system

Abstract

The system according to this embodiment aims to integrate the entire process, from predicting natural disasters to formulating evacuation plans and managing disaster prevention supplies. [Solution] The system according to the embodiment comprises a prediction unit, a planning unit, an evacuation unit, and a management unit. The prediction unit predicts natural disasters. The planning unit creates individual evacuation plans based on the information predicted by the prediction unit. The evacuation unit provides the optimal evacuation route based on the evacuation plan created by the planning unit. The management unit manages disaster prevention supplies.

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, there is a problem that prediction of natural disasters, formulation of evacuation plans, and management of disaster prevention goods are carried out individually, and there is a lack of integrated disaster prevention measures.

[0005] The system according to the embodiment aims to integrally perform from prediction of natural disasters to formulation of evacuation plans and management of disaster prevention goods.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a prediction unit, a planning unit, an evacuation unit, and a management unit. The prediction unit predicts natural disasters. The planning unit creates individual evacuation plans based on the information predicted by the prediction unit. The evacuation unit provides the optimal evacuation route based on the evacuation plan created by the planning unit. The management unit manages disaster prevention supplies. [Effects of the Invention]

[0007] The system according to this embodiment can comprehensively handle everything from predicting natural disasters to formulating evacuation plans and managing disaster prevention supplies. [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 applied 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 next-generation disaster prevention advisor system according to an embodiment of the present invention is a system that predicts disaster risks and supports disaster preparedness in homes and communities. This system provides a comprehensive solution to enhance response capabilities in the event of a disaster, including natural disaster prediction, evacuation plan development, and management of disaster preparedness supplies. The next-generation disaster prevention advisor system uses machine learning and real-time data analysis to predict natural disasters such as earthquakes, tsunamis, hurricanes, and floods. For example, the next-generation disaster prevention advisor system collects data from various sources such as satellite imagery, weather information, and social media, and predicts natural disasters through advanced algorithms and analysis. Next, the next-generation disaster prevention advisor system creates an individualized evacuation plan based on the user's living environment and family structure. For example, the next-generation disaster prevention advisor system recommends the optimal evacuation route and shelter depending on the number of family members and where they live. The next-generation disaster prevention advisor system also provides the optimal evacuation route in real time when a disaster occurs and recommends shelters and evacuation centers. Furthermore, the next-generation disaster prevention advisor system provides a checklist for users to manage the supplies they need in an emergency. For example, the next-generation disaster prevention advisor system lists necessary supplies such as food, water, first-aid kits, and flashlights, helping users to always be prepared. This means the next-generation disaster prevention advisor system will provide a comprehensive solution to enhance disaster response capabilities, including natural disaster prediction, evacuation plan development, disaster response, and disaster preparedness management. In this way, the next-generation disaster prevention advisor system can support disaster preparedness in homes and communities and improve their ability to respond when a disaster occurs.

[0029] The next-generation disaster prevention advisor system according to this embodiment comprises a prediction unit, a planning unit, an evacuation unit, and a management unit. The prediction unit predicts natural disasters. The prediction unit predicts natural disasters such as earthquakes, tsunamis, hurricanes, and floods, for example, using machine learning and real-time data analysis. The prediction unit collects data from various sources such as satellite images, weather information, and social media, and predicts natural disasters using advanced algorithms and analysis. The planning unit creates individual evacuation plans based on the information predicted by the prediction unit. The planning unit creates individual evacuation plans based on the user's living environment and family structure, for example. The planning unit recommends optimal evacuation routes and shelters, for example, depending on the number of family members and where they live. The evacuation unit provides the optimal evacuation route based on the evacuation plan created by the planning unit. The evacuation unit provides the optimal evacuation route in real time when a disaster occurs, for example. The evacuation unit guides the user to the safest evacuation route from their current location in real time. The management unit manages disaster prevention supplies. The management unit provides a checklist for the user to manage the supplies needed in an emergency, for example. The management department, for example, lists necessary supplies such as food, water, first-aid kits, and flashlights, and helps users keep them readily available. This enables the next-generation disaster prevention advisor system according to the embodiment to predict natural disasters, create evacuation plans, provide optimal evacuation routes, and manage disaster prevention supplies.

[0030] The prediction unit predicts natural disasters. For example, it uses machine learning and real-time data analysis to predict natural disasters such as earthquakes, tsunamis, hurricanes, and floods. Specifically, the prediction unit collects data from various sources such as satellite imagery, weather information, and social media, and predicts natural disasters through advanced algorithms and analysis. Satellite imagery is used to understand surface deformation and cloud movement in real time, weather information provides meteorological data such as temperature, humidity, wind speed, and precipitation, and social media collects real-time information posted by users and is used to understand the occurrence and progression of disasters. The prediction unit integrates this data and uses machine learning algorithms to predict the probability of disaster occurrence and the extent of impact. For example, in earthquake prediction, it learns past earthquake data and crustal deformation patterns and compares them with current crustal deformation data to calculate the probability of earthquake occurrence. In tsunami prediction, it predicts the occurrence, direction of movement, and arrival time of tsunamis based on the location and magnitude of earthquakes. In hurricane prediction, it predicts the direction of movement and intensity of hurricanes based on meteorological data and identifies affected areas. In flood prediction, it predicts the risk of flood occurrence and the extent of inundation based on precipitation and river water level data. In this way, the prediction unit can provide information that enables advance detection of natural disasters and rapid response.

[0031] The planning department creates individual evacuation plans based on information predicted by the forecasting department. For example, the planning department creates individual evacuation plans based on the user's living environment and family structure. For example, the planning department recommends the optimal evacuation route and shelter depending on the number of family members and where they live. Specifically, the planning department creates evacuation plans considering the user's residence location, family structure, and special needs (e.g., presence of elderly people or infants). For example, in households with elderly people, barrier-free evacuation routes and shelters are prioritized, and in households with infants, shelters equipped with necessary supplies and facilities are recommended. The planning department also considers whether the user has pets and can guide them to pet-friendly shelters. Furthermore, the planning department also considers the user's living area, such as their workplace or school, and prepares multiple evacuation route patterns in the event of a disaster. For example, it provides different evacuation routes depending on whether the user is at home or at work, enabling rapid evacuation in any situation. Based on this information, the planning department creates and provides a customized evacuation plan for each user. In this way, the planning department can provide the optimal evacuation plan according to the user's individual circumstances and support rapid and safe evacuation in the event of a disaster.

[0032] The evacuation unit provides the optimal evacuation route based on the evacuation plan created by the planning unit. For example, the evacuation unit provides the optimal evacuation route in real time when a disaster occurs. For example, the evacuation unit guides the user to the safest evacuation route from their current location in real time. Specifically, the evacuation unit obtains the user's current location information and calculates the optimal evacuation route considering the progress of the disaster and road conditions. For example, if a flood occurs, it will prioritize selecting roads that are not flooded, and if an earthquake occurs, it will select a route with a low risk of collapse. The evacuation unit can also modify the evacuation route as needed based on information updated in real time. For example, if a new obstacle appears during evacuation, the evacuation unit will immediately calculate a new route and guide the user. The evacuation unit provides the user with an evacuation route using means such as voice guidance, vibration notifications, and map displays. This allows the user to evacuate quickly and safely when a disaster occurs. Furthermore, the evacuation unit can present multiple evacuation routes and allow the user to choose. For example, it can present the shortest route and a safe route, allowing the user to choose according to the situation. This allows the evacuation unit to provide flexible evacuation support that meets the user's needs and ensures safety during disasters.

[0033] The Management Department manages disaster preparedness supplies. For example, the Management Department provides users with checklists to manage the supplies they will need in an emergency. Specifically, the Management Department lists necessary supplies such as food, water, first-aid kits, and flashlights, and helps users keep them on hand. For example, in households with elderly people, medicines and care products are added, and in households with infants, milk and diapers are added. The Management Department also considers whether the user has pets and lists pet food, water, and other supplies. In addition to including items such as pages in the list, the management department manages the expiration dates and best-before dates of supplies and notifies users regularly. For example, when the expiration dates of food or water are approaching, the management department will notify users and encourage them to replenish at the appropriate time. The management department will also provide advice on the storage location and quantity of supplies. For example, it will recommend storing food and water in a cool, dry place, and storing first-aid kits in a place where they can be easily accessed. In this way, the management department can help users properly manage the supplies they need in an emergency and respond quickly in the event of a disaster.

[0034] The notification unit provides real-time alerts and notifications. For example, the notification unit provides real-time alerts and notifications when a disaster occurs. For example, the notification unit sends notifications to the user's smartphone or personal computer. For example, the notification unit provides notifications at the appropriate time depending on the type and urgency of the disaster. For example, the notification unit provides immediate notifications when an earthquake occurs and provides advance notifications when a flood is predicted. This enables a rapid response by providing real-time alerts and notifications. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input real-time data when a disaster occurs into a generating AI and have the generating AI generate the notification content.

[0035] The recommendation unit recommends evacuation shelters and evacuation centers. For example, in the event of a disaster, the recommendation unit recommends the most suitable evacuation shelter or evacuation center based on the user's current location. The recommendation unit makes recommendations considering, for example, the capacity and facilities of the evacuation shelters. The recommendation unit makes recommendations considering, for example, the congestion level and accessibility of the evacuation shelters. The recommendation unit makes recommendations considering, for example, the user's special needs (e.g., facilities for people with disabilities or the elderly). In this way, the recommendation unit can recommend evacuation shelters and evacuation centers, enabling users to evacuate to a safe place. Some or all of the above processing in the recommendation unit may be performed using, for example, AI, or not using AI. For example, the recommendation unit can input information about evacuation shelters into a generating AI and have the generating AI perform the optimal evacuation shelter recommendation.

[0036] The support department provides information and resources necessary for post-disaster recovery efforts. For example, the support department provides information necessary for post-disaster recovery efforts. For example, the support department provides procedures and precautions necessary for recovery efforts. For example, the support department provides information on supplies and equipment necessary for recovery efforts. For example, the support department provides information on resources necessary for recovery efforts (e.g., government agencies and non-profit organizations). For example, the support department provides contact information and methods for applying for support necessary for recovery efforts. In this way, the support department enables rapid recovery by providing information and resources necessary for post-disaster recovery efforts. Some or all of the above processes in the support department may be performed using AI, for example, or not using AI. For example, the support department can input information necessary for recovery efforts into a generating AI and have the generating AI provide the support information.

[0037] The prediction unit can collect data such as satellite imagery, weather information, and social media information to predict natural disasters. For example, the prediction unit can analyze satellite imagery to predict earthquake occurrences. For example, the prediction unit can analyze weather information to predict hurricane paths. For example, the prediction unit can analyze social media posts to assess flood risk. By collecting diverse data to predict natural disasters, the prediction unit can improve its prediction accuracy. Some or all of the above-described processes in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input satellite imagery and weather information into a generating AI and have the generating AI perform natural disaster predictions.

[0038] The planning unit can create individual evacuation plans based on the user's living environment and family structure. For example, the planning unit creates an evacuation plan considering the user's living environment (e.g., type and location of residence). For example, the planning unit creates an evacuation plan considering the user's family structure (e.g., number and ages of family members). For example, the planning unit creates an evacuation plan considering special needs (e.g., facilities for people with disabilities or the elderly). This enables appropriate evacuation by allowing the planning unit to create individual evacuation plans based on the user's living environment and family structure. Some or all of the above processing in the planning unit may be performed using AI, for example, or without AI. For example, the planning unit can input data on the user's living environment and family structure into a generating AI and have the generating AI create an individual evacuation plan.

[0039] The evacuation unit can provide the optimal evacuation route in real time when a disaster occurs. For example, the evacuation unit can guide the user to the safest evacuation route from their current location in real time. For example, the evacuation unit can provide the optimal evacuation route considering traffic conditions and terrain. For example, the evacuation unit can adjust the evacuation route according to the type and scale of the disaster. As a result, the evacuation unit can provide the optimal evacuation route in real time when a disaster occurs, enabling rapid evacuation. Some or all of the above processing in the evacuation unit may be performed using AI, for example, or without AI. For example, the evacuation unit can input data on the user's current location and traffic conditions into a generating AI and have the generating AI perform the task of providing the optimal evacuation route.

[0040] The management department can provide a checklist of supplies needed in an emergency. The management department can list necessary supplies such as food, water, a first-aid kit, and a flashlight. The management department can provide a checklist of supplies that users should always keep on hand. The management department can notify users of how to manage their supplies and how often to update them. This allows the management department to prepare quickly by providing a checklist of supplies needed in an emergency. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input the user's supplies management data into a generating AI and have the generating AI generate the checklist.

[0041] The prediction unit can optimize its prediction algorithm by referring to past disaster data during the prediction process. For example, the prediction unit can refer to past earthquake data to learn earthquake occurrence patterns and improve prediction accuracy. For example, the prediction unit can analyze past flood data to identify flood occurrence conditions and reflect them in the prediction algorithm. For example, the prediction unit can use past hurricane data to improve the accuracy of hurricane path predictions. In this way, the prediction unit improves prediction accuracy by optimizing its prediction algorithm by referring to past disaster data. Some or all of the above processes in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input past disaster data into a generating AI and have the generating AI perform the optimization of the prediction algorithm.

[0042] The prediction unit can improve the accuracy of its predictions by considering the characteristics of each region. For example, the prediction unit can predict the impact of earthquakes by considering topographic data for each region. For example, the prediction unit can assess the risk of flooding by referring to meteorological data for each region. For example, the prediction unit can predict damage in the event of a disaster by using building structure data for each region. In this way, the prediction unit can make more accurate predictions by improving the accuracy of its predictions by considering the characteristics of each region. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without using AI. For example, the prediction unit can input regional characteristic data into a generating AI and have the generating AI perform the task of improving the accuracy of the predictions.

[0043] The prediction unit can improve the accuracy of its predictions by considering the user's geographical location information. For example, the prediction unit predicts the extent of earthquake impact based on the user's current location. For example, the prediction unit evaluates flood risk by referring to the user's location information. For example, the prediction unit improves the accuracy of hurricane path predictions by using the user's geographical location information. As a result, the prediction unit can make more accurate predictions by improving the accuracy of its predictions by considering the user's geographical location information. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input the user's geographical location information into a generating AI and have the generating AI perform the task of improving the accuracy of the prediction.

[0044] The prediction unit can improve the reliability of its predictions by analyzing social media trends during the prediction process. For example, the prediction unit can analyze posts on social media about earthquakes to predict the occurrence of earthquakes. For example, the prediction unit can collect information on social media about floods to assess the risk of flooding. For example, the prediction unit can analyze posts on social media about hurricanes to improve the accuracy of hurricane path predictions. In this way, the prediction unit can make more accurate predictions by improving the reliability of its predictions by analyzing social media trends. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input social media trend data into a generating AI and have the generating AI perform the task of improving the reliability of the predictions.

[0045] The planning unit can create an optimal plan by referring to the user's past evacuation history when creating a plan. For example, the planning unit can create an optimal evacuation plan based on the evacuation route the user has used in the past. For example, the planning unit can create an evacuation plan that avoids congestion based on the user's past evacuation history. For example, the planning unit can analyze the user's past evacuation history and create the most efficient evacuation plan. In this way, the planning unit can enable appropriate evacuation by creating an optimal plan by referring to the user's past evacuation history. Some or all of the above processes in the planning unit may be performed using AI, for example, or without AI. For example, the planning unit can input the user's past evacuation history data into a generating AI and have the generating AI create an optimal evacuation plan.

[0046] The planning unit can customize plans when creating them, taking into account family structure and whether or not pets are present. For example, the planning unit can create an optimal evacuation plan based on the number and ages of family members. For example, the planning unit can create a plan that allows evacuation with pets, taking into account whether or not pets are present. For example, the planning unit can create a plan that includes the division of roles at the evacuation center, based on family structure. In this way, the planning unit can enable appropriate evacuation by customizing plans to take into account family structure and whether or not pets are present. Some or all of the above processes in the planning unit may be performed using AI, for example, or not. For example, the planning unit can input data on family structure and whether or not pets are present into a generating AI and have the generating AI perform the customization of the evacuation plan.

[0047] The planning unit can create an optimal evacuation plan by considering the user's geographical location information when creating the plan. For example, the planning unit plans the optimal evacuation route based on the user's current location. For example, the planning unit selects evacuation shelters by referring to the user's location information. For example, the planning unit customizes the evacuation plan using the user's geographical location information. In this way, the planning unit enables appropriate evacuation by creating an optimal evacuation plan that considers the user's geographical location information. Some or all of the above processes in the planning unit may be performed using AI, for example, or without AI. For example, the planning unit can input the user's geographical location information into a generating AI and have the generating AI create an optimal evacuation plan.

[0048] The planning department can improve the reliability of its plans by analyzing users' social media activity during the planning process. For example, the planning department can analyze users' social media posts related to evacuation and incorporate them into the plan. For example, the planning department can identify evacuation-related concerns from users' social media activity and incorporate them into the plan. For example, the planning department can improve the reliability of its plans by referencing the evacuation plans of users' social media followers. In this way, the planning department can create more reliable evacuation plans by analyzing users' social media activity and improving the reliability of the plan. Some or all of the above processes in the planning department may be performed using AI, for example, or not using AI. For example, the planning department can input users' social media activity data into a generating AI and have the generating AI perform the task of improving the reliability of the evacuation plan.

[0049] The evacuation unit can provide the optimal route by referring to real-time traffic information when providing evacuation routes. For example, the evacuation unit can provide the optimal evacuation route based on real-time traffic congestion information. For example, the evacuation unit can provide the optimal evacuation route by considering the real-time operating status of public transportation. For example, the evacuation unit can provide detour routes based on real-time road construction information. In this way, the evacuation unit can provide the optimal route by referring to real-time traffic information, enabling rapid evacuation. Some or all of the above processing in the evacuation unit may be performed using AI, for example, or without AI. For example, the evacuation unit can input real-time traffic information into a generating AI and have the generating AI perform the task of providing the optimal evacuation route.

[0050] The evacuation unit can adjust evacuation routes when providing them, taking into account the congestion status of evacuation shelters. For example, the evacuation unit can check the congestion status of evacuation shelters in real time and provide the optimal evacuation route. For example, the evacuation unit can suggest alternative evacuation shelters to avoid crowded ones. For example, the evacuation unit can adjust evacuation routes in real time according to the congestion status. This allows the evacuation unit to enable appropriate evacuation by adjusting routes while considering the congestion status of evacuation shelters. Some or all of the above processes in the evacuation unit may be performed using AI, for example, or without AI. For example, the evacuation unit can input evacuation shelter congestion data into a generating AI and have the generating AI perform the adjustment of evacuation routes.

[0051] The evacuation unit can provide the optimal evacuation route by considering the user's geographical location information when providing evacuation routes. For example, the evacuation unit provides the optimal evacuation route based on the user's current location. For example, the evacuation unit selects an evacuation shelter by referring to the user's location information. For example, the evacuation unit customizes the evacuation route using the user's geographical location information. In this way, the evacuation unit enables appropriate evacuation by providing the optimal route by considering the user's geographical location information. Some or all of the above processes in the evacuation unit may be performed using AI, for example, or without AI. For example, the evacuation unit can input the user's geographical location information into a generating AI and have the generating AI perform the task of providing the optimal evacuation route.

[0052] The evacuation unit can improve the reliability of evacuation routes by analyzing the user's social media activity when providing evacuation routes. For example, the evacuation unit can analyze the user's social media posts related to evacuation and reflect them in the route. For example, the evacuation unit can identify evacuation-related concerns from the user's social media activity and reflect them in the route. For example, the evacuation unit can improve the reliability of routes by referring to the evacuation routes of the user's social media followers. In this way, the evacuation unit can enable appropriate evacuation by analyzing the user's social media activity and improving the reliability of routes. Some or all of the above processing in the evacuation unit may be performed using AI, for example, or without AI. For example, the evacuation 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 reliability of evacuation routes.

[0053] The management department can create an optimal checklist by referring to the user's past use history of disaster preparedness supplies when providing the checklist. For example, the management department can create an optimal checklist based on the disaster preparedness supplies the user has used in the past. For example, the management department can prioritize and include necessary disaster preparedness supplies in the list based on the user's past use history. For example, the management department can analyze the user's past use history to create the most efficient checklist. This allows the management department to create an optimal list by referring to the user's past use history of disaster preparedness supplies, enabling proper preparation. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input the user's past use history of disaster preparedness supplies into a generating AI and have the generating AI create an optimal checklist.

[0054] The management department can customize the checklist when providing it, taking into account the user's family structure and whether or not they have pets. For example, the management department can create a checklist of optimal disaster preparedness items based on the number and ages of family members. For example, the management department can create a checklist that includes disaster preparedness items for pets, taking into account whether or not the user has pets. For example, the management department can include necessary disaster preparedness items in the list based on the family structure. This allows the management department to make appropriate preparations by customizing the list to take into account the user's family structure and whether or not they have pets. Some or all of the above processes in the management department may be performed using AI, for example, or not. For example, the management department can input data on family structure and whether or not the user has pets into a generating AI and have the generating AI perform the checklist customization.

[0055] The management department can create an optimal checklist by considering the user's geographical location when providing the checklist. For example, the management department can create an optimal checklist of disaster preparedness items based on the user's current location. For example, the management department can refer to the user's location information and include disaster preparedness items in the list that are appropriate for the region. For example, the management department can customize the checklist using the user's geographical location information. This allows the management department to create an optimal list by considering the user's geographical location information, enabling appropriate preparation. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input the user's geographical location information into a generating AI and have the generating AI create an optimal checklist.

[0056] The management department can improve the reliability of checklists by analyzing users' social media activity when providing them. For example, the management department can analyze users' social media posts about disaster preparedness items and reflect them in the list. For example, the management department can identify users' interests regarding disaster preparedness items from their social media activity and reflect them in the list. For example, the management department can improve the reliability of the list by referring to the disaster preparedness item lists of the user's social media followers. This allows the management department to make appropriate preparations by analyzing users' social media activity and improving the reliability of the list. Some or all of the above processes by the management department may be performed using AI, for example, or not using AI. For example, the management department can input user social media activity data into a generating AI and have the generating AI perform checklist reliability improvements.

[0057] The notification unit can select the optimal notification method by referring to the user's past notification history when providing a notification. For example, the notification unit may select the optimal notification method based on the notification methods the user has preferred to use in the past. For example, the notification unit may identify effective notification methods from the user's past notification history. For example, the notification unit may analyze the user's past notification history and select the most efficient notification method. This enables the notification unit to appropriately convey information by selecting the optimal notification method by referring to the user's past notification history. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit may input the user's past notification history data into a generating AI and have the generating AI select the optimal notification method.

[0058] The notification unit can select the optimal notification method when providing notifications, taking into account the user's geographical location information. For example, the notification unit may select the optimal notification method based on the user's current location. For example, the notification unit may refer to the user's location information and select a notification method appropriate to the characteristics of the region. For example, the notification unit may customize the notification method using the user's geographical location information. This enables appropriate information transmission by allowing the notification unit to select the optimal notification method considering the user's geographical location information. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit may input the user's geographical location information into a generating AI and have the generating AI select the optimal notification method.

[0059] The recommendation unit can recommend the most suitable evacuation shelter by referring to the user's past evacuation history when providing recommendations. For example, the recommendation unit may recommend the most suitable evacuation shelter based on the evacuation shelters the user has used in the past. For example, the recommendation unit may recommend an evacuation shelter that avoids congestion based on the user's past evacuation history. For example, the recommendation unit may analyze the user's past evacuation history and recommend the most efficient evacuation shelter. In this way, the recommendation unit enables appropriate evacuation by recommending the most suitable evacuation shelter by referring to the user's past evacuation history. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit may input the user's past evacuation history data into a generating AI and have the generating AI perform the optimal evacuation shelter recommendation.

[0060] The recommendation unit can recommend the most suitable evacuation shelter by considering the user's geographical location information when providing recommendations. For example, the recommendation unit recommends the most suitable evacuation shelter based on the user's current location. For example, the recommendation unit refers to the user's location information and recommends an evacuation shelter that is appropriate for the characteristics of the region. For example, the recommendation unit customizes the evacuation shelter using the user's geographical location information. As a result, the recommendation unit can make appropriate evacuation possible by recommending the most suitable evacuation shelter by considering the user's geographical location information. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input the user's geographical location information into a generating AI and have the generating AI perform the optimal evacuation shelter recommendation.

[0061] The support unit can provide optimal support information by referring to the user's past support history when providing support information. For example, the support unit provides optimal support information based on the support the user has received in the past. For example, the support unit identifies effective support methods from the user's past support history. For example, the support unit analyzes the user's past support history and provides the most efficient support information. In this way, the support unit can provide appropriate support by referring to the user's past support history and providing optimal support information. Some or all of the above processes in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the user's past support history data into a generating AI and have the generating AI perform the task of providing optimal support information.

[0062] The support unit can provide optimal support information by considering the user's geographical location when providing support information. For example, the support unit can provide optimal support information based on the user's current location. For example, the support unit can refer to the user's location information and provide support information according to the characteristics of the region. For example, the support unit can customize support information using the user's geographical location information. In this way, the support unit can provide appropriate support by considering the user's geographical location information and providing optimal support information. Some or all of the above processing in the support unit may be performed using AI, for example, or without using AI. For example, the support unit can input the user's geographical location information into a generating AI and have the generating AI perform the task of providing optimal support information.

[0063] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0064] The next-generation disaster prevention advisor system can monitor users' health status and incorporate this information into disaster evacuation plans. For example, it can recommend evacuation shelters closer to medical facilities to users whose health is deteriorating. It can also select evacuation routes and provide special care at evacuation shelters based on health status. Furthermore, it can notify users of their health status monitoring results in real time and collaborate with medical institutions as needed.

[0065] The next-generation disaster prevention advisor system can manage information about users' pets and incorporate it into disaster evacuation plans. For example, it can recommend evacuation shelters that allow pets based on the type and number of pets. It can also provide evacuation routes and care at shelters based on the pet's health condition and special needs. Furthermore, it can notify users of pet information in real time and, if necessary, collaborate with specialized pet support organizations.

[0066] The next-generation disaster prevention advisor system can improve evacuation plans by referencing users' past evacuation experiences. For example, if problems occurred during past evacuations, the system can incorporate measures to resolve those problems into the evacuation plan. It can also improve the accuracy of evacuation plans based on lessons learned from past evacuation experiences. Furthermore, it is possible to share past evacuation experiences and use them as a reference for other users' evacuation plans.

[0067] The next-generation disaster prevention advisor system leverages users' social networks to facilitate information sharing during disasters. For example, users can share information in real time with friends and family and adjust evacuation plans. Furthermore, information can be gathered on the congestion status of evacuation centers and evacuation routes through social networking. Users can also refer to other users' evacuation plans via social networks.

[0068] The next-generation disaster prevention advisor system can optimize evacuation plans during disasters by utilizing the user's geographical location information. For example, it can provide the optimal evacuation route based on the user's current location. It can also select evacuation shelters and adjust evacuation routes by referring to geographical location information. Furthermore, it is possible to customize evacuation plans using geographical location information.

[0069] The following briefly describes the processing flow for example form 1.

[0070] Step 1: The prediction unit predicts natural disasters. The prediction unit uses machine learning and real-time data analysis to predict natural disasters such as earthquakes, tsunamis, hurricanes, and floods. The prediction unit collects data from various sources such as satellite imagery, weather information, and social media, and predicts natural disasters through advanced algorithms and analysis. Step 2: The planning department creates individual evacuation plans based on the information predicted by the forecasting department. The planning department creates individual evacuation plans based on the user's living environment and family structure. The planning department recommends the optimal evacuation routes and shelters according to the number of family members and where they live. Step 3: The evacuation unit provides the optimal evacuation route based on the evacuation plan created by the planning unit. The evacuation unit provides the optimal evacuation route in real time when a disaster occurs. The evacuation unit guides the user to the safest evacuation route from their current location in real time. Step 4: The management department manages the disaster preparedness supplies. The management department provides users with a checklist to manage the supplies they will need in an emergency. The management department lists necessary supplies such as food, water, first-aid kits, and flashlights, and helps users keep them on hand at all times.

[0071] (Example of form 2) The next-generation disaster prevention advisor system according to an embodiment of the present invention is a system that predicts disaster risks and supports disaster preparedness in homes and communities. This system provides a comprehensive solution to enhance response capabilities in the event of a disaster, including natural disaster prediction, evacuation plan development, and management of disaster preparedness supplies. The next-generation disaster prevention advisor system uses machine learning and real-time data analysis to predict natural disasters such as earthquakes, tsunamis, hurricanes, and floods. For example, the next-generation disaster prevention advisor system collects data from various sources such as satellite imagery, weather information, and social media, and predicts natural disasters through advanced algorithms and analysis. Next, the next-generation disaster prevention advisor system creates an individualized evacuation plan based on the user's living environment and family structure. For example, the next-generation disaster prevention advisor system recommends the optimal evacuation route and shelter depending on the number of family members and where they live. The next-generation disaster prevention advisor system also provides the optimal evacuation route in real time when a disaster occurs and recommends shelters and evacuation centers. Furthermore, the next-generation disaster prevention advisor system provides a checklist for users to manage the supplies they need in an emergency. For example, the next-generation disaster prevention advisor system lists necessary supplies such as food, water, first-aid kits, and flashlights, helping users to always be prepared. This means the next-generation disaster prevention advisor system will provide a comprehensive solution to enhance disaster response capabilities, including natural disaster prediction, evacuation plan development, disaster response, and disaster preparedness management. In this way, the next-generation disaster prevention advisor system can support disaster preparedness in homes and communities and improve their ability to respond when a disaster occurs.

[0072] The next-generation disaster prevention advisor system according to this embodiment comprises a prediction unit, a planning unit, an evacuation unit, and a management unit. The prediction unit predicts natural disasters. The prediction unit predicts natural disasters such as earthquakes, tsunamis, hurricanes, and floods, for example, using machine learning and real-time data analysis. The prediction unit collects data from various sources such as satellite images, weather information, and social media, and predicts natural disasters using advanced algorithms and analysis. The planning unit creates individual evacuation plans based on the information predicted by the prediction unit. The planning unit creates individual evacuation plans based on the user's living environment and family structure, for example. The planning unit recommends optimal evacuation routes and shelters, for example, depending on the number of family members and where they live. The evacuation unit provides the optimal evacuation route based on the evacuation plan created by the planning unit. The evacuation unit provides the optimal evacuation route in real time when a disaster occurs, for example. The evacuation unit guides the user to the safest evacuation route from their current location in real time. The management unit manages disaster prevention supplies. The management unit provides a checklist for the user to manage the supplies needed in an emergency, for example. The management department, for example, lists necessary supplies such as food, water, first-aid kits, and flashlights, and helps users keep them readily available. This enables the next-generation disaster prevention advisor system according to the embodiment to predict natural disasters, create evacuation plans, provide optimal evacuation routes, and manage disaster prevention supplies.

[0073] The prediction unit predicts natural disasters. For example, it uses machine learning and real-time data analysis to predict natural disasters such as earthquakes, tsunamis, hurricanes, and floods. Specifically, the prediction unit collects data from various sources such as satellite imagery, weather information, and social media, and predicts natural disasters through advanced algorithms and analysis. Satellite imagery is used to understand surface deformation and cloud movement in real time, weather information provides meteorological data such as temperature, humidity, wind speed, and precipitation, and social media collects real-time information posted by users and is used to understand the occurrence and progression of disasters. The prediction unit integrates this data and uses machine learning algorithms to predict the probability of disaster occurrence and the extent of impact. For example, in earthquake prediction, it learns past earthquake data and crustal deformation patterns and compares them with current crustal deformation data to calculate the probability of earthquake occurrence. In tsunami prediction, it predicts the occurrence, direction of movement, and arrival time of tsunamis based on the location and magnitude of earthquakes. In hurricane prediction, it predicts the direction of movement and intensity of hurricanes based on meteorological data and identifies affected areas. In flood prediction, it predicts the risk of flood occurrence and the extent of inundation based on precipitation and river water level data. In this way, the prediction unit can provide information that enables advance detection of natural disasters and rapid response.

[0074] The planning department creates individual evacuation plans based on information predicted by the forecasting department. For example, the planning department creates individual evacuation plans based on the user's living environment and family structure. For example, the planning department recommends the optimal evacuation route and shelter depending on the number of family members and where they live. Specifically, the planning department creates evacuation plans considering the user's residence location, family structure, and special needs (e.g., presence of elderly people or infants). For example, in households with elderly people, barrier-free evacuation routes and shelters are prioritized, and in households with infants, shelters equipped with necessary supplies and facilities are recommended. The planning department also considers whether the user has pets and can guide them to pet-friendly shelters. Furthermore, the planning department also considers the user's living area, such as their workplace or school, and prepares multiple evacuation route patterns in the event of a disaster. For example, it provides different evacuation routes depending on whether the user is at home or at work, enabling rapid evacuation in any situation. Based on this information, the planning department creates and provides a customized evacuation plan for each user. In this way, the planning department can provide the optimal evacuation plan according to the user's individual circumstances and support rapid and safe evacuation in the event of a disaster.

[0075] The evacuation unit provides the optimal evacuation route based on the evacuation plan created by the planning unit. For example, the evacuation unit provides the optimal evacuation route in real time when a disaster occurs. For example, the evacuation unit guides the user to the safest evacuation route from their current location in real time. Specifically, the evacuation unit obtains the user's current location information and calculates the optimal evacuation route considering the progress of the disaster and road conditions. For example, if a flood occurs, it will prioritize selecting roads that are not flooded, and if an earthquake occurs, it will select a route with a low risk of collapse. The evacuation unit can also modify the evacuation route as needed based on information updated in real time. For example, if a new obstacle appears during evacuation, the evacuation unit will immediately calculate a new route and guide the user. The evacuation unit provides the user with an evacuation route using means such as voice guidance, vibration notifications, and map displays. This allows the user to evacuate quickly and safely when a disaster occurs. Furthermore, the evacuation unit can present multiple evacuation routes and allow the user to choose. For example, it can present the shortest route and a safe route, allowing the user to choose according to the situation. This allows the evacuation unit to provide flexible evacuation support that meets the user's needs and ensures safety during disasters.

[0076] The Management Department manages disaster preparedness supplies. For example, the Management Department provides users with checklists to manage the supplies they will need in an emergency. Specifically, the Management Department lists necessary supplies such as food, water, first-aid kits, and flashlights, and helps users keep them on hand. For example, in households with elderly people, medicines and care products are added, and in households with infants, milk and diapers are added. The Management Department also considers whether the user has pets and lists pet food, water, and other supplies. In addition to including items such as pages in the list, the management department manages the expiration dates and best-before dates of supplies and notifies users regularly. For example, when the expiration dates of food or water are approaching, the management department will notify users and encourage them to replenish at the appropriate time. The management department will also provide advice on the storage location and quantity of supplies. For example, it will recommend storing food and water in a cool, dry place, and storing first-aid kits in a place where they can be easily accessed. In this way, the management department can help users properly manage the supplies they need in an emergency and respond quickly in the event of a disaster.

[0077] The notification unit provides real-time alerts and notifications. For example, the notification unit provides real-time alerts and notifications when a disaster occurs. For example, the notification unit sends notifications to the user's smartphone or personal computer. For example, the notification unit provides notifications at the appropriate time depending on the type and urgency of the disaster. For example, the notification unit provides immediate notifications when an earthquake occurs and provides advance notifications when a flood is predicted. This enables a rapid response by providing real-time alerts and notifications. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input real-time data when a disaster occurs into a generating AI and have the generating AI generate the notification content.

[0078] The recommendation unit recommends evacuation shelters and evacuation centers. For example, in the event of a disaster, the recommendation unit recommends the most suitable evacuation shelter or evacuation center based on the user's current location. The recommendation unit makes recommendations considering, for example, the capacity and facilities of the evacuation shelters. The recommendation unit makes recommendations considering, for example, the congestion level and accessibility of the evacuation shelters. The recommendation unit makes recommendations considering, for example, the user's special needs (e.g., facilities for people with disabilities or the elderly). In this way, the recommendation unit can recommend evacuation shelters and evacuation centers, enabling users to evacuate to a safe place. Some or all of the above processing in the recommendation unit may be performed using, for example, AI, or not using AI. For example, the recommendation unit can input information about evacuation shelters into a generating AI and have the generating AI perform the optimal evacuation shelter recommendation.

[0079] The support department provides information and resources necessary for post-disaster recovery efforts. For example, the support department provides information necessary for post-disaster recovery efforts. For example, the support department provides procedures and precautions necessary for recovery efforts. For example, the support department provides information on supplies and equipment necessary for recovery efforts. For example, the support department provides information on resources necessary for recovery efforts (e.g., government agencies and non-profit organizations). For example, the support department provides contact information and methods for applying for support necessary for recovery efforts. In this way, the support department enables rapid recovery by providing information and resources necessary for post-disaster recovery efforts. Some or all of the above processes in the support department may be performed using AI, for example, or not using AI. For example, the support department can input information necessary for recovery efforts into a generating AI and have the generating AI provide the support information.

[0080] The prediction unit can collect data such as satellite imagery, weather information, and social media information to predict natural disasters. For example, the prediction unit can analyze satellite imagery to predict earthquake occurrences. For example, the prediction unit can analyze weather information to predict hurricane paths. For example, the prediction unit can analyze social media posts to assess flood risk. By collecting diverse data to predict natural disasters, the prediction unit can improve its prediction accuracy. Some or all of the above-described processes in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input satellite imagery and weather information into a generating AI and have the generating AI perform natural disaster predictions.

[0081] The planning unit can create individual evacuation plans based on the user's living environment and family structure. For example, the planning unit creates an evacuation plan considering the user's living environment (e.g., type and location of residence). For example, the planning unit creates an evacuation plan considering the user's family structure (e.g., number and ages of family members). For example, the planning unit creates an evacuation plan considering special needs (e.g., facilities for people with disabilities or the elderly). This enables appropriate evacuation by allowing the planning unit to create individual evacuation plans based on the user's living environment and family structure. Some or all of the above processing in the planning unit may be performed using AI, for example, or without AI. For example, the planning unit can input data on the user's living environment and family structure into a generating AI and have the generating AI create an individual evacuation plan.

[0082] The evacuation unit can provide the optimal evacuation route in real time when a disaster occurs. For example, the evacuation unit can guide the user to the safest evacuation route from their current location in real time. For example, the evacuation unit can provide the optimal evacuation route considering traffic conditions and terrain. For example, the evacuation unit can adjust the evacuation route according to the type and scale of the disaster. As a result, the evacuation unit can provide the optimal evacuation route in real time when a disaster occurs, enabling rapid evacuation. Some or all of the above processing in the evacuation unit may be performed using AI, for example, or without AI. For example, the evacuation unit can input data on the user's current location and traffic conditions into a generating AI and have the generating AI perform the task of providing the optimal evacuation route.

[0083] The management department can provide a checklist of supplies needed in an emergency. The management department can list necessary supplies such as food, water, a first-aid kit, and a flashlight. The management department can provide a checklist of supplies that users should always keep on hand. The management department can notify users of how to manage their supplies and how often to update them. This allows the management department to prepare quickly by providing a checklist of supplies needed in an emergency. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input the user's supplies management data into a generating AI and have the generating AI generate the checklist.

[0084] The prediction unit can estimate the user's emotions and adjust the notification method of the prediction result based on the estimated user emotions. For example, if the user is feeling anxious, the prediction unit provides a notification method that explains the prediction result in detail and provides reassurance. For example, if the user is calm, the prediction unit provides a concise and to-the-point notification method. For example, if the user is in a state of panic, the prediction unit provides a notification in a calm tone and includes specific action instructions. In this way, the prediction unit can provide appropriate information transmission by providing a notification method that is appropriate 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 prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the notification method.

[0085] The prediction unit can optimize its prediction algorithm by referring to past disaster data during the prediction process. For example, the prediction unit can refer to past earthquake data to learn earthquake occurrence patterns and improve prediction accuracy. For example, the prediction unit can analyze past flood data to identify flood occurrence conditions and reflect them in the prediction algorithm. For example, the prediction unit can use past hurricane data to improve the accuracy of hurricane path predictions. In this way, the prediction unit improves prediction accuracy by optimizing its prediction algorithm by referring to past disaster data. Some or all of the above processes in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input past disaster data into a generating AI and have the generating AI perform the optimization of the prediction algorithm.

[0086] The prediction unit can improve the accuracy of its predictions by considering the characteristics of each region. For example, the prediction unit can predict the impact of earthquakes by considering topographic data for each region. For example, the prediction unit can assess the risk of flooding by referring to meteorological data for each region. For example, the prediction unit can predict damage in the event of a disaster by using building structure data for each region. In this way, the prediction unit can make more accurate predictions by improving the accuracy of its predictions by considering the characteristics of each region. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without using AI. For example, the prediction unit can input regional characteristic data into a generating AI and have the generating AI perform the task of improving the accuracy of the predictions.

[0087] The prediction unit can estimate the user's emotions and adjust the display method of the prediction results based on the estimated user emotions. For example, if the user is feeling anxious, the prediction unit may display the prediction results in a visually easy-to-understand manner to provide reassurance. For example, if the user is calm, the prediction unit may provide a concise and to-the-point display method. For example, if the user is in a state of panic, the prediction unit may display the information in a calm tone and include specific action instructions. In this way, the prediction unit can provide appropriate information transmission by offering a display method that corresponds 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 prediction unit may be performed using AI, for example, or not using AI. For example, the prediction unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the display method.

[0088] The prediction unit can improve the accuracy of its predictions by considering the user's geographical location information. For example, the prediction unit predicts the extent of earthquake impact based on the user's current location. For example, the prediction unit evaluates flood risk by referring to the user's location information. For example, the prediction unit improves the accuracy of hurricane path predictions by using the user's geographical location information. As a result, the prediction unit can make more accurate predictions by improving the accuracy of its predictions by considering the user's geographical location information. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input the user's geographical location information into a generating AI and have the generating AI perform the task of improving the accuracy of the prediction.

[0089] The prediction unit can improve the reliability of its predictions by analyzing social media trends during the prediction process. For example, the prediction unit can analyze posts on social media about earthquakes to predict the occurrence of earthquakes. For example, the prediction unit can collect information on social media about floods to assess the risk of flooding. For example, the prediction unit can analyze posts on social media about hurricanes to improve the accuracy of hurricane path predictions. In this way, the prediction unit can make more accurate predictions by improving the reliability of its predictions by analyzing social media trends. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input social media trend data into a generating AI and have the generating AI perform the task of improving the reliability of the predictions.

[0090] The planning 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 planning unit will provide a detailed evacuation plan to reassure them. For example, if the user is calm, the planning unit will provide a concise and to-the-point evacuation plan. For example, if the user is panicking, the planning unit will provide an evacuation plan in a calm tone and include specific action instructions. This allows the planning unit to provide an appropriate evacuation plan by adjusting the level of detail according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the planning unit may be performed using AI or not. For example, the planning unit can input user emotion data into a generative AI and have the generative AI adjust the level of detail in the evacuation plan.

[0091] The planning unit can create an optimal plan by referring to the user's past evacuation history when creating a plan. For example, the planning unit can create an optimal evacuation plan based on the evacuation route the user has used in the past. For example, the planning unit can create an evacuation plan that avoids congestion based on the user's past evacuation history. For example, the planning unit can analyze the user's past evacuation history and create the most efficient evacuation plan. In this way, the planning unit can enable appropriate evacuation by creating an optimal plan by referring to the user's past evacuation history. Some or all of the above processes in the planning unit may be performed using AI, for example, or without AI. For example, the planning unit can input the user's past evacuation history data into a generating AI and have the generating AI create an optimal evacuation plan.

[0092] The planning unit can customize plans when creating them, taking into account family structure and whether or not pets are present. For example, the planning unit can create an optimal evacuation plan based on the number and ages of family members. For example, the planning unit can create a plan that allows evacuation with pets, taking into account whether or not pets are present. For example, the planning unit can create a plan that includes the division of roles at the evacuation center, based on family structure. In this way, the planning unit can enable appropriate evacuation by customizing plans to take into account family structure and whether or not pets are present. Some or all of the above processes in the planning unit may be performed using AI, for example, or not. For example, the planning unit can input data on family structure and whether or not pets are present into a generating AI and have the generating AI perform the customization of the evacuation plan.

[0093] The planning unit can estimate the user's emotions and determine the priorities of the evacuation plan based on the estimated emotions. For example, if the user is feeling anxious, the planning unit will create an evacuation plan prioritizing important items. For example, if the user is calm, the planning unit will create an evacuation plan considering the overall balance. For example, if the user is in a state of panic, the planning unit will set priorities to allow for quick action. This enables the planning unit to create an appropriate evacuation plan by providing priorities that correspond 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 planning unit may be performed using AI or not using AI. For example, the planning unit can input user emotion data into a generative AI and have the generative AI determine the priorities of the evacuation plan.

[0094] The planning unit can create an optimal evacuation plan by considering the user's geographical location information when creating the plan. For example, the planning unit plans the optimal evacuation route based on the user's current location. For example, the planning unit selects evacuation shelters by referring to the user's location information. For example, the planning unit customizes the evacuation plan using the user's geographical location information. In this way, the planning unit enables appropriate evacuation by creating an optimal evacuation plan that considers the user's geographical location information. Some or all of the above processes in the planning unit may be performed using AI, for example, or without AI. For example, the planning unit can input the user's geographical location information into a generating AI and have the generating AI create an optimal evacuation plan.

[0095] The planning department can improve the reliability of its plans by analyzing users' social media activity during the planning process. For example, the planning department can analyze users' social media posts related to evacuation and incorporate them into the plan. For example, the planning department can identify evacuation-related concerns from users' social media activity and incorporate them into the plan. For example, the planning department can improve the reliability of its plans by referencing the evacuation plans of users' social media followers. In this way, the planning department can create more reliable evacuation plans by analyzing users' social media activity and improving the reliability of the plan. Some or all of the above processes in the planning department may be performed using AI, for example, or not using AI. For example, the planning department can input users' social media activity data into a generating AI and have the generating AI perform the task of improving the reliability of the evacuation plan.

[0096] The evacuation unit can estimate the user's emotions and adjust the display method of the evacuation route based on the estimated user emotions. For example, if the user is feeling anxious, the evacuation unit will provide a detailed explanation of the evacuation route to reassure them. For example, if the user is calm, the evacuation unit will provide a concise and to-the-point display method. For example, if the user is in a state of panic, the evacuation unit will display information in a calm tone and include specific action instructions. In this way, the evacuation unit can provide appropriate information by offering an evacuation route display method that corresponds 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 evacuation unit may be performed using AI, for example, or without AI. For example, the evacuation unit can input user emotion data into the generative AI and have the generative AI adjust the display method of the evacuation route.

[0097] The evacuation unit can provide the optimal route by referring to real-time traffic information when providing evacuation routes. For example, the evacuation unit can provide the optimal evacuation route based on real-time traffic congestion information. For example, the evacuation unit can provide the optimal evacuation route by considering the real-time operating status of public transportation. For example, the evacuation unit can provide detour routes based on real-time road construction information. In this way, the evacuation unit can provide the optimal route by referring to real-time traffic information, enabling rapid evacuation. Some or all of the above processing in the evacuation unit may be performed using AI, for example, or without AI. For example, the evacuation unit can input real-time traffic information into a generating AI and have the generating AI perform the task of providing the optimal evacuation route.

[0098] The evacuation unit can adjust evacuation routes when providing them, taking into account the congestion status of evacuation shelters. For example, the evacuation unit can check the congestion status of evacuation shelters in real time and provide the optimal evacuation route. For example, the evacuation unit can suggest alternative evacuation shelters to avoid crowded ones. For example, the evacuation unit can adjust evacuation routes in real time according to the congestion status. This allows the evacuation unit to enable appropriate evacuation by adjusting routes while considering the congestion status of evacuation shelters. Some or all of the above processes in the evacuation unit may be performed using AI, for example, or without AI. For example, the evacuation unit can input evacuation shelter congestion data into a generating AI and have the generating AI perform the adjustment of evacuation routes.

[0099] The evacuation unit can estimate the user's emotions and determine the priority of evacuation routes based on the estimated emotions. For example, if the user is feeling anxious, the evacuation unit will prioritize important routes. For example, if the user is calm, the evacuation unit will provide evacuation routes considering the overall balance. For example, if the user is in a state of panic, the evacuation unit will set priorities to allow for quick action. In this way, the evacuation unit enables appropriate evacuation by providing evacuation route priorities 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 evacuation unit may be performed using AI or not using AI. For example, the evacuation unit can input user emotion data into a generative AI and have the generative AI determine the priority of evacuation routes.

[0100] The evacuation unit can provide the optimal evacuation route by considering the user's geographical location information when providing evacuation routes. For example, the evacuation unit provides the optimal evacuation route based on the user's current location. For example, the evacuation unit selects an evacuation shelter by referring to the user's location information. For example, the evacuation unit customizes the evacuation route using the user's geographical location information. In this way, the evacuation unit enables appropriate evacuation by providing the optimal route by considering the user's geographical location information. Some or all of the above processes in the evacuation unit may be performed using AI, for example, or without AI. For example, the evacuation unit can input the user's geographical location information into a generating AI and have the generating AI perform the task of providing the optimal evacuation route.

[0101] The evacuation unit can improve the reliability of evacuation routes by analyzing the user's social media activity when providing evacuation routes. For example, the evacuation unit can analyze the user's social media posts related to evacuation and reflect them in the route. For example, the evacuation unit can identify evacuation-related concerns from the user's social media activity and reflect them in the route. For example, the evacuation unit can improve the reliability of routes by referring to the evacuation routes of the user's social media followers. In this way, the evacuation unit can enable appropriate evacuation by analyzing the user's social media activity and improving the reliability of routes. Some or all of the above processing in the evacuation unit may be performed using AI, for example, or without AI. For example, the evacuation 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 reliability of evacuation routes.

[0102] The management department can estimate the user's emotions and adjust the disaster preparedness checklist based on those emotions. For example, if the user is feeling anxious, the management department can provide a detailed checklist to reassure them. If the user is calm, the management department can provide a concise and to-the-point checklist. If the user is panicking, the management department can provide a checklist in a calm tone and include specific action instructions. This allows the management department to provide a disaster preparedness checklist tailored to the user's emotions, enabling proper preparation. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the management department may be performed using AI or not. For example, the management department can input user emotion data into a generative AI and have the generative AI adjust the disaster preparedness checklist.

[0103] The management department can create an optimal checklist by referring to the user's past use history of disaster preparedness supplies when providing the checklist. For example, the management department can create an optimal checklist based on the disaster preparedness supplies the user has used in the past. For example, the management department can prioritize and include necessary disaster preparedness supplies in the list based on the user's past use history. For example, the management department can analyze the user's past use history to create the most efficient checklist. This allows the management department to create an optimal list by referring to the user's past use history of disaster preparedness supplies, enabling proper preparation. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input the user's past use history of disaster preparedness supplies into a generating AI and have the generating AI create an optimal checklist.

[0104] The management department can customize the checklist when providing it, taking into account the user's family structure and whether or not they have pets. For example, the management department can create a checklist of optimal disaster preparedness items based on the number and ages of family members. For example, the management department can create a checklist that includes disaster preparedness items for pets, taking into account whether or not the user has pets. For example, the management department can include necessary disaster preparedness items in the list based on the family structure. This allows the management department to make appropriate preparations by customizing the list to take into account the user's family structure and whether or not they have pets. Some or all of the above processes in the management department may be performed using AI, for example, or not. For example, the management department can input data on family structure and whether or not the user has pets into a generating AI and have the generating AI perform the checklist customization.

[0105] The management unit can estimate the user's emotions and determine the priority of disaster preparedness items based on those emotions. For example, if the user is feeling anxious, the management unit will prioritize and include important disaster preparedness items in the list. If the user is calm, the management unit will include disaster preparedness items in the list considering the overall balance. If the user is panicking, the management unit will set priorities to allow for quick preparation. This enables appropriate preparation by providing a priority of disaster preparedness items according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the management unit may be performed using AI or not. For example, the management unit can input user emotion data into a generative AI and have the generative AI determine the priority of disaster preparedness items.

[0106] The management department can create an optimal checklist by considering the user's geographical location when providing the checklist. For example, the management department can create an optimal checklist of disaster preparedness items based on the user's current location. For example, the management department can refer to the user's location information and include disaster preparedness items in the list that are appropriate for the region. For example, the management department can customize the checklist using the user's geographical location information. This allows the management department to create an optimal list by considering the user's geographical location information, enabling appropriate preparation. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input the user's geographical location information into a generating AI and have the generating AI create an optimal checklist.

[0107] The management department can improve the reliability of checklists by analyzing users' social media activity when providing them. For example, the management department can analyze users' social media posts about disaster preparedness items and reflect them in the list. For example, the management department can identify users' interests regarding disaster preparedness items from their social media activity and reflect them in the list. For example, the management department can improve the reliability of the list by referring to the disaster preparedness item lists of the user's social media followers. This allows the management department to make appropriate preparations by analyzing users' social media activity and improving the reliability of the list. Some or all of the above processes by the management department may be performed using AI, for example, or not using AI. For example, the management department can input user social media activity data into a generating AI and have the generating AI perform checklist reliability improvements.

[0108] The notification unit can estimate the user's emotions and adjust the timing of notifications based on the estimated emotions. For example, if the user is feeling anxious, the notification unit may provide an early notification to reassure them. If the user is calm, the notification unit may provide a notification at an appropriate time. If the user is panicking, the notification unit may provide a notification in a calm tone and include specific action instructions. This enables the notification unit to deliver information appropriately by providing notification timing that matches the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input user emotion data into a generative AI and have the generative AI adjust the timing of notifications.

[0109] The notification unit can select the optimal notification method by referring to the user's past notification history when providing a notification. For example, the notification unit may select the optimal notification method based on the notification methods the user has preferred to use in the past. For example, the notification unit may identify effective notification methods from the user's past notification history. For example, the notification unit may analyze the user's past notification history and select the most efficient notification method. This enables the notification unit to appropriately convey information by selecting the optimal notification method by referring to the user's past notification history. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit may input the user's past notification history data into a generating AI and have the generating AI select the optimal notification method.

[0110] The notification unit can estimate the user's emotions and determine notification priorities based on the estimated emotions. For example, if the user is feeling anxious, the notification unit will prioritize important notifications. If the user is calm, the notification unit will send notifications considering the overall balance. If the user is panicking, the notification unit will set priorities to allow for quick action. This enables appropriate information delivery by providing notification priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input user emotion data into a generative AI and have the generative AI determine the notification priorities.

[0111] The notification unit can select the optimal notification method when providing notifications, taking into account the user's geographical location information. For example, the notification unit may select the optimal notification method based on the user's current location. For example, the notification unit may refer to the user's location information and select a notification method appropriate to the characteristics of the region. For example, the notification unit may customize the notification method using the user's geographical location information. This enables appropriate information transmission by allowing the notification unit to select the optimal notification method considering the user's geographical location information. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit may input the user's geographical location information into a generating AI and have the generating AI select the optimal notification method.

[0112] The recommendation unit can estimate the user's emotions and adjust its shelter recommendation method based on the estimated emotions. For example, if the user is feeling anxious, the recommendation unit will provide detailed shelter information to reassure them. For example, if the user is calm, the recommendation unit will provide concise and to-the-point shelter information. For example, if the user is panicking, the recommendation unit will provide shelter information in a calm tone and include specific action instructions. In this way, the recommendation unit enables appropriate evacuation by providing shelter recommendations that are appropriate 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 recommendation unit may be performed using AI or not using AI. For example, the recommendation unit can input user emotion data into a generative AI and have the generative AI adjust the shelter recommendation method.

[0113] The recommendation unit can recommend the most suitable evacuation shelter by referring to the user's past evacuation history when providing recommendations. For example, the recommendation unit may recommend the most suitable evacuation shelter based on the evacuation shelters the user has used in the past. For example, the recommendation unit may recommend an evacuation shelter that avoids congestion based on the user's past evacuation history. For example, the recommendation unit may analyze the user's past evacuation history and recommend the most efficient evacuation shelter. In this way, the recommendation unit enables appropriate evacuation by recommending the most suitable evacuation shelter by referring to the user's past evacuation history. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit may input the user's past evacuation history data into a generating AI and have the generating AI perform the optimal evacuation shelter recommendation.

[0114] The recommendation unit can estimate the user's emotions and determine the priority of shelters based on the estimated emotions. For example, if the user is feeling anxious, the recommendation unit will prioritize and recommend important shelters. For example, if the user is calm, the recommendation unit will recommend shelters considering the overall balance. For example, if the user is panicking, the recommendation unit will set priorities to allow for quick action. In this way, the recommendation unit enables appropriate evacuation by providing shelter priorities 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 recommendation unit may be performed using AI or not using AI. For example, the recommendation unit can input user emotion data into a generative AI and have the generative AI determine the priority of shelters.

[0115] The recommendation unit can recommend the most suitable evacuation shelter by considering the user's geographical location information when providing recommendations. For example, the recommendation unit recommends the most suitable evacuation shelter based on the user's current location. For example, the recommendation unit refers to the user's location information and recommends an evacuation shelter that is appropriate for the characteristics of the region. For example, the recommendation unit customizes the evacuation shelter using the user's geographical location information. As a result, the recommendation unit can make appropriate evacuation possible by recommending the most suitable evacuation shelter by considering the user's geographical location information. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input the user's geographical location information into a generating AI and have the generating AI perform the optimal evacuation shelter recommendation.

[0116] The support unit can estimate the user's emotions and adjust the method of providing support information based on the estimated emotions. For example, if the user is feeling anxious, the support unit can provide detailed support information to reassure them. For example, if the user is calm, the support unit can provide concise and to-the-point support information. For example, if the user is panicking, the support unit can provide support information in a calm tone and include specific action instructions. This allows the support unit to provide appropriate support by offering support information in a way that suits 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 support unit may be performed using AI or not using AI. For example, the support unit can input user emotion data into a generative AI and have the generative AI adjust the method of providing support information.

[0117] The support unit can provide optimal support information by referring to the user's past support history when providing support information. For example, the support unit provides optimal support information based on the support the user has received in the past. For example, the support unit identifies effective support methods from the user's past support history. For example, the support unit analyzes the user's past support history and provides the most efficient support information. In this way, the support unit can provide appropriate support by referring to the user's past support history and providing optimal support information. Some or all of the above processes in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the user's past support history data into a generating AI and have the generating AI perform the task of providing optimal support information.

[0118] The support unit can estimate the user's emotions and prioritize support information based on the estimated emotions. For example, if the user is feeling anxious, the support unit will prioritize providing important support information. If the user is calm, the support unit will provide support information considering the overall balance. If the user is panicking, the support unit will set priorities to allow for quick action. This enables the support unit to provide appropriate support by prioritizing support information 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 support unit may be performed using AI or not using AI. For example, the support unit can input user emotion data into a generative AI and have the generative AI determine the priority of support information.

[0119] The support unit can provide optimal support information by considering the user's geographical location when providing support information. For example, the support unit can provide optimal support information based on the user's current location. For example, the support unit can refer to the user's location information and provide support information according to the characteristics of the region. For example, the support unit can customize support information using the user's geographical location information. In this way, the support unit can provide appropriate support by considering the user's geographical location information and providing optimal support information. Some or all of the above processing in the support unit may be performed using AI, for example, or without using AI. For example, the support unit can input the user's geographical location information into a generating AI and have the generating AI perform the task of providing optimal support information.

[0120] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0121] The next-generation disaster prevention advisor system can monitor users' health status and incorporate this information into disaster evacuation plans. For example, it can recommend evacuation shelters closer to medical facilities to users whose health is deteriorating. It can also select evacuation routes and provide special care at evacuation shelters based on health status. Furthermore, it can notify users of their health status monitoring results in real time and collaborate with medical institutions as needed.

[0122] The next-generation disaster prevention advisor system can estimate the user's emotions and adjust the flexibility of the evacuation plan based on those emotions. For example, if the user is feeling anxious, it can provide multiple options in the evacuation plan to allow the user to choose with confidence. If the user is calm, it can provide the most efficient evacuation route. Furthermore, if the user is in a state of panic, it can simplify the evacuation plan to enable quick action.

[0123] The next-generation disaster prevention advisor system can manage information about users' pets and incorporate it into disaster evacuation plans. For example, it can recommend evacuation shelters that allow pets based on the type and number of pets. It can also provide evacuation routes and care at shelters based on the pet's health condition and special needs. Furthermore, it can notify users of pet information in real time and, if necessary, collaborate with specialized pet support organizations.

[0124] The next-generation disaster prevention advisor system can estimate a user's emotions and adjust its communication methods in evacuation shelters based on those emotions. For example, if a user is feeling anxious, it will communicate carefully and reassuringly. If a user is calm, it will communicate concisely and to the point. Furthermore, if a user is in a state of panic, it can communicate in a calm tone, including specific action instructions.

[0125] The next-generation disaster prevention advisor system can improve evacuation plans by referencing users' past evacuation experiences. For example, if problems occurred during past evacuations, the system can incorporate measures to resolve those problems into the evacuation plan. It can also improve the accuracy of evacuation plans based on lessons learned from past evacuation experiences. Furthermore, it is possible to share past evacuation experiences and use them as a reference for other users' evacuation plans.

[0126] The next-generation disaster preparedness advisor system can estimate the user's emotions and adjust the management method of disaster preparedness supplies based on those emotions. For example, if the user is feeling anxious, it will provide detailed management instructions to reassure them. If the user is calm, it will provide concise and to-the-point management instructions. Furthermore, if the user is in a state of panic, it can provide management instructions in a calm tone, including specific action instructions.

[0127] The next-generation disaster prevention advisor system leverages users' social networks to facilitate information sharing during disasters. For example, users can share information in real time with friends and family and adjust evacuation plans. Furthermore, information can be gathered on the congestion status of evacuation centers and evacuation routes through social networking. Users can also refer to other users' evacuation plans via social networks.

[0128] The next-generation disaster prevention advisor system can estimate the user's emotions and adjust the content of notifications based on those emotions. For example, if the user is feeling anxious, it will provide detailed notifications to reassure them. If the user is calm, it will provide concise and to-the-point notifications. Furthermore, if the user is in a state of panic, it can provide notifications in a calm tone that include specific action instructions.

[0129] The next-generation disaster prevention advisor system can optimize evacuation plans during disasters by utilizing the user's geographical location information. For example, it can provide the optimal evacuation route based on the user's current location. It can also select evacuation shelters and adjust evacuation routes by referring to geographical location information. Furthermore, it is possible to customize evacuation plans using geographical location information.

[0130] The next-generation disaster prevention advisor system can estimate the user's emotions and adjust the way support information is provided based on those emotions. For example, if the user is feeling anxious, it will provide detailed support information to reassure them. If the user is calm, it will provide concise and to-the-point support information. Furthermore, if the user is in a state of panic, it can provide support information in a calm tone, including specific action instructions.

[0131] The following briefly describes the processing flow for example form 2.

[0132] Step 1: The prediction unit predicts natural disasters. The prediction unit uses machine learning and real-time data analysis to predict natural disasters such as earthquakes, tsunamis, hurricanes, and floods. The prediction unit collects data from various sources such as satellite imagery, weather information, and social media, and predicts natural disasters through advanced algorithms and analysis. Step 2: The planning department creates individual evacuation plans based on the information predicted by the forecasting department. The planning department creates individual evacuation plans based on the user's living environment and family structure. The planning department recommends the optimal evacuation routes and shelters according to the number of family members and where they live. Step 3: The evacuation unit provides the optimal evacuation route based on the evacuation plan created by the planning unit. The evacuation unit provides the optimal evacuation route in real time when a disaster occurs. The evacuation unit guides the user to the safest evacuation route from their current location in real time. Step 4: The management department manages the disaster preparedness supplies. The management department provides users with a checklist to manage the supplies they will need in an emergency. The management department lists necessary supplies such as food, water, first-aid kits, and flashlights, and helps users keep them on hand at all times.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] Each of the multiple elements described above, including the prediction unit, planning unit, evacuation unit, management unit, notification unit, recommendation unit, and support unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the prediction unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12. The planning unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12. The evacuation unit is implemented by, for example, the control unit 46A of the smart device 14. The management unit is implemented by, for example, the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12. The notification unit is implemented by, for example, the control unit 46A of the smart device 14. The recommendation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12. The support unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0137] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0138] 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.

[0139] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0140] The 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.

[0141] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0143] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0144] Figure 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.

[0145] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0146] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0147] In the 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.

[0148] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0149] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0150] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0151] The data processing system 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.

[0152] Each of the multiple elements described above, including the prediction unit, planning unit, evacuation unit, management unit, notification unit, recommendation unit, and support unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the prediction unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The planning unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12. The evacuation unit is implemented, for example, by the control unit 46A of the smart glasses 214. The management unit is implemented, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The notification unit is implemented, for example, by the control unit 46A of the smart glasses 214. The recommendation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12. The support unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0153] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0154] 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.

[0155] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0156] The 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.

[0157] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0158] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (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).

[0159] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] Each of the multiple elements described above, including the prediction unit, planning unit, evacuation unit, management unit, notification unit, recommendation unit, and support unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the prediction unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The planning unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12. The evacuation unit is implemented by, for example, the control unit 46A of the headset terminal 314. The management unit is implemented by, for example, the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The notification unit is implemented by, for example, the control unit 46A of the headset terminal 314. The recommendation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12. The support unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0169] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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).

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.).

[0182] 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.

[0183] 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.

[0184] 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.

[0185] Each of the multiple elements described above, including the prediction unit, planning unit, evacuation unit, management unit, notification unit, recommendation unit, and support unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the prediction unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The planning unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12. The evacuation unit is implemented by, for example, the control unit 46A of the robot 414. The management unit is implemented by, for example, the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The notification unit is implemented by, for example, the control unit 46A of the robot 414. The recommendation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12. The support unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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."

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] (Note 1) A prediction unit that predicts natural disasters, A planning unit that creates individual evacuation plans based on the information predicted by the prediction unit, An evacuation unit that provides the optimal evacuation route based on the evacuation plan created by the aforementioned planning unit, It includes a management department that manages disaster prevention supplies. A system characterized by the following features. (Note 2) It includes a notification unit that provides real-time alerts and notifications. The system described in Appendix 1, characterized by the features described herein. (Note 3) It includes a recommendation section that provides recommendations for evacuation shelters and evacuation centers. The system described in Appendix 1, characterized by the features described herein. (Note 4) The department is equipped to provide information and resources necessary for post-disaster recovery efforts. The system described in Appendix 1, characterized by the features described herein. (Note 5) The prediction unit, By collecting data such as satellite imagery, weather information, and social media, natural disasters can be predicted. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned planning department, Create individualized evacuation plans based on the user's living environment and family structure. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned evacuation section is, Provides the optimal evacuation route in real time during a disaster. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned management department, Provide a checklist of supplies needed in an emergency. The system described in Appendix 1, characterized by the features described herein. (Note 9) The prediction unit, It estimates the user's emotions and adjusts the notification method of the prediction results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The prediction unit, During the prediction process, the prediction algorithm is optimized by referring to past disaster data. The system described in Appendix 1, characterized by the features described herein. (Note 11) The prediction unit, When making predictions, we improve the accuracy of the predictions by taking into account the characteristics of each region. The system described in Appendix 1, characterized by the features described herein. (Note 12) The prediction unit, It estimates the user's emotions and adjusts how the prediction results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The prediction unit, When making predictions, the system takes the user's geographical location into account to improve prediction accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 14) The prediction unit, Analyzing social media trends during the prediction process improves the reliability of the predictions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned planning department, 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 16) The aforementioned planning department, When creating a plan, the system references the user's past evacuation history to create the optimal plan. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned planning department, When creating a plan, customize it to take into account family structure and whether or not you have pets. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned planning department, 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 19) The aforementioned planning department, When creating a plan, the system takes into account the user's geographical location to create the optimal evacuation plan. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned planning department, Analyzing users' social media activity during plan creation improves the reliability of the plan. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned evacuation section is, The system estimates the user's emotions and adjusts how evacuation routes are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned evacuation section is, When providing evacuation routes, the system uses real-time traffic information to provide the optimal route. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned evacuation section is, When providing evacuation routes, adjust the routes considering the congestion level of the evacuation shelters. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned evacuation section is, The system estimates the user's emotions and prioritizes evacuation routes based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned evacuation section is, When providing evacuation routes, the system will provide the optimal route considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned evacuation section is, When providing evacuation routes, we analyze users' social media activity to improve the reliability of those routes. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned management department, The system estimates the user's emotions and adjusts the disaster preparedness checklist based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned management department, When providing a checklist, the system references the user's past use history of disaster preparedness supplies to create the most optimal list. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned management department, When providing a checklist, customize the list to take into account the user's family structure and whether or not they have pets. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned management department, The system estimates the user's emotions and prioritizes disaster preparedness items based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned management department, When providing checklists, we create optimal lists that take into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned management department, When providing checklists, we analyze users' social media activity to improve the reliability of the lists. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned notification unit, It estimates the user's emotions and adjusts the timing of notifications based on those emotions. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned notification unit, When providing notifications, the system will refer to the user's past notification history to select the most suitable notification method. The system described in Appendix 2, characterized by the features described herein. (Note 35) The aforementioned notification unit, It estimates the user's emotions and prioritizes notifications based on those emotions. The system described in Appendix 2, characterized by the features described herein. (Note 36) The aforementioned notification unit, When providing notifications, the system will select the most suitable notification method, taking into account the user's geographical location. The system described in Appendix 2, characterized by the features described herein. (Note 37) The aforementioned recommendation section is, The system estimates the user's emotions and adjusts the method of recommending shelters based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 38) The aforementioned recommendation section is, When providing recommendations, the system will refer to the user's past evacuation history to recommend the most suitable evacuation shelter. The system described in Appendix 3, characterized by the features described herein. (Note 39) The aforementioned recommendation section is, The system estimates user emotions and determines the priority of shelters based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 40) The aforementioned recommendation section is, When providing recommendations, the system will take into account the user's geographical location to suggest the most suitable evacuation shelter. The system described in Appendix 3, characterized by the features described herein. (Note 41) The aforementioned support unit, The system estimates the user's emotions and adjusts how support information is provided based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 42) The aforementioned support unit, When providing support information, we refer to the user's past support history to provide the most appropriate support information. The system described in Appendix 4, characterized by the features described herein. (Note 43) The aforementioned support unit, It estimates the user's emotions and prioritizes support information based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 44) The aforementioned support unit, When providing support information, we will provide the most appropriate support information by taking into account the user's geographical location. The system described in Appendix 4, characterized by the features described herein. [Explanation of symbols]

[0205] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A prediction unit that predicts natural disasters, A planning unit that creates individual evacuation plans based on the information predicted by the prediction unit, An evacuation unit that provides the optimal evacuation route based on the evacuation plan created by the aforementioned planning unit, It includes a management department that manages disaster prevention supplies. A system characterized by the following features.

2. It includes a notification unit that provides real-time alerts and notifications. The system according to feature 1.

3. It includes a recommendation section that provides recommendations for evacuation shelters and evacuation centers. The system according to feature 1.

4. The department is equipped to provide information and resources necessary for post-disaster recovery efforts. The system according to feature 1.

5. The prediction unit, By collecting data such as satellite imagery, weather information, and social media, natural disasters can be predicted. The system according to feature 1.

6. The aforementioned planning department, Create individualized evacuation plans based on the user's living environment and family structure. The system according to feature 1.

7. The aforementioned evacuation section is, Provides the optimal evacuation route in real time during a disaster. The system according to feature 1.

8. The aforementioned management department, Provide a checklist of supplies needed in an emergency. The system according to feature 1.

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