System

Abstract

An object of a system according to an embodiment is to continuously support safety and hygiene activities without imposing a burden on a site.SOLUTION: A system according to an embodiment includes a network camera, a drone, and a generation AI. The network camera monitors a work situation and an environment. The drone patrols the site. The production data analysis analyzes AI collected from network cameras and drones and suggests safety measures.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, safety and health activities do not directly generate profits, so they require a lot of time and effort and are likely to be put off.

[0005] The system according to the embodiment aims to continuously support safety and health activities without placing a burden on the workplace. [Means for solving the problem]

[0006] The system according to the embodiment includes a network camera, a drone, and a generating AI. The network camera monitors the work situation and environment. The drone patrols the site. The generating AI analyzes data collected from the network camera and drone and proposes safety measures. [Effects of the Invention]

[0007] The system according to the embodiment can continuously support safety and health activities without placing a burden on the site. [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 safety and health activity support system according to an embodiment of the present invention utilizes IoT devices such as network cameras and drones, as well as generative AI, to efficiently promote safety and health activities at work sites and provide an environment in which both the elderly and young people can work with peace of mind. As a result, the safety and health activity support system can address issues such as long working hours and labor shortages, and support the sustainable development of the entire industry.

[0029] A safety and health activity support system according to an embodiment includes a network camera, a drone, and a generation AI. The network camera monitors the work situation and environment. For example, the network camera collects data on worker movements, machine operation status, and on-site environmental data (such as temperature and humidity). The network camera is capable of collecting data in real time and provides the data to be input to the generation AI. The drone patrols the site. For example, the drone checks the safety of work at heights and grasps the situation over a wide area of ​​the site. The drone also monitors and collects data from the air and provides the data to be input to the generation AI. The generation AI analyzes the data collected from the network camera and the drone and proposes safety measures. For example, if a worker's movements show a dangerous pattern or if a machine abnormality is detected, the generation AI proposes safety measures based on that information. The generation AI analyzes the data using a text generation AI (e.g., LLM) or a multimodal generation AI and proposes specific safety measures. As a result, the safety and health activity support system according to an embodiment efficiently promotes safety and health activities at the site and provides an environment where elderly and young people can work with peace of mind.

[0030] Network cameras can collect data on worker movements, machine operation status, and on-site environmental data. For example, network cameras issue alerts if worker movements differ from normal or dangerous behavior is detected. Network cameras can also detect abnormal machine operation or signs of fire and respond immediately. Furthermore, network cameras collect environmental data (temperature, humidity, etc.) in real time and input it into the generative AI. This allows for improved safety on-site by collecting data on worker movements, machine operation status, and environmental data.

[0031] Drones can be used to check the safety of work at height and grasp the situation over a wide area of ​​a site. For example, drones can check the safety of work at height to ensure that workers can work safely. Drones can also grasp the situation over a wide area of ​​a site and evaluate the safety of the entire site. Furthermore, drones can monitor and collect data from the sky, providing data that is input into generative AI. This can improve site safety by checking the safety of work at height and grasping the situation over a wide area of ​​a site.

[0032] Generative AI can suggest safety measures when worker movements show a dangerous pattern or when a machine abnormality is detected. For example, when a worker's movements show a dangerous pattern, generative AI suggests safety measures based on that information. In addition, when a machine abnormality is detected, generative AI suggests safety measures based on that information. Furthermore, generative AI analyzes data using text generation AI (e.g., LLM) or multimodal generation AI to suggest specific safety measures. This makes it possible to improve safety on site by detecting worker movements and machine abnormalities and suggesting safety measures.

[0033] The generative AI can suggest safety training for workers and machine maintenance. For example, the generative AI can suggest safety training for workers. For example, it can suggest a program to educate workers on safe work procedures. The generative AI can also suggest machine maintenance. For example, it can suggest regular inspections and repairs of machines. Furthermore, the generative AI can analyze data using text generation AI (e.g., LLM) or multimodal generation AI to suggest specific safety training and maintenance. This can improve safety on site by suggesting safety training for workers and machine maintenance.

[0034] Generative AI can analyze data to assign less demanding tasks to the elderly and suggest appropriate educational programs for younger people. Generative AI can, for example, perform data analysis to assign less demanding tasks to the elderly. For example, it can assign appropriate tasks taking into account the physical strength and health of the elderly. Generative AI can also suggest appropriate educational programs for younger people. For example, it can suggest programs to teach necessary skills and knowledge to young people. Furthermore, generative AI can analyze data using text generation AI (e.g., LLM) or multimodal generation AI to suggest specific task assignments and educational programs. This can improve the working environment and secure human resources by assigning less demanding tasks to the elderly and suggesting appropriate educational programs for younger people.

[0035] Network cameras can analyze collected video data in real time using generative AI, instantly detecting abnormal behavior and dangerous situations. For example, network cameras input collected video data into generative AI for real-time analysis. For example, an alert is issued if a worker's movements are unusual or dangerous behavior is detected. Generative AI can also be used to detect abnormal behavior from network camera video data. For example, an immediate notification is sent if a worker falls or enters a dangerous area. Furthermore, generative AI can analyze network camera video data to detect dangerous situations in real time. For example, it can detect abnormal machine operation or signs of a fire and respond immediately. This allows for real-time analysis of collected video data and instantly detect abnormal behavior and dangerous situations, improving safety on site.

[0036] Network cameras can incorporate algorithms that automatically adjust the viewpoint and maintain the optimal monitoring range. For example, algorithms can be developed to automatically adjust the viewpoint of network cameras to maintain the optimal monitoring range. For example, the camera viewpoint can be moved to match the movements of workers. Systems that automatically adjust the camera viewpoint can also be introduced to optimize the monitoring range. For example, multiple cameras can work together to adjust the viewpoint to eliminate blind spots. Furthermore, algorithms that automatically adjust the viewpoint of network cameras can be used to optimize the monitoring range. For example, the camera viewpoint can be moved so that important work areas can always be monitored. This allows the camera viewpoint to be automatically adjusted and the optimal monitoring range to be maintained, thereby improving safety on site.

[0037] Network cameras can be equipped with voice recognition functionality, allowing them to evaluate safety conditions from the voices of workers and environmental sounds. Network cameras can be equipped with voice recognition functionality to analyze the voices of workers and environmental sounds. For example, they can detect workers' cries or abnormal sounds and immediately issue an alert. Voice recognition functionality can also be used to analyze the voice data collected by the network camera and evaluate safety conditions. For example, it can detect abnormal machine sounds or workers' cries for help. Furthermore, voice recognition functionality can be added to network cameras to evaluate safety conditions from the voices of workers and environmental sounds. For example, it can analyze the content of conversations between workers and predict dangerous situations. This makes it possible to improve safety on site by evaluating safety conditions from the voices of workers and environmental sounds.

[0038] Network cameras can be linked with other IoT devices to perform comprehensive safety assessments. Network cameras can be linked with other IoT devices (such as wearable devices) to perform comprehensive safety assessments. For example, a worker's heart rate and body temperature data can be integrated with the camera's video data. Camera data can also be linked with other IoT devices to build a system that comprehensively evaluates safety conditions. For example, environmental sensor data can be integrated with the camera's video data. Furthermore, network camera data can be linked with wearable devices to perform comprehensive safety assessments. For example, a worker's movements and biometric data can be combined with the camera's video data for analysis. In this way, by linking with other IoT devices and performing comprehensive safety assessments, safety on site can be improved.

[0039] Drones can analyze footage from their onboard cameras using generative AI to automatically identify dangerous areas and abnormalities. For example, drones input footage from their onboard cameras into generative AI, which automatically identifies dangerous areas and abnormalities. For example, this can be used to check the safety of work at heights or detect machine abnormalities. Generative AI can also be used to build systems that identify dangerous areas and abnormalities from drone camera footage. For example, it can analyze the movements of workers and the operation of machines to detect abnormalities. Furthermore, generative AI can analyze drone camera footage to automatically identify dangerous areas and abnormalities. For example, it can monitor the situation on site in real time and detect abnormalities. This allows for the analysis of drone camera footage with generative AI to automatically identify dangerous areas and abnormalities, thereby improving site safety.

[0040] Algorithms can be developed for drones to optimize flight routes and efficiently cover wide areas. For example, algorithms can be developed to optimize flight routes, allowing drones to efficiently cover wide areas. For example, routes can be automatically adjusted according to the terrain and work conditions of the site. Systems can also be built to optimize drone flight routes, allowing for efficient coverage of wide areas. For example, routes can be planned for multiple drones to patrol in coordination. Furthermore, algorithms can be used to optimize drone flight routes, allowing for efficient coverage of wide areas. For example, routes can be set that prioritize patrol of important work areas. This allows drone flight routes to be optimized, allowing for efficient coverage of wide areas, thereby improving safety on site.

[0041] Drones can be equipped with additional sensors to simultaneously collect environmental data such as temperature, humidity, and gas concentration. For example, drones can be equipped with temperature and humidity sensors to simultaneously collect environmental data. For example, the temperature and humidity of the work environment can be monitored in real time. A gas concentration sensor can also be added to the drone to build a system for collecting environmental data. For example, the concentration of harmful gases can be measured and anomalies detected. Furthermore, multiple sensors can be installed on the drone to simultaneously collect environmental data such as temperature, humidity, and gas concentration. For example, a comprehensive safety assessment of the work environment can be performed. By adding sensors to the drone and simultaneously collecting environmental data such as temperature, humidity, and gas concentration, safety at the work site can be improved.

[0042] Drones can work in conjunction with ground sensors to grasp the situation on site in more detail. For example, drones can work in conjunction with ground sensors to build a system that grasps the situation on site in more detail. For example, data from temperature and humidity sensors on the ground can be integrated. Drone data can also be linked with ground sensors to comprehensively evaluate the situation on site. For example, data from vibration and sound sensors on the ground can be integrated. Drone data can also be linked with ground sensors to grasp the situation on site in more detail. For example, data from environmental sensors on the ground can be integrated with drone video data. This allows drones to work in conjunction with ground sensors to grasp the situation on site in more detail, thereby improving safety at the site.

[0043] Generative AI can build a hazard prediction model from collected data and propose measures to prevent accidents before they happen. For example, generative AI analyzes data collected from network cameras and drones to build a hazard prediction model. For example, it learns dangerous patterns based on past data and proposes measures to prevent accidents before they happen. Generative AI can also be used to build a hazard prediction model from collected data and develop a system that predicts hazards in real time. For example, it can analyze the movements of workers and the operation of machines to predict dangerous situations. Generative AI can also be used to build a hazard prediction model from collected data and propose measures to prevent accidents before they happen. For example, it can analyze environmental data and work data to predict dangerous situations. This allows for the construction of a hazard prediction model from collected data and the proposal of measures to prevent accidents before they happen, thereby improving safety on site.

[0044] Generative AI can compare the effectiveness of proposed safety measures with past data and select the most appropriate measures. For example, generative AI can build a system that compares proposed safety measures with past data and evaluates their effectiveness. For example, it can evaluate based on past accident data and the results of implementing safety measures. Generative AI can also compare proposed safety measures with past data and select the most appropriate measures. For example, it can analyze past data and prioritize highly effective measures. Furthermore, generative AI can develop a system that compares proposed safety measures with past data and evaluates their effectiveness. For example, it can simulate the effectiveness of safety measures based on past data. This allows the generative AI to compare proposed safety measures with past data to evaluate their effectiveness and select the most appropriate measures, thereby improving safety at the work site.

[0045] The analysis results by generative AI can be applied to other industries to build a general-purpose safety system. For example, generative AI can apply the analysis results to the manufacturing industry to build a general-purpose safety system. For example, it can evaluate the safety of a production line and propose appropriate measures. Generative AI can also apply the analysis results to the medical industry to build a general-purpose safety system. For example, it can evaluate the safety of a medical site and propose appropriate measures. Generative AI can also apply the analysis results to other industries to build a general-purpose safety system. For example, it can evaluate the safety of the logistics industry or the construction industry and propose appropriate measures. In this way, the analysis results by generative AI can be applied to other industries to build a general-purpose safety system, thereby improving safety in a variety of industries.

[0046] The analysis results of the generative AI can be displayed on a visual dashboard to enable intuitive understanding. The generative AI, for example, builds a system that displays the analysis results on a visual dashboard to enable intuitive understanding. For example, the analysis results are displayed using graphs and charts. The generative AI also displays the analysis results on a visual dashboard to enable users to easily understand. For example, important data is displayed color-coded. The generative AI also displays the analysis results on a visual dashboard to enable intuitive understanding. For example, data is updated in real time to display the latest information. In this way, the analysis results of the generative AI can be displayed on a visual dashboard to enable intuitive understanding, allowing users to easily grasp the information.

[0047] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0048] The safety and health activity support system can also be equipped with a voice recognition function. The voice recognition function can analyze the voices of workers and environmental sounds to detect abnormalities. For example, it can detect a worker's shout or an abnormal sound and immediately issue an alert. The voice recognition function can also be used to analyze the content of conversations between workers and predict dangerous situations. Furthermore, the voice recognition function can be used to detect abnormal machine sounds and signs of fire and respond immediately. In this way, adding the voice recognition function can improve safety on site.

[0049] Network cameras can also incorporate algorithms to automatically adjust the viewpoint and maintain the optimal monitoring range. Algorithms for automatically adjusting the viewpoint have been developed, allowing the camera viewpoint to move in accordance with the movements of workers. Multiple cameras can also work together to adjust the viewpoint to eliminate blind spots. Furthermore, the camera viewpoint can be moved so that important work areas can be constantly monitored. This allows the camera viewpoint to be automatically adjusted and the optimal monitoring range to be maintained, improving safety on site.

[0050] Drones can be equipped with additional sensors to simultaneously collect environmental data such as temperature, humidity, and gas concentration. Equipped with temperature and humidity sensors, the temperature and humidity of the work environment can be monitored in real time. Gas concentration sensors can also be added to measure the concentration of harmful gases and detect abnormalities. Furthermore, multiple sensors can be installed to simultaneously collect environmental data such as temperature, humidity, and gas concentration, allowing for comprehensive safety assessment of the work environment. By adding sensors to drones and simultaneously collecting environmental data, safety on-site can be improved.

[0051] The generating AI can further compare the proposed safety measures with past data to evaluate their effectiveness and select the most appropriate measures. It is possible to build a system that evaluates proposed safety measures based on past accident data and the implementation results of safety measures. It can also analyze past data and prioritize the selection of highly effective measures. Furthermore, it can simulate the effectiveness of safety measures based on past data. This allows the generating AI to compare the proposed safety measures with past data to evaluate their effectiveness and select the most appropriate measures, thereby improving safety at work sites.

[0052] Generative AI can further build a hazard prediction model from collected data and propose measures to prevent accidents before they occur. It can analyze data collected from network cameras and drones, learn dangerous patterns based on past data, and propose measures to prevent accidents before they occur. It can also build a hazard prediction model from collected data and develop a system that predicts hazards in real time. It can also analyze environmental data and work data to predict dangerous situations. This allows it to build a hazard prediction model from collected data and propose measures to prevent accidents before they occur, thereby improving safety on site.

[0053] The generative AI can further display the analysis results on a visual dashboard, making them easy to understand. The analysis results can be displayed on a visual dashboard, using graphs and charts. Important data can also be color-coded to make it easy for users to understand. Furthermore, data can be updated in real time to display the latest information. In this way, the generative AI's analysis results can be displayed on a visual dashboard, making them easy to understand, allowing users to easily grasp the information.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: Network cameras monitor the work situation and environment. For example, network cameras collect data on worker movements, machine operation status, and on-site environmental data (temperature, humidity, etc.). Network cameras also collect data in real time, providing the data to be input into the generative AI. Step 2: The drone patrols the site. For example, the drone can check the safety of work at heights and grasp the situation over a wide area of ​​the site. The drone also monitors from the sky and collects data, providing the data to be input into the generative AI. Step 3: Generative AI analyzes the data collected from the network cameras and drones and proposes safety measures. For example, if a worker's movements show a dangerous pattern or a machine abnormality is detected, the generative AI will propose safety measures based on that information. The generative AI analyzes the data using text generation AI (e.g., LLM) or multimodal generation AI and proposes specific safety measures.

[0056] (Example 2) The safety and health activity support system according to an embodiment of the present invention utilizes IoT devices such as network cameras and drones, as well as generative AI, to efficiently promote safety and health activities at work sites and provide an environment in which both the elderly and young people can work with peace of mind. As a result, the safety and health activity support system can address issues such as long working hours and labor shortages, and support the sustainable development of the entire industry.

[0057] A safety and health activity support system according to an embodiment includes a network camera, a drone, and a generation AI. The network camera monitors the work situation and environment. For example, the network camera collects data on worker movements, machine operation status, and on-site environmental data (such as temperature and humidity). The network camera is capable of collecting data in real time and provides the data to be input to the generation AI. The drone patrols the site. For example, the drone checks the safety of work at heights and grasps the situation over a wide area of ​​the site. The drone also monitors and collects data from the air and provides the data to be input to the generation AI. The generation AI analyzes the data collected from the network camera and the drone and proposes safety measures. For example, if a worker's movements show a dangerous pattern or if a machine abnormality is detected, the generation AI proposes safety measures based on that information. The generation AI analyzes the data using a text generation AI (e.g., LLM) or a multimodal generation AI and proposes specific safety measures. As a result, the safety and health activity support system according to an embodiment efficiently promotes safety and health activities at the site and provides an environment where elderly and young people can work with peace of mind.

[0058] Network cameras can collect data on worker movements, machine operation status, and on-site environmental data. For example, network cameras issue alerts if worker movements differ from normal or dangerous behavior is detected. Network cameras can also detect abnormal machine operation or signs of fire and respond immediately. Furthermore, network cameras collect environmental data (temperature, humidity, etc.) in real time and input it into the generative AI. This allows for improved safety on-site by collecting data on worker movements, machine operation status, and environmental data.

[0059] Drones can be used to check the safety of work at height and grasp the situation over a wide area of ​​a site. For example, drones can check the safety of work at height to ensure that workers can work safely. Drones can also grasp the situation over a wide area of ​​a site and evaluate the safety of the entire site. Furthermore, drones can monitor and collect data from the sky, providing data that is input into generative AI. This can improve site safety by checking the safety of work at height and grasping the situation over a wide area of ​​a site.

[0060] Generative AI can suggest safety measures when worker movements show a dangerous pattern or when a machine abnormality is detected. For example, when a worker's movements show a dangerous pattern, generative AI suggests safety measures based on that information. In addition, when a machine abnormality is detected, generative AI suggests safety measures based on that information. Furthermore, generative AI analyzes data using text generation AI (e.g., LLM) or multimodal generation AI to suggest specific safety measures. This makes it possible to improve safety on site by detecting worker movements and machine abnormalities and suggesting safety measures.

[0061] The generative AI can suggest safety training for workers and machine maintenance. For example, the generative AI can suggest safety training for workers. For example, it can suggest a program to educate workers on safe work procedures. The generative AI can also suggest machine maintenance. For example, it can suggest regular inspections and repairs of machines. Furthermore, the generative AI can analyze data using text generation AI (e.g., LLM) or multimodal generation AI to suggest specific safety training and maintenance. This can improve safety on site by suggesting safety training for workers and machine maintenance.

[0062] Generative AI can analyze data to assign less demanding tasks to the elderly and suggest appropriate educational programs for younger people. Generative AI can, for example, perform data analysis to assign less demanding tasks to the elderly. For example, it can assign appropriate tasks taking into account the physical strength and health of the elderly. Generative AI can also suggest appropriate educational programs for younger people. For example, it can suggest programs to teach necessary skills and knowledge to young people. Furthermore, generative AI can analyze data using text generation AI (e.g., LLM) or multimodal generation AI to suggest specific task assignments and educational programs. This can improve the working environment and secure human resources by assigning less demanding tasks to the elderly and suggesting appropriate educational programs for younger people.

[0063] Network cameras can analyze collected video data in real time using generative AI, instantly detecting abnormal behavior and dangerous situations. For example, network cameras input collected video data into generative AI for real-time analysis. For example, an alert is issued if a worker's movements are unusual or dangerous behavior is detected. Generative AI can also be used to detect abnormal behavior from network camera video data. For example, an immediate notification is sent if a worker falls or enters a dangerous area. Furthermore, generative AI can analyze network camera video data to detect dangerous situations in real time. For example, it can detect abnormal machine operation or signs of a fire and respond immediately. This allows for real-time analysis of collected video data and instantly detect abnormal behavior and dangerous situations, improving safety on site.

[0064] Network cameras can incorporate algorithms that automatically adjust the viewpoint and maintain the optimal monitoring range. For example, algorithms can be developed to automatically adjust the viewpoint of network cameras to maintain the optimal monitoring range. For example, the camera viewpoint can be moved to match the movements of workers. Systems that automatically adjust the camera viewpoint can also be introduced to optimize the monitoring range. For example, multiple cameras can work together to adjust the viewpoint to eliminate blind spots. Furthermore, algorithms that automatically adjust the viewpoint of network cameras can be used to optimize the monitoring range. For example, the camera viewpoint can be moved so that important work areas can always be monitored. This allows the camera viewpoint to be automatically adjusted and the optimal monitoring range to be maintained, thereby improving safety on site.

[0065] Network cameras can use their emotion estimation function to estimate a worker's stress and fatigue levels from their facial expressions and movements, and suggest appropriate breaks and work assignments. Network cameras estimate a worker's stress and fatigue levels from their facial expressions and movements, for example, by detecting changes in facial expressions and movements and suggesting breaks. They can also use their emotion estimation function to monitor a worker's stress and fatigue levels in real time, for example, by measuring stress levels using facial recognition technology and suggesting appropriate breaks. They can also estimate a worker's stress and fatigue levels based on video data from network cameras and adjust work assignments. For example, workers with high levels of fatigue can be assigned lighter work. In this way, the health and safety of workers can be ensured by estimating a worker's stress and fatigue levels from their facial expressions and movements and suggesting appropriate breaks and work assignments.

[0066] Network cameras can be equipped with voice recognition functionality, allowing them to evaluate safety conditions from the voices of workers and environmental sounds. Network cameras can be equipped with voice recognition functionality to analyze the voices of workers and environmental sounds. For example, they can detect workers' cries or abnormal sounds and immediately issue an alert. Voice recognition functionality can also be used to analyze the voice data collected by the network camera and evaluate safety conditions. For example, it can detect abnormal machine sounds or workers' cries for help. Furthermore, voice recognition functionality can be added to network cameras to evaluate safety conditions from the voices of workers and environmental sounds. For example, it can analyze the content of conversations between workers and predict dangerous situations. This makes it possible to improve safety on site by evaluating safety conditions from the voices of workers and environmental sounds.

[0067] Network cameras can be linked with other IoT devices to perform comprehensive safety assessments. Network cameras can be linked with other IoT devices (such as wearable devices) to perform comprehensive safety assessments. For example, a worker's heart rate and body temperature data can be integrated with the camera's video data. Camera data can also be linked with other IoT devices to build a system that comprehensively evaluates safety conditions. For example, environmental sensor data can be integrated with the camera's video data. Furthermore, network camera data can be linked with wearable devices to perform comprehensive safety assessments. For example, a worker's movements and biometric data can be combined with the camera's video data for analysis. In this way, by linking with other IoT devices and performing comprehensive safety assessments, safety on site can be improved.

[0068] A network camera equipped with an emotion estimation function can monitor the emotional state of a worker in real time and adjust the environment to elicit positive emotions. A network camera, for example, is equipped with an emotion estimation function and monitors the emotional state of a worker in real time. For example, it analyzes the worker's facial expressions and movements and calculates an emotion score. The network camera also monitors the emotional state of a worker in real time and adjusts the environment to elicit positive emotions. For example, it adjusts the temperature and lighting of the work environment. Furthermore, a camera equipped with an emotion estimation function can be used to monitor the emotional state of a worker and provide feedback to elicit positive emotions. For example, it can display an encouraging message. In this way, the psychological burden on the worker can be reduced by monitoring the emotional state of a worker in real time and adjusting the environment to elicit positive emotions.

[0069] Drones can analyze footage from their onboard cameras using generative AI to automatically identify dangerous areas and abnormalities. For example, drones input footage from their onboard cameras into generative AI, which automatically identifies dangerous areas and abnormalities. For example, this can be used to check the safety of work at heights or detect machine abnormalities. Generative AI can also be used to build systems that identify dangerous areas and abnormalities from drone camera footage. For example, it can analyze the movements of workers and the operation of machines to detect abnormalities. Furthermore, generative AI can analyze drone camera footage to automatically identify dangerous areas and abnormalities. For example, it can monitor the situation on site in real time and detect abnormalities. This allows for the analysis of drone camera footage with generative AI to automatically identify dangerous areas and abnormalities, thereby improving site safety.

[0070] Algorithms can be developed for drones to optimize flight routes and efficiently cover wide areas. For example, algorithms can be developed to optimize flight routes, allowing drones to efficiently cover wide areas. For example, routes can be automatically adjusted according to the terrain and work conditions of the site. Systems can also be built to optimize drone flight routes, allowing for efficient coverage of wide areas. For example, routes can be planned for multiple drones to patrol in coordination. Furthermore, algorithms can be used to optimize drone flight routes, allowing for efficient coverage of wide areas. For example, routes can be set that prioritize patrol of important work areas. This allows drone flight routes to be optimized, allowing for efficient coverage of wide areas, thereby improving safety on site.

[0071] Drones can use emotion estimation functions to estimate the emotional state of workers from video footage captured by the drone and provide appropriate feedback. For example, drones analyze video footage captured by the drone to estimate the emotional state of workers. For example, they analyze the worker's facial expressions and movements and calculate an emotion score. Furthermore, using the emotion estimation function, they can estimate the emotional state of workers from video footage captured by the drone and provide appropriate feedback. For example, they can measure the worker's stress level and suggest a break. Furthermore, a system can be built that estimates the emotional state of workers based on video footage captured by the drone and provides feedback. For example, it can display encouraging messages in response to changes in the worker's emotions. This allows the system to estimate the emotional state of workers from video footage captured by the drone and provide appropriate feedback, thereby reducing the psychological burden on workers.

[0072] Drones can be equipped with additional sensors to simultaneously collect environmental data such as temperature, humidity, and gas concentration. For example, drones can be equipped with temperature and humidity sensors to simultaneously collect environmental data. For example, the temperature and humidity of the work environment can be monitored in real time. A gas concentration sensor can also be added to the drone to build a system for collecting environmental data. For example, the concentration of harmful gases can be measured and anomalies detected. Furthermore, multiple sensors can be installed on the drone to simultaneously collect environmental data such as temperature, humidity, and gas concentration. For example, a comprehensive safety assessment of the work environment can be performed. By adding sensors to the drone and simultaneously collecting environmental data such as temperature, humidity, and gas concentration, safety at the work site can be improved.

[0073] Drones can work in conjunction with ground sensors to grasp the situation on site in more detail. For example, drones can work in conjunction with ground sensors to build a system that grasps the situation on site in more detail. For example, data from temperature and humidity sensors on the ground can be integrated. Drone data can also be linked with ground sensors to comprehensively evaluate the situation on site. For example, data from vibration and sound sensors on the ground can be integrated. Drone data can also be linked with ground sensors to grasp the situation on site in more detail. For example, data from environmental sensors on the ground can be integrated with drone video data. This allows drones to work in conjunction with ground sensors to grasp the situation on site in more detail, thereby improving safety at the site.

[0074] Drones equipped with emotion estimation functions can monitor the emotional state of workers in real time and adjust the environment to elicit positive emotions. For example, drones equipped with emotion estimation functions can monitor the emotional state of workers in real time. For example, they can analyze the worker's facial expressions and movements and calculate an emotion score. They can also monitor the worker's emotional state in real time and adjust the environment to elicit positive emotions. For example, they can adjust the temperature and lighting of the work environment. Furthermore, drones equipped with emotion estimation functions can monitor the worker's emotional state and provide feedback to elicit positive emotions. For example, they can display encouraging messages. In this way, by monitoring the worker's emotional state in real time and adjusting the environment to elicit positive emotions, the psychological burden on the worker can be reduced.

[0075] Generative AI can build a hazard prediction model from collected data and propose measures to prevent accidents before they happen. For example, generative AI analyzes data collected from network cameras and drones to build a hazard prediction model. For example, it learns dangerous patterns based on past data and proposes measures to prevent accidents before they happen. Generative AI can also be used to build a hazard prediction model from collected data and develop a system that predicts hazards in real time. For example, it can analyze the movements of workers and the operation of machines to predict dangerous situations. Generative AI can also be used to build a hazard prediction model from collected data and propose measures to prevent accidents before they happen. For example, it can analyze environmental data and work data to predict dangerous situations. This allows for the construction of a hazard prediction model from collected data and the proposal of measures to prevent accidents before they happen, thereby improving safety on site.

[0076] Generative AI can compare the effectiveness of proposed safety measures with past data and select the most appropriate measures. For example, generative AI can build a system that compares proposed safety measures with past data and evaluates their effectiveness. For example, it can evaluate based on past accident data and the results of implementing safety measures. Generative AI can also compare proposed safety measures with past data and select the most appropriate measures. For example, it can analyze past data and prioritize highly effective measures. Furthermore, generative AI can develop a system that compares proposed safety measures with past data and evaluates their effectiveness. For example, it can simulate the effectiveness of safety measures based on past data. This allows the generative AI to compare proposed safety measures with past data to evaluate their effectiveness and select the most appropriate measures, thereby improving safety at the work site.

[0077] The generative AI uses its emotion estimation function to propose safety measures that take into account the emotional state of workers, thereby reducing the psychological burden on workers. For example, the generative AI uses its emotion estimation function to analyze the emotional state of workers and propose safety measures to reduce psychological burden. For example, it suggests breaks for workers with high stress levels. It also monitors workers' emotional states in real time and proposes safety measures based on the emotional data. For example, it displays encouraging messages to workers with low emotion scores. Furthermore, the emotion estimation function is used to build a system that proposes safety measures that take into account the emotional state of workers. For example, it adjusts work allocation based on the emotional data. This makes it possible to use the emotion estimation function to propose safety measures that take into account the emotional state of workers, reducing the psychological burden on workers and improving safety on site.

[0078] The analysis results by generative AI can be applied to other industries to build a general-purpose safety system. For example, generative AI can apply the analysis results to the manufacturing industry to build a general-purpose safety system. For example, it can evaluate the safety of a production line and propose appropriate measures. Generative AI can also apply the analysis results to the medical industry to build a general-purpose safety system. For example, it can evaluate the safety of a medical site and propose appropriate measures. Generative AI can also apply the analysis results to other industries to build a general-purpose safety system. For example, it can evaluate the safety of the logistics industry or the construction industry and propose appropriate measures. In this way, the analysis results by generative AI can be applied to other industries to build a general-purpose safety system, thereby improving safety in a variety of industries.

[0079] The analysis results of the generative AI can be displayed on a visual dashboard to enable intuitive understanding. The generative AI, for example, builds a system that displays the analysis results on a visual dashboard to enable intuitive understanding. For example, the analysis results are displayed using graphs and charts. The generative AI also displays the analysis results on a visual dashboard to enable users to easily understand. For example, important data is displayed color-coded. The generative AI also displays the analysis results on a visual dashboard to enable intuitive understanding. For example, data is updated in real time to display the latest information. In this way, the analysis results of the generative AI can be displayed on a visual dashboard to enable intuitive understanding, allowing users to easily grasp the information.

[0080] The generative AI can use the emotion estimation function to monitor the emotional state of workers in real time and adjust the environment to elicit positive emotions. For example, the generative AI uses the emotion estimation function to monitor the emotional state of workers in real time. For example, it analyzes the worker's facial expressions and movements and calculates an emotion score. It also monitors the worker's emotional state in real time and adjusts the environment to elicit positive emotions. For example, it adjusts the temperature and lighting of the work environment. It also uses the emotion estimation function to monitor the worker's emotional state and provide feedback to elicit positive emotions. For example, it displays an encouraging message. In this way, by using the emotion estimation function to monitor the worker's emotional state in real time and adjusting the environment to elicit positive emotions, it is possible to reduce the psychological burden on the worker.

[0081] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0082] The safety and health activity support system can also be equipped with a voice recognition function. The voice recognition function can analyze the voices of workers and environmental sounds to detect abnormalities. For example, it can detect a worker's shout or an abnormal sound and immediately issue an alert. The voice recognition function can also be used to analyze the content of conversations between workers and predict dangerous situations. Furthermore, the voice recognition function can be used to detect abnormal machine sounds and signs of fire and respond immediately. In this way, adding the voice recognition function can improve safety on site.

[0083] Network cameras can also incorporate algorithms to automatically adjust the viewpoint and maintain the optimal monitoring range. Algorithms for automatically adjusting the viewpoint have been developed, allowing the camera viewpoint to move in accordance with the movements of workers. Multiple cameras can also work together to adjust the viewpoint to eliminate blind spots. Furthermore, the camera viewpoint can be moved so that important work areas can be constantly monitored. This allows the camera viewpoint to be automatically adjusted and the optimal monitoring range to be maintained, improving safety on site.

[0084] Drones can be equipped with additional sensors to simultaneously collect environmental data such as temperature, humidity, and gas concentration. Equipped with temperature and humidity sensors, the temperature and humidity of the work environment can be monitored in real time. Gas concentration sensors can also be added to measure the concentration of harmful gases and detect abnormalities. Furthermore, multiple sensors can be installed to simultaneously collect environmental data such as temperature, humidity, and gas concentration, allowing for comprehensive safety assessment of the work environment. By adding sensors to drones and simultaneously collecting environmental data, safety on-site can be improved.

[0085] The generating AI can further compare the proposed safety measures with past data to evaluate their effectiveness and select the most appropriate measures. It is possible to build a system that evaluates proposed safety measures based on past accident data and the implementation results of safety measures. It can also analyze past data and prioritize the selection of highly effective measures. Furthermore, it can simulate the effectiveness of safety measures based on past data. This allows the generating AI to compare the proposed safety measures with past data to evaluate their effectiveness and select the most appropriate measures, thereby improving safety at work sites.

[0086] The generative AI can further use its emotion estimation function to propose safety measures that take into account the emotional state of workers, thereby reducing their psychological burden. Using the emotion estimation function, it can analyze workers' emotional states and suggest breaks for workers with high stress levels. It can also propose safety measures based on the emotional data and display encouraging messages to workers with low emotion scores. It can also adjust work assignments based on the emotional data. This allows the emotion estimation function to propose safety measures that take into account the emotional state of workers, reducing their psychological burden and improving safety on site.

[0087] The network camera can also use its emotion estimation function to estimate stress and fatigue levels from workers' facial expressions and movements, and suggest appropriate breaks and work assignments. It can detect changes in workers' facial expressions and movements and suggest breaks. The emotion estimation function can also be used to monitor workers' stress and fatigue levels in real time, measure stress levels using facial recognition technology, and suggest appropriate breaks. It can also estimate workers' stress and fatigue levels, and assign lighter work to workers with high levels of fatigue. This makes it possible to ensure the health and safety of workers by estimating stress and fatigue levels from workers' facial expressions and movements, and suggesting appropriate breaks and work assignments.

[0088] The drone can also use its emotion estimation function to estimate the emotional state of workers from the video footage captured by the drone and provide appropriate feedback. The drone can analyze the video footage captured by the drone, analyze the worker's facial expressions and movements, and calculate an emotion score. The emotion estimation function can also be used to measure the worker's stress level and suggest a break. Furthermore, encouraging messages can be displayed depending on the worker's emotional changes. This makes it possible to reduce the psychological burden on workers by estimating the worker's emotional state from the video footage captured by the drone and providing appropriate feedback.

[0089] The generative AI can further use its emotion estimation function to monitor the emotional state of workers in real time and adjust the environment to elicit positive emotions. Using the emotion estimation function, it can analyze the worker's facial expressions and movements and calculate an emotion score. It can also monitor the worker's emotional state in real time and adjust the temperature and lighting of the work environment. It can also use the emotion estimation function to monitor the worker's emotional state and display encouraging messages. This makes it possible to reduce the psychological burden on workers by using the emotion estimation function to monitor the worker's emotional state in real time and adjust the environment to elicit positive emotions.

[0090] Generative AI can further build a hazard prediction model from collected data and propose measures to prevent accidents before they occur. It can analyze data collected from network cameras and drones, learn dangerous patterns based on past data, and propose measures to prevent accidents before they occur. It can also build a hazard prediction model from collected data and develop a system that predicts hazards in real time. It can also analyze environmental data and work data to predict dangerous situations. This allows it to build a hazard prediction model from collected data and propose measures to prevent accidents before they occur, thereby improving safety on site.

[0091] The generative AI can further display the analysis results on a visual dashboard, making them easy to understand. The analysis results can be displayed on a visual dashboard, using graphs and charts. Important data can also be color-coded to make it easy for users to understand. Furthermore, data can be updated in real time to display the latest information. In this way, the generative AI's analysis results can be displayed on a visual dashboard, making them easy to understand, allowing users to easily grasp the information.

[0092] The processing flow of the second embodiment will be briefly explained below.

[0093] Step 1: Network cameras monitor the work situation and environment. For example, network cameras collect data on worker movements, machine operation status, and on-site environmental data (temperature, humidity, etc.). Network cameras also collect data in real time, providing the data to be input into the generative AI. Step 2: The drone patrols the site. For example, the drone can check the safety of work at heights and grasp the situation over a wide area of ​​the site. The drone also monitors from the sky and collects data, providing the data to be input into the generative AI. Step 3: Generative AI analyzes the data collected from the network cameras and drones and proposes safety measures. For example, if a worker's movements show a dangerous pattern or a machine abnormality is detected, the generative AI will propose safety measures based on that information. The generative AI analyzes the data using text generation AI (e.g., LLM) or multimodal generation AI and proposes specific safety measures.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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).

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0113] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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).

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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."

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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]

[0161] 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. Equipped with a network camera, The network camera monitors the work situation and the environment, Equipped with drones, The drone patrols the site, Equipped with generative AI, The generative AI analyzes data collected from the network cameras and drones and proposes safety measures. A system characterized by:

2. The network camera includes: Collect data on worker movements, machine operation status, and the site environment 2. The system of claim 1.

3. The drone is Check the safety of work at heights and grasp the situation at a wide range of work sites 2. The system of claim 1.

4. The generated AI is If a worker's movements show a dangerous pattern or if a machine malfunction is detected, the system will suggest the above safety measures.

2. The system of claim 1.

5. The generated AI is Propose safety training for workers and machine maintenance 2. The system of claim 1.

6. The generated AI is Data analysis to assign less burdensome tasks to older workers and propose appropriate educational programs for younger workers 2. The system of claim 1.

7. The network camera includes: The collected video data is analyzed in real time using the generative AI to instantly detect abnormal behavior and dangerous situations.

2. The system of claim 1.

8. The network camera includes: Implementing algorithms to automatically adjust viewpoints and maintain optimal surveillance coverage 2. The system of claim 1.

Images