system

Abstract

An object of a system according to an embodiment is to suppress occurrence of workers' accidents at a construction site.SOLUTION: A system according to an embodiment includes a dangerous work washout unit, a past example collection unit, and a danger prevention measure proposal unit. The dangerous work screening unit uses the generated AI to screen dangerous work at the building site based on the MECE principles. The past example collection unit collects and analyzes a past example generated in a similar construction site using the generated AI. The risk prevention measure proposing part proposes a risk prevention measure suitable for the site based on the collected and analyzed AI.SELECTED DRAWING: Figure 1

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] With conventional technology, measures to prevent industrial accidents at construction sites rely on tacit knowledge, which can lead to a decline in safety awareness due to staleness.

[0005] The system according to the embodiment aims to prevent industrial accidents from occurring at construction sites. [Means for solving the problem]

[0006] The system according to the embodiment comprises a dangerous work identification unit, a past example collection unit, and a danger prevention measure proposal unit. The dangerous work identification unit uses a generation AI to identify dangerous work at construction sites based on the MECE principle. The past example collection unit uses a generation AI to collect and analyze past examples that have occurred at similar construction sites. The danger prevention measure proposal unit uses a generation AI to propose danger prevention measures suited to the site based on the collected and analyzed data. [Effects of the Invention]

[0007] The system according to the embodiment can prevent industrial accidents from occurring at construction sites. [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) The industrial accident prevention system according to the embodiment of the present invention is a system that identifies dangerous work at construction sites based on the MECE principle, collects and analyzes past examples from similar sites, and proposes risk prevention measures suited to the site. As a result, the industrial accident prevention system can effectively prevent industrial accidents at construction sites.

[0029] The industrial accident prevention system according to the embodiment includes a dangerous work identification unit, a past case collection unit, and a hazard prevention measure proposal unit. The dangerous work identification unit uses a generation AI to identify dangerous work at construction sites based on the MECE principle. For example, the generation AI lists dangerous work, such as working at heights, operating heavy machinery, and electrical work, based on prompts containing specific conditions and work content at the construction site. The generation AI also collects and analyzes past cases that occurred at similar construction sites based on a database of past industrial accident cases. For example, the generation AI collects data on accidents such as falls while working at heights and entrapment accidents while operating heavy machinery, and identifies common risk factors and prevention measures. Furthermore, the generation AI proposes hazard prevention measures tailored to the site based on the collected and analyzed data. For example, the generation AI suggests using safety harnesses and inspecting scaffolding when working at heights, checking operating procedures for operating heavy machinery, and checking surrounding safety. As a result, the industrial accident prevention system according to the embodiment can effectively prevent industrial accidents at construction sites. For example, the output unit notifies on-site workers of proposed preventive measures and encourages them to implement them. The proposed preventive measures are displayed via a web application or mobile application. If feedback is desired in paper form, the results can be printed using a printer. Sending the results via email provides quick feedback by sending the results directly to workers or managers.

[0030] The dangerous work identification unit analyzes workers' past behavioral data and can predict potentially dangerous work. For example, the dangerous work identification unit collects workers' past behavioral data, and the generation AI analyzes that data to predict potentially dangerous work. For example, it analyzes patterns of frequent mistakes and accidents that have occurred in the past and lists dangerous work in similar situations. In addition, the dangerous work identification unit uses the generation AI to predict the possibility that specific work procedures or actions are dangerous based on the worker's behavioral data. For example, it identifies risks that arise from repeated specific actions and adds those actions to the dangerous work list. In addition, the dangerous work identification unit analyzes workers' behavioral data in real time, and the generation AI predicts potentially dangerous work on the spot. For example, if a worker moves in an unusual way, it warns that the action may be dangerous. This makes it possible to predict potentially dangerous work and prevent industrial accidents from occurring.

[0031] The dangerous work identification unit can identify dangerous work under specific conditions by taking into account environmental data at the site. For example, the dangerous work identification unit collects weather data at the site, and the generation AI identifies dangerous work under specific weather conditions based on that data. For example, work done in slippery areas during rainy weather is added to the dangerous work list. The dangerous work identification unit also analyzes topographical data at the site, and the generation AI identifies dangerous work under specific topographical conditions based on that data. For example, work done on steep slopes or unstable ground is added to the dangerous work list. The dangerous work identification unit also takes into account time-of-day data at the site, and the generation AI identifies dangerous work under specific time periods based on that data. For example, dangerous work due to poor visibility during night or early morning work is listed. This makes it possible to identify dangerous work under specific conditions and prevent industrial accidents from occurring.

[0032] When analyzing past industrial accident cases, the past case collection unit can take into account detailed environmental data at the time of the accident. For example, the past case collection unit collects temperature data from past industrial accident cases, and the generation AI uses that data to analyze the environmental conditions at the time of the accident. For example, it evaluates the impact of high and low temperatures on the accident. In addition, the past case collection unit collects humidity data from past industrial accident cases, and the generation AI uses that data to analyze the environmental conditions at the time of the accident. For example, it evaluates the impact of high and low humidity on the accident. In addition, the past case collection unit collects wind speed data from past industrial accident cases, and the generation AI uses that data to analyze the environmental conditions at the time of the accident. For example, it evaluates the impact of strong winds or no wind on the accident. This makes it possible to evaluate the risk of an industrial accident occurring by taking into account detailed environmental data at the time of the accident.

[0033] When analyzing past industrial accident cases, the past case collection unit can identify risk factors by taking into account the worker's years of experience and qualification information. For example, the past case collection unit collects data on the years of experience of workers in past industrial accident cases, and the generation AI identifies risk factors based on that data. For example, it evaluates the risk of accidents involving workers with few years of experience. The past case collection unit also collects the worker's qualification information in past industrial accident cases, and the generation AI identifies risk factors based on that data. For example, it evaluates the risk of accidents involving workers without specific qualifications. The past case collection unit also analyzes the worker's years of experience in combination with the qualification information, and the generation AI identifies risk factors based on that data. For example, it evaluates the risk of accidents involving workers with few years of experience and no qualifications. In this way, it is possible to identify risk factors by taking into account the worker's years of experience and qualification information.

[0034] The hazard prevention measure proposal unit can simulate the effects of proposed prevention measures and select the optimal measures. For example, the hazard prevention measure proposal unit builds a system that simulates the effects of prevention measures proposed by the generation AI and selects the optimal measures. For example, it prioritizes the proposal of highly effective measures based on the simulation results. The hazard prevention measure proposal unit also simulates prevention measures, and the generation AI selects the optimal measures based on the results. For example, it proposes measures that reduce accident risk through simulation. The hazard prevention measure proposal unit also develops a system that uses the generation AI to simulate the effects of prevention measures and selects the optimal measures based on the results. For example, it proposes measures whose effectiveness has been confirmed through simulation. In this way, it is possible to simulate the effects of prevention measures and select the optimal measures.

[0035] The hazard prevention measure proposal unit can collect prevention measures from different industries and propose measures that can be applied to construction sites. For example, the hazard prevention measure proposal unit collects prevention measure data from different industries, and the generation AI proposes measures that can be applied to construction sites based on that data. For example, safety measures from the manufacturing industry can be applied to construction sites. The hazard prevention measure proposal unit also analyzes prevention measures from different industries, and the generation AI proposes measures that can be applied to construction sites based on that data. For example, safety measures from the logistics industry can be applied to construction sites. The hazard prevention measure proposal unit also integrates prevention measure data from different industries, and builds a system in which the generation AI proposes measures that can be applied to construction sites based on that data. For example, common safety measures can be applied to construction sites. This makes it possible to collect prevention measures from different industries and propose measures that can be applied to construction sites.

[0036] The hazard prevention measure proposal unit can visualize the proposed prevention measures so that workers can intuitively understand them. For example, the hazard prevention measure proposal unit builds a system that visualizes the prevention measures proposed by the generation AI so that workers can intuitively understand them. For example, it explains the prevention measures using diagrams and illustrations. The hazard prevention measure proposal unit also visualizes the prevention measures so that the generation AI can intuitively understand them based on the data. For example, it explains the prevention measures using videos and animations. The hazard prevention measure proposal unit also visualizes the prevention measures proposed by the generation AI so that workers can intuitively understand them. For example, it explains the prevention measures using 3D models. In this way, it is possible to visualize the prevention measures so that workers can intuitively understand them.

[0037] The dangerous work identification unit collects data on dangerous work in different industries and can identify dangerous work that can be applied to construction sites. For example, the dangerous work identification unit collects data on dangerous work in the manufacturing industry, and the generation AI uses that data to identify dangerous work that can be applied to construction sites. For example, dangerous work related to machine operation is applied to operating heavy equipment at construction sites. The dangerous work identification unit also collects data on dangerous work in the logistics industry, and the generation AI uses that data to identify dangerous work that can be applied to construction sites. For example, dangerous work related to loading and unloading luggage is applied to transporting materials at construction sites. The dangerous work identification unit also integrates data on dangerous work from different industries, and the generation AI uses that data to identify dangerous work that can be applied to construction sites. For example, it extracts common risk factors and adds them to a list of dangerous work at construction sites. This makes it possible to collect data on dangerous work in different industries and identify dangerous work that can be applied to construction sites.

[0038] The dangerous work identification unit can analyze worker conversations using voice recognition technology and identify potential dangers. For example, the dangerous work identification unit analyzes worker conversations using voice recognition technology, and the generation AI identifies potential dangers based on that data. For example, it analyzes the content of conversations when workers sense danger and adds it to a dangerous work list. The dangerous work identification unit also collects worker conversation data, and the generation AI identifies dangerous work based on that data. For example, it lists dangerous situations and work that workers frequently mention. The dangerous work identification unit also uses voice recognition technology to identify dangerous work from worker conversations in real time, and the generation AI updates the dangerous work list based on that data. For example, it identifies new dangerous work from conversations during work. This makes it possible to analyze worker conversations and identify potential dangers.

[0039] The past case collection unit can collect industrial accident cases from different countries and regions and analyze them taking into account differences in culture and regulations. For example, the past case collection unit collects industrial accident cases from different countries, and the generation AI analyzes the data taking into account differences in culture and regulations. For example, it evaluates the impact of safety regulations in a specific country on accidents. The past case collection unit also collects industrial accident cases from different regions, and the generation AI analyzes the data taking into account differences in culture and regulations. For example, it evaluates the impact of regional work culture on accidents. The past case collection unit also integrates industrial accident cases from different countries and regions, and the generation AI analyzes the data taking into account differences in culture and regulations. For example, it evaluates the impact of international safety standards on accidents. This makes it possible to collect industrial accident cases from different countries and regions and analyze them taking into account differences in culture and regulations.

[0040] The hazard prevention measure proposal unit can simulate the effects of proposed prevention measures and select the optimal measures. For example, the hazard prevention measure proposal unit builds a system that simulates the effects of prevention measures proposed by the generation AI and selects the optimal measures. For example, it prioritizes the proposal of highly effective measures based on the simulation results. The hazard prevention measure proposal unit also simulates prevention measures, and the generation AI selects the optimal measures based on the results. For example, it proposes measures that reduce accident risk through simulation. The hazard prevention measure proposal unit also develops a system that uses the generation AI to simulate the effects of prevention measures and selects the optimal measures based on the results. For example, it proposes measures whose effectiveness has been confirmed through simulation. In this way, it is possible to simulate the effects of prevention measures and select the optimal measures.

[0041] The hazard prevention measure proposal unit can evaluate the cost and feasibility of proposed prevention measures and propose the most appropriate measures for the site. For example, the hazard prevention measure proposal unit builds a system that evaluates the cost of prevention measures proposed by the generation AI and proposes the most appropriate measures by taking feasibility into consideration. For example, it prioritizes the proposal of measures with high cost performance. The hazard prevention measure proposal unit also evaluates the feasibility of prevention measures, and the generation AI proposes the most appropriate measures based on the results. For example, it proposes measures that are feasible taking into consideration on-site resources and time. The hazard prevention measure proposal unit also develops a system that uses the generation AI to evaluate the cost and feasibility of prevention measures and proposes the most appropriate measures based on the results. For example, it proposes measures that are low-cost and feasible. This makes it possible to evaluate the cost and feasibility of prevention measures and propose the most appropriate measures for the site.

[0042] The hazard prevention measure proposal unit can collect prevention measures from different industries and propose measures that can be applied to construction sites. For example, the hazard prevention measure proposal unit collects prevention measure data from different industries, and the generation AI proposes measures that can be applied to construction sites based on that data. For example, safety measures from the manufacturing industry can be applied to construction sites. The hazard prevention measure proposal unit also analyzes prevention measures from different industries, and the generation AI proposes measures that can be applied to construction sites based on that data. For example, safety measures from the logistics industry can be applied to construction sites. The hazard prevention measure proposal unit also integrates prevention measure data from different industries, and builds a system in which the generation AI proposes measures that can be applied to construction sites based on that data. For example, common safety measures can be applied to construction sites. This makes it possible to collect prevention measures from different industries and propose measures that can be applied to construction sites.

[0043] The hazard prevention measure proposal unit can visualize the proposed prevention measures so that workers can intuitively understand them. For example, the hazard prevention measure proposal unit builds a system that visualizes the prevention measures proposed by the generation AI so that workers can intuitively understand them. For example, it explains the prevention measures using diagrams and illustrations. The hazard prevention measure proposal unit also visualizes the prevention measures so that the generation AI can intuitively understand them based on the data. For example, it explains the prevention measures using videos and animations. The hazard prevention measure proposal unit also visualizes the prevention measures proposed by the generation AI so that workers can intuitively understand them. For example, it explains the prevention measures using 3D models. In this way, it is possible to visualize the prevention measures so that workers can intuitively understand them.

[0044] The dangerous work identification unit analyzes workers' past behavioral data and can predict potentially dangerous work. For example, the dangerous work identification unit collects workers' past behavioral data, and the generation AI analyzes that data to predict potentially dangerous work. For example, it analyzes patterns of frequent mistakes and accidents that have occurred in the past and lists dangerous work in similar situations. In addition, the dangerous work identification unit uses the generation AI to predict the possibility that specific work procedures or actions are dangerous based on the worker's behavioral data. For example, it identifies risks that arise from repeated specific actions and adds those actions to the dangerous work list. In addition, the dangerous work identification unit analyzes workers' behavioral data in real time, and the generation AI predicts potentially dangerous work on the spot. For example, if a worker moves in an unusual way, it warns that the action may be dangerous. This makes it possible to predict potentially dangerous work and prevent industrial accidents from occurring.

[0045] When analyzing past industrial accident cases, the past case collection unit can take into account detailed environmental data at the time of the accident. For example, the past case collection unit collects temperature data from past industrial accident cases, and the generation AI uses that data to analyze the environmental conditions at the time of the accident. For example, it evaluates the impact of high and low temperatures on the accident. In addition, the past case collection unit collects humidity data from past industrial accident cases, and the generation AI uses that data to analyze the environmental conditions at the time of the accident. For example, it evaluates the impact of high and low humidity on the accident. In addition, the past case collection unit collects wind speed data from past industrial accident cases, and the generation AI uses that data to analyze the environmental conditions at the time of the accident. For example, it evaluates the impact of strong winds or no wind on the accident. This makes it possible to evaluate the risk of an industrial accident occurring by taking into account detailed environmental data at the time of the accident.

[0046] When analyzing past industrial accident cases, the past case collection unit can identify risk factors by taking into account the worker's years of experience and qualification information. For example, the past case collection unit collects data on the years of experience of workers in past industrial accident cases, and the generation AI identifies risk factors based on that data. For example, it evaluates the risk of accidents involving workers with few years of experience. The past case collection unit also collects the worker's qualification information in past industrial accident cases, and the generation AI identifies risk factors based on that data. For example, it evaluates the risk of accidents involving workers without specific qualifications. The past case collection unit also analyzes the worker's years of experience in combination with the qualification information, and the generation AI identifies risk factors based on that data. For example, it evaluates the risk of accidents involving workers with few years of experience and no qualifications. In this way, it is possible to identify risk factors by taking into account the worker's years of experience and qualification information.

[0047] The past case collection unit can collect industrial accident cases from different countries and regions and analyze them taking into account differences in culture and regulations. For example, the past case collection unit collects industrial accident cases from different countries, and the generation AI analyzes the data taking into account differences in culture and regulations. For example, it evaluates the impact of safety regulations in a specific country on accidents. The past case collection unit also collects industrial accident cases from different regions, and the generation AI analyzes the data taking into account differences in culture and regulations. For example, it evaluates the impact of regional work culture on accidents. The past case collection unit also integrates industrial accident cases from different countries and regions, and the generation AI analyzes the data taking into account differences in culture and regulations. For example, it evaluates the impact of international safety standards on accidents. This makes it possible to collect industrial accident cases from different countries and regions and analyze them taking into account differences in culture and regulations.

[0048] The past case collection unit can use visual data to recreate detailed situations when analyzing past industrial accident cases. For example, the past case collection unit collects photo data of the scene of past industrial accident cases, and the generation AI recreates detailed situations based on that data. For example, it analyzes photos of the accident scene to identify the cause of the accident. The past case collection unit also collects video data of past industrial accident cases, and the generation AI recreates detailed situations based on that data. For example, it analyzes videos of the time of the accident to identify the circumstances of the accident. The past case collection unit also uses visual data to build a system in which the generation AI recreates detailed situations of past industrial accident cases. For example, it recreates the accident scene using a 3D model to analyze the cause of the accident. This makes it possible to recreate detailed situations using visual data when analyzing past industrial accident cases.

[0049] The hazard prevention measure proposal unit can simulate the effects of proposed prevention measures and select the optimal measures. For example, the hazard prevention measure proposal unit builds a system that simulates the effects of prevention measures proposed by the generation AI and selects the optimal measures. For example, it prioritizes the proposal of highly effective measures based on the simulation results. The hazard prevention measure proposal unit also simulates prevention measures, and the generation AI selects the optimal measures based on the results. For example, it proposes measures that reduce accident risk through simulation. The hazard prevention measure proposal unit also develops a system that uses the generation AI to simulate the effects of prevention measures and selects the optimal measures based on the results. For example, it proposes measures whose effectiveness has been confirmed through simulation. In this way, it is possible to simulate the effects of prevention measures and select the optimal measures.

[0050] The hazard prevention measure proposal unit can collect prevention measures from different industries and propose measures that can be applied to construction sites. For example, the hazard prevention measure proposal unit collects prevention measure data from different industries, and the generation AI proposes measures that can be applied to construction sites based on that data. For example, safety measures from the manufacturing industry can be applied to construction sites. The hazard prevention measure proposal unit also analyzes prevention measures from different industries, and the generation AI proposes measures that can be applied to construction sites based on that data. For example, safety measures from the logistics industry can be applied to construction sites. The hazard prevention measure proposal unit also integrates prevention measure data from different industries, and builds a system in which the generation AI proposes measures that can be applied to construction sites based on that data. For example, common safety measures can be applied to construction sites. This makes it possible to collect prevention measures from different industries and propose measures that can be applied to construction sites.

[0051] The hazard prevention measure proposal unit can visualize the proposed prevention measures so that workers can intuitively understand them. For example, the hazard prevention measure proposal unit builds a system that visualizes the prevention measures proposed by the generation AI so that workers can intuitively understand them. For example, it explains the prevention measures using diagrams and illustrations. The hazard prevention measure proposal unit also visualizes the prevention measures so that the generation AI can intuitively understand them based on the data. For example, it explains the prevention measures using videos and animations. The hazard prevention measure proposal unit also visualizes the prevention measures proposed by the generation AI so that workers can intuitively understand them. For example, it explains the prevention measures using 3D models. In this way, it is possible to visualize the prevention measures so that workers can intuitively understand them.

[0052] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0053] The dangerous work identification unit can analyze the past behavioral data of workers and predict potentially dangerous work. For example, it analyzes patterns of mistakes and accidents that have frequently occurred in the past and lists dangerous work in similar situations. The dangerous work identification unit also predicts the possibility that specific work procedures or actions are dangerous based on the worker's behavioral data. For example, it identifies risks that arise from repeated specific actions and adds those actions to the dangerous work list. The dangerous work identification unit also analyzes the worker's behavioral data in real time and predicts potentially dangerous work on the spot. For example, if a worker moves in an unusual way, it warns that the action may be dangerous. This makes it possible to predict potentially dangerous work and prevent industrial accidents from occurring.

[0054] The dangerous work identification unit can identify dangerous work under specific conditions by taking into account environmental data on the site. For example, it collects weather data on the site and identifies dangerous work under specific weather conditions. For example, work done in slippery areas during rainy weather can be added to the dangerous work list. It can also analyze topographical data on the site and identify dangerous work under specific topographical conditions. For example, work done on steep slopes or unstable ground can be added to the dangerous work list. It can also identify dangerous work during specific time periods by taking into account time-of-day data on the site. For example, it can list dangerous work done at night or early in the morning due to poor visibility. This makes it possible to identify dangerous work under specific conditions and prevent industrial accidents from occurring.

[0055] When analyzing past industrial accident cases, the past case collection unit can take into account detailed environmental data at the time of the accident. For example, it collects temperature data from past industrial accident cases and analyzes the environmental conditions at the time of the accident based on that data. For example, it evaluates the impact of high or low temperatures on the accident. It also collects humidity data from past industrial accident cases and analyzes the environmental conditions at the time of the accident based on that data. For example, it evaluates the impact of high or low humidity on the accident. It also collects wind speed data from past industrial accident cases and analyzes the environmental conditions at the time of the accident based on that data. For example, it evaluates the impact of strong winds or no wind on the accident. This makes it possible to evaluate the risk of an industrial accident occurring by taking into account detailed environmental data at the time of the accident.

[0056] When analyzing past industrial accident cases, the past case collection unit can identify risk factors by taking into account the worker's years of experience and qualification information. For example, it collects data on the worker's years of experience in past industrial accident cases and identifies risk factors based on that data. For example, it evaluates the risk of accidents involving workers with few years of experience. It also collects worker qualification information in past industrial accident cases and identifies risk factors based on that data. For example, it evaluates the risk of accidents involving workers without specific qualifications. It also analyzes a combination of the worker's years of experience and qualification information and identifies risk factors based on that data. For example, it evaluates the risk of accidents involving workers with few years of experience and no qualifications. In this way, it is possible to identify risk factors by taking into account the worker's years of experience and qualification information.

[0057] The danger prevention measures proposal unit can simulate the effects of proposed prevention measures and select the optimal measures. For example, a system can be constructed that simulates the effects of proposed prevention measures and selects the optimal measures based on the results. For example, measures with high effectiveness can be proposed preferentially based on the simulation results. Also, prevention measures can be simulated and the optimal measures can be selected based on the results. For example, measures that reduce accident risk can be proposed through simulation. Also, a system can be developed that simulates the effects of proposed prevention measures and selects the optimal measures based on the results. For example, measures whose effectiveness has been confirmed through simulation can be proposed. In this way, the effects of prevention measures can be simulated and the optimal measures can be selected.

[0058] The hazard prevention measures proposal unit can collect prevention measures from different industries and propose measures that can be applied to construction sites. For example, it collects data on prevention measures from different industries and proposes measures that can be applied to construction sites based on that data. For example, it applies safety measures from the manufacturing industry to construction sites. It also analyzes prevention measures from different industries and proposes measures that can be applied to construction sites based on that data. For example, it applies safety measures from the logistics industry to construction sites. It also integrates prevention measure data from different industries and builds a system that proposes measures that can be applied to construction sites based on that data. For example, it applies common safety measures to construction sites. This makes it possible to collect prevention measures from different industries and propose measures that can be applied to construction sites.

[0059] The hazard prevention measure proposal unit can visualize the proposed prevention measures so that workers can intuitively understand them. For example, a system can be constructed that visualizes the proposed prevention measures so that workers can intuitively understand them. For example, the prevention measures can be explained using diagrams and illustrations. The prevention measures can also be visualized so that workers can intuitively understand them based on the data. For example, the prevention measures can be explained using videos and animations. Furthermore, a system can be developed that visualizes the proposed prevention measures so that workers can intuitively understand them. For example, the prevention measures can be explained using 3D models. In this way, the prevention measures can be visualized so that workers can intuitively understand them.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The dangerous work identification module uses a generative AI to identify dangerous work at construction sites based on the MECE principle. For example, the generative AI lists dangerous work, such as working at heights, operating heavy machinery, and electrical work, based on prompts that include the specific situation and work content at the construction site. Step 2: The past case collection unit uses the generation AI to collect and analyze past cases that occurred at similar construction sites. For example, the generation AI collects data on accidents such as falls while working at height or getting caught in heavy machinery while operating, and extracts common risk factors and preventative measures. Step 3: The Hazard Prevention Measures Proposal Unit uses the Generative AI to propose hazard prevention measures suited to the site based on the collected and analyzed data. For example, the Generative AI may suggest using safety harnesses when working at height, inspecting scaffolding, checking operating procedures when operating heavy machinery, and checking the safety of the surrounding area.

[0062] (Example 2) The industrial accident prevention system according to the embodiment of the present invention is a system that identifies dangerous work at construction sites based on the MECE principle, collects and analyzes past examples from similar sites, and proposes risk prevention measures suited to the site. As a result, the industrial accident prevention system can effectively prevent industrial accidents at construction sites.

[0063] The industrial accident prevention system according to the embodiment includes a dangerous work identification unit, a past case collection unit, and a hazard prevention measure proposal unit. The dangerous work identification unit uses a generation AI to identify dangerous work at construction sites based on the MECE principle. For example, the generation AI lists dangerous work, such as working at heights, operating heavy machinery, and electrical work, based on prompts containing specific conditions and work content at the construction site. The generation AI also collects and analyzes past cases that occurred at similar construction sites based on a database of past industrial accident cases. For example, the generation AI collects data on accidents such as falls while working at heights and entrapment accidents while operating heavy machinery, and identifies common risk factors and prevention measures. Furthermore, the generation AI proposes hazard prevention measures tailored to the site based on the collected and analyzed data. For example, the generation AI suggests using safety harnesses and inspecting scaffolding when working at heights, checking operating procedures for operating heavy machinery, and checking surrounding safety. As a result, the industrial accident prevention system according to the embodiment can effectively prevent industrial accidents at construction sites. For example, the output unit notifies on-site workers of proposed preventive measures and encourages them to implement them. The proposed preventive measures are displayed via a web application or mobile application. If feedback is desired in paper form, the results can be printed using a printer. Sending the results via email provides quick feedback by sending the results directly to workers or managers.

[0064] The dangerous work identification unit analyzes workers' past behavioral data and can predict potentially dangerous work. For example, the dangerous work identification unit collects workers' past behavioral data, and the generation AI analyzes that data to predict potentially dangerous work. For example, it analyzes patterns of frequent mistakes and accidents that have occurred in the past and lists dangerous work in similar situations. In addition, the dangerous work identification unit uses the generation AI to predict the possibility that specific work procedures or actions are dangerous based on the worker's behavioral data. For example, it identifies risks that arise from repeated specific actions and adds those actions to the dangerous work list. In addition, the dangerous work identification unit analyzes workers' behavioral data in real time, and the generation AI predicts potentially dangerous work on the spot. For example, if a worker moves in an unusual way, it warns that the action may be dangerous. This makes it possible to predict potentially dangerous work and prevent industrial accidents from occurring.

[0065] The dangerous work identification unit can identify dangerous work under specific conditions by taking into account environmental data at the site. For example, the dangerous work identification unit collects weather data at the site, and the generation AI identifies dangerous work under specific weather conditions based on that data. For example, work done in slippery areas during rainy weather is added to the dangerous work list. The dangerous work identification unit also analyzes topographical data at the site, and the generation AI identifies dangerous work under specific topographical conditions based on that data. For example, work done on steep slopes or unstable ground is added to the dangerous work list. The dangerous work identification unit also takes into account time-of-day data at the site, and the generation AI identifies dangerous work under specific time periods based on that data. For example, dangerous work due to poor visibility during night or early morning work is listed. This makes it possible to identify dangerous work under specific conditions and prevent industrial accidents from occurring.

[0066] The dangerous work identification unit can use the emotion estimation function to evaluate the stress levels and fatigue levels of workers and dynamically update the list of dangerous work based on that. For example, the dangerous work identification unit monitors the stress levels of workers in real time, and the generation AI updates the dangerous work list based on that data. For example, work performed by workers with high stress levels is added to the dangerous work list. The dangerous work identification unit also evaluates the fatigue levels of workers, and the generation AI dynamically updates the dangerous work list based on that data. For example, work performed by workers who work long hours is added to the dangerous work list. The dangerous work identification unit also uses the emotion estimation function to evaluate the emotional state of workers, and the generation AI updates the dangerous work list based on that data. For example, work performed by workers in a negative emotional state is added to the dangerous work list. This allows the list of dangerous work to be dynamically updated based on the stress levels and fatigue levels of workers, preventing industrial accidents from occurring.

[0067] When analyzing past industrial accident cases, the past case collection unit can take into account detailed environmental data at the time of the accident. For example, the past case collection unit collects temperature data from past industrial accident cases, and the generation AI uses that data to analyze the environmental conditions at the time of the accident. For example, it evaluates the impact of high and low temperatures on the accident. In addition, the past case collection unit collects humidity data from past industrial accident cases, and the generation AI uses that data to analyze the environmental conditions at the time of the accident. For example, it evaluates the impact of high and low humidity on the accident. In addition, the past case collection unit collects wind speed data from past industrial accident cases, and the generation AI uses that data to analyze the environmental conditions at the time of the accident. For example, it evaluates the impact of strong winds or no wind on the accident. This makes it possible to evaluate the risk of an industrial accident occurring by taking into account detailed environmental data at the time of the accident.

[0068] When analyzing past industrial accident cases, the past case collection unit can identify risk factors by taking into account the worker's years of experience and qualification information. For example, the past case collection unit collects data on the years of experience of workers in past industrial accident cases, and the generation AI identifies risk factors based on that data. For example, it evaluates the risk of accidents involving workers with few years of experience. The past case collection unit also collects the worker's qualification information in past industrial accident cases, and the generation AI identifies risk factors based on that data. For example, it evaluates the risk of accidents involving workers without specific qualifications. The past case collection unit also analyzes the worker's years of experience in combination with the qualification information, and the generation AI identifies risk factors based on that data. For example, it evaluates the risk of accidents involving workers with few years of experience and no qualifications. In this way, it is possible to identify risk factors by taking into account the worker's years of experience and qualification information.

[0069] The past case collection unit uses the emotion estimation function to analyze the emotional states of workers in past industrial accident cases and evaluate the impact of emotional factors on accidents. For example, the past case collection unit collects data on the emotional states of workers in past industrial accident cases, and the generation AI evaluates the impact of emotional factors on accidents based on that data. For example, it evaluates the risk of accidents when stress or anxiety is high. The past case collection unit also uses the emotion estimation function to analyze the emotional states of workers in past industrial accident cases, and the generation AI evaluates the impact of emotional factors on accidents based on that data. For example, it evaluates the risk of accidents when anger or impatience is high. The past case collection unit also uses the emotional state data in past industrial accident cases, and the generation AI evaluates the impact of emotional factors on accidents based on that data. For example, it evaluates the risk of accidents when positive emotions are low. This makes it possible to evaluate the impact of emotional factors on accidents by taking into account the emotional states of workers.

[0070] The hazard prevention measure proposal unit can simulate the effects of proposed prevention measures and select the optimal measures. For example, the hazard prevention measure proposal unit builds a system that simulates the effects of prevention measures proposed by the generation AI and selects the optimal measures. For example, it prioritizes the proposal of highly effective measures based on the simulation results. The hazard prevention measure proposal unit also simulates prevention measures, and the generation AI selects the optimal measures based on the results. For example, it proposes measures that reduce accident risk through simulation. The hazard prevention measure proposal unit also develops a system that uses the generation AI to simulate the effects of prevention measures and selects the optimal measures based on the results. For example, it proposes measures whose effectiveness has been confirmed through simulation. In this way, it is possible to simulate the effects of prevention measures and select the optimal measures.

[0071] The hazard prevention measure proposal unit uses the emotion estimation function to evaluate the emotional impact of proposed preventive measures on workers and select measures that are easy to accept. For example, the hazard prevention measure proposal unit uses the emotion estimation function to evaluate the emotional impact of proposed preventive measures on workers, and the generation AI selects measures that are easy to accept based on that data. For example, it proposes measures that have a high positive emotional response. The hazard prevention measure proposal unit also collects workers' emotional responses to proposed preventive measures in real time, and the generation AI selects measures that are easy to accept based on that data. For example, it prioritizes the proposal of measures with a high emotional score. The hazard prevention measure proposal unit also uses the emotion estimation function to evaluate the emotional impact of proposed preventive measures on workers, and builds a system in which the generation AI selects measures that are easy to accept based on that data. For example, it proposes measures that have a low negative emotional response. This makes it possible to evaluate the emotional impact of preventive measures on workers and select measures that are easy to accept.

[0072] The hazard prevention measure proposal unit can collect prevention measures from different industries and propose measures that can be applied to construction sites. For example, the hazard prevention measure proposal unit collects prevention measure data from different industries, and the generation AI proposes measures that can be applied to construction sites based on that data. For example, safety measures from the manufacturing industry can be applied to construction sites. The hazard prevention measure proposal unit also analyzes prevention measures from different industries, and the generation AI proposes measures that can be applied to construction sites based on that data. For example, safety measures from the logistics industry can be applied to construction sites. The hazard prevention measure proposal unit also integrates prevention measure data from different industries, and builds a system in which the generation AI proposes measures that can be applied to construction sites based on that data. For example, common safety measures can be applied to construction sites. This makes it possible to collect prevention measures from different industries and propose measures that can be applied to construction sites.

[0073] The hazard prevention measure proposal unit can visualize the proposed prevention measures so that workers can intuitively understand them. For example, the hazard prevention measure proposal unit builds a system that visualizes the prevention measures proposed by the generation AI so that workers can intuitively understand them. For example, it explains the prevention measures using diagrams and illustrations. The hazard prevention measure proposal unit also visualizes the prevention measures so that the generation AI can intuitively understand them based on the data. For example, it explains the prevention measures using videos and animations. The hazard prevention measure proposal unit also visualizes the prevention measures proposed by the generation AI so that workers can intuitively understand them. For example, it explains the prevention measures using 3D models. In this way, it is possible to visualize the prevention measures so that workers can intuitively understand them.

[0074] The hazard prevention measure proposal unit uses the emotion estimation function to monitor workers' emotional reactions to proposed preventive measures in real time, and can continuously update the optimal measures. For example, the hazard prevention measure proposal unit uses the emotion estimation function to monitor workers' emotional reactions to proposed preventive measures in real time, and the generation AI continuously updates the optimal measures based on that data. For example, it prioritizes proposing measures with a high number of positive emotional reactions. The hazard prevention measure proposal unit also collects workers' emotional reactions to proposed preventive measures in real time, and builds a system in which the generation AI continuously updates the optimal measures based on that data. For example, it prioritizes proposing measures with a high emotional score. The hazard prevention measure proposal unit also uses the emotion estimation function to monitor workers' emotional reactions to proposed preventive measures in real time, and develops a system in which the generation AI continuously updates the optimal measures based on that data. For example, it proposes measures with a low number of negative emotional reactions. This makes it possible to monitor workers' emotional reactions to preventive measures in real time, and continuously update the optimal measures.

[0075] The dangerous work identification unit collects data on dangerous work in different industries and can identify dangerous work that can be applied to construction sites. For example, the dangerous work identification unit collects data on dangerous work in the manufacturing industry, and the generation AI uses that data to identify dangerous work that can be applied to construction sites. For example, dangerous work related to machine operation is applied to operating heavy equipment at construction sites. The dangerous work identification unit also collects data on dangerous work in the logistics industry, and the generation AI uses that data to identify dangerous work that can be applied to construction sites. For example, dangerous work related to loading and unloading luggage is applied to transporting materials at construction sites. The dangerous work identification unit also integrates data on dangerous work from different industries, and the generation AI uses that data to identify dangerous work that can be applied to construction sites. For example, it extracts common risk factors and adds them to a list of dangerous work at construction sites. This makes it possible to collect data on dangerous work in different industries and identify dangerous work that can be applied to construction sites.

[0076] The dangerous work identification unit can analyze worker conversations using voice recognition technology and identify potential dangers. For example, the dangerous work identification unit analyzes worker conversations using voice recognition technology, and the generation AI identifies potential dangers based on that data. For example, it analyzes the content of conversations when workers sense danger and adds it to a dangerous work list. The dangerous work identification unit also collects worker conversation data, and the generation AI identifies dangerous work based on that data. For example, it lists dangerous situations and work that workers frequently mention. The dangerous work identification unit also uses voice recognition technology to identify dangerous work from worker conversations in real time, and the generation AI updates the dangerous work list based on that data. For example, it identifies new dangerous work from conversations during work. This makes it possible to analyze worker conversations and identify potential dangers.

[0077] The dangerous work identification unit uses the emotion estimation function to monitor the emotional reactions of workers when they perform dangerous work in real time, and can identify highly dangerous work. For example, the dangerous work identification unit monitors the emotional reactions of workers when they perform dangerous work in real time, and the generation AI identifies highly dangerous work based on that data. For example, work that elicits strong emotions of fear or anxiety is added to the dangerous work list. The dangerous work identification unit also uses the emotion estimation function to analyze the emotional reactions of workers, and the generation AI identifies highly dangerous work based on that data. For example, work that elicits high stress is added to the dangerous work list. The dangerous work identification unit also collects the emotional reactions of workers in real time, and the generation AI identifies highly dangerous work based on that data. For example, work that elicits strong negative emotions is added to the dangerous work list. This makes it possible to monitor the emotional reactions of workers when they perform dangerous work in real time, and identify highly dangerous work.

[0078] The past case collection unit can collect industrial accident cases from different countries and regions and analyze them taking into account differences in culture and regulations. For example, the past case collection unit collects industrial accident cases from different countries, and the generation AI analyzes the data taking into account differences in culture and regulations. For example, it evaluates the impact of safety regulations in a specific country on accidents. The past case collection unit also collects industrial accident cases from different regions, and the generation AI analyzes the data taking into account differences in culture and regulations. For example, it evaluates the impact of regional work culture on accidents. The past case collection unit also integrates industrial accident cases from different countries and regions, and the generation AI analyzes the data taking into account differences in culture and regulations. For example, it evaluates the impact of international safety standards on accidents. This makes it possible to collect industrial accident cases from different countries and regions and analyze them taking into account differences in culture and regulations.

[0079] The past case collection unit uses the emotion estimation function to collect workers' emotional reactions to past industrial accident cases and identify preventive measures that are likely to be emotionally relatable. For example, the past case collection unit collects data on workers' emotional reactions to past industrial accident cases, and the generation AI identifies preventive measures that are likely to be emotionally relatable based on that data. For example, it prioritizes proposing preventive measures that workers strongly resonate with. The past case collection unit also uses the emotion estimation function to analyze workers' emotional reactions to past industrial accident cases, and the generation AI identifies preventive measures that are likely to be emotionally relatable based on that data. For example, it proposes preventive measures that have a high number of positive emotional reactions. The past case collection unit also builds a system that allows the generation AI to identify preventive measures that are likely to be emotionally relatable based on workers' emotional reaction data. For example, it prioritizes proposing preventive measures with a high emotion score. This makes it possible to collect workers' emotional reactions to past industrial accident cases and identify preventive measures that are likely to be emotionally relatable.

[0080] The hazard prevention measure proposal unit can simulate the effects of proposed prevention measures and select the optimal measures. For example, the hazard prevention measure proposal unit builds a system that simulates the effects of prevention measures proposed by the generation AI and selects the optimal measures. For example, it prioritizes the proposal of highly effective measures based on the simulation results. The hazard prevention measure proposal unit also simulates prevention measures, and the generation AI selects the optimal measures based on the results. For example, it proposes measures that reduce accident risk through simulation. The hazard prevention measure proposal unit also develops a system that uses the generation AI to simulate the effects of prevention measures and selects the optimal measures based on the results. For example, it proposes measures whose effectiveness has been confirmed through simulation. In this way, it is possible to simulate the effects of prevention measures and select the optimal measures.

[0081] The hazard prevention measure proposal unit can evaluate the cost and feasibility of proposed prevention measures and propose the most appropriate measures for the site. For example, the hazard prevention measure proposal unit builds a system that evaluates the cost of prevention measures proposed by the generation AI and proposes the most appropriate measures by taking feasibility into consideration. For example, it prioritizes the proposal of measures with high cost performance. The hazard prevention measure proposal unit also evaluates the feasibility of prevention measures, and the generation AI proposes the most appropriate measures based on the results. For example, it proposes measures that are feasible taking into consideration on-site resources and time. The hazard prevention measure proposal unit also develops a system that uses the generation AI to evaluate the cost and feasibility of prevention measures and proposes the most appropriate measures based on the results. For example, it proposes measures that are low-cost and feasible. This makes it possible to evaluate the cost and feasibility of prevention measures and propose the most appropriate measures for the site.

[0082] The hazard prevention measure proposal unit uses the emotion estimation function to evaluate the emotional impact of proposed preventive measures on workers and select measures that are easy to accept. For example, the hazard prevention measure proposal unit uses the emotion estimation function to evaluate the emotional impact of proposed preventive measures on workers, and the generation AI selects measures that are easy to accept based on that data. For example, it proposes measures that have a high positive emotional response. The hazard prevention measure proposal unit also collects workers' emotional responses to proposed preventive measures in real time, and the generation AI selects measures that are easy to accept based on that data. For example, it prioritizes the proposal of measures with a high emotional score. The hazard prevention measure proposal unit also uses the emotion estimation function to evaluate the emotional impact of proposed preventive measures on workers, and builds a system in which the generation AI selects measures that are easy to accept based on that data. For example, it proposes measures that have a low negative emotional response. This makes it possible to evaluate the emotional impact of preventive measures on workers and select measures that are easy to accept.

[0083] The hazard prevention measure proposal unit can collect prevention measures from different industries and propose measures that can be applied to construction sites. For example, the hazard prevention measure proposal unit collects prevention measure data from different industries, and the generation AI proposes measures that can be applied to construction sites based on that data. For example, safety measures from the manufacturing industry can be applied to construction sites. The hazard prevention measure proposal unit also analyzes prevention measures from different industries, and the generation AI proposes measures that can be applied to construction sites based on that data. For example, safety measures from the logistics industry can be applied to construction sites. The hazard prevention measure proposal unit also integrates prevention measure data from different industries, and builds a system in which the generation AI proposes measures that can be applied to construction sites based on that data. For example, common safety measures can be applied to construction sites. This makes it possible to collect prevention measures from different industries and propose measures that can be applied to construction sites.

[0084] The hazard prevention measure proposal unit can visualize the proposed prevention measures so that workers can intuitively understand them. For example, the hazard prevention measure proposal unit builds a system that visualizes the prevention measures proposed by the generation AI so that workers can intuitively understand them. For example, it explains the prevention measures using diagrams and illustrations. The hazard prevention measure proposal unit also visualizes the prevention measures so that the generation AI can intuitively understand them based on the data. For example, it explains the prevention measures using videos and animations. The hazard prevention measure proposal unit also visualizes the prevention measures proposed by the generation AI so that workers can intuitively understand them. For example, it explains the prevention measures using 3D models. In this way, it is possible to visualize the prevention measures so that workers can intuitively understand them.

[0085] The hazard prevention measure proposal unit uses the emotion estimation function to monitor workers' emotional reactions to proposed preventive measures in real time, and can continuously update the optimal measures. For example, the hazard prevention measure proposal unit uses the emotion estimation function to monitor workers' emotional reactions to proposed preventive measures in real time, and the generation AI continuously updates the optimal measures based on that data. For example, it prioritizes proposing measures with a high number of positive emotional reactions. The hazard prevention measure proposal unit also collects workers' emotional reactions to proposed preventive measures in real time, and builds a system in which the generation AI continuously updates the optimal measures based on that data. For example, it prioritizes proposing measures with a high emotional score. The hazard prevention measure proposal unit also uses the emotion estimation function to monitor workers' emotional reactions to proposed preventive measures in real time, and develops a system in which the generation AI continuously updates the optimal measures based on that data. For example, it proposes measures with a low number of negative emotional reactions. This makes it possible to monitor workers' emotional reactions to preventive measures in real time, and continuously update the optimal measures.

[0086] The dangerous work identification unit analyzes workers' past behavioral data and can predict potentially dangerous work. For example, the dangerous work identification unit collects workers' past behavioral data, and the generation AI analyzes that data to predict potentially dangerous work. For example, it analyzes patterns of frequent mistakes and accidents that have occurred in the past and lists dangerous work in similar situations. In addition, the dangerous work identification unit uses the generation AI to predict the possibility that specific work procedures or actions are dangerous based on the worker's behavioral data. For example, it identifies risks that arise from repeated specific actions and adds those actions to the dangerous work list. In addition, the dangerous work identification unit analyzes workers' behavioral data in real time, and the generation AI predicts potentially dangerous work on the spot. For example, if a worker moves in an unusual way, it warns that the action may be dangerous. This makes it possible to predict potentially dangerous work and prevent industrial accidents from occurring.

[0087] The dangerous work identification unit can use the emotion estimation function to evaluate the stress levels and fatigue levels of workers and dynamically update the list of dangerous work based on that. For example, the dangerous work identification unit monitors the stress levels of workers in real time, and the generation AI updates the dangerous work list based on that data. For example, work performed by workers with high stress levels is added to the dangerous work list. The dangerous work identification unit also evaluates the fatigue levels of workers, and the generation AI dynamically updates the dangerous work list based on that data. For example, work performed by workers who work long hours is added to the dangerous work list. The dangerous work identification unit also uses the emotion estimation function to evaluate the emotional state of workers, and the generation AI updates the dangerous work list based on that data. For example, work performed by workers in a negative emotional state is added to the dangerous work list. This allows the list of dangerous work to be dynamically updated based on the stress levels and fatigue levels of workers, preventing industrial accidents from occurring.

[0088] When analyzing past industrial accident cases, the past case collection unit can take into account detailed environmental data at the time of the accident. For example, the past case collection unit collects temperature data from past industrial accident cases, and the generation AI uses that data to analyze the environmental conditions at the time of the accident. For example, it evaluates the impact of high and low temperatures on the accident. In addition, the past case collection unit collects humidity data from past industrial accident cases, and the generation AI uses that data to analyze the environmental conditions at the time of the accident. For example, it evaluates the impact of high and low humidity on the accident. In addition, the past case collection unit collects wind speed data from past industrial accident cases, and the generation AI uses that data to analyze the environmental conditions at the time of the accident. For example, it evaluates the impact of strong winds or no wind on the accident. This makes it possible to evaluate the risk of an industrial accident occurring by taking into account detailed environmental data at the time of the accident.

[0089] When analyzing past industrial accident cases, the past case collection unit can identify risk factors by taking into account the worker's years of experience and qualification information. For example, the past case collection unit collects data on the years of experience of workers in past industrial accident cases, and the generation AI identifies risk factors based on that data. For example, it evaluates the risk of accidents involving workers with few years of experience. The past case collection unit also collects the worker's qualification information in past industrial accident cases, and the generation AI identifies risk factors based on that data. For example, it evaluates the risk of accidents involving workers without specific qualifications. The past case collection unit also analyzes the worker's years of experience in combination with the qualification information, and the generation AI identifies risk factors based on that data. For example, it evaluates the risk of accidents involving workers with few years of experience and no qualifications. In this way, it is possible to identify risk factors by taking into account the worker's years of experience and qualification information.

[0090] The past case collection unit uses the emotion estimation function to analyze the emotional states of workers in past industrial accident cases and evaluate the impact of emotional factors on accidents. For example, the past case collection unit collects data on the emotional states of workers in past industrial accident cases, and the generation AI evaluates the impact of emotional factors on accidents based on that data. For example, it evaluates the risk of accidents when stress or anxiety is high. The past case collection unit also uses the emotion estimation function to analyze the emotional states of workers in past industrial accident cases, and the generation AI evaluates the impact of emotional factors on accidents based on that data. For example, it evaluates the risk of accidents when anger or impatience is high. The past case collection unit also uses the emotional state data in past industrial accident cases, and the generation AI evaluates the impact of emotional factors on accidents based on that data. For example, it evaluates the risk of accidents when positive emotions are low. This makes it possible to evaluate the impact of emotional factors on accidents by taking into account the emotional states of workers.

[0091] The past case collection unit can collect industrial accident cases from different countries and regions and analyze them taking into account differences in culture and regulations. For example, the past case collection unit collects industrial accident cases from different countries, and the generation AI analyzes the data taking into account differences in culture and regulations. For example, it evaluates the impact of safety regulations in a specific country on accidents. The past case collection unit also collects industrial accident cases from different regions, and the generation AI analyzes the data taking into account differences in culture and regulations. For example, it evaluates the impact of regional work culture on accidents. The past case collection unit also integrates industrial accident cases from different countries and regions, and the generation AI analyzes the data taking into account differences in culture and regulations. For example, it evaluates the impact of international safety standards on accidents. This makes it possible to collect industrial accident cases from different countries and regions and analyze them taking into account differences in culture and regulations.

[0092] The past case collection unit can use visual data to recreate detailed situations when analyzing past industrial accident cases. For example, the past case collection unit collects photo data of the scene of past industrial accident cases, and the generation AI recreates detailed situations based on that data. For example, it analyzes photos of the accident scene to identify the cause of the accident. The past case collection unit also collects video data of past industrial accident cases, and the generation AI recreates detailed situations based on that data. For example, it analyzes videos of the time of the accident to identify the circumstances of the accident. The past case collection unit also uses visual data to build a system in which the generation AI recreates detailed situations of past industrial accident cases. For example, it recreates the accident scene using a 3D model to analyze the cause of the accident. This makes it possible to recreate detailed situations using visual data when analyzing past industrial accident cases.

[0093] The past case collection unit uses the emotion estimation function to collect workers' emotional reactions to past industrial accident cases and identify preventive measures that are likely to be emotionally relatable. For example, the past case collection unit collects data on workers' emotional reactions to past industrial accident cases, and the generation AI identifies preventive measures that are likely to be emotionally relatable based on that data. For example, it prioritizes proposing preventive measures that workers strongly resonate with. The past case collection unit also uses the emotion estimation function to analyze workers' emotional reactions to past industrial accident cases, and the generation AI identifies preventive measures that are likely to be emotionally relatable based on that data. For example, it proposes preventive measures that have a high number of positive emotional reactions. The past case collection unit also builds a system that allows the generation AI to identify preventive measures that are likely to be emotionally relatable based on workers' emotional reaction data. For example, it prioritizes proposing preventive measures with a high emotion score. This makes it possible to collect workers' emotional reactions to past industrial accident cases and identify preventive measures that are likely to be emotionally relatable.

[0094] The hazard prevention measure proposal unit can simulate the effects of proposed prevention measures and select the optimal measures. For example, the hazard prevention measure proposal unit builds a system that simulates the effects of prevention measures proposed by the generation AI and selects the optimal measures. For example, it prioritizes the proposal of highly effective measures based on the simulation results. The hazard prevention measure proposal unit also simulates prevention measures, and the generation AI selects the optimal measures based on the results. For example, it proposes measures that reduce accident risk through simulation. The hazard prevention measure proposal unit also develops a system that uses the generation AI to simulate the effects of prevention measures and selects the optimal measures based on the results. For example, it proposes measures whose effectiveness has been confirmed through simulation. In this way, it is possible to simulate the effects of prevention measures and select the optimal measures.

[0095] The hazard prevention measure proposal unit uses the emotion estimation function to evaluate the emotional impact of proposed preventive measures on workers and select measures that are easy to accept. For example, the hazard prevention measure proposal unit uses the emotion estimation function to evaluate the emotional impact of proposed preventive measures on workers, and the generation AI selects measures that are easy to accept based on that data. For example, it proposes measures that have a high positive emotional response. The hazard prevention measure proposal unit also collects workers' emotional responses to proposed preventive measures in real time, and the generation AI selects measures that are easy to accept based on that data. For example, it prioritizes the proposal of measures with a high emotional score. The hazard prevention measure proposal unit also uses the emotion estimation function to evaluate the emotional impact of proposed preventive measures on workers, and builds a system in which the generation AI selects measures that are easy to accept based on that data. For example, it proposes measures that have a low negative emotional response. This makes it possible to evaluate the emotional impact of preventive measures on workers and select measures that are easy to accept.

[0096] The hazard prevention measure proposal unit can collect prevention measures from different industries and propose measures that can be applied to construction sites. For example, the hazard prevention measure proposal unit collects prevention measure data from different industries, and the generation AI proposes measures that can be applied to construction sites based on that data. For example, safety measures from the manufacturing industry can be applied to construction sites. The hazard prevention measure proposal unit also analyzes prevention measures from different industries, and the generation AI proposes measures that can be applied to construction sites based on that data. For example, safety measures from the logistics industry can be applied to construction sites. The hazard prevention measure proposal unit also integrates prevention measure data from different industries, and builds a system in which the generation AI proposes measures that can be applied to construction sites based on that data. For example, common safety measures can be applied to construction sites. This makes it possible to collect prevention measures from different industries and propose measures that can be applied to construction sites.

[0097] The hazard prevention measure proposal unit can visualize the proposed prevention measures so that workers can intuitively understand them. For example, the hazard prevention measure proposal unit builds a system that visualizes the prevention measures proposed by the generation AI so that workers can intuitively understand them. For example, it explains the prevention measures using diagrams and illustrations. The hazard prevention measure proposal unit also visualizes the prevention measures so that the generation AI can intuitively understand them based on the data. For example, it explains the prevention measures using videos and animations. The hazard prevention measure proposal unit also visualizes the prevention measures proposed by the generation AI so that workers can intuitively understand them. For example, it explains the prevention measures using 3D models. In this way, it is possible to visualize the prevention measures so that workers can intuitively understand them.

[0098] The hazard prevention measure proposal unit uses the emotion estimation function to monitor workers' emotional reactions to proposed preventive measures in real time, and can continuously update the optimal measures. For example, the hazard prevention measure proposal unit uses the emotion estimation function to monitor workers' emotional reactions to proposed preventive measures in real time, and the generation AI continuously updates the optimal measures based on that data. For example, it prioritizes proposing measures with a high number of positive emotional reactions. The hazard prevention measure proposal unit also collects workers' emotional reactions to proposed preventive measures in real time, and builds a system in which the generation AI continuously updates the optimal measures based on that data. For example, it prioritizes proposing measures with a high emotional score. The hazard prevention measure proposal unit also uses the emotion estimation function to monitor workers' emotional reactions to proposed preventive measures in real time, and develops a system in which the generation AI continuously updates the optimal measures based on that data. For example, it proposes measures with a low number of negative emotional reactions. This makes it possible to monitor workers' emotional reactions to preventive measures in real time, and continuously update the optimal measures.

[0099] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0100] The dangerous work identification unit can analyze the past behavioral data of workers and predict potentially dangerous work. For example, it analyzes patterns of mistakes and accidents that have frequently occurred in the past and lists dangerous work in similar situations. The dangerous work identification unit also predicts the possibility that specific work procedures or actions are dangerous based on the worker's behavioral data. For example, it identifies risks that arise from repeated specific actions and adds those actions to the dangerous work list. The dangerous work identification unit also analyzes the worker's behavioral data in real time and predicts potentially dangerous work on the spot. For example, if a worker moves in an unusual way, it warns that the action may be dangerous. This makes it possible to predict potentially dangerous work and prevent industrial accidents from occurring.

[0101] The dangerous work identification unit can identify dangerous work under specific conditions by taking into account environmental data on the site. For example, it collects weather data on the site and identifies dangerous work under specific weather conditions. For example, work done in slippery areas during rainy weather can be added to the dangerous work list. It can also analyze topographical data on the site and identify dangerous work under specific topographical conditions. For example, work done on steep slopes or unstable ground can be added to the dangerous work list. It can also identify dangerous work during specific time periods by taking into account time-of-day data on the site. For example, it can list dangerous work done at night or early in the morning due to poor visibility. This makes it possible to identify dangerous work under specific conditions and prevent industrial accidents from occurring.

[0102] The dangerous work identification unit can use the emotion estimation function to evaluate the stress levels and fatigue levels of workers and dynamically update the list of dangerous work based on the evaluation. For example, it can monitor the stress levels of workers in real time and update the dangerous work list based on the data. For example, it can add work performed by workers with high stress levels to the dangerous work list. It can also evaluate the fatigue levels of workers and dynamically update the dangerous work list based on the data. For example, it can add work performed by workers who work long hours to the dangerous work list. It can also use the emotion estimation function to evaluate the emotional state of workers and update the dangerous work list based on the data. For example, it can add work performed by workers in a negative emotional state to the dangerous work list. This allows the list of dangerous work to be dynamically updated based on the stress levels and fatigue levels of workers, thereby preventing industrial accidents from occurring.

[0103] When analyzing past industrial accident cases, the past case collection unit can take into account detailed environmental data at the time of the accident. For example, it collects temperature data from past industrial accident cases and analyzes the environmental conditions at the time of the accident based on that data. For example, it evaluates the impact of high or low temperatures on the accident. It also collects humidity data from past industrial accident cases and analyzes the environmental conditions at the time of the accident based on that data. For example, it evaluates the impact of high or low humidity on the accident. It also collects wind speed data from past industrial accident cases and analyzes the environmental conditions at the time of the accident based on that data. For example, it evaluates the impact of strong winds or no wind on the accident. This makes it possible to evaluate the risk of an industrial accident occurring by taking into account detailed environmental data at the time of the accident.

[0104] When analyzing past industrial accident cases, the past case collection unit can identify risk factors by taking into account the worker's years of experience and qualification information. For example, it collects data on the worker's years of experience in past industrial accident cases and identifies risk factors based on that data. For example, it evaluates the risk of accidents involving workers with few years of experience. It also collects worker qualification information in past industrial accident cases and identifies risk factors based on that data. For example, it evaluates the risk of accidents involving workers without specific qualifications. It also analyzes a combination of the worker's years of experience and qualification information and identifies risk factors based on that data. For example, it evaluates the risk of accidents involving workers with few years of experience and no qualifications. In this way, it is possible to identify risk factors by taking into account the worker's years of experience and qualification information.

[0105] The past case collection unit can use the emotion estimation function to analyze the emotional states of workers in past industrial accident cases and evaluate the impact of emotional factors on accidents. For example, it collects data on the emotional states of workers in past industrial accident cases and evaluates the impact of emotional factors on accidents based on that data. For example, it evaluates the risk of accidents when stress or anxiety is high. It can also use the emotion estimation function to analyze the emotional states of workers in past industrial accident cases and evaluate the impact of emotional factors on accidents based on that data. For example, it evaluates the risk of accidents when anger or impatience is high. It can also evaluate the impact of emotional factors on accidents based on emotional state data in past industrial accident cases. For example, it evaluates the risk of accidents when positive emotions are low. This makes it possible to evaluate the impact of emotional factors on accidents by taking into account the emotional states of workers.

[0106] The danger prevention measures proposal unit can simulate the effects of proposed prevention measures and select the optimal measures. For example, a system can be constructed that simulates the effects of proposed prevention measures and selects the optimal measures based on the results. For example, measures with high effectiveness can be proposed preferentially based on the simulation results. Also, prevention measures can be simulated and the optimal measures can be selected based on the results. For example, measures that reduce accident risk can be proposed through simulation. Also, a system can be developed that simulates the effects of proposed prevention measures and selects the optimal measures based on the results. For example, measures whose effectiveness has been confirmed through simulation can be proposed. In this way, the effects of prevention measures can be simulated and the optimal measures can be selected.

[0107] The danger prevention measure proposal unit can use the emotion estimation function to evaluate the emotional impact that the proposed prevention measures have on workers and select measures that are easy to accept. For example, the emotion estimation function can be used to evaluate the emotional impact that the proposed prevention measures have on workers and select measures that are easy to accept based on that data. For example, measures that elicit a large number of positive emotional responses can be proposed. In addition, the emotional responses of workers to the proposed prevention measures can be collected in real time and measures that are easy to accept can be selected based on that data. For example, measures with a high emotional score can be proposed preferentially. In addition, the emotion estimation function can be used to evaluate the emotional impact that the proposed prevention measures have on workers and a system can be constructed to select measures that are easy to accept based on that data. For example, measures that elicit a small number of negative emotional responses can be proposed. In this way, the emotional impact that the proposed prevention measures have on workers can be evaluated and measures that are easy to accept can be selected.

[0108] The hazard prevention measures proposal unit can collect prevention measures from different industries and propose measures that can be applied to construction sites. For example, it collects data on prevention measures from different industries and proposes measures that can be applied to construction sites based on that data. For example, it applies safety measures from the manufacturing industry to construction sites. It also analyzes prevention measures from different industries and proposes measures that can be applied to construction sites based on that data. For example, it applies safety measures from the logistics industry to construction sites. It also integrates prevention measure data from different industries and builds a system that proposes measures that can be applied to construction sites based on that data. For example, it applies common safety measures to construction sites. This makes it possible to collect prevention measures from different industries and propose measures that can be applied to construction sites.

[0109] The hazard prevention measure proposal unit can visualize the proposed prevention measures so that workers can intuitively understand them. For example, a system can be constructed that visualizes the proposed prevention measures so that workers can intuitively understand them. For example, the prevention measures can be explained using diagrams and illustrations. The prevention measures can also be visualized so that workers can intuitively understand them based on the data. For example, the prevention measures can be explained using videos and animations. Furthermore, a system can be developed that visualizes the proposed prevention measures so that workers can intuitively understand them. For example, the prevention measures can be explained using 3D models. In this way, the prevention measures can be visualized so that workers can intuitively understand them.

[0110] The processing flow of the second embodiment will be briefly explained below.

[0111] Step 1: The dangerous work identification module uses a generative AI to identify dangerous work at construction sites based on the MECE principle. For example, the generative AI lists dangerous work, such as working at heights, operating heavy machinery, and electrical work, based on prompts that include the specific situation and work content at the construction site. Step 2: The past case collection unit uses the generation AI to collect and analyze past cases that occurred at similar construction sites. For example, the generation AI collects data on accidents such as falls while working at height or getting caught in heavy machinery while operating, and extracts common risk factors and preventative measures. Step 3: The Hazard Prevention Measures Proposal Unit uses the Generative AI to propose hazard prevention measures suited to the site based on the collected and analyzed data. For example, the Generative AI may suggest using safety harnesses when working at height, inspecting scaffolding, checking operating procedures when operating heavy machinery, and checking the safety of the surrounding area.

[0112] 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.

[0113] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<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.

[0114] 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.

[0115] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] In the headset type terminal 314, 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. 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 specific processing unit 290 using these models.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0146] 7, the 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] In the robot 414, 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 robot 414 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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).

[0165] 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.

[0166] 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."

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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. [Explanation of symbols]

[0179] 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 dangerous work identification section uses generative AI to identify dangerous work at construction sites based on the MECE principle. A past example collection unit that uses the generation AI to collect and analyze past examples that have occurred at similar construction sites; and a danger prevention measure proposal unit that proposes danger prevention measures suited to the site based on the collected and analyzed data using the generation AI. A system characterized by:

2. The dangerous work identification unit Analyzing worker behavior data to predict potentially dangerous work 2. The system of claim 1.

3. The dangerous work identification unit Considering environmental data of the site, identify the hazardous operations under specific conditions.

2. The system of claim 1.

4. The dangerous work identification unit Evaluate worker stress and fatigue levels and dynamically update the list of dangerous tasks based on that.

2. The system of claim 1.

5. The past case collection unit When analyzing past workplace accident cases, consider detailed environmental data at the time of the accident.

2. The system of claim 1.

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