0032] When collecting past accident data, the collection unit can collect detailed information about the cause and circumstances of the accident. The collection unit collects detailed information, such as the weather, time of day, and work details when the accident occurred. The collection unit can also collect information about the years of experience and health status of workers involved in the accident. The collection unit can also collect photos and videos of the scene when the accident occurred and record the detailed circumstances. This allows the collection unit to collect detailed accident data and improve the accuracy of risk assessment.

[0033] The collection unit can also collect environmental factors such as weather and time of day when collecting information about the site conditions. For example, the collection unit collects weather information about the site in real time and stores it in a database. The collection unit can also collect information about the time period when work is performed and evaluate the safety of the work. The collection unit can also collect environmental data such as the temperature and humidity of the site and use it to improve the work environment. In this way, the collection unit can collect data that takes environmental factors into consideration and use it to improve the work environment.

[0034] When collecting data, the collection unit can take into account the individual work history and health condition of each worker. For example, the collection unit can refer to the worker's past work history to evaluate the risk of a specific task. The collection unit can also monitor the worker's health condition and refrain from collecting data if the worker is in poor health. The collection unit can also adjust the data collection method taking into account the worker's years of experience and skill level. This allows the collection unit to evaluate the risk of the worker and collect appropriate data.

[0035] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the geographical information of the site. For example, the collection unit prioritizes collecting data from high-risk areas based on the geographical information of the site. The collection unit can also collect data by taking into account the movement lines of workers based on the geographical information of the site. The collection unit can also prioritize collecting data from specific work areas based on the geographical information of the site. This allows the collection unit to prioritize collecting data from high-risk areas based on the geographical information.

[0036] The collection unit can analyze the social media activities of the workers and collect related data when collecting data. For example, the collection unit analyzes the content of the workers' posts on social media and collects related data. The collection unit can also grasp the situation at the work site based on the workers' check-in information on social media. The collection unit can also collect related data by referring to the activities of the workers' friends on social media. In this way, the collection unit can analyze the social media activities and grasp the situation at the work site.

[0037] The collection unit can customize the collection method by reflecting past feedback when collecting data. For example, the collection unit can adjust the frequency and timing of data collection based on past feedback. The collection unit can also customize the type of data to be collected based on past feedback. The collection unit can also improve the data collection method based on past feedback. This allows the collection unit to improve the data collection method based on past feedback.

[0038] When making a prediction, the prediction unit performs a detailed cause analysis of past accident data, enabling more accurate predictions. For example, the prediction unit performs a detailed cause analysis based on past accident data and identifies high-risk areas. The prediction unit can also evaluate the risks of specific tasks based on past accident data. The prediction unit can also identify high-risk time periods and weather conditions based on past accident data. This allows the prediction unit to perform a detailed cause analysis and identify high-risk areas.

[0039] The prediction unit can make predictions taking into account environmental factors (weather, time of day, etc.) at the site. The prediction unit predicts high-risk areas based on, for example, weather information at the site. The prediction unit can also predict high-risk time periods based on time period information at the site. The prediction unit can also predict high-risk areas based on environmental data such as the temperature and humidity at the site. This allows the prediction unit to perform risk assessments that take environmental factors into account, thereby improving the accuracy of predictions.

[0040] The prediction unit can make predictions taking into account the individual work history and health condition of each worker. For example, the prediction unit can refer to the worker's past work history to evaluate the risk of a specific task. The prediction unit can also monitor the worker's health condition and predict areas where risk increases when the worker is in poor health. The prediction unit can also predict high-risk areas taking into account the worker's years of experience and skill level. This allows the prediction unit to evaluate the risk of the worker and make appropriate predictions.

[0041] When making predictions, the prediction unit can predict dangerous locations by taking into account geographical information of the work site. For example, the prediction unit predicts high-risk areas based on the geographical information of the work site. The prediction unit can also evaluate risks by taking into account the movement lines of workers based on the geographical information of the work site. The prediction unit can also predict risks in specific work areas based on the geographical information of the work site. This allows the prediction unit to predict high-risk areas based on geographical information.

[0042] The prediction unit can improve the accuracy of the prediction by referring to related literature and data when making a prediction. The prediction unit can improve the accuracy of the risk assessment by referring to, for example, related literature. The prediction unit can also improve the accuracy of the risk assessment by referring to related data. The prediction unit can also improve the accuracy of the risk assessment by referring to related research results. In this way, the prediction unit can improve the accuracy of the risk assessment by referring to related information.

[0043] The prediction unit can predict dangerous locations by taking into account the market value of the site when making predictions. The prediction unit can predict high-risk areas, for example, based on the market value of the site. The prediction unit can also evaluate risks by taking into account the movement lines of workers based on the market value of the site. The prediction unit can also predict risks in specific work areas based on the market value of the site. This allows the prediction unit to predict high-risk areas based on the market value.

[0044] During monitoring, the monitoring unit can analyze detailed patterns of worker movement to improve the accuracy of monitoring. For example, the monitoring unit analyzes patterns of worker movement to detect abnormal movement. The monitoring unit can also analyze patterns of worker movement to identify high-risk movement. The monitoring unit can also analyze patterns of worker movement to propose an efficient monitoring method. In this way, the monitoring unit can analyze movement patterns and detect abnormal movement.

[0045] The monitoring unit can take into consideration the individual work history and health condition of each worker when monitoring. For example, the monitoring unit can refer to the worker's past work history to evaluate the risk of a specific task. The monitoring unit can also monitor the worker's health condition and intensify monitoring if the worker is in poor health. The monitoring unit can also adjust the monitoring method taking into consideration the worker's years of experience and skill level. This allows the monitoring unit to evaluate the risk of the worker and perform appropriate monitoring.

[0046] The monitoring unit can perform monitoring taking into consideration environmental factors (weather, time of day, etc.) at the site. For example, the monitoring unit can monitor high-risk areas based on weather information at the site. The monitoring unit can also monitor high-risk time periods based on time period information at the site. The monitoring unit can also monitor high-risk areas based on environmental data such as the temperature and humidity at the site. This allows the monitoring unit to perform monitoring taking environmental factors into consideration and monitor high-risk areas.

[0047] The monitoring unit can perform monitoring while taking into consideration the geographical information of the site. For example, the monitoring unit monitors high-risk areas based on the geographical information of the site. The monitoring unit can also perform monitoring while taking into consideration the movement lines of workers based on the geographical information of the site. The monitoring unit can also focus on monitoring specific work areas based on the geographical information of the site. This allows the monitoring unit to monitor high-risk areas based on the geographical information.

[0048] The monitoring unit can improve the accuracy of monitoring by referring to related literature and data during monitoring. For example, the monitoring unit can improve the accuracy of monitoring by referring to related literature. The monitoring unit can also improve the accuracy of monitoring by referring to related data. The monitoring unit can also improve the accuracy of monitoring by referring to related research results. In this way, the monitoring unit can improve the accuracy of monitoring by referring to related information.

[0049] The monitoring unit can perform monitoring taking into account the market value of the site. For example, the monitoring unit monitors high-risk areas based on the market value of the site. The monitoring unit can also perform monitoring taking into account the movement of workers based on the market value of the site. The monitoring unit can also focus on monitoring specific work areas based on the market value of the site. This allows the monitoring unit to monitor high-risk areas based on market value.

[0050] The detection unit can improve the accuracy of detection by analyzing detailed patterns of the worker's movements during detection. For example, the detection unit can analyze the patterns of the worker's movements and detect abnormal movements. The detection unit can also analyze the patterns of the worker's movements and identify high-risk movements. The detection unit can also analyze the patterns of the worker's movements and propose an efficient detection method. In this way, the detection unit can analyze the patterns of movements and detect abnormal movements.

[0051] The detection unit can perform detection by taking into consideration the individual work history and health condition of each worker. For example, the detection unit can refer to the worker's past work history to evaluate the risk of a specific task. The detection unit can also monitor the worker's health condition and detect behavior that increases risk when the worker is in poor health. The detection unit can also detect high-risk behavior by taking into consideration the worker's years of experience and skill level. This allows the detection unit to evaluate the risk of the worker and perform appropriate detection.

[0052] The detection unit can perform detection while taking into consideration environmental factors (weather, time of day, etc.) at the site. For example, the detection unit detects high-risk behavior based on weather information at the site. The detection unit can also detect behavior during high-risk time periods based on time period information at the site. The detection unit can also detect high-risk behavior based on environmental data such as the temperature and humidity at the site. This allows the detection unit to perform detection while taking environmental factors into consideration and detect high-risk behavior.

[0053] During detection, the detection unit can detect dangerous behavior by taking into account geographical information of the work site. For example, the detection unit detects behavior in high-risk areas based on the geographical information of the work site. The detection unit can also detect dangerous behavior by taking into account the movement lines of workers based on the geographical information of the work site. The detection unit can also detect dangerous behavior in a specific work area based on the geographical information of the work site. This allows the detection unit to detect behavior in high-risk areas based on geographical information.

[0054] The detection unit can improve the accuracy of detection by referring to related literature and data during detection. For example, the detection unit improves the accuracy of detection by referring to related literature. The detection unit can also improve the accuracy of detection by referring to related data. The detection unit can also improve the accuracy of detection by referring to related research results. In this way, the detection unit can improve the accuracy of detection by referring to related information.

[0055] The detection unit can detect risky behavior by taking into account the market value of the site during detection. For example, the detection unit detects behavior in high-risk areas based on the market value of the site. The detection unit can also detect risky behavior by taking into account the movement of workers based on the market value of the site. The detection unit can also detect risky behavior in a specific work area based on the market value of the site. This allows the detection unit to detect behavior in high-risk areas based on the market value.

[0056] When issuing a warning, the warning unit can analyze detailed patterns of the worker's movements to improve the accuracy of the warning. For example, the warning unit analyzes the patterns of the worker's movements and issues a warning when abnormal movements are detected. The warning unit can also analyze the patterns of the worker's movements and identify high-risk movements and issue a warning. The warning unit can also analyze the patterns of the worker's movements and suggest an efficient warning method. This allows the warning unit to analyze the movement patterns and issue a warning when abnormal movements are detected.

[0057] When issuing a warning, the warning unit can take into consideration the individual work history and health condition of the worker. For example, the warning unit can refer to the worker's past work history, evaluate the risk of a specific task, and issue a warning. The warning unit can also monitor the worker's health condition and intensify the warning when the worker is in poor health. The warning unit can also issue a warning about high-risk behavior, taking into consideration the worker's years of experience and skill level. This allows the warning unit to evaluate the risk of the worker and issue an appropriate warning.

[0058] The warning unit can issue a warning taking into consideration environmental factors (weather, time of day, etc.) at the site. For example, the warning unit issues a warning about high-risk behavior based on weather information at the site. The warning unit can also issue a warning about behavior during high-risk time periods based on time period information at the site. The warning unit can also issue a warning about high-risk behavior based on environmental data such as the temperature and humidity at the site. In this way, the warning unit can issue a warning taking into consideration environmental factors and issue a warning about high-risk behavior.

[0059] When issuing a warning, the warning unit can issue a warning taking into account geographical information of the work site. For example, the warning unit issues a warning against actions in high-risk areas based on the geographical information of the work site. The warning unit can also issue a warning taking into account the movement lines of workers based on the geographical information of the work site. The warning unit can also issue a warning against dangerous actions in a specific work area based on the geographical information of the work site. This allows the warning unit to issue a warning against actions in high-risk areas based on geographical information.

[0060] The warning unit can improve the accuracy of the warning by referring to related literature and data when issuing a warning. For example, the warning unit can improve the accuracy of the warning by referring to related literature. The warning unit can also improve the accuracy of the warning by referring to related data. The warning unit can also improve the accuracy of the warning by referring to related research results. In this way, the warning unit can improve the accuracy of the warning by referring to related information.

[0061] When issuing a warning, the warning unit can issue a warning taking into account the market value of the work site. For example, the warning unit issues a warning against behavior in a high-risk area based on the market value of the work site. The warning unit can also issue a warning taking into account the movement of workers based on the market value of the work site. The warning unit can also issue a warning against dangerous behavior in a specific work area based on the market value of the work site. This allows the warning unit to issue a warning against behavior in a high-risk area based on the market value.

[0062] During the simulation, the simulation unit can improve the accuracy of the simulation by referring to past accident data. For example, the simulation unit performs a detailed cause analysis based on the past accident data, thereby improving the accuracy of the simulation. The simulation unit can also evaluate the risk of a specific task based on the past accident data. The simulation unit can also identify high-risk time periods and weather conditions based on the past accident data. This allows the simulation unit to perform a detailed cause analysis based on the past accident data, thereby improving the accuracy of the simulation.

[0063] The simulation unit can perform a simulation taking into account the individual work history and health condition of each worker. For example, the simulation unit can refer to the worker's past work history to evaluate the risk of a specific task. The simulation unit can also monitor the worker's health condition and perform a simulation that increases the risk when the worker is in poor health. The simulation unit can also perform a simulation with a high risk taking into account the worker's years of experience and skill level. This allows the simulation unit to evaluate the risk of the worker and perform an appropriate simulation.

[0064] The simulation unit can perform a simulation taking into account environmental factors (weather, time of day, etc.) at the site. For example, the simulation unit simulates high-risk areas based on weather information at the site. The simulation unit can also simulate high-risk time periods based on time period information at the site. The simulation unit can also simulate high-risk areas based on environmental data such as the temperature and humidity at the site. In this way, the simulation unit can perform a simulation taking into account environmental factors and simulate high-risk areas.

[0065] The simulation unit can perform a simulation taking into account geographical information of the site. For example, the simulation unit simulates high-risk areas based on the geographical information of the site. The simulation unit can also perform a simulation taking into account the movement lines of workers based on the geographical information of the site. The simulation unit can also focus the simulation on specific work areas based on the geographical information of the site. This allows the simulation unit to simulate high-risk areas based on the geographical information.

[0066] The simulation unit can improve the accuracy of the simulation by referring to related literature and data during the simulation. For example, the simulation unit can improve the accuracy of the simulation by referring to related literature. The simulation unit can also improve the accuracy of the simulation by referring to related data. The simulation unit can also improve the accuracy of the simulation by referring to related research results. In this way, the simulation unit can improve the accuracy of the simulation by referring to related information.

[0067] The simulation unit can perform a simulation taking into account the market value of the site. For example, the simulation unit simulates high-risk areas based on the market value of the site. The simulation unit can also perform a simulation taking into account the movement lines of workers based on the market value of the site. The simulation unit can also focus the simulation on specific work areas based on the market value of the site. This allows the simulation unit to simulate high-risk areas based on the market value.

[0068] The creation unit can improve the accuracy of safety documents by referring to past accident data when creating the documents. For example, the creation unit can perform detailed cause analysis based on past accident data to improve the accuracy of the safety documents. The creation unit can also evaluate the risks of specific tasks based on past accident data. The creation unit can also identify high-risk time periods and weather conditions based on past accident data. This allows the creation unit to perform detailed cause analysis based on past accident data to improve the accuracy of the safety documents.

[0069] When creating safety documents, the creation department can take into account the individual work history and health status of each worker. For example, the creation department can refer to the worker's past work history to evaluate the risk of a specific task. The creation department can also monitor the worker's health status and create safety documents that increase the risk when the worker is in poor health. The creation department can also create safety documents with higher risks by taking into account the worker's years of experience and skill level. This allows the creation department to evaluate the risk of workers and create appropriate safety documents.

[0070] The creation unit can create safety documents taking into consideration environmental factors (weather, time of day, etc.) at the work site. For example, the creation unit can indicate high-risk areas in the safety documents based on weather information at the work site. The creation unit can also indicate high-risk time periods in the safety documents based on time period information at the work site. The creation unit can also indicate high-risk areas in the safety documents based on environmental data such as the temperature and humidity at the work site. This allows the creation unit to create safety documents taking environmental factors into consideration and indicate high-risk areas.

[0071] The creation unit can create safety documents taking into account the geographical information of the work site when creating them. For example, the creation unit describes high-risk areas in the safety documents based on the geographical information of the work site. The creation unit can also create safety documents taking into account the movement lines of workers based on the geographical information of the work site. The creation unit can also focus on specific work areas when describing safety documents based on the geographical information of the work site. This allows the creation unit to describe high-risk areas in the safety documents based on the geographical information.

[0072] The creation unit can improve the accuracy of the safety documents by referring to related literature and data when creating the safety documents. For example, the creation unit can improve the accuracy of the safety documents by referring to related literature. The creation unit can also improve the accuracy of the safety documents by referring to related data. The creation unit can also improve the accuracy of the safety documents by referring to related research results. In this way, the creation unit can improve the accuracy of the safety documents by referring to related information.

[0073] The creation department can create safety documents taking into account the market value of the site when creating them. For example, the creation department can list high-risk areas in the safety documents based on the market value of the site. The creation department can also create safety documents taking into account the movement lines of workers based on the market value of the site. The creation department can also focus on specific work areas when creating safety documents based on the market value of the site. This allows the creation department to list high-risk areas in the safety documents based on the market value.

[0074] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0075] A safety management system can be equipped with a health monitoring unit that monitors the health status of workers in real time and immediately issues an alert if an abnormality is detected. For example, sensors can measure a worker's heart rate and body temperature, and an alert can be issued if an abnormal value is detected. It can also issue an alert to encourage a worker to take a break if the worker is overworked. Furthermore, it can adjust work assignments based on the worker's health status to prevent overwork. In this way, the health monitoring unit can protect the health of workers and reduce the risk of workplace accidents.

[0076] A safety management system can include a training evaluation department that evaluates the skill level of workers and provides appropriate training programs. For example, basic safety training can be provided to new employees, while more experienced workers can receive more advanced training. It can also recommend additional training if a skill deficiency is detected in a particular task. It can also monitor the progress of training and adjust the training content as needed. This allows the training evaluation department to improve worker skills and increase safety.

[0077] The safety management system can be equipped with an efficiency suggestion unit that analyzes worker behavior data and makes suggestions to improve work efficiency. For example, it analyzes worker movement lines and suggests efficient work procedures. It can also analyze worker rest patterns and suggest appropriate break timing. It can also make suggestions to optimize the placement of tools and equipment used by workers. In this way, the efficiency suggestion unit can improve work efficiency, reduce worker burden, and increase safety.

[0078] A safety management system can be equipped with an environment improvement department that makes suggestions for improving the work environment based on worker behavior data. For example, it analyzes worker movement lines and makes suggestions for optimizing the layout of the work space. It can also review the placement of tools and equipment used by workers and suggest efficient placement. It can also monitor the temperature and humidity of the work environment and make suggestions for providing a comfortable work environment. This allows the environment improvement department to optimize the work environment and improve work efficiency and safety.

[0079] A safety management system can be equipped with a procedure improvement unit that proposes improvements to work procedures based on worker behavior data. For example, it can analyze worker movements and propose efficient work procedures. It can also analyze worker working time and propose ways to reduce unnecessary movements. It can also review the operating procedures for tools and equipment used by workers and propose safe and efficient operating procedures. In this way, the procedure improvement unit can optimize work procedures and improve work efficiency and safety.

[0080] The processing flow of the first embodiment will be briefly explained below.

[0081] Step 1: The collection unit collects past accident data and on-site conditions. Past accident data includes the type of accident, the date and time of the accident, and the cause, while on-site conditions include the work environment, equipment status, and worker deployment. The collection unit obtains past accident data from a database and collects on-site conditions using sensors and cameras. Step 2: The prediction unit analyzes the data collected by the collection unit and predicts dangerous locations. The prediction unit predicts dangerous locations such as falls based on the collected data using the generation AI. The generation AI analyzes past accident data and evaluates the degree of danger in specific work areas and work procedures. Step 3: The monitoring unit monitors the movements of workers based on the dangerous locations predicted by the prediction unit. The monitoring unit uses AI cameras to monitor the movements of workers in real time, detecting instances such as when a worker is not using a safety harness while working at height or when the worker is making inappropriate movements near heavy machinery. Step 4: The detection unit detects dangerous behavior based on the worker's movements monitored by the monitoring unit. The detection unit uses the generation AI to detect dangerous behavior from the monitored worker's movements and identifies the dangerous behavior by analyzing the worker's movement patterns. Step 5: The warning unit issues a warning based on the dangerous behavior detected by the detection unit. The warning unit warns the worker in real time using audio and visual alerts.

[0082] (Example 2) A safety management system according to an embodiment of the present invention is a system that uses generative AI to analyze past accident data and site conditions, predict dangerous locations, monitor worker behavior, detect dangerous behavior, and issue warnings to improve safety at construction sites. The safety management system collects past accident data and site conditions, and generative AI analyzes the data to predict dangerous locations. Next, it uses AI cameras to monitor worker movements and detect dangerous behavior. Furthermore, it issues warnings based on the detected dangerous behavior. For example, the safety management system collects past accident data and site conditions. For example, the safety management system identifies locations where many accidents have occurred in the past and locations where specific tasks are performed, and evaluates the level of risk. Next, the safety management system uses AI cameras to monitor worker movements. For example, it detects when workers are not using safety harnesses while working at heights or when they are behaving inappropriately near heavy machinery, and issues warnings in real time. This significantly improves safety at construction sites and reduces work-related accidents. Furthermore, automating safety checks and document creation improves work efficiency, and streamlining safety education and training can foster safety-conscious personnel. As a result, the safety management system significantly improves safety at construction sites, leading to a reduction in work-related accidents. In addition, work efficiency is improved by automating safety checks and document creation, and safety education and training can be made more efficient, fostering safety-conscious personnel.

[0083] A safety management system according to an embodiment includes a collection unit, a prediction unit, a monitoring unit, a detection unit, and a warning unit. The collection unit collects past accident data and on-site conditions. The past accident data includes, for example, but is not limited to, the type of accident, the date and time of the accident, and the cause. The on-site conditions include, for example, but are not limited to, the work environment, the state of the equipment, and the deployment of workers. The collection unit, for example, acquires past accident data from a database and collects on-site conditions using sensors and cameras. The prediction unit analyzes the data collected by the collection unit and predicts dangerous areas. The prediction unit predicts dangerous areas, such as falls, based on the collected data, using, for example, a generation AI. For example, the generation AI analyzes past accident data and evaluates the risk level in a specific work area or work procedure. The monitoring unit monitors the movements of workers based on the dangerous areas predicted by the prediction unit. The monitoring unit monitors the movements of workers in real time, for example, using an AI camera. For example, the monitoring unit detects when a worker is not using a safety harness while working at a height or when the worker is behaving inappropriately near heavy machinery. The detection unit detects dangerous behavior based on the movements of the worker monitored by the monitoring unit. The detection unit detects dangerous behavior from the monitored movements of the worker, for example, using a generation AI. For example, the generation AI analyzes patterns of the worker's movements and identifies the dangerous behavior. The warning unit issues a warning based on the dangerous behavior detected by the detection unit. The warning unit issues a warning to the worker, for example, in real time. For example, the warning unit issues a warning to the worker using an audio or visual alert. As a result, the safety management system according to the embodiment improves safety at construction sites and contributes to reducing work-related accidents.

[0084] The safety management system includes a simulation unit that uses VR and AR technology to simulate safe work procedures. The simulation unit uses VR and AR technology to simulate safe work procedures. VR technology, for example, uses a head-mounted display to create a virtual reality space in which workers can experience safe work procedures. For example, the simulation unit can allow workers to experience procedures for working at heights or how to operate heavy machinery in a virtual space. AR technology, for example, uses smart glasses to overlay virtual information on the real work environment, allowing workers to visually confirm safe work procedures. For example, the simulation unit can display safe work procedures at the work site, allowing workers to perform the work according to those procedures. This allows the simulation unit to increase workers' safety awareness and reduce the risk of accidents.

[0085] The safety management system includes a creation unit that creates safety documents to automatically add dangerous points based on past accident data and on-site conditions. The creation unit creates safety documents to automatically add dangerous points based on past accident data and on-site conditions. Safety documents include, but are not limited to, risk assessment sheets and safety procedure manuals. For example, the creation unit analyzes past accident data and automatically generates documents that list points to be careful of in specific tasks and details of accidents that have occurred in the past. For example, the creation unit uses a generation AI to create a risk assessment sheet based on past accident data and provides information to workers to work safely. In this way, the creation unit can provide workers with information to work safely and reduce the risk of accidents.

[0086] The collection unit can estimate the worker's emotions and adjust the timing of data collection based on the estimated worker's emotions. For example, if the worker is feeling stressed, the collection unit can reduce the frequency of data collection to reduce the worker's burden. Furthermore, if the worker is relaxed, the collection unit can increase the frequency of data collection to collect more detailed data. Furthermore, if the worker is tired, the collection unit can adjust the timing of data collection to collect data during breaks. This allows the collection unit to reduce the worker's burden and collect more detailed data. The estimation of the worker's emotions is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0087] When collecting past accident data, the collection unit can collect detailed information about the cause and circumstances of the accident. The collection unit collects detailed information, such as the weather, time of day, and work details when the accident occurred. The collection unit can also collect information about the years of experience and health status of workers involved in the accident. The collection unit can also collect photos and videos of the scene when the accident occurred and record the detailed circumstances. This allows the collection unit to collect detailed accident data and improve the accuracy of risk assessment.

[0088] The collection unit can also collect environmental factors such as weather and time of day when collecting information about the site conditions. For example, the collection unit collects weather information about the site in real time and stores it in a database. The collection unit can also collect information about the time period when work is performed and evaluate the safety of the work. The collection unit can also collect environmental data such as the temperature and humidity of the site and use it to improve the work environment. In this way, the collection unit can collect data that takes environmental factors into consideration and use it to improve the work environment.

[0089] When collecting data, the collection unit can take into account the individual work history and health condition of each worker. For example, the collection unit can refer to the worker's past work history to evaluate the risk of a specific task. The collection unit can also monitor the worker's health condition and refrain from collecting data if the worker is in poor health. The collection unit can also adjust the data collection method taking into account the worker's years of experience and skill level. This allows the collection unit to evaluate the risk of the worker and collect appropriate data.

[0090] The collection unit can estimate the worker's emotions and determine the priority of data to be collected based on the estimated worker's emotions. For example, if the worker is feeling stressed, the collection unit can prioritize collecting data of high importance. Furthermore, if the worker is relaxed, the collection unit can prioritize collecting detailed data. Furthermore, if the worker is tired, the collection unit can prioritize collecting the minimum amount of data necessary. This allows the collection unit to adjust the priority of data collection according to the worker's emotions and perform efficient data collection. The estimation of the worker's emotions is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0091] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the geographical information of the site. For example, the collection unit prioritizes collecting data from high-risk areas based on the geographical information of the site. The collection unit can also collect data by taking into account the movement lines of workers based on the geographical information of the site. The collection unit can also prioritize collecting data from specific work areas based on the geographical information of the site. This allows the collection unit to prioritize collecting data from high-risk areas based on the geographical information.

[0092] The collection unit can analyze the social media activities of the workers and collect related data when collecting data. For example, the collection unit analyzes the content of the workers' posts on social media and collects related data. The collection unit can also grasp the situation at the work site based on the workers' check-in information on social media. The collection unit can also collect related data by referring to the activities of the workers' friends on social media. In this way, the collection unit can analyze the social media activities and grasp the situation at the work site.

[0093] The collection unit can customize the collection method by reflecting past feedback when collecting data. For example, the collection unit can adjust the frequency and timing of data collection based on past feedback. The collection unit can also customize the type of data to be collected based on past feedback. The collection unit can also improve the data collection method based on past feedback. This allows the collection unit to improve the data collection method based on past feedback.

[0094] The prediction unit can estimate the worker's emotions and adjust the method for predicting dangerous locations based on the estimated worker's emotions. For example, if the worker is feeling stressed, the prediction unit can focus on predicting high-risk areas. Furthermore, if the worker is relaxed, the prediction unit can also perform a detailed risk assessment. Furthermore, if the worker is tired, the prediction unit can also perform a simplified risk assessment. This allows the prediction unit to adjust the prediction method according to the worker's emotions and improve the accuracy of the risk assessment. The estimation of the worker's emotions is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0095] When making a prediction, the prediction unit performs a detailed cause analysis of past accident data, enabling more accurate predictions. For example, the prediction unit performs a detailed cause analysis based on past accident data and identifies high-risk areas. The prediction unit can also evaluate the risks of specific tasks based on past accident data. The prediction unit can also identify high-risk time periods and weather conditions based on past accident data. This allows the prediction unit to perform a detailed cause analysis and identify high-risk areas.

[0096] The prediction unit can make predictions taking into account environmental factors (weather, time of day, etc.) at the site. The prediction unit predicts high-risk areas based on, for example, weather information at the site. The prediction unit can also predict high-risk time periods based on time period information at the site. The prediction unit can also predict high-risk areas based on environmental data such as the temperature and humidity at the site. This allows the prediction unit to perform risk assessments that take environmental factors into account, thereby improving the accuracy of predictions.

[0097] The prediction unit can make predictions taking into account the individual work history and health condition of each worker. For example, the prediction unit can refer to the worker's past work history to evaluate the risk of a specific task. The prediction unit can also monitor the worker's health condition and predict areas where risk increases when the worker is in poor health. The prediction unit can also predict high-risk areas taking into account the worker's years of experience and skill level. This allows the prediction unit to evaluate the risk of the worker and make appropriate predictions.

[0098] The prediction unit can estimate the worker's emotions and adjust the display method of the prediction results based on the estimated worker's emotions. For example, if the worker is feeling stressed, the prediction unit can provide a simple, highly visible display method. If the worker is relaxed, the prediction unit can also provide a display method that includes detailed information. If the worker is tired, the prediction unit can also provide a display method that focuses on the main points. In this way, the prediction unit can adjust the display method according to the worker's emotions and improve visibility. The estimation of the worker's emotions is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0099] When making predictions, the prediction unit can predict dangerous locations by taking into account geographical information of the work site. For example, the prediction unit predicts high-risk areas based on the geographical information of the work site. The prediction unit can also evaluate risks by taking into account the movement lines of workers based on the geographical information of the work site. The prediction unit can also predict risks in specific work areas based on the geographical information of the work site. This allows the prediction unit to predict high-risk areas based on geographical information.

[0100] The prediction unit can improve the accuracy of the prediction by referring to related literature and data when making a prediction. The prediction unit can improve the accuracy of the risk assessment by referring to, for example, related literature. The prediction unit can also improve the accuracy of the risk assessment by referring to related data. The prediction unit can also improve the accuracy of the risk assessment by referring to related research results. In this way, the prediction unit can improve the accuracy of the risk assessment by referring to related information.

[0101] The prediction unit can predict dangerous locations by taking into account the market value of the site when making predictions. The prediction unit can predict high-risk areas, for example, based on the market value of the site. The prediction unit can also evaluate risks by taking into account the movement lines of workers based on the market value of the site. The prediction unit can also predict risks in specific work areas based on the market value of the site. This allows the prediction unit to predict high-risk areas based on the market value.

[0102] The monitoring unit can estimate the worker's emotions and adjust the monitoring standards based on the estimated worker's emotions. For example, if the worker is feeling stressed, the monitoring unit can reduce the frequency of monitoring to reduce the worker's burden. Furthermore, if the worker is relaxed, the monitoring unit can increase the monitoring frequency and collect more detailed data. Furthermore, if the worker is tired, the monitoring unit can adjust the timing of monitoring and monitor during breaks. This allows the monitoring unit to adjust the monitoring standards according to the worker's emotions and reduce the worker's burden. The estimation of the worker's emotions is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0103] During monitoring, the monitoring unit can analyze detailed patterns of worker movement to improve the accuracy of monitoring. For example, the monitoring unit analyzes patterns of worker movement to detect abnormal movement. The monitoring unit can also analyze patterns of worker movement to identify high-risk movement. The monitoring unit can also analyze patterns of worker movement to propose an efficient monitoring method. In this way, the monitoring unit can analyze movement patterns and detect abnormal movement.

[0104] The monitoring unit can take into consideration the individual work history and health condition of each worker when monitoring. For example, the monitoring unit can refer to the worker's past work history to evaluate the risk of a specific task. The monitoring unit can also monitor the worker's health condition and intensify monitoring if the worker is in poor health. The monitoring unit can also adjust the monitoring method taking into consideration the worker's years of experience and skill level. This allows the monitoring unit to evaluate the risk of the worker and perform appropriate monitoring.

[0105] The monitoring unit can perform monitoring taking into consideration environmental factors (weather, time of day, etc.) at the site. For example, the monitoring unit can monitor high-risk areas based on weather information at the site. The monitoring unit can also monitor high-risk time periods based on time period information at the site. The monitoring unit can also monitor high-risk areas based on environmental data such as the temperature and humidity at the site. This allows the monitoring unit to perform monitoring taking environmental factors into consideration and monitor high-risk areas.

[0106] The monitoring unit can estimate the worker's emotions and adjust the display method of the monitoring results based on the estimated worker's emotions. For example, if the worker is feeling stressed, the monitoring unit can provide a simple, highly visible display method. If the worker is relaxed, the monitoring unit can also provide a display method that includes detailed information. If the worker is tired, the monitoring unit can also provide a display method that focuses on the main points. This allows the monitoring unit to adjust the display method according to the worker's emotions and improve visibility. The estimation of the worker's emotions is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0107] The monitoring unit can perform monitoring while taking into consideration the geographical information of the site. For example, the monitoring unit monitors high-risk areas based on the geographical information of the site. The monitoring unit can also perform monitoring while taking into consideration the movement lines of workers based on the geographical information of the site. The monitoring unit can also focus on monitoring specific work areas based on the geographical information of the site. This allows the monitoring unit to monitor high-risk areas based on the geographical information.

[0108] The monitoring unit can improve the accuracy of monitoring by referring to related literature and data during monitoring. For example, the monitoring unit can improve the accuracy of monitoring by referring to related literature. The monitoring unit can also improve the accuracy of monitoring by referring to related data. The monitoring unit can also improve the accuracy of monitoring by referring to related research results. In this way, the monitoring unit can improve the accuracy of monitoring by referring to related information.

[0109] The monitoring unit can perform monitoring taking into account the market value of the site. For example, the monitoring unit monitors high-risk areas based on the market value of the site. The monitoring unit can also perform monitoring taking into account the movement of workers based on the market value of the site. The monitoring unit can also focus on monitoring specific work areas based on the market value of the site. This allows the monitoring unit to monitor high-risk areas based on market value.

[0110] The detection unit can estimate the worker's emotions and adjust the detection criteria for risky behavior based on the estimated worker's emotions. For example, when the worker is stressed, the detection unit focuses on detecting high-risk behavior. The detection unit can also detect detailed behavioral patterns when the worker is relaxed. The detection unit can also detect simple behavioral patterns when the worker is tired. This allows the detection unit to adjust the detection criteria according to the worker's emotions and detect high-risk behavior. The estimation of the worker's emotions is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0111] The detection unit can improve the accuracy of detection by analyzing detailed patterns of the worker's movements during detection. For example, the detection unit can analyze the patterns of the worker's movements and detect abnormal movements. The detection unit can also analyze the patterns of the worker's movements and identify high-risk movements. The detection unit can also analyze the patterns of the worker's movements and propose an efficient detection method. In this way, the detection unit can analyze the patterns of movements and detect abnormal movements.

[0112] The detection unit can perform detection by taking into consideration the individual work history and health condition of each worker. For example, the detection unit can refer to the worker's past work history to evaluate the risk of a specific task. The detection unit can also monitor the worker's health condition and detect behavior that increases risk when the worker is in poor health. The detection unit can also detect high-risk behavior by taking into consideration the worker's years of experience and skill level. This allows the detection unit to evaluate the risk of the worker and perform appropriate detection.

[0113] The detection unit can perform detection while taking into consideration environmental factors (weather, time of day, etc.) at the site. For example, the detection unit detects high-risk behavior based on weather information at the site. The detection unit can also detect behavior during high-risk time periods based on time period information at the site. The detection unit can also detect high-risk behavior based on environmental data such as the temperature and humidity at the site. This allows the detection unit to perform detection while taking environmental factors into consideration and detect high-risk behavior.

[0114] The detection unit can estimate the worker's emotion and adjust the display method of the detection results based on the estimated worker's emotion. For example, if the worker is feeling stressed, the detection unit provides a simple, highly visible display method. If the worker is relaxed, the detection unit can also provide a display method that includes detailed information. If the worker is tired, the detection unit can also provide a display method that focuses on the main points. This allows the detection unit to adjust the display method according to the worker's emotion and improve visibility. The worker's emotion is estimated using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0115] During detection, the detection unit can detect dangerous behavior by taking into account geographical information of the work site. For example, the detection unit detects behavior in high-risk areas based on the geographical information of the work site. The detection unit can also detect dangerous behavior by taking into account the movement lines of workers based on the geographical information of the work site. The detection unit can also detect dangerous behavior in a specific work area based on the geographical information of the work site. This allows the detection unit to detect behavior in high-risk areas based on geographical information.

[0116] The detection unit can improve the accuracy of detection by referring to related literature and data during detection. For example, the detection unit improves the accuracy of detection by referring to related literature. The detection unit can also improve the accuracy of detection by referring to related data. The detection unit can also improve the accuracy of detection by referring to related research results. In this way, the detection unit can improve the accuracy of detection by referring to related information.

[0117] The detection unit can detect risky behavior by taking into account the market value of the site during detection. For example, the detection unit detects behavior in high-risk areas based on the market value of the site. The detection unit can also detect risky behavior by taking into account the movement of workers based on the market value of the site. The detection unit can also detect risky behavior in a specific work area based on the market value of the site. This allows the detection unit to detect behavior in high-risk areas based on the market value.

[0118] The warning unit can estimate the worker's emotions and adjust the warning method based on the estimated worker's emotions. For example, if the worker is feeling stressed, the warning unit can issue a warning in a calm voice. If the worker is relaxed, the warning unit can also issue a warning including a detailed explanation. If the worker is tired, the warning unit can also issue a concise and to-the-point warning. This allows the warning unit to adjust the warning method according to the worker's emotions and issue an effective warning. The worker's emotions are estimated using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0119] When issuing a warning, the warning unit can analyze detailed patterns of the worker's movements to improve the accuracy of the warning. For example, the warning unit analyzes the patterns of the worker's movements and issues a warning when abnormal movements are detected. The warning unit can also analyze the patterns of the worker's movements and identify high-risk movements and issue a warning. The warning unit can also analyze the patterns of the worker's movements and suggest an efficient warning method. This allows the warning unit to analyze the movement patterns and issue a warning when abnormal movements are detected.

[0120] When issuing a warning, the warning unit can take into consideration the individual work history and health condition of the worker. For example, the warning unit can refer to the worker's past work history, evaluate the risk of a specific task, and issue a warning. The warning unit can also monitor the worker's health condition and intensify the warning when the worker is in poor health. The warning unit can also issue a warning about high-risk behavior, taking into consideration the worker's years of experience and skill level. This allows the warning unit to evaluate the risk of the worker and issue an appropriate warning.

[0121] The warning unit can issue a warning taking into consideration environmental factors (weather, time of day, etc.) at the site. For example, the warning unit issues a warning about high-risk behavior based on weather information at the site. The warning unit can also issue a warning about behavior during high-risk time periods based on time period information at the site. The warning unit can also issue a warning about high-risk behavior based on environmental data such as the temperature and humidity at the site. In this way, the warning unit can issue a warning taking into consideration environmental factors and issue a warning about high-risk behavior.

[0122] The warning unit can estimate the worker's emotions and determine the priority of warnings based on the estimated worker's emotions. For example, if the worker is feeling stressed, the warning unit can prioritize issuing a more important warning. Furthermore, if the worker is relaxed, the warning unit can prioritize issuing a more detailed warning. Furthermore, if the worker is tired, the warning unit can prioritize issuing the minimum necessary warning. In this way, the warning unit can adjust the priority of warnings according to the worker's emotions and prioritize issuing important warnings. The estimation of the worker's emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0123] When issuing a warning, the warning unit can issue a warning taking into account geographical information of the work site. For example, the warning unit issues a warning against actions in high-risk areas based on the geographical information of the work site. The warning unit can also issue a warning taking into account the movement lines of workers based on the geographical information of the work site. The warning unit can also issue a warning against dangerous actions in a specific work area based on the geographical information of the work site. This allows the warning unit to issue a warning against actions in high-risk areas based on geographical information.

[0124] The warning unit can improve the accuracy of the warning by referring to related literature and data when issuing a warning. For example, the warning unit can improve the accuracy of the warning by referring to related literature. The warning unit can also improve the accuracy of the warning by referring to related data. The warning unit can also improve the accuracy of the warning by referring to related research results. In this way, the warning unit can improve the accuracy of the warning by referring to related information.

[0125] When issuing a warning, the warning unit can issue a warning taking into account the market value of the work site. For example, the warning unit issues a warning against behavior in a high-risk area based on the market value of the work site. The warning unit can also issue a warning taking into account the movement of workers based on the market value of the work site. The warning unit can also issue a warning against dangerous behavior in a specific work area based on the market value of the work site. This allows the warning unit to issue a warning against behavior in a high-risk area based on the market value.

[0126] The simulation unit can estimate the worker's emotions and adjust the content of the simulation based on the estimated worker's emotions. For example, if the worker is feeling stressed, the simulation unit can provide a simple, highly visible simulation. If the worker is relaxed, the simulation unit can provide a simulation that includes detailed information. If the worker is tired, the simulation unit can provide a simulation that focuses on the main points. This allows the simulation unit to adjust the content of the simulation according to the worker's emotions, enabling effective training. The estimation of the worker's emotions is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0127] During the simulation, the simulation unit can improve the accuracy of the simulation by referring to past accident data. For example, the simulation unit performs a detailed cause analysis based on the past accident data, thereby improving the accuracy of the simulation. The simulation unit can also evaluate the risk of a specific task based on the past accident data. The simulation unit can also identify high-risk time periods and weather conditions based on the past accident data. This allows the simulation unit to perform a detailed cause analysis based on the past accident data, thereby improving the accuracy of the simulation.

[0128] The simulation unit can perform a simulation taking into account the individual work history and health condition of each worker. For example, the simulation unit can refer to the worker's past work history to evaluate the risk of a specific task. The simulation unit can also monitor the worker's health condition and perform a simulation that increases the risk when the worker is in poor health. The simulation unit can also perform a simulation with a high risk taking into account the worker's years of experience and skill level. This allows the simulation unit to evaluate the risk of the worker and perform an appropriate simulation.

[0129] The simulation unit can perform a simulation taking into account environmental factors (weather, time of day, etc.) at the site. For example, the simulation unit simulates high-risk areas based on weather information at the site. The simulation unit can also simulate high-risk time periods based on time period information at the site. The simulation unit can also simulate high-risk areas based on environmental data such as the temperature and humidity at the site. In this way, the simulation unit can perform a simulation taking into account environmental factors and simulate high-risk areas.

[0130] The simulation unit can estimate the emotions of the worker and determine the priority of simulations based on the estimated emotions of the worker. For example, if the worker is feeling stressed, the simulation unit can prioritize simulations with high importance. Furthermore, if the worker is relaxed, the simulation unit can prioritize detailed simulations. Furthermore, if the worker is tired, the simulation unit can prioritize the minimum necessary simulations. In this way, the simulation unit can adjust the priority of simulations according to the emotions of the worker and prioritize important simulations. The estimation of the emotions of the worker is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0131] The simulation unit can perform a simulation taking into account geographical information of the site. For example, the simulation unit simulates high-risk areas based on the geographical information of the site. The simulation unit can also perform a simulation taking into account the movement lines of workers based on the geographical information of the site. The simulation unit can also focus the simulation on specific work areas based on the geographical information of the site. This allows the simulation unit to simulate high-risk areas based on the geographical information.

[0132] The simulation unit can improve the accuracy of the simulation by referring to related literature and data during the simulation. For example, the simulation unit can improve the accuracy of the simulation by referring to related literature. The simulation unit can also improve the accuracy of the simulation by referring to related data. The simulation unit can also improve the accuracy of the simulation by referring to related research results. In this way, the simulation unit can improve the accuracy of the simulation by referring to related information.

[0133] The simulation unit can perform a simulation taking into account the market value of the site. For example, the simulation unit simulates high-risk areas based on the market value of the site. The simulation unit can also perform a simulation taking into account the movement lines of workers based on the market value of the site. The simulation unit can also focus the simulation on specific work areas based on the market value of the site. This allows the simulation unit to simulate high-risk areas based on the market value.

[0134] The creation unit can estimate the worker's emotions and adjust the content of the safety document based on the estimated worker's emotions. For example, if the worker is feeling stressed, the creation unit can provide a simple, highly visible safety document. If the worker is relaxed, the creation unit can also provide a safety document that includes detailed information. If the worker is tired, the creation unit can also provide a safety document that focuses on the main points. This allows the creation unit to adjust the content of the safety document according to the worker's emotions and improve visibility. The estimation of the worker's emotions is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0135] The creation unit can improve the accuracy of safety documents by referring to past accident data when creating the documents. For example, the creation unit can perform detailed cause analysis based on past accident data to improve the accuracy of the safety documents. The creation unit can also evaluate the risks of specific tasks based on past accident data. The creation unit can also identify high-risk time periods and weather conditions based on past accident data. This allows the creation unit to perform detailed cause analysis based on past accident data to improve the accuracy of the safety documents.

[0136] When creating safety documents, the creation department can take into account the individual work history and health status of each worker. For example, the creation department can refer to the worker's past work history to evaluate the risk of a specific task. The creation department can also monitor the worker's health status and create safety documents that increase the risk when the worker is in poor health. The creation department can also create safety documents with higher risks by taking into account the worker's years of experience and skill level. This allows the creation department to evaluate the risk of workers and create appropriate safety documents.

[0137] The creation unit can create safety documents taking into consideration environmental factors (weather, time of day, etc.) at the work site. For example, the creation unit can indicate high-risk areas in the safety documents based on weather information at the work site. The creation unit can also indicate high-risk time periods in the safety documents based on time period information at the work site. The creation unit can also indicate high-risk areas in the safety documents based on environmental data such as the temperature and humidity at the work site. This allows the creation unit to create safety documents taking environmental factors into consideration and indicate high-risk areas.

[0138] The creation unit can estimate the worker's emotions and determine the priority of safety documents based on the estimated worker's emotions. For example, if the worker is feeling stressed, the creation unit can prioritize creating safety documents with high importance. Furthermore, if the worker is relaxed, the creation unit can prioritize creating detailed safety documents. Furthermore, if the worker is tired, the creation unit can prioritize creating the minimum necessary safety documents. This allows the creation unit to adjust the priority of safety documents according to the worker's emotions and prioritize creating important documents. The estimation of the worker's emotions is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0139] The creation unit can create safety documents taking into account the geographical information of the work site when creating them. For example, the creation unit describes high-risk areas in the safety documents based on the geographical information of the work site. The creation unit can also create safety documents taking into account the movement lines of workers based on the geographical information of the work site. The creation unit can also focus on specific work areas when describing safety documents based on the geographical information of the work site. This allows the creation unit to describe high-risk areas in the safety documents based on the geographical information.

[0140] The creation unit can improve the accuracy of the safety documents by referring to related literature and data when creating the safety documents. For example, the creation unit can improve the accuracy of the safety documents by referring to related literature. The creation unit can also improve the accuracy of the safety documents by referring to related data. The creation unit can also improve the accuracy of the safety documents by referring to related research results. In this way, the creation unit can improve the accuracy of the safety documents by referring to related information.

[0141] The creation department can create safety documents taking into account the market value of the site when creating them. For example, the creation department can list high-risk areas in the safety documents based on the market value of the site. The creation department can also create safety documents taking into account the movement lines of workers based on the market value of the site. The creation department can also focus on specific work areas when creating safety documents based on the market value of the site. This allows the creation department to list high-risk areas in the safety documents based on the market value. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, prediction unit, monitoring unit, detection unit, warning unit, simulation unit, and creation unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects on-site information using the camera 42 and sensors of the smart device 14, and acquires past accident data from the database 24 using the specific processing unit 290 of the data processing device 12. The prediction unit predicts dangerous locations using a generation AI using the specific processing unit 290 of the data processing device 12. The monitoring unit monitors worker movements in real time using the camera 42 of the smart device 14. The detection unit detects dangerous behavior using a generation AI using the specific processing unit 290 of the data processing device 12. The warning unit issues a warning in real time using the output device 40 of the smart device 14. The simulation unit realizes VR or AR technology using the display 40A of the smart device 14, for example. The creation unit automatically generates safety documents using the specific processing unit 290 of the data processing device 12, for example. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, prediction unit, monitoring unit, detection unit, warning unit, simulation unit, and creation unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects on-site conditions using the camera 42 and sensors of the smart glasses 214, and acquires past accident data from the database 24 using the specific processing unit 290 of the data processing device 12. The prediction unit predicts dangerous locations using a generation AI using the specific processing unit 290 of the data processing device 12. The monitoring unit monitors worker movements in real time using the camera 42 of the smart glasses 214. The detection unit detects dangerous behavior using a generation AI using the specific processing unit 290 of the data processing device 12. The warning unit issues a warning in real time using the speaker 240 of the smart glasses 214. The simulation unit realizes AR technology using the display of the smart glasses 214, for example. The creation unit automatically generates safety documents using the specific processing unit 290 of the data processing device 12, for example. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, prediction unit, monitoring unit, detection unit, warning unit, simulation unit, and creation unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects on-site conditions using the camera 42 and sensors of the headset-type terminal 314, and acquires past accident data from the database 24 using the identification processing unit 290 of the data processing device 12. The prediction unit predicts dangerous locations using a generation AI using the identification processing unit 290 of the data processing device 12. The monitoring unit monitors the movements of workers in real time using the camera 42 of the headset-type terminal 314. The detection unit detects dangerous behavior using a generation AI using the identification processing unit 290 of the data processing device 12. The warning unit issues a warning in real time using the speaker 240 of the headset-type terminal 314, for example. The simulation unit realizes VR technology using the display 343 of the headset-type terminal 314, for example. The creation unit automatically creates the security document using, for example, the specific processing unit 290 of the data processing device 12 . === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, prediction unit, monitoring unit, detection unit, warning unit, simulation unit, and creation unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects on-site information using the camera 42 and sensors of the robot 414, and acquires past accident data from the database 24 using the specific processing unit 290 of the data processing device 12. The prediction unit predicts dangerous locations using a generation AI using the specific processing unit 290 of the data processing device 12. The monitoring unit monitors the movements of workers in real time using the camera 42 of the robot 414. The detection unit detects dangerous behavior using a generation AI using the specific processing unit 290 of the data processing device 12. The warning unit issues a warning in real time using the speaker 240 of the robot 414. The simulation unit implements AR technology using the display of the robot 414, for example. The creation unit automatically generates safety documents using the specific processing unit 290 of the data processing device 12, for example.

[0142] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0143] A safety management system can be equipped with a health monitoring unit that monitors the health status of workers in real time and immediately issues an alert if an abnormality is detected. For example, sensors can measure a worker's heart rate and body temperature, and an alert can be issued if an abnormal value is detected. It can also issue an alert to encourage a worker to take a break if the worker is overworked. Furthermore, it can adjust work assignments based on the worker's health status to prevent overwork. In this way, the health monitoring unit can protect the health of workers and reduce the risk of workplace accidents.

[0144] The safety management system can include an environment adjustment unit that estimates the emotions of workers and adjusts the work environment based on the estimated emotions. For example, if a worker feels stressed, the lighting in the work environment can be adjusted to provide a relaxing environment. If a worker feels tired, an audio message encouraging the worker to take a break can be issued. Furthermore, if a worker lacks concentration, music can be played to help the worker concentrate. In this way, the environment adjustment unit can optimize the work environment according to the worker's emotions, thereby improving work efficiency and safety.

[0145] A safety management system can include a training evaluation department that evaluates the skill level of workers and provides appropriate training programs. For example, basic safety training can be provided to new employees, while more experienced workers can receive more advanced training. It can also recommend additional training if a skill deficiency is detected in a particular task. It can also monitor the progress of training and adjust the training content as needed. This allows the training evaluation department to improve worker skills and increase safety.

[0146] The safety management system can include a communication adjustment unit that estimates the emotions of workers and adjusts the communication method based on the estimated emotions. For example, if a worker is feeling stressed, instructions can be given in a calm tone. If the worker is relaxed, instructions can be given that include detailed explanations. Furthermore, if the worker is tired, instructions can be given that are concise and to the point. In this way, the communication adjustment unit can optimize the communication method according to the emotions of the worker, thereby improving work efficiency and safety.

[0147] The safety management system can be equipped with an efficiency suggestion unit that analyzes worker behavior data and makes suggestions to improve work efficiency. For example, it analyzes worker movement lines and suggests efficient work procedures. It can also analyze worker rest patterns and suggest appropriate break timing. It can also make suggestions to optimize the placement of tools and equipment used by workers. In this way, the efficiency suggestion unit can improve work efficiency, reduce worker burden, and increase safety.

[0148] The safety management system can include a break management unit that estimates the emotions of workers and adjusts break timing based on the estimated emotions. For example, if a worker feels stressed, it can encourage them to take an early break. If the worker feels relaxed, it can maintain the normal break timing. Furthermore, if the worker feels tired, it can suggest extending the break time. In this way, the break management unit can optimize break timing according to the worker's emotions, improving work efficiency and safety.

[0149] A safety management system can be equipped with an environment improvement department that makes suggestions for improving the work environment based on worker behavior data. For example, it analyzes worker movement lines and makes suggestions for optimizing the layout of the work space. It can also review the placement of tools and equipment used by workers and suggest efficient placement. It can also monitor the temperature and humidity of the work environment and make suggestions for providing a comfortable work environment. This allows the environment improvement department to optimize the work environment and improve work efficiency and safety.

[0150] The safety management system can include a work allocation unit that estimates the emotions of workers and adjusts work assignments based on the estimated emotions. For example, if a worker is feeling stressed, it can assign less burdensome work. If the worker is relaxed, it can also assign normal work. Furthermore, if the worker is tired, it can also assign work that prioritizes rest. In this way, the work allocation unit can optimize work assignments according to the emotions of workers, thereby improving work efficiency and safety.

[0151] A safety management system can be equipped with a procedure improvement unit that proposes improvements to work procedures based on worker behavior data. For example, it can analyze worker movements and propose efficient work procedures. It can also analyze worker working time and propose ways to reduce unnecessary movements. It can also review the operating procedures for tools and equipment used by workers and propose safe and efficient operating procedures. In this way, the procedure improvement unit can optimize work procedures and improve work efficiency and safety.

[0152] The safety management system can include an education adjustment unit that estimates the emotions of workers and adjusts the content of safety education based on the estimated emotions. For example, if a worker is feeling stressed, simple, highly visible education materials can be provided. If a worker is relaxed, education materials containing detailed information can be provided. Furthermore, if a worker is tired, education materials that focus on the main points can be provided. In this way, the education adjustment unit can optimize the education content according to the emotions of workers and achieve effective safety education.

[0153] The processing flow of the second embodiment will be briefly explained below.

[0154] Step 1: The collection unit collects past accident data and on-site conditions. Past accident data includes the type of accident, the date and time of the accident, and the cause, while on-site conditions include the work environment, equipment status, and worker deployment. The collection unit obtains past accident data from a database and collects on-site conditions using sensors and cameras. Step 2: The prediction unit analyzes the data collected by the collection unit and predicts dangerous locations. The prediction unit predicts dangerous locations such as falls based on the collected data using the generation AI. The generation AI analyzes past accident data and evaluates the degree of danger in specific work areas and work procedures. Step 3: The monitoring unit monitors the movements of workers based on the dangerous locations predicted by the prediction unit. The monitoring unit uses AI cameras to monitor the movements of workers in real time, detecting instances such as when a worker is not using a safety harness while working at height or when the worker is making inappropriate movements near heavy machinery. Step 4: The detection unit detects dangerous behavior based on the worker's movements monitored by the monitoring unit. The detection unit uses the generation AI to detect dangerous behavior from the monitored worker's movements and identifies the dangerous behavior by analyzing the worker's movement patterns. Step 5: The warning unit issues a warning based on the dangerous behavior detected by the detection unit. The warning unit warns the worker in real time using audio and visual alerts.

[0155] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0156] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0157] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0158] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0159] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0160] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0161] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0162] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0163] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0164] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0165] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0166] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0167] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0168] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0169] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0170] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0171] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0172] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0173] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0174] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0175] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0176] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0177] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0178] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0179] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0180] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0181] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0182] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0183] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0184] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0185] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0186] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0187] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0188] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0189] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0190] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0191] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0192] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0193] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0194] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0195] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0196] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0197] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0198] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0199] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0200] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0201] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0202] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0203] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0204] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0205] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0206] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0207] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0208] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0209] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0210] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0211] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0212] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0213] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0214] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0215] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0216] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0217] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0218] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0219] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0220] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0221] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0222] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0223] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0224] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0225] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0226] [Explanation of symbols]

[0227] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A collection department that collects past accident data and on-site conditions; a prediction unit that analyzes the data collected by the collection unit and predicts dangerous locations; a monitoring unit that monitors the movement of workers based on the dangerous locations predicted by the prediction unit; a detection unit that detects dangerous behavior based on the movement of the worker monitored by the monitoring unit; a warning unit that issues a warning based on the dangerous behavior detected by the detection unit. A system characterized by:

2. Equipped with a simulation section that uses VR and AR technology to simulate safe work procedures 2. The system of claim 1.

3. Equipped with a creation section that creates safety documents that automatically add dangerous points based on past accident data and on-site conditions 2. The system of claim 1.

4. The collecting unit Estimate the emotions of workers and adjust the timing of data collection based on the estimated emotions of workers.

2. The system of claim 1.

5. The collecting unit When collecting past accident data, collect detailed information on the causes and circumstances of the accidents.

2. The system of claim 1.

6. The collecting unit When collecting information on the site, include environmental factors such as weather and time of day.

2. The system of claim 1.

7. The collecting unit When collecting data, consider the individual work history and health status of each worker.

2. The system of claim 1.

8. The collecting unit Estimate the emotions of workers and prioritize the data to be collected based on the estimated emotions of workers 2. The system of claim 1.

9. The collecting unit When collecting data, prioritize collecting the most relevant data by taking into account the geographical location of the site.

2. The system of claim 1.

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