system

Abstract

The system according to this embodiment aims to quickly and accurately confirm the safety of employees and issue evacuation orders in the event of a disaster. [Solution] The system according to the embodiment comprises an evaluation unit, a guidance unit, a collection unit, and a management unit. The evaluation unit evaluates disaster risk. The guidance unit guides the user to the optimal evacuation route in the event of a disaster based on the risk evaluated by the evaluation unit. The collection unit collects the safety status of employees. The management unit centrally manages the safety status collected by the collection unit on a dashboard.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the confirmation of the safety of employees and evacuation instructions at the time of a disaster were not carried out quickly and accurately, and there were problems in the disaster response of enterprises.

[0005] The system according to the embodiment aims to quickly and accurately confirm the safety of employees and give evacuation instructions at the time of a disaster.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an evaluation unit, a guidance unit, a data collection unit, and a management unit. The evaluation unit evaluates disaster risk. The guidance unit guides users to the optimal evacuation route in the event of a disaster based on the risk evaluated by the evaluation unit. The data collection unit collects the safety status of employees. The management unit centrally manages the safety status collected by the data collection unit on a dashboard. [Effects of the Invention]

[0007] The system according to this embodiment can quickly and accurately confirm the safety of employees and issue evacuation orders in the event of a disaster. [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 labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applicable to the communication I / F include wireless communication standards including 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 Emergency Compass, according to an embodiment of the present invention, is an AI agent system that supports corporate disaster response. This Emergency Compass provides real-time information during a disaster, comprehensively supporting employee safety checks, evacuation orders, and recovery activities. The Emergency Compass uses AI to conduct risk assessments in advance and, in the event of a disaster, guides users to the optimal evacuation route in real time. Furthermore, the Emergency Compass automatically collects employee safety information and manages it centrally on a dashboard, enabling rapid and accurate situation assessment. After a disaster, the Emergency Compass visualizes the extent of the damage and supports the development of recovery plans. In addition, the Emergency Compass records all data and creates detailed reports that can be used for future disaster preparedness. For example, the Emergency Compass uses AI to conduct risk assessments in advance. This allows companies to understand potential risks before a disaster occurs and take appropriate measures. For instance, it conducts risk assessments against natural disasters such as earthquakes and typhoons, and checks the seismic resistance of buildings and evacuation routes. When a disaster occurs, the Emergency Compass uses AI to guide users to the optimal evacuation route in real time. For example, in the event of an earthquake, Emergency Compass will instruct employees on the optimal evacuation route, taking into account traffic conditions and building damage. It also automatically collects employee safety information and centrally manages it on a dashboard. This allows companies to quickly and accurately ascertain the safety of their employees. After a disaster, Emergency Compass's AI visualizes the damage and assists in developing recovery plans. For instance, it analyzes the condition of damaged buildings and equipment and proposes the resources and procedures necessary for recovery. Furthermore, Emergency Compass records all data and creates detailed reports that can be used for future disaster preparedness. This allows companies to continuously improve the accuracy of their disaster response. In addition, Emergency Compass automatically generates situation reports based on the data collected during a disaster. For example, it compiles damage information and response instructions, generating real-time situation reports.Furthermore, Emergency Compass automatically generates and sends safety confirmation messages and evacuation instruction messages, encouraging a quick response. This allows employees to take appropriate action quickly. In this way, Emergency Compass is a tool that comprehensively supports a company's disaster response, improving overall corporate safety through rapid response and enhanced risk management.

[0029] The emergency compass according to this embodiment comprises an evaluation unit, a guidance unit, a data collection unit, and a management unit. The evaluation unit evaluates disaster risks. The evaluation unit evaluates risks such as earthquakes, typhoons, and floods. The evaluation unit can use AI to evaluate risks based on past disaster data and real-time information. For example, the evaluation unit can evaluate the seismic resistance of buildings based on past earthquake data. The evaluation unit can also evaluate flood risks based on past typhoon data. Furthermore, the evaluation unit can evaluate fire risks based on past fire data. The guidance unit guides the user to the optimal evacuation route in the event of a disaster based on the risks evaluated by the evaluation unit. The guidance unit instructs the user to the optimal evacuation route considering, for example, traffic conditions and the extent of building damage. The guidance unit can use AI to guide the user to the optimal evacuation route based on real-time traffic information and the extent of building damage. For example, the guidance unit can provide a route that avoids congestion based on traffic congestion information. Furthermore, the guidance unit can provide an optimal route based on the operating status of public transportation. Furthermore, the guidance unit can provide a detour route based on road construction information. The data collection unit collects employee safety status. The data collection unit automatically collects employee safety status using, for example, sensors and GPS data. The data collection unit can also collect employee safety status in real time using AI. For example, the data collection unit can determine safety status based on employee location information. Furthermore, the data collection unit can monitor employee health status and collect safety status information. In addition, the data collection unit can analyze employee social media activity and collect safety status information. The management unit centrally manages the safety status collected by the data collection unit on a dashboard. For example, the management unit displays the collected safety status on the dashboard in real time. The management unit can manage the collected safety status in real time using AI. For example, the management unit can display safety status on the dashboard using graphs and maps. Furthermore, the management unit updates the safety status in real time, enabling rapid situation assessment. In addition, the management unit can support the development of recovery plans based on the safety status.As a result, the Emergency Compass according to this embodiment can assess disaster risks, provide guidance on evacuation routes, and collect and manage information on the safety status of individuals.

[0030] The evaluation department assesses disaster risks. For example, it assesses risks such as earthquakes, typhoons, and floods. The evaluation department can use AI to assess risks based on past disaster data and real-time information. Specifically, the evaluation department can assess the seismic resistance of buildings based on past earthquake data. The AI ​​analyzes earthquake intensity, frequency, and building structure information to predict how much earthquake a building can withstand. The evaluation department can also assess flood risks based on past typhoon data. The AI ​​analyzes rainfall, river water levels, and topographic information to predict the probability of flooding and the extent of its impact. Furthermore, the evaluation department can assess fire risks based on past fire data. The AI ​​analyzes the location and cause of fires, building materials, etc., to predict the risk of fire and its potential for spread. As a result, the evaluation department can comprehensively assess various disaster risks and provide information to take appropriate countermeasures. In addition, the evaluation department can quickly grasp changes in disaster risks by utilizing real-time information. For example, it can acquire data from seismometers and weather sensors in real time to immediately assess the occurrence and progression of disasters. Furthermore, the evaluation department can improve the accuracy of its assessments by continuously training its AI-based predictive models. This allows the evaluation department to always provide highly accurate risk assessments based on the latest information, supporting quick and appropriate responses.

[0031] The guidance unit guides users to the optimal evacuation route in the event of a disaster, based on the risks assessed by the evaluation unit. For example, the guidance unit directs users to the optimal evacuation route considering traffic conditions and the extent of building damage. Specifically, the guidance unit can use AI to guide users to the optimal evacuation route based on real-time traffic information and building damage. The AI ​​analyzes traffic congestion information and provides routes that avoid congestion. It also analyzes the operation status of public transportation and provides the optimal route considering available means of transport. Furthermore, it can analyze road construction information and road closure information due to disasters and provide detour routes. The guidance unit integrates this information to guide users to the optimal evacuation route in real time. For example, through a smartphone app, it displays the optimal route based on the user's current location and evacuation destination, and provides warnings through voice guidance and vibration notifications. In addition to guiding users to evacuation routes, the guidance unit can also provide information on evacuation destinations and points to note during evacuation. In this way, the guidance unit can help users evacuate quickly and safely. Furthermore, the guidance unit can collect user feedback and continuously improve the accuracy and effectiveness of the guidance content. For example, based on feedback from users who have received evacuation route guidance, routes are reviewed and guidance content is improved. This allows the guidance department to always provide highly accurate evacuation route guidance based on the latest information, supporting a quick and appropriate response.

[0032] The data collection unit collects information on the safety status of employees. For example, the unit automatically collects employee safety status using sensors and GPS data. Specifically, the unit can use AI to collect employee safety status in real time. The AI ​​analyzes employee location information to understand their current location and movement. It also monitors employee health and immediately notifies if any abnormalities are detected. Furthermore, the unit can analyze employees' social media activity to collect safety status information. For example, it analyzes the content and location information posted by employees on social media to understand their safety status. This allows the unit to quickly and accurately collect employee safety status information, supporting disaster response. Additionally, the unit centrally manages the collected safety status information and can collaborate with other systems and departments as needed. For example, collected data is stored on a cloud server and made accessible to the management unit. The unit can also adjust the frequency and accuracy of data collection to enable flexible responses to specific situations and conditions. This allows the unit to collect data efficiently and effectively, improving the overall system performance.

[0033] The Management Department centrally manages the safety status collected by the Collection Department on a dashboard. For example, the Management Department displays the safety status collected in real time on the dashboard. Specifically, the Management Department can use AI to manage the collected safety status in real time. The AI ​​analyzes the collected data and displays it on the dashboard in graphs and maps. It also updates the safety status in real time, enabling rapid situation assessment. Furthermore, the Management Department can support the formulation of recovery plans based on the safety status. For example, it analyzes the safety status to understand the number of victims and the extent of damage, providing the information necessary for formulating recovery plans. This allows the Management Department to support a rapid and appropriate response during a disaster. Furthermore, the Management Department can store the collected data long-term and perform analysis and predictions based on past disaster data. This allows the Management Department to provide information useful for assessing disaster risks and formulating countermeasures. Furthermore, the Management Department can collaborate with other systems and departments to strengthen information sharing and coordination. For example, by collaborating with other disaster response systems and emergency response departments to strengthen information sharing and coordination, disaster responses can be made more effective. This allows the management department to support a swift and appropriate response during disasters and minimize the risk of damage.

[0034] The evaluation unit can perform risk assessments against natural disasters such as earthquakes and typhoons. For example, to assess earthquake risk, the evaluation unit can refer to past earthquake data and assess the seismic resistance of a building. For example, to assess typhoon risk, the evaluation unit can refer to past typhoon data and assess flood risk. For example, to assess fire risk, the evaluation unit can refer to past fire data and assess fire risk. This makes it possible to perform risk assessments against natural disasters. Some or all of the above processing in the evaluation unit may be performed using AI, or it may be performed without AI. For example, the evaluation unit can input past disaster data into a generating AI, and the generating AI can perform risk assessments.

[0035] The guidance unit can provide the optimal evacuation route considering traffic conditions and the extent of building damage. For example, the guidance unit can provide a route that avoids congestion based on traffic congestion information. For example, the guidance unit can provide the optimal route based on the operating status of public transportation. For example, the guidance unit can provide a detour route based on road construction information. This makes it possible to provide evacuation route instructions that take into account traffic conditions and the extent of building damage. Some or all of the above processing in the guidance unit may be performed using AI or not. For example, the guidance unit can input real-time traffic information into a generating AI, and the generating AI can guide users to the optimal evacuation route.

[0036] The data collection unit can automatically collect employee safety status. The data collection unit can collect employee safety status using, for example, sensors. The data collection unit can collect employee safety status using, for example, GPS data. The data collection unit can collect employee safety status by analyzing employee social media activity. This makes it possible to automatically collect employee safety status. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input data acquired from sensors into a generating AI, and the generating AI can collect safety status.

[0037] The management department can centrally manage the collected safety status on a dashboard. For example, the management department can display the collected safety status on the dashboard in real time. For example, the management department can display the safety status using graphs or maps. For example, the management department can update the safety status in real time. This makes it possible to centrally manage the collected safety status on a dashboard. Some or all of the above processes in the management department may be performed using AI or not. For example, the management department can input the collected safety status into a generating AI, and the generating AI can display it on the dashboard.

[0038] The management department can visualize the extent of the damage and support the development of recovery plans. For example, the management department can analyze the condition of damaged buildings and equipment and propose the resources and procedures necessary for recovery. For example, the management department can display the extent of the damage using graphs and maps. For example, the management department can update the extent of the damage in real time to support the development of recovery plans. This enables the visualization of the extent of the damage and support for the development of recovery plans. Some or all of the above processes performed by the management department may be carried out using AI or not. For example, the management department can input damage data into a generating AI, which can then support the development of recovery plans.

[0039] The management department can record all data and create detailed reports that can be used for future disaster response. For example, the management department can automatically generate situation reports based on data collected when a disaster occurs. For example, the management department can compile damage reports and response instructions and generate real-time situation reports. For example, the management department can automatically generate and send safety confirmation messages and evacuation instruction messages. This makes it possible to create detailed reports that can be used for future disaster response. Some or all of the above processes in the management department may be performed using AI or not. For example, the management department can input collected data into a generating AI, which can then create a detailed report.

[0040] The management department can automatically generate situation reports based on data collected during a disaster. For example, the management department can compile damage reports and response instructions and generate real-time situation reports. For example, the management department can automatically generate and send safety confirmation messages and evacuation instruction messages. This enables the automatic generation of situation reports during a disaster. Some or all of the above processes in the management department may be performed using AI, or they may not. For example, the management department can input collected data into a generation AI, which can then automatically generate situation reports.

[0041] The management department can automatically generate and send safety confirmation messages and evacuation instruction messages. For example, the management department can automatically generate safety confirmation messages and send them to employees. For example, the management department can automatically generate evacuation instruction messages and send them to employees. This makes it possible to automatically generate and send safety confirmation messages and evacuation instruction messages. Some or all of the above processes in the management department may be performed using AI, or they may not be performed using AI. For example, the management department can input safety confirmation messages and evacuation instruction messages into a generation AI, and the generation AI can automatically generate and send them.

[0042] The evaluation unit can improve the accuracy of its risk assessment by referring to past disaster data. For example, the evaluation unit can refer to past earthquake data to assess the seismic resistance of a building. For example, the evaluation unit can refer to past typhoon data to assess flood risk. For example, the evaluation unit can refer to past fire data to assess fire risk. In this way, the accuracy of the risk assessment is improved by referring to past disaster data. Some or all of the above processing in the evaluation unit may be performed using AI or not. For example, the evaluation unit can input past disaster data into a generating AI, which can then improve the accuracy of the risk assessment.

[0043] The evaluation unit can apply different evaluation algorithms to a company's specific industry or business type during risk assessment. For example, in the case of a manufacturing company, the evaluation unit might focus on evaluating factory equipment risks. In the case of a service industry company, the evaluation unit might focus on evaluating customer service risks. In the case of an IT company, the evaluation unit might focus on evaluating data center risks. This enables risk assessment tailored to a company's specific industry or business type. Some or all of the above processing in the evaluation unit may be performed using AI or not. For example, the evaluation unit can input company industry and business type data into a generating AI, and the generating AI can apply different evaluation algorithms.

[0044] The evaluation unit can perform risk assessments while taking into account the geographical location information of companies. For example, if a company is located in an earthquake-prone area, the evaluation unit will focus on assessing earthquake risk. For example, if a company is located in a flood-prone area, the evaluation unit will focus on assessing flood risk. For example, if a company is located in a fire-prone area, the evaluation unit will focus on assessing fire risk. This makes it possible to perform risk assessments that take into account the geographical location information of companies. Some or all of the above processing in the evaluation unit may be performed using AI, or it may be performed without using AI. For example, the evaluation unit can input the geographical location information of companies into a generating AI, and the generating AI can perform the risk assessment.

[0045] The assessment department can take into account the condition of a company's equipment and infrastructure when conducting risk assessments. For example, if a company has aging equipment, the assessment department will focus on assessing equipment risk. If a company has new equipment, the assessment department will lower its assessment of equipment risk. If a company is located in an area with vulnerable infrastructure, the assessment department will focus on assessing infrastructure risk. This makes it possible to conduct risk assessments that take into account the condition of a company's equipment and infrastructure. Some or all of the above processing in the assessment department may be performed using AI, or it may be performed without AI. For example, the assessment department can input data on the condition of a company's equipment and infrastructure into a generating AI, and the generating AI can perform the risk assessment.

[0046] The guidance unit can provide the optimal route by referring to real-time traffic information when guiding people on evacuation routes. For example, the guidance unit can provide a route that avoids congestion based on traffic congestion information. For example, the guidance unit can provide the optimal route based on the operating status of public transportation. For example, the guidance unit can provide a detour route based on road construction information. This makes it possible to provide the optimal evacuation route by referring to real-time traffic information. Some or all of the above processing in the guidance unit may be performed using AI or not. For example, the guidance unit can input real-time traffic information into a generating AI, and the generating AI can provide the optimal evacuation route.

[0047] The guidance unit can provide evacuation route guidance while considering the attribute information of the evacuees. For example, the guidance unit can provide an easy-to-walk route for the elderly. For example, the guidance unit can provide the shortest route for the person in poor health. For example, the guidance unit can provide a safe route for the child. This makes it possible to provide evacuation route guidance that takes the attribute information of the evacuees into consideration. Some or all of the above processing in the guidance unit may be performed using AI or not. For example, the guidance unit can input the attribute information of the evacuees into a generating AI, and the generating AI can provide the optimal evacuation route.

[0048] The guidance unit can provide guidance while considering the capacity of evacuation destinations when guiding people along evacuation routes. For example, if the capacity of an evacuation destination is limited, the guidance unit will suggest alternative evacuation destinations. For example, if the capacity of an evacuation destination is sufficient, the guidance unit will prioritize guiding people to that destination. For example, if the capacity of an evacuation destination is unknown, the guidance unit will suggest multiple evacuation destinations. This makes it possible to guide people along evacuation routes that take into account the capacity of evacuation destinations. Some or all of the above processing in the guidance unit may be performed using AI, or it may be performed without AI. For example, the guidance unit can input evacuation destination capacity data into a generating AI, and the generating AI can provide the optimal evacuation route.

[0049] The guidance unit can use the location information of evacuees' mobile devices to provide guidance when guiding them along evacuation routes. For example, the guidance unit can provide the optimal evacuation route based on the location information of the mobile device. For example, the guidance unit can determine the current location of evacuees based on the location information of the mobile device and provide appropriate guidance. For example, the guidance unit can grasp the movement status of evacuees in real time based on the location information of the mobile device and provide guidance. This makes it possible to provide evacuation route guidance using the location information of evacuees' mobile devices. Some or all of the above processing in the guidance unit may be performed using AI or not. For example, the guidance unit can input the location information of the mobile device into a generating AI, and the generating AI can provide the optimal evacuation route.

[0050] The data collection unit can improve the accuracy of data collection by referring to past safety confirmation data when collecting safety status information. For example, the data collection unit can perform detailed safety confirmation based on past safety confirmation data. For example, the data collection unit can perform a brief safety confirmation based on past safety confirmation data. For example, the data collection unit can perform a rapid safety confirmation based on past safety confirmation data. As a result, the accuracy of safety status collection is improved by referring to past safety confirmation data. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input past safety confirmation data into a generating AI, which can then improve the accuracy of safety status collection.

[0051] The collection unit can collect safety status information while considering the attribute information of the person being collected. For example, if the person holds a high position, the collection unit will conduct a detailed safety check. For example, if the department is important, the collection unit will conduct a detailed safety check. For example, the collection unit will conduct a safety check for everyone regardless of position or department. This makes it possible to collect safety status information while considering the attribute information of the person being collected. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input the attribute information of the person being collected into a generating AI, and the generating AI can collect safety status information.

[0052] The collection unit can collect safety status information while considering the geographical location information of the person being collected. For example, the collection unit can perform a detailed safety check based on the geographical location information. For example, the collection unit can perform a brief safety check based on the geographical location information. For example, the collection unit can perform a rapid safety check based on the geographical location information. This makes it possible to collect safety status information while considering the geographical location information of the person being collected. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input the geographical location information of the person being collected into a generating AI, and the generating AI can collect safety status information.

[0053] The collection unit can analyze and collect data on the social media activities of the individuals being collected when collecting information on their safety status. For example, the collection unit can perform detailed safety checks based on social media activities. For example, the collection unit can perform brief safety checks based on social media activities. For example, the collection unit can perform rapid safety checks based on social media activities. This makes it possible to collect information on the safety status of individuals by analyzing their social media activities. Some or all of the above-described processes in the collection unit may be performed using AI or not. For example, the collection unit can input the social media activity data of the individuals being collected into a generating AI, which can then collect information on their safety status.

[0054] The management department can select the optimal display method by referring to past disaster response data when displaying the dashboard. For example, the management department can provide a detailed display based on past disaster response data. For example, the management department can provide a concise display based on past disaster response data. For example, the management department can provide a rapid display based on past disaster response data. This makes it possible to select the optimal dashboard display method by referring to past disaster response data. Some or all of the above processing in the management department may be performed using AI or not. For example, the management department can input past disaster response data into a generating AI, and the generating AI can select the optimal display method.

[0055] The management department can apply different display algorithms to the dashboard display depending on the specific industry or business type of the company. For example, in the case of a manufacturing company, the management department might focus on displaying the operating status of equipment. In the case of a service industry company, the management department might focus on displaying the customer service status. In the case of an IT company, the management department might focus on displaying the operating status of data centers. This enables dashboard displays tailored to the specific industry or business type of the company. Some or all of the above processing in the management department may be performed using AI or not. For example, the management department can input company industry and business type data into a generating AI, and the generating AI can apply different display algorithms.

[0056] The management department can select the optimal display method when displaying the dashboard, taking into account the company's geographical location information. For example, the management department can provide a detailed display based on geographical location information. For example, the management department can provide a concise display based on geographical location information. For example, the management department can provide a rapid display based on geographical location information. This makes it possible to select the optimal dashboard display method that takes into account the company's geographical location information. Some or all of the above processing in the management department may be performed using AI, or not. For example, the management department can input the company's geographical location information into a generating AI, and the generating AI can select the optimal display method.

[0057] The management department can display dashboards that take into account the status of a company's equipment and infrastructure. For example, if a company has aging equipment, the management department can display the equipment status in detail. If a company has new equipment, the management department can display the equipment status concisely. If a company is located in an area with vulnerable infrastructure, the management department can display the infrastructure status in detail. This makes it possible to display dashboards that take into account the status of a company's equipment and infrastructure. Some or all of the above processing in the management department may be performed using AI, or not. For example, the management department can input data on the status of a company's equipment and infrastructure into a generating AI, which can then display the dashboard.

[0058] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0059] The assessment department can conduct disaster risk assessments tailored to the specific industry and business type of a company. For example, in the manufacturing industry, it can focus on assessing factory equipment risks. In the service industry, it can focus on assessing customer service risks. Furthermore, in the IT industry, it can focus on assessing data center risks. This enables risk assessments that are tailored to the specific industry and business type of a company.

[0060] The guidance system can provide evacuation route guidance while considering the attributes of the evacuees. For example, it can provide an easy-to-walk route for the elderly, the shortest route for those in poor health, and a safe route for children. This makes it possible to provide evacuation route guidance that takes into account the attributes of the evacuees.

[0061] The data collection unit can collect safety status information while considering the geographical location of the person being collected. For example, it can perform detailed safety checks based on geographical location information. It can also perform concise safety checks based on geographical location information. Furthermore, it can perform rapid safety checks based on geographical location information. This makes it possible to collect safety status information while considering the geographical location of the person being collected.

[0062] The management department can apply different display algorithms to the dashboard depending on the specific industry or business type of the company. For example, in the manufacturing industry, the display can focus on equipment operating status. In the service industry, the display can focus on customer service status. Furthermore, in the IT industry, the display can focus on data center operating status. This enables dashboard displays tailored to the specific industry or business type of the company.

[0063] The assessment department can take into account a company's geographical location when conducting risk assessments. For example, a company located in an earthquake-prone area can have its earthquake risk assessed with greater emphasis. Similarly, a company located in a flood-prone area can have its flood risk assessed with greater emphasis. Furthermore, a company located in a fire-prone area can have its fire risk assessed with greater emphasis. This makes it possible to conduct risk assessments that take into account a company's geographical location.

[0064] The following briefly describes the processing flow for example form 1.

[0065] Step 1: The evaluation unit assesses disaster risks. The evaluation unit assesses risks such as earthquakes, typhoons, and floods. The evaluation unit can use AI to assess risks based on past disaster data and real-time information. For example, the evaluation unit can assess the seismic resistance of a building based on past earthquake data. The evaluation unit can also assess flood risk based on past typhoon data. Furthermore, the evaluation unit can assess fire risk based on past fire data. Step 2: The guidance unit guides people to the optimal evacuation route in the event of a disaster, based on the risks assessed by the evaluation unit. The guidance unit will, for example, indicate the optimal evacuation route considering traffic conditions and the extent of building damage. The guidance unit can use AI to guide people to the optimal evacuation route based on real-time traffic information and building damage. For example, the guidance unit can provide routes that avoid congestion based on traffic congestion information. It can also provide optimal routes based on the operating status of public transportation. Furthermore, the guidance unit can provide detour routes based on road construction information. Step 3: The data collection unit collects employee safety information. The data collection unit automatically collects employee safety information using, for example, sensors and GPS data. The data collection unit can collect employee safety information in real time using AI. For example, the data collection unit can determine safety based on employee location information. The data collection unit can also monitor employees' health status and collect safety information. Furthermore, the data collection unit can analyze employees' social media activity and collect safety information. Step 4: The management department centrally manages the safety status collected by the collection department on a dashboard. For example, the management department displays the safety status collected in real time on the dashboard. The management department can use AI to manage the collected safety status in real time. For example, the management department can display the safety status on the dashboard using graphs and maps. In addition, the management department can update the safety status in real time, enabling rapid situation assessment. Furthermore, the management department can support the formulation of recovery plans based on the safety status.

[0066] (Example of form 2) The Emergency Compass, according to an embodiment of the present invention, is an AI agent system that supports corporate disaster response. This Emergency Compass provides real-time information during a disaster, comprehensively supporting employee safety checks, evacuation orders, and recovery activities. The Emergency Compass uses AI to conduct risk assessments in advance and, in the event of a disaster, guides users to the optimal evacuation route in real time. Furthermore, the Emergency Compass automatically collects employee safety information and manages it centrally on a dashboard, enabling rapid and accurate situation assessment. After a disaster, the Emergency Compass visualizes the extent of the damage and supports the development of recovery plans. In addition, the Emergency Compass records all data and creates detailed reports that can be used for future disaster preparedness. For example, the Emergency Compass uses AI to conduct risk assessments in advance. This allows companies to understand potential risks before a disaster occurs and take appropriate measures. For instance, it conducts risk assessments against natural disasters such as earthquakes and typhoons, and checks the seismic resistance of buildings and evacuation routes. When a disaster occurs, the Emergency Compass uses AI to guide users to the optimal evacuation route in real time. For example, in the event of an earthquake, Emergency Compass will instruct employees on the optimal evacuation route, taking into account traffic conditions and building damage. It also automatically collects employee safety information and centrally manages it on a dashboard. This allows companies to quickly and accurately ascertain the safety of their employees. After a disaster, Emergency Compass's AI visualizes the damage and assists in developing recovery plans. For instance, it analyzes the condition of damaged buildings and equipment and proposes the resources and procedures necessary for recovery. Furthermore, Emergency Compass records all data and creates detailed reports that can be used for future disaster preparedness. This allows companies to continuously improve the accuracy of their disaster response. In addition, Emergency Compass automatically generates situation reports based on the data collected during a disaster. For example, it compiles damage information and response instructions, generating real-time situation reports.Furthermore, Emergency Compass automatically generates and sends safety confirmation messages and evacuation instruction messages, encouraging a quick response. This allows employees to take appropriate action quickly. In this way, Emergency Compass is a tool that comprehensively supports a company's disaster response, improving overall corporate safety through rapid response and enhanced risk management.

[0067] The emergency compass according to this embodiment comprises an evaluation unit, a guidance unit, a data collection unit, and a management unit. The evaluation unit evaluates disaster risks. The evaluation unit evaluates risks such as earthquakes, typhoons, and floods. The evaluation unit can use AI to evaluate risks based on past disaster data and real-time information. For example, the evaluation unit can evaluate the seismic resistance of buildings based on past earthquake data. The evaluation unit can also evaluate flood risks based on past typhoon data. Furthermore, the evaluation unit can evaluate fire risks based on past fire data. The guidance unit guides the user to the optimal evacuation route in the event of a disaster based on the risks evaluated by the evaluation unit. The guidance unit instructs the user to the optimal evacuation route considering, for example, traffic conditions and the extent of building damage. The guidance unit can use AI to guide the user to the optimal evacuation route based on real-time traffic information and the extent of building damage. For example, the guidance unit can provide a route that avoids congestion based on traffic congestion information. Furthermore, the guidance unit can provide an optimal route based on the operating status of public transportation. Furthermore, the guidance unit can provide a detour route based on road construction information. The data collection unit collects employee safety status. The data collection unit automatically collects employee safety status using, for example, sensors and GPS data. The data collection unit can also collect employee safety status in real time using AI. For example, the data collection unit can determine safety status based on employee location information. Furthermore, the data collection unit can monitor employee health status and collect safety status information. In addition, the data collection unit can analyze employee social media activity and collect safety status information. The management unit centrally manages the safety status collected by the data collection unit on a dashboard. For example, the management unit displays the collected safety status on the dashboard in real time. The management unit can manage the collected safety status in real time using AI. For example, the management unit can display safety status on the dashboard using graphs and maps. Furthermore, the management unit updates the safety status in real time, enabling rapid situation assessment. In addition, the management unit can support the development of recovery plans based on the safety status.As a result, the Emergency Compass according to this embodiment can assess disaster risks, provide guidance on evacuation routes, and collect and manage information on the safety status of individuals.

[0068] The evaluation department assesses disaster risks. For example, it assesses risks such as earthquakes, typhoons, and floods. The evaluation department can use AI to assess risks based on past disaster data and real-time information. Specifically, the evaluation department can assess the seismic resistance of buildings based on past earthquake data. The AI ​​analyzes earthquake intensity, frequency, and building structure information to predict how much earthquake a building can withstand. The evaluation department can also assess flood risks based on past typhoon data. The AI ​​analyzes rainfall, river water levels, and topographic information to predict the probability of flooding and the extent of its impact. Furthermore, the evaluation department can assess fire risks based on past fire data. The AI ​​analyzes the location and cause of fires, building materials, etc., to predict the risk of fire and its potential for spread. As a result, the evaluation department can comprehensively assess various disaster risks and provide information to take appropriate countermeasures. In addition, the evaluation department can quickly grasp changes in disaster risks by utilizing real-time information. For example, it can acquire data from seismometers and weather sensors in real time to immediately assess the occurrence and progression of disasters. Furthermore, the evaluation department can improve the accuracy of its assessments by continuously training its AI-based predictive models. This allows the evaluation department to always provide highly accurate risk assessments based on the latest information, supporting quick and appropriate responses.

[0069] The guidance unit guides users to the optimal evacuation route in the event of a disaster, based on the risks assessed by the evaluation unit. For example, the guidance unit directs users to the optimal evacuation route considering traffic conditions and the extent of building damage. Specifically, the guidance unit can use AI to guide users to the optimal evacuation route based on real-time traffic information and building damage. The AI ​​analyzes traffic congestion information and provides routes that avoid congestion. It also analyzes the operation status of public transportation and provides the optimal route considering available means of transport. Furthermore, it can analyze road construction information and road closure information due to disasters and provide detour routes. The guidance unit integrates this information to guide users to the optimal evacuation route in real time. For example, through a smartphone app, it displays the optimal route based on the user's current location and evacuation destination, and provides warnings through voice guidance and vibration notifications. In addition to guiding users to evacuation routes, the guidance unit can also provide information on evacuation destinations and points to note during evacuation. In this way, the guidance unit can help users evacuate quickly and safely. Furthermore, the guidance unit can collect user feedback and continuously improve the accuracy and effectiveness of the guidance content. For example, based on feedback from users who have received evacuation route guidance, routes are reviewed and guidance content is improved. This allows the guidance department to always provide highly accurate evacuation route guidance based on the latest information, supporting a quick and appropriate response.

[0070] The data collection unit collects information on the safety status of employees. For example, the unit automatically collects employee safety status using sensors and GPS data. Specifically, the unit can use AI to collect employee safety status in real time. The AI ​​analyzes employee location information to understand their current location and movement. It also monitors employee health and immediately notifies if any abnormalities are detected. Furthermore, the unit can analyze employees' social media activity to collect safety status information. For example, it analyzes the content and location information posted by employees on social media to understand their safety status. This allows the unit to quickly and accurately collect employee safety status information, supporting disaster response. Additionally, the unit centrally manages the collected safety status information and can collaborate with other systems and departments as needed. For example, collected data is stored on a cloud server and made accessible to the management unit. The unit can also adjust the frequency and accuracy of data collection to enable flexible responses to specific situations and conditions. This allows the unit to collect data efficiently and effectively, improving the overall system performance.

[0071] The Management Department centrally manages the safety status collected by the Collection Department on a dashboard. For example, the Management Department displays the safety status collected in real time on the dashboard. Specifically, the Management Department can use AI to manage the collected safety status in real time. The AI ​​analyzes the collected data and displays it on the dashboard in graphs and maps. It also updates the safety status in real time, enabling rapid situation assessment. Furthermore, the Management Department can support the formulation of recovery plans based on the safety status. For example, it analyzes the safety status to understand the number of victims and the extent of damage, providing the information necessary for formulating recovery plans. This allows the Management Department to support a rapid and appropriate response during a disaster. Furthermore, the Management Department can store the collected data long-term and perform analysis and predictions based on past disaster data. This allows the Management Department to provide information useful for assessing disaster risks and formulating countermeasures. Furthermore, the Management Department can collaborate with other systems and departments to strengthen information sharing and coordination. For example, by collaborating with other disaster response systems and emergency response departments to strengthen information sharing and coordination, disaster responses can be made more effective. This allows the management department to support a swift and appropriate response during disasters and minimize the risk of damage.

[0072] The evaluation unit can perform risk assessments against natural disasters such as earthquakes and typhoons. For example, to assess earthquake risk, the evaluation unit can refer to past earthquake data and assess the seismic resistance of a building. For example, to assess typhoon risk, the evaluation unit can refer to past typhoon data and assess flood risk. For example, to assess fire risk, the evaluation unit can refer to past fire data and assess fire risk. This makes it possible to perform risk assessments against natural disasters. Some or all of the above processing in the evaluation unit may be performed using AI, or it may be performed without AI. For example, the evaluation unit can input past disaster data into a generating AI, and the generating AI can perform risk assessments.

[0073] The guidance unit can provide the optimal evacuation route considering traffic conditions and the extent of building damage. For example, the guidance unit can provide a route that avoids congestion based on traffic congestion information. For example, the guidance unit can provide the optimal route based on the operating status of public transportation. For example, the guidance unit can provide a detour route based on road construction information. This makes it possible to provide evacuation route instructions that take into account traffic conditions and the extent of building damage. Some or all of the above processing in the guidance unit may be performed using AI or not. For example, the guidance unit can input real-time traffic information into a generating AI, and the generating AI can guide users to the optimal evacuation route.

[0074] The data collection unit can automatically collect employee safety status. The data collection unit can collect employee safety status using, for example, sensors. The data collection unit can collect employee safety status using, for example, GPS data. The data collection unit can collect employee safety status by analyzing employee social media activity. This makes it possible to automatically collect employee safety status. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input data acquired from sensors into a generating AI, and the generating AI can collect safety status.

[0075] The management department can centrally manage the collected safety status on a dashboard. For example, the management department can display the collected safety status on the dashboard in real time. For example, the management department can display the safety status using graphs or maps. For example, the management department can update the safety status in real time. This makes it possible to centrally manage the collected safety status on a dashboard. Some or all of the above processes in the management department may be performed using AI or not. For example, the management department can input the collected safety status into a generating AI, and the generating AI can display it on the dashboard.

[0076] The management department can visualize the extent of the damage and support the development of recovery plans. For example, the management department can analyze the condition of damaged buildings and equipment and propose the resources and procedures necessary for recovery. For example, the management department can display the extent of the damage using graphs and maps. For example, the management department can update the extent of the damage in real time to support the development of recovery plans. This enables the visualization of the extent of the damage and support for the development of recovery plans. Some or all of the above processes performed by the management department may be carried out using AI or not. For example, the management department can input damage data into a generating AI, which can then support the development of recovery plans.

[0077] The management department can record all data and create detailed reports that can be used for future disaster response. For example, the management department can automatically generate situation reports based on data collected when a disaster occurs. For example, the management department can compile damage reports and response instructions and generate real-time situation reports. For example, the management department can automatically generate and send safety confirmation messages and evacuation instruction messages. This makes it possible to create detailed reports that can be used for future disaster response. Some or all of the above processes in the management department may be performed using AI or not. For example, the management department can input collected data into a generating AI, which can then create a detailed report.

[0078] The management department can automatically generate situation reports based on data collected during a disaster. For example, the management department can compile damage reports and response instructions and generate real-time situation reports. For example, the management department can automatically generate and send safety confirmation messages and evacuation instruction messages. This enables the automatic generation of situation reports during a disaster. Some or all of the above processes in the management department may be performed using AI, or they may not. For example, the management department can input collected data into a generation AI, which can then automatically generate situation reports.

[0079] The management department can automatically generate and send safety confirmation messages and evacuation instruction messages. For example, the management department can automatically generate safety confirmation messages and send them to employees. For example, the management department can automatically generate evacuation instruction messages and send them to employees. This makes it possible to automatically generate and send safety confirmation messages and evacuation instruction messages. Some or all of the above processes in the management department may be performed using AI, or they may not be performed using AI. For example, the management department can input safety confirmation messages and evacuation instruction messages into a generation AI, and the generation AI can automatically generate and send them.

[0080] The evaluation unit can estimate the user's emotions and adjust the priority of risk assessment based on the estimated user emotions. For example, if the user is feeling anxious, the evaluation unit will prioritize evaluating the highest-risk items. For example, if the user is calm, the evaluation unit will perform a balanced overall risk assessment. For example, if the user is facing an emergency, the evaluation unit will prioritize evaluating risks that require immediate attention. This makes it possible to adjust the priority of risk assessment based on the user'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 evaluation unit may be performed using AI or not. For example, the evaluation unit can input user emotion data into a generative AI, which can then adjust the priority of risk assessment.

[0081] The evaluation unit can improve the accuracy of its risk assessment by referring to past disaster data. For example, the evaluation unit can refer to past earthquake data to assess the seismic resistance of a building. For example, the evaluation unit can refer to past typhoon data to assess flood risk. For example, the evaluation unit can refer to past fire data to assess fire risk. In this way, the accuracy of the risk assessment is improved by referring to past disaster data. Some or all of the above processing in the evaluation unit may be performed using AI or not. For example, the evaluation unit can input past disaster data into a generating AI, which can then improve the accuracy of the risk assessment.

[0082] The evaluation unit can apply different evaluation algorithms to a company's specific industry or business type during risk assessment. For example, in the case of a manufacturing company, the evaluation unit might focus on evaluating factory equipment risks. In the case of a service industry company, the evaluation unit might focus on evaluating customer service risks. In the case of an IT company, the evaluation unit might focus on evaluating data center risks. This enables risk assessment tailored to a company's specific industry or business type. Some or all of the above processing in the evaluation unit may be performed using AI or not. For example, the evaluation unit can input company industry and business type data into a generating AI, and the generating AI can apply different evaluation algorithms.

[0083] The evaluation unit can estimate the user's emotions and adjust the order in which the risk assessment results are displayed based on the estimated user emotions. For example, if the user is feeling anxious, the evaluation unit will display the highest-risk items first. For example, if the user is calm, the evaluation unit will display the overall risk assessment in a balanced manner. For example, if the user is facing an emergency, the evaluation unit will prioritize displaying risks that require immediate attention. This makes it possible to adjust the display order of risk assessment results based on the user'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 evaluation unit may be performed using AI or not. For example, the evaluation unit can input user emotion data into a generative AI, which can then adjust the order in which the risk assessment results are displayed.

[0084] The evaluation unit can perform risk assessments while taking into account the geographical location information of companies. For example, if a company is located in an earthquake-prone area, the evaluation unit will focus on assessing earthquake risk. For example, if a company is located in a flood-prone area, the evaluation unit will focus on assessing flood risk. For example, if a company is located in a fire-prone area, the evaluation unit will focus on assessing fire risk. This makes it possible to perform risk assessments that take into account the geographical location information of companies. Some or all of the above processing in the evaluation unit may be performed using AI, or it may be performed without using AI. For example, the evaluation unit can input the geographical location information of companies into a generating AI, and the generating AI can perform the risk assessment.

[0085] The assessment department can take into account the condition of a company's equipment and infrastructure when conducting risk assessments. For example, if a company has aging equipment, the assessment department will focus on assessing equipment risk. If a company has new equipment, the assessment department will lower its assessment of equipment risk. If a company is located in an area with vulnerable infrastructure, the assessment department will focus on assessing infrastructure risk. This makes it possible to conduct risk assessments that take into account the condition of a company's equipment and infrastructure. Some or all of the above processing in the assessment department may be performed using AI, or it may be performed without AI. For example, the assessment department can input data on the condition of a company's equipment and infrastructure into a generating AI, and the generating AI can perform the risk assessment.

[0086] The guidance unit can estimate the user's emotions and adjust the evacuation route guidance method based on the estimated user emotions. For example, if the user is feeling anxious, the guidance unit will provide detailed guidance. For example, if the user is calm, the guidance unit will provide concise guidance. For example, if the user is facing an emergency, the guidance unit will provide rapid guidance. This makes it possible to adjust the evacuation route guidance method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 guidance unit may be performed using AI or not. For example, the guidance unit can input user emotion data into a generative AI, and the generative AI can adjust the evacuation route guidance method.

[0087] The guidance unit can provide the optimal route by referring to real-time traffic information when guiding people on evacuation routes. For example, the guidance unit can provide a route that avoids congestion based on traffic congestion information. For example, the guidance unit can provide the optimal route based on the operating status of public transportation. For example, the guidance unit can provide a detour route based on road construction information. This makes it possible to provide the optimal evacuation route by referring to real-time traffic information. Some or all of the above processing in the guidance unit may be performed using AI or not. For example, the guidance unit can input real-time traffic information into a generating AI, and the generating AI can provide the optimal evacuation route.

[0088] The guidance unit can provide evacuation route guidance while considering the attribute information of the evacuees. For example, the guidance unit can provide an easy-to-walk route for the elderly. For example, the guidance unit can provide the shortest route for the person in poor health. For example, the guidance unit can provide a safe route for the child. This makes it possible to provide evacuation route guidance that takes the attribute information of the evacuees into consideration. Some or all of the above processing in the guidance unit may be performed using AI or not. For example, the guidance unit can input the attribute information of the evacuees into a generating AI, and the generating AI can provide the optimal evacuation route.

[0089] The guidance unit can estimate the user's emotions and adjust the display method of the evacuation route based on the estimated user emotions. For example, if the user is feeling anxious, the guidance unit will provide a detailed display. For example, if the user is calm, the guidance unit will provide a concise display. For example, if the user is facing an emergency, the guidance unit will provide a rapid display. This makes it possible to adjust the display method of the evacuation route based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the guidance unit may be performed using AI or not. For example, the guidance unit can input user emotion data into a generative AI, and the generative AI can adjust the display method of the evacuation route.

[0090] The guidance unit can provide guidance while considering the capacity of evacuation destinations when guiding people along evacuation routes. For example, if the capacity of an evacuation destination is limited, the guidance unit will suggest alternative evacuation destinations. For example, if the capacity of an evacuation destination is sufficient, the guidance unit will prioritize guiding people to that destination. For example, if the capacity of an evacuation destination is unknown, the guidance unit will suggest multiple evacuation destinations. This makes it possible to guide people along evacuation routes that take into account the capacity of evacuation destinations. Some or all of the above processing in the guidance unit may be performed using AI, or it may be performed without AI. For example, the guidance unit can input evacuation destination capacity data into a generating AI, and the generating AI can provide the optimal evacuation route.

[0091] The guidance unit can use the location information of evacuees' mobile devices to provide guidance when guiding them along evacuation routes. For example, the guidance unit can provide the optimal evacuation route based on the location information of the mobile device. For example, the guidance unit can determine the current location of evacuees based on the location information of the mobile device and provide appropriate guidance. For example, the guidance unit can grasp the movement status of evacuees in real time based on the location information of the mobile device and provide guidance. This makes it possible to provide evacuation route guidance using the location information of evacuees' mobile devices. Some or all of the above processing in the guidance unit may be performed using AI or not. For example, the guidance unit can input the location information of the mobile device into a generating AI, and the generating AI can provide the optimal evacuation route.

[0092] The data collection unit can estimate the user's emotions and adjust the method of collecting safety status information based on the estimated emotions. For example, if the user is feeling anxious, the data collection unit will perform a detailed safety check. For example, if the user is calm, the data collection unit will perform a brief safety check. For example, if the user is facing an emergency, the data collection unit will perform a rapid safety check. This makes it possible to adjust the method of collecting safety status information based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a 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 user emotion data into a generative AI, which can then adjust the method of collecting safety status information.

[0093] The data collection unit can improve the accuracy of data collection by referring to past safety confirmation data when collecting safety status information. For example, the data collection unit can perform detailed safety confirmation based on past safety confirmation data. For example, the data collection unit can perform a brief safety confirmation based on past safety confirmation data. For example, the data collection unit can perform a rapid safety confirmation based on past safety confirmation data. As a result, the accuracy of safety status collection is improved by referring to past safety confirmation data. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input past safety confirmation data into a generating AI, which can then improve the accuracy of safety status collection.

[0094] The collection unit can collect safety status information while considering the attribute information of the person being collected. For example, if the person holds a high position, the collection unit will conduct a detailed safety check. For example, if the department is important, the collection unit will conduct a detailed safety check. For example, the collection unit will conduct a safety check for everyone regardless of position or department. This makes it possible to collect safety status information while considering the attribute information of the person being collected. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input the attribute information of the person being collected into a generating AI, and the generating AI can collect safety status information.

[0095] The data collection unit can estimate the user's emotions and adjust the frequency of collecting safety status information based on the estimated emotions. For example, if the user is feeling anxious, the data collection unit will frequently check on their safety. For example, if the user is calm, the data collection unit will check on their safety at a moderate frequency. For example, if the user is facing an emergency, the data collection unit will quickly check on their safety. This makes it possible to adjust the frequency of collecting safety status information based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a 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 user emotion data into a generative AI, which can then adjust the frequency of collecting safety status information.

[0096] The collection unit can collect safety status information while considering the geographical location information of the person being collected. For example, the collection unit can perform a detailed safety check based on the geographical location information. For example, the collection unit can perform a brief safety check based on the geographical location information. For example, the collection unit can perform a rapid safety check based on the geographical location information. This makes it possible to collect safety status information while considering the geographical location information of the person being collected. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input the geographical location information of the person being collected into a generating AI, and the generating AI can collect safety status information.

[0097] The collection unit can analyze and collect data on the social media activities of the individuals being collected when collecting information on their safety status. For example, the collection unit can perform detailed safety checks based on social media activities. For example, the collection unit can perform brief safety checks based on social media activities. For example, the collection unit can perform rapid safety checks based on social media activities. This makes it possible to collect information on the safety status of individuals by analyzing their social media activities. Some or all of the above-described processes in the collection unit may be performed using AI or not. For example, the collection unit can input the social media activity data of the individuals being collected into a generating AI, which can then collect information on their safety status.

[0098] The management unit can estimate the user's emotions and adjust the dashboard display based on the estimated emotions. For example, if the user is feeling anxious, the management unit can provide a detailed display. For example, if the user is calm, the management unit can provide a concise display. For example, if the user is facing an emergency, the management unit can provide a rapid display. This makes it possible to adjust the dashboard display based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 management unit may be performed using AI or not. For example, the management unit can input user emotion data into a generative AI, which can then adjust the dashboard display.

[0099] The management department can select the optimal display method by referring to past disaster response data when displaying the dashboard. For example, the management department can provide a detailed display based on past disaster response data. For example, the management department can provide a concise display based on past disaster response data. For example, the management department can provide a rapid display based on past disaster response data. This makes it possible to select the optimal dashboard display method by referring to past disaster response data. Some or all of the above processing in the management department may be performed using AI or not. For example, the management department can input past disaster response data into a generating AI, and the generating AI can select the optimal display method.

[0100] The management department can apply different display algorithms to the dashboard display depending on the specific industry or business type of the company. For example, in the case of a manufacturing company, the management department might focus on displaying the operating status of equipment. In the case of a service industry company, the management department might focus on displaying the customer service status. In the case of an IT company, the management department might focus on displaying the operating status of data centers. This enables dashboard displays tailored to the specific industry or business type of the company. Some or all of the above processing in the management department may be performed using AI or not. For example, the management department can input company industry and business type data into a generating AI, and the generating AI can apply different display algorithms.

[0101] The management unit can estimate the user's emotions and adjust the dashboard's operating procedures based on the estimated emotions. For example, if the user is feeling anxious, the management unit can provide detailed operating procedures. For example, if the user is calm, the management unit can provide concise operating procedures. For example, if the user is facing an emergency, the management unit can provide quick operating procedures. This makes it possible to adjust the dashboard's operating procedures based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 management unit may be performed using AI or not. For example, the management unit can input user emotion data into a generative AI, and the generative AI can adjust the dashboard's operating procedures.

[0102] The management department can select the optimal display method when displaying the dashboard, taking into account the company's geographical location information. For example, the management department can provide a detailed display based on geographical location information. For example, the management department can provide a concise display based on geographical location information. For example, the management department can provide a rapid display based on geographical location information. This makes it possible to select the optimal dashboard display method that takes into account the company's geographical location information. Some or all of the above processing in the management department may be performed using AI, or not. For example, the management department can input the company's geographical location information into a generating AI, and the generating AI can select the optimal display method.

[0103] The management department can display dashboards that take into account the status of a company's equipment and infrastructure. For example, if a company has aging equipment, the management department can display the equipment status in detail. If a company has new equipment, the management department can display the equipment status concisely. If a company is located in an area with vulnerable infrastructure, the management department can display the infrastructure status in detail. This makes it possible to display dashboards that take into account the status of a company's equipment and infrastructure. Some or all of the above processing in the management department may be performed using AI, or not. For example, the management department can input data on the status of a company's equipment and infrastructure into a generating AI, which can then display the dashboard.

[0104] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0105] The assessment department can conduct disaster risk assessments tailored to the specific industry and business type of a company. For example, in the manufacturing industry, it can focus on assessing factory equipment risks. In the service industry, it can focus on assessing customer service risks. Furthermore, in the IT industry, it can focus on assessing data center risks. This enables risk assessments that are tailored to the specific industry and business type of a company.

[0106] The guidance system can provide evacuation route guidance while considering the attributes of the evacuees. For example, it can provide an easy-to-walk route for the elderly, the shortest route for those in poor health, and a safe route for children. This makes it possible to provide evacuation route guidance that takes into account the attributes of the evacuees.

[0107] The data collection unit can collect safety status information while considering the geographical location of the person being collected. For example, it can perform detailed safety checks based on geographical location information. It can also perform concise safety checks based on geographical location information. Furthermore, it can perform rapid safety checks based on geographical location information. This makes it possible to collect safety status information while considering the geographical location of the person being collected.

[0108] The management department can apply different display algorithms to the dashboard depending on the specific industry or business type of the company. For example, in the manufacturing industry, the display can focus on equipment operating status. In the service industry, the display can focus on customer service status. Furthermore, in the IT industry, the display can focus on data center operating status. This enables dashboard displays tailored to the specific industry or business type of the company.

[0109] The assessment department can take into account a company's geographical location when conducting risk assessments. For example, a company located in an earthquake-prone area can have its earthquake risk assessed with greater emphasis. Similarly, a company located in a flood-prone area can have its flood risk assessed with greater emphasis. Furthermore, a company located in a fire-prone area can have its fire risk assessed with greater emphasis. This makes it possible to conduct risk assessments that take into account a company's geographical location.

[0110] The evaluation unit can estimate the user's emotions and adjust the priority of risk assessment based on those emotions. For example, if the user is feeling anxious, the highest-risk items can be prioritized in the evaluation. Conversely, if the user is calm, the overall risk assessment can be balanced. Furthermore, if the user is facing an emergency, risks requiring immediate attention can be prioritized in the evaluation. This makes it possible to adjust the priority of risk assessment based on the user's emotions.

[0111] The guidance system can estimate the user's emotions and adjust the evacuation route guidance based on those emotions. For example, if the user is feeling anxious, it can provide detailed guidance. If the user is calm, it can provide concise guidance. Furthermore, if the user is facing an emergency, it can provide rapid guidance. This makes it possible to adjust the evacuation route guidance based on the user's emotions.

[0112] The data collection unit can estimate the user's emotions and adjust the method of collecting safety information based on those emotions. For example, if the user is feeling anxious, a detailed safety check can be performed. If the user is calm, a concise safety check can be performed. Furthermore, if the user is facing an emergency, a rapid safety check can be performed. This makes it possible to adjust the method of collecting safety information based on the user's emotions.

[0113] The management team can estimate the user's emotions and adjust how the dashboard is displayed based on those emotions. For example, if the user is feeling anxious, a detailed display can be provided. If the user is calm, a concise display can be provided. Furthermore, if the user is facing an emergency, a rapid display can be provided. This allows for the adjustment of the dashboard display based on the user's emotions.

[0114] The management department can estimate the user's emotions and adjust the dashboard's operating procedures based on those estimates. For example, if the user is feeling anxious, detailed instructions can be provided. If the user is calm, concise instructions can be provided. Furthermore, if the user is facing an emergency, quick instructions can be provided. This enables the adjustment of dashboard operating procedures based on the user's emotions.

[0115] The following briefly describes the processing flow for example form 2.

[0116] Step 1: The evaluation unit assesses disaster risks. The evaluation unit assesses risks such as earthquakes, typhoons, and floods. The evaluation unit can use AI to assess risks based on past disaster data and real-time information. For example, the evaluation unit can assess the seismic resistance of a building based on past earthquake data. The evaluation unit can also assess flood risk based on past typhoon data. Furthermore, the evaluation unit can assess fire risk based on past fire data. Step 2: The guidance unit guides people to the optimal evacuation route in the event of a disaster, based on the risks assessed by the evaluation unit. The guidance unit will, for example, indicate the optimal evacuation route considering traffic conditions and the extent of building damage. The guidance unit can use AI to guide people to the optimal evacuation route based on real-time traffic information and building damage. For example, the guidance unit can provide routes that avoid congestion based on traffic congestion information. It can also provide optimal routes based on the operating status of public transportation. Furthermore, the guidance unit can provide detour routes based on road construction information. Step 3: The data collection unit collects employee safety information. The data collection unit automatically collects employee safety information using, for example, sensors and GPS data. The data collection unit can collect employee safety information in real time using AI. For example, the data collection unit can determine safety based on employee location information. The data collection unit can also monitor employees' health status and collect safety information. Furthermore, the data collection unit can analyze employees' social media activity and collect safety information. Step 4: The management department centrally manages the safety status collected by the collection department on a dashboard. For example, the management department displays the safety status collected in real time on the dashboard. The management department can use AI to manage the collected safety status in real time. For example, the management department can display the safety status on the dashboard using graphs and maps. In addition, the management department can update the safety status in real time, enabling rapid situation assessment. Furthermore, the management department can support the formulation of recovery plans based on the safety status.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] Each of the multiple elements described above, including the evaluation unit, guidance unit, collection unit, and management unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the evaluation unit is implemented by the specific processing unit 290 of the data processing unit 12 and evaluates risk based on past disaster data and real-time information. The guidance unit is implemented by, for example, the control unit 46A of the smart device 14 and guides the optimal evacuation route considering traffic conditions and building damage. The collection unit automatically collects the safety status of employees using, for example, sensors and GPS data from the smart device 14. The management unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and centrally manages the collected safety status on a dashboard. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0121] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] Each of the multiple elements described above, including the evaluation unit, guidance unit, collection unit, and management unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the evaluation unit is implemented by the specific processing unit 290 of the data processing unit 12 and evaluates risk based on past disaster data and real-time information. The guidance unit is implemented by the control unit 46A of the smart glasses 214 and guides the user to the optimal evacuation route considering traffic conditions and building damage. The collection unit automatically collects the safety status of employees using the sensors and GPS data of the smart glasses 214. The management unit is implemented by the specific processing unit 290 of the data processing unit 12 and centrally manages the collected safety status on a dashboard. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be changed in various ways.

[0137] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] Each of the multiple elements described above, including the evaluation unit, guidance unit, collection unit, and management unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the evaluation unit is implemented by the specific processing unit 290 of the data processing unit 12 and evaluates risk based on past disaster data and real-time information. The guidance unit is implemented by, for example, the control unit 46A of the headset terminal 314 and guides the user to the optimal evacuation route considering traffic conditions and building damage. The collection unit automatically collects the safety status of employees using, for example, sensors and GPS data from the headset terminal 314. The management unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and centrally manages the collected safety status on a dashboard. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be changed in various ways.

[0153] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.).

[0166] 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.

[0167] 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.

[0168] 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.

[0169] Each of the multiple elements described above, including the evaluation unit, guidance unit, collection unit, and management unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the evaluation unit is implemented by the specific processing unit 290 of the data processing unit 12 and evaluates risk based on past disaster data and real-time information. The guidance unit is implemented by, for example, the control unit 46A of the robot 414 and guides the optimal evacuation route considering traffic conditions and building damage. The collection unit automatically collects the safety status of employees using, for example, the sensors and GPS data of the robot 414. The management unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and centrally manages the collected safety status on a dashboard. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be changed in various ways.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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."

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] (Note 1) The assessment department evaluates disaster risk, Based on the risk assessed by the aforementioned evaluation unit, a guidance unit provides the optimal evacuation route in the event of a disaster. The collection department is responsible for gathering information on the safety status of employees, The system includes a management unit that centrally manages the safety status collected by the collection unit on a dashboard. A system characterized by the following features. (Note 2) The evaluation unit, Conduct risk assessments for natural disasters such as earthquakes and typhoons. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned guide section is The system will instruct you on the optimal evacuation route, taking into account traffic conditions and the extent of damage to buildings. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is Automatically collect information on the safety status of employees. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned management department, Collected safety information is centrally managed on a dashboard. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned management department, Visualize the extent of the damage and support the development of recovery plans. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned management department, Record all data and create a detailed report that can be used for future disaster preparedness. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned management department, The system automatically generates situation reports based on data collected during a disaster. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned management department, Automatically generates and sends safety confirmation messages and evacuation instruction messages. The system described in Appendix 1, characterized by the features described herein. (Note 10) The evaluation unit, It estimates user sentiment and adjusts risk assessment priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 11) The evaluation unit, When conducting risk assessments, referencing past disaster data improves the accuracy of the assessment. The system described in Appendix 1, characterized by the features described herein. (Note 12) The evaluation unit, When assessing risk, different assessment algorithms are applied depending on the specific industry or business type of the company. The system described in Appendix 1, characterized by the features described herein. (Note 13) The evaluation unit, It estimates the user's emotions and adjusts the order in which the risk assessment results are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The evaluation unit, When conducting a risk assessment, the company's geographical location information should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 15) The evaluation unit, When conducting a risk assessment, the condition of the company's facilities and infrastructure should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned guide section is The system estimates the user's emotions and adjusts the evacuation route guidance method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned guide section is When providing evacuation route guidance, the system uses real-time traffic information to provide the optimal route. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned guide section is When providing evacuation route guidance, the guidance should take into account the attributes of the evacuees. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned guide section is The system estimates the user's emotions and adjusts how evacuation routes are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned guide section is When guiding people along evacuation routes, the capacity of the evacuation sites should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned guide section is When guiding evacuees along evacuation routes, the system uses location information from evacuees' mobile devices to provide directions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned collection unit is The system estimates the user's emotions and adjusts the method of collecting safety status information based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned collection unit is When collecting information on the safety status of individuals, past safety confirmation data is referenced to improve the accuracy of the collection. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned collection unit is When collecting information on the safety status of individuals, the data should be collected while taking into consideration the attribute information of the individuals being collected. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned collection unit is The system estimates the user's emotions and adjusts the frequency of collecting safety status information based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned collection unit is When collecting information on the safety status of individuals, the collection process takes into account the geographical location of those being collected. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned collection unit is When collecting information on the safety status of individuals, the social media activity of those being collected is analyzed and analyzed. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned management department, It estimates the user's emotions and adjusts how the dashboard is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned management department, When displaying the dashboard, the system selects the optimal display method by referring to past disaster response data. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned management department, When displaying the dashboard, different display algorithms are applied depending on the company's specific industry or business type. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned management department, It estimates the user's emotions and adjusts the dashboard's operation procedures based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned management department, When displaying the dashboard, the system selects the optimal display method considering the company's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned management department, When displaying the dashboard, the system should take into account the status of the company's equipment and infrastructure. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0189] 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. The assessment department evaluates disaster risk, Based on the risk assessed by the aforementioned evaluation unit, a guidance unit provides the optimal evacuation route in the event of a disaster. The collection department is responsible for gathering information on the safety status of employees, The system includes a management unit that centrally manages the safety status collected by the collection unit on a dashboard. A system characterized by the following features.

2. The evaluation unit, Conduct risk assessments for natural disasters such as earthquakes and typhoons. The system according to feature 1.

3. The aforementioned guide section is The system will instruct you on the optimal evacuation route, taking into account traffic conditions and the extent of damage to buildings. The system according to feature 1.

4. The aforementioned collection unit is Automatically collect information on the safety status of employees. The system according to feature 1.

5. The aforementioned management department, Collected safety information is centrally managed on a dashboard. The system according to feature 1.

6. The aforementioned management department, Visualize the extent of the damage and support the development of recovery plans. The system according to feature 1.

7. The aforementioned management department, Record all data and create a detailed report that can be used for future disaster preparedness. The system according to feature 1.

8. The aforementioned management department, The system automatically generates situation reports based on data collected during a disaster. The system according to feature 1.

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