system

Abstract

The system according to this embodiment aims to identify hazardous areas in the workplace and quickly propose appropriate countermeasures. [Solution] The system according to the embodiment comprises a collection unit, a health information collection unit, an analysis unit, a proposal unit, and a provision unit. The collection unit collects information from the work site. The health information collection unit collects health information of workers. The analysis unit analyzes the information collected by the collection unit and the health information collection unit to identify hazardous areas. The proposal unit proposes countermeasures based on the hazardous areas identified by the analysis unit. The provision unit provides the countermeasures proposed by the proposal unit to the workers at the site.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including 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 prior art, there was a problem that it was difficult to identify dangerous locations at the workplace and quickly propose appropriate countermeasures.

[0005] The system according to the embodiment aims to identify dangerous locations at the workplace and quickly propose appropriate countermeasures.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, a health information collection unit, an analysis unit, a proposal unit, and a provision unit. The collection unit collects information from the work site. The health information collection unit collects health information from workers. The analysis unit analyzes the information collected by the collection unit and the health information collection unit to identify hazardous areas. The proposal unit proposes countermeasures based on the hazardous areas identified by the analysis unit. The provision unit provides the countermeasures proposed by the proposal unit to the workers at the work site. [Effects of the Invention]

[0007] The system according to this embodiment can identify hazardous areas in the workplace and quickly propose appropriate 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 applicable 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 receiving 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 receiving 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) The safety measures system according to an embodiment of the present invention is an innovative solution that dramatically improves workplace safety. This safety measures system instantly analyzes common mistakes and hazards that senior workers are prone to by taking and submitting images of the work site, and visually proposes safety measures. This service improves workplace safety, reduces accidents involving senior workers, who currently account for about half of all industrial accidents, and allows them to work with peace of mind. Specifically, it consists of the following steps. First, the safety measures system inputs information about the work site in the form of drawings and photographs. Next, the safety measures system inputs the health status of the elderly workers working there (motor skills, organ conditions such as vision and hearing, etc.). Based on this information, the safety measures system uses AI to analyze hazards in the work site and identify common mistakes and hazards that senior workers are prone to. For example, the safety measures system identifies areas with a high risk of falls and proposes warnings and countermeasures. Furthermore, the safety measures system's AI has learned from past industrial accident cases and countermeasures, giving it strong skills in improving work sites. As a result, the safety measures system presents the optimal countermeasure plan according to the situation at the site. For example, the safety measures system proposes measures such as warnings about footing, countermeasures for areas prone to collisions, and measures against temperature and humidity. Through this mechanism, the safety measures system can improve workplace safety and reduce accidents involving senior workers. In this way, the safety measures system will create an environment where all generations can work with peace of mind and will greatly contribute to solving problems in Japan's labor market. In this way, the safety measures system can dramatically improve workplace safety and reduce accidents involving senior workers.

[0029] The safety measures system according to this embodiment comprises a collection unit, a health information collection unit, an analysis unit, a proposal unit, and a provision unit. The collection unit collects information from the workplace. For example, the collection unit collects drawings and photographs of the workplace. For example, the collection unit can take a panoramic photograph of the workplace to obtain detailed information. The collection unit can also collect floor plans and elevations of the workplace. For example, the collection unit can scan a floor plan of the workplace and save it as digital data. Furthermore, the collection unit can also collect information on the condition of equipment and work procedures at the workplace. For example, the collection unit can record the condition of equipment with photographs and record work procedures with videos. The health information collection unit collects health information of workers. For example, the health information collection unit collects health information such as the workers' physical ability and their vision and hearing. For example, the health information collection unit can measure the workers' heart rate and blood pressure and collect data. The health information collection unit can also examine the workers' vision and hearing and collect data. For example, the health information collection unit conducts vision tests and records vision data. Furthermore, the health information collection unit can also measure workers' stress levels and collect data. For example, the health information collection unit conducts stress checks and evaluates stress levels. The analysis unit analyzes the information collected by the collection unit and the health information collection unit to identify hazardous areas. For example, the analysis unit identifies areas with a high risk of falls or slips based on the collected information. For example, the analysis unit can evaluate the presence or absence of slippery floors or handrails to identify the risk of falls. The analysis unit can also identify risks in high-altitude work areas based on the collected information. For example, the analysis unit evaluates the presence or absence of safety devices in high-altitude work areas to identify the risk of falls. Furthermore, the analysis unit can also identify risks in the work environment based on the collected information. For example, the analysis unit evaluates the temperature and humidity of the work environment to identify the risk of heatstroke. The proposal unit proposes countermeasures based on the hazardous areas identified by the analysis unit. For example, the proposal unit proposes countermeasures such as warnings about footing, measures for areas prone to collisions, and measures against temperature and humidity. The proposal department could, for example, suggest installing non-slip mats on slippery floors. They could also suggest installing handrails.For example, the proposal department proposes installing handrails in high-altitude work areas. Furthermore, the proposal department can also propose improvements to the work environment. For example, the proposal department proposes installing air conditioning equipment to regulate the temperature and humidity of the work environment. The supply department provides the proposed countermeasures plan to the workers on site. The supply department provides the countermeasures plan visually, for example. The supply department can provide the countermeasures plan using, for example, a display or projection mapping. The supply department can also provide the countermeasures plan using voice guidance. For example, the supply department explains the countermeasures plan to the workers using voice guidance. Furthermore, the supply department can provide the countermeasures plan using digital displays. For example, the supply department displays the countermeasures plan using a tablet or smartphone. As a result, the safety countermeasures system according to this embodiment can dramatically improve workplace safety and reduce accidents involving senior workers.

[0030] The data collection department collects information from the work site. For example, it collects drawings and photographs of the work site. Specifically, it can take panoramic photographs of the work site to obtain detailed information. This provides basic data for visually confirming the overall layout of the site and identifying potential hazards. The data collection department can also collect floor plans and elevations of the work site. For example, it can scan floor plans of the work site and save them as digital data. This allows for an accurate understanding of the site layout and equipment placement, enabling the creation of efficient work plans. Furthermore, the data collection department can collect information on the condition of equipment and work procedures at the work site. For example, it can record the condition of equipment with photographs and work procedures with videos. This allows for a detailed understanding of equipment maintenance status and work flow, providing information for taking appropriate measures. The data collection department centrally manages this data and can link it with other systems and departments as needed. For example, the collected data can be stored on a cloud server and made accessible to the analysis department and the proposal department. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The Health Information Collection Department collects health information from workers. For example, it collects health information such as workers' physical abilities and their vision and hearing. Specifically, it can measure and collect data on workers' heart rate and blood pressure. This allows for real-time monitoring of workers' health status and prompt response if abnormalities are detected. The Health Information Collection Department can also examine and collect data on workers' vision and hearing. For example, it conducts vision tests and records vision data. This allows for appropriate measures to be taken for workers with vision problems. Furthermore, the Health Information Collection Department can measure and collect data on workers' stress levels. For example, it conducts stress checks and evaluates stress levels. This allows for appropriate support to be provided to workers with high stress levels, leading to improvements in the work environment. The Health Information Collection Department centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible to the Analysis Department and the Proposal Department. Adjusting the frequency and accuracy of data collection allows for flexible responses to specific situations and conditions. This allows the health information collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0032] The analysis unit analyzes information collected by the data collection unit and the health information collection unit to identify hazardous areas. For example, based on the collected information, the analysis unit can identify areas with a high risk of falls. Specifically, it can evaluate the presence or absence of slippery floors or handrails to identify the risk of falls. This makes it possible to take measures to ensure workers can perform their tasks safely. The analysis unit can also identify risks in high-altitude work areas based on the collected information. For example, the analysis unit can evaluate the presence or absence of safety devices in high-altitude work areas to identify the risk of falls. This makes it possible to take measures to ensure the safety of workers performing high-altitude work. Furthermore, the analysis unit can identify risks in the work environment based on the collected information. For example, the analysis unit can evaluate the temperature and humidity of the work environment to identify the risk of heatstroke. This makes it possible to take measures to ensure workers can work comfortably. The analysis unit centrally manages this data and can link with other systems and departments as needed. For example, the collected data is stored on a cloud server and made accessible to the proposal unit. In addition, by adjusting the frequency and accuracy of data analysis, flexible responses can be made according to specific situations and conditions. This allows the analysis unit to analyze data efficiently and effectively, improving the overall system performance.

[0033] The proposal department proposes countermeasures based on the hazardous areas identified by the analysis department. For example, the proposal department might propose measures such as warnings about footing, countermeasures for areas prone to collisions, and measures for temperature and humidity. Specifically, it could propose the installation of non-slip mats on slippery floors, enabling workers to perform their tasks safely. The proposal department could also propose the installation of handrails, such as installing handrails in high-altitude work areas, ensuring the safety of workers performing high-altitude tasks. Furthermore, the proposal department could propose improvements to the work environment, such as installing air conditioning to regulate temperature and humidity, enabling workers to work comfortably. The proposal department centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible to the provision department. Adjusting the frequency and accuracy of data proposals allows for flexible responses to specific situations and conditions. This enables the proposal department to propose data efficiently and effectively, improving the overall system performance.

[0034] The provision department provides the proposed countermeasures plan to on-site workers. The provision department can provide the countermeasures plan visually, for example. Specifically, it can use display screens or projection mapping to provide the countermeasures plan. This makes it easier for workers to visually understand the countermeasures plan. The provision department can also provide the countermeasures plan using audio guidance. For example, the provision department can explain the countermeasures plan to workers using audio guides. This ensures that the countermeasures plan is communicated to workers with visual impairments. Furthermore, the provision department can provide the countermeasures plan using digital displays. For example, the provision department can display the countermeasures plan using tablets or smartphones. This allows workers to check the countermeasures plan anytime, anywhere. The provision department centrally manages this data and can link with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible to the proposal department. By adjusting the frequency and accuracy of data provision, flexible responses can be made to specific situations and conditions. This allows the provision department to provide data efficiently and effectively, improving the overall system performance.

[0035] The data collection unit can collect drawings and photographs of the work site. For example, the data collection unit can collect floor plans and elevation drawings of the work site. For example, the data collection unit can take panoramic photographs of the work site to obtain detailed information. The data collection unit can also collect information on the condition of equipment and work procedures at the work site. For example, the data collection unit can record the condition of equipment with photographs and work procedures with videos. By collecting drawings and photographs of the work site, detailed information about the site can be obtained. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input drawings and photographs of the work site into AI, and the AI ​​can automatically analyze the information.

[0036] The health information collection unit can collect health information such as workers' physical abilities and vision and hearing. For example, the health information collection unit can measure workers' heart rate and blood pressure and collect data. For example, the health information collection unit can examine workers' vision and hearing and collect data. The health information collection unit can also measure workers' stress levels and collect data. For example, the health information collection unit can conduct stress checks and evaluate stress levels. By collecting workers' health information in this way, it is possible to propose safety measures that are appropriate for each individual worker. Some or all of the above processing in the health information collection unit may be performed using AI, for example, or not using AI. For example, the health information collection unit can input workers' health data into AI, and the AI ​​can automatically analyze the data.

[0037] The analysis unit can analyze the collected information and identify areas with a high risk of falls or slips. For example, based on the collected information, the analysis unit can evaluate the presence or absence of slippery floors or handrails to identify the risk of falls. For example, the analysis unit can evaluate the presence or absence of safety devices in high-altitude work areas to identify the risk of slips or slips. The analysis unit can also evaluate the temperature and humidity of the work environment based on the collected information to identify the risk of heatstroke. This ensures the safety of workers by identifying areas with a high risk of falls or slips. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected information into AI, which can automatically identify risks.

[0038] The proposal department can propose countermeasures based on identified hazardous areas, such as warnings about footing, measures to prevent collisions, and measures to control temperature and humidity. For example, the proposal department might propose installing non-slip mats on slippery floors. For example, it might propose installing handrails in high-altitude work areas. The proposal department might also propose installing air conditioning equipment to regulate the temperature and humidity of the work environment. This improves worker safety by proposing countermeasures based on identified hazardous areas. Some or all of the above processing in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can input information on identified hazardous areas into AI, and the AI ​​can automatically propose countermeasures.

[0039] The service provider can visually present the proposed countermeasure plan to on-site workers. For example, the service provider can provide the countermeasure plan using display screens or projection mapping. The service provider can also explain the countermeasure plan to workers using audio guidance. Furthermore, the service provider can display the countermeasure plan using tablets or smartphones. This visual presentation of the countermeasure plan makes it easier for workers to understand. Some or all of the above processes in the service provider may be performed using AI, or not. For example, the service provider can input the proposed countermeasure plan into AI, which can then automatically select a visual display method.

[0040] The data collection unit can optimize the collection range by referring to past accident data when collecting information from the work site. For example, the data collection unit can prioritize collecting data from areas where accidents have frequently occurred in the past. For example, the data collection unit can collect detailed information from specific work areas based on past accident data. The data collection unit can also analyze past accident data and focus on collecting data from high-risk areas. This allows for focused collection of data from high-risk areas by referring to past accident data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past accident data into AI, which can then automatically optimize the collection range.

[0041] The data collection unit can apply different data collection methods depending on the nature of the work at the worksite. For example, in the case of work at height, the data collection unit can use a drone to collect information. For example, in the case of work in a confined space, the data collection unit can use a robot to collect information. Furthermore, in the case of a large-scale worksite, the data collection unit can use multiple cameras to collect information over a wide area. This allows for efficient data collection by applying a data collection method appropriate to the nature of the work. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the details of the work at the worksite into the AI, which can then automatically select the appropriate data collection method.

[0042] The data collection unit can adjust its collection method when collecting information at a work site, taking into account the environmental conditions of the site. For example, if the lighting is dim, the data collection unit can use an infrared camera to collect information. For example, if the noise level is high, the data collection unit can use voice recognition technology to collect information. In addition, if the environment is humid, the data collection unit can use a waterproof camera to collect information. This allows for accurate information to be collected by applying a collection method appropriate to the environmental conditions. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the environmental conditions of the site into the AI, and the AI ​​can automatically select an appropriate collection method.

[0043] The data collection unit can select the optimal timing for information gathering at a work site by referring to the work schedule. For example, the data collection unit can collect information during periods of low workload. For example, it can collect information after work has finished. It can also collect information in between tasks. This allows for efficient information gathering by selecting the timing based on the work schedule. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the work schedule at the site into the AI, which can then automatically select the optimal timing for collection.

[0044] The health information collection unit can optimize the scope of health information collection by referring to the worker's past health data. For example, the health information collection unit can prioritize the collection of specific health information based on the worker's past health data. For example, the health information collection unit can analyze the worker's past health data to collect high-risk health information. The health information collection unit can also adjust the scope of health information to be collected by referring to the worker's past health data. This allows for the focused collection of high-risk health information by referring to past health data. Some or all of the above processing in the health information collection unit may be performed using AI, for example, or without AI. For example, the health information collection unit can input the worker's past health data into AI, which can then automatically optimize the collection scope.

[0045] The health information collection unit can adjust its collection methods when collecting health information, taking into account the workers' lifestyles. For example, the health information collection unit can collect health information regarding nutritional status by considering the workers' eating habits. For example, the health information collection unit can collect health information regarding exercise capacity by considering the workers' exercise habits. Furthermore, the health information collection unit can analyze the workers' lifestyles and collect health information that indicates a high risk. This allows for the collection of accurate health information by applying collection methods tailored to lifestyles. Some or all of the above-described processes in the health information collection unit may be performed using AI, for example, or without AI. For example, the health information collection unit can input workers' lifestyle data into AI, which can then automatically select an appropriate collection method.

[0046] The analysis unit can optimize its analysis algorithm by referring to past industrial accident data during the analysis process. For example, the analysis unit can optimize an algorithm to identify high-risk areas based on past industrial accident data. For example, the analysis unit can optimize an algorithm to identify the causes of industrial accidents by analyzing past industrial accident data. The analysis unit can also optimize an algorithm to propose preventive measures for industrial accidents by referring to past industrial accident data. In this way, the algorithm to identify high-risk areas can be optimized by referring to past industrial accident data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past industrial accident data into AI, and the AI ​​can automatically optimize the analysis algorithm.

[0047] The analysis unit can apply different analysis methods depending on the work content at the work site during the analysis. For example, in the case of work at height, the analysis unit can apply an analysis method specialized for work at height. For example, in the case of work in a confined space, the analysis unit can apply an analysis method specialized for confined spaces. Furthermore, in the case of a large-scale work site, the analysis unit can apply an analysis method specialized for large-scale work. This allows for efficient analysis by applying an analysis method appropriate to the work content. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the work content at the site into the AI, and the AI ​​can automatically select an appropriate analysis method.

[0048] The analysis unit can perform analyses while considering the environmental conditions of the work site. For example, in a high-temperature environment, the analysis unit can consider the risk of heatstroke. For example, in a low-temperature environment, the analysis unit can consider the risk of freezing. Furthermore, in a high-humidity environment, the analysis unit can consider slipperiness. By considering environmental conditions, more accurate analysis results can be obtained. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the environmental conditions of the work site into the AI, and the AI ​​can perform the analysis automatically.

[0049] The analysis unit can perform analysis while considering the worker's attribute information. For example, in the case of elderly workers, the analysis unit can consider the decline in their vision and hearing. For example, in the case of inexperienced workers, the analysis unit can consider the risk of work errors. Furthermore, in the case of experienced workers, the analysis unit can consider work efficiency. In this way, by considering the worker's attribute information, it is possible to provide analysis results that are appropriate for each individual worker. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the worker's attribute information into AI, and the AI ​​can perform the analysis automatically.

[0050] The proposal unit can adjust the level of detail of its proposals based on the importance of the identified hazardous areas. For example, the proposal unit can propose detailed countermeasures for high-importance hazardous areas, and concise countermeasures for low-importance hazardous areas. The proposal unit can also adjust the level of detail of its proposals in stages according to importance. This allows for the provision of optimal countermeasures for workers by adjusting the level of detail of proposals based on the importance of the hazardous areas. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not. For example, the proposal unit can input the importance of the identified hazardous areas into the AI, and the AI ​​can automatically adjust the level of detail of its proposals.

[0051] The proposal unit can apply different proposal algorithms depending on the work content at the work site when making a proposal. For example, in the case of work at height, the proposal unit can apply a proposal algorithm specialized for work at height. For example, in the case of work in a confined space, the proposal unit can apply a proposal algorithm specialized for confined spaces. Furthermore, in the case of a large-scale work site, the proposal unit can apply a proposal algorithm specialized for large-scale work. This allows for efficient proposals by applying a proposal algorithm appropriate to the work content. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the work content at the site into AI, and the AI ​​can automatically select an appropriate proposal algorithm.

[0052] The proposal department can adjust its proposals when making them, taking into account the environmental conditions of the work site. For example, if the lighting is dim, the proposal department can propose lighting improvements. For example, if the noise level is high, the proposal department can propose noise reduction measures. Furthermore, if the environment is humid, the proposal department can propose humidity reduction measures. By providing proposals tailored to the environmental conditions, the department can offer the most optimal solutions for workers. Some or all of the above processing in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input the environmental conditions of the work site into the AI, and the AI ​​can automatically adjust the proposals.

[0053] The proposal department can adjust the content of its proposals by taking into account the worker's attribute information. For example, in the case of an elderly worker, the proposal department can make suggestions that take into account the decline in their vision or hearing. For example, in the case of an inexperienced worker, the proposal department can make suggestions that reduce the risk of work errors. Furthermore, in the case of an experienced worker, the proposal department can make suggestions that improve work efficiency. In this way, by adjusting the content of the proposals based on the worker's attribute information, it is possible to provide suggestions that are suitable for each individual worker. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input the worker's attribute information into the AI, and the AI ​​can automatically adjust the content of the suggestions.

[0054] The service provider can select the optimal service delivery method by referring to the worker's past feedback at the time of delivery. For example, the service provider can prioritize providing the display method that the worker has preferred in the past. For example, the service provider can provide a display method that reflects improvements based on the worker's past feedback. The service provider can also analyze the worker's past feedback and select the optimal service delivery method. This allows the service provider to select the best service delivery method for the worker by referring to past feedback. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the worker's past feedback into AI, and the AI ​​can automatically select the optimal service delivery method.

[0055] The information delivery unit can select the optimal delivery method at the time of delivery, taking into account the worker's device information. For example, if the worker is using a smartphone, the delivery unit can provide a display method that matches the screen size. For example, if the worker is using a tablet, the delivery unit can provide a display method optimized for a larger screen. Furthermore, if the worker is using a smartwatch, the delivery unit can provide a concise and highly visible display method. In this way, by selecting the delivery method based on device information, information can be delivered in the most optimal form for the worker. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input the worker's device information into AI, and the AI ​​can automatically select the optimal delivery method.

[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 safety system can also be equipped with a real-time monitoring unit. This unit can monitor the work environment in real time and issue immediate warnings if an anomaly is detected. For example, it can track workers' movements and issue warnings if the risk of falls or slips increases. It can also monitor the temperature and humidity of the work environment and issue warnings if the risk of heatstroke increases. Furthermore, it can detect abnormal machine operation and suggest machine shutdown or maintenance. This real-time monitoring and warning system can further improve worker safety.

[0058] The safety measures system can also be equipped with a predictive analytics unit. This unit can predict future risks based on collected data and take preventative measures. For example, it can predict accidents that are more likely to occur during specific seasons or times of day based on past data and propose countermeasures. It can also predict periods when specific workers are more prone to fatigue or stress based on worker health data and propose breaks or work adjustments. Furthermore, it can predict the risk of machine failure based on machine usage data and propose maintenance in advance. This allows for further improvements in worker safety through proactive measures based on predictive analytics.

[0059] The safety measures system can also include a communication section. This section provides functions to facilitate communication between workers and between workers and managers. For example, it can provide a chat function for workers to report hazardous areas. It can also provide a messaging function for managers to send safety instructions to workers in real time. Furthermore, it can provide a survey function for workers to provide feedback on safety measures. This strengthens communication between workers and managers and enhances the effectiveness of safety measures.

[0060] The safety measures system can also include an education and training department. This department provides functions for offering safety education and training to workers. For example, it can offer online courses for workers to learn about safety measures. It can also provide simulation training for workers to practice safety measures in actual work environments. Furthermore, it can provide a news feed to ensure workers stay up-to-date on the latest safety information. This helps to raise workers' safety awareness and prevent accidents.

[0061] The safety measures system may also include a feedback collection unit. This unit can collect feedback from workers and use it to improve the system. For example, the feedback collection unit can provide forms for workers to submit opinions and suggestions regarding safety measures. It can also provide questionnaires for workers to evaluate the effectiveness of specific measures. Furthermore, the feedback collection unit can provide a function for workers to provide feedback anonymously. This allows for system improvements that reflect worker opinions, thereby enhancing the effectiveness of safety measures.

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

[0063] Step 1: The collection unit gathers information from the work site. For example, it collects drawings and photographs of the work site, panoramic photos, floor plans and elevations, equipment status, and work procedures. This allows for a detailed understanding of the work site. Step 2: The health information collection department collects health information about workers. For example, it collects data on workers' physical abilities, vision and hearing, heart rate and blood pressure, visual acuity and hearing, and stress levels. This allows for a detailed understanding of the workers' health status. Step 3: The analysis unit analyzes the information collected by the data collection unit and the health information collection unit to identify hazardous areas. For example, it evaluates areas with a high risk of falls, the presence or absence of slippery floors or handrails, the risks in high-altitude work areas, and the temperature and humidity of the work environment to identify risks. Step 4: The proposal department proposes countermeasures based on the hazardous areas identified by the analysis department. For example, they may propose countermeasures such as warnings about footing, installation of non-slip mats, installation of handrails, and adjustment of the temperature and humidity of the work environment. Step 5: The provision department provides the proposed countermeasures plan to the workers on site. For example, the countermeasures plan may be provided using display screens, projection mapping, audio guidance, tablets, or smartphones.

[0064] (Example of form 2) The safety measures system according to an embodiment of the present invention is an innovative solution that dramatically improves workplace safety. This safety measures system instantly analyzes common mistakes and hazards that senior workers are prone to by taking and submitting images of the work site, and visually proposes safety measures. This service improves workplace safety, reduces accidents involving senior workers, who currently account for about half of all industrial accidents, and allows them to work with peace of mind. Specifically, it consists of the following steps. First, the safety measures system inputs information about the work site in the form of drawings and photographs. Next, the safety measures system inputs the health status of the elderly workers working there (motor skills, organ conditions such as vision and hearing, etc.). Based on this information, the safety measures system uses AI to analyze hazards in the work site and identify common mistakes and hazards that senior workers are prone to. For example, the safety measures system identifies areas with a high risk of falls and proposes warnings and countermeasures. Furthermore, the safety measures system's AI has learned from past industrial accident cases and countermeasures, giving it strong skills in improving work sites. As a result, the safety measures system presents the optimal countermeasure plan according to the situation at the site. For example, the safety measures system proposes measures such as warnings about footing, countermeasures for areas prone to collisions, and measures against temperature and humidity. Through this mechanism, the safety measures system can improve workplace safety and reduce accidents involving senior workers. In this way, the safety measures system will create an environment where all generations can work with peace of mind and will greatly contribute to solving problems in Japan's labor market. In this way, the safety measures system can dramatically improve workplace safety and reduce accidents involving senior workers.

[0065] The safety measures system according to this embodiment comprises a collection unit, a health information collection unit, an analysis unit, a proposal unit, and a provision unit. The collection unit collects information from the workplace. For example, the collection unit collects drawings and photographs of the workplace. For example, the collection unit can take a panoramic photograph of the workplace to obtain detailed information. The collection unit can also collect floor plans and elevations of the workplace. For example, the collection unit can scan a floor plan of the workplace and save it as digital data. Furthermore, the collection unit can also collect information on the condition of equipment and work procedures at the workplace. For example, the collection unit can record the condition of equipment with photographs and record work procedures with videos. The health information collection unit collects health information of workers. For example, the health information collection unit collects health information such as the workers' physical ability and their vision and hearing. For example, the health information collection unit can measure the workers' heart rate and blood pressure and collect data. The health information collection unit can also examine the workers' vision and hearing and collect data. For example, the health information collection unit conducts vision tests and records vision data. Furthermore, the health information collection unit can also measure workers' stress levels and collect data. For example, the health information collection unit conducts stress checks and evaluates stress levels. The analysis unit analyzes the information collected by the collection unit and the health information collection unit to identify hazardous areas. For example, the analysis unit identifies areas with a high risk of falls or slips based on the collected information. For example, the analysis unit can evaluate the presence or absence of slippery floors or handrails to identify the risk of falls. The analysis unit can also identify risks in high-altitude work areas based on the collected information. For example, the analysis unit evaluates the presence or absence of safety devices in high-altitude work areas to identify the risk of falls. Furthermore, the analysis unit can also identify risks in the work environment based on the collected information. For example, the analysis unit evaluates the temperature and humidity of the work environment to identify the risk of heatstroke. The proposal unit proposes countermeasures based on the hazardous areas identified by the analysis unit. For example, the proposal unit proposes countermeasures such as warnings about footing, measures for areas prone to collisions, and measures against temperature and humidity. The proposal department could, for example, suggest installing non-slip mats on slippery floors. They could also suggest installing handrails.For example, the proposal department proposes installing handrails in high-altitude work areas. Furthermore, the proposal department can also propose improvements to the work environment. For example, the proposal department proposes installing air conditioning equipment to regulate the temperature and humidity of the work environment. The supply department provides the proposed countermeasures plan to the workers on site. The supply department provides the countermeasures plan visually, for example. The supply department can provide the countermeasures plan using, for example, a display or projection mapping. The supply department can also provide the countermeasures plan using voice guidance. For example, the supply department explains the countermeasures plan to the workers using voice guidance. Furthermore, the supply department can provide the countermeasures plan using digital displays. For example, the supply department displays the countermeasures plan using a tablet or smartphone. As a result, the safety countermeasures system according to this embodiment can dramatically improve workplace safety and reduce accidents involving senior workers.

[0066] The data collection department collects information from the work site. For example, it collects drawings and photographs of the work site. Specifically, it can take panoramic photographs of the work site to obtain detailed information. This provides basic data for visually confirming the overall layout of the site and identifying potential hazards. The data collection department can also collect floor plans and elevations of the work site. For example, it can scan floor plans of the work site and save them as digital data. This allows for an accurate understanding of the site layout and equipment placement, enabling the creation of efficient work plans. Furthermore, the data collection department can collect information on the condition of equipment and work procedures at the work site. For example, it can record the condition of equipment with photographs and work procedures with videos. This allows for a detailed understanding of equipment maintenance status and work flow, providing information for taking appropriate measures. The data collection department centrally manages this data and can link it with other systems and departments as needed. For example, the collected data can be stored on a cloud server and made accessible to the analysis department and the proposal department. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0067] The Health Information Collection Department collects health information from workers. For example, it collects health information such as workers' physical abilities and their vision and hearing. Specifically, it can measure and collect data on workers' heart rate and blood pressure. This allows for real-time monitoring of workers' health status and prompt response if abnormalities are detected. The Health Information Collection Department can also examine and collect data on workers' vision and hearing. For example, it conducts vision tests and records vision data. This allows for appropriate measures to be taken for workers with vision problems. Furthermore, the Health Information Collection Department can measure and collect data on workers' stress levels. For example, it conducts stress checks and evaluates stress levels. This allows for appropriate support to be provided to workers with high stress levels, leading to improvements in the work environment. The Health Information Collection Department centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible to the Analysis Department and the Proposal Department. Adjusting the frequency and accuracy of data collection allows for flexible responses to specific situations and conditions. This allows the health information collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0068] The analysis unit analyzes information collected by the data collection unit and the health information collection unit to identify hazardous areas. For example, based on the collected information, the analysis unit can identify areas with a high risk of falls. Specifically, it can evaluate the presence or absence of slippery floors or handrails to identify the risk of falls. This makes it possible to take measures to ensure workers can perform their tasks safely. The analysis unit can also identify risks in high-altitude work areas based on the collected information. For example, the analysis unit can evaluate the presence or absence of safety devices in high-altitude work areas to identify the risk of falls. This makes it possible to take measures to ensure the safety of workers performing high-altitude work. Furthermore, the analysis unit can identify risks in the work environment based on the collected information. For example, the analysis unit can evaluate the temperature and humidity of the work environment to identify the risk of heatstroke. This makes it possible to take measures to ensure workers can work comfortably. The analysis unit centrally manages this data and can link with other systems and departments as needed. For example, the collected data is stored on a cloud server and made accessible to the proposal unit. In addition, by adjusting the frequency and accuracy of data analysis, flexible responses can be made according to specific situations and conditions. This allows the analysis unit to analyze data efficiently and effectively, improving the overall system performance.

[0069] The proposal department proposes countermeasures based on the hazardous areas identified by the analysis department. For example, the proposal department might propose measures such as warnings about footing, countermeasures for areas prone to collisions, and measures for temperature and humidity. Specifically, it could propose the installation of non-slip mats on slippery floors, enabling workers to perform their tasks safely. The proposal department could also propose the installation of handrails, such as installing handrails in high-altitude work areas, ensuring the safety of workers performing high-altitude tasks. Furthermore, the proposal department could propose improvements to the work environment, such as installing air conditioning to regulate temperature and humidity, enabling workers to work comfortably. The proposal department centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible to the provision department. Adjusting the frequency and accuracy of data proposals allows for flexible responses to specific situations and conditions. This enables the proposal department to propose data efficiently and effectively, improving the overall system performance.

[0070] The provision department provides the proposed countermeasures plan to on-site workers. The provision department can provide the countermeasures plan visually, for example. Specifically, it can use display screens or projection mapping to provide the countermeasures plan. This makes it easier for workers to visually understand the countermeasures plan. The provision department can also provide the countermeasures plan using audio guidance. For example, the provision department can explain the countermeasures plan to workers using audio guides. This ensures that the countermeasures plan is communicated to workers with visual impairments. Furthermore, the provision department can provide the countermeasures plan using digital displays. For example, the provision department can display the countermeasures plan using tablets or smartphones. This allows workers to check the countermeasures plan anytime, anywhere. The provision department centrally manages this data and can link with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible to the proposal department. By adjusting the frequency and accuracy of data provision, flexible responses can be made to specific situations and conditions. This allows the provision department to provide data efficiently and effectively, improving the overall system performance.

[0071] The data collection unit can collect drawings and photographs of the work site. For example, the data collection unit can collect floor plans and elevation drawings of the work site. For example, the data collection unit can take panoramic photographs of the work site to obtain detailed information. The data collection unit can also collect information on the condition of equipment and work procedures at the work site. For example, the data collection unit can record the condition of equipment with photographs and work procedures with videos. By collecting drawings and photographs of the work site, detailed information about the site can be obtained. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input drawings and photographs of the work site into AI, and the AI ​​can automatically analyze the information.

[0072] The health information collection unit can collect health information such as workers' physical abilities and vision and hearing. For example, the health information collection unit can measure workers' heart rate and blood pressure and collect data. For example, the health information collection unit can examine workers' vision and hearing and collect data. The health information collection unit can also measure workers' stress levels and collect data. For example, the health information collection unit can conduct stress checks and evaluate stress levels. By collecting workers' health information in this way, it is possible to propose safety measures that are appropriate for each individual worker. Some or all of the above processing in the health information collection unit may be performed using AI, for example, or not using AI. For example, the health information collection unit can input workers' health data into AI, and the AI ​​can automatically analyze the data.

[0073] The analysis unit can analyze the collected information and identify areas with a high risk of falls or slips. For example, based on the collected information, the analysis unit can evaluate the presence or absence of slippery floors or handrails to identify the risk of falls. For example, the analysis unit can evaluate the presence or absence of safety devices in high-altitude work areas to identify the risk of slips or slips. The analysis unit can also evaluate the temperature and humidity of the work environment based on the collected information to identify the risk of heatstroke. This ensures the safety of workers by identifying areas with a high risk of falls or slips. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected information into AI, which can automatically identify risks.

[0074] The proposal department can propose countermeasures based on identified hazardous areas, such as warnings about footing, measures to prevent collisions, and measures to control temperature and humidity. For example, the proposal department might propose installing non-slip mats on slippery floors. For example, it might propose installing handrails in high-altitude work areas. The proposal department might also propose installing air conditioning equipment to regulate the temperature and humidity of the work environment. This improves worker safety by proposing countermeasures based on identified hazardous areas. Some or all of the above processing in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can input information on identified hazardous areas into AI, and the AI ​​can automatically propose countermeasures.

[0075] The service provider can visually present the proposed countermeasure plan to on-site workers. For example, the service provider can provide the countermeasure plan using display screens or projection mapping. The service provider can also explain the countermeasure plan to workers using audio guidance. Furthermore, the service provider can display the countermeasure plan using tablets or smartphones. This visual presentation of the countermeasure plan makes it easier for workers to understand. Some or all of the above processes in the service provider may be performed using AI, or not. For example, the service provider can input the proposed countermeasure plan into AI, which can then automatically select a visual display method.

[0076] The data collection unit can estimate the emotions of workers and adjust the timing of information collection at the workplace based on the estimated emotions. For example, if a worker is feeling stressed, the data collection unit can adjust the collection timing to a time when the worker is relaxed. For example, if a worker is tired, the data collection unit can adjust the collection timing to coincide with a break time. The data collection unit can also set the collection timing to after work is finished if the worker is concentrating. This reduces the burden on workers by adjusting the information collection timing based on their emotions. 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 data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input worker emotion data into an AI, which can then automatically adjust the collection timing.

[0077] The data collection unit can optimize the collection range by referring to past accident data when collecting information from the work site. For example, the data collection unit can prioritize collecting data from areas where accidents have frequently occurred in the past. For example, the data collection unit can collect detailed information from specific work areas based on past accident data. The data collection unit can also analyze past accident data and focus on collecting data from high-risk areas. This allows for focused collection of data from high-risk areas by referring to past accident data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past accident data into AI, which can then automatically optimize the collection range.

[0078] The data collection unit can apply different data collection methods depending on the nature of the work at the worksite. For example, in the case of work at height, the data collection unit can use a drone to collect information. For example, in the case of work in a confined space, the data collection unit can use a robot to collect information. Furthermore, in the case of a large-scale worksite, the data collection unit can use multiple cameras to collect information over a wide area. This allows for efficient data collection by applying a data collection method appropriate to the nature of the work. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the details of the work at the worksite into the AI, which can then automatically select the appropriate data collection method.

[0079] The data collection unit can estimate the worker's emotions and determine the priority of information to collect based on the estimated emotions. For example, if a worker is feeling anxious, the data collection unit may prioritize collecting information about hazardous areas. If a worker is tired, the data collection unit may prioritize collecting information about rest areas. If a worker is focused, the data collection unit may also prioritize collecting detailed information about the work area. This allows for the priority collection of important information by prioritizing information 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 an AI, which can then automatically determine the priority of information.

[0080] The data collection unit can adjust its collection method when collecting information at a work site, taking into account the environmental conditions of the site. For example, if the lighting is dim, the data collection unit can use an infrared camera to collect information. For example, if the noise level is high, the data collection unit can use voice recognition technology to collect information. In addition, if the environment is humid, the data collection unit can use a waterproof camera to collect information. This allows for accurate information to be collected by applying a collection method appropriate to the environmental conditions. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the environmental conditions of the site into the AI, and the AI ​​can automatically select an appropriate collection method.

[0081] The data collection unit can select the optimal timing for information gathering at a work site by referring to the work schedule. For example, the data collection unit can collect information during periods of low workload. For example, it can collect information after work has finished. It can also collect information in between tasks. This allows for efficient information gathering by selecting the timing based on the work schedule. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the work schedule at the site into the AI, which can then automatically select the optimal timing for collection.

[0082] The health information collection unit can estimate the worker's emotions and adjust the timing of health information collection based on the estimated emotions. For example, if a worker is stressed, the health information collection unit can collect health information during times when the worker is relaxed. For example, if a worker is tired, the health information collection unit can collect health information during break times. Furthermore, if a worker is concentrating, the health information collection unit can collect health information after work is completed. This reduces the burden on workers by adjusting the timing of health information collection based on their emotions. 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 health information collection unit may be performed using AI, for example, or without AI. For example, the health information collection unit can input worker emotion data into AI, and the AI ​​can automatically adjust the collection timing.

[0083] The health information collection unit can optimize the scope of health information collection by referring to the worker's past health data. For example, the health information collection unit can prioritize the collection of specific health information based on the worker's past health data. For example, the health information collection unit can analyze the worker's past health data to collect high-risk health information. The health information collection unit can also adjust the scope of health information to be collected by referring to the worker's past health data. This allows for the focused collection of high-risk health information by referring to past health data. Some or all of the above processing in the health information collection unit may be performed using AI, for example, or without AI. For example, the health information collection unit can input the worker's past health data into AI, which can then automatically optimize the collection scope.

[0084] The health information collection unit can estimate a worker's emotions and determine the priority of health information to collect based on the estimated emotions. For example, if a worker is feeling anxious, the health information collection unit will prioritize collecting stress-related health information. For example, if a worker is tired, the health information collection unit can prioritize collecting fatigue-related health information. Furthermore, if a worker is focused, the health information collection unit can prioritize collecting health information related to work performance. This allows for the priority collection of important health information by determining the priority of health information 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 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 health information collection unit may be performed using AI, for example, or without AI. For example, the health information collection unit can input worker emotion data into an AI, which can then automatically determine the priority of health information.

[0085] The health information collection unit can adjust its collection methods when collecting health information, taking into account the workers' lifestyles. For example, the health information collection unit can collect health information regarding nutritional status by considering the workers' eating habits. For example, the health information collection unit can collect health information regarding exercise capacity by considering the workers' exercise habits. Furthermore, the health information collection unit can analyze the workers' lifestyles and collect health information that indicates a high risk. This allows for the collection of accurate health information by applying collection methods tailored to lifestyles. Some or all of the above-described processes in the health information collection unit may be performed using AI, for example, or without AI. For example, the health information collection unit can input workers' lifestyle data into AI, which can then automatically select an appropriate collection method.

[0086] The analysis unit can estimate the worker's emotions and adjust the analysis criteria based on the estimated emotions. For example, if the worker is stressed, the analysis unit will prioritize stress-related data in its analysis. For example, if the worker is tired, the analysis unit can prioritize fatigue-related data in its analysis. Furthermore, if the worker is focused, the analysis unit can prioritize data related to work performance in its analysis. By adjusting the analysis criteria based on the worker's emotions, more appropriate analysis results can be obtained. 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 analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input worker emotion data into AI, and the AI ​​can automatically adjust the analysis criteria.

[0087] The analysis unit can optimize its analysis algorithm by referring to past industrial accident data during the analysis process. For example, the analysis unit can optimize an algorithm to identify high-risk areas based on past industrial accident data. For example, the analysis unit can optimize an algorithm to identify the causes of industrial accidents by analyzing past industrial accident data. The analysis unit can also optimize an algorithm to propose preventive measures for industrial accidents by referring to past industrial accident data. In this way, the algorithm to identify high-risk areas can be optimized by referring to past industrial accident data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past industrial accident data into AI, and the AI ​​can automatically optimize the analysis algorithm.

[0088] The analysis unit can apply different analysis methods depending on the work content at the work site during the analysis. For example, in the case of work at height, the analysis unit can apply an analysis method specialized for work at height. For example, in the case of work in a confined space, the analysis unit can apply an analysis method specialized for confined spaces. Furthermore, in the case of a large-scale work site, the analysis unit can apply an analysis method specialized for large-scale work. This allows for efficient analysis by applying an analysis method appropriate to the work content. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the work content at the site into the AI, and the AI ​​can automatically select an appropriate analysis method.

[0089] 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 can provide a simple and highly visible display method. For example, if the worker is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the worker is in a hurry, the analysis unit can provide a concise display method. In this way, by adjusting the display method based on the worker's emotions, the analysis results can be provided in a way that is easy for the worker to understand. 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 analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input worker emotion data into AI, and the AI ​​can automatically adjust the display method.

[0090] The analysis unit can perform analyses while considering the environmental conditions of the work site. For example, in a high-temperature environment, the analysis unit can consider the risk of heatstroke. For example, in a low-temperature environment, the analysis unit can consider the risk of freezing. Furthermore, in a high-humidity environment, the analysis unit can consider slipperiness. By considering environmental conditions, more accurate analysis results can be obtained. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the environmental conditions of the work site into the AI, and the AI ​​can perform the analysis automatically.

[0091] The analysis unit can perform analysis while considering the worker's attribute information. For example, in the case of elderly workers, the analysis unit can consider the decline in their vision and hearing. For example, in the case of inexperienced workers, the analysis unit can consider the risk of work errors. Furthermore, in the case of experienced workers, the analysis unit can consider work efficiency. In this way, by considering the worker's attribute information, it is possible to provide analysis results that are appropriate for each individual worker. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the worker's attribute information into AI, and the AI ​​can perform the analysis automatically.

[0092] The suggestion unit can estimate the worker's emotions and adjust the way the suggestion is presented based on the estimated emotions. For example, if the worker is tense, the suggestion unit can provide a simple and easily understandable suggestion. If the worker is relaxed, the suggestion unit can provide a suggestion that includes detailed information. If the worker is in a hurry, the suggestion unit can also provide a concise suggestion. In this way, by adjusting the way the suggestion is presented based on the worker's emotions, the suggestion can be presented in a way that is easy for the worker to understand. 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 using AI. For example, the suggestion unit can input worker emotion data into AI, and the AI ​​can automatically adjust the way the suggestion is presented.

[0093] The proposal unit can adjust the level of detail of its proposals based on the importance of the identified hazardous areas. For example, the proposal unit can propose detailed countermeasures for high-importance hazardous areas, and concise countermeasures for low-importance hazardous areas. The proposal unit can also adjust the level of detail of its proposals in stages according to importance. This allows for the provision of optimal countermeasures for workers by adjusting the level of detail of proposals based on the importance of the hazardous areas. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not. For example, the proposal unit can input the importance of the identified hazardous areas into the AI, and the AI ​​can automatically adjust the level of detail of its proposals.

[0094] The proposal unit can apply different proposal algorithms depending on the work content at the work site when making a proposal. For example, in the case of work at height, the proposal unit can apply a proposal algorithm specialized for work at height. For example, in the case of work in a confined space, the proposal unit can apply a proposal algorithm specialized for confined spaces. Furthermore, in the case of a large-scale work site, the proposal unit can apply a proposal algorithm specialized for large-scale work. This allows for efficient proposals by applying a proposal algorithm appropriate to the work content. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the work content at the site into AI, and the AI ​​can automatically select an appropriate proposal algorithm.

[0095] 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 feeling anxious, the suggestion unit may prioritize suggesting measures to address hazardous areas. If a worker is feeling tired, the suggestion unit may prioritize suggesting improvements to rest areas. If a worker is concentrating, the suggestion unit may also prioritize suggestions to improve work efficiency. By prioritizing suggestions based on the worker's emotions, important suggestions can be provided preferentially. 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 suggestion unit may be performed using AI or not. For example, the suggestion unit can input worker emotion data into an AI, which can then automatically determine the priority of suggestions.

[0096] The proposal department can adjust its proposals when making them, taking into account the environmental conditions of the work site. For example, if the lighting is dim, the proposal department can propose lighting improvements. For example, if the noise level is high, the proposal department can propose noise reduction measures. Furthermore, if the environment is humid, the proposal department can propose humidity reduction measures. By providing proposals tailored to the environmental conditions, the department can offer the most optimal solutions for workers. Some or all of the above processing in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input the environmental conditions of the work site into the AI, and the AI ​​can automatically adjust the proposals.

[0097] The proposal department can adjust the content of its proposals by taking into account the worker's attribute information. For example, in the case of an elderly worker, the proposal department can make suggestions that take into account the decline in their vision or hearing. For example, in the case of an inexperienced worker, the proposal department can make suggestions that reduce the risk of work errors. Furthermore, in the case of an experienced worker, the proposal department can make suggestions that improve work efficiency. In this way, by adjusting the content of the proposals based on the worker's attribute information, it is possible to provide suggestions that are suitable for each individual worker. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input the worker's attribute information into the AI, and the AI ​​can automatically adjust the content of the suggestions.

[0098] The information provider can estimate a worker's emotions and adjust the way the information is displayed based on the estimated emotions. For example, if a worker is tense, the provider can provide a simple and highly visible display. If a worker is relaxed, the provider can provide a display that includes detailed information. If a worker is in a hurry, the provider can also provide a concise display. By adjusting the display method based on the worker's emotions, information can be provided in a way that is easy for the worker to understand. 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 information provider may be performed using AI or not using AI. For example, the information provider can input worker emotion data into AI, and the AI ​​can automatically adjust the display method.

[0099] The service provider can select the optimal service delivery method by referring to the worker's past feedback at the time of delivery. For example, the service provider can prioritize providing the display method that the worker has preferred in the past. For example, the service provider can provide a display method that reflects improvements based on the worker's past feedback. The service provider can also analyze the worker's past feedback and select the optimal service delivery method. This allows the service provider to select the best service delivery method for the worker by referring to past feedback. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the worker's past feedback into AI, and the AI ​​can automatically select the optimal service delivery method.

[0100] The information provider can estimate the worker's emotions and determine the priority of the information to provide based on the estimated emotions. For example, if a worker is feeling anxious, the information provider can prioritize providing information about hazardous areas. For example, if a worker is tired, the information provider can prioritize providing information about rest areas. Furthermore, if a worker is concentrating, the information provider can prioritize providing detailed information about the work area. In this way, important information can be prioritized by determining the priority of information 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 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 information provider may be performed using AI, for example, or not using AI. For example, the information provider can input worker emotion data into an AI, and the AI ​​can automatically determine the priority of the information.

[0101] The information delivery unit can select the optimal delivery method at the time of delivery, taking into account the worker's device information. For example, if the worker is using a smartphone, the delivery unit can provide a display method that matches the screen size. For example, if the worker is using a tablet, the delivery unit can provide a display method optimized for a larger screen. Furthermore, if the worker is using a smartwatch, the delivery unit can provide a concise and highly visible display method. In this way, by selecting the delivery method based on device information, information can be delivered in the most optimal form for the worker. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input the worker's device information into AI, and the AI ​​can automatically select the optimal delivery method.

[0102] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0103] The safety system can also be equipped with a real-time monitoring unit. This unit can monitor the work environment in real time and issue immediate warnings if an anomaly is detected. For example, it can track workers' movements and issue warnings if the risk of falls or slips increases. It can also monitor the temperature and humidity of the work environment and issue warnings if the risk of heatstroke increases. Furthermore, it can detect abnormal machine operation and suggest machine shutdown or maintenance. This real-time monitoring and warning system can further improve worker safety.

[0104] The safety measures system can also be equipped with a predictive analytics unit. This unit can predict future risks based on collected data and take preventative measures. For example, it can predict accidents that are more likely to occur during specific seasons or times of day based on past data and propose countermeasures. It can also predict periods when specific workers are more prone to fatigue or stress based on worker health data and propose breaks or work adjustments. Furthermore, it can predict the risk of machine failure based on machine usage data and propose maintenance in advance. This allows for further improvements in worker safety through proactive measures based on predictive analytics.

[0105] The safety measures system can also include a communication section. This section provides functions to facilitate communication between workers and between workers and managers. For example, it can provide a chat function for workers to report hazardous areas. It can also provide a messaging function for managers to send safety instructions to workers in real time. Furthermore, it can provide a survey function for workers to provide feedback on safety measures. This strengthens communication between workers and managers and enhances the effectiveness of safety measures.

[0106] The safety measures system can also include an education and training department. This department provides functions for offering safety education and training to workers. For example, it can offer online courses for workers to learn about safety measures. It can also provide simulation training for workers to practice safety measures in actual work environments. Furthermore, it can provide a news feed to ensure workers stay up-to-date on the latest safety information. This helps to raise workers' safety awareness and prevent accidents.

[0107] The safety measures system may also include a feedback collection unit. This unit can collect feedback from workers and use it to improve the system. For example, the feedback collection unit can provide forms for workers to submit opinions and suggestions regarding safety measures. It can also provide questionnaires for workers to evaluate the effectiveness of specific measures. Furthermore, the feedback collection unit can provide a function for workers to provide feedback anonymously. This allows for system improvements that reflect worker opinions, thereby enhancing the effectiveness of safety measures.

[0108] The safety measures system can further analyze workers' emotions using emotion estimation functionality and propose measures to reduce their stress levels. For example, if a worker's stress level is high, the system can suggest adjusting break times to provide a more relaxing environment. If a worker is feeling anxious, the system can suggest mental health support to provide reassurance. Furthermore, if a worker is fatigued, the system can suggest adjusting their work schedule to reduce their workload. By implementing measures based on workers' emotions, the system can reduce their stress and provide a safer working environment.

[0109] The safety measures system can further analyze workers' emotions using emotion estimation capabilities and propose measures to improve their motivation. For example, if a worker is feeling motivated, the system can suggest new tasks to provide a further challenge. If a worker is feeling tired, the system can suggest a break time for refreshment. Furthermore, if a worker is feeling anxious, the system can suggest support to provide reassurance. By implementing measures based on workers' emotions, the system can improve worker motivation and increase productivity.

[0110] The safety measures system can further analyze workers' emotions using emotion estimation functionality and propose measures to facilitate communication among workers. For example, if a worker is feeling stressed, the system can suggest team-building activities to reduce stress. If a worker is feeling anxious, the system can suggest mental health support to provide a sense of security. Furthermore, if a worker is feeling tired, the system can suggest a break time for refreshment. By implementing measures based on workers' emotions, the system can improve communication among workers and promote team cooperation.

[0111] The safety measures system can further analyze workers' emotions using emotion estimation functionality and propose measures to maintain their health. For example, if a worker is stressed, the system can suggest a relaxation program to reduce stress. If a worker is tired, the system can suggest a break time to allow them to recover. Furthermore, if a worker is anxious, the system can suggest mental health support to provide reassurance. By implementing measures based on workers' emotions, the system can maintain their health and provide a safe working environment.

[0112] The safety measures system can further analyze workers' emotions using emotion estimation capabilities and propose measures to improve worker performance. For example, if a worker is feeling motivated, the system can suggest new tasks to provide a further challenge. If a worker is feeling tired, the system can suggest a break time for refreshment. Furthermore, if a worker is feeling anxious, the system can suggest support to provide reassurance. By implementing measures based on workers' emotions, the system can improve worker performance and increase productivity.

[0113] The following briefly describes the processing flow for example form 2.

[0114] Step 1: The collection unit gathers information from the work site. For example, it collects drawings and photographs of the work site, panoramic photos, floor plans and elevations, equipment status, and work procedures. This allows for a detailed understanding of the work site. Step 2: The health information collection department collects health information about workers. For example, it collects data on workers' physical abilities, vision and hearing, heart rate and blood pressure, visual acuity and hearing, and stress levels. This allows for a detailed understanding of the workers' health status. Step 3: The analysis unit analyzes the information collected by the data collection unit and the health information collection unit to identify hazardous areas. For example, it evaluates areas with a high risk of falls, the presence or absence of slippery floors or handrails, the risks in high-altitude work areas, and the temperature and humidity of the work environment to identify risks. Step 4: The proposal department proposes countermeasures based on the hazardous areas identified by the analysis department. For example, they may propose countermeasures such as warnings about footing, installation of non-slip mats, installation of handrails, and adjustment of the temperature and humidity of the work environment. Step 5: The provision department provides the proposed countermeasures plan to the workers on site. For example, the countermeasures plan may be provided using display screens, projection mapping, audio guidance, tablets, or smartphones.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] Each of the multiple elements described above, including the collection unit, health information collection unit, analysis unit, proposal unit, and provision unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects information from the work site using the camera 42 of the smart device 14 and transmits it to the data processing unit 12 via the control unit 46A. The health information collection unit collects health information of workers using the sensors of the smart device 14 and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit analyzes the collected information using the identification processing unit 290 of the data processing unit 12 and identifies hazardous areas. The proposal unit proposes a countermeasure plan based on the analysis results using the identification processing unit 290 of the data processing unit 12. The provision unit provides the countermeasure plan visually and audibly using the display 40A and speaker 40B of the smart device 14. 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.

[0119] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] Each of the multiple elements described above, including the collection unit, health information collection unit, analysis unit, proposal unit, and provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects information from the work site using the camera 42 of the smart glasses 214 and transmits it to the data processing unit 12 via the control unit 46A. The health information collection unit collects health information of workers using the sensors of the smart glasses 214 and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit analyzes the collected information using the identification processing unit 290 of the data processing unit 12 and identifies hazardous areas. The proposal unit proposes a countermeasure plan based on the analysis results using the identification processing unit 290 of the data processing unit 12. The provision unit provides the countermeasure plan visually and audibly using the display and speaker of the smart glasses 214. 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.

[0135] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] Each of the multiple elements described above, including the collection unit, health information collection unit, analysis unit, proposal unit, and provision unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects information from the work site using the camera 42 of the headset terminal 314 and transmits it to the data processing unit 12 by the control unit 46A. The health information collection unit collects health information of workers using the sensors of the headset terminal 314 and transmits it to the data processing unit 12 by the control unit 46A. The analysis unit analyzes the collected information using the identification processing unit 290 of the data processing unit 12 and identifies hazardous areas. The proposal unit proposes a countermeasure plan based on the analysis results using the identification processing unit 290 of the data processing unit 12. The provision unit provides the countermeasure plan visually and audibly using the display and speaker of the headset terminal 314. 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.

[0151] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.).

[0164] 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.

[0165] 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.

[0166] 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.

[0167] Each of the multiple elements described above, including the collection unit, health information collection unit, analysis unit, proposal unit, and provision unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects information from the work site using the camera 42 of the robot 414 and transmits it to the data processing unit 12 via the control unit 46A. The health information collection unit collects health information of workers using the sensors of the robot 414 and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit analyzes the collected information using the identification processing unit 290 of the data processing unit 12 and identifies hazardous areas. The proposal unit proposes a countermeasure plan based on the analysis results using the identification processing unit 290 of the data processing unit 12. The provision unit provides the countermeasure plan visually and audibly using the display and speaker of the robot 414. 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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."

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] (Note 1) A collection department that collects information from the workplace, The Health Information Collection Department collects health information on workers, An analysis unit analyzes the information collected by the aforementioned collection unit and the aforementioned health information collection unit to identify dangerous areas, A proposal unit proposes a countermeasure plan based on the hazardous areas identified by the analysis unit, The system includes a provision unit that provides the proposed countermeasures plan to on-site workers. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect drawings and photographs of the workplace. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned health information collection unit, Collect health information on workers' physical abilities and vision / hearing. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, The collected information is analyzed to identify areas with a high risk of falls and slips. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, Based on the identified hazardous areas, we propose countermeasures such as warnings about footing, measures to prevent collisions, and measures to address temperature and humidity. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, Provide the proposed countermeasures plan to the workers on site in a visual format. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is The system estimates workers' emotions and adjusts the timing of information gathering at the workplace based on the estimated emotions of the workers. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When collecting information from the workplace, refer to past accident data to optimize the scope of data collection. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting information at a work site, different collection methods should be applied depending on the nature of the work being done. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is Estimate workers' sentiments and prioritize the information to collect based on those estimated sentiments. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting information from the workplace, adjust the collection method considering the environmental conditions of the site. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When gathering information from the workplace, refer to the work schedule at the site to select the optimal timing for collection. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned health information collection unit, The system estimates workers' emotions and adjusts the timing of health information collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned health information collection unit, When collecting health information, we optimize the scope of data collection by referring to the worker's past health data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned health information collection unit, Estimate workers' sentiments and prioritize the health information to collect based on those estimated sentiments. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned health information collection unit, When collecting health information, adjust the collection method to take into account the worker's lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, We estimate the workers' emotions and adjust the analysis criteria based on the estimated workers' emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the analysis algorithm is optimized by referring to past industrial accident data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, During the analysis, different analytical methods are applied depending on the type of work being done at the workplace. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit, The system estimates the workers' emotions and adjusts how the analysis results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned analysis unit, During the analysis, the environmental conditions of the work site will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned analysis unit, During the analysis, the attribute information of the workers will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, We estimate the workers' feelings and adjust the way the proposal is expressed based on those estimated feelings. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the identified hazards. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the work content at the workplace. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned proposal section is, Estimate workers' sentiments and prioritize proposals based on those estimated sentiments. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned proposal section is, When making a proposal, adjust the proposal content to take into account the environmental conditions of the work site. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned proposal section is, When making a proposal, adjust the proposal content to take into account the attribute information of the workers. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, We estimate workers' sentiments and adjust how information is displayed based on those estimated sentiments. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When providing services, the optimal delivery method is selected by referring to past feedback from workers. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned supply unit is, Estimate workers' sentiments and prioritize the information provided based on those estimated sentiments. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned supply unit is, When providing the service, the optimal delivery method will be selected, taking into account the worker's device information. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0187] 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 department that collects information from the workplace, The Health Information Collection Department collects health information on workers, An analysis unit analyzes the information collected by the aforementioned collection unit and the aforementioned health information collection unit to identify dangerous areas, A proposal unit proposes a countermeasure plan based on the hazardous areas identified by the analysis unit, The system includes a provision unit that provides the proposed countermeasures plan to on-site workers. A system characterized by the following features.

2. The aforementioned collection unit is Collect drawings and photographs of the workplace. The system according to feature 1.

3. The aforementioned health information collection unit, Collect health information on workers' physical abilities and vision / hearing. The system according to feature 1.

4. The aforementioned analysis unit, The collected information is analyzed to identify areas with a high risk of falls and slips. The system according to feature 1.

5. The aforementioned proposal section is, Based on the identified hazardous areas, we propose countermeasures such as warnings about footing, measures to prevent collisions, and measures to address temperature and humidity. The system according to feature 1.

6. The aforementioned supply unit is, Provide the proposed countermeasures plan to the workers on site in a visual format. The system according to feature 1.

7. The aforementioned collection unit is The system estimates workers' emotions and adjusts the timing of information gathering at the workplace based on the estimated emotions of the workers. The system according to feature 1.

8. The aforementioned collection unit is When collecting information from the workplace, refer to past accident data to optimize the scope of data collection. The system according to feature 1.

9. The aforementioned collection unit is When collecting information at a work site, different collection methods should be applied depending on the nature of the work being done. The system according to feature 1.

10. The aforementioned collection unit is Estimate workers' sentiments and prioritize the information to collect based on those estimated sentiments. The system according to feature 1.

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