system

Abstract

The system according to this embodiment aims to assess an individual's risk in real time and provide an optimal evacuation plan. [Solution] The system according to the embodiment comprises a risk assessment unit, an evacuation plan generation unit, and a disaster prevention education provision unit. The risk assessment unit analyzes the user's location information, weather data, earthquake information, etc., in real time and constantly assesses the individual's risk. Based on the risk assessed by the risk assessment unit, the evacuation plan generation unit proposes the optimal evacuation route and shelter, taking into account the user's health condition, family structure, presence of pets, etc. The disaster prevention education provision unit provides the user with disaster prevention quizzes and simulation games based on the evacuation plan proposed by the evacuation plan generation unit.

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 character of the chatbot, 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 individual risks are not sufficiently evaluated in real time and an optimal evacuation plan is not provided, and there is room for improvement.

[0005] The system according to the embodiment aims to evaluate an individual's risk in real time and provide an optimal evacuation plan.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a risk assessment unit, an evacuation plan generation unit, and a disaster prevention education provision unit. The risk assessment unit analyzes the user's location information, weather data, earthquake information, etc., in real time and constantly assesses the individual's risk. Based on the risk assessed by the risk assessment unit, the evacuation plan generation unit proposes the optimal evacuation route and shelter, taking into account the user's health condition, family structure, presence of pets, etc. Based on the evacuation plan proposed by the evacuation plan generation unit, the disaster prevention education provision unit provides the user with disaster prevention quizzes and simulation games. [Effects of the Invention]

[0007] The system according to this embodiment can assess an individual's risk in real time and provide an optimal evacuation plan. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls 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 disaster prevention support system according to an embodiment of the present invention is a comprehensive disaster prevention support system tailored to the needs of the community and individuals. This disaster prevention support system analyzes the user's location information, weather data, earthquake information, etc., in real time to constantly assess the individual's risk. Next, it proposes the optimal evacuation route and shelter, taking into account the user's health condition, family structure, presence of pets, etc. It also provides disaster prevention quizzes and simulation games tailored to the user's knowledge level, allowing them to acquire disaster prevention knowledge while having fun. Furthermore, it matches users with neighbors and local volunteers to promote mutual assistance during disasters. In addition, it provides information and support in multiple languages ​​for foreign residents and tourists. Finally, it provides stress management and psychological support before and after disasters. Through this mechanism, it is possible to create an environment where everyone can live safely, regardless of their level of disaster prevention awareness. For example, it analyzes the user's location information, weather data, earthquake information, etc., in real time to constantly assess the individual's risk. In this process, AI is used to analyze the data and perform risk assessment. For example, if a user is in an earthquake-prone area, the risk is assessed highly, and appropriate evacuation actions are proposed. Next, it proposes the optimal evacuation route and shelter, taking into account the user's health condition, family structure, presence of pets, etc. For example, if a user is elderly, a barrier-free evacuation shelter will be suggested. If a user has a pet, a pet-friendly evacuation shelter will be suggested. Furthermore, disaster preparedness quizzes and simulation games tailored to the user's knowledge level will be provided, allowing them to acquire disaster preparedness knowledge while having fun. For example, simple quizzes will be offered for children, and simulation games for adults, disseminating disaster preparedness knowledge to a wide range of people. Matching users with neighbors and local volunteers will also be facilitated to promote mutual assistance during disasters. For example, participating in local disaster preparedness drills can deepen cooperation with neighbors. In addition, information and support will be provided in multiple languages ​​for foreign residents and tourists. For example, providing disaster preparedness information in multiple languages ​​such as English and Chinese will allow foreigners to evacuate with confidence. Finally, stress management and psychological support will be provided before and after disasters. For example, counseling services will be offered to reduce stress after a disaster, providing mental health care.In this way, by providing a comprehensive disaster prevention support system tailored to the needs of the community and individuals, it is possible to create an environment where everyone can live safely, regardless of their level of disaster preparedness. This allows the disaster prevention support system to assess user risks in real time and provide optimal evacuation plans and disaster prevention education, thereby creating a safe living environment.

[0029] The disaster prevention support system according to this embodiment comprises a risk assessment unit, an evacuation plan generation unit, and a disaster prevention education provision unit. The risk assessment unit analyzes the user's location information, weather data, earthquake information, etc., in real time and constantly assesses the individual's risk. For example, the risk assessment unit obtains the user's location information from GPS, Wi-Fi location information, mobile base station information, etc. Weather data uses data from the Japan Meteorological Agency or data updated in real time. Earthquake information uses data from seismometer networks or data updated in real time. The risk assessment unit analyzes the data in real time using data streaming technology and real-time data processing algorithms. The risk assessment unit constantly assesses the individual's risk based on the risk score calculation method and evaluation frequency. For example, if the user is in an earthquake-prone area, the risk assessment unit will rate the risk highly and propose appropriate evacuation actions. Based on the risk assessed by the risk assessment unit, the evacuation plan generation unit proposes the optimal evacuation route and shelter, taking into account the user's health condition, family structure, presence of pets, etc. For example, the evacuation plan generation unit obtains the user's health condition from data such as heart rate, blood pressure, and health app data. The family structure information includes the number of family members, their ages, and any members requiring special care. The pet information includes the type and number of pets, and any pets requiring special care. The evacuation plan generation unit proposes the optimal evacuation route based on criteria such as distance, time, and safety. Evacuation shelters are proposed based on conditions such as capacity, facilities, and access methods. For example, if the user is elderly, the evacuation plan generation unit will propose a barrier-free shelter. If the user has pets, it will propose a pet-friendly shelter. The disaster prevention education provision unit provides users with disaster prevention quizzes and simulation games based on the evacuation plan proposed by the evacuation plan generation unit. For example, the disaster prevention education provision unit provides disaster prevention quizzes and simulation games tailored to the user's knowledge level. Disaster prevention quizzes are provided based on the type and difficulty of questions and the presentation method. Simulation games are provided based on the game scenario, interactivity, and educational effectiveness. For example, the disaster prevention education provision unit provides simple quizzes for children and simulation games for adults.As a result, the disaster prevention support system according to this embodiment can assess the user's risks in real time and provide optimal evacuation plans and disaster prevention education, thereby creating an environment in which people can live safely.

[0030] The Risk Assessment Unit analyzes user location information, weather data, earthquake information, etc., in real time to continuously assess individual risks. For example, the Risk Assessment Unit obtains user location information from GPS, Wi-Fi location information, and cell tower information. This allows it to accurately determine where the user is currently located. Weather data uses data from the Japan Meteorological Agency and real-time updated data. This allows it to assess risk based on current weather conditions and future forecasts. Earthquake information uses data from seismometer networks and real-time updated data. This allows it to quickly obtain information such as earthquake occurrences, epicenters, and seismic intensity, and reflect this in risk assessments. The Risk Assessment Unit analyzes data in real time using data streaming technology and real-time data processing algorithms. This allows it to process collected data immediately and quickly assess user risks. The Risk Assessment Unit continuously assesses individual risks based on the risk score calculation method and evaluation frequency. For example, if a user is in an earthquake-prone area, the Risk Assessment Unit will rate the risk higher and suggest appropriate evacuation actions. The risk score is calculated based on the user's location information, weather data, earthquake information, etc., and is notified to the user in real time. This allows users to constantly understand their own risks and take appropriate action. Furthermore, the risk assessment unit can utilize historical data and statistical information to conduct long-term risk assessments and trend analyses. For example, it can predict risk fluctuations in specific regions and time periods based on past earthquake data and formulate future countermeasures. In addition, the risk assessment unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, and issue warnings early. As a result, the risk assessment unit can not only grasp the situation in real time but also handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0031] The evacuation plan generation unit proposes the optimal evacuation route and shelter based on the risks assessed by the risk assessment unit, taking into account the user's health condition, family structure, and whether or not they have pets. For example, the evacuation plan generation unit obtains the user's health condition from data such as heart rate, blood pressure, and health app data. This allows for an accurate understanding of the user's physical condition and health status, which can then be reflected in the evacuation plan. For family structure, it obtains information such as the number of family members, their ages, and any members requiring special care. This allows for a plan that ensures the safe evacuation of all family members. For pet ownership, it obtains information such as the type and number of pets, and any pets requiring special care. This allows for an evacuation plan that includes pets. The evacuation plan generation unit proposes the optimal evacuation route based on criteria such as distance, time, and safety. For example, if the user is elderly, the evacuation plan generation unit will propose a barrier-free evacuation route. If the user has pets, it will propose a pet-friendly evacuation route. Shelters are proposed based on conditions such as capacity, facilities, and access methods. For example, if the user is elderly, the evacuation plan generation unit will propose a barrier-free shelter. If the user has pets, it will propose a pet-friendly shelter. This allows the evacuation plan generation unit to provide an optimal evacuation plan tailored to each user's individual circumstances. Furthermore, the evacuation plan generation unit can continuously revise the evacuation plan based on real-time updated data, enabling it to respond to the latest situations. For example, if an evacuation route is blocked or an evacuation center becomes full, the evacuation plan generation unit immediately incorporates new data and updates the evacuation plan. In addition, the evacuation plan generation unit can create more accurate evacuation plans by considering the characteristics of each region and past disaster history. As a result, the evacuation plan generation unit can always provide highly accurate evacuation plans based on the latest information, supporting a quick and appropriate response.

[0032] The Disaster Prevention Education Provision Department provides users with disaster prevention quizzes and simulation games based on evacuation plans proposed by the Evacuation Plan Generation Department. For example, the Disaster Prevention Education Provision Department provides disaster prevention quizzes and simulation games tailored to the user's knowledge level. Disaster prevention quizzes are provided based on the type and difficulty level of questions and the method of presentation. For example, simple quizzes are provided for children, and simulation games are provided for adults. Simulation games are provided based on the game's scenario, interactivity, and educational effect. For example, games that simulate actions taken during an earthquake or games that allow users to select evacuation routes are possible. This allows users to acquire disaster prevention knowledge while having fun. The Disaster Prevention Education Provision Department can collect user feedback and continuously improve the accuracy and effectiveness of the educational content. For example, the difficulty level and method of presentation of questions are reviewed based on feedback from users who have played disaster prevention quizzes and simulation games. In addition, the Disaster Prevention Education Provision Department can reliably transmit information using multiple communication methods. For example, important information is reliably delivered not only through smartphone notifications but also through voice calls, SMS, and email. This allows the Disaster Prevention Education Provision Department to provide disaster prevention education to users quickly and reliably, minimizing the risk of disaster. Furthermore, the disaster prevention education department can provide more effective disaster prevention education by taking into account the characteristics of each region and past disaster history. For example, in areas prone to earthquakes, education will focus on earthquake countermeasures, and in areas prone to floods, education will focus on flood countermeasures. In this way, the disaster prevention education department can assess the user's risk in real time and provide optimal evacuation plans and disaster prevention education, thereby creating an environment where people can live safely.

[0033] The disaster prevention education provider can provide disaster prevention quizzes and simulation games tailored to the user's knowledge level. The disaster prevention education provider can, for example, evaluate the user's knowledge level based on pre-tests and past learning history. The disaster prevention education provider provides simple quizzes and simulation games according to the user's knowledge level. For example, the disaster prevention education provider can provide simple quizzes for children and simulation games for adults. In this way, the disaster prevention education provider can effectively help users acquire disaster prevention knowledge by providing disaster prevention education tailored to their knowledge level. Some or all of the above processing in the disaster prevention education provider may be performed using, for example, a generative AI, or without a generative AI. For example, the disaster prevention education provider can input the user's knowledge level into a generative AI, and the generative AI can generate appropriate disaster prevention quizzes and simulation games.

[0034] The risk assessment unit can improve the accuracy of its risk assessments by referring to the user's past evacuation history. For example, the risk assessment unit can record the locations and routes the user has evacuated to in the past and reflect this in risk assessments for similar situations. The risk assessment unit can also analyze the success rate and problems of evacuations from the user's past evacuation history and utilize this in risk assessments. The risk assessment unit can also improve the accuracy of its risk assessments by considering the time of day and weather conditions when the user evacuated in the past. In this way, the risk assessment unit can improve the accuracy of its risk assessments by referring to past evacuation history. Some or all of the above processes in the risk assessment unit may be performed using AI, for example, or without AI. For example, the risk assessment unit can input the user's past evacuation history data into a generating AI and have the generating AI perform the task of improving the accuracy of the risk assessment.

[0035] The risk assessment unit can perform risk assessments while considering the user's lifestyle patterns. For example, the risk assessment unit can consider the user's commuting time and perform risk assessments during that time period. The risk assessment unit can also consider how the user spends their holidays and vacations and perform risk assessments at specific locations. The risk assessment unit can also customize risk assessments at specific times and locations based on the user's lifestyle patterns. This allows the risk assessment unit to perform more accurate risk assessments by considering the user's lifestyle patterns. Some or all of the above processing in the risk assessment unit may be performed using AI, for example, or without AI. For example, the risk assessment unit can input user lifestyle pattern data into a generating AI and have the generating AI perform the risk assessment.

[0036] The risk assessment unit can perform risk assessments while considering the geographical distribution of users. For example, the risk assessment unit can perform risk assessments while considering the geographical characteristics of the area where the user lives. If the user is traveling, the risk assessment unit can also perform risk assessments while considering the geographical characteristics of the current location. If the user lives in multiple areas, the risk assessment unit can also perform risk assessments while considering the geographical characteristics of each area. This allows the risk assessment unit to perform more accurate risk assessments by considering the geographical distribution of users. Some or all of the above processing in the risk assessment unit may be performed using AI, for example, or without using AI. For example, the risk assessment unit can input the geographical distribution data of users into a generating AI and have the generating AI perform the risk assessment.

[0037] The risk assessment unit can analyze a user's social media activity and obtain relevant risk information during risk assessment. For example, the risk assessment unit can perform risk assessment based on location information shared by the user on social media. The risk assessment unit can also analyze information on accounts that the user follows on social media and reflect this in the risk assessment. The risk assessment unit can also analyze content posted by the user on social media to improve the accuracy of the risk assessment. In this way, the risk assessment unit can improve the accuracy of the risk assessment by analyzing social media activity. Some or all of the above processing in the risk assessment unit may be performed using AI, for example, or without AI. For example, the risk assessment unit can input the user's social media activity data into a generating AI and have the generating AI perform the task of improving the accuracy of the risk assessment.

[0038] The evacuation plan generation unit can generate an optimal evacuation plan by referring to the user's past evacuation actions. For example, the evacuation plan generation unit can record the locations and routes the user has evacuated to in the past and reflect this in evacuation plans for similar situations. The evacuation plan generation unit can also analyze the success rate and problems of evacuation from the user's past evacuation actions and utilize this in the evacuation plan. The evacuation plan generation unit can also generate an optimal evacuation plan by considering the time of day and weather conditions when the user evacuated in the past. In this way, the evacuation plan generation unit can generate an optimal evacuation plan by referring to past evacuation actions. Some or all of the above processing in the evacuation plan generation unit may be performed using AI, for example, or without using AI. For example, the evacuation plan generation unit can input the user's past evacuation action data into a generation AI and have the generation AI execute the generation of an optimal evacuation plan.

[0039] The evacuation plan generation unit can monitor the user's current health status in real time and adjust the evacuation plan accordingly. For example, the unit can monitor the user's heart rate and blood pressure and provide an evacuation plan tailored to their health condition. If the user is feeling unwell, the unit can adjust the evacuation plan and suggest a safe evacuation route. The unit can also monitor the user's health status in real time and update the evacuation plan as needed. This allows the unit to provide an appropriate evacuation plan by monitoring the user's health status in real time. Some or all of the above-described processes in the evacuation plan generation unit may be performed using AI, for example, or without AI. For example, the evacuation plan generation unit can input the user's health status data into a generation AI and have the generation AI perform the adjustment of the evacuation plan.

[0040] The evacuation plan generation unit can propose the optimal evacuation route by considering the user's geographical location information when generating an evacuation plan. For example, the evacuation plan generation unit can propose the optimal evacuation route by considering the geographical characteristics of the area where the user lives. If the user is traveling, the evacuation plan generation unit can also propose the optimal evacuation route by considering the geographical characteristics of the current location. If the user lives in multiple areas, the evacuation plan generation unit can also propose the optimal evacuation route by considering the geographical characteristics of each area. In this way, the evacuation plan generation unit can propose the optimal evacuation route by considering geographical location information. Some or all of the above processing in the evacuation plan generation unit may be performed using AI, for example, or without AI. For example, the evacuation plan generation unit can input the user's geographical location information into a generation AI and have the generation AI propose the optimal evacuation route.

[0041] The evacuation plan generation unit can analyze the user's social media activity and provide relevant evacuation information when generating an evacuation plan. For example, the evacuation plan generation unit can propose the optimal evacuation route based on location information shared by the user on social media. The evacuation plan generation unit can also analyze information from accounts that the user follows on social media and provide relevant evacuation information. The evacuation plan generation unit can also analyze content posted by the user on social media to improve the accuracy of the evacuation plan. In this way, the evacuation plan generation unit can improve the accuracy of the evacuation plan by analyzing social media activity. Some or all of the above processing in the evacuation plan generation unit may be performed using AI, for example, or without AI. For example, the evacuation plan generation unit can input the user's social media activity data into a generation AI and have the generation AI provide relevant evacuation information.

[0042] The disaster prevention education provision unit can provide optimal educational content by referring to the user's past learning history when providing disaster prevention education. For example, the disaster prevention education provision unit can provide optimal educational content as the next step based on what the user has learned in the past. The disaster prevention education provision unit can also identify areas where the user has a low level of understanding from the user's past learning history and provide educational content intensively in those areas. The disaster prevention education provision unit can also review what the user has learned in the past to reinforce their knowledge. In this way, the disaster prevention education provision unit can provide optimal disaster prevention education content by referring to the user's past learning history. Some or all of the above processes in the disaster prevention education provision unit may be performed using AI, for example, or without using AI. For example, the disaster prevention education provision unit can input the user's past learning history data into a generating AI and have the generating AI perform the task of providing optimal educational content.

[0043] The disaster prevention education delivery unit can evaluate the user's current knowledge level in real time when providing disaster prevention education and adjust the educational content accordingly. For example, the disaster prevention education delivery unit can evaluate the user's current knowledge level in a quiz format and provide appropriate educational content. The disaster prevention education delivery unit can also monitor the user's knowledge level in real time and adjust the educational content according to their level of understanding. The disaster prevention education delivery unit can also evaluate the user's knowledge level and provide additional educational content as needed. In this way, the disaster prevention education delivery unit can provide appropriate disaster prevention education content by evaluating the current knowledge level in real time. Some or all of the above processes in the disaster prevention education delivery unit may be performed using AI, for example, or without AI. For example, the disaster prevention education delivery unit can input the user's knowledge level data into a generating AI and have the generating AI perform the adjustment of the educational content.

[0044] The disaster prevention education provision unit can provide optimal educational content by considering the user's geographical location information when providing disaster prevention education. For example, the disaster prevention education provision unit can provide optimal disaster prevention education content by considering the geographical characteristics of the area where the user lives. If the user is traveling, the disaster prevention education provision unit can also provide disaster prevention education content by considering the geographical characteristics of the current location. If the user lives in multiple areas, the disaster prevention education provision unit can also provide disaster prevention education content by considering the geographical characteristics of each area. In this way, the disaster prevention education provision unit can provide optimal disaster prevention education content by considering geographical location information. Some or all of the above processing in the disaster prevention education provision unit may be performed using AI, for example, or without using AI. For example, the disaster prevention education provision unit can input the user's geographical location information into a generating AI and have the generating AI execute the provision of optimal educational content.

[0045] The disaster prevention education provision department can analyze users' social media activity and provide relevant educational information when providing disaster prevention education. For example, the disaster prevention education provision department can provide relevant disaster prevention education content based on information shared by users on social media. The disaster prevention education provision department can also analyze information on accounts that users follow on social media and provide relevant disaster prevention education content. The disaster prevention education provision department can also analyze content posted by users on social media to improve the accuracy of disaster prevention education. In this way, the disaster prevention education provision department can improve the accuracy of disaster prevention education by analyzing social media activity. Some or all of the above processing in the disaster prevention education provision department may be performed using AI, for example, or without AI. For example, the disaster prevention education provision department can input user social media activity data into a generating AI and have the generating AI provide relevant educational information.

[0046] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0047] The risk assessment unit can improve the accuracy of its risk assessments by referring to the user's past evacuation history. For example, it can record the locations and routes the user has evacuated to in the past and reflect this in risk assessments for similar situations. It can also analyze the success rate and problems of evacuations from the user's past evacuation history and utilize this information in risk assessments. Furthermore, it can improve the accuracy of risk assessments by considering the time of day and weather conditions when the user evacuated in the past. In this way, the risk assessment unit can improve the accuracy of its risk assessments by referring to past evacuation history. Some or all of the above processing in the risk assessment unit may be performed using AI, for example, or without AI. For example, the risk assessment unit can input the user's past evacuation history data into a generating AI and have the generating AI perform the task of improving the accuracy of the risk assessment.

[0048] The risk assessment unit can perform risk assessments while considering the user's lifestyle patterns. For example, it can consider the user's commuting time and perform risk assessments for that time period. It can also consider how the user spends their holidays and vacations and perform risk assessments for specific locations. Furthermore, it can customize risk assessments for specific time periods and locations based on the user's lifestyle patterns. This allows the risk assessment unit to perform more accurate risk assessments by considering the user's lifestyle patterns. Some or all of the above processing in the risk assessment unit may be performed using AI, for example, or without AI. For example, the risk assessment unit can input user lifestyle pattern data into a generating AI and have the generating AI perform the risk assessment.

[0049] The risk assessment unit can perform risk assessments while considering the geographical distribution of users. For example, it can perform risk assessments while considering the geographical characteristics of the area where the user lives. Furthermore, if the user is traveling, it can perform risk assessments while considering the geographical characteristics of their current location. In addition, if the user lives in multiple areas, it can perform risk assessments while considering the geographical characteristics of each area. This allows the risk assessment unit to perform more accurate risk assessments by considering the geographical distribution of users. Some or all of the above processing in the risk assessment unit may be performed using AI, for example, or without AI. For example, the risk assessment unit can input the user's geographical distribution data into a generating AI and have the generating AI perform the risk assessment.

[0050] The risk assessment unit can analyze a user's social media activity and obtain relevant risk information. For example, it can perform a risk assessment based on location information shared by the user on social media. It can also analyze information about accounts that the user follows on social media and reflect this in the risk assessment. Furthermore, it can analyze the content that the user posts on social media to improve the accuracy of the risk assessment. In this way, the risk assessment unit can improve the accuracy of the risk assessment by analyzing social media activity. Some or all of the above processing in the risk assessment unit may be performed using AI, for example, or without AI. For example, the risk assessment unit can input user social media activity data into a generating AI and have the generating AI perform the task of improving the accuracy of the risk assessment.

[0051] The evacuation plan generation unit can generate an optimal evacuation plan by referring to the user's past evacuation actions. For example, it can record the locations and routes the user has evacuated to in the past and reflect this in evacuation plans for similar situations. It can also analyze the success rate and problems of evacuation from the user's past evacuation actions and utilize this information in the evacuation plan. Furthermore, it can generate an optimal evacuation plan by considering the time of day and weather conditions when the user evacuated in the past. In this way, the evacuation plan generation unit can generate an optimal evacuation plan by referring to past evacuation actions. Some or all of the above processing in the evacuation plan generation unit may be performed using AI, for example, or without AI. For example, the evacuation plan generation unit can input the user's past evacuation action data into a generation AI and have the generation AI execute the generation of an optimal evacuation plan.

[0052] The evacuation plan generation unit can monitor the user's current health status in real time and adjust the evacuation plan accordingly. For example, it can monitor the user's heart rate and blood pressure and provide an evacuation plan tailored to their health condition. Furthermore, if the user is feeling unwell, it can adjust the evacuation plan and suggest a safe evacuation route. It can also monitor the user's health status in real time and update the evacuation plan as needed. This allows the evacuation plan generation unit to provide an appropriate evacuation plan by monitoring the user's health status in real time. Some or all of the above-described processes in the evacuation plan generation unit may be performed using AI, for example, or without AI. For example, the evacuation plan generation unit can input the user's health status data into a generation AI and have the generation AI perform the adjustment of the evacuation plan.

[0053] The following briefly describes the processing flow for example form 1.

[0054] Step 1: The Risk Assessment Unit analyzes user location information, weather data, earthquake information, etc., in real time to continuously assess individual risk. The Risk Assessment Unit obtains user location information from GPS, Wi-Fi location information, mobile base station information, etc., uses weather data from the Japan Meteorological Agency and real-time updated data, and uses earthquake information from seismometer networks and real-time updated data. The Risk Assessment Unit analyzes the data in real time using data streaming technology and real-time data processing algorithms, and continuously assesses individual risk based on the risk score calculation method and assessment frequency. Step 2: The evacuation plan generation unit proposes the optimal evacuation route and shelter based on the risks assessed by the risk assessment unit, taking into account the user's health condition, family structure, and whether or not they have pets. The evacuation plan generation unit obtains the user's health condition from data such as heart rate, blood pressure, and health app data, obtains information on family structure such as the number of family members, their ages, and members requiring special care, and obtains information on pets such as the type and number of pets and pets requiring special care. The evacuation plan generation unit proposes the optimal evacuation route based on criteria such as distance, time, and safety, and proposes shelters based on conditions such as capacity, facilities, and access methods. Step 3: The disaster prevention education department provides users with disaster prevention quizzes and simulation games based on the evacuation plans proposed by the evacuation plan generation department. The disaster prevention education department provides disaster prevention quizzes and simulation games tailored to the user's knowledge level. Disaster prevention quizzes are provided based on the type and difficulty of questions and the method of presentation, while simulation games are provided based on the game scenario, interactivity, and educational effect.

[0055] (Example of form 2) The disaster prevention support system according to an embodiment of the present invention is a comprehensive disaster prevention support system tailored to the needs of the community and individuals. This disaster prevention support system analyzes the user's location information, weather data, earthquake information, etc., in real time to constantly assess the individual's risk. Next, it proposes the optimal evacuation route and shelter, taking into account the user's health condition, family structure, presence of pets, etc. It also provides disaster prevention quizzes and simulation games tailored to the user's knowledge level, allowing them to acquire disaster prevention knowledge while having fun. Furthermore, it matches users with neighbors and local volunteers to promote mutual assistance during disasters. In addition, it provides information and support in multiple languages ​​for foreign residents and tourists. Finally, it provides stress management and psychological support before and after disasters. Through this mechanism, it is possible to create an environment where everyone can live safely, regardless of their level of disaster prevention awareness. For example, it analyzes the user's location information, weather data, earthquake information, etc., in real time to constantly assess the individual's risk. In this process, AI is used to analyze the data and perform risk assessment. For example, if a user is in an earthquake-prone area, the risk is assessed highly, and appropriate evacuation actions are proposed. Next, it proposes the optimal evacuation route and shelter, taking into account the user's health condition, family structure, presence of pets, etc. For example, if a user is elderly, a barrier-free evacuation shelter will be suggested. If a user has a pet, a pet-friendly evacuation shelter will be suggested. Furthermore, disaster preparedness quizzes and simulation games tailored to the user's knowledge level will be provided, allowing them to acquire disaster preparedness knowledge while having fun. For example, simple quizzes will be offered for children, and simulation games for adults, disseminating disaster preparedness knowledge to a wide range of people. Matching users with neighbors and local volunteers will also be facilitated to promote mutual assistance during disasters. For example, participating in local disaster preparedness drills can deepen cooperation with neighbors. In addition, information and support will be provided in multiple languages ​​for foreign residents and tourists. For example, providing disaster preparedness information in multiple languages ​​such as English and Chinese will allow foreigners to evacuate with confidence. Finally, stress management and psychological support will be provided before and after disasters. For example, counseling services will be offered to reduce stress after a disaster, providing mental health care.In this way, by providing a comprehensive disaster prevention support system tailored to the needs of the community and individuals, it is possible to create an environment where everyone can live safely, regardless of their level of disaster preparedness. This allows the disaster prevention support system to assess user risks in real time and provide optimal evacuation plans and disaster prevention education, thereby creating a safe living environment.

[0056] The disaster prevention support system according to this embodiment comprises a risk assessment unit, an evacuation plan generation unit, and a disaster prevention education provision unit. The risk assessment unit analyzes the user's location information, weather data, earthquake information, etc., in real time and constantly assesses the individual's risk. For example, the risk assessment unit obtains the user's location information from GPS, Wi-Fi location information, mobile base station information, etc. Weather data uses data from the Japan Meteorological Agency or data updated in real time. Earthquake information uses data from seismometer networks or data updated in real time. The risk assessment unit analyzes the data in real time using data streaming technology and real-time data processing algorithms. The risk assessment unit constantly assesses the individual's risk based on the risk score calculation method and evaluation frequency. For example, if the user is in an earthquake-prone area, the risk assessment unit will rate the risk highly and propose appropriate evacuation actions. Based on the risk assessed by the risk assessment unit, the evacuation plan generation unit proposes the optimal evacuation route and shelter, taking into account the user's health condition, family structure, presence of pets, etc. For example, the evacuation plan generation unit obtains the user's health condition from data such as heart rate, blood pressure, and health app data. The family structure information includes the number of family members, their ages, and any members requiring special care. The pet information includes the type and number of pets, and any pets requiring special care. The evacuation plan generation unit proposes the optimal evacuation route based on criteria such as distance, time, and safety. Evacuation shelters are proposed based on conditions such as capacity, facilities, and access methods. For example, if the user is elderly, the evacuation plan generation unit will propose a barrier-free shelter. If the user has pets, it will propose a pet-friendly shelter. The disaster prevention education provision unit provides users with disaster prevention quizzes and simulation games based on the evacuation plan proposed by the evacuation plan generation unit. For example, the disaster prevention education provision unit provides disaster prevention quizzes and simulation games tailored to the user's knowledge level. Disaster prevention quizzes are provided based on the type and difficulty of questions and the presentation method. Simulation games are provided based on the game scenario, interactivity, and educational effectiveness. For example, the disaster prevention education provision unit provides simple quizzes for children and simulation games for adults.As a result, the disaster prevention support system according to this embodiment can assess the user's risks in real time and provide optimal evacuation plans and disaster prevention education, thereby creating an environment in which people can live safely.

[0057] The Risk Assessment Unit analyzes user location information, weather data, earthquake information, etc., in real time to continuously assess individual risks. For example, the Risk Assessment Unit obtains user location information from GPS, Wi-Fi location information, and cell tower information. This allows it to accurately determine where the user is currently located. Weather data uses data from the Japan Meteorological Agency and real-time updated data. This allows it to assess risk based on current weather conditions and future forecasts. Earthquake information uses data from seismometer networks and real-time updated data. This allows it to quickly obtain information such as earthquake occurrences, epicenters, and seismic intensity, and reflect this in risk assessments. The Risk Assessment Unit analyzes data in real time using data streaming technology and real-time data processing algorithms. This allows it to process collected data immediately and quickly assess user risks. The Risk Assessment Unit continuously assesses individual risks based on the risk score calculation method and evaluation frequency. For example, if a user is in an earthquake-prone area, the Risk Assessment Unit will rate the risk higher and suggest appropriate evacuation actions. The risk score is calculated based on the user's location information, weather data, earthquake information, etc., and is notified to the user in real time. This allows users to constantly understand their own risks and take appropriate action. Furthermore, the risk assessment unit can utilize historical data and statistical information to conduct long-term risk assessments and trend analyses. For example, it can predict risk fluctuations in specific regions and time periods based on past earthquake data and formulate future countermeasures. In addition, the risk assessment unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, and issue warnings early. As a result, the risk assessment unit can not only grasp the situation in real time but also handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0058] The evacuation plan generation unit proposes the optimal evacuation route and shelter based on the risks assessed by the risk assessment unit, taking into account the user's health condition, family structure, and whether or not they have pets. For example, the evacuation plan generation unit obtains the user's health condition from data such as heart rate, blood pressure, and health app data. This allows for an accurate understanding of the user's physical condition and health status, which can then be reflected in the evacuation plan. For family structure, it obtains information such as the number of family members, their ages, and any members requiring special care. This allows for a plan that ensures the safe evacuation of all family members. For pet ownership, it obtains information such as the type and number of pets, and any pets requiring special care. This allows for an evacuation plan that includes pets. The evacuation plan generation unit proposes the optimal evacuation route based on criteria such as distance, time, and safety. For example, if the user is elderly, the evacuation plan generation unit will propose a barrier-free evacuation route. If the user has pets, it will propose a pet-friendly evacuation route. Shelters are proposed based on conditions such as capacity, facilities, and access methods. For example, if the user is elderly, the evacuation plan generation unit will propose a barrier-free shelter. If the user has pets, it will propose a pet-friendly shelter. This allows the evacuation plan generation unit to provide an optimal evacuation plan tailored to each user's individual circumstances. Furthermore, the evacuation plan generation unit can continuously revise the evacuation plan based on real-time updated data, enabling it to respond to the latest situations. For example, if an evacuation route is blocked or an evacuation center becomes full, the evacuation plan generation unit immediately incorporates new data and updates the evacuation plan. In addition, the evacuation plan generation unit can create more accurate evacuation plans by considering the characteristics of each region and past disaster history. As a result, the evacuation plan generation unit can always provide highly accurate evacuation plans based on the latest information, supporting a quick and appropriate response.

[0059] The Disaster Prevention Education Provision Department provides users with disaster prevention quizzes and simulation games based on evacuation plans proposed by the Evacuation Plan Generation Department. For example, the Disaster Prevention Education Provision Department provides disaster prevention quizzes and simulation games tailored to the user's knowledge level. Disaster prevention quizzes are provided based on the type and difficulty level of questions and the method of presentation. For example, simple quizzes are provided for children, and simulation games are provided for adults. Simulation games are provided based on the game's scenario, interactivity, and educational effect. For example, games that simulate actions taken during an earthquake or games that allow users to select evacuation routes are possible. This allows users to acquire disaster prevention knowledge while having fun. The Disaster Prevention Education Provision Department can collect user feedback and continuously improve the accuracy and effectiveness of the educational content. For example, the difficulty level and method of presentation of questions are reviewed based on feedback from users who have played disaster prevention quizzes and simulation games. In addition, the Disaster Prevention Education Provision Department can reliably transmit information using multiple communication methods. For example, important information is reliably delivered not only through smartphone notifications but also through voice calls, SMS, and email. This allows the Disaster Prevention Education Provision Department to provide disaster prevention education to users quickly and reliably, minimizing the risk of disaster. Furthermore, the disaster prevention education department can provide more effective disaster prevention education by taking into account the characteristics of each region and past disaster history. For example, in areas prone to earthquakes, education will focus on earthquake countermeasures, and in areas prone to floods, education will focus on flood countermeasures. In this way, the disaster prevention education department can assess the user's risk in real time and provide optimal evacuation plans and disaster prevention education, thereby creating an environment where people can live safely.

[0060] The disaster prevention education provider can provide disaster prevention quizzes and simulation games tailored to the user's knowledge level. The disaster prevention education provider can, for example, evaluate the user's knowledge level based on pre-tests and past learning history. The disaster prevention education provider provides simple quizzes and simulation games according to the user's knowledge level. For example, the disaster prevention education provider can provide simple quizzes for children and simulation games for adults. In this way, the disaster prevention education provider can effectively help users acquire disaster prevention knowledge by providing disaster prevention education tailored to their knowledge level. Some or all of the above processing in the disaster prevention education provider may be performed using, for example, a generative AI, or without a generative AI. For example, the disaster prevention education provider can input the user's knowledge level into a generative AI, and the generative AI can generate appropriate disaster prevention quizzes and simulation games.

[0061] The risk assessment unit can estimate the user's emotions and adjust the frequency of risk assessments based on the estimated emotions. For example, the risk assessment unit can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, the risk assessment unit can calculate an emotion score based on changes in facial expressions. The risk assessment unit can also record the user's voice and estimate emotions using voice analysis technology. For example, the risk assessment unit can analyze the tone and speed of the voice and calculate an emotion score. The risk assessment unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the risk assessment unit can calculate an emotion score based on fluctuations in heart rate. This allows the risk assessment unit to provide appropriate information by adjusting the frequency of risk assessments according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the risk assessment unit may be performed using AI, for example, or without AI. For example, the risk assessment unit can input user image data captured by a camera into a generating AI and have the generating AI perform the estimation of the user's emotions.

[0062] The risk assessment unit can improve the accuracy of its risk assessments by referring to the user's past evacuation history. For example, the risk assessment unit can record the locations and routes the user has evacuated to in the past and reflect this in risk assessments for similar situations. The risk assessment unit can also analyze the success rate and problems of evacuations from the user's past evacuation history and utilize this in risk assessments. The risk assessment unit can also improve the accuracy of its risk assessments by considering the time of day and weather conditions when the user evacuated in the past. In this way, the risk assessment unit can improve the accuracy of its risk assessments by referring to past evacuation history. Some or all of the above processes in the risk assessment unit may be performed using AI, for example, or without AI. For example, the risk assessment unit can input the user's past evacuation history data into a generating AI and have the generating AI perform the task of improving the accuracy of the risk assessment.

[0063] The risk assessment unit can perform risk assessments while considering the user's lifestyle patterns. For example, the risk assessment unit can consider the user's commuting time and perform risk assessments during that time period. The risk assessment unit can also consider how the user spends their holidays and vacations and perform risk assessments at specific locations. The risk assessment unit can also customize risk assessments at specific times and locations based on the user's lifestyle patterns. This allows the risk assessment unit to perform more accurate risk assessments by considering the user's lifestyle patterns. Some or all of the above processing in the risk assessment unit may be performed using AI, for example, or without AI. For example, the risk assessment unit can input user lifestyle pattern data into a generating AI and have the generating AI perform the risk assessment.

[0064] The risk assessment unit can estimate the user's emotions and adjust the order in which the risk assessment results are displayed based on the estimated emotions. For example, if the user is feeling anxious, the risk assessment unit will prioritize displaying the most important risk information. If the user is relaxed, the risk assessment unit can also display detailed risk information in a sequential manner. If the user is feeling stressed, the risk assessment unit can also display concise and to-the-point risk information. In this way, the risk assessment unit can provide appropriate information by adjusting the order in which the risk assessment results are displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the risk assessment unit may be performed using AI, for example, or without AI. For example, the risk assessment unit can input user emotion data into the generative AI and have the generative AI adjust the display order of the risk assessment results.

[0065] The risk assessment unit can perform risk assessments while considering the geographical distribution of users. For example, the risk assessment unit can perform risk assessments while considering the geographical characteristics of the area where the user lives. If the user is traveling, the risk assessment unit can also perform risk assessments while considering the geographical characteristics of the current location. If the user lives in multiple areas, the risk assessment unit can also perform risk assessments while considering the geographical characteristics of each area. This allows the risk assessment unit to perform more accurate risk assessments by considering the geographical distribution of users. Some or all of the above processing in the risk assessment unit may be performed using AI, for example, or without using AI. For example, the risk assessment unit can input the geographical distribution data of users into a generating AI and have the generating AI perform the risk assessment.

[0066] The risk assessment unit can analyze a user's social media activity and obtain relevant risk information during risk assessment. For example, the risk assessment unit can perform risk assessment based on location information shared by the user on social media. The risk assessment unit can also analyze information on accounts that the user follows on social media and reflect this in the risk assessment. The risk assessment unit can also analyze content posted by the user on social media to improve the accuracy of the risk assessment. In this way, the risk assessment unit can improve the accuracy of the risk assessment by analyzing social media activity. Some or all of the above processing in the risk assessment unit may be performed using AI, for example, or without AI. For example, the risk assessment unit can input the user's social media activity data into a generating AI and have the generating AI perform the task of improving the accuracy of the risk assessment.

[0067] The evacuation plan generation unit can estimate the user's emotions and adjust the level of detail in the evacuation plan based on the estimated emotions. For example, if the user is feeling anxious, the evacuation plan generation unit can provide a detailed evacuation plan to reassure them. If the user is relaxed, the evacuation plan generation unit can also provide a concise evacuation plan, conveying only the necessary information. If the user is feeling stressed, the evacuation plan generation unit can appropriately adjust the level of detail in the evacuation plan to avoid providing excessive information. In this way, the evacuation plan generation unit can provide an appropriate evacuation plan by adjusting the level of detail in the evacuation plan according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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-described processes in the evacuation plan generation unit may be performed using AI, for example, or without AI. For example, the evacuation plan generation unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the level of detail in the evacuation plan.

[0068] The evacuation plan generation unit can generate an optimal evacuation plan by referring to the user's past evacuation actions. For example, the evacuation plan generation unit can record the locations and routes the user has evacuated to in the past and reflect this in evacuation plans for similar situations. The evacuation plan generation unit can also analyze the success rate and problems of evacuation from the user's past evacuation actions and utilize this in the evacuation plan. The evacuation plan generation unit can also generate an optimal evacuation plan by considering the time of day and weather conditions when the user evacuated in the past. In this way, the evacuation plan generation unit can generate an optimal evacuation plan by referring to past evacuation actions. Some or all of the above processing in the evacuation plan generation unit may be performed using AI, for example, or without using AI. For example, the evacuation plan generation unit can input the user's past evacuation action data into a generation AI and have the generation AI execute the generation of an optimal evacuation plan.

[0069] The evacuation plan generation unit can monitor the user's current health status in real time and adjust the evacuation plan accordingly. For example, the unit can monitor the user's heart rate and blood pressure and provide an evacuation plan tailored to their health condition. If the user is feeling unwell, the unit can adjust the evacuation plan and suggest a safe evacuation route. The unit can also monitor the user's health status in real time and update the evacuation plan as needed. This allows the unit to provide an appropriate evacuation plan by monitoring the user's health status in real time. Some or all of the above-described processes in the evacuation plan generation unit may be performed using AI, for example, or without AI. For example, the evacuation plan generation unit can input the user's health status data into a generation AI and have the generation AI perform the adjustment of the evacuation plan.

[0070] The evacuation plan generation unit can estimate the user's emotions and determine the priority of the evacuation plan based on the estimated emotions. For example, if the user is feeling anxious, the evacuation plan generation unit will prioritize suggesting the safest evacuation route. If the user is relaxed, the evacuation plan generation unit can also prioritize suggesting the most efficient evacuation route. If the user is feeling stressed, the evacuation plan generation unit can appropriately adjust the priority of the evacuation plan and avoid providing excessive information. In this way, the evacuation plan generation unit can provide an appropriate evacuation plan by determining the priority of the evacuation plan according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the evacuation plan generation unit may be performed using AI, for example, or without AI. For example, the evacuation plan generation unit can input the user's emotion data into a generative AI and have the generative AI perform the determination of the priority of the evacuation plan.

[0071] The evacuation plan generation unit can propose the optimal evacuation route by considering the user's geographical location information when generating an evacuation plan. For example, the evacuation plan generation unit can propose the optimal evacuation route by considering the geographical characteristics of the area where the user lives. If the user is traveling, the evacuation plan generation unit can also propose the optimal evacuation route by considering the geographical characteristics of the current location. If the user lives in multiple areas, the evacuation plan generation unit can also propose the optimal evacuation route by considering the geographical characteristics of each area. In this way, the evacuation plan generation unit can propose the optimal evacuation route by considering geographical location information. Some or all of the above processing in the evacuation plan generation unit may be performed using AI, for example, or without AI. For example, the evacuation plan generation unit can input the user's geographical location information into a generation AI and have the generation AI propose the optimal evacuation route.

[0072] The evacuation plan generation unit can analyze the user's social media activity and provide relevant evacuation information when generating an evacuation plan. For example, the evacuation plan generation unit can propose the optimal evacuation route based on location information shared by the user on social media. The evacuation plan generation unit can also analyze information from accounts that the user follows on social media and provide relevant evacuation information. The evacuation plan generation unit can also analyze content posted by the user on social media to improve the accuracy of the evacuation plan. In this way, the evacuation plan generation unit can improve the accuracy of the evacuation plan by analyzing social media activity. Some or all of the above processing in the evacuation plan generation unit may be performed using AI, for example, or without AI. For example, the evacuation plan generation unit can input the user's social media activity data into a generation AI and have the generation AI provide relevant evacuation information.

[0073] The disaster prevention education provider can estimate the user's emotions and adjust the content of the disaster prevention education based on the estimated emotions. For example, if the user is feeling anxious, the provider can provide disaster prevention education that provides a sense of security. If the user is relaxed, the provider can also provide detailed disaster prevention education to deepen their knowledge. If the user is stressed, the provider can also provide concise and to-the-point disaster prevention education. In this way, the disaster prevention education provider can provide effective disaster prevention education by adjusting the content of the education according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the disaster prevention education provider may be performed using AI, for example, or not using AI. For example, the disaster prevention education provider can input user emotion data into the generative AI and have the generative AI perform the adjustment of the disaster prevention education content.

[0074] The disaster prevention education provision unit can provide optimal educational content by referring to the user's past learning history when providing disaster prevention education. For example, the disaster prevention education provision unit can provide optimal educational content as the next step based on what the user has learned in the past. The disaster prevention education provision unit can also identify areas where the user has a low level of understanding from the user's past learning history and provide educational content intensively in those areas. The disaster prevention education provision unit can also review what the user has learned in the past to reinforce their knowledge. In this way, the disaster prevention education provision unit can provide optimal disaster prevention education content by referring to the user's past learning history. Some or all of the above processes in the disaster prevention education provision unit may be performed using AI, for example, or without using AI. For example, the disaster prevention education provision unit can input the user's past learning history data into a generating AI and have the generating AI perform the task of providing optimal educational content.

[0075] The disaster prevention education delivery unit can evaluate the user's current knowledge level in real time when providing disaster prevention education and adjust the educational content accordingly. For example, the disaster prevention education delivery unit can evaluate the user's current knowledge level in a quiz format and provide appropriate educational content. The disaster prevention education delivery unit can also monitor the user's knowledge level in real time and adjust the educational content according to their level of understanding. The disaster prevention education delivery unit can also evaluate the user's knowledge level and provide additional educational content as needed. In this way, the disaster prevention education delivery unit can provide appropriate disaster prevention education content by evaluating the current knowledge level in real time. Some or all of the above processes in the disaster prevention education delivery unit may be performed using AI, for example, or without AI. For example, the disaster prevention education delivery unit can input the user's knowledge level data into a generating AI and have the generating AI perform the adjustment of the educational content.

[0076] The disaster prevention education provider can estimate the user's emotions and determine the priority of disaster prevention education based on the estimated emotions. For example, if the user is feeling anxious, the provider will prioritize providing the most important disaster prevention education content. If the user is relaxed, the provider can also provide detailed disaster prevention education content in a sequential manner. If the user is stressed, the provider can also provide concise and to-the-point disaster prevention education content. In this way, the provider can provide effective disaster prevention education by determining the priority of disaster prevention education according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the disaster prevention education provider may be performed using AI, for example, or not using AI. For example, the disaster prevention education provider can input user emotion data into a generative AI and have the generative AI determine the priority of disaster prevention education.

[0077] The disaster prevention education provision unit can provide optimal educational content by considering the user's geographical location information when providing disaster prevention education. For example, the disaster prevention education provision unit can provide optimal disaster prevention education content by considering the geographical characteristics of the area where the user lives. If the user is traveling, the disaster prevention education provision unit can also provide disaster prevention education content by considering the geographical characteristics of the current location. If the user lives in multiple areas, the disaster prevention education provision unit can also provide disaster prevention education content by considering the geographical characteristics of each area. In this way, the disaster prevention education provision unit can provide optimal disaster prevention education content by considering geographical location information. Some or all of the above processing in the disaster prevention education provision unit may be performed using AI, for example, or without using AI. For example, the disaster prevention education provision unit can input the user's geographical location information into a generating AI and have the generating AI execute the provision of optimal educational content.

[0078] The disaster prevention education provision department can analyze users' social media activity and provide relevant educational information when providing disaster prevention education. For example, the disaster prevention education provision department can provide relevant disaster prevention education content based on information shared by users on social media. The disaster prevention education provision department can also analyze information on accounts that users follow on social media and provide relevant disaster prevention education content. The disaster prevention education provision department can also analyze content posted by users on social media to improve the accuracy of disaster prevention education. In this way, the disaster prevention education provision department can improve the accuracy of disaster prevention education by analyzing social media activity. Some or all of the above processing in the disaster prevention education provision department may be performed using AI, for example, or without AI. For example, the disaster prevention education provision department can input user social media activity data into a generating AI and have the generating AI provide relevant educational information.

[0079] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0080] The risk assessment unit can estimate the user's emotions and adjust the frequency of risk assessments based on the estimated emotions. For example, if the user is feeling anxious, the frequency of risk assessments can be increased to provide more detailed information. Conversely, if the user is relaxed, the frequency of risk assessments can be decreased to provide only the minimum necessary information. Furthermore, if the user is feeling stressed, the frequency of risk assessments can be appropriately adjusted to avoid providing excessive information. In this way, the risk assessment unit can provide appropriate information by adjusting the frequency of risk assessments according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the risk assessment unit may be performed using AI, or not using AI. For example, the risk assessment unit can input user image data captured by a camera into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0081] The risk assessment unit can improve the accuracy of its risk assessments by referring to the user's past evacuation history. For example, it can record the locations and routes the user has evacuated to in the past and reflect this in risk assessments for similar situations. It can also analyze the success rate and problems of evacuations from the user's past evacuation history and utilize this information in risk assessments. Furthermore, it can improve the accuracy of risk assessments by considering the time of day and weather conditions when the user evacuated in the past. In this way, the risk assessment unit can improve the accuracy of its risk assessments by referring to past evacuation history. Some or all of the above processing in the risk assessment unit may be performed using AI, for example, or without AI. For example, the risk assessment unit can input the user's past evacuation history data into a generating AI and have the generating AI perform the task of improving the accuracy of the risk assessment.

[0082] The risk assessment unit can perform risk assessments while considering the user's lifestyle patterns. For example, it can consider the user's commuting time and perform risk assessments for that time period. It can also consider how the user spends their holidays and vacations and perform risk assessments for specific locations. Furthermore, it can customize risk assessments for specific time periods and locations based on the user's lifestyle patterns. This allows the risk assessment unit to perform more accurate risk assessments by considering the user's lifestyle patterns. Some or all of the above processing in the risk assessment unit may be performed using AI, for example, or without AI. For example, the risk assessment unit can input user lifestyle pattern data into a generating AI and have the generating AI perform the risk assessment.

[0083] The risk assessment unit can estimate the user's emotions and adjust the order in which the risk assessment results are displayed based on the estimated emotions. For example, if the user is feeling anxious, the most important risk information can be displayed first. If the user is relaxed, detailed risk information can be displayed in a sequential manner. Furthermore, if the user is feeling stressed, concise and to-the-point risk information can be displayed. In this way, the risk assessment unit can provide appropriate information by adjusting the order in which the risk assessment results are displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the risk assessment unit may be performed using AI, or not using AI. For example, the risk assessment unit can input user emotion data into the generative AI and have the generative AI adjust the display order of the risk assessment results.

[0084] The risk assessment unit can perform risk assessments while considering the geographical distribution of users. For example, it can perform risk assessments while considering the geographical characteristics of the area where the user lives. Furthermore, if the user is traveling, it can perform risk assessments while considering the geographical characteristics of their current location. In addition, if the user lives in multiple areas, it can perform risk assessments while considering the geographical characteristics of each area. This allows the risk assessment unit to perform more accurate risk assessments by considering the geographical distribution of users. Some or all of the above processing in the risk assessment unit may be performed using AI, for example, or without AI. For example, the risk assessment unit can input the user's geographical distribution data into a generating AI and have the generating AI perform the risk assessment.

[0085] The risk assessment unit can analyze a user's social media activity and obtain relevant risk information. For example, it can perform a risk assessment based on location information shared by the user on social media. It can also analyze information about accounts that the user follows on social media and reflect this in the risk assessment. Furthermore, it can analyze the content that the user posts on social media to improve the accuracy of the risk assessment. In this way, the risk assessment unit can improve the accuracy of the risk assessment by analyzing social media activity. Some or all of the above processing in the risk assessment unit may be performed using AI, for example, or without AI. For example, the risk assessment unit can input user social media activity data into a generating AI and have the generating AI perform the task of improving the accuracy of the risk assessment.

[0086] The evacuation plan generation unit can estimate the user's emotions and adjust the level of detail in the evacuation plan based on the estimated emotions. For example, if the user is feeling anxious, it can provide a detailed evacuation plan to reassure them. If the user is relaxed, it can provide a concise evacuation plan, conveying only the necessary information. Furthermore, if the user is feeling stressed, it can appropriately adjust the level of detail in the evacuation plan to avoid providing excessive information. In this way, the evacuation plan generation unit can provide an appropriate evacuation plan by adjusting the level of detail in the evacuation plan according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The 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 evacuation plan generation unit may be performed using AI, for example, or without AI. For example, the evacuation plan generation unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the level of detail in the evacuation plan.

[0087] The evacuation plan generation unit can generate an optimal evacuation plan by referring to the user's past evacuation actions. For example, it can record the locations and routes the user has evacuated to in the past and reflect this in evacuation plans for similar situations. It can also analyze the success rate and problems of evacuation from the user's past evacuation actions and utilize this information in the evacuation plan. Furthermore, it can generate an optimal evacuation plan by considering the time of day and weather conditions when the user evacuated in the past. In this way, the evacuation plan generation unit can generate an optimal evacuation plan by referring to past evacuation actions. Some or all of the above processing in the evacuation plan generation unit may be performed using AI, for example, or without AI. For example, the evacuation plan generation unit can input the user's past evacuation action data into a generation AI and have the generation AI execute the generation of an optimal evacuation plan.

[0088] The evacuation plan generation unit can monitor the user's current health status in real time and adjust the evacuation plan accordingly. For example, it can monitor the user's heart rate and blood pressure and provide an evacuation plan tailored to their health condition. Furthermore, if the user is feeling unwell, it can adjust the evacuation plan and suggest a safe evacuation route. It can also monitor the user's health status in real time and update the evacuation plan as needed. This allows the evacuation plan generation unit to provide an appropriate evacuation plan by monitoring the user's health status in real time. Some or all of the above-described processes in the evacuation plan generation unit may be performed using AI, for example, or without AI. For example, the evacuation plan generation unit can input the user's health status data into a generation AI and have the generation AI perform the adjustment of the evacuation plan.

[0089] The evacuation plan generation unit can estimate the user's emotions and determine the priority of the evacuation plan based on the estimated emotions. For example, if the user is feeling anxious, it can prioritize suggesting the safest evacuation route. If the user is relaxed, it can prioritize suggesting the most efficient evacuation route. Furthermore, if the user is feeling stressed, it can appropriately adjust the priority of the evacuation plan to avoid providing excessive information. In this way, the evacuation plan generation unit can provide an appropriate evacuation plan by determining the priority of the evacuation plan according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The 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 evacuation plan generation unit may be performed using AI, for example, or without AI. For example, the evacuation plan generation unit can input user emotion data into a generative AI and have the generative AI perform the determination of the priority of the evacuation plan.

[0090] The following briefly describes the processing flow for example form 2.

[0091] Step 1: The Risk Assessment Unit analyzes user location information, weather data, earthquake information, etc., in real time to continuously assess individual risk. The Risk Assessment Unit obtains user location information from GPS, Wi-Fi location information, mobile base station information, etc., uses weather data from the Japan Meteorological Agency and real-time updated data, and uses earthquake information from seismometer networks and real-time updated data. The Risk Assessment Unit analyzes the data in real time using data streaming technology and real-time data processing algorithms, and continuously assesses individual risk based on the risk score calculation method and assessment frequency. Step 2: The evacuation plan generation unit proposes the optimal evacuation route and shelter based on the risks assessed by the risk assessment unit, taking into account the user's health condition, family structure, and whether or not they have pets. The evacuation plan generation unit obtains the user's health condition from data such as heart rate, blood pressure, and health app data, obtains information on family structure such as the number of family members, their ages, and members requiring special care, and obtains information on pets such as the type and number of pets and pets requiring special care. The evacuation plan generation unit proposes the optimal evacuation route based on criteria such as distance, time, and safety, and proposes shelters based on conditions such as capacity, facilities, and access methods. Step 3: The disaster prevention education department provides users with disaster prevention quizzes and simulation games based on the evacuation plans proposed by the evacuation plan generation department. The disaster prevention education department provides disaster prevention quizzes and simulation games tailored to the user's knowledge level. Disaster prevention quizzes are provided based on the type and difficulty of questions and the method of presentation, while simulation games are provided based on the game scenario, interactivity, and educational effect.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] Each of the multiple elements described above, including the risk assessment unit, evacuation plan generation unit, and disaster prevention education provision unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the risk assessment unit acquires the user's location information using the control unit 46A of the smart device 14 and analyzes weather data and earthquake information using the specific processing unit 290 of the data processing unit 12. The evacuation plan generation unit considers the user's health condition, family structure, and whether or not they have pets using the specific processing unit 290 of the data processing unit 12 and proposes the optimal evacuation route and shelter. The disaster prevention education provision unit provides disaster prevention quizzes and simulation games using, for example, the control unit 46A of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0096] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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).

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] Each of the multiple elements described above, including the risk assessment unit, evacuation plan generation unit, and disaster prevention education provision unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the risk assessment unit acquires the user's location information using the control unit 46A of the smart glasses 214 and analyzes weather data and earthquake information using the identification processing unit 290 of the data processing unit 12. The evacuation plan generation unit, for example, uses the identification processing unit 290 of the data processing unit 12 to consider the user's health condition, family structure, and whether or not they have pets, and proposes the optimal evacuation route and shelter. The disaster prevention education provision unit, for example, provides disaster prevention quizzes and simulation games using the control unit 46A of the smart glasses 214. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

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

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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).

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.).

[0124] 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.

[0125] 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.

[0126] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is 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.

[0127] Each of the multiple elements described above, including the risk assessment unit, evacuation plan generation unit, and disaster prevention education provision unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the risk assessment unit acquires the user's location information using the control unit 46A of the headset terminal 314 and analyzes weather data and earthquake information using the identification processing unit 290 of the data processing unit 12. The evacuation plan generation unit considers the user's health condition, family structure, and whether or not they have pets using the identification processing unit 290 of the data processing unit 12 and proposes the optimal evacuation route and shelter. The disaster prevention education provision unit provides disaster prevention quizzes and simulation games using, for example, the control unit 46A of the headset terminal 314. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0128] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] Each of the multiple elements described above, including the risk assessment unit, evacuation plan generation unit, and disaster prevention education provision unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the risk assessment unit acquires the user's location information using the control unit 46A of the robot 414 and analyzes weather data and earthquake information using the specific processing unit 290 of the data processing unit 12. The evacuation plan generation unit considers the user's health condition, family structure, and whether or not they have pets using the specific processing unit 290 of the data processing unit 12 and proposes the optimal evacuation route and shelter. The disaster prevention education provision unit provides disaster prevention quizzes and simulation games using, for example, the control unit 46A of the robot 414. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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."

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] (Note 1) The Risk Assessment Department analyzes user location information, weather data, earthquake information, etc. in real time to constantly assess individual risks. Based on the risks assessed by the aforementioned risk assessment unit, the evacuation plan generation unit proposes the optimal evacuation route and shelter, taking into account the user's health condition, family structure, presence of pets, etc. The system includes a disaster prevention education provision unit that provides users with disaster prevention quizzes and simulation games based on the evacuation plan proposed by the aforementioned evacuation plan generation unit. A system characterized by the following features. (Note 2) The aforementioned disaster prevention education provision department, We provide disaster prevention quizzes and simulation games tailored to the user's knowledge level. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned risk assessment unit, The system estimates the user's emotions and adjusts the frequency of risk assessments based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned risk assessment unit, During risk assessment, referencing the user's past evacuation history improves the accuracy of the risk assessment. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned risk assessment unit, When conducting a risk assessment, the user's lifestyle patterns should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned risk assessment 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 7) The aforementioned risk assessment unit, When conducting a risk assessment, the geographical distribution of users should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned risk assessment unit, During risk assessment, we analyze users' social media activity and obtain relevant risk information. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned evacuation plan generation unit, The system estimates the user's emotions and adjusts the level of detail in the evacuation plan based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned evacuation plan generation unit, When generating an evacuation plan, the system references the user's past evacuation actions to create the optimal evacuation plan. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned evacuation plan generation unit, During the evacuation plan generation process, the system monitors the user's current health status in real time and adjusts the evacuation plan accordingly. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned evacuation plan generation unit, The system estimates user emotions and determines the priority of evacuation plans based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned evacuation plan generation unit, When generating an evacuation plan, the system proposes the optimal evacuation route, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned evacuation plan generation unit, When generating evacuation plans, the system analyzes the user's social media activity and provides relevant evacuation information. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned disaster prevention education provision department, The system estimates the user's emotions and adjusts the content of disaster prevention education based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned disaster prevention education provision department, When providing disaster prevention education, the system provides optimal educational content by referring to the user's past learning history. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned disaster prevention education provision department, When providing disaster prevention education, the system assesses the user's current knowledge level in real time and adjusts the educational content accordingly. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned disaster prevention education provision department, The system estimates user emotions and determines the priority of disaster prevention education based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned disaster prevention education provision department, When providing disaster prevention education, we will provide optimal educational content while taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned disaster prevention education provision department, When providing disaster prevention education, we analyze users' social media activity and provide relevant educational information. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0164] 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 Risk Assessment Department analyzes user location information, weather data, earthquake information, etc. in real time to constantly assess individual risks. Based on the risks assessed by the aforementioned risk assessment unit, the evacuation plan generation unit proposes the optimal evacuation route and shelter, taking into account the user's health condition, family structure, presence of pets, etc. The system includes a disaster prevention education provision unit that provides users with disaster prevention quizzes and simulation games based on the evacuation plan proposed by the aforementioned evacuation plan generation unit. A system characterized by the following features.

2. The aforementioned disaster prevention education provision department, We provide disaster prevention quizzes and simulation games tailored to the user's knowledge level. The system according to feature 1.

3. The aforementioned risk assessment unit, The system estimates the user's emotions and adjusts the frequency of risk assessments based on those estimated emotions. The system according to feature 1.

4. The aforementioned risk assessment unit, During risk assessment, referencing the user's past evacuation history improves the accuracy of the risk assessment. The system according to feature 1.

5. The aforementioned risk assessment unit, When conducting a risk assessment, the user's lifestyle patterns should be taken into consideration. The system according to feature 1.

6. The aforementioned risk assessment 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 according to feature 1.

7. The aforementioned risk assessment unit, When conducting a risk assessment, the geographical distribution of users should be taken into consideration. The system according to feature 1.

8. The aforementioned risk assessment unit, During risk assessment, we analyze users' social media activity and obtain relevant risk information. The system according to feature 1.

9. The aforementioned evacuation plan generation unit, The system estimates the user's emotions and adjusts the level of detail in the evacuation plan based on those estimated emotions. The system according to feature 1.

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