system

Abstract

The system according to this embodiment aims to analyze environmental data from construction sites, detect dangerous situations, and propose countermeasures. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a proposal unit, and a tracking unit. The collection unit collects environmental data from the construction site using sensors. The analysis unit analyzes the data collected by the collection unit and detects dangerous situations. The proposal unit proposes countermeasures based on the dangerous situations detected by the analysis unit. The tracking unit tracks the location information and body movements of workers.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult to manage the safety of a construction site, and there is room for improvement to prevent accidents.

[0005] The system according to the embodiment aims to analyze environmental data of a construction site, detect dangerous situations, and propose countermeasures.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, and a tracking unit. The data collection unit collects environmental data from the construction site using sensors. The analysis unit analyzes the data collected by the data collection unit and detects dangerous situations. The proposal unit proposes countermeasures based on the dangerous situations detected by the analysis unit. The tracking unit tracks the location information and body movements of workers. [Effects of the Invention]

[0007] The system according to this embodiment can analyze environmental data from a construction site, detect dangerous situations, and propose countermeasures. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) An AI monitoring system according to an embodiment of the present invention is a system for improving safety management at construction sites. This system uses sensors to collect data such as the environment of the construction site, the physical condition of workers, and the weather in real time, and the AI ​​analyzes this data to detect dangerous situations and propose countermeasures. For example, the AI ​​monitoring system proposes appropriate countermeasures when environmental conditions such as high or low temperature, high or low humidity reach dangerous levels. It also tracks the location information and body movements of workers to detect dangerous actions and locations. For example, if a worker approaches a high-altitude work area or an area where hazardous materials are used, the AI ​​issues a warning. In this way, the AI ​​monitoring system can improve safety at construction sites and prevent accidents. For example, the AI ​​monitoring system uses sensors attached to heavy machinery such as cranes, excavators, and forklifts to monitor the operating status and location of the machinery. Wearable sensors worn by workers and sensors attached to helmets track the location information and body movements of workers in real time. Furthermore, cameras that provide an overview of the site and stationary sensors that monitor specific areas are used to ensure a safe working environment. Next, the AI ​​analyzes the collected data. AI detects dangerous situations by considering environmental data, worker health data, weather data, and more. For example, if environmental conditions such as high or low temperatures, or high or low humidity reach dangerous levels, the AI ​​will suggest appropriate countermeasures. It also analyzes workers' location information and body movements to detect dangerous actions and locations. For example, if a worker approaches a high-altitude work area or an area where hazardous materials are used, the AI ​​will issue a warning. Furthermore, when a dangerous situation is detected, the AI ​​will suggest quick and accurate countermeasures. For example, if high or low temperatures are detected, it will instruct workers to take appropriate breaks. Also, if a worker approaches a dangerous area, it will issue a warning and guide the worker to a safe location. In this way, the AI ​​monitoring system can improve safety at construction sites and prevent accidents.

[0029] The AI ​​monitoring system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, and a tracking unit. The data collection unit collects environmental data from the construction site using sensors. The data collection unit monitors the operating status and location of heavy machinery, such as cranes, excavators, and forklifts, using sensors attached to them. The data collection unit can also track the location and body movements of workers in real time using wearable sensors worn by workers or sensors attached to helmets. Furthermore, the data collection unit ensures a safe working environment using cameras that provide an overview of the site and stationary sensors that monitor specific areas. For example, the data collection unit monitors the operating status of cranes and detects abnormal operation. The data collection unit can also track the location of workers in real time and detect approach to dangerous areas. Furthermore, the data collection unit collects environmental data from the site and converts it into a format that is easy for AI to analyze. The analysis unit analyzes the data collected by the data collection unit and detects dangerous situations. The analysis unit detects dangerous situations by considering, for example, environmental data, worker health data, and weather data. For example, the analysis unit proposes appropriate countermeasures when environmental conditions such as high or low temperatures, high or low humidity reach dangerous levels. The analysis unit can also analyze worker location information and body movements to detect dangerous actions or locations. For example, the analysis unit issues a warning if a worker approaches a high-altitude work area or an area where hazardous materials are used. Furthermore, the analysis unit proposes quick and accurate countermeasures when a dangerous situation is detected. The proposal unit proposes countermeasures based on the dangerous situation detected by the analysis unit. For example, the proposal unit proposes appropriate countermeasures when environmental conditions such as high or low temperatures, high or low humidity reach dangerous levels. The proposal unit can also issue a warning if a worker approaches a high-altitude work area or an area where hazardous materials are used. For example, if high temperatures are detected, the proposal unit instructs the worker to take an appropriate break. The proposal unit also issues a warning and guides the worker to a safe location if they approach a dangerous area. The tracking unit tracks the worker's location information and body movements. The tracking unit tracks the worker's location and body movements in real time, for example, using wearable sensors worn by the worker or sensors attached to their helmet.Furthermore, the tracking unit can analyze the worker's movement patterns and predict dangerous actions. For example, the tracking unit can analyze the worker's movement patterns and predict dangerous actions. The tracking unit can also track the worker's location information in real time and detect approach to dangerous areas. As a result, the AI ​​monitoring system according to this embodiment can improve safety at construction sites and prevent accidents.

[0030] The data collection unit uses sensors to collect environmental data from construction sites. Specifically, it monitors the operating status and location of heavy machinery such as cranes, excavators, and forklifts using sensors attached to the machines. These sensors collect data such as machine operating time, location, speed, vibration, and temperature in real time and transmit it to a central database. For example, sensors on cranes can detect changes in the angle and load of the crane boom, enabling early detection of abnormal operation. Sensors on excavators measure the depth of excavation and soil hardness, providing data to improve work efficiency. Sensors on forklifts monitor the position and load balance of the forklift, supporting safe operation. Furthermore, the data collection unit tracks workers' location and body movements in real time using wearable sensors worn by workers and sensors attached to their helmets. The wearable sensors collect biometric data such as the worker's heart rate, body temperature, and movement patterns to monitor the worker's health. The sensors attached to the helmets detect the worker's head movements, allowing for early detection of falls and impacts. This ensures worker safety and enables health management. Furthermore, the data collection unit ensures a safe working environment using cameras that provide an overview of the site and stationary sensors that monitor specific areas. The cameras capture wide-area images in real time and transmit them to a central monitoring system. This allows for an understanding of the overall situation at the site and the early detection of abnormal activity or dangerous situations. Stationary sensors are installed in specific areas to collect environmental data such as temperature, humidity, and gas concentration. This enables a rapid response if environmental conditions reach dangerous levels. For example, the data collection unit monitors the crane's operating status and detects abnormal movements. It can also track workers' locations in real time and detect their approach to dangerous areas. Furthermore, the unit collects on-site environmental data and converts it into a format easily analyzed by AI. This allows the data collection unit to gather a wide range of data from various devices and understand the situation in real time. The collected data is stored on a cloud server, making it accessible to the analysis and proposal units. By adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. This enables the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit analyzes data collected by the data collection unit to detect dangerous situations. Specifically, it detects dangerous situations by considering environmental data, worker health data, weather data, etc. For example, if environmental conditions such as high or low temperatures, high or low humidity reach dangerous levels, the analysis unit will propose appropriate countermeasures. The analysis unit uses AI to analyze this data in real time and quickly detect dangerous situations. The AI ​​learns from past data using machine learning algorithms and detects abnormal patterns and signs of danger with high accuracy. For example, the AI ​​analyzes crane operation data to detect abnormal vibrations and changes in load. It can also analyze worker location information and body movements to detect dangerous actions and locations. For example, it will issue a warning if a worker approaches a high-altitude work area or an area where hazardous materials are used. Furthermore, the analysis unit proposes quick and accurate countermeasures when dangerous situations are detected. For example, it proposes appropriate measures when environmental conditions such as high or low temperatures, high or low humidity reach dangerous levels. It also issues warnings and guides workers to safe locations if they approach dangerous areas. The analysis unit can also perform long-term risk assessments and trend analyses using historical data and statistical information. For example, it can predict risk fluctuations in specific seasons or time periods based on historical environmental data and formulate future countermeasures. In addition, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, and issue warnings early. As a result, the analysis unit can not only grasp the situation in real time but also handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0032] The proposal department will propose countermeasures based on the hazardous conditions detected by the analysis department. Specifically, if environmental conditions such as high or low temperatures, high or low humidity reach dangerous levels, appropriate measures will be proposed. For example, if high temperatures are detected, workers will be instructed to take appropriate breaks. Similarly, if low temperatures are detected, workers will be instructed to take measures to protect themselves from the cold. In the event of high or low humidity, appropriate measures will also be proposed to protect the health of the workers. Furthermore, the system can issue warnings if workers approach high-altitude work areas or areas where hazardous materials are used. For example, if a worker approaches a high-altitude work area, it will issue a warning and guide the worker to a safe location. Similarly, if a worker approaches an area where hazardous materials are used, it will issue a warning and guide the worker to a safe location. The system uses AI to automatically propose these measures and respond quickly and accurately. The AI ​​learns algorithms to propose optimal measures based on past data and statistical information, and always provides measures based on the latest information. The proposal department can also collect feedback from workers and continuously improve the accuracy and effectiveness of its proposals. For example, it can evaluate the results after the proposed measures are implemented and incorporate them into future proposals. Furthermore, the proposal department can reliably transmit information using multiple communication methods. For instance, it can use not only smartphone notifications but also voice calls, SMS, and email to ensure that important information is delivered reliably. This allows the proposal department to propose measures quickly and accurately, and to ensure the safety of workers.

[0033] The tracking unit tracks the location and body movements of workers. Specifically, it uses wearable sensors worn by workers and sensors attached to helmets to track their location and body movements in real time. Wearable sensors collect biometric data such as the worker's heart rate, body temperature, and movement patterns to monitor the worker's health. Sensors attached to helmets detect the worker's head movements, allowing for early detection of falls and impacts. This ensures worker safety and enables health management. The tracking unit can analyze workers' movement patterns and predict dangerous actions. For example, it can analyze workers' movement patterns and detect unusual or abnormal movements. This allows for warnings to be issued before workers perform dangerous actions, preventing accidents. The tracking unit can also track workers' location information in real time and detect approach to dangerous areas. For example, if a worker approaches a high-altitude work area or an area where hazardous materials are used, it can issue a warning and guide the worker to a safe location. Furthermore, the tracking unit can collect worker feedback and continuously improve the accuracy and effectiveness of the tracking system. For example, it can evaluate the analysis results of worker movement patterns and location information based on the tracked data and reflect them in the next tracking. The tracking unit can also reliably transmit information using multiple communication methods. For example, it can reliably deliver important information by using not only smartphone notifications but also voice calls, SMS, and email. As a result, the tracking unit can quickly and accurately track the location information and physical movements of workers, ensuring worker safety. Consequently, the AI ​​monitoring system according to this embodiment can improve safety at construction sites and prevent accidents.

[0034] The data collection unit can monitor the operating status and location of heavy machinery such as cranes, excavators, and forklifts using sensors attached to the machinery. For example, the data collection unit can monitor the operating status of a crane and detect abnormal movements. It can also track the location of an excavator in real time and detect its approach to a dangerous area. Furthermore, the data collection unit can monitor the operating status of a forklift and ensure safe operation. In this way, the safety of the machinery is ensured by monitoring the operating status and location of heavy machinery. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input data from sensors that monitor the operating status of a crane into a generating AI and have the generating AI perform the detection of abnormal movements.

[0035] The data collection unit can track the location and body movements of workers in real time using wearable sensors worn by workers or sensors attached to their helmets. For example, the data collection unit can track location information using a wristwatch-type wearable sensor worn by a worker. The data collection unit can also track the body movements of workers in real time using sensors attached to their helmets. Furthermore, the data collection unit can track the location information of workers using a belt-type wearable sensor to ensure a safe working environment. This ensures worker safety by tracking the location information and body movements of workers in real time. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input data from sensors that track the worker's location information in real time into a generating AI and have the generating AI perform location information analysis.

[0036] The data collection unit can ensure a safe working environment by using cameras that provide an overview of the site and stationary sensors that monitor specific areas. For example, the data collection unit monitors the working environment using cameras installed at high altitudes that provide an overview of the site. The data collection unit can also ensure a safe working environment by using infrared sensors that monitor specific areas. Furthermore, the data collection unit can monitor specific areas using ultrasonic sensors to ensure a safe working environment. In this way, a safe working environment is ensured by using cameras that provide an overview of the site and stationary sensors that monitor specific areas. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input video data from cameras that provide an overview of the site into a generating AI and have the generating AI perform monitoring of the working environment.

[0037] The analysis unit can detect dangerous situations by considering environmental data, worker health data, weather data, etc. For example, the analysis unit considers temperature, humidity, noise level, etc. as environmental data. The analysis unit can also consider heart rate, body temperature, etc. as worker health data. Furthermore, the analysis unit can also consider temperature, precipitation, wind speed, etc. as weather data. By considering environmental data, worker health data, weather data, etc., the analysis unit can accurately detect dangerous situations. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input environmental data, worker health data, and weather data into a generating AI and have the generating AI perform the detection of dangerous situations.

[0038] The suggestion unit can propose appropriate countermeasures when environmental conditions, such as high or low temperatures, high or low humidity, reach dangerous levels. For example, if the temperature reaches 35 degrees Celsius or higher, the suggestion unit can instruct workers to take appropriate breaks. It can also instruct workers to take cold weather precautions if the temperature falls below 0 degrees Celsius. Furthermore, if the humidity reaches 80% or higher, the suggestion unit can encourage workers to rehydrate. This ensures worker safety by proposing appropriate countermeasures when environmental conditions reach dangerous levels. Some or all of the above-described processes in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input environmental data into a generating AI and have the generating AI propose appropriate countermeasures.

[0039] The proposed system can issue a warning if a worker approaches a work area at height or an area where hazardous materials are used. For example, the proposed system will issue a warning if a worker approaches a work area at height of 10 meters or more above the ground. The proposed system can also issue a warning if a worker approaches an area where chemicals are used or other hazardous materials are used. Furthermore, the proposed system can issue a warning and guide the worker to a safe location if they approach a work area at height or an area where hazardous materials are used. This ensures the safety of workers by issuing a warning when they approach a work area at height or an area where hazardous materials are used. Some or all of the above processing in the proposed system may be performed using AI or not. For example, the proposed system can input the worker's location information into a generating AI and have the generating AI issue a warning.

[0040] The data collection unit can detect abnormal changes in a worker's physical condition by referring to the worker's past health data. For example, the data collection unit can detect abnormal fluctuations in heart rate based on the worker's past health data. It can also detect abnormal fluctuations in body temperature based on the worker's past health data. Furthermore, it can detect abnormal fluctuations in blood pressure based on the worker's past health data. In this way, abnormal changes in physical condition can be detected early by referring to the worker's past health data. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the worker's past health data into a generating AI and have the generating AI perform the detection of abnormal changes in physical condition.

[0041] The data collection unit can monitor noise levels and vibrations at the site in real time and evaluate the safety of the work environment. For example, the data collection unit can monitor noise levels at the site in real time and issue a warning if they exceed acceptable limits. It can also monitor vibration levels at the site in real time and issue a warning if it detects abnormal vibrations. Furthermore, the data collection unit can integrate noise and vibration levels to evaluate the overall safety of the work environment. This allows for the evaluation of work environment safety by monitoring noise levels and vibrations at the site in real time. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input noise level and vibration data from the site into a generating AI and have the generating AI perform an evaluation of work environment safety.

[0042] The data collection unit can monitor workers' rest periods and working hours to collect data for preventing overwork. For example, the data collection unit can monitor workers' rest periods and instruct them to take appropriate breaks. It can also monitor workers' working hours and suggest breaks to prevent overwork. Furthermore, the data collection unit can integrate workers' rest periods and working hours to assess the overall risk of overwork. This allows for the collection of data for preventing overwork by monitoring workers' rest periods and working hours. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input data on workers' rest periods and working hours into a generating AI and have the generating AI perform an assessment of the risk of overwork.

[0043] The data collection unit can monitor the air quality and hazardous substance concentrations at a site and assess health risks. For example, the data collection unit can monitor the air quality at the site in real time and issue a warning if it detects abnormal concentrations of hazardous substances. It can also monitor the concentration of hazardous substances at the site in real time and issue a warning if it exceeds acceptable limits. Furthermore, the data collection unit can integrate air quality and hazardous substance concentrations to assess overall health risks. This allows for the assessment of health risks by monitoring the air quality and hazardous substance concentrations at the site. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input site air quality and hazardous substance concentration data into a generating AI and have the generating AI perform a health risk assessment.

[0044] The analysis unit can predict similar hazardous situations by referring to past accident data. For example, the analysis unit predicts similar hazardous situations based on past accident data. The analysis unit can also identify hazardous work patterns based on past accident data. Furthermore, the analysis unit can identify hazardous environmental conditions based on past accident data. This allows the analysis unit to predict similar hazardous situations by referring to past accident data. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input past accident data into a generating AI and have the generating AI perform predictions of similar hazardous situations.

[0045] The analysis unit can integrate worker health data and environmental data to perform a comprehensive risk assessment. For example, the analysis unit can integrate worker health data and environmental data to perform a comprehensive risk assessment. The analysis unit can also predict dangerous situations based on worker health data and environmental data. Furthermore, the analysis unit can propose appropriate countermeasures based on worker health data and environmental data. In this way, a comprehensive risk assessment is performed by integrating worker health data and environmental data. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input worker health data and environmental data into a generating AI and have the generating AI perform a comprehensive risk assessment.

[0046] The analysis unit can perform risk assessments while considering the skill levels and experience of the workers. For example, the analysis unit can perform risk assessments while considering the skill levels of the workers. The analysis unit can also perform risk assessments while considering the experience of the workers. Furthermore, the analysis unit can integrate the skill levels and experience of the workers to perform a comprehensive risk assessment. This allows for a more accurate risk assessment by considering the skill levels and experience of the workers. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input worker skill level and experience data into a generating AI and have the generating AI perform the risk assessment.

[0047] The analysis unit can analyze on-site weather data and assess the risks associated with weather changes. For example, the analysis unit can analyze on-site weather data and assess the risks associated with weather changes. The analysis unit can also predict dangerous situations based on the weather data. Furthermore, the analysis unit can propose appropriate countermeasures based on the weather data. In this way, the risks associated with weather changes are assessed by analyzing on-site weather data. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input on-site weather data into a generating AI and have the generating AI perform a risk assessment due to weather changes.

[0048] The proposal unit can propose the optimal countermeasure by referring to past countermeasure data. For example, the proposal unit can propose the optimal countermeasure based on past countermeasure data. The proposal unit can also identify effective countermeasures based on past countermeasure data. Furthermore, the proposal unit can propose appropriate countermeasures based on past countermeasure data. In this way, the optimal countermeasure is proposed by referring to past countermeasure data. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit can input past countermeasure data into a generating AI and have the generating AI execute a proposal for the optimal countermeasure.

[0049] The proposal department can propose specific countermeasures based on the worker's skill level and experience. For example, the proposal department can propose specific countermeasures based on the worker's skill level. Furthermore, the proposal department can propose specific countermeasures based on the worker's experience. In addition, the proposal department can integrate the worker's skill level and experience to propose comprehensive countermeasures. This ensures that appropriate countermeasures are implemented by proposing specific countermeasures based on the worker's skill level and experience. Some or all of the above-described processes in the proposal department may be performed using AI, or not. For example, the proposal department can input worker skill level and experience data into a generating AI and have the generating AI propose specific countermeasures.

[0050] The proposal department can propose the optimal timing for countermeasures, taking into account the on-site work schedule. For example, the proposal department can propose the optimal timing for countermeasures, taking into account the on-site work schedule. The proposal department can also identify effective timing for countermeasures based on the work schedule. Furthermore, the proposal department can propose appropriate timing for countermeasures based on the work schedule. In this way, by taking into account the on-site work schedule, the optimal timing for countermeasures is proposed. Some or all of the above processes in the proposal department may be performed using AI or not. For example, the proposal department can input on-site work schedule data into a generating AI and have the generating AI execute a proposal for the optimal timing for countermeasures.

[0051] The proposal department can propose health management measures, taking into account the health status of the workers. For example, the proposal department can propose health management measures, taking into account the health status of the workers. The proposal department can also identify effective health management measures based on the health status. Furthermore, the proposal department can propose appropriate health management measures based on the health status. In this way, appropriate health management measures are proposed by taking into account the health status of the workers. Some or all of the above processing in the proposal department may be performed using AI or not. For example, the proposal department can input worker health status data into a generating AI and have the generating AI execute proposals for health management measures.

[0052] The tracking unit can detect abnormal movements by referring to the worker's past movement history. For example, the tracking unit can detect abnormal movement patterns based on the worker's past movement history. The tracking unit can also detect approach to dangerous areas based on the worker's past movement history. Furthermore, the tracking unit can detect unusual movements based on the worker's past movement history. This allows for early detection of abnormal movements by referring to the worker's past movement history. Some or all of the above processing in the tracking unit may be performed using AI or not. For example, the tracking unit can input the worker's past movement history data into a generating AI and have the generating AI perform abnormal movement detection.

[0053] The tracking unit can analyze the worker's movement patterns and predict dangerous actions. For example, the tracking unit can analyze the worker's movement patterns and predict dangerous actions. The tracking unit can also identify dangerous work procedures based on the movement patterns. Furthermore, the tracking unit can propose appropriate countermeasures based on the movement patterns. In this way, dangerous actions can be predicted by analyzing the worker's movement patterns. Some or all of the above processing in the tracking unit may be performed using AI or not. For example, the tracking unit can input worker movement pattern data into a generating AI and have the generating AI perform the prediction of dangerous actions.

[0054] The tracking unit can monitor workers' rest periods and working hours and collect data to prevent overwork. For example, the tracking unit can monitor workers' rest periods and instruct them to take appropriate breaks. It can also monitor workers' working hours and suggest breaks to prevent overwork. Furthermore, the tracking unit can integrate workers' rest periods and working hours to assess the overall risk of overwork. This allows for the collection of data to prevent overwork by monitoring workers' rest periods and working hours. Some or all of the above processing in the tracking unit may be performed using AI or not. For example, the tracking unit can input data on workers' rest periods and working hours into a generating AI and have the generating AI perform an assessment of the risk of overwork.

[0055] The tracking unit can analyze the worker's movement patterns and propose efficient work procedures. For example, the tracking unit can analyze the worker's movement patterns and propose efficient work procedures. The tracking unit can also identify inefficiencies in the work based on the movement patterns. Furthermore, the tracking unit can propose appropriate work procedures based on the movement patterns. In this way, by analyzing the worker's movement patterns, efficient work procedures are proposed. Some or all of the above processing in the tracking unit may be performed using AI or not. For example, the tracking unit can input worker movement pattern data into a generating AI and have the generating AI propose efficient work procedures.

[0056] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0057] The data collection unit can evaluate the efficiency of work by referring to the worker's past work history. For example, the data collection unit can identify inefficiencies in work based on the worker's past work history. Furthermore, the data collection unit can propose more efficient work procedures based on past work history. In addition, the data collection unit can identify areas for improvement in work based on past work history. This allows for the evaluation of work efficiency and the suggestion of improvements by referring to the worker's past work history.

[0058] The analysis unit can perform risk assessments of tasks while taking into account the skill levels of the workers. For example, the analysis unit can identify high-risk tasks based on the skill levels of the workers. Furthermore, the analysis unit can perform risk assessments tailored to skill levels. In addition, the analysis unit can propose appropriate countermeasures while considering skill levels. This allows for more accurate risk assessments and the proposal of appropriate countermeasures by taking into account the skill levels of the workers.

[0059] The proposal department can make health management suggestions by referring to workers' past health data. For example, the proposal department can propose appropriate health management measures based on workers' past health data. It can also identify effective health management methods based on past health data. Furthermore, it can assess health risks based on past health data and propose appropriate countermeasures. In this way, appropriate health management suggestions can be made by referring to workers' past health data.

[0060] The collection unit can monitor the air quality and hazardous substance concentrations at the site and assess health risks. For example, the collection unit can monitor the air quality at the site in real time and issue a warning if it detects abnormal concentrations of hazardous substances. It can also monitor the concentration of hazardous substances at the site in real time and issue a warning if it exceeds acceptable limits. Furthermore, it can integrate air quality and hazardous substance concentrations to assess overall health risks. In this way, health risks can be assessed by monitoring the air quality and hazardous substance concentrations at the site.

[0061] The tracking unit can detect abnormal movements by referring to the worker's past movement history. For example, the tracking unit can detect abnormal movement patterns based on the worker's past movement history. It can also detect approach to dangerous areas based on past movement history. Furthermore, it can detect unusual movements based on past movement history. As a result, abnormal movements can be detected early by referring to the worker's past movement history.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The data collection unit collects environmental data from the construction site using sensors. The data collection unit monitors the operating status and location of heavy machinery such as cranes, excavators, and forklifts using sensors attached to the machinery. It also tracks the location and body movements of workers in real time using wearable sensors worn by workers and sensors attached to their helmets. Furthermore, it ensures a safe working environment using cameras that provide an overview of the site and stationary sensors that monitor specific areas. Step 2: The analysis unit analyzes the data collected by the data collection unit to detect dangerous situations. The analysis unit detects dangerous situations by considering environmental data, worker health data, weather data, etc. For example, if environmental conditions such as high or low temperature, high or low humidity reach dangerous levels, it will propose appropriate countermeasures. It also analyzes the worker's location information and body movements to detect dangerous actions and positions. Step 3: The proposal department proposes countermeasures based on the hazardous conditions detected by the analysis department. The proposal department proposes appropriate countermeasures when environmental conditions such as high or low temperatures, high or low humidity reach dangerous levels. It also issues warnings when workers approach high-altitude work areas or areas where hazardous materials are used. Step 4: The tracking unit tracks the worker's location and body movements. The tracking unit uses wearable sensors worn by the worker and sensors attached to their helmet to track the worker's location and body movements in real time. It can also analyze the worker's movement patterns and predict dangerous actions.

[0064] (Example of form 2) An AI monitoring system according to an embodiment of the present invention is a system for improving safety management at construction sites. This system uses sensors to collect data such as the environment of the construction site, the physical condition of workers, and the weather in real time, and the AI ​​analyzes this data to detect dangerous situations and propose countermeasures. For example, the AI ​​monitoring system proposes appropriate countermeasures when environmental conditions such as high or low temperature, high or low humidity reach dangerous levels. It also tracks the location information and body movements of workers to detect dangerous actions and locations. For example, if a worker approaches a high-altitude work area or an area where hazardous materials are used, the AI ​​issues a warning. In this way, the AI ​​monitoring system can improve safety at construction sites and prevent accidents. For example, the AI ​​monitoring system uses sensors attached to heavy machinery such as cranes, excavators, and forklifts to monitor the operating status and location of the machinery. Wearable sensors worn by workers and sensors attached to helmets track the location information and body movements of workers in real time. Furthermore, cameras that provide an overview of the site and stationary sensors that monitor specific areas are used to ensure a safe working environment. Next, the AI ​​analyzes the collected data. AI detects dangerous situations by considering environmental data, worker health data, weather data, and more. For example, if environmental conditions such as high or low temperatures, or high or low humidity reach dangerous levels, the AI ​​will suggest appropriate countermeasures. It also analyzes workers' location information and body movements to detect dangerous actions and locations. For example, if a worker approaches a high-altitude work area or an area where hazardous materials are used, the AI ​​will issue a warning. Furthermore, when a dangerous situation is detected, the AI ​​will suggest quick and accurate countermeasures. For example, if high or low temperatures are detected, it will instruct workers to take appropriate breaks. Also, if a worker approaches a dangerous area, it will issue a warning and guide the worker to a safe location. In this way, the AI ​​monitoring system can improve safety at construction sites and prevent accidents.

[0065] The AI ​​monitoring system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, and a tracking unit. The data collection unit collects environmental data from the construction site using sensors. The data collection unit monitors the operating status and location of heavy machinery, such as cranes, excavators, and forklifts, using sensors attached to them. The data collection unit can also track the location and body movements of workers in real time using wearable sensors worn by workers or sensors attached to helmets. Furthermore, the data collection unit ensures a safe working environment using cameras that provide an overview of the site and stationary sensors that monitor specific areas. For example, the data collection unit monitors the operating status of cranes and detects abnormal operation. The data collection unit can also track the location of workers in real time and detect approach to dangerous areas. Furthermore, the data collection unit collects environmental data from the site and converts it into a format that is easy for AI to analyze. The analysis unit analyzes the data collected by the data collection unit and detects dangerous situations. The analysis unit detects dangerous situations by considering, for example, environmental data, worker health data, and weather data. For example, the analysis unit proposes appropriate countermeasures when environmental conditions such as high or low temperatures, high or low humidity reach dangerous levels. The analysis unit can also analyze worker location information and body movements to detect dangerous actions or locations. For example, the analysis unit issues a warning if a worker approaches a high-altitude work area or an area where hazardous materials are used. Furthermore, the analysis unit proposes quick and accurate countermeasures when a dangerous situation is detected. The proposal unit proposes countermeasures based on the dangerous situation detected by the analysis unit. For example, the proposal unit proposes appropriate countermeasures when environmental conditions such as high or low temperatures, high or low humidity reach dangerous levels. The proposal unit can also issue a warning if a worker approaches a high-altitude work area or an area where hazardous materials are used. For example, if high temperatures are detected, the proposal unit instructs the worker to take an appropriate break. The proposal unit also issues a warning and guides the worker to a safe location if they approach a dangerous area. The tracking unit tracks the worker's location information and body movements. The tracking unit tracks the worker's location and body movements in real time, for example, using wearable sensors worn by the worker or sensors attached to their helmet.Furthermore, the tracking unit can analyze the worker's movement patterns and predict dangerous actions. For example, the tracking unit can analyze the worker's movement patterns and predict dangerous actions. The tracking unit can also track the worker's location information in real time and detect approach to dangerous areas. As a result, the AI ​​monitoring system according to this embodiment can improve safety at construction sites and prevent accidents.

[0066] The data collection unit uses sensors to collect environmental data from construction sites. Specifically, it monitors the operating status and location of heavy machinery such as cranes, excavators, and forklifts using sensors attached to the machines. These sensors collect data such as machine operating time, location, speed, vibration, and temperature in real time and transmit it to a central database. For example, sensors on cranes can detect changes in the angle and load of the crane boom, enabling early detection of abnormal operation. Sensors on excavators measure the depth of excavation and soil hardness, providing data to improve work efficiency. Sensors on forklifts monitor the position and load balance of the forklift, supporting safe operation. Furthermore, the data collection unit tracks workers' location and body movements in real time using wearable sensors worn by workers and sensors attached to their helmets. The wearable sensors collect biometric data such as the worker's heart rate, body temperature, and movement patterns to monitor the worker's health. The sensors attached to the helmets detect the worker's head movements, allowing for early detection of falls and impacts. This ensures worker safety and enables health management. Furthermore, the data collection unit ensures a safe working environment using cameras that provide an overview of the site and stationary sensors that monitor specific areas. The cameras capture wide-area images in real time and transmit them to a central monitoring system. This allows for an understanding of the overall situation at the site and the early detection of abnormal activity or dangerous situations. Stationary sensors are installed in specific areas to collect environmental data such as temperature, humidity, and gas concentration. This enables a rapid response if environmental conditions reach dangerous levels. For example, the data collection unit monitors the crane's operating status and detects abnormal movements. It can also track workers' locations in real time and detect their approach to dangerous areas. Furthermore, the unit collects on-site environmental data and converts it into a format easily analyzed by AI. This allows the data collection unit to gather a wide range of data from various devices and understand the situation in real time. The collected data is stored on a cloud server, making it accessible to the analysis and proposal units. By adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. This enables the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0067] The analysis unit analyzes data collected by the data collection unit to detect dangerous situations. Specifically, it detects dangerous situations by considering environmental data, worker health data, weather data, etc. For example, if environmental conditions such as high or low temperatures, high or low humidity reach dangerous levels, the analysis unit will propose appropriate countermeasures. The analysis unit uses AI to analyze this data in real time and quickly detect dangerous situations. The AI ​​learns from past data using machine learning algorithms and detects abnormal patterns and signs of danger with high accuracy. For example, the AI ​​analyzes crane operation data to detect abnormal vibrations and changes in load. It can also analyze worker location information and body movements to detect dangerous actions and locations. For example, it will issue a warning if a worker approaches a high-altitude work area or an area where hazardous materials are used. Furthermore, the analysis unit proposes quick and accurate countermeasures when dangerous situations are detected. For example, it proposes appropriate measures when environmental conditions such as high or low temperatures, high or low humidity reach dangerous levels. It also issues warnings and guides workers to safe locations if they approach dangerous areas. The analysis unit can also perform long-term risk assessments and trend analyses using historical data and statistical information. For example, it can predict risk fluctuations in specific seasons or time periods based on historical environmental data and formulate future countermeasures. In addition, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, and issue warnings early. As a result, the analysis unit can not only grasp the situation in real time but also handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0068] The proposal department will propose countermeasures based on the hazardous conditions detected by the analysis department. Specifically, if environmental conditions such as high or low temperatures, high or low humidity reach dangerous levels, appropriate measures will be proposed. For example, if high temperatures are detected, workers will be instructed to take appropriate breaks. Similarly, if low temperatures are detected, workers will be instructed to take measures to protect themselves from the cold. In the event of high or low humidity, appropriate measures will also be proposed to protect the health of the workers. Furthermore, the system can issue warnings if workers approach high-altitude work areas or areas where hazardous materials are used. For example, if a worker approaches a high-altitude work area, it will issue a warning and guide the worker to a safe location. Similarly, if a worker approaches an area where hazardous materials are used, it will issue a warning and guide the worker to a safe location. The system uses AI to automatically propose these measures and respond quickly and accurately. The AI ​​learns algorithms to propose optimal measures based on past data and statistical information, and always provides measures based on the latest information. The proposal department can also collect feedback from workers and continuously improve the accuracy and effectiveness of its proposals. For example, it can evaluate the results after the proposed measures are implemented and incorporate them into future proposals. Furthermore, the proposal department can reliably transmit information using multiple communication methods. For instance, it can use not only smartphone notifications but also voice calls, SMS, and email to ensure that important information is delivered reliably. This allows the proposal department to propose measures quickly and accurately, and to ensure the safety of workers.

[0069] The tracking unit tracks the location and body movements of workers. Specifically, it uses wearable sensors worn by workers and sensors attached to helmets to track their location and body movements in real time. Wearable sensors collect biometric data such as the worker's heart rate, body temperature, and movement patterns to monitor the worker's health. Sensors attached to helmets detect the worker's head movements, allowing for early detection of falls and impacts. This ensures worker safety and enables health management. The tracking unit can analyze workers' movement patterns and predict dangerous actions. For example, it can analyze workers' movement patterns and detect unusual or abnormal movements. This allows for warnings to be issued before workers perform dangerous actions, preventing accidents. The tracking unit can also track workers' location information in real time and detect approach to dangerous areas. For example, if a worker approaches a high-altitude work area or an area where hazardous materials are used, it can issue a warning and guide the worker to a safe location. Furthermore, the tracking unit can collect worker feedback and continuously improve the accuracy and effectiveness of the tracking system. For example, it can evaluate the analysis results of worker movement patterns and location information based on the tracked data and reflect them in the next tracking. The tracking unit can also reliably transmit information using multiple communication methods. For example, it can reliably deliver important information by using not only smartphone notifications but also voice calls, SMS, and email. As a result, the tracking unit can quickly and accurately track the location information and physical movements of workers, ensuring worker safety. Consequently, the AI ​​monitoring system according to this embodiment can improve safety at construction sites and prevent accidents.

[0070] The data collection unit can monitor the operating status and location of heavy machinery such as cranes, excavators, and forklifts using sensors attached to the machinery. For example, the data collection unit can monitor the operating status of a crane and detect abnormal movements. It can also track the location of an excavator in real time and detect its approach to a dangerous area. Furthermore, the data collection unit can monitor the operating status of a forklift and ensure safe operation. In this way, the safety of the machinery is ensured by monitoring the operating status and location of heavy machinery. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input data from sensors that monitor the operating status of a crane into a generating AI and have the generating AI perform the detection of abnormal movements.

[0071] The data collection unit can track the location and body movements of workers in real time using wearable sensors worn by workers or sensors attached to their helmets. For example, the data collection unit can track location information using a wristwatch-type wearable sensor worn by a worker. The data collection unit can also track the body movements of workers in real time using sensors attached to their helmets. Furthermore, the data collection unit can track the location information of workers using a belt-type wearable sensor to ensure a safe working environment. This ensures worker safety by tracking the location information and body movements of workers in real time. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input data from sensors that track the worker's location information in real time into a generating AI and have the generating AI perform location information analysis.

[0072] The data collection unit can ensure a safe working environment by using cameras that provide an overview of the site and stationary sensors that monitor specific areas. For example, the data collection unit monitors the working environment using cameras installed at high altitudes that provide an overview of the site. The data collection unit can also ensure a safe working environment by using infrared sensors that monitor specific areas. Furthermore, the data collection unit can monitor specific areas using ultrasonic sensors to ensure a safe working environment. In this way, a safe working environment is ensured by using cameras that provide an overview of the site and stationary sensors that monitor specific areas. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input video data from cameras that provide an overview of the site into a generating AI and have the generating AI perform monitoring of the working environment.

[0073] The analysis unit can detect dangerous situations by considering environmental data, worker health data, weather data, etc. For example, the analysis unit considers temperature, humidity, noise level, etc. as environmental data. The analysis unit can also consider heart rate, body temperature, etc. as worker health data. Furthermore, the analysis unit can also consider temperature, precipitation, wind speed, etc. as weather data. By considering environmental data, worker health data, weather data, etc., the analysis unit can accurately detect dangerous situations. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input environmental data, worker health data, and weather data into a generating AI and have the generating AI perform the detection of dangerous situations.

[0074] The suggestion unit can propose appropriate countermeasures when environmental conditions, such as high or low temperatures, high or low humidity, reach dangerous levels. For example, if the temperature reaches 35 degrees Celsius or higher, the suggestion unit can instruct workers to take appropriate breaks. It can also instruct workers to take cold weather precautions if the temperature falls below 0 degrees Celsius. Furthermore, if the humidity reaches 80% or higher, the suggestion unit can encourage workers to rehydrate. This ensures worker safety by proposing appropriate countermeasures when environmental conditions reach dangerous levels. Some or all of the above-described processes in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input environmental data into a generating AI and have the generating AI propose appropriate countermeasures.

[0075] The proposed system can issue a warning if a worker approaches a work area at height or an area where hazardous materials are used. For example, the proposed system will issue a warning if a worker approaches a work area at height of 10 meters or more above the ground. The proposed system can also issue a warning if a worker approaches an area where chemicals are used or other hazardous materials are used. Furthermore, the proposed system can issue a warning and guide the worker to a safe location if they approach a work area at height or an area where hazardous materials are used. This ensures the safety of workers by issuing a warning when they approach a work area at height or an area where hazardous materials are used. Some or all of the above processing in the proposed system may be performed using AI or not. For example, the proposed system can input the worker's location information into a generating AI and have the generating AI issue a warning.

[0076] The data collection unit can estimate the worker's emotions and adjust the frequency of data collection based on the estimated emotions. For example, if a worker is stressed, the data collection unit can increase the frequency of data collection to detect anomalies early. Conversely, if a worker is relaxed, the data collection unit can decrease the frequency of data collection to reduce the system load. Furthermore, if a worker is tired, the data collection unit can appropriately adjust the frequency of data collection to monitor changes in the worker's physical condition. This allows for early detection of anomalies and reduces the system load by adjusting the data collection frequency based on the worker's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input worker emotion data into the generative AI and have the generative AI adjust the data collection frequency.

[0077] The data collection unit can detect abnormal changes in a worker's physical condition by referring to the worker's past health data. For example, the data collection unit can detect abnormal fluctuations in heart rate based on the worker's past health data. It can also detect abnormal fluctuations in body temperature based on the worker's past health data. Furthermore, it can detect abnormal fluctuations in blood pressure based on the worker's past health data. In this way, abnormal changes in physical condition can be detected early by referring to the worker's past health data. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the worker's past health data into a generating AI and have the generating AI perform the detection of abnormal changes in physical condition.

[0078] The data collection unit can monitor noise levels and vibrations at the site in real time and evaluate the safety of the work environment. For example, the data collection unit can monitor noise levels at the site in real time and issue a warning if they exceed acceptable limits. It can also monitor vibration levels at the site in real time and issue a warning if it detects abnormal vibrations. Furthermore, the data collection unit can integrate noise and vibration levels to evaluate the overall safety of the work environment. This allows for the evaluation of work environment safety by monitoring noise levels and vibrations at the site in real time. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input noise level and vibration data from the site into a generating AI and have the generating AI perform an evaluation of work environment safety.

[0079] The data collection unit can estimate the emotions of workers and determine the priority of data to collect based on the estimated emotions. For example, if a worker is stressed, the data collection unit may prioritize the collection of health data. It may also prioritize the collection of environmental data if the worker is relaxed. Furthermore, if the worker is tired, it may prioritize the collection of physical condition data. This prioritizes the collection of important data by determining the priority of data to collect based on the worker's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input worker emotion data into a generative AI and have the generative AI determine the priority of data collection.

[0080] The data collection unit can monitor workers' rest periods and working hours to collect data for preventing overwork. For example, the data collection unit can monitor workers' rest periods and instruct them to take appropriate breaks. It can also monitor workers' working hours and suggest breaks to prevent overwork. Furthermore, the data collection unit can integrate workers' rest periods and working hours to assess the overall risk of overwork. This allows for the collection of data for preventing overwork by monitoring workers' rest periods and working hours. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input data on workers' rest periods and working hours into a generating AI and have the generating AI perform an assessment of the risk of overwork.

[0081] The data collection unit can monitor the air quality and hazardous substance concentrations at a site and assess health risks. For example, the data collection unit can monitor the air quality at the site in real time and issue a warning if it detects abnormal concentrations of hazardous substances. It can also monitor the concentration of hazardous substances at the site in real time and issue a warning if it exceeds acceptable limits. Furthermore, the data collection unit can integrate air quality and hazardous substance concentrations to assess overall health risks. This allows for the assessment of health risks by monitoring the air quality and hazardous substance concentrations at the site. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input site air quality and hazardous substance concentration data into a generating AI and have the generating AI perform a health risk assessment.

[0082] The analysis unit can estimate the worker's emotions and adjust the display method of the analysis results based on the estimated worker's emotions. For example, if the worker is tense, the analysis unit can provide a simple and highly visible display method. It can also provide a display method that includes detailed information if the worker is relaxed. Furthermore, if the worker is in a hurry, the analysis unit can provide a concise display method. This allows for a highly visible display method by adjusting the display method of the analysis results based on the worker's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input worker emotion data into the generative AI and have the generative AI adjust the display method of the analysis results.

[0083] The analysis unit can predict similar hazardous situations by referring to past accident data. For example, the analysis unit predicts similar hazardous situations based on past accident data. The analysis unit can also identify hazardous work patterns based on past accident data. Furthermore, the analysis unit can identify hazardous environmental conditions based on past accident data. This allows the analysis unit to predict similar hazardous situations by referring to past accident data. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input past accident data into a generating AI and have the generating AI perform predictions of similar hazardous situations.

[0084] The analysis unit can integrate worker health data and environmental data to perform a comprehensive risk assessment. For example, the analysis unit can integrate worker health data and environmental data to perform a comprehensive risk assessment. The analysis unit can also predict dangerous situations based on worker health data and environmental data. Furthermore, the analysis unit can propose appropriate countermeasures based on worker health data and environmental data. In this way, a comprehensive risk assessment is performed by integrating worker health data and environmental data. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input worker health data and environmental data into a generating AI and have the generating AI perform a comprehensive risk assessment.

[0085] The analysis unit can estimate the worker's emotions and determine the priority of analysis based on the estimated worker's emotions. For example, if the worker is stressed, the analysis unit may prioritize the analysis of health data. It may also prioritize the analysis of environmental data if the worker is relaxed. Furthermore, if the worker is tired, it may prioritize the analysis of physical condition data. This allows for the prioritization of important data by determining the priority of analysis based on the worker's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input worker emotion data into a generative AI and have the generative AI determine the priority of analysis.

[0086] The analysis unit can perform risk assessments while considering the skill levels and experience of the workers. For example, the analysis unit can perform risk assessments while considering the skill levels of the workers. The analysis unit can also perform risk assessments while considering the experience of the workers. Furthermore, the analysis unit can integrate the skill levels and experience of the workers to perform a comprehensive risk assessment. This allows for a more accurate risk assessment by considering the skill levels and experience of the workers. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input worker skill level and experience data into a generating AI and have the generating AI perform the risk assessment.

[0087] The analysis unit can analyze on-site weather data and assess the risks associated with weather changes. For example, the analysis unit can analyze on-site weather data and assess the risks associated with weather changes. The analysis unit can also predict dangerous situations based on the weather data. Furthermore, the analysis unit can propose appropriate countermeasures based on the weather data. In this way, the risks associated with weather changes are assessed by analyzing on-site weather data. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input on-site weather data into a generating AI and have the generating AI perform a risk assessment due to weather changes.

[0088] The suggestion unit can estimate the worker's emotions and adjust the suggestion content based on the estimated emotions. For example, if the worker is tense, the suggestion unit can provide simple and easy-to-understand suggestions. If the worker is relaxed, the suggestion unit can also provide suggestions that include detailed information. Furthermore, if the worker is in a hurry, the suggestion unit can provide suggestions that get straight to the point. In this way, by adjusting the suggestion content based on the worker's emotions, it provides highly easy-to-understand suggestions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input worker emotion data into a generative AI and have the generative AI perform the adjustment of the suggestion content.

[0089] The proposal unit can propose the optimal countermeasure by referring to past countermeasure data. For example, the proposal unit can propose the optimal countermeasure based on past countermeasure data. The proposal unit can also identify effective countermeasures based on past countermeasure data. Furthermore, the proposal unit can propose appropriate countermeasures based on past countermeasure data. In this way, the optimal countermeasure is proposed by referring to past countermeasure data. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit can input past countermeasure data into a generating AI and have the generating AI execute a proposal for the optimal countermeasure.

[0090] The proposal department can propose specific countermeasures based on the worker's skill level and experience. For example, the proposal department can propose specific countermeasures based on the worker's skill level. Furthermore, the proposal department can propose specific countermeasures based on the worker's experience. In addition, the proposal department can integrate the worker's skill level and experience to propose comprehensive countermeasures. This ensures that appropriate countermeasures are implemented by proposing specific countermeasures based on the worker's skill level and experience. Some or all of the above-described processes in the proposal department may be performed using AI, or not. For example, the proposal department can input worker skill level and experience data into a generating AI and have the generating AI propose specific countermeasures.

[0091] The suggestion unit can estimate the worker's emotions and determine the priority of suggestions based on the estimated emotions. For example, if a worker is stressed, the suggestion unit may prioritize suggestions related to health data. It may also prioritize suggestions related to environmental data if the worker is relaxed. Furthermore, if the worker is tired, it may prioritize suggestions related to physical condition data. This allows for prioritizing important suggestions based on the worker's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input worker emotion data into a generative AI and have the generative AI determine the priority of suggestions.

[0092] The proposal department can propose the optimal timing for countermeasures, taking into account the on-site work schedule. For example, the proposal department can propose the optimal timing for countermeasures, taking into account the on-site work schedule. The proposal department can also identify effective timing for countermeasures based on the work schedule. Furthermore, the proposal department can propose appropriate timing for countermeasures based on the work schedule. In this way, by taking into account the on-site work schedule, the optimal timing for countermeasures is proposed. Some or all of the above processes in the proposal department may be performed using AI or not. For example, the proposal department can input on-site work schedule data into a generating AI and have the generating AI execute a proposal for the optimal timing for countermeasures.

[0093] The proposal department can propose health management measures, taking into account the health status of the workers. For example, the proposal department can propose health management measures, taking into account the health status of the workers. The proposal department can also identify effective health management measures based on the health status. Furthermore, the proposal department can propose appropriate health management measures based on the health status. In this way, appropriate health management measures are proposed by taking into account the health status of the workers. Some or all of the above processing in the proposal department may be performed using AI or not. For example, the proposal department can input worker health status data into a generating AI and have the generating AI execute proposals for health management measures.

[0094] The tracking unit can detect abnormal movements by referring to the worker's past movement history. For example, the tracking unit can detect abnormal movement patterns based on the worker's past movement history. The tracking unit can also detect approach to dangerous areas based on the worker's past movement history. Furthermore, the tracking unit can detect unusual movements based on the worker's past movement history. This allows for early detection of abnormal movements by referring to the worker's past movement history. Some or all of the above processing in the tracking unit may be performed using AI or not. For example, the tracking unit can input the worker's past movement history data into a generating AI and have the generating AI perform abnormal movement detection.

[0095] The tracking unit can analyze the worker's movement patterns and predict dangerous actions. For example, the tracking unit can analyze the worker's movement patterns and predict dangerous actions. The tracking unit can also identify dangerous work procedures based on the movement patterns. Furthermore, the tracking unit can propose appropriate countermeasures based on the movement patterns. In this way, dangerous actions can be predicted by analyzing the worker's movement patterns. Some or all of the above processing in the tracking unit may be performed using AI or not. For example, the tracking unit can input worker movement pattern data into a generating AI and have the generating AI perform the prediction of dangerous actions.

[0096] The tracking unit can estimate the worker's emotions and adjust the display method of the tracking results based on the estimated worker's emotions. For example, if the worker is tense, the tracking unit can provide a simple and highly visible display method. It can also provide a display method that includes detailed information if the worker is relaxed. Furthermore, if the worker is in a hurry, the tracking unit can provide a concise display method. This provides a highly visible display method by adjusting the display method of the tracking results based on the worker's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the tracking unit may be performed using AI or not. For example, the tracking unit can input worker emotion data into the generative AI and have the generative AI adjust the display method of the tracking results.

[0097] The tracking unit can monitor workers' rest periods and working hours and collect data to prevent overwork. For example, the tracking unit can monitor workers' rest periods and instruct them to take appropriate breaks. It can also monitor workers' working hours and suggest breaks to prevent overwork. Furthermore, the tracking unit can integrate workers' rest periods and working hours to assess the overall risk of overwork. This allows for the collection of data to prevent overwork by monitoring workers' rest periods and working hours. Some or all of the above processing in the tracking unit may be performed using AI or not. For example, the tracking unit can input data on workers' rest periods and working hours into a generating AI and have the generating AI perform an assessment of the risk of overwork.

[0098] The tracking unit can analyze the worker's movement patterns and propose efficient work procedures. For example, the tracking unit can analyze the worker's movement patterns and propose efficient work procedures. The tracking unit can also identify inefficiencies in the work based on the movement patterns. Furthermore, the tracking unit can propose appropriate work procedures based on the movement patterns. In this way, by analyzing the worker's movement patterns, efficient work procedures are proposed. Some or all of the above processing in the tracking unit may be performed using AI or not. For example, the tracking unit can input worker movement pattern data into a generating AI and have the generating AI propose efficient work procedures.

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

[0100] The analysis unit can estimate a worker's emotions and evaluate their stress level based on those emotions. For example, if a worker is experiencing high stress, the analysis unit evaluates their stress level and suggests appropriate countermeasures. If a worker is relaxed, the analysis unit can also suggest ways to maintain that state. Furthermore, if a worker is feeling fatigued, the analysis unit can evaluate their fatigue level and suggest taking a break. In this way, by evaluating stress levels based on workers' emotions and suggesting appropriate countermeasures, the health and safety of workers can be ensured.

[0101] The data collection unit can evaluate the efficiency of work by referring to the worker's past work history. For example, the data collection unit can identify inefficiencies in work based on the worker's past work history. Furthermore, the data collection unit can propose more efficient work procedures based on past work history. In addition, the data collection unit can identify areas for improvement in work based on past work history. This allows for the evaluation of work efficiency and the suggestion of improvements by referring to the worker's past work history.

[0102] The proposal department can estimate the emotions of workers and make suggestions for improving the work environment based on those estimates. For example, if a worker is feeling stressed, the proposal department will suggest improvements to the work environment. If a worker is relaxed, the proposal department can also make suggestions to maintain that state. Furthermore, if a worker is feeling fatigued, the proposal department can suggest taking a break. In this way, by making suggestions for improving the work environment based on the workers' emotions, the health and safety of the workers can be ensured.

[0103] The analysis unit can perform risk assessments of tasks while taking into account the skill levels of the workers. For example, the analysis unit can identify high-risk tasks based on the skill levels of the workers. Furthermore, the analysis unit can perform risk assessments tailored to skill levels. In addition, the analysis unit can propose appropriate countermeasures while considering skill levels. This allows for more accurate risk assessments and the proposal of appropriate countermeasures by taking into account the skill levels of the workers.

[0104] The data collection unit can estimate the worker's emotions and determine the priority of data collection based on those estimates. For example, if a worker is stressed, the unit will prioritize collecting health data. If a worker is relaxed, it can prioritize collecting environmental data. Furthermore, if a worker is tired, it can prioritize collecting physical condition data. By prioritizing data collection based on the worker's emotions, important data can be collected preferentially.

[0105] The proposal department can make health management suggestions by referring to workers' past health data. For example, the proposal department can propose appropriate health management measures based on workers' past health data. It can also identify effective health management methods based on past health data. Furthermore, it can assess health risks based on past health data and propose appropriate countermeasures. In this way, appropriate health management suggestions can be made by referring to workers' past health data.

[0106] The analysis unit can estimate the worker's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the worker is tense, the analysis unit provides a simple and easy-to-read display method. If the worker is relaxed, it can also provide a display method that includes detailed information. Furthermore, if the worker is in a hurry, it can provide a display method that focuses on the essentials. In this way, by adjusting the display method of the analysis results based on the worker's emotions, a highly easy-to-read display method can be provided.

[0107] The collection unit can monitor the air quality and hazardous substance concentrations at the site and assess health risks. For example, the collection unit can monitor the air quality at the site in real time and issue a warning if it detects abnormal concentrations of hazardous substances. It can also monitor the concentration of hazardous substances at the site in real time and issue a warning if it exceeds acceptable limits. Furthermore, it can integrate air quality and hazardous substance concentrations to assess overall health risks. In this way, health risks can be assessed by monitoring the air quality and hazardous substance concentrations at the site.

[0108] The proposal department can estimate the worker's emotions and adjust the proposal content based on those estimates. For example, if the worker is stressed, the proposal department can provide a simple and easy-to-understand proposal. If the worker is relaxed, it can provide a proposal that includes more detailed information. Furthermore, if the worker is in a hurry, it can provide a proposal that gets straight to the point. In this way, by adjusting the proposal content based on the worker's emotions, it is possible to provide a highly understandable proposal.

[0109] The tracking unit can detect abnormal movements by referring to the worker's past movement history. For example, the tracking unit can detect abnormal movement patterns based on the worker's past movement history. It can also detect approach to dangerous areas based on past movement history. Furthermore, it can detect unusual movements based on past movement history. As a result, abnormal movements can be detected early by referring to the worker's past movement history.

[0110] The following briefly describes the processing flow for example form 2.

[0111] Step 1: The data collection unit collects environmental data from the construction site using sensors. The data collection unit monitors the operating status and location of heavy machinery such as cranes, excavators, and forklifts using sensors attached to the machinery. It also tracks the location and body movements of workers in real time using wearable sensors worn by workers and sensors attached to their helmets. Furthermore, it ensures a safe working environment using cameras that provide an overview of the site and stationary sensors that monitor specific areas. Step 2: The analysis unit analyzes the data collected by the data collection unit to detect dangerous situations. The analysis unit detects dangerous situations by considering environmental data, worker health data, weather data, etc. For example, if environmental conditions such as high or low temperature, high or low humidity reach dangerous levels, it will propose appropriate countermeasures. It also analyzes the worker's location information and body movements to detect dangerous actions and positions. Step 3: The proposal department proposes countermeasures based on the hazardous conditions detected by the analysis department. The proposal department proposes appropriate countermeasures when environmental conditions such as high or low temperatures, high or low humidity reach dangerous levels. It also issues warnings when workers approach high-altitude work areas or areas where hazardous materials are used. Step 4: The tracking unit tracks the worker's location and body movements. The tracking unit uses wearable sensors worn by the worker and sensors attached to their helmet to track the worker's location and body movements in real time. It can also analyze the worker's movement patterns and predict dangerous actions.

[0112] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0113] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0114] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0115] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, and tracking unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit uses the sensors of the smart device 14 to collect environmental data from the construction site and tracks the location information and body movements of workers in real time. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected data to detect dangerous situations. The proposal unit is implemented by the identification processing unit 290 of the data processing unit 12 and proposes appropriate countermeasures based on the analysis results. The tracking unit is implemented by the control unit 46A of the smart device 14 and analyzes the movement patterns of workers and predicts dangerous movements. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0116] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0117] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0118] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0119] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0120] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0122] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0123] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0124] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0125] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0126] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0127] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0128] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0129] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0130] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0131] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, and tracking unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit uses the sensors of the smart glasses 214 to collect environmental data from the construction site and tracks the location information and body movements of workers in real time. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected data to detect dangerous situations. The proposal unit is implemented by the identification processing unit 290 of the data processing unit 12 and proposes appropriate countermeasures based on the analysis results. The tracking unit is implemented by the control unit 46A of the smart glasses 214 and analyzes the worker's movement patterns and predicts dangerous movements. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0132] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0133] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0134] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0135] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0136] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0137] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0138] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0139] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0140] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0141] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0142] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0143] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0144] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0145] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0146] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0147] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, and tracking unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit uses the sensors of the headset terminal 314 to collect environmental data from the construction site and tracks the location information and body movements of workers in real time. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected data to detect dangerous situations. The proposal unit is implemented by the identification processing unit 290 of the data processing unit 12 and proposes appropriate countermeasures based on the analysis results. The tracking unit is implemented by the control unit 46A of the headset terminal 314 and analyzes the movement patterns of workers and predicts dangerous movements. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0148] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0149] As shown in Figure 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.

[0150] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0151] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0152] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0153] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0154] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0155] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0156] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0157] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0158] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0159] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0160] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0161] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0162] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0163] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0164] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, and tracking unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the data collection unit uses the sensors of the robot 414 to collect environmental data from the construction site and tracks the location information and body movements of workers in real time. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected data to detect dangerous situations. The proposal unit is implemented by the identification processing unit 290 of the data processing unit 12 and proposes appropriate countermeasures based on the analysis results. The tracking unit is implemented by the control unit 46A of the robot 414 and analyzes the movement patterns of workers and predicts dangerous movements. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0165] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0166] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0167] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0168] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0169] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0170] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0171] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0172] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0173] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0174] 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.

[0175] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0176] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0177] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0178] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0179] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0180] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0181] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0182] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0183] (Note 1) A collection unit that collects environmental data from the construction site using sensors, An analysis unit analyzes the data collected by the aforementioned collection unit and detects dangerous situations, Based on the dangerous situation detected by the analysis unit, the proposal unit proposes countermeasures, It includes a tracking unit that tracks the location information and body movements of workers. A system characterized by the following features. (Note 2) The aforementioned collection unit is Sensors attached to heavy machinery such as cranes, excavators, and forklifts are used to monitor the operating status and position of the machines. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned collection unit is The system uses wearable sensors worn by workers and sensors attached to their helmets to track their location and body movements in real time. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is We ensure a safe working environment by using cameras that provide an overview of the site and stationary sensors that monitor specific areas. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit, Dangerous situations are detected by considering environmental data, worker health data, weather data, etc. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, If environmental conditions such as high or low temperatures, high or low humidity reach dangerous levels, we will propose appropriate countermeasures. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned proposal section is, A warning will be issued if a worker approaches an area where work is being done at height or an area where hazardous materials are being used. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is The system estimates the workers' emotions and adjusts the frequency of data collection based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is By referring to the worker's past health data, abnormal changes in physical condition are detected. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is The system monitors noise levels and vibrations at the work site in real time to assess the safety of the work environment. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is The system estimates the emotions of the workers and prioritizes the data to be collected based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is Monitor workers' break times and working hours to collect data to prevent overwork. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is Monitor the air quality and concentration of harmful substances at the site and assess the health risks. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, The system estimates the emotions of the workers and adjusts the display method of the analysis results based on the estimated emotions of the workers. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, By referring to past accident data, we can predict similar hazardous situations. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, Integrate worker health data and environmental data to conduct a comprehensive risk assessment. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, The system estimates the emotions of the workers and determines the priority of analysis based on the estimated emotions of the workers. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, Risk assessment is conducted taking into account the skill level and experience of the workers. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, Analyze on-site weather data to assess risks associated with weather changes. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, The system estimates the emotions of the workers and adjusts the proposals based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, We will propose the optimal countermeasures by referring to past countermeasure data. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, We will propose specific countermeasures based on the skill level and experience of the workers. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, The system estimates the workers' emotions and prioritizes proposals based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, We will propose the optimal timing for taking action, taking into consideration the on-site work schedule. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, We will propose health management measures, taking into consideration the health status of the workers. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned tracking unit is The system detects abnormal movements by referring to the worker's past movement history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned tracking unit is Analyze worker movement patterns to predict dangerous actions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned tracking unit is The system estimates the worker's emotions and adjusts how tracking results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned tracking unit is Monitor workers' break times and working hours to collect data to prevent overwork. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned tracking unit is We analyze workers' movement patterns and propose efficient work procedures. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0184] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that collects environmental data from the construction site using sensors, An analysis unit analyzes the data collected by the aforementioned collection unit and detects dangerous situations, Based on the dangerous situation detected by the analysis unit, the proposal unit proposes countermeasures, It includes a tracking unit that tracks the location information and body movements of workers. A system characterized by the following features.

2. The aforementioned collection unit is Sensors attached to heavy machinery such as cranes, excavators, and forklifts are used to monitor the operating status and position of the machines. The system according to feature 1.

3. The aforementioned collection unit is The system uses wearable sensors worn by workers and sensors attached to their helmets to track their location and body movements in real time. The system according to feature 1.

4. The aforementioned collection unit is We ensure a safe working environment by using cameras that provide an overview of the site and stationary sensors that monitor specific areas. The system according to feature 1.

5. The aforementioned analysis unit, Dangerous situations are detected by considering environmental data, worker health data, weather data, etc. The system according to feature 1.

6. The aforementioned proposal section is, If environmental conditions such as high or low temperatures, high or low humidity reach dangerous levels, we will propose appropriate countermeasures. The system according to feature 1.

7. The aforementioned proposal section is, A warning will be issued if a worker approaches an area where work is being done at height or an area where hazardous materials are being used. The system according to feature 1.

8. The aforementioned collection unit is The system estimates the workers' emotions and adjusts the frequency of data collection based on the estimated emotions. The system according to feature 1.

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