System

Abstract

A system is provided.SOLUTION: A system, comprising: means for obtaining earthquake information; means for obtaining terrain information and weather data; means for performing a tsunami simulation based on the earthquake information and the terrain information; means for evaluating a risk of a user based on a simulation result; means for sending a notification to the user based on an evaluation result; and means for generating a personalized evacuation instruction based on location information of the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] This invention relates to a system for minimizing damage caused by tsunamis when an earthquake occurs. Conventional tsunami evacuation systems have the problem that information is general and lacks specificity, making it difficult for users to take appropriate evacuation actions. In addition, tsunami simulations and risk assessments are not performed in real time, which can lead to delays in issuing evacuation instructions. This can increase human and property damage during disasters. Therefore, there is a need for a system that can quickly and accurately assess tsunami risk when an earthquake occurs and provide personalized evacuation instructions to each user. [Means for solving the problem]

[0005] The present invention solves the above-mentioned problems by providing a system including a means for acquiring earthquake information, a means for acquiring topographical information and meteorological data, a means for performing a tsunami simulation based on the acquired information, a means for assessing a user's risk based on the simulation results, a means for sending a notification to the user based on the assessment results, and a means for generating personalized evacuation instructions based on the user's location information. This system further includes a means for updating the topographical information using crowdsourcing or public datasets, a means for periodically updating the user's location information, and a means for performing risk assessment in real time, thereby enabling more accurate and rapid assessment of disaster risk and instruction of appropriate evacuation actions. This allows for minimizing human and property damage in the event of a disaster.

[0006] "Means for acquiring earthquake information" refers to means for collecting information such as the seismic intensity, epicenter, and time of occurrence of an earthquake in real time when it occurs.

[0007] "Means for acquiring topographical information and meteorological data" refers to means for acquiring coastline and seabed topographical data, as well as meteorological data, which are necessary for tsunami simulations.

[0008] "Means for performing tsunami simulations" refers to means for calculating the progress, wave height, arrival time, and range of impact of a tsunami based on acquired earthquake information and topographical information.

[0009] The "means for assessing user risk" is a means for assessing the risk of a tsunami for a specific user's current location based on the results of a tsunami simulation.

[0010] "Means for sending notifications to users based on the assessment results" refers to means for notifying users of appropriate evacuation instructions or warnings based on the results of the user's risk assessment.

[0011] "Means for generating personalized evacuation instructions based on the user's location information" refers to a means for generating and providing optimal evacuation instructions by taking into account the user's current location, surrounding terrain and building information, and past behavioral data.

[0012] "Means for updating topographical information using crowdsourcing and public datasets" refers to a means for collecting the latest coastline and seabed topography data through crowdsourcing and public datasets and updating the database within the system.

[0013] "Means for periodically updating user location information and conducting real-time risk assessment" refers to means for periodically obtaining location information from the user's device and conducting the latest risk assessment in real time. [Brief explanation of the drawings]

[0014] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0015] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0018] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0019] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0020] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple 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), Bluetooth (registered trademark), etc.

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0022] [First embodiment]

[0023] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0024] 1, a 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.

[0025] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. 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. The database 24 and the communication I / F 26 are also 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).

[0026] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0027] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0030] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0032] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0033] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0034] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0035] This invention relates to a hazard map service for minimizing damage caused by tsunamis when an earthquake occurs. This service acquires earthquake information, topographical information, and meteorological data, and performs tsunami simulations based on this data to assess the risk to each user and provide appropriate evacuation instructions promptly.

[0036] System Configuration

[0037] The system of the present invention consists of the following main components:

[0038] 1. Earthquake detection subsystem (server)

[0039] 2. Data collection subsystem (server)

[0040] 3. Tsunami Simulation Subsystem (Server)

[0041] 4. Risk Assessment Subsystem (Server)

[0042] 5. Notification sending subsystem (server)

[0043] 6. Personalized Advice Generation Subsystem (Server)

[0044] 7. User terminal (terminal)

[0045] Earthquake detection and data collection

[0046] The server connects with multiple earthquake detection sensors and public institution APIs to obtain earthquake occurrence information in real time. For example, it uses the Japan Meteorological Agency's API to collect data such as earthquake intensity, epicenter, and occurrence time. In addition, the device periodically sends the user's location information to the server. The server uses crowdsourcing and public datasets to obtain and store the latest coastline and seabed topography data. Weather data is also obtained in the same way and used in simulations.

[0047] Tsunami Simulation

[0048] Based on the earthquake information and the collected topographical information, the server runs an advanced tsunami simulation, which calculates the tsunami's progress, wave height, arrival time, and impact area, and stores the results in a database. The simulation is performed quickly and updated in real time.

[0049] Risk Assessment

[0050] Based on the results of the simulation, the server assesses the risk to the user's current location. Users' location information is compared with the simulation data, and users who are determined to be at high risk are required to take appropriate action based on the risk assessment. This risk assessment is updated regularly and is based on the latest information.

[0051] Notifications and personalized advice

[0052] Based on the evaluation results, the server sends notifications to high-risk users, including evacuation instructions, recommended evacuation routes, and information on the nearest evacuation shelters. High-risk users are especially provided with personalized evacuation instructions, allowing them to take optimal evacuation actions based on their current location and surrounding environment.

[0053] Specific examples

[0054] 1. Earthquake occurs

[0055] The server receives information from a seismograph about a magnitude 6 earthquake and verifies that it occurred near the coast. The earthquake information includes the epicenter, time of occurrence, and seismic intensity.

[0056] 2. Data Collection

[0057] The server retrieves the latest coastline and bathymetry data from crowdsourcing and public datasets and updates the database.

[0058] 3. Run the simulation

[0059] Based on the acquired earthquake and topographical information, the server starts a tsunami simulation, which calculates the tsunami's progress, wave height, arrival time, and affected area.

[0060] 4. Risk Assessment

[0061] The server obtains the location information of users who are deemed high risk and evaluates whether the area will be affected by a tsunami. Users who are deemed high risk are given a high risk score.

[0062] 5. Notice and Advice

[0063] The server sends a personalized notification to User A with evacuation instructions, including the nearest evacuation shelter and the specific evacuation route.

[0064] Users will receive a notification and can begin evacuation immediately.

[0065] In this way, the system of the present invention can assist users in taking prompt and appropriate evacuation actions when an earthquake occurs, thereby minimizing damage caused by tsunamis.

[0066] The processing flow will be explained below.

[0067] Step 1:

[0068] The server polls earthquake detectors and APIs of public institutions to obtain earthquake information. Specifically, it monitors the seismic intensity, epicenter, and occurrence time in real time, and triggers an earthquake detection event when an earthquake occurs that exceeds a threshold (for example, seismic intensity 5 or higher).

[0069] Step 2:

[0070] The device acquires the user's current location information using GPS and periodically sends it to the server. The user's location information must be highly accurate and is usually updated every few minutes.

[0071] Step 3:

[0072] The server collects up-to-date coastline and bathymetry data from crowdsourcing and public datasets, storing the data in a database that is updated in real time.

[0073] Step 4:

[0074] The server obtains meteorological data (e.g., tides, wind speed, air pressure, etc.) from the API of a public institution. This data is also stored and updated in the database and used during simulation.

[0075] Step 5:

[0076] The server receives a trigger from the earthquake detection subsystem and starts a tsunami simulation. It uses earthquake information and collected topographical and meteorological data to calculate the tsunami's progress, arrival time, wave height, and affected area. The simulation results are stored in a database.

[0077] Step 6:

[0078] The server retrieves the user's current location information from the database and compares it with the results of the tsunami simulation. If a specific user is in a high-risk area, it calculates the user's risk score and generates an evaluation result for each user.

[0079] Step 7:

[0080] The server generates notifications for users based on the risk assessment results, including evacuation instructions, recommended evacuation routes, and information on the nearest evacuation shelters. The content of the notifications is personalized and optimized based on the user's current location and surrounding terrain and building information.

[0081] Step 8:

[0082] The server sends a notification to the high-risk user's device, delivers the information quickly using push notifications or SMS, and logs whether the notification was successful.

[0083] Step 9:

[0084] The device receives the notification and displays an alert to the user, which is visually and audibly communicated to the user so that the user can see it immediately.

[0085] Step 10:

[0086] The user checks the notification and follows the instructions to begin evacuation. The user heads to the nearest evacuation shelter, selects a safe route, and moves quickly. During evacuation, the user can check updates and additional instructions on their smartphone.

[0087] This system's process continues in this way, supporting users from the moment an earthquake occurs until they can safely evacuate. Distributed data collection and advanced simulations enable quick and accurate evacuation instructions.

[0088] Example 1

[0089] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0090] A system is needed to minimize damage from earthquake-induced tsunamis and support rapid and effective evacuation. In particular, there is a need for risk assessments and personalized evacuation instructions that differ for each user. However, current systems have difficulty updating data and assessing risks in real time, making it difficult to provide users with appropriate evacuation instructions.

[0091] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0092] In this invention, the server includes means for acquiring earthquake information, means for acquiring location data, means for collecting topographical data and meteorological data, means for simulating tsunami behavior based on the earthquake information and topographical data, means for assessing a user's risk based on the simulation results, means for transmitting information to the user based on the assessment results, and means for generating personalized evacuation instructions based on the user's location data, thereby enabling the user to take optimal evacuation actions based on the risk assessment updated in real time.

[0093] "Earthquake information" is data related to the occurrence of an earthquake, including information such as seismic intensity, epicenter, and time of occurrence.

[0094] "Location data" is information that indicates a user's current location, and is typically obtained by a location information system such as a GPS.

[0095] "Topographic data" refers to information about the shape of the earth's surface and seabed, and is geographic data used in tsunami simulations.

[0096] "Meteorological data" refers to information about weather and climate, and is data used to predict the progression and impact of tsunamis.

[0097] "Means for simulating tsunami behavior" refers to methods and means for calculating tsunami progression, wave height, arrival time, area of ​​impact, etc., using earthquake information and topographical data.

[0098] "Means for assessing risk" refers to methods and means for comparing the results of tsunami simulations with user location data to assess the degree to which a user is exposed to tsunami risk.

[0099] "Personalized evacuation instructions" are evacuation instructions that are individually generated based on the user's current location and risk assessment, including recommended evacuation routes and information about the nearest evacuation shelters.

[0100] "Crowdsourcing" is a method of collecting information or data from a large number of people and is a means used to update information used within a system.

[0101] "Public Dataset" means a dataset provided by a government agency or other public body that contains reliable topographical and meteorological data.

[0102] This invention relates to a hazard map service for minimizing damage caused by tsunamis when an earthquake occurs. The system of the present invention consists of the following main components:

[0103] 1. Earthquake detection subsystem (server)

[0104] 2. Data collection subsystem (server)

[0105] 3. Tsunami Simulation Subsystem (Server)

[0106] 4. Risk Assessment Subsystem (Server)

[0107] 5. Notification sending subsystem (server)

[0108] 6. Personalized Advice Generation Subsystem (Server)

[0109] 7. User terminal (terminal)

[0110] Earthquake detection and data collection

[0111] The server connects with multiple sensors and public institution APIs to detect earthquakes and obtains earthquake occurrence information in real time. Specifically, the server sends a request to an API (e.g., the Japan Meteorological Agency API) and receives information such as seismic intensity, epicenter, and time of occurrence, which it then stores in a database.

[0112] The device also periodically transmits the user's location information to the server, which receives the location information acquired using the GPS function and stores it in a database, allowing the device to track the user's location in real time.

[0113] Additionally, the server will use crowdsourcing and public datasets to obtain the latest coastline and bathymetry data to update the database, as well as weather data, which will be used in the tsunami simulation.

[0114] Tsunami Simulation

[0115] Based on the earthquake information and collected topographical data, the server performs advanced tsunami simulations. The server uses dedicated simulation software (e.g., NEOWAVE) to calculate the tsunami's progress, wave height, arrival time, and impact area. The simulation results are stored in a database and updated in real time.

[0116] Risk Assessment

[0117] Based on the simulation results, the server assesses the risk to the user's current location. The server compares the simulation results with the user's location information to calculate a risk score, which allows the server to assess the user's exposure to tsunami danger. The risk assessment is updated regularly, ensuring that the risk assessment is always based on the latest information.

[0118] Notifications and personalized advice

[0119] Based on the assessment results, the server sends notifications to high-risk users, including evacuation instructions, recommended evacuation routes, and information on the nearest evacuation shelters. High-risk users can also receive personalized evacuation instructions, allowing them to take optimal evacuation actions based on their current location and surrounding environment.

[0120] Specific examples

[0121] 1. Earthquake occurs

[0122] The server receives information from a seismograph about a magnitude 6 earthquake and verifies that it occurred near the coast. The earthquake information includes the epicenter, time of occurrence, and seismic intensity.

[0123] 2. Data Collection

[0124] The server updates the database with the latest coastline and bathymetry data from crowdsourcing and public datasets.

[0125] 3. Tsunami Simulation

[0126] Based on the acquired earthquake and topographical information, the server starts a tsunami simulation, which calculates the tsunami's progress, wave height, arrival time, and affected area.

[0127] 4. Risk Assessment

[0128] The server obtains the location information of users who are deemed high risk and evaluates whether the area will be affected by a tsunami. Users who are deemed high risk are given a high risk score.

[0129] 5. Notice and Advice

[0130] The server sends a personalized notification to User A containing evacuation instructions, including the nearest evacuation shelter and a safe evacuation route.

[0131] Users will receive a notification and can begin evacuation immediately.

[0132] Prompt Sentence Examples

[0133] Below are some example prompts to input to the generative AI model:

[0134] "Please tell me the process of the system that obtains the latest earthquake information, performs a tsunami simulation, and provides evacuation instructions to users. Please explain in detail the APIs, sensors, and simulation content used, and explain the flow of how users evacuate."

[0135] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0136] Step 1: Obtaining earthquake information

[0137] The server obtains earthquake information from earthquake sensors and APIs of public institutions to detect earthquakes.

[0138] Input: Earthquake sensor data and data obtained from public APIs (seismic intensity, epicenter, and time of occurrence).

[0139] Specific operation: The server sends a request to the API, receives earthquake information, and stores it in a database.

[0140] Output: Earthquake information stored in a database.

[0141] Step 2: Get user location

[0142] The device periodically sends the user's location information to the server.

[0143] Input: Location data from the user device.

[0144] Specific operation: The device uses the GPS function to obtain its current location and sends the location data to the server, which receives the data and stores it in a database.

[0145] Output: User location stored in a database.

[0146] Step 3: Collect topographic and meteorological data

[0147] The server collects topographical and meteorological data from crowdsourcing and public datasets.

[0148] Input: Topographic and meteorological data from crowdsourcing platforms and public datasets.

[0149] Specific operation: The server accesses multiple data sources, automatically obtains the latest coastline, ocean floor topography and weather data, and updates the database.

[0150] Output: Latest terrain and weather data stored in a database.

[0151] Step 4: Run the tsunami simulation

[0152] The server runs a tsunami simulation based on the collected earthquake information and topographical data.

[0153] Input: Earthquake information, topographical data, meteorological data.

[0154] Specific operation: The server uses simulation software (e.g., NEOWAVE) to calculate the tsunami's progress, wave height, arrival time, and impact area. The simulation results are saved in a database.

[0155] Output: Tsunami simulation results stored in a database.

[0156] Step 5: Conduct a risk assessment

[0157] The server assesses risk based on the tsunami simulation results and the user's location information.

[0158] Input: Tsunami simulation results, user location information.

[0159] Specific operation: The server compares the simulation results with the location information, calculates a risk score, and saves the risk assessment results in a database.

[0160] Output: Risk assessment results stored in a database.

[0161] Step 6: Send notifications and provide personalized advice

[0162] The server will send information to users based on the risk assessment results and provide personalized evacuation instructions.

[0163] Input: Risk assessment results, user location information.

[0164] What it does: The server sends notifications to high-risk users with evacuation instructions, including the nearest evacuation shelter and recommended evacuation routes.

[0165] Output: Personalized evacuation instructions and notifications sent to user devices.

[0166] (Application example 1)

[0167] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0168] Current tsunami evacuation systems struggle to provide users with optimal evacuation routes in real time when an earthquake occurs. Many systems only provide text-based notifications, making it difficult for users to take prompt and appropriate evacuation actions during an emergency. Furthermore, there is a need for systems that can provide personalized evacuation instructions based on the user's current location in real time.

[0169] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0170] In this invention, the server includes means for acquiring earthquake information, means for acquiring topographical information and meteorological data, means for running a tsunami simulation based on the earthquake information and topographical information, means for assessing a user's risk based on the simulation results, means for sending a notification to the user based on the assessment results, means for generating personalized evacuation instructions based on the user's location information, and means for displaying evacuation routes in AR on the smart device. This allows the user to be visually guided to the optimal evacuation route in real time, enabling them to take prompt and appropriate evacuation action.

[0171] "Earthquake information" refers to data such as the epicenter, seismic intensity, and time of occurrence at the time of an earthquake.

[0172] "Topographic information" refers to geographic information including coastline, ocean floor topography, and elevation data for areas where earthquakes occur.

[0173] "Weather Data" refers to information regarding weather conditions such as weather, temperature, wind speed, and air pressure.

[0174] "Tsunami simulation" refers to the process of calculating the progression, wave height, arrival time, and area of ​​impact of a tsunami based on earthquake and topographical information.

[0175] "Risk assessment" refers to the act of assessing the tsunami risk at the user's current location based on the simulation results.

[0176] "Notification" refers to the act of sending evacuation instructions and evacuation route information to users based on the results of risk assessment.

[0177] "Personalized evacuation instructions" refers to evacuation suggestions that are customized based on the user's current location and surrounding conditions.

[0178] "Smart devices" refers to devices that can connect to the Internet, such as smartphones, smart glasses, and head-mounted displays.

[0179] "AR display" refers to the act of overlaying digital information on real-world scenery using augmented reality technology.

[0180] This invention relates to a system that operates as a security service to minimize damage caused by tsunamis when an earthquake occurs. This system acquires earthquake information, topographical information, and meteorological data, and performs tsunami simulations based on these data. Furthermore, it performs risk assessments for each user based on the simulation results and promptly provides appropriate evacuation instructions.

[0181] System Configuration

[0182] The system of the present invention consists of the following main components:

[0183] 1. Earthquake detection subsystem (server)

[0184] 2. Data collection subsystem (server)

[0185] 3. Tsunami Simulation Subsystem (Server)

[0186] 4. Risk Assessment Subsystem (Server)

[0187] 5. Notification sending subsystem (server)

[0188] 6. Personalized Advice Generation Subsystem (Server)

[0189] 7. User terminal (smart device)

[0190] Earthquake detection and data collection

[0191] The server obtains real-time earthquake occurrence information by linking with multiple earthquake detection sensors and public institution APIs. For example, it uses the Japan Meteorological Agency's API to collect data such as seismic intensity, epicenter, and time of occurrence. In addition, user devices periodically send their location information to the server. The server obtains and stores the latest coastline and seabed topography data using crowdsourcing and public datasets. Weather data is also collected and used in the simulations.

[0192] Tsunami Simulation

[0193] Based on the collected seismic and topographical information, the server runs an advanced tsunami simulation, calculating the tsunami's progress, wave height, arrival time, and impact area, and storing the results in a database. The simulation is performed quickly and updated in real time.

[0194] Risk Assessment

[0195] Based on the results of the tsunami simulation, the server assesses the risk to the user's current location. The server compares the user's location information with the simulation data, and users who are determined to be at high risk are required to take appropriate action. This risk assessment is updated regularly and is based on the latest information.

[0196] Notifications and personalized advice

[0197] Based on the results of the risk assessment, the server sends notifications to high-risk users. The notifications include evacuation instructions, recommended evacuation routes, and information on the nearest evacuation shelters. High-risk users are provided with personalized evacuation instructions. The optimal evacuation route is visually displayed on the user's smart device, such as a smartphone or smart glasses, using augmented reality (AR) technology.

[0198] Specific examples

[0199] For example, if a magnitude 6 earthquake occurs in a coastal area, the server detects this information in real time and runs a tsunami simulation using the latest coastline data. The resulting simulation data is used to assess the risk to the user's area. Users in high-risk areas are notified of the optimal evacuation route and the nearest evacuation shelter. The evacuation route is visually displayed on the user's smart device through the AR function, enabling quick evacuation.

[0200] Example prompts to input to a generative AI model:

[0201] Generate a notification that provides evacuation instructions based on the user's current location in the event of a tsunami. The notification should include:

[0202] 1. Earthquake Information

[0203] 2. Risk assessment using tsunami simulation

[0204] 3. Information on the nearest evacuation shelter

[0205] 4. Best evacuation route

[0206] For example, include, "An earthquake of magnitude 6 has occurred and a tsunami is expected. The nearest evacuation shelter is AAA evacuation shelter, and the best evacuation route is BBB. Please evacuate immediately."

[0207] By feeding this prompt into a generative AI model, a message providing evacuation instructions in natural language is generated.

[0208] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0209] Step 1: Obtaining earthquake information

[0210] The server connects with multiple sensors and public APIs to obtain real-time earthquake information. This information includes the epicenter, seismic intensity, and time of occurrence. The input is data from the sensors and APIs, and the output is stored internally as earthquake information. This data is used in subsequent processing.

[0211] Step 2: Collect terrain and weather data

[0212] The server collects the latest topographic and meteorological data from crowdsourcing and public datasets. The input is data from external databases, and the output is stored internally as topographic and meteorological data. This data is used for tsunami simulation.

[0213] Step 3: Run the tsunami simulation

[0214] The server runs a tsunami simulation based on the collected earthquake and topographical information. The input is earthquake information and topographical information, and through calculations, the progress, wave height, arrival time, and affected area of ​​the tsunami are simulated. The output is saved in a database as the simulation results.

[0215] Step 4: User Risk Assessment

[0216] The server performs risk assessment based on the user's current location and simulation data. The input is the user's location information and the simulation results, and a risk score is calculated by comparing these. The output is the risk assessment result for each user, and notification content is determined based on this.

[0217] Step 5: Generate and send notifications

[0218] The server generates notifications for users based on the risk assessment results. These notifications include evacuation instructions, recommended evacuation routes, and information on the nearest evacuation shelters. The input is the risk assessment results, and the output is the notification message sent to the user. The notification content is personalized and sent to the user's smart device.

[0219] Step 6: Evacuation route guidance using AR display

[0220] The user's smart device (smart glasses or smartphone) displays an AR evacuation route based on the received notification. The input is the notification data from the server, and the output is the evacuation route displayed on the device. Using augmented reality technology, users can visually confirm the evacuation route and evacuate quickly.

[0221] The above process enables users to take swift and appropriate evacuation action in the event of an earthquake.

[0222] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0223] This invention relates to a hazard map service for minimizing damage caused by tsunamis in the event of an earthquake. In particular, it focuses on a system that combines methods for recognizing users' emotions to optimize the content and notification method of evacuation instructions. This service acquires earthquake information, topographical information, and meteorological data, and performs tsunami simulations based on this information. Furthermore, it uses an emotion engine to recognize the user's emotional state and personalize evacuation instructions.

[0224] System Configuration

[0225] The system of the present invention consists of the following main components:

[0226] 1. Earthquake detection subsystem (server)

[0227] 2. Data collection subsystem (server)

[0228] 3. Tsunami Simulation Subsystem (Server)

[0229] 4. Risk Assessment Subsystem (Server)

[0230] 5. Notification sending subsystem (server)

[0231] 6. Personalized Advice Generation Subsystem (Server)

[0232] 7. Emotion engine (server)

[0233] 8. User terminal (terminal)

[0234] Earthquake detection and data collection

[0235] The server connects with multiple earthquake detection sensors and public institution APIs to obtain earthquake occurrence information in real time. For example, it uses the Japan Meteorological Agency's API to collect data such as earthquake intensity, epicenter, and occurrence time. In addition, the device periodically sends the user's location information to the server. The server uses crowdsourcing and public datasets to obtain and store the latest coastline and seabed topography data. Weather data is also obtained in the same way and used in simulations.

[0236] Tsunami Simulation

[0237] Based on the earthquake information and the collected topographical information, the server runs an advanced tsunami simulation, which calculates the tsunami's progress, wave height, arrival time, and impact area, and stores the results in a database. The simulation is performed quickly and updated in real time.

[0238] Risk Assessment

[0239] Based on the results of the simulation, the server assesses the risk to the user's current location. Users' location information is compared with the simulation data, and users who are determined to be at high risk are required to take appropriate action based on the risk assessment. This risk assessment is updated regularly and is based on the latest information.

[0240] Emotion Recognition and Personalized Advice

[0241] The emotion engine analyzes data (e.g., voice, facial expressions, typing speed) obtained from the user's smartphone or other mobile device to recognize the user's emotional state in real time. The server takes the results of the emotion engine into consideration and optimizes the content and notification method of evacuation instructions based on the user's emotional state. For example, it provides simple and intuitive instructions to a panicked user and detailed information to a calm user.

[0242] Notifications and personalized advice

[0243] Based on the evaluation results, the server generates and sends notifications to high-risk users, including evacuation instructions, recommended evacuation routes, and information on the nearest evacuation shelters. In particular, high-risk users are provided with personalized evacuation instructions that reflect their emotional state, allowing them to take optimal evacuation actions based on their current location and surrounding environment.

[0244] Specific examples

[0245] 1. Earthquake occurs

[0246] The server receives information from a seismograph about a magnitude 6 earthquake and verifies that it occurred near the coast. The earthquake information includes the epicenter, time of occurrence, and seismic intensity.

[0247] 2. Data Collection

[0248] The server retrieves the latest coastline and bathymetry data from crowdsourcing and public datasets and updates the database.

[0249] 3. Run the simulation

[0250] Based on the acquired earthquake and topographical information, the server starts a tsunami simulation, which calculates the tsunami's progress, wave height, arrival time, and affected area.

[0251] 4. Risk Assessment

[0252] The server obtains the location information of users who are deemed high risk and evaluates whether the area will be affected by a tsunami. Users who are deemed high risk are given a high risk score.

[0253] 5. Emotion recognition

[0254] The device collects the user's voice and facial expression data, which is then analyzed by an emotion engine to assess the user's emotional state.

[0255] 6. Notice and Advice

[0256] The server generates evacuation instructions that take into account User A's emotional state and sends a personalized notification containing the evacuation instructions, including the nearest evacuation shelter and the specific evacuation route.

[0257] Users will receive a notification and can begin evacuation immediately.

[0258] This system will help users take prompt and appropriate evacuation actions in the event of an earthquake, minimizing damage from tsunamis. Furthermore, emotion recognition will enable responses based on the user's mental state, further improving the effectiveness of evacuation actions.

[0259] The processing flow will be explained below.

[0260] Step 1:

[0261] The server polls earthquake detectors and APIs of public institutions to obtain earthquake information. Specifically, it monitors the seismic intensity, epicenter, and occurrence time in real time, and triggers an earthquake detection event when an earthquake occurs that exceeds a threshold (for example, seismic intensity 5 or higher).

[0262] Step 2:

[0263] The device acquires the user's current location information using GPS and periodically sends it to the server. The user's location information must be highly accurate and is usually updated every few minutes.

[0264] Step 3:

[0265] The server collects up-to-date coastline and bathymetry data from crowdsourcing and public datasets, storing the data in a database that is updated in real time.

[0266] Step 4:

[0267] The server obtains meteorological data (e.g., tides, wind speed, air pressure, etc.) from the API of a public institution. This data is also stored and updated in the database and used during simulation.

[0268] Step 5:

[0269] The server receives a trigger from the earthquake detection subsystem and starts a tsunami simulation. It uses earthquake information and collected topographical and meteorological data to calculate the tsunami's progress, arrival time, wave height, and affected area. The simulation results are stored in a database.

[0270] Step 6:

[0271] The server retrieves the user's current location information from the database and compares it with the results of the tsunami simulation. If a specific user is in a high-risk area, it calculates the user's risk score and generates an evaluation result for each user.

[0272] Step 7:

[0273] The device collects the user's voice and facial expression data and sends it to the emotion engine. Voice data is obtained using a microphone, and facial expression data is obtained using a camera.

[0274] Step 8:

[0275] The emotion engine analyzes the received voice and facial expression data to recognize the user's emotional state. For example, it analyzes the tone of voice and facial features to determine whether the user is panicked or calm.

[0276] Step 9:

[0277] The server receives the results of the emotion engine and optimizes the content and notification method of evacuation instructions based on the user's emotional state, for example, providing simple and intuitive instructions to a panicked user and detailed information to a calm user.

[0278] Step 10:

[0279] The server generates evacuation instructions that take into account the emotional state of the user and sends them to the devices of high-risk users. The server quickly delivers information using push notifications and SMS. It also logs whether the transmission was successful.

[0280] Step 11:

[0281] The device receives the notification and displays an alert to the user, which is visually and audibly communicated to the user so that the user can see it immediately.

[0282] Step 12:

[0283] The user checks the notification and follows the instructions to begin evacuation. The user heads to the nearest evacuation shelter, selects a safe route, and moves quickly. During evacuation, the user can check updates and additional instructions on their smartphone.

[0284] This is how the system's process progresses, supporting users from the moment an earthquake occurs to the moment they decide to evacuate safely. In addition to distributed data collection and advanced simulation, emotion recognition enables optimal responses for each user.

[0285] Example 2

[0286] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0287] In order to minimize damage caused by tsunamis when an earthquake occurs, quick and accurate evacuation instructions are necessary. However, conventional systems have difficulty providing appropriate evacuation instructions that take into account the individual situation and emotional state of the user. This has led to problems such as users panicking and delaying evacuation behavior.

[0288] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for acquiring earthquake information, means for acquiring topographical information and meteorological data, means for executing a tsunami simulation based on the earthquake information and topographical information, means for assessing a user's risk based on the simulation results, means for sending a notification to the user based on the assessment results, means for recognizing the user's emotional state, and means for generating personalized evacuation instructions based on the recognized emotional state. This makes it possible to quickly provide appropriate evacuation instructions according to the user's emotional state in the event of an earthquake, and support the user's evacuation behavior.

[0289] "Earthquake information" is detailed data about earthquakes, including the magnitude, epicenter, and time of occurrence of the earthquake.

[0290] "Topographic information" is detailed data about the terrain, including coastline and undersea topography data.

[0291] "Weather data" refers to data related to weather, such as precipitation, wind speed, and temperature.

[0292] The "means for performing a tsunami simulation" is a means for calculating the progress, wave height, arrival time, and range of impact of a tsunami based on earthquake information and topographical information.

[0293] The "means for assessing the user's risk" is a means for comparing the results of a tsunami simulation with the user's current location to assess the possibility that the user will be affected by a tsunami.

[0294] The "means for sending notifications to users based on the assessment results" refers to the means for sending evacuation instructions or warnings to users based on the users' risk assessment.

[0295] The "means for recognizing the user's emotional state" refers to a means for analyzing data such as the user's voice, facial expression, typing speed, etc., and recognizing the user's emotional state in real time.

[0296] The "means for generating personalized evacuation instructions" is a means for creating evacuation instructions that are best suited to the user's situation based on the recognized emotional state.

[0297] "Crowdsourcing" is a method of collecting information from a large number of Internet users.

[0298] A "public dataset" is a collection of highly reliable data provided by governments, public institutions, etc.

[0299] "Real-time" refers to information being updated immediately without delay.

[0300] This invention is a system for providing hazard maps to minimize damage caused by tsunamis during earthquakes. The system acquires earthquake, topographical, and meteorological data, and performs tsunami simulations based on this information. It also recognizes the user's emotional state and provides personalized evacuation instructions.

[0301] System Configuration

[0302] The system of the present invention consists of the following main components:

[0303] 1. Earthquake detection subsystem (server)

[0304] 2. Data collection subsystem (server)

[0305] 3. Tsunami Simulation Subsystem (Server)

[0306] 4. Risk Assessment Subsystem (Server)

[0307] 5. Notification sending subsystem (server)

[0308] 6. Personalized Advice Generation Subsystem (Server)

[0309] 7. Emotion engine (server)

[0310] 8. User terminal (terminal)

[0311] Earthquake detection and data collection

[0312] The server connects with multiple sensors and public institution APIs to detect earthquakes and obtains earthquake occurrence information (e.g., seismic intensity, epicenter, and time of occurrence) in real time. For example, data is collected using the Japan Meteorological Agency's API. The device periodically sends the user's location information to the server. The server uses crowdsourcing and public datasets to obtain and store the latest coastline and seabed topography data. Weather data is also obtained in the same way and used in simulations.

[0313] Tsunami Simulation

[0314] The server runs an advanced tsunami simulation based on earthquake information and collected topographical information. This simulation calculates the tsunami's progress, wave height, arrival time, and impact area. The simulation results are stored in a database and are continuously updated. For example, GEOWAVE may be used as the tsunami simulation software.

[0315] Risk Assessment

[0316] The server evaluates the risk to the user's current location based on the simulation results. By comparing the user's location information with the simulation data, users who are deemed to be at high risk will be given special evacuation instructions. This risk assessment is updated in real time.

[0317] Emotion Recognition and Personalized Advice

[0318] The emotion engine analyzes data such as the user's voice, facial expressions, and typing speed to recognize their emotional state in real time. The server then uses the results of this emotion engine to personalize the evacuation instructions and notification method based on the user's emotional state. For example, it provides simple and intuitive instructions to a panicked user and detailed information to a calm user.

[0319] Notification and Action Support

[0320] The server generates optimal evacuation instructions based on the risk assessment and emotion recognition results, and sends a notification to the user's device. This notification includes evacuation instructions, recommended evacuation routes, and information on the nearest evacuation shelter. The user receives this notification and promptly begins appropriate evacuation actions.

[0321] Specific examples

[0322] 1. Earthquake occurs

[0323] The server receives information from a seismograph about a magnitude 6 earthquake and verifies that it occurred near the coast. The earthquake information includes the epicenter, time of occurrence, and seismic intensity.

[0324] 2. Data Collection

[0325] The server retrieves the latest coastline and bathymetry data from crowdsourcing and public datasets and updates the database.

[0326] 3. Run the simulation

[0327] Based on the acquired earthquake and topographical information, the server starts a tsunami simulation, which calculates the tsunami's progress, wave height, arrival time, and affected area.

[0328] 4. Risk Assessment

[0329] The server obtains the location information of users who are deemed high risk and evaluates whether the area will be affected by a tsunami. Users who are deemed high risk are given a high risk score.

[0330] 5. Emotion recognition

[0331] The device collects the user's voice and facial expression data, which is then analyzed by an emotion engine to assess the user's emotional state.

[0332] 6. Notice and Advice

[0333] The server generates evacuation instructions that take into account User A's emotional state and sends a personalized notification containing the evacuation instructions, including the nearest evacuation shelter and the specific evacuation route.

[0334] Users will receive a notification and can begin evacuation immediately.

[0335] Prompt Sentence Examples

[0336] "If you live in a coastal area, please suggest multiple scenarios for what evacuation route you should take in the event of a magnitude 6 earthquake, depending on your emotional state."

[0337] This system enables users to take prompt and appropriate evacuation actions in the event of an earthquake, minimizing damage from tsunamis. Furthermore, emotion recognition makes it possible to respond according to the user's mental state, further improving the effectiveness of evacuation actions.

[0338] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0339] Step 1: Earthquake detection

[0340] The server collects earthquake information from seismometers and public institutions (e.g., Japan Meteorological Agency API) to detect earthquakes.

[0341] Input: Seismic intensity, epicenter, time of occurrence

[0342] How it works: The server polls data from the seismometer and generates an alert if an earthquake of magnitude 5 or greater is detected.

[0343] Output: Earthquake alerts and data on seismic intensity, epicenter, and occurrence time

[0344] Step 2: Data collection

[0345] The device periodically transmits the user's location information to the server.

[0346] Input: GPS data

[0347] How it works: The device receives a GPS signal and sends location information to the server at specified intervals.

[0348] Output: Current location of the user

[0349] Step 3: Update terrain and weather data

[0350] The server uses crowdsourcing and public datasets to obtain the latest terrain and weather data and update the database.

[0351] Input: Crowdsourced data, public datasets

[0352] How it works: The server periodically checks for updates to the dataset and automatically updates the database if new data is found.

[0353] Output: Latest terrain and weather data

[0354] Step 4: Run the tsunami simulation

[0355] The server runs a tsunami simulation based on the collected earthquake and topographical information.

[0356] Input: Earthquake information, topographical information

[0357] How it works: The server launches simulation software (e.g., GEOWAVE), inputs data, and calculates the tsunami's progress, wave height, arrival time, and area of ​​impact.

[0358] Output: Tsunami simulation results (wave height, arrival time, affected area)

[0359] Step 5: Risk assessment

[0360] The server evaluates the risk to the user's current location based on the simulation results.

[0361] Input: User location information, tsunami simulation results

[0362] How it works: The server compares the user's location with simulated data to identify users in high-risk areas and calculate a risk score.

[0363] Output: Risk assessment result (risk score)

[0364] Step 6: Collect and analyze emotion data

[0365] The device collects the user's voice and facial expression data, which is analyzed by an emotion engine to recognize the user's emotional state.

[0366] Input: Voice data, facial expression data

[0367] How it works: The device uses a microphone and camera to collect voice and facial expression data and sends it to a server. The emotion engine analyzes the data and evaluates the user's emotional state.

[0368] Output: Emotion evaluation result (e.g., panicked, calm)

[0369] Step 7: Generate personalized evacuation instructions

[0370] The server takes into account the results of the emotion engine to generate evacuation instructions based on the user's emotional state.

[0371] Input: Emotion assessment results, risk assessment results

[0372] How it works: Based on the analysis results of the emotion engine, the server selects different evacuation instruction templates and customizes evacuation instructions that best suit the user's situation.

[0373] Output: Personalized evacuation instructions

[0374] Step 8: Send notification

[0375] The server notifies the user of the generated evacuation instructions.

[0376] Input: personalized evacuation instructions

[0377] What it does: The server generates the notification and pushes it to the user's device. It also saves it in the notification history so the user can review it later.

[0378] Output: Evacuation notice sent to user

[0379] Step 9: User Actions

[0380] Users will receive a notification and will be able to quickly take action to evacuate.

[0381] Input: Evacuation order notice

[0382] Action: The user confirms the notification and follows the evacuation route provided. During the evacuation, the device automatically updates its location and sends it to the server.

[0383] Output: Evacuation action start and progress

[0384] In this way, the system can provide users with quick and accurate evacuation instructions, minimizing damage during disasters. Furthermore, personalized instructions based on emotion recognition can respond according to the user's mental state.

[0385] (Application example 2)

[0386] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0387] Conventional earthquake and tsunami evacuation systems do not take into account the user's psychological state when issuing evacuation instructions, which can lead to panic and confusion. It is particularly difficult for users to take appropriate evacuation actions when an earthquake occurs while they are shopping in a physical store. Therefore, it is necessary to optimize personalized evacuation instructions and notification methods according to each user's emotional state so that they can evacuate quickly and effectively.

[0388] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for acquiring earthquake information, means for acquiring topographical information and meteorological data, means for running a tsunami simulation based on the earthquake information and topographical information, means for assessing the user's risk based on the simulation results, means for sending a notification to the user based on the assessment results, means for generating personalized evacuation instructions based on the user's location information, means for recognizing the user's emotional state using an emotion engine, and means for optimizing the content of the evacuation instructions and the notification method based on the user's emotional state. This enables the user to take evacuation action quickly and effectively when they encounter an earthquake while shopping in a physical store.

[0389] "Means for acquiring earthquake information" refers to a system or device for detecting and collecting information on earthquake occurrence.

[0390] The "means for acquiring topographical information and meteorological data" is a system or device for collecting topographical data and meteorological information.

[0391] "Means for performing tsunami simulations based on earthquake information and topographical information" refers to a system or device for predicting and calculating the occurrence and progression of tsunamis using earthquake and topographical data.

[0392] "Means for assessing user risk based on simulation results" refers to a system or device that determines the risk level of the area where the user is located based on the results of a tsunami simulation.

[0393] "Means for sending a notification to a user based on the assessment result" refers to a system or device that sends an appropriate notification to a user depending on the result of the risk assessment.

[0394] "Means for generating personalized evacuation instructions based on the user's location information" refers to a system or device that generates individual evacuation instructions tailored to each situation based on the user's current location.

[0395] "Means for recognizing a user's emotional state using an emotion engine" refers to a system or device that uses emotion recognition technology to analyze and determine a user's current emotional state.

[0396] The "means for optimizing the content and notification method of evacuation instructions based on the emotional state of the user" is a system or device that generates evacuation instructions in the optimal format and content, taking into account the emotional state of the user.

[0397] This invention is a hazard map service for minimizing damage caused by tsunamis when an earthquake occurs. The system acquires earthquake information, topographical information, and meteorological data, and based on this information, performs tsunami simulations to assess risk and provides optimal evacuation instructions to users. Furthermore, it uses an emotion engine to recognize the user's emotional state and optimizes notification content according to that emotional state, helping users take quick and effective evacuation actions.

[0398] System Configuration

[0399] The system consists of the following main components:

[0400] 1. Earthquake detection subsystem (server): Detects earthquakes and collects earthquake information in real time.

[0401] 2. Data collection subsystem (server): Collects and updates terrain and meteorological data.

[0402] 3. Tsunami simulation subsystem (server): Simulates tsunami progression, wave height, arrival time, and impact area based on earthquake and topographical data.

[0403] 4. Risk assessment subsystem (server): Evaluates the user's risk based on the results of the tsunami simulation.

[0404] 5. Notification sending subsystem (server): Sends appropriate notifications to users based on the results of risk assessment.

[0405] 6. Personalized advice generation subsystem (server): Generates individual evacuation instructions based on the user's location and emotional state.

[0406] 7. Emotion Engine (Server): Recognizes the user's emotional state in real time.

[0407] 8. User terminal (terminal): Transmits the user's location information and emotional state data to the server and receives evacuation instructions.

[0408] Earthquake detection and data collection

[0409] The server connects with multiple sensors and public institution APIs to detect earthquakes and obtains earthquake occurrence information in real time. For example, it uses the Japan Meteorological Agency's API to collect data such as earthquake intensity, epicenter, and occurrence time. Furthermore, the device periodically sends location information to the server, allowing the server to determine the user's current location. It also uses crowdsourcing and public datasets to collect and store the latest topographical and meteorological data.

[0410] Tsunami Simulation

[0411] Based on the earthquake information and the collected topographical information, the server runs an advanced tsunami simulation, which calculates the tsunami's progress, wave height, arrival time and impact area, and stores the results in a database. The simulation is performed quickly and updated in real time.

[0412] Risk Assessment

[0413] Based on the results of the simulation, the server assesses the risk to the user's current location. The server compares the user's location information with the simulation data, and if a user is determined to be at high risk, they will be required to take appropriate action based on the risk assessment. This risk assessment is updated regularly and is based on the latest information.

[0414] Emotion Recognition and Personalized Advice

[0415] The emotion engine analyzes data (e.g., voice, facial expressions, typing speed) obtained from the user's smartphone or other mobile device to recognize the user's emotional state in real time. The server takes the results of the emotion engine into consideration and optimizes the content and notification method of evacuation instructions based on the user's emotional state. For example, it provides simple and intuitive instructions to a panicked user and detailed information to a calm user.

[0416] Notifications and personalized advice

[0417] Based on the risk assessment results, the server generates and sends notifications to high-risk users, including evacuation instructions, recommended evacuation routes, and information on the nearest evacuation shelters. In particular, high-risk users are provided with personalized evacuation instructions that reflect their emotional state, allowing them to take optimal evacuation actions based on their current location and surrounding environment.

[0418] Specific examples

[0419] If an earthquake occurs while a user is shopping in a physical store, real-time earthquake information is acquired to confirm that the user is in a coastal area. At the same time, a tsunami simulation is run to identify the user's area as high-risk. The emotion engine analyzes the user's facial expression data and determines that the user is in a state of panic.

[0420] When this happens, the server will send a notification to the user:

[0421] "High-risk area. Evacuate immediately to the nearest evacuation center. Please remain calm and follow simple instructions."

[0422] Example prompt sentence:

[0423] "Create a notification for tsunami evacuation instructions and personalized evacuation routes using emotion recognition. Earthquake information is as follows: Epicenter, Magnitude 6, Occurrence time 18:00. User is in a panic and in a high-risk area."

[0424] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0425] Step 1:

[0426] The server acquires earthquake information. First, the server acquires real-time earthquake occurrence information through the API of a public institution (e.g., the Japan Meteorological Agency API), collecting data such as seismic intensity, epicenter, and time of occurrence. The input data is data distributed by earthquake sensor networks and public institutions, and the output is detailed information about the earthquake.

[0427] Step 2:

[0428] The server obtains terrain information and weather data. The server obtains and stores the latest coastline and bathymetry data, as well as weather data, using crowdsourcing or public datasets. The input data is data from the terrain database and weather information API, and the output is updated terrain and weather data.

[0429] Step 3:

[0430] The server runs the tsunami simulation. It performs an advanced tsunami simulation based on earthquake information and collected topographical information. Specifically, it simulates the tsunami's progress, wave height, arrival time, and affected area, and stores the results in a database. The input data are earthquake information and topographical information, and the output is the simulation results.

[0431] Step 4:

[0432] The server performs risk assessment. Based on the simulation results, the server assesses the risk for the user's current location. Specifically, it compares the user's location information with the simulation data and determines whether the area is high-risk. The input data is the user's location information and the tsunami simulation results, and the output is the risk assessment results.

[0433] Step 5:

[0434] The device collects emotional state data. Emotional data such as the user's voice, facial expression, and typing speed are collected from smartphones and other mobile devices and sent to a server. The input data is the user's emotional expression data, and the output is the data sent to the server.

[0435] Step 6:

[0436] The server recognizes the emotional state using an emotion engine. Based on the received emotional state data, the server uses the emotion engine to analyze and determine the user's emotional state (e.g., panic, calm, etc.) in real time. The input data is emotional expression data, and the output is the determined emotional state.

[0437] Step 7:

[0438] The server generates personalized evacuation instructions. It integrates the emotion engine's judgment results and risk assessment results to generate optimal evacuation instructions according to the user's emotional state and risk level. As a specific example, it provides simple and intuitive instructions to a panicked user, and a detailed evacuation route to a calm user. The input data are the emotional state judgment results and risk assessment results, and the output is optimized evacuation instructions.

[0439] Step 8:

[0440] The server sends a notification to the user. Based on the optimized evacuation instructions, the server sends a notification to the user's device. The notification content includes specific evacuation instructions, recommended evacuation routes, and information about the nearest evacuation shelter. The input data is the optimized evacuation instructions, and the output is a notification to the user.

[0441] Specific examples

[0442] If an earthquake occurs while a user is shopping in a physical store, earthquake information is obtained in real time. At the same time, a tsunami simulation is run and the user's area is identified as high-risk. The emotion engine analyzes the user's facial expression data and recognizes that the user is in a state of panic. In this case, the server sends the following notification to the user:

[0443] "High-risk area. Evacuate immediately to the nearest evacuation center. Please remain calm and follow simple instructions."

[0444] Example prompt sentence:

[0445] "Create a notification for tsunami evacuation instructions and personalized evacuation routes using emotion recognition. Earthquake information is as follows: Epicenter, Magnitude 6, Occurrence time 18:00. User is in a panic and in a high-risk area."

[0446] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0447] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0448] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0449] [Second embodiment]

[0450] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0451] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0452] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. 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. The database 24 and the communication I / F 26 are also 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).

[0453] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0454] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0455] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0456] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0457] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0458] The specific processing program 56 is an example of a "program" according to the technology of the present 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0459] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0460] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0461] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0462] This invention relates to a hazard map service for minimizing damage caused by tsunamis when an earthquake occurs. This service acquires earthquake information, topographical information, and meteorological data, and performs tsunami simulations based on this data to assess the risk to each user and provide appropriate evacuation instructions promptly.

[0463] System Configuration

[0464] The system of the present invention consists of the following main components:

[0465] 1. Earthquake detection subsystem (server)

[0466] 2. Data collection subsystem (server)

[0467] 3. Tsunami Simulation Subsystem (Server)

[0468] 4. Risk Assessment Subsystem (Server)

[0469] 5. Notification sending subsystem (server)

[0470] 6. Personalized Advice Generation Subsystem (Server)

[0471] 7. User terminal (terminal)

[0472] Earthquake detection and data collection

[0473] The server connects with multiple earthquake detection sensors and public institution APIs to obtain earthquake occurrence information in real time. For example, it uses the Japan Meteorological Agency's API to collect data such as earthquake intensity, epicenter, and occurrence time. In addition, the device periodically sends the user's location information to the server. The server uses crowdsourcing and public datasets to obtain and store the latest coastline and seabed topography data. Weather data is also obtained in the same way and used in simulations.

[0474] Tsunami Simulation

[0475] Based on the earthquake information and the collected topographical information, the server runs an advanced tsunami simulation, which calculates the tsunami's progress, wave height, arrival time, and impact area, and stores the results in a database. The simulation is performed quickly and updated in real time.

[0476] Risk Assessment

[0477] Based on the results of the simulation, the server assesses the risk to the user's current location. Users' location information is compared with the simulation data, and users who are determined to be at high risk are required to take appropriate action based on the risk assessment. This risk assessment is updated regularly and is based on the latest information.

[0478] Notifications and personalized advice

[0479] Based on the evaluation results, the server sends notifications to high-risk users, including evacuation instructions, recommended evacuation routes, and information on the nearest evacuation shelters. High-risk users are especially provided with personalized evacuation instructions, allowing them to take optimal evacuation actions based on their current location and surrounding environment.

[0480] Specific examples

[0481] 1. Earthquake occurs

[0482] The server receives information from a seismograph about a magnitude 6 earthquake and verifies that it occurred near the coast. The earthquake information includes the epicenter, time of occurrence, and seismic intensity.

[0483] 2. Data Collection

[0484] The server retrieves the latest coastline and bathymetry data from crowdsourcing and public datasets and updates the database.

[0485] 3. Run the simulation

[0486] Based on the acquired earthquake and topographical information, the server starts a tsunami simulation, which calculates the tsunami's progress, wave height, arrival time, and affected area.

[0487] 4. Risk Assessment

[0488] The server obtains the location information of users who are deemed high risk and evaluates whether the area will be affected by a tsunami. Users who are deemed high risk are given a high risk score.

[0489] 5. Notice and Advice

[0490] The server sends a personalized notification to User A with evacuation instructions, including the nearest evacuation shelter and the specific evacuation route.

[0491] Users will receive a notification and can begin evacuation immediately.

[0492] In this way, the system of the present invention can assist users in taking prompt and appropriate evacuation actions when an earthquake occurs, thereby minimizing damage caused by tsunamis.

[0493] The processing flow will be explained below.

[0494] Step 1:

[0495] The server polls earthquake detectors and APIs of public institutions to obtain earthquake information. Specifically, it monitors the seismic intensity, epicenter, and occurrence time in real time, and triggers an earthquake detection event when an earthquake occurs that exceeds a threshold (for example, seismic intensity 5 or higher).

[0496] Step 2:

[0497] The device acquires the user's current location information using GPS and periodically sends it to the server. The user's location information must be highly accurate and is usually updated every few minutes.

[0498] Step 3:

[0499] The server collects up-to-date coastline and bathymetry data from crowdsourcing and public datasets, storing the data in a database that is updated in real time.

[0500] Step 4:

[0501] The server obtains meteorological data (e.g., tides, wind speed, air pressure, etc.) from the API of a public institution. This data is also stored and updated in the database and used during simulation.

[0502] Step 5:

[0503] The server receives a trigger from the earthquake detection subsystem and starts a tsunami simulation. It uses earthquake information and collected topographical and meteorological data to calculate the tsunami's progress, arrival time, wave height, and affected area. The simulation results are stored in a database.

[0504] Step 6:

[0505] The server retrieves the user's current location information from the database and compares it with the results of the tsunami simulation. If a specific user is in a high-risk area, it calculates the user's risk score and generates an evaluation result for each user.

[0506] Step 7:

[0507] The server generates notifications for users based on the risk assessment results, including evacuation instructions, recommended evacuation routes, and information on the nearest evacuation shelters. The content of the notifications is personalized and optimized based on the user's current location and surrounding terrain and building information.

[0508] Step 8:

[0509] The server sends a notification to the high-risk user's device, delivers the information quickly using push notifications or SMS, and logs whether the notification was successful.

[0510] Step 9:

[0511] The device receives the notification and displays an alert to the user, which is visually and audibly communicated to the user so that the user can see it immediately.

[0512] Step 10:

[0513] The user checks the notification and follows the instructions to begin evacuation. The user heads to the nearest evacuation shelter, selects a safe route, and moves quickly. During evacuation, the user can check updates and additional instructions on their smartphone.

[0514] This system's process continues in this way, supporting users from the moment an earthquake occurs until they can safely evacuate. Distributed data collection and advanced simulations enable quick and accurate evacuation instructions.

[0515] Example 1

[0516] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0517] A system is needed to minimize damage from earthquake-induced tsunamis and support rapid and effective evacuation. In particular, there is a need for risk assessments and personalized evacuation instructions that differ for each user. However, current systems have difficulty updating data and assessing risks in real time, making it difficult to provide users with appropriate evacuation instructions.

[0518] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0519] In this invention, the server includes means for acquiring earthquake information, means for acquiring location data, means for collecting topographical data and meteorological data, means for simulating tsunami behavior based on the earthquake information and topographical data, means for assessing a user's risk based on the simulation results, means for transmitting information to the user based on the assessment results, and means for generating personalized evacuation instructions based on the user's location data, thereby enabling the user to take optimal evacuation actions based on the risk assessment updated in real time.

[0520] "Earthquake information" is data related to the occurrence of an earthquake, including information such as seismic intensity, epicenter, and time of occurrence.

[0521] "Location data" is information that indicates a user's current location, and is typically obtained by a location information system such as a GPS.

[0522] "Topographic data" refers to information about the shape of the earth's surface and seabed, and is geographic data used in tsunami simulations.

[0523] "Meteorological data" refers to information about weather and climate, and is data used to predict the progression and impact of tsunamis.

[0524] "Means for simulating tsunami behavior" refers to methods and means for calculating tsunami progression, wave height, arrival time, area of ​​impact, etc., using earthquake information and topographical data.

[0525] "Means for assessing risk" refers to methods and means for comparing the results of tsunami simulations with user location data to assess the degree to which a user is exposed to tsunami risk.

[0526] "Personalized evacuation instructions" are evacuation instructions that are individually generated based on the user's current location and risk assessment, including recommended evacuation routes and information about the nearest evacuation shelters.

[0527] "Crowdsourcing" is a method of collecting information or data from a large number of people and is a means used to update information used within a system.

[0528] "Public Dataset" means a dataset provided by a government agency or other public body that contains reliable topographical and meteorological data.

[0529] This invention relates to a hazard map service for minimizing damage caused by tsunamis when an earthquake occurs. The system of the present invention consists of the following main components:

[0530] 1. Earthquake detection subsystem (server)

[0531] 2. Data collection subsystem (server)

[0532] 3. Tsunami Simulation Subsystem (Server)

[0533] 4. Risk Assessment Subsystem (Server)

[0534] 5. Notification sending subsystem (server)

[0535] 6. Personalized Advice Generation Subsystem (Server)

[0536] 7. User terminal (terminal)

[0537] Earthquake detection and data collection

[0538] The server connects with multiple sensors and public institution APIs to detect earthquakes and obtains earthquake occurrence information in real time. Specifically, the server sends a request to an API (e.g., the Japan Meteorological Agency API) and receives information such as seismic intensity, epicenter, and time of occurrence, which it then stores in a database.

[0539] The device also periodically transmits the user's location information to the server, which receives the location information acquired using the GPS function and stores it in a database, allowing the device to track the user's location in real time.

[0540] Additionally, the server will use crowdsourcing and public datasets to obtain the latest coastline and bathymetry data to update the database, as well as weather data, which will be used in the tsunami simulation.

[0541] Tsunami Simulation

[0542] Based on the earthquake information and collected topographical data, the server performs advanced tsunami simulations. The server uses dedicated simulation software (e.g., NEOWAVE) to calculate the tsunami's progress, wave height, arrival time, and impact area. The simulation results are stored in a database and updated in real time.

[0543] Risk Assessment

[0544] Based on the simulation results, the server assesses the risk to the user's current location. The server compares the simulation results with the user's location information to calculate a risk score, which allows the server to assess the user's exposure to tsunami danger. The risk assessment is updated regularly, ensuring that the risk assessment is always based on the latest information.

[0545] Notifications and personalized advice

[0546] Based on the assessment results, the server sends notifications to high-risk users, including evacuation instructions, recommended evacuation routes, and information on the nearest evacuation shelters. High-risk users can also receive personalized evacuation instructions, allowing them to take optimal evacuation actions based on their current location and surrounding environment.

[0547] Specific examples

[0548] 1. Earthquake occurs

[0549] The server receives information from a seismograph about a magnitude 6 earthquake and verifies that it occurred near the coast. The earthquake information includes the epicenter, time of occurrence, and seismic intensity.

[0550] 2. Data Collection

[0551] The server updates the database with the latest coastline and bathymetry data from crowdsourcing and public datasets.

[0552] 3. Tsunami Simulation

[0553] Based on the acquired earthquake and topographical information, the server starts a tsunami simulation, which calculates the tsunami's progress, wave height, arrival time, and affected area.

[0554] 4. Risk Assessment

[0555] The server obtains the location information of users who are deemed high risk and evaluates whether the area will be affected by a tsunami. Users who are deemed high risk are given a high risk score.

[0556] 5. Notice and Advice

[0557] The server sends a personalized notification to User A containing evacuation instructions, including the nearest evacuation shelter and a safe evacuation route.

[0558] Users will receive a notification and can begin evacuation immediately.

[0559] Prompt Sentence Examples

[0560] Below are some example prompts to input to the generative AI model:

[0561] "Please tell me the process of the system that obtains the latest earthquake information, performs a tsunami simulation, and provides evacuation instructions to users. Please explain in detail the APIs, sensors, and simulation content used, and explain the flow of how users evacuate."

[0562] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0563] Step 1: Obtaining earthquake information

[0564] The server obtains earthquake information from earthquake sensors and APIs of public institutions to detect earthquakes.

[0565] Input: Earthquake sensor data and data obtained from public APIs (seismic intensity, epicenter, and time of occurrence).

[0566] Specific operation: The server sends a request to the API, receives earthquake information, and stores it in a database.

[0567] Output: Earthquake information stored in a database.

[0568] Step 2: Get user location

[0569] The device periodically sends the user's location information to the server.

[0570] Input: Location data from the user device.

[0571] Specific operation: The device uses the GPS function to obtain its current location and sends the location data to the server, which receives the data and stores it in a database.

[0572] Output: User location stored in a database.

[0573] Step 3: Collect topographic and meteorological data

[0574] The server collects topographical and meteorological data from crowdsourcing and public datasets.

[0575] Input: Topographic and meteorological data from crowdsourcing platforms and public datasets.

[0576] Specific operation: The server accesses multiple data sources, automatically obtains the latest coastline, ocean floor topography and weather data, and updates the database.

[0577] Output: Latest terrain and weather data stored in a database.

[0578] Step 4: Run the tsunami simulation

[0579] The server runs a tsunami simulation based on the collected earthquake information and topographical data.

[0580] Input: Earthquake information, topographical data, meteorological data.

[0581] Specific operation: The server uses simulation software (e.g., NEOWAVE) to calculate the tsunami's progress, wave height, arrival time, and impact area. The simulation results are saved in a database.

[0582] Output: Tsunami simulation results stored in a database.

[0583] Step 5: Conduct a risk assessment

[0584] The server assesses risk based on the tsunami simulation results and the user's location information.

[0585] Input: Tsunami simulation results, user location information.

[0586] Specific operation: The server compares the simulation results with the location information, calculates a risk score, and saves the risk assessment results in a database.

[0587] Output: Risk assessment results stored in a database.

[0588] Step 6: Send notifications and provide personalized advice

[0589] The server will send information to users based on the risk assessment results and provide personalized evacuation instructions.

[0590] Input: Risk assessment results, user location information.

[0591] What it does: The server sends notifications to high-risk users with evacuation instructions, including the nearest evacuation shelter and recommended evacuation routes.

[0592] Output: Personalized evacuation instructions and notifications sent to user devices.

[0593] (Application example 1)

[0594] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0595] Current tsunami evacuation systems struggle to provide users with optimal evacuation routes in real time when an earthquake occurs. Many systems only provide text-based notifications, making it difficult for users to take prompt and appropriate evacuation actions during an emergency. Furthermore, there is a need for systems that can provide personalized evacuation instructions based on the user's current location in real time.

[0596] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0597] In this invention, the server includes means for acquiring earthquake information, means for acquiring topographical information and meteorological data, means for running a tsunami simulation based on the earthquake information and topographical information, means for assessing a user's risk based on the simulation results, means for sending a notification to the user based on the assessment results, means for generating personalized evacuation instructions based on the user's location information, and means for displaying evacuation routes in AR on the smart device. This allows the user to be visually guided to the optimal evacuation route in real time, enabling them to take prompt and appropriate evacuation action.

[0598] "Earthquake information" refers to data such as the epicenter, seismic intensity, and time of occurrence at the time of an earthquake.

[0599] "Topographic information" refers to geographic information including coastline, ocean floor topography, and elevation data for areas where earthquakes occur.

[0600] "Weather Data" refers to information regarding weather conditions such as weather, temperature, wind speed, and air pressure.

[0601] "Tsunami simulation" refers to the process of calculating the progression, wave height, arrival time, and area of ​​impact of a tsunami based on earthquake and topographical information.

[0602] "Risk assessment" refers to the act of assessing the tsunami risk at the user's current location based on the simulation results.

[0603] "Notification" refers to the act of sending evacuation instructions and evacuation route information to users based on the results of risk assessment.

[0604] "Personalized evacuation instructions" refers to evacuation suggestions that are customized based on the user's current location and surrounding conditions.

[0605] "Smart devices" refers to devices that can connect to the Internet, such as smartphones, smart glasses, and head-mounted displays.

[0606] "AR display" refers to the act of overlaying digital information on real-world scenery using augmented reality technology.

[0607] This invention relates to a system that operates as a security service to minimize damage caused by tsunamis when an earthquake occurs. This system acquires earthquake information, topographical information, and meteorological data, and performs tsunami simulations based on these data. Furthermore, it performs risk assessments for each user based on the simulation results and promptly provides appropriate evacuation instructions.

[0608] System Configuration

[0609] The system of the present invention consists of the following main components:

[0610] 1. Earthquake detection subsystem (server)

[0611] 2. Data collection subsystem (server)

[0612] 3. Tsunami Simulation Subsystem (Server)

[0613] 4. Risk Assessment Subsystem (Server)

[0614] 5. Notification sending subsystem (server)

[0615] 6. Personalized Advice Generation Subsystem (Server)

[0616] 7. User terminal (smart device)

[0617] Earthquake detection and data collection

[0618] The server obtains real-time earthquake occurrence information by linking with multiple earthquake detection sensors and public institution APIs. For example, it uses the Japan Meteorological Agency's API to collect data such as seismic intensity, epicenter, and time of occurrence. In addition, user devices periodically send their location information to the server. The server obtains and stores the latest coastline and seabed topography data using crowdsourcing and public datasets. Weather data is also collected and used in the simulations.

[0619] Tsunami Simulation

[0620] Based on the collected seismic and topographical information, the server runs an advanced tsunami simulation, calculating the tsunami's progress, wave height, arrival time, and impact area, and storing the results in a database. The simulation is performed quickly and updated in real time.

[0621] Risk Assessment

[0622] Based on the results of the tsunami simulation, the server assesses the risk to the user's current location. The server compares the user's location information with the simulation data, and users who are determined to be at high risk are required to take appropriate action. This risk assessment is updated regularly and is based on the latest information.

[0623] Notifications and personalized advice

[0624] Based on the results of the risk assessment, the server sends notifications to high-risk users. The notifications include evacuation instructions, recommended evacuation routes, and information on the nearest evacuation shelters. High-risk users are provided with personalized evacuation instructions. The optimal evacuation route is visually displayed on the user's smart device, such as a smartphone or smart glasses, using augmented reality (AR) technology.

[0625] Specific examples

[0626] For example, if a magnitude 6 earthquake occurs in a coastal area, the server detects this information in real time and runs a tsunami simulation using the latest coastline data. The resulting simulation data is used to assess the risk to the user's area. Users in high-risk areas are notified of the optimal evacuation route and the nearest evacuation shelter. The evacuation route is visually displayed on the user's smart device through the AR function, enabling quick evacuation.

[0627] Example prompts to input to a generative AI model:

[0628] Generate a notification that provides evacuation instructions based on the user's current location in the event of a tsunami. The notification should include:

[0629] 1. Earthquake Information

[0630] 2. Risk assessment using tsunami simulation

[0631] 3. Information on the nearest evacuation shelter

[0632] 4. Best evacuation route

[0633] For example, include, "An earthquake of magnitude 6 has occurred and a tsunami is expected. The nearest evacuation shelter is AAA evacuation shelter, and the best evacuation route is BBB. Please evacuate immediately."

[0634] By feeding this prompt into a generative AI model, a message providing evacuation instructions in natural language is generated.

[0635] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0636] Step 1: Obtaining earthquake information

[0637] The server connects with multiple sensors and public APIs to obtain real-time earthquake information. This information includes the epicenter, seismic intensity, and time of occurrence. The input is data from the sensors and APIs, and the output is stored internally as earthquake information. This data is used in subsequent processing.

[0638] Step 2: Collect terrain and weather data

[0639] The server collects the latest topographic and meteorological data from crowdsourcing and public datasets. The input is data from external databases, and the output is stored internally as topographic and meteorological data. This data is used for tsunami simulation.

[0640] Step 3: Run the tsunami simulation

[0641] The server runs a tsunami simulation based on the collected earthquake and topographical information. The input is earthquake information and topographical information, and through calculations, the progress, wave height, arrival time, and affected area of ​​the tsunami are simulated. The output is saved in a database as the simulation results.

[0642] Step 4: User Risk Assessment

[0643] The server performs risk assessment based on the user's current location and simulation data. The input is the user's location information and the simulation results, and a risk score is calculated by comparing these. The output is the risk assessment result for each user, and notification content is determined based on this.

[0644] Step 5: Generate and send notifications

[0645] The server generates notifications for users based on the risk assessment results. These notifications include evacuation instructions, recommended evacuation routes, and information on the nearest evacuation shelters. The input is the risk assessment results, and the output is the notification message sent to the user. The notification content is personalized and sent to the user's smart device.

[0646] Step 6: Evacuation route guidance using AR display

[0647] The user's smart device (smart glasses or smartphone) displays an AR evacuation route based on the received notification. The input is the notification data from the server, and the output is the evacuation route displayed on the device. Using augmented reality technology, users can visually confirm the evacuation route and evacuate quickly.

[0648] The above process enables users to take swift and appropriate evacuation action in the event of an earthquake.

[0649] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0650] This invention relates to a hazard map service for minimizing damage caused by tsunamis in the event of an earthquake. In particular, it focuses on a system that combines methods for recognizing users' emotions to optimize the content and notification method of evacuation instructions. This service acquires earthquake information, topographical information, and meteorological data, and performs tsunami simulations based on this information. Furthermore, it uses an emotion engine to recognize the user's emotional state and personalize evacuation instructions.

[0651] System Configuration

[0652] The system of the present invention consists of the following main components:

[0653] 1. Earthquake detection subsystem (server)

[0654] 2. Data collection subsystem (server)

[0655] 3. Tsunami Simulation Subsystem (Server)

[0656] 4. Risk Assessment Subsystem (Server)

[0657] 5. Notification sending subsystem (server)

[0658] 6. Personalized Advice Generation Subsystem (Server)

[0659] 7. Emotion engine (server)

[0660] 8. User terminal (terminal)

[0661] Earthquake detection and data collection

[0662] The server connects with multiple earthquake detection sensors and public institution APIs to obtain earthquake occurrence information in real time. For example, it uses the Japan Meteorological Agency's API to collect data such as earthquake intensity, epicenter, and occurrence time. In addition, the device periodically sends the user's location information to the server. The server uses crowdsourcing and public datasets to obtain and store the latest coastline and seabed topography data. Weather data is also obtained in the same way and used in simulations.

[0663] Tsunami Simulation

[0664] Based on the earthquake information and the collected topographical information, the server runs an advanced tsunami simulation, which calculates the tsunami's progress, wave height, arrival time, and impact area, and stores the results in a database. The simulation is performed quickly and updated in real time.

[0665] Risk Assessment

[0666] Based on the results of the simulation, the server assesses the risk to the user's current location. Users' location information is compared with the simulation data, and users who are determined to be at high risk are required to take appropriate action based on the risk assessment. This risk assessment is updated regularly and is based on the latest information.

[0667] Emotion Recognition and Personalized Advice

[0668] The emotion engine analyzes data (e.g., voice, facial expressions, typing speed) obtained from the user's smartphone or other mobile device to recognize the user's emotional state in real time. The server takes the results of the emotion engine into consideration and optimizes the content and notification method of evacuation instructions based on the user's emotional state. For example, it provides simple and intuitive instructions to a panicked user and detailed information to a calm user.

[0669] Notifications and personalized advice

[0670] Based on the evaluation results, the server generates and sends notifications to high-risk users, including evacuation instructions, recommended evacuation routes, and information on the nearest evacuation shelters. In particular, high-risk users are provided with personalized evacuation instructions that reflect their emotional state, allowing them to take optimal evacuation actions based on their current location and surrounding environment.

[0671] Specific examples

[0672] 1. Earthquake occurs

[0673] The server receives information from a seismograph about a magnitude 6 earthquake and verifies that it occurred near the coast. The earthquake information includes the epicenter, time of occurrence, and seismic intensity.

[0674] 2. Data Collection

[0675] The server retrieves the latest coastline and bathymetry data from crowdsourcing and public datasets and updates the database.

[0676] 3. Run the simulation

[0677] Based on the acquired earthquake and topographical information, the server starts a tsunami simulation, which calculates the tsunami's progress, wave height, arrival time, and affected area.

[0678] 4. Risk Assessment

[0679] The server obtains the location information of users who are deemed high risk and evaluates whether the area will be affected by a tsunami. Users who are deemed high risk are given a high risk score.

[0680] 5. Emotion recognition

[0681] The device collects the user's voice and facial expression data, which is then analyzed by an emotion engine to assess the user's emotional state.

[0682] 6. Notice and Advice

[0683] The server generates evacuation instructions that take into account User A's emotional state and sends a personalized notification containing the evacuation instructions, including the nearest evacuation shelter and the specific evacuation route.

[0684] Users will receive a notification and can begin evacuation immediately.

[0685] This system will help users take prompt and appropriate evacuation actions in the event of an earthquake, minimizing damage from tsunamis. Furthermore, emotion recognition will enable responses based on the user's mental state, further improving the effectiveness of evacuation actions.

[0686] The processing flow will be explained below.

[0687] Step 1:

[0688] The server polls earthquake detectors and APIs of public institutions to obtain earthquake information. Specifically, it monitors the seismic intensity, epicenter, and occurrence time in real time, and triggers an earthquake detection event when an earthquake occurs that exceeds a threshold (for example, seismic intensity 5 or higher).

[0689] Step 2:

[0690] The device acquires the user's current location information using GPS and periodically sends it to the server. The user's location information must be highly accurate and is usually updated every few minutes.

[0691] Step 3:

[0692] The server collects up-to-date coastline and bathymetry data from crowdsourcing and public datasets, storing the data in a database that is updated in real time.

[0693] Step 4:

[0694] The server obtains meteorological data (e.g., tides, wind speed, air pressure, etc.) from the API of a public institution. This data is also stored and updated in the database and used during simulation.

[0695] Step 5:

[0696] The server receives a trigger from the earthquake detection subsystem and starts a tsunami simulation. It uses earthquake information and collected topographical and meteorological data to calculate the tsunami's progress, arrival time, wave height, and affected area. The simulation results are stored in a database.

[0697] Step 6:

[0698] The server retrieves the user's current location information from the database and compares it with the results of the tsunami simulation. If a specific user is in a high-risk area, it calculates the user's risk score and generates an evaluation result for each user.

[0699] Step 7:

[0700] The device collects the user's voice and facial expression data and sends it to the emotion engine. Voice data is obtained using a microphone, and facial expression data is obtained using a camera.

[0701] Step 8:

[0702] The emotion engine analyzes the received voice and facial expression data to recognize the user's emotional state. For example, it analyzes the tone of voice and facial features to determine whether the user is panicked or calm.

[0703] Step 9:

[0704] The server receives the results of the emotion engine and optimizes the content and notification method of evacuation instructions based on the user's emotional state, for example, providing simple and intuitive instructions to a panicked user and detailed information to a calm user.

[0705] Step 10:

[0706] The server generates evacuation instructions that take into account the emotional state of the user and sends them to the devices of high-risk users. The server quickly delivers information using push notifications and SMS. It also logs whether the transmission was successful.

[0707] Step 11:

[0708] The device receives the notification and displays an alert to the user, which is visually and audibly communicated to the user so that the user can see it immediately.

[0709] Step 12:

[0710] The user checks the notification and follows the instructions to begin evacuation. The user heads to the nearest evacuation shelter, selects a safe route, and moves quickly. During evacuation, the user can check updates and additional instructions on their smartphone.

[0711] This is how the system's process progresses, supporting users from the moment an earthquake occurs to the moment they decide to evacuate safely. In addition to distributed data collection and advanced simulation, emotion recognition enables optimal responses for each user.

[0712] Example 2

[0713] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0714] In order to minimize damage caused by tsunamis when an earthquake occurs, quick and accurate evacuation instructions are necessary. However, conventional systems have difficulty providing appropriate evacuation instructions that take into account the individual situation and emotional state of the user. This has led to problems such as users panicking and delaying evacuation behavior.

[0715] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for acquiring earthquake information, means for acquiring topographical information and meteorological data, means for executing a tsunami simulation based on the earthquake information and topographical information, means for assessing a user's risk based on the simulation results, means for sending a notification to the user based on the assessment results, means for recognizing the user's emotional state, and means for generating personalized evacuation instructions based on the recognized emotional state. This makes it possible to quickly provide appropriate evacuation instructions according to the user's emotional state in the event of an earthquake, and support the user's evacuation behavior.

[0716] "Earthquake information" is detailed data about earthquakes, including the magnitude, epicenter, and time of occurrence of the earthquake.

[0717] "Topographic information" is detailed data about the terrain, including coastline and undersea topography data.

[0718] "Weather data" refers to data related to weather, such as precipitation, wind speed, and temperature.

[0719] The "means for performing a tsunami simulation" is a means for calculating the progress, wave height, arrival time, and range of impact of a tsunami based on earthquake information and topographical information.

[0720] The "means for assessing the user's risk" is a means for comparing the results of a tsunami simulation with the user's current location to assess the possibility that the user will be affected by a tsunami.

[0721] The "means for sending notifications to users based on the assessment results" refers to the means for sending evacuation instructions or warnings to users based on the users' risk assessment.

[0722] The "means for recognizing the user's emotional state" refers to a means for analyzing data such as the user's voice, facial expression, typing speed, etc., and recognizing the user's emotional state in real time.

[0723] The "means for generating personalized evacuation instructions" is a means for creating evacuation instructions that are best suited to the user's situation based on the recognized emotional state.

[0724] "Crowdsourcing" is a method of collecting information from a large number of Internet users.

[0725] A "public dataset" is a collection of highly reliable data provided by governments, public institutions, etc.

[0726] "Real-time" refers to information being updated immediately without delay.

[0727] This invention is a system for providing hazard maps to minimize damage caused by tsunamis during earthquakes. The system acquires earthquake, topographical, and meteorological data, and performs tsunami simulations based on this information. It also recognizes the user's emotional state and provides personalized evacuation instructions.

[0728] System Configuration

[0729] The system of the present invention consists of the following main components:

[0730] 1. Earthquake detection subsystem (server)

[0731] 2. Data collection subsystem (server)

[0732] 3. Tsunami Simulation Subsystem (Server)

[0733] 4. Risk Assessment Subsystem (Server)

[0734] 5. Notification sending subsystem (server)

[0735] 6. Personalized Advice Generation Subsystem (Server)

[0736] 7. Emotion engine (server)

[0737] 8. User terminal (terminal)

[0738] Earthquake detection and data collection

[0739] The server connects with multiple sensors and public institution APIs to detect earthquakes and obtains earthquake occurrence information (e.g., seismic intensity, epicenter, and time of occurrence) in real time. For example, data is collected using the Japan Meteorological Agency's API. The device periodically sends the user's location information to the server. The server uses crowdsourcing and public datasets to obtain and store the latest coastline and seabed topography data. Weather data is also obtained in the same way and used in simulations.

[0740] Tsunami Simulation

[0741] The server runs an advanced tsunami simulation based on earthquake information and collected topographical information. This simulation calculates the tsunami's progress, wave height, arrival time, and impact area. The simulation results are stored in a database and are continuously updated. For example, GEOWAVE may be used as the tsunami simulation software.

[0742] Risk Assessment

[0743] The server evaluates the risk to the user's current location based on the simulation results. By comparing the user's location information with the simulation data, users who are deemed to be at high risk will be given special evacuation instructions. This risk assessment is updated in real time.

[0744] Emotion Recognition and Personalized Advice

[0745] The emotion engine analyzes data such as the user's voice, facial expressions, and typing speed to recognize their emotional state in real time. The server then uses the results of this emotion engine to personalize the evacuation instructions and notification method based on the user's emotional state. For example, it provides simple and intuitive instructions to a panicked user and detailed information to a calm user.

[0746] Notification and Action Support

[0747] The server generates optimal evacuation instructions based on the risk assessment and emotion recognition results, and sends a notification to the user's device. This notification includes evacuation instructions, recommended evacuation routes, and information on the nearest evacuation shelter. The user receives this notification and promptly begins appropriate evacuation actions.

[0748] Specific examples

[0749] 1. Earthquake occurs

[0750] The server receives information from a seismograph about a magnitude 6 earthquake and verifies that it occurred near the coast. The earthquake information includes the epicenter, time of occurrence, and seismic intensity.

[0751] 2. Data Collection

[0752] The server retrieves the latest coastline and bathymetry data from crowdsourcing and public datasets and updates the database.

[0753] 3. Run the simulation

[0754] Based on the acquired earthquake and topographical information, the server starts a tsunami simulation, which calculates the tsunami's progress, wave height, arrival time, and affected area.

[0755] 4. Risk Assessment

[0756] The server obtains the location information of users who are deemed high risk and evaluates whether the area will be affected by a tsunami. Users who are deemed high risk are given a high risk score.

[0757] 5. Emotion recognition

[0758] The device collects the user's voice and facial expression data, which is then analyzed by an emotion engine to assess the user's emotional state.

[0759] 6. Notice and Advice

[0760] The server generates evacuation instructions that take into account User A's emotional state and sends a personalized notification containing the evacuation instructions, including the nearest evacuation shelter and the specific evacuation route.

[0761] Users will receive a notification and can begin evacuation immediately.

[0762] Prompt Sentence Examples

[0763] "If you live in a coastal area, please suggest multiple scenarios for what evacuation route you should take in the event of a magnitude 6 earthquake, depending on your emotional state."

[0764] This system enables users to take prompt and appropriate evacuation actions in the event of an earthquake, minimizing damage from tsunamis. Furthermore, emotion recognition makes it possible to respond according to the user's mental state, further improving the effectiveness of evacuation actions.

[0765] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0766] Step 1: Earthquake detection

[0767] The server collects earthquake information from seismometers and public institutions (e.g., Japan Meteorological Agency API) to detect earthquakes.

[0768] Input: Seismic intensity, epicenter, time of occurrence

[0769] How it works: The server polls data from the seismometer and generates an alert if an earthquake of magnitude 5 or greater is detected.

[0770] Output: Earthquake alerts and data on seismic intensity, epicenter, and occurrence time

[0771] Step 2: Data collection

[0772] The device periodically transmits the user's location information to the server.

[0773] Input: GPS data

[0774] How it works: The device receives a GPS signal and sends location information to the server at specified intervals.

[0775] Output: Current location of the user

[0776] Step 3: Update terrain and weather data

[0777] The server uses crowdsourcing and public datasets to obtain the latest terrain and weather data and update the database.

[0778] Input: Crowdsourced data, public datasets

[0779] How it works: The server periodically checks for updates to the dataset and automatically updates the database if new data is found.

[0780] Output: Latest terrain and weather data

[0781] Step 4: Run the tsunami simulation

[0782] The server runs a tsunami simulation based on the collected earthquake and topographical information.

[0783] Input: Earthquake information, topographical information

[0784] How it works: The server launches simulation software (e.g., GEOWAVE), inputs data, and calculates the tsunami's progress, wave height, arrival time, and area of ​​impact.

[0785] Output: Tsunami simulation results (wave height, arrival time, affected area)

[0786] Step 5: Risk assessment

[0787] The server evaluates the risk to the user's current location based on the simulation results.

[0788] Input: User location information, tsunami simulation results

[0789] How it works: The server compares the user's location with simulated data to identify users in high-risk areas and calculate a risk score.

[0790] Output: Risk assessment result (risk score)

[0791] Step 6: Collect and analyze emotion data

[0792] The device collects the user's voice and facial expression data, which is analyzed by an emotion engine to recognize the user's emotional state.

[0793] Input: Voice data, facial expression data

[0794] How it works: The device uses a microphone and camera to collect voice and facial expression data and sends it to a server. The emotion engine analyzes the data and evaluates the user's emotional state.

[0795] Output: Emotion evaluation result (e.g., panicked, calm)

[0796] Step 7: Generate personalized evacuation instructions

[0797] The server takes into account the results of the emotion engine to generate evacuation instructions based on the user's emotional state.

[0798] Input: Emotion assessment results, risk assessment results

[0799] How it works: Based on the analysis results of the emotion engine, the server selects different evacuation instruction templates and customizes evacuation instructions that best suit the user's situation.

[0800] Output: Personalized evacuation instructions

[0801] Step 8: Send notification

[0802] The server notifies the user of the generated evacuation instructions.

[0803] Input: personalized evacuation instructions

[0804] What it does: The server generates the notification and pushes it to the user's device. It also saves it in the notification history so the user can review it later.

[0805] Output: Evacuation notice sent to user

[0806] Step 9: User Actions

[0807] Users will receive a notification and will be able to quickly take action to evacuate.

[0808] Input: Evacuation order notice

[0809] Action: The user confirms the notification and follows the evacuation route provided. During the evacuation, the device automatically updates its location and sends it to the server.

[0810] Output: Evacuation action start and progress

[0811] In this way, the system can provide users with quick and accurate evacuation instructions, minimizing damage during disasters. Furthermore, personalized instructions based on emotion recognition can respond according to the user's mental state.

[0812] (Application example 2)

[0813] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0814] Conventional earthquake and tsunami evacuation systems do not take into account the user's psychological state when issuing evacuation instructions, which can lead to panic and confusion. It is particularly difficult for users to take appropriate evacuation actions when an earthquake occurs while they are shopping in a physical store. Therefore, it is necessary to optimize personalized evacuation instructions and notification methods according to each user's emotional state so that they can evacuate quickly and effectively.

[0815] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for acquiring earthquake information, means for acquiring topographical information and meteorological data, means for running a tsunami simulation based on the earthquake information and topographical information, means for assessing the user's risk based on the simulation results, means for sending a notification to the user based on the assessment results, means for generating personalized evacuation instructions based on the user's location information, means for recognizing the user's emotional state using an emotion engine, and means for optimizing the content of the evacuation instructions and the notification method based on the user's emotional state. This enables the user to take evacuation action quickly and effectively when they encounter an earthquake while shopping in a physical store.

[0816] "Means for acquiring earthquake information" refers to a system or device for detecting and collecting information on earthquake occurrence.

[0817] The "means for acquiring topographical information and meteorological data" is a system or device for collecting topographical data and meteorological information.

[0818] "Means for performing tsunami simulations based on earthquake information and topographical information" refers to a system or device for predicting and calculating the occurrence and progression of tsunamis using earthquake and topographical data.

[0819] "Means for assessing user risk based on simulation results" refers to a system or device that determines the risk level of the area where the user is located based on the results of a tsunami simulation.

[0820] "Means for sending a notification to a user based on the assessment result" refers to a system or device that sends an appropriate notification to a user depending on the result of the risk assessment.

[0821] "Means for generating personalized evacuation instructions based on the user's location information" refers to a system or device that generates individual evacuation instructions tailored to each situation based on the user's current location.

[0822] "Means for recognizing a user's emotional state using an emotion engine" refers to a system or device that uses emotion recognition technology to analyze and determine a user's current emotional state.

[0823] The "means for optimizing the content and notification method of evacuation instructions based on the emotional state of the user" is a system or device that generates evacuation instructions in the optimal format and content, taking into account the emotional state of the user.

[0824] This invention is a hazard map service for minimizing damage caused by tsunamis when an earthquake occurs. The system acquires earthquake information, topographical information, and meteorological data, and based on this information, performs tsunami simulations to assess risk and provides optimal evacuation instructions to users. Furthermore, it uses an emotion engine to recognize the user's emotional state and optimizes notification content according to that emotional state, helping users take quick and effective evacuation actions.

[0825] System Configuration

[0826] The system consists of the following main components:

[0827] 1. Earthquake detection subsystem (server): Detects earthquakes and collects earthquake information in real time.

[0828] 2. Data collection subsystem (server): Collects and updates terrain and meteorological data.

[0829] 3. Tsunami simulation subsystem (server): Simulates tsunami progression, wave height, arrival time, and impact area based on earthquake and topographical data.

[0830] 4. Risk assessment subsystem (server): Evaluates the user's risk based on the results of the tsunami simulation.

[0831] 5. Notification sending subsystem (server): Sends appropriate notifications to users based on the results of risk assessment.

[0832] 6. Personalized advice generation subsystem (server): Generates individual evacuation instructions based on the user's location and emotional state.

[0833] 7. Emotion Engine (Server): Recognizes the user's emotional state in real time.

[0834] 8. User terminal (terminal): Transmits the user's location information and emotional state data to the server and receives evacuation instructions.

[0835] Earthquake detection and data collection

[0836] The server connects with multiple sensors and public institution APIs to detect earthquakes and obtains earthquake occurrence information in real time. For example, it uses the Japan Meteorological Agency's API to collect data such as earthquake intensity, epicenter, and occurrence time. Furthermore, the device periodically sends location information to the server, allowing the server to determine the user's current location. It also uses crowdsourcing and public datasets to collect and store the latest topographical and meteorological data.

[0837] Tsunami Simulation

[0838] Based on the earthquake information and the collected topographical information, the server runs an advanced tsunami simulation, which calculates the tsunami's progress, wave height, arrival time and impact area, and stores the results in a database. The simulation is performed quickly and updated in real time.

[0839] Risk Assessment

[0840] Based on the results of the simulation, the server assesses the risk to the user's current location. The server compares the user's location information with the simulation data, and if a user is determined to be at high risk, they will be required to take appropriate action based on the risk assessment. This risk assessment is updated regularly and is based on the latest information.

[0841] Emotion Recognition and Personalized Advice

[0842] The emotion engine analyzes data (e.g., voice, facial expressions, typing speed) obtained from the user's smartphone or other mobile device to recognize the user's emotional state in real time. The server takes the results of the emotion engine into consideration and optimizes the content and notification method of evacuation instructions based on the user's emotional state. For example, it provides simple and intuitive instructions to a panicked user and detailed information to a calm user.

[0843] Notifications and personalized advice

[0844] Based on the risk assessment results, the server generates and sends notifications to high-risk users, including evacuation instructions, recommended evacuation routes, and information on the nearest evacuation shelters. In particular, high-risk users are provided with personalized evacuation instructions that reflect their emotional state, allowing them to take optimal evacuation actions based on their current location and surrounding environment.

[0845] Specific examples

[0846] If an earthquake occurs while a user is shopping in a physical store, real-time earthquake information is acquired to confirm that the user is in a coastal area. At the same time, a tsunami simulation is run to identify the user's area as high-risk. The emotion engine analyzes the user's facial expression data and determines that the user is in a state of panic.

[0847] When this happens, the server will send a notification to the user:

[0848] "High-risk area. Evacuate immediately to the nearest evacuation center. Please remain calm and follow simple instructions."

[0849] Example prompt sentence:

[0850] "Create a notification for tsunami evacuation instructions and personalized evacuation routes using emotion recognition. Earthquake information is as follows: Epicenter, Magnitude 6, Occurrence time 18:00. User is in a panic and in a high-risk area."

[0851] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0852] Step 1:

[0853] The server acquires earthquake information. First, the server acquires real-time earthquake occurrence information through the API of a public institution (e.g., the Japan Meteorological Agency API), collecting data such as seismic intensity, epicenter, and time of occurrence. The input data is data distributed by earthquake sensor networks and public institutions, and the output is detailed information about the earthquake.

[0854] Step 2:

[0855] The server obtains terrain information and weather data. The server obtains and stores the latest coastline and bathymetry data, as well as weather data, using crowdsourcing or public datasets. The input data is data from the terrain database and weather information API, and the output is updated terrain and weather data.

[0856] Step 3:

[0857] The server runs the tsunami simulation. It performs an advanced tsunami simulation based on earthquake information and collected topographical information. Specifically, it simulates the tsunami's progress, wave height, arrival time, and affected area, and stores the results in a database. The input data are earthquake information and topographical information, and the output is the simulation results.

[0858] Step 4:

[0859] The server performs risk assessment. Based on the simulation results, the server assesses the risk for the user's current location. Specifically, it compares the user's location information with the simulation data and determines whether the area is high-risk. The input data is the user's location information and the tsunami simulation results, and the output is the risk assessment results.

[0860] Step 5:

[0861] The device collects emotional state data. Emotional data such as the user's voice, facial expression, and typing speed are collected from smartphones and other mobile devices and sent to a server. The input data is the user's emotional expression data, and the output is the data sent to the server.

[0862] Step 6:

[0863] The server recognizes the emotional state using an emotion engine. Based on the received emotional state data, the server uses the emotion engine to analyze and determine the user's emotional state (e.g., panic, calm, etc.) in real time. The input data is emotional expression data, and the output is the determined emotional state.

[0864] Step 7:

[0865] The server generates personalized evacuation instructions. It integrates the emotion engine's judgment results and risk assessment results to generate optimal evacuation instructions according to the user's emotional state and risk level. As a specific example, it provides simple and intuitive instructions to a panicked user, and a detailed evacuation route to a calm user. The input data are the emotional state judgment results and risk assessment results, and the output is optimized evacuation instructions.

[0866] Step 8:

[0867] The server sends a notification to the user. Based on the optimized evacuation instructions, the server sends a notification to the user's device. The notification content includes specific evacuation instructions, recommended evacuation routes, and information about the nearest evacuation shelter. The input data is the optimized evacuation instructions, and the output is a notification to the user.

[0868] Specific examples

[0869] If an earthquake occurs while a user is shopping in a physical store, earthquake information is obtained in real time. At the same time, a tsunami simulation is run and the user's area is identified as high-risk. The emotion engine analyzes the user's facial expression data and recognizes that the user is in a state of panic. In this case, the server sends the following notification to the user:

[0870] "High-risk area. Evacuate immediately to the nearest evacuation center. Please remain calm and follow simple instructions."

[0871] Example prompt sentence:

[0872] "Create a notification for tsunami evacuation instructions and personalized evacuation routes using emotion recognition. Earthquake information is as follows: Epicenter, Magnitude 6, Occurrence time 18:00. User is in a panic and in a high-risk area."

[0873] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0874] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0875] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0876] [Third embodiment]

[0877] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0878] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0879] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. 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. The database 24 and the communication I / F 26 are also 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).

[0880] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0881] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0882] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0883] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0884] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0885] The specific processing program 56 is an example of a "program" according to the technology of the present 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0886] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0887] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0888] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0889] This invention relates to a hazard map service for minimizing damage caused by tsunamis when an earthquake occurs. This service acquires earthquake information, topographical information, and meteorological data, and performs tsunami simulations based on this data to assess the risk to each user and provide appropriate evacuation instructions promptly.

[0890] System Configuration

[0891] The system of the present invention consists of the following main components:

[0892] 1. Earthquake detection subsystem (server)

[0893] 2. Data collection subsystem (server)

[0894] 3. Tsunami Simulation Subsystem (Server)

[0895] 4. Risk Assessment Subsystem (Server)

[0896] 5. Notification sending subsystem (server)

[0897] 6. Personalized Advice Generation Subsystem (Server)

[0898] 7. User terminal (terminal)

[0899] Earthquake detection and data collection

[0900] The server connects with multiple earthquake detection sensors and public institution APIs to obtain earthquake occurrence information in real time. For example, it uses the Japan Meteorological Agency's API to collect data such as earthquake intensity, epicenter, and occurrence time. In addition, the device periodically sends the user's location information to the server. The server uses crowdsourcing and public datasets to obtain and store the latest coastline and seabed topography data. Weather data is also obtained in the same way and used in simulations.

[0901] Tsunami Simulation

[0902] Based on the earthquake information and the collected topographical information, the server runs an advanced tsunami simulation, which calculates the tsunami's progress, wave height, arrival time, and impact area, and stores the results in a database. The simulation is performed quickly and updated in real time.

[0903] Risk Assessment

[0904] Based on the results of the simulation, the server assesses the risk to the user's current location. Users' location information is compared with the simulation data, and users who are determined to be at high risk are required to take appropriate action based on the risk assessment. This risk assessment is updated regularly and is based on the latest information.

[0905] Notifications and personalized advice

[0906] Based on the evaluation results, the server sends notifications to high-risk users, including evacuation instructions, recommended evacuation routes, and information on the nearest evacuation shelters. High-risk users are especially provided with personalized evacuation instructions, allowing them to take optimal evacuation actions based on their current location and surrounding environment.

[0907] Specific examples

[0908] 1. Earthquake occurs

[0909] The server receives information from a seismograph about a magnitude 6 earthquake and verifies that it occurred near the coast. The earthquake information includes the epicenter, time of occurrence, and seismic intensity.

[0910] 2. Data Collection

[0911] The server retrieves the latest coastline and bathymetry data from crowdsourcing and public datasets and updates the database.

[0912] 3. Run the simulation

[0913] Based on the acquired earthquake and topographical information, the server starts a tsunami simulation, which calculates the tsunami's progress, wave height, arrival time, and affected area.

[0914] 4. Risk Assessment

[0915] The server obtains the location information of users who are deemed high risk and evaluates whether the area will be affected by a tsunami. Users who are deemed high risk are given a high risk score.

[0916] 5. Notice and Advice

[0917] The server sends a personalized notification to User A with evacuation instructions, including the nearest evacuation shelter and the specific evacuation route.

[0918] Users will receive a notification and can begin evacuation immediately.

[0919] In this way, the system of the present invention can assist users in taking prompt and appropriate evacuation actions when an earthquake occurs, thereby minimizing damage caused by tsunamis.

[0920] The processing flow will be explained below.

[0921] Step 1:

[0922] The server polls earthquake detectors and APIs of public institutions to obtain earthquake information. Specifically, it monitors the seismic intensity, epicenter, and occurrence time in real time, and triggers an earthquake detection event when an earthquake occurs that exceeds a threshold (for example, seismic intensity 5 or higher).

[0923] Step 2:

[0924] The device acquires the user's current location information using GPS and periodically sends it to the server. The user's location information must be highly accurate and is usually updated every few minutes.

[0925] Step 3:

[0926] The server collects up-to-date coastline and bathymetry data from crowdsourcing and public datasets, storing the data in a database that is updated in real time.

[0927] Step 4:

[0928] The server obtains meteorological data (e.g., tides, wind speed, air pressure, etc.) from the API of a public institution. This data is also stored and updated in the database and used during simulation.

[0929] Step 5:

[0930] The server receives a trigger from the earthquake detection subsystem and starts a tsunami simulation. It uses earthquake information and collected topographical and meteorological data to calculate the tsunami's progress, arrival time, wave height, and affected area. The simulation results are stored in a database.

[0931] Step 6:

[0932] The server retrieves the user's current location information from the database and compares it with the results of the tsunami simulation. If a specific user is in a high-risk area, it calculates the user's risk score and generates an evaluation result for each user.

[0933] Step 7:

[0934] The server generates notifications for users based on the risk assessment results, including evacuation instructions, recommended evacuation routes, and information on the nearest evacuation shelters. The content of the notifications is personalized and optimized based on the user's current location and surrounding terrain and building information.

[0935] Step 8:

[0936] The server sends a notification to the high-risk user's device, delivers the information quickly using push notifications or SMS, and logs whether the notification was successful.

[0937] Step 9:

[0938] The device receives the notification and displays an alert to the user, which is visually and audibly communicated to the user so that the user can see it immediately.

[0939] Step 10:

[0940] The user checks the notification and follows the instructions to begin evacuation. The user heads to the nearest evacuation shelter, selects a safe route, and moves quickly. During evacuation, the user can check updates and additional instructions on their smartphone.

[0941] This system's process continues in this way, supporting users from the moment an earthquake occurs until they can safely evacuate. Distributed data collection and advanced simulations enable quick and accurate evacuation instructions.

[0942] Example 1

[0943] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0944] A system is needed to minimize damage from earthquake-induced tsunamis and support rapid and effective evacuation. In particular, there is a need for risk assessments and personalized evacuation instructions that differ for each user. However, current systems have difficulty updating data and assessing risks in real time, making it difficult to provide users with appropriate evacuation instructions.

[0945] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0946] In this invention, the server includes means for acquiring earthquake information, means for acquiring location data, means for collecting topographical data and meteorological data, means for simulating tsunami behavior based on the earthquake information and topographical data, means for assessing a user's risk based on the simulation results, means for transmitting information to the user based on the assessment results, and means for generating personalized evacuation instructions based on the user's location data, thereby enabling the user to take optimal evacuation actions based on the risk assessment updated in real time.

[0947] "Earthquake information" is data related to the occurrence of an earthquake, including information such as seismic intensity, epicenter, and time of occurrence.

[0948] "Location data" is information that indicates a user's current location, and is typically obtained by a location information system such as a GPS.

[0949] "Topographic data" refers to information about the shape of the earth's surface and seabed, and is geographic data used in tsunami simulations.

[0950] "Meteorological data" refers to information about weather and climate, and is data used to predict the progression and impact of tsunamis.

[0951] "Means for simulating tsunami behavior" refers to methods and means for calculating tsunami progression, wave height, arrival time, area of ​​impact, etc., using earthquake information and topographical data.

[0952] "Means for assessing risk" refers to methods and means for comparing the results of tsunami simulations with user location data to assess the degree to which a user is exposed to tsunami risk.

[0953] "Personalized evacuation instructions" are evacuation instructions that are individually generated based on the user's current location and risk assessment, including recommended evacuation routes and information about the nearest evacuation shelters.

[0954] "Crowdsourcing" is a method of collecting information or data from a large number of people and is a means used to update information used within a system.

[0955] "Public Dataset" means a dataset provided by a government agency or other public body that contains reliable topographical and meteorological data.

[0956] This invention relates to a hazard map service for minimizing damage caused by tsunamis when an earthquake occurs. The system of the present invention consists of the following main components:

[0957] 1. Earthquake detection subsystem (server)

[0958] 2. Data collection subsystem (server)

[0959] 3. Tsunami Simulation Subsystem (Server)

[0960] 4. Risk Assessment Subsystem (Server)

[0961] 5. Notification sending subsystem (server)

[0962] 6. Personalized Advice Generation Subsystem (Server)

[0963] 7. User terminal (terminal)

[0964] Earthquake detection and data collection

[0965] The server connects with multiple sensors and public institution APIs to detect earthquakes and obtains earthquake occurrence information in real time. Specifically, the server sends a request to an API (e.g., the Japan Meteorological Agency API) and receives information such as seismic intensity, epicenter, and time of occurrence, which it then stores in a database.

[0966] The device also periodically transmits the user's location information to the server, which receives the location information acquired using the GPS function and stores it in a database, allowing the device to track the user's location in real time.

[0967] Additionally, the server will use crowdsourcing and public datasets to obtain the latest coastline and bathymetry data to update the database, as well as weather data, which will be used in the tsunami simulation.

[0968] Tsunami Simulation

[0969] Based on the earthquake information and collected topographical data, the server performs advanced tsunami simulations. The server uses dedicated simulation software (e.g., NEOWAVE) to calculate the tsunami's progress, wave height, arrival time, and impact area. The simulation results are stored in a database and updated in real time.

[0970] Risk Assessment

[0971] Based on the simulation results, the server assesses the risk to the user's current location. The server compares the simulation results with the user's location information to calculate a risk score, which allows the server to assess the user's exposure to tsunami danger. The risk assessment is updated regularly, ensuring that the risk assessment is always based on the latest information.

[0972] Notifications and personalized advice

[0973] Based on the assessment results, the server sends notifications to high-risk users, including evacuation instructions, recommended evacuation routes, and information on the nearest evacuation shelters. High-risk users can also receive personalized evacuation instructions, allowing them to take optimal evacuation actions based on their current location and surrounding environment.

[0974] Specific examples

[0975] 1. Earthquake occurs

[0976] The server receives information from a seismograph about a magnitude 6 earthquake and verifies that it occurred near the coast. The earthquake information includes the epicenter, time of occurrence, and seismic intensity.

[0977] 2. Data Collection

[0978] The server updates the database with the latest coastline and bathymetry data from crowdsourcing and public datasets.

[0979] 3. Tsunami Simulation

[0980] Based on the acquired earthquake and topographical information, the server starts a tsunami simulation, which calculates the tsunami's progress, wave height, arrival time, and affected area.

[0981] 4. Risk Assessment

[0982] The server obtains the location information of users who are deemed high risk and evaluates whether the area will be affected by a tsunami. Users who are deemed high risk are given a high risk score.

[0983] 5. Notice and Advice

[0984] The server sends a personalized notification to User A containing evacuation instructions, including the nearest evacuation shelter and a safe evacuation route.

[0985] Users will receive a notification and can begin evacuation immediately.

[0986] Prompt Sentence Examples

[0987] Below are some example prompts to input to the generative AI model:

[0988] "Please tell me the process of the system that obtains the latest earthquake information, performs a tsunami simulation, and provides evacuation instructions to users. Please explain in detail the APIs, sensors, and simulation content used, and explain the flow of how users evacuate."

[0989] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0990] Step 1: Obtaining earthquake information

[0991] The server obtains earthquake information from earthquake sensors and APIs of public institutions to detect earthquakes.

[0992] Input: Earthquake sensor data and data obtained from public APIs (seismic intensity, epicenter, and time of occurrence).

[0993] Specific operation: The server sends a request to the API, receives earthquake information, and stores it in a database.

[0994] Output: Earthquake information stored in a database.

[0995] Step 2: Get user location

[0996] The device periodically sends the user's location information to the server.

[0997] Input: Location data from the user device.

[0998] Specific operation: The device uses the GPS function to obtain its current location and sends the location data to the server, which receives the data and stores it in a database.

[0999] Output: User location stored in a database.

[1000] Step 3: Collect topographic and meteorological data

[1001] The server collects topographical and meteorological data from crowdsourcing and public datasets.

[1002] Input: Topographic and meteorological data from crowdsourcing platforms and public datasets.

[1003] Specific operation: The server accesses multiple data sources, automatically obtains the latest coastline, ocean floor topography and weather data, and updates the database.

[1004] Output: Latest terrain and weather data stored in a database.

[1005] Step 4: Run the tsunami simulation

[1006] The server runs a tsunami simulation based on the collected earthquake information and topographical data.

[1007] Input: Earthquake information, topographical data, meteorological data.

[1008] Specific operation: The server uses simulation software (e.g., NEOWAVE) to calculate the tsunami's progress, wave height, arrival time, and impact area. The simulation results are saved in a database.

[1009] Output: Tsunami simulation results stored in a database.

[1010] Step 5: Conduct a risk assessment

[1011] The server assesses risk based on the tsunami simulation results and the user's location information.

[1012] Input: Tsunami simulation results, user location information.

[1013] Specific operation: The server compares the simulation results with the location information, calculates a risk score, and saves the risk assessment results in a database.

[1014] Output: Risk assessment results stored in a database.

[1015] Step 6: Send notifications and provide personalized advice

[1016] The server will send information to users based on the risk assessment results and provide personalized evacuation instructions.

[1017] Input: Risk assessment results, user location information.

[1018] What it does: The server sends notifications to high-risk users with evacuation instructions, including the nearest evacuation shelter and recommended evacuation routes.

[1019] Output: Personalized evacuation instructions and notifications sent to user devices.

[1020] (Application example 1)

[1021] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1022] Current tsunami evacuation systems struggle to provide users with optimal evacuation routes in real time when an earthquake occurs. Many systems only provide text-based notifications, making it difficult for users to take prompt and appropriate evacuation actions during an emergency. Furthermore, there is a need for systems that can provide personalized evacuation instructions based on the user's current location in real time.

[1023] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1024] In this invention, the server includes means for acquiring earthquake information, means for acquiring topographical information and meteorological data, means for running a tsunami simulation based on the earthquake information and topographical information, means for assessing a user's risk based on the simulation results, means for sending a notification to the user based on the assessment results, means for generating personalized evacuation instructions based on the user's location information, and means for displaying evacuation routes in AR on the smart device. This allows the user to be visually guided to the optimal evacuation route in real time, enabling them to take prompt and appropriate evacuation action.

[1025] "Earthquake information" refers to data such as the epicenter, seismic intensity, and time of occurrence at the time of an earthquake.

[1026] "Topographic information" refers to geographic information including coastline, ocean floor topography, and elevation data for areas where earthquakes occur.

[1027] "Weather Data" refers to information regarding weather conditions such as weather, temperature, wind speed, and air pressure.

[1028] "Tsunami simulation" refers to the process of calculating the progression, wave height, arrival time, and area of ​​impact of a tsunami based on earthquake and topographical information.

[1029] "Risk assessment" refers to the act of assessing the tsunami risk at the user's current location based on the simulation results.

[1030] "Notification" refers to the act of sending evacuation instructions and evacuation route information to users based on the results of risk assessment.

[1031] "Personalized evacuation instructions" refers to evacuation suggestions that are customized based on the user's current location and surrounding conditions.

[1032] "Smart devices" refers to devices that can connect to the Internet, such as smartphones, smart glasses, and head-mounted displays.

[1033] "AR display" refers to the act of overlaying digital information on real-world scenery using augmented reality technology.

[1034] This invention relates to a system that operates as a security service to minimize damage caused by tsunamis when an earthquake occurs. This system acquires earthquake information, topographical information, and meteorological data, and performs tsunami simulations based on these data. Furthermore, it performs risk assessments for each user based on the simulation results and promptly provides appropriate evacuation instructions.

[1035] System Configuration

[1036] The system of the present invention consists of the following main components:

[1037] 1. Earthquake detection subsystem (server)

[1038] 2. Data collection subsystem (server)

[1039] 3. Tsunami Simulation Subsystem (Server)

[1040] 4. Risk Assessment Subsystem (Server)

[1041] 5. Notification sending subsystem (server)

[1042] 6. Personalized Advice Generation Subsystem (Server)

[1043] 7. User terminal (smart device)

[1044] Earthquake detection and data collection

[1045] The server obtains real-time earthquake occurrence information by linking with multiple earthquake detection sensors and public institution APIs. For example, it uses the Japan Meteorological Agency's API to collect data such as seismic intensity, epicenter, and time of occurrence. In addition, user devices periodically send their location information to the server. The server obtains and stores the latest coastline and seabed topography data using crowdsourcing and public datasets. Weather data is also collected and used in the simulations.

[1046] Tsunami Simulation

[1047] Based on the collected seismic and topographical information, the server runs an advanced tsunami simulation, calculating the tsunami's progress, wave height, arrival time, and impact area, and storing the results in a database. The simulation is performed quickly and updated in real time.

[1048] Risk Assessment

[1049] Based on the results of the tsunami simulation, the server assesses the risk to the user's current location. The server compares the user's location information with the simulation data, and users who are determined to be at high risk are required to take appropriate action. This risk assessment is updated regularly and is based on the latest information.

[1050] Notifications and personalized advice

[1051] Based on the results of the risk assessment, the server sends notifications to high-risk users. The notifications include evacuation instructions, recommended evacuation routes, and information on the nearest evacuation shelters. High-risk users are provided with personalized evacuation instructions. The optimal evacuation route is visually displayed on the user's smart device, such as a smartphone or smart glasses, using augmented reality (AR) technology.

[1052] Specific examples

[1053] For example, if a magnitude 6 earthquake occurs in a coastal area, the server detects this information in real time and runs a tsunami simulation using the latest coastline data. The resulting simulation data is used to assess the risk to the user's area. Users in high-risk areas are notified of the optimal evacuation route and the nearest evacuation shelter. The evacuation route is visually displayed on the user's smart device through the AR function, enabling quick evacuation.

[1054] Example prompts to input to a generative AI model:

[1055] Generate a notification that provides evacuation instructions based on the user's current location in the event of a tsunami. The notification should include:

[1056] 1. Earthquake Information

[1057] 2. Risk assessment using tsunami simulation

[1058] 3. Information on the nearest evacuation shelter

[1059] 4. Best evacuation route

[1060] For example, include, "An earthquake of magnitude 6 has occurred and a tsunami is expected. The nearest evacuation shelter is AAA evacuation shelter, and the best evacuation route is BBB. Please evacuate immediately."

[1061] By feeding this prompt into a generative AI model, a message providing evacuation instructions in natural language is generated.

[1062] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1063] Step 1: Obtaining earthquake information

[1064] The server connects with multiple sensors and public APIs to obtain real-time earthquake information. This information includes the epicenter, seismic intensity, and time of occurrence. The input is data from the sensors and APIs, and the output is stored internally as earthquake information. This data is used in subsequent processing.

[1065] Step 2: Collect terrain and weather data

[1066] The server collects the latest topographic and meteorological data from crowdsourcing and public datasets. The input is data from external databases, and the output is stored internally as topographic and meteorological data. This data is used for tsunami simulation.

[1067] Step 3: Run the tsunami simulation

[1068] The server runs a tsunami simulation based on the collected earthquake and topographical information. The input is earthquake information and topographical information, and through calculations, the progress, wave height, arrival time, and affected area of ​​the tsunami are simulated. The output is saved in a database as the simulation results.

[1069] Step 4: User Risk Assessment

[1070] The server performs risk assessment based on the user's current location and simulation data. The input is the user's location information and the simulation results, and a risk score is calculated by comparing these. The output is the risk assessment result for each user, and notification content is determined based on this.

[1071] Step 5: Generate and send notifications

[1072] The server generates notifications for users based on the risk assessment results. These notifications include evacuation instructions, recommended evacuation routes, and information on the nearest evacuation shelters. The input is the risk assessment results, and the output is the notification message sent to the user. The notification content is personalized and sent to the user's smart device.

[1073] Step 6: Evacuation route guidance using AR display

[1074] The user's smart device (smart glasses or smartphone) displays an AR evacuation route based on the received notification. The input is the notification data from the server, and the output is the evacuation route displayed on the device. Using augmented reality technology, users can visually confirm the evacuation route and evacuate quickly.

[1075] The above process enables users to take swift and appropriate evacuation action in the event of an earthquake.

[1076] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1077] This invention relates to a hazard map service for minimizing damage caused by tsunamis in the event of an earthquake. In particular, it focuses on a system that combines methods for recognizing users' emotions to optimize the content and notification method of evacuation instructions. This service acquires earthquake information, topographical information, and meteorological data, and performs tsunami simulations based on this information. Furthermore, it uses an emotion engine to recognize the user's emotional state and personalize evacuation instructions.

[1078] System Configuration

[1079] The system of the present invention consists of the following main components:

[1080] 1. Earthquake detection subsystem (server)

[1081] 2. Data collection subsystem (server)

[1082] 3. Tsunami Simulation Subsystem (Server)

[1083] 4. Risk Assessment Subsystem (Server)

[1084] 5. Notification sending subsystem (server)

[1085] 6. Personalized Advice Generation Subsystem (Server)

[1086] 7. Emotion engine (server)

[1087] 8. User terminal (terminal)

[1088] Earthquake detection and data collection

[1089] The server connects with multiple earthquake detection sensors and public institution APIs to obtain earthquake occurrence information in real time. For example, it uses the Japan Meteorological Agency's API to collect data such as earthquake intensity, epicenter, and occurrence time. In addition, the device periodically sends the user's location information to the server. The server uses crowdsourcing and public datasets to obtain and store the latest coastline and seabed topography data. Weather data is also obtained in the same way and used in simulations.

[1090] Tsunami Simulation

[1091] Based on the earthquake information and the collected topographical information, the server runs an advanced tsunami simulation, which calculates the tsunami's progress, wave height, arrival time, and impact area, and stores the results in a database. The simulation is performed quickly and updated in real time.

[1092] Risk Assessment

[1093] Based on the results of the simulation, the server assesses the risk to the user's current location. Users' location information is compared with the simulation data, and users who are determined to be at high risk are required to take appropriate action based on the risk assessment. This risk assessment is updated regularly and is based on the latest information.

[1094] Emotion Recognition and Personalized Advice

[1095] The emotion engine analyzes data (e.g., voice, facial expressions, typing speed) obtained from the user's smartphone or other mobile device to recognize the user's emotional state in real time. The server takes the results of the emotion engine into consideration and optimizes the content and notification method of evacuation instructions based on the user's emotional state. For example, it provides simple and intuitive instructions to a panicked user and detailed information to a calm user.

[1096] Notifications and personalized advice

[1097] Based on the evaluation results, the server generates and sends notifications to high-risk users, including evacuation instructions, recommended evacuation routes, and information on the nearest evacuation shelters. In particular, high-risk users are provided with personalized evacuation instructions that reflect their emotional state, allowing them to take optimal evacuation actions based on their current location and surrounding environment.

[1098] Specific examples

[1099] 1. Earthquake occurs

[1100] The server receives information from a seismograph about a magnitude 6 earthquake and verifies that it occurred near the coast. The earthquake information includes the epicenter, time of occurrence, and seismic intensity.

[1101] 2. Data Collection

[1102] The server retrieves the latest coastline and bathymetry data from crowdsourcing and public datasets and updates the database.

[1103] 3. Run the simulation

[1104] Based on the acquired earthquake and topographical information, the server starts a tsunami simulation, which calculates the tsunami's progress, wave height, arrival time, and affected area.

[1105] 4. Risk Assessment

[1106] The server obtains the location information of users who are deemed high risk and evaluates whether the area will be affected by a tsunami. Users who are deemed high risk are given a high risk score.

[1107] 5. Emotion recognition

[1108] The device collects the user's voice and facial expression data, which is then analyzed by an emotion engine to assess the user's emotional state.

[1109] 6. Notice and Advice

[1110] The server generates evacuation instructions that take into account User A's emotional state and sends a personalized notification containing the evacuation instructions, including the nearest evacuation shelter and the specific evacuation route.

[1111] Users will receive a notification and can begin evacuation immediately.

[1112] This system will help users take prompt and appropriate evacuation actions in the event of an earthquake, minimizing damage from tsunamis. Furthermore, emotion recognition will enable responses based on the user's mental state, further improving the effectiveness of evacuation actions.

[1113] The processing flow will be explained below.

[1114] Step 1:

[1115] The server polls earthquake detectors and APIs of public institutions to obtain earthquake information. Specifically, it monitors the seismic intensity, epicenter, and occurrence time in real time, and triggers an earthquake detection event when an earthquake occurs that exceeds a threshold (for example, seismic intensity 5 or higher).

[1116] Step 2:

[1117] The device acquires the user's current location information using GPS and periodically sends it to the server. The user's location information must be highly accurate and is usually updated every few minutes.

[1118] Step 3:

[1119] The server collects up-to-date coastline and bathymetry data from crowdsourcing and public datasets, storing the data in a database that is updated in real time.

[1120] Step 4:

[1121] The server obtains meteorological data (e.g., tides, wind speed, air pressure, etc.) from the API of a public institution. This data is also stored and updated in the database and used during simulation.

[1122] Step 5:

[1123] The server receives a trigger from the earthquake detection subsystem and starts a tsunami simulation. It uses earthquake information and collected topographical and meteorological data to calculate the tsunami's progress, arrival time, wave height, and affected area. The simulation results are stored in a database.

[1124] Step 6:

[1125] The server retrieves the user's current location information from the database and compares it with the results of the tsunami simulation. If a specific user is in a high-risk area, it calculates the user's risk score and generates an evaluation result for each user.

[1126] Step 7:

[1127] The device collects the user's voice and facial expression data and sends it to the emotion engine. Voice data is obtained using a microphone, and facial expression data is obtained using a camera.

[1128] Step 8:

[1129] The emotion engine analyzes the received voice and facial expression data to recognize the user's emotional state. For example, it analyzes the tone of voice and facial features to determine whether the user is panicked or calm.

[1130] Step 9:

[1131] The server receives the results of the emotion engine and optimizes the content and notification method of evacuation instructions based on the user's emotional state, for example, providing simple and intuitive instructions to a panicked user and detailed information to a calm user.

[1132] Step 10:

[1133] The server generates evacuation instructions that take into account the emotional state of the user and sends them to the devices of high-risk users. The server quickly delivers information using push notifications and SMS. It also logs whether the transmission was successful.

[1134] Step 11:

[1135] The device receives the notification and displays an alert to the user, which is visually and audibly communicated to the user so that the user can see it immediately.

[1136] Step 12:

[1137] The user checks the notification and follows the instructions to begin evacuation. The user heads to the nearest evacuation shelter, selects a safe route, and moves quickly. During evacuation, the user can check updates and additional instructions on their smartphone.

[1138] This is how the system's process progresses, supporting users from the moment an earthquake occurs to the moment they decide to evacuate safely. In addition to distributed data collection and advanced simulation, emotion recognition enables optimal responses for each user.

[1139] Example 2

[1140] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1141] In order to minimize damage caused by tsunamis when an earthquake occurs, quick and accurate evacuation instructions are necessary. However, conventional systems have difficulty providing appropriate evacuation instructions that take into account the individual situation and emotional state of the user. This has led to problems such as users panicking and delaying evacuation behavior.

[1142] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for acquiring earthquake information, means for acquiring topographical information and meteorological data, means for executing a tsunami simulation based on the earthquake information and topographical information, means for assessing a user's risk based on the simulation results, means for sending a notification to the user based on the assessment results, means for recognizing the user's emotional state, and means for generating personalized evacuation instructions based on the recognized emotional state. This makes it possible to quickly provide appropriate evacuation instructions according to the user's emotional state in the event of an earthquake, and support the user's evacuation behavior.

[1143] "Earthquake information" is detailed data about earthquakes, including the magnitude, epicenter, and time of occurrence of the earthquake.

[1144] "Topographic information" is detailed data about the terrain, including coastline and undersea topography data.

[1145] "Weather data" refers to data related to weather, such as precipitation, wind speed, and temperature.

[1146] The "means for performing a tsunami simulation" is a means for calculating the progress, wave height, arrival time, and range of impact of a tsunami based on earthquake information and topographical information.

[1147] The "means for assessing the user's risk" is a means for comparing the results of a tsunami simulation with the user's current location to assess the possibility that the user will be affected by a tsunami.

[1148] The "means for sending notifications to users based on the assessment results" refers to the means for sending evacuation instructions or warnings to users based on the users' risk assessment.

[1149] The "means for recognizing the user's emotional state" refers to a means for analyzing data such as the user's voice, facial expression, typing speed, etc., and recognizing the user's emotional state in real time.

[1150] The "means for generating personalized evacuation instructions" is a means for creating evacuation instructions that are best suited to the user's situation based on the recognized emotional state.

[1151] "Crowdsourcing" is a method of collecting information from a large number of Internet users.

[1152] A "public dataset" is a collection of highly reliable data provided by governments, public institutions, etc.

[1153] "Real-time" refers to information being updated immediately without delay.

[1154] This invention is a system for providing hazard maps to minimize damage caused by tsunamis during earthquakes. The system acquires earthquake, topographical, and meteorological data, and performs tsunami simulations based on this information. It also recognizes the user's emotional state and provides personalized evacuation instructions.

[1155] System Configuration

[1156] The system of the present invention consists of the following main components:

[1157] 1. Earthquake detection subsystem (server)

[1158] 2. Data collection subsystem (server)

[1159] 3. Tsunami Simulation Subsystem (Server)

[1160] 4. Risk Assessment Subsystem (Server)

[1161] 5. Notification sending subsystem (server)

[1162] 6. Personalized Advice Generation Subsystem (Server)

[1163] 7. Emotion engine (server)

[1164] 8. User terminal (terminal)

[1165] Earthquake detection and data collection

[1166] The server connects with multiple sensors and public institution APIs to detect earthquakes and obtains earthquake occurrence information (e.g., seismic intensity, epicenter, and time of occurrence) in real time. For example, data is collected using the Japan Meteorological Agency's API. The device periodically sends the user's location information to the server. The server uses crowdsourcing and public datasets to obtain and store the latest coastline and seabed topography data. Weather data is also obtained in the same way and used in simulations.

[1167] Tsunami Simulation

[1168] The server runs an advanced tsunami simulation based on earthquake information and collected topographical information. This simulation calculates the tsunami's progress, wave height, arrival time, and impact area. The simulation results are stored in a database and are continuously updated. For example, GEOWAVE may be used as the tsunami simulation software.

[1169] Risk Assessment

[1170] The server evaluates the risk to the user's current location based on the simulation results. By comparing the user's location information with the simulation data, users who are deemed to be at high risk will be given special evacuation instructions. This risk assessment is updated in real time.

[1171] Emotion Recognition and Personalized Advice

[1172] The emotion engine analyzes data such as the user's voice, facial expressions, and typing speed to recognize their emotional state in real time. The server then uses the results of this emotion engine to personalize the evacuation instructions and notification method based on the user's emotional state. For example, it provides simple and intuitive instructions to a panicked user and detailed information to a calm user.

[1173] Notification and Action Support

[1174] The server generates optimal evacuation instructions based on the risk assessment and emotion recognition results, and sends a notification to the user's device. This notification includes evacuation instructions, recommended evacuation routes, and information on the nearest evacuation shelter. The user receives this notification and promptly begins appropriate evacuation actions.

[1175] Specific examples

[1176] 1. Earthquake occurs

[1177] The server receives information from a seismograph about a magnitude 6 earthquake and verifies that it occurred near the coast. The earthquake information includes the epicenter, time of occurrence, and seismic intensity.

[1178] 2. Data Collection

[1179] The server retrieves the latest coastline and bathymetry data from crowdsourcing and public datasets and updates the database.

[1180] 3. Run the simulation

[1181] Based on the acquired earthquake and topographical information, the server starts a tsunami simulation, which calculates the tsunami's progress, wave height, arrival time, and affected area.

[1182] 4. Risk Assessment

[1183] The server obtains the location information of users who are deemed high risk and evaluates whether the area will be affected by a tsunami. Users who are deemed high risk are given a high risk score.

[1184] 5. Emotion recognition

[1185] The device collects the user's voice and facial expression data, which is then analyzed by an emotion engine to assess the user's emotional state.

[1186] 6. Notice and Advice

[1187] The server generates evacuation instructions that take into account User A's emotional state and sends a personalized notification containing the evacuation instructions, including the nearest evacuation shelter and the specific evacuation route.

[1188] Users will receive a notification and can begin evacuation immediately.

[1189] Prompt Sentence Examples

[1190] "If you live in a coastal area, please suggest multiple scenarios for what evacuation route you should take in the event of a magnitude 6 earthquake, depending on your emotional state."

[1191] This system enables users to take prompt and appropriate evacuation actions in the event of an earthquake, minimizing damage from tsunamis. Furthermore, emotion recognition makes it possible to respond according to the user's mental state, further improving the effectiveness of evacuation actions.

[1192] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1193] Step 1: Earthquake detection

[1194] The server collects earthquake information from seismometers and public institutions (e.g., Japan Meteorological Agency API) to detect earthquakes.

[1195] Input: Seismic intensity, epicenter, time of occurrence

[1196] How it works: The server polls data from the seismometer and generates an alert if an earthquake of magnitude 5 or greater is detected.

[1197] Output: Earthquake alerts and data on seismic intensity, epicenter, and occurrence time

[1198] Step 2: Data collection

[1199] The device periodically transmits the user's location information to the server.

[1200] Input: GPS data

[1201] How it works: The device receives a GPS signal and sends location information to the server at specified intervals.

[1202] Output: Current location of the user

[1203] Step 3: Update terrain and weather data

[1204] The server uses crowdsourcing and public datasets to obtain the latest terrain and weather data and update the database.

[1205] Input: Crowdsourced data, public datasets

[1206] How it works: The server periodically checks for updates to the dataset and automatically updates the database if new data is found.

[1207] Output: Latest terrain and weather data

[1208] Step 4: Run the tsunami simulation

[1209] The server runs a tsunami simulation based on the collected earthquake and topographical information.

[1210] Input: Earthquake information, topographical information

[1211] How it works: The server launches simulation software (e.g., GEOWAVE), inputs data, and calculates the tsunami's progress, wave height, arrival time, and area of ​​impact.

[1212] Output: Tsunami simulation results (wave height, arrival time, affected area)

[1213] Step 5: Risk assessment

[1214] The server evaluates the risk to the user's current location based on the simulation results.

[1215] Input: User location information, tsunami simulation results

[1216] How it works: The server compares the user's location with simulated data to identify users in high-risk areas and calculate a risk score.

[1217] Output: Risk assessment result (risk score)

[1218] Step 6: Collect and analyze emotion data

[1219] The device collects the user's voice and facial expression data, which is analyzed by an emotion engine to recognize the user's emotional state.

[1220] Input: Voice data, facial expression data

[1221] How it works: The device uses a microphone and camera to collect voice and facial expression data and sends it to a server. The emotion engine analyzes the data and evaluates the user's emotional state.

[1222] Output: Emotion evaluation result (e.g., panicked, calm)

[1223] Step 7: Generate personalized evacuation instructions

[1224] The server takes into account the results of the emotion engine to generate evacuation instructions based on the user's emotional state.

[1225] Input: Emotion assessment results, risk assessment results

[1226] How it works: Based on the analysis results of the emotion engine, the server selects different evacuation instruction templates and customizes evacuation instructions that best suit the user's situation.

[1227] Output: Personalized evacuation instructions

[1228] Step 8: Send notification

[1229] The server notifies the user of the generated evacuation instructions.

[1230] Input: personalized evacuation instructions

[1231] What it does: The server generates the notification and pushes it to the user's device. It also saves it in the notification history so the user can review it later.

[1232] Output: Evacuation notice sent to user

[1233] Step 9: User Actions

[1234] Users will receive a notification and will be able to quickly take action to evacuate.

[1235] Input: Evacuation order notice

[1236] Action: The user confirms the notification and follows the evacuation route provided. During the evacuation, the device automatically updates its location and sends it to the server.

[1237] Output: Evacuation action start and progress

[1238] In this way, the system can provide users with quick and accurate evacuation instructions, minimizing damage during disasters. Furthermore, personalized instructions based on emotion recognition can respond according to the user's mental state.

[1239] (Application example 2)

[1240] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1241] Conventional earthquake and tsunami evacuation systems do not take into account the user's psychological state when issuing evacuation instructions, which can lead to panic and confusion. It is particularly difficult for users to take appropriate evacuation actions when an earthquake occurs while they are shopping in a physical store. Therefore, it is necessary to optimize personalized evacuation instructions and notification methods according to each user's emotional state so that they can evacuate quickly and effectively.

[1242] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for acquiring earthquake information, means for acquiring topographical information and meteorological data, means for running a tsunami simulation based on the earthquake information and topographical information, means for assessing the user's risk based on the simulation results, means for sending a notification to the user based on the assessment results, means for generating personalized evacuation instructions based on the user's location information, means for recognizing the user's emotional state using an emotion engine, and means for optimizing the content of the evacuation instructions and the notification method based on the user's emotional state. This enables the user to take evacuation action quickly and effectively when they encounter an earthquake while shopping in a physical store.

[1243] "Means for acquiring earthquake information" refers to a system or device for detecting and collecting information on earthquake occurrence.

[1244] The "means for acquiring topographical information and meteorological data" is a system or device for collecting topographical data and meteorological information.

[1245] "Means for performing tsunami simulations based on earthquake information and topographical information" refers to a system or device for predicting and calculating the occurrence and progression of tsunamis using earthquake and topographical data.

[1246] "Means for assessing user risk based on simulation results" refers to a system or device that determines the risk level of the area where the user is located based on the results of a tsunami simulation.

[1247] "Means for sending a notification to a user based on the assessment result" refers to a system or device that sends an appropriate notification to a user depending on the result of the risk assessment.

[1248] "Means for generating personalized evacuation instructions based on the user's location information" refers to a system or device that generates individual evacuation instructions tailored to each situation based on the user's current location.

[1249] "Means for recognizing a user's emotional state using an emotion engine" refers to a system or device that uses emotion recognition technology to analyze and determine a user's current emotional state.

[1250] The "means for optimizing the content and notification method of evacuation instructions based on the emotional state of the user" is a system or device that generates evacuation instructions in the optimal format and content, taking into account the emotional state of the user.

[1251] This invention is a hazard map service for minimizing damage caused by tsunamis when an earthquake occurs. The system acquires earthquake information, topographical information, and meteorological data, and based on this information, performs tsunami simulations to assess risk and provides optimal evacuation instructions to users. Furthermore, it uses an emotion engine to recognize the user's emotional state and optimizes notification content according to that emotional state, helping users take quick and effective evacuation actions.

[1252] System Configuration

[1253] The system consists of the following main components:

[1254] 1. Earthquake detection subsystem (server): Detects earthquakes and collects earthquake information in real time.

[1255] 2. Data collection subsystem (server): Collects and updates terrain and meteorological data.

[1256] 3. Tsunami simulation subsystem (server): Simulates tsunami progression, wave height, arrival time, and impact area based on earthquake and topographical data.

[1257] 4. Risk assessment subsystem (server): Evaluates the user's risk based on the results of the tsunami simulation.

[1258] 5. Notification sending subsystem (server): Sends appropriate notifications to users based on the results of risk assessment.

[1259] 6. Personalized advice generation subsystem (server): Generates individual evacuation instructions based on the user's location and emotional state.

[1260] 7. Emotion Engine (Server): Recognizes the user's emotional state in real time.

[1261] 8. User terminal (terminal): Transmits the user's location information and emotional state data to the server and receives evacuation instructions.

[1262] Earthquake detection and data collection

[1263] The server connects with multiple sensors and public institution APIs to detect earthquakes and obtains earthquake occurrence information in real time. For example, it uses the Japan Meteorological Agency's API to collect data such as earthquake intensity, epicenter, and occurrence time. Furthermore, the device periodically sends location information to the server, allowing the server to determine the user's current location. It also uses crowdsourcing and public datasets to collect and store the latest topographical and meteorological data.

[1264] Tsunami Simulation

[1265] Based on the earthquake information and the collected topographical information, the server runs an advanced tsunami simulation, which calculates the tsunami's progress, wave height, arrival time and impact area, and stores the results in a database. The simulation is performed quickly and updated in real time.

[1266] Risk Assessment

[1267] Based on the results of the simulation, the server assesses the risk to the user's current location. The server compares the user's location information with the simulation data, and if a user is determined to be at high risk, they will be required to take appropriate action based on the risk assessment. This risk assessment is updated regularly and is based on the latest information.

[1268] Emotion Recognition and Personalized Advice

[1269] The emotion engine analyzes data (e.g., voice, facial expressions, typing speed) obtained from the user's smartphone or other mobile device to recognize the user's emotional state in real time. The server takes the results of the emotion engine into consideration and optimizes the content and notification method of evacuation instructions based on the user's emotional state. For example, it provides simple and intuitive instructions to a panicked user and detailed information to a calm user.

[1270] Notifications and personalized advice

[1271] Based on the risk assessment results, the server generates and sends notifications to high-risk users, including evacuation instructions, recommended evacuation routes, and information on the nearest evacuation shelters. In particular, high-risk users are provided with personalized evacuation instructions that reflect their emotional state, allowing them to take optimal evacuation actions based on their current location and surrounding environment.

[1272] Specific examples

[1273] If an earthquake occurs while a user is shopping in a physical store, real-time earthquake information is acquired to confirm that the user is in a coastal area. At the same time, a tsunami simulation is run to identify the user's area as high-risk. The emotion engine analyzes the user's facial expression data and determines that the user is in a state of panic.

[1274] When this happens, the server will send a notification to the user:

[1275] "High-risk area. Evacuate immediately to the nearest evacuation center. Please remain calm and follow simple instructions."

[1276] Example prompt sentence:

[1277] "Create a notification for tsunami evacuation instructions and personalized evacuation routes using emotion recognition. Earthquake information is as follows: Epicenter, Magnitude 6, Occurrence time 18:00. User is in a panic and in a high-risk area."

[1278] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1279] Step 1:

[1280] The server acquires earthquake information. First, the server acquires real-time earthquake occurrence information through the API of a public institution (e.g., the Japan Meteorological Agency API), collecting data such as seismic intensity, epicenter, and time of occurrence. The input data is data distributed by earthquake sensor networks and public institutions, and the output is detailed information about the earthquake.

[1281] Step 2:

[1282] The server obtains terrain information and weather data. The server obtains and stores the latest coastline and bathymetry data, as well as weather data, using crowdsourcing or public datasets. The input data is data from the terrain database and weather information API, and the output is updated terrain and weather data.

[1283] Step 3:

[1284] The server runs the tsunami simulation. It performs an advanced tsunami simulation based on earthquake information and collected topographical information. Specifically, it simulates the tsunami's progress, wave height, arrival time, and affected area, and stores the results in a database. The input data are earthquake information and topographical information, and the output is the simulation results.

[1285] Step 4:

[1286] The server performs risk assessment. Based on the simulation results, the server assesses the risk for the user's current location. Specifically, it compares the user's location information with the simulation data and determines whether the area is high-risk. The input data is the user's location information and the tsunami simulation results, and the output is the risk assessment results.

[1287] Step 5:

[1288] The device collects emotional state data. Emotional data such as the user's voice, facial expression, and typing speed are collected from smartphones and other mobile devices and sent to a server. The input data is the user's emotional expression data, and the output is the data sent to the server.

[1289] Step 6:

[1290] The server recognizes the emotional state using an emotion engine. Based on the received emotional state data, the server uses the emotion engine to analyze and determine the user's emotional state (e.g., panic, calm, etc.) in real time. The input data is emotional expression data, and the output is the determined emotional state.

[1291] Step 7:

[1292] The server generates personalized evacuation instructions. It integrates the emotion engine's judgment results and risk assessment results to generate optimal evacuation instructions according to the user's emotional state and risk level. As a specific example, it provides simple and intuitive instructions to a panicked user, and a detailed evacuation route to a calm user. The input data are the emotional state judgment results and risk assessment results, and the output is optimized evacuation instructions.

[1293] Step 8:

[1294] The server sends a notification to the user. Based on the optimized evacuation instructions, the server sends a notification to the user's device. The notification content includes specific evacuation instructions, recommended evacuation routes, and information about the nearest evacuation shelter. The input data is the optimized evacuation instructions, and the output is a notification to the user.

[1295] Specific examples

[1296] If an earthquake occurs while a user is shopping in a physical store, earthquake information is obtained in real time. At the same time, a tsunami simulation is run and the user's area is identified as high-risk. The emotion engine analyzes the user's facial expression data and recognizes that the user is in a state of panic. In this case, the server sends the following notification to the user:

[1297] "High-risk area. Evacuate immediately to the nearest evacuation center. Please remain calm and follow simple instructions."

[1298] Example prompt sentence:

[1299] "Create a notification for tsunami evacuation instructions and personalized evacuation routes using emotion recognition. Earthquake information is as follows: Epicenter, Magnitude 6, Occurrence time 18:00. User is in a panic and in a high-risk area."

[1300] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1301] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1302] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1303] [Fourth embodiment]

[1304] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1305] 7, a 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.

[1306] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. 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. The database 24 and the communication I / F 26 are also 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).

[1307] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1308] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1309] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1310] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1311] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1312] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1313] The specific processing program 56 is an example of a "program" according to the technology of the present 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 in accordance with the specific processing program 56 executed on the RAM 30.

[1314] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1315] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1316] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1317] This invention relates to a hazard map service for minimizing damage caused by tsunamis when an earthquake occurs. This service acquires earthquake information, topographical information, and meteorological data, and performs tsunami simulations based on this data to assess the risk to each user and provide appropriate evacuation instructions promptly.

[1318] System Configuration

[1319] The system of the present invention consists of the following main components:

[1320] 1. Earthquake detection subsystem (server)

[1321] 2. Data collection subsystem (server)

[1322] 3. Tsunami Simulation Subsystem (Server)

[1323] 4. Risk Assessment Subsystem (Server)

[1324] 5. Notification sending subsystem (server)

[1325] 6. Personalized Advice Generation Subsystem (Server)

[1326] 7. User terminal (terminal)

[1327] Earthquake detection and data collection

[1328] The server connects with multiple earthquake detection sensors and public institution APIs to obtain earthquake occurrence information in real time. For example, it uses the Japan Meteorological Agency's API to collect data such as earthquake intensity, epicenter, and occurrence time. In addition, the device periodically sends the user's location information to the server. The server uses crowdsourcing and public datasets to obtain and store the latest coastline and seabed topography data. Weather data is also obtained in the same way and used in simulations.

[1329] Tsunami Simulation

[1330] Based on the earthquake information and the collected topographical information, the server runs an advanced tsunami simulation, which calculates the tsunami's progress, wave height, arrival time, and impact area, and stores the results in a database. The simulation is performed quickly and updated in real time.

[1331] Risk Assessment

[1332] Based on the results of the simulation, the server assesses the risk to the user's current location. Users' location information is compared with the simulation data, and users who are determined to be at high risk are required to take appropriate action based on the risk assessment. This risk assessment is updated regularly and is based on the latest information.

[1333] Notifications and personalized advice

[1334] Based on the evaluation results, the server sends notifications to high-risk users, including evacuation instructions, recommended evacuation routes, and information on the nearest evacuation shelters. High-risk users are especially provided with personalized evacuation instructions, allowing them to take optimal evacuation actions based on their current location and surrounding environment.

[1335] Specific examples

[1336] 1. Earthquake occurs

[1337] The server receives information from a seismograph about a magnitude 6 earthquake and verifies that it occurred near the coast. The earthquake information includes the epicenter, time of occurrence, and seismic intensity.

[1338] 2. Data Collection

[1339] The server retrieves the latest coastline and bathymetry data from crowdsourcing and public datasets and updates the database.

[1340] 3. Run the simulation

[1341] Based on the acquired earthquake and topographical information, the server starts a tsunami simulation, which calculates the tsunami's progress, wave height, arrival time, and affected area.

[1342] 4. Risk Assessment

[1343] The server obtains the location information of users who are deemed high risk and evaluates whether the area will be affected by a tsunami. Users who are deemed high risk are given a high risk score.

[1344] 5. Notice and Advice

[1345] The server sends a personalized notification to User A with evacuation instructions, including the nearest evacuation shelter and the specific evacuation route.

[1346] Users will receive a notification and can begin evacuation immediately.

[1347] In this way, the system of the present invention can assist users in taking prompt and appropriate evacuation actions when an earthquake occurs, thereby minimizing damage caused by tsunamis.

[1348] The processing flow will be explained below.

[1349] Step 1:

[1350] The server polls earthquake detectors and APIs of public institutions to obtain earthquake information. Specifically, it monitors the seismic intensity, epicenter, and occurrence time in real time, and triggers an earthquake detection event when an earthquake occurs that exceeds a threshold (for example, seismic intensity 5 or higher).

[1351] Step 2:

[1352] The device acquires the user's current location information using GPS and periodically sends it to the server. The user's location information must be highly accurate and is usually updated every few minutes.

[1353] Step 3:

[1354] The server collects up-to-date coastline and bathymetry data from crowdsourcing and public datasets, storing the data in a database that is updated in real time.

[1355] Step 4:

[1356] The server obtains meteorological data (e.g., tides, wind speed, air pressure, etc.) from the API of a public institution. This data is also stored and updated in the database and used during simulation.

[1357] Step 5:

[1358] The server receives a trigger from the earthquake detection subsystem and starts a tsunami simulation. It uses earthquake information and collected topographical and meteorological data to calculate the tsunami's progress, arrival time, wave height, and affected area. The simulation results are stored in a database.

[1359] Step 6:

[1360] The server retrieves the user's current location information from the database and compares it with the results of the tsunami simulation. If a specific user is in a high-risk area, it calculates the user's risk score and generates an evaluation result for each user.

[1361] Step 7:

[1362] The server generates notifications for users based on the risk assessment results, including evacuation instructions, recommended evacuation routes, and information on the nearest evacuation shelters. The content of the notifications is personalized and optimized based on the user's current location and surrounding terrain and building information.

[1363] Step 8:

[1364] The server sends a notification to the high-risk user's device, delivers the information quickly using push notifications or SMS, and logs whether the notification was successful.

[1365] Step 9:

[1366] The device receives the notification and displays an alert to the user, which is visually and audibly communicated to the user so that the user can see it immediately.

[1367] Step 10:

[1368] The user checks the notification and follows the instructions to begin evacuation. The user heads to the nearest evacuation shelter, selects a safe route, and moves quickly. During evacuation, the user can check updates and additional instructions on their smartphone.

[1369] This system's process continues in this way, supporting users from the moment an earthquake occurs until they can safely evacuate. Distributed data collection and advanced simulations enable quick and accurate evacuation instructions.

[1370] Example 1

[1371] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1372] A system is needed to minimize damage from earthquake-induced tsunamis and support rapid and effective evacuation. In particular, there is a need for risk assessments and personalized evacuation instructions that differ for each user. However, current systems have difficulty updating data and assessing risks in real time, making it difficult to provide users with appropriate evacuation instructions.

[1373] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1374] In this invention, the server includes means for acquiring earthquake information, means for acquiring location data, means for collecting topographical data and meteorological data, means for simulating tsunami behavior based on the earthquake information and topographical data, means for assessing a user's risk based on the simulation results, means for transmitting information to the user based on the assessment results, and means for generating personalized evacuation instructions based on the user's location data, thereby enabling the user to take optimal evacuation actions based on the risk assessment updated in real time.

[1375] "Earthquake information" is data related to the occurrence of an earthquake, including information such as seismic intensity, epicenter, and time of occurrence.

[1376] "Location data" is information that indicates a user's current location, and is typically obtained by a location information system such as a GPS.

[1377] "Topographic data" refers to information about the shape of the earth's surface and seabed, and is geographic data used in tsunami simulations.

[1378] "Meteorological data" refers to information about weather and climate, and is data used to predict the progression and impact of tsunamis.

[1379] "Means for simulating tsunami behavior" refers to methods and means for calculating tsunami progression, wave height, arrival time, area of ​​impact, etc., using earthquake information and topographical data.

[1380] "Means for assessing risk" refers to methods and means for comparing the results of tsunami simulations with user location data to assess the degree to which a user is exposed to tsunami risk.

[1381] "Personalized evacuation instructions" are evacuation instructions that are individually generated based on the user's current location and risk assessment, including recommended evacuation routes and information about the nearest evacuation shelters.

[1382] "Crowdsourcing" is a method of collecting information or data from a large number of people and is a means used to update information used within a system.

[1383] "Public Dataset" means a dataset provided by a government agency or other public body that contains reliable topographical and meteorological data.

[1384] This invention relates to a hazard map service for minimizing damage caused by tsunamis when an earthquake occurs. The system of the present invention consists of the following main components:

[1385] 1. Earthquake detection subsystem (server)

[1386] 2. Data collection subsystem (server)

[1387] 3. Tsunami Simulation Subsystem (Server)

[1388] 4. Risk Assessment Subsystem (Server)

[1389] 5. Notification sending subsystem (server)

[1390] 6. Personalized Advice Generation Subsystem (Server)

[1391] 7. User terminal (terminal)

[1392] Earthquake detection and data collection

[1393] The server connects with multiple sensors and public institution APIs to detect earthquakes and obtains earthquake occurrence information in real time. Specifically, the server sends a request to an API (e.g., the Japan Meteorological Agency API) and receives information such as seismic intensity, epicenter, and time of occurrence, which it then stores in a database.

[1394] The device also periodically transmits the user's location information to the server, which receives the location information acquired using the GPS function and stores it in a database, allowing the device to track the user's location in real time.

[1395] Additionally, the server will use crowdsourcing and public datasets to obtain the latest coastline and bathymetry data to update the database, as well as weather data, which will be used in the tsunami simulation.

[1396] Tsunami Simulation

[1397] Based on the earthquake information and collected topographical data, the server performs advanced tsunami simulations. The server uses dedicated simulation software (e.g., NEOWAVE) to calculate the tsunami's progress, wave height, arrival time, and impact area. The simulation results are stored in a database and updated in real time.

[1398] Risk Assessment

[1399] Based on the simulation results, the server assesses the risk to the user's current location. The server compares the simulation results with the user's location information to calculate a risk score, which allows the server to assess the user's exposure to tsunami danger. The risk assessment is updated regularly, ensuring that the risk assessment is always based on the latest information.

[1400] Notifications and personalized advice

[1401] Based on the assessment results, the server sends notifications to high-risk users, including evacuation instructions, recommended evacuation routes, and information on the nearest evacuation shelters. High-risk users can also receive personalized evacuation instructions, allowing them to take optimal evacuation actions based on their current location and surrounding environment.

[1402] Specific examples

[1403] 1. Earthquake occurs

[1404] The server receives information from a seismograph about a magnitude 6 earthquake and verifies that it occurred near the coast. The earthquake information includes the epicenter, time of occurrence, and seismic intensity.

[1405] 2. Data Collection

[1406] The server updates the database with the latest coastline and bathymetry data from crowdsourcing and public datasets.

[1407] 3. Tsunami Simulation

[1408] Based on the acquired earthquake and topographical information, the server starts a tsunami simulation, which calculates the tsunami's progress, wave height, arrival time, and affected area.

[1409] 4. Risk Assessment

[1410] The server obtains the location information of users who are deemed high risk and evaluates whether the area will be affected by a tsunami. Users who are deemed high risk are given a high risk score.

[1411] 5. Notice and Advice

[1412] The server sends a personalized notification to User A containing evacuation instructions, including the nearest evacuation shelter and a safe evacuation route.

[1413] Users will receive a notification and can begin evacuation immediately.

[1414] Prompt Sentence Examples

[1415] Below are some example prompts to input to the generative AI model:

[1416] "Please tell me the process of the system that obtains the latest earthquake information, performs a tsunami simulation, and provides evacuation instructions to users. Please explain in detail the APIs, sensors, and simulation content used, and explain the flow of how users evacuate."

[1417] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1418] Step 1: Obtaining earthquake information

[1419] The server obtains earthquake information from earthquake sensors and APIs of public institutions to detect earthquakes.

[1420] Input: Earthquake sensor data and data obtained from public APIs (seismic intensity, epicenter, and time of occurrence).

[1421] Specific operation: The server sends a request to the API, receives earthquake information, and stores it in a database.

[1422] Output: Earthquake information stored in a database.

[1423] Step 2: Get user location

[1424] The device periodically sends the user's location information to the server.

[1425] Input: Location data from the user device.

[1426] Specific operation: The device uses the GPS function to obtain its current location and sends the location data to the server, which receives the data and stores it in a database.

[1427] Output: User location stored in a database.

[1428] Step 3: Collect topographic and meteorological data

[1429] The server collects topographical and meteorological data from crowdsourcing and public datasets.

[1430] Input: Topographic and meteorological data from crowdsourcing platforms and public datasets.

[1431] Specific operation: The server accesses multiple data sources, automatically obtains the latest coastline, ocean floor topography and weather data, and updates the database.

[1432] Output: Latest terrain and weather data stored in a database.

[1433] Step 4: Run the tsunami simulation

[1434] The server runs a tsunami simulation based on the collected earthquake information and topographical data.

[1435] Input: Earthquake information, topographical data, meteorological data.

[1436] Specific operation: The server uses simulation software (e.g., NEOWAVE) to calculate the tsunami's progress, wave height, arrival time, and impact area. The simulation results are saved in a database.

[1437] Output: Tsunami simulation results stored in a database.

[1438] Step 5: Conduct a risk assessment

[1439] The server assesses risk based on the tsunami simulation results and the user's location information.

[1440] Input: Tsunami simulation results, user location information.

[1441] Specific operation: The server compares the simulation results with the location information, calculates a risk score, and saves the risk assessment results in a database.

[1442] Output: Risk assessment results stored in a database.

[1443] Step 6: Send notifications and provide personalized advice

[1444] The server will send information to users based on the risk assessment results and provide personalized evacuation instructions.

[1445] Input: Risk assessment results, user location information.

[1446] What it does: The server sends notifications to high-risk users with evacuation instructions, including the nearest evacuation shelter and recommended evacuation routes.

[1447] Output: Personalized evacuation instructions and notifications sent to user devices.

[1448] (Application example 1)

[1449] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1450] Current tsunami evacuation systems struggle to provide users with optimal evacuation routes in real time when an earthquake occurs. Many systems only provide text-based notifications, making it difficult for users to take prompt and appropriate evacuation actions during an emergency. Furthermore, there is a need for systems that can provide personalized evacuation instructions based on the user's current location in real time.

[1451] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1452] In this invention, the server includes means for acquiring earthquake information, means for acquiring topographical information and meteorological data, means for running a tsunami simulation based on the earthquake information and topographical information, means for assessing a user's risk based on the simulation results, means for sending a notification to the user based on the assessment results, means for generating personalized evacuation instructions based on the user's location information, and means for displaying evacuation routes in AR on the smart device. This allows the user to be visually guided to the optimal evacuation route in real time, enabling them to take prompt and appropriate evacuation action.

[1453] "Earthquake information" refers to data such as the epicenter, seismic intensity, and time of occurrence at the time of an earthquake.

[1454] "Topographic information" refers to geographic information including coastline, ocean floor topography, and elevation data for areas where earthquakes occur.

[1455] "Weather Data" refers to information regarding weather conditions such as weather, temperature, wind speed, and air pressure.

[1456] "Tsunami simulation" refers to the process of calculating the progression, wave height, arrival time, and area of ​​impact of a tsunami based on earthquake and topographical information.

[1457] "Risk assessment" refers to the act of assessing the tsunami risk at the user's current location based on the simulation results.

[1458] "Notification" refers to the act of sending evacuation instructions and evacuation route information to users based on the results of risk assessment.

[1459] "Personalized evacuation instructions" refers to evacuation suggestions that are customized based on the user's current location and surrounding conditions.

[1460] "Smart devices" refers to devices that can connect to the Internet, such as smartphones, smart glasses, and head-mounted displays.

[1461] "AR display" refers to the act of overlaying digital information on real-world scenery using augmented reality technology.

[1462] This invention relates to a system that operates as a security service to minimize damage caused by tsunamis when an earthquake occurs. This system acquires earthquake information, topographical information, and meteorological data, and performs tsunami simulations based on these data. Furthermore, it performs risk assessments for each user based on the simulation results and promptly provides appropriate evacuation instructions.

[1463] System Configuration

[1464] The system of the present invention consists of the following main components:

[1465] 1. Earthquake detection subsystem (server)

[1466] 2. Data collection subsystem (server)

[1467] 3. Tsunami Simulation Subsystem (Server)

[1468] 4. Risk Assessment Subsystem (Server)

[1469] 5. Notification sending subsystem (server)

[1470] 6. Personalized Advice Generation Subsystem (Server)

[1471] 7. User terminal (smart device)

[1472] Earthquake detection and data collection

[1473] The server obtains real-time earthquake occurrence information by linking with multiple earthquake detection sensors and public institution APIs. For example, it uses the Japan Meteorological Agency's API to collect data such as seismic intensity, epicenter, and time of occurrence. In addition, user devices periodically send their location information to the server. The server obtains and stores the latest coastline and seabed topography data using crowdsourcing and public datasets. Weather data is also collected and used in the simulations.

[1474] Tsunami Simulation

[1475] Based on the collected seismic and topographical information, the server runs an advanced tsunami simulation, calculating the tsunami's progress, wave height, arrival time, and impact area, and storing the results in a database. The simulation is performed quickly and updated in real time.

[1476] Risk Assessment

[1477] Based on the results of the tsunami simulation, the server assesses the risk to the user's current location. The server compares the user's location information with the simulation data, and users who are determined to be at high risk are required to take appropriate action. This risk assessment is updated regularly and is based on the latest information.

[1478] Notifications and personalized advice

[1479] Based on the results of the risk assessment, the server sends notifications to high-risk users. The notifications include evacuation instructions, recommended evacuation routes, and information on the nearest evacuation shelters. High-risk users are provided with personalized evacuation instructions. The optimal evacuation route is visually displayed on the user's smart device, such as a smartphone or smart glasses, using augmented reality (AR) technology.

[1480] Specific examples

[1481] For example, if a magnitude 6 earthquake occurs in a coastal area, the server detects this information in real time and runs a tsunami simulation using the latest coastline data. The resulting simulation data is used to assess the risk to the user's area. Users in high-risk areas are notified of the optimal evacuation route and the nearest evacuation shelter. The evacuation route is visually displayed on the user's smart device through the AR function, enabling quick evacuation.

[1482] Example prompts to input to a generative AI model:

[1483] Generate a notification that provides evacuation instructions based on the user's current location in the event of a tsunami. The notification should include:

[1484] 1. Earthquake Information

[1485] 2. Risk assessment using tsunami simulation

[1486] 3. Information on the nearest evacuation shelter

[1487] 4. Best evacuation route

[1488] For example, include, "An earthquake of magnitude 6 has occurred and a tsunami is expected. The nearest evacuation shelter is AAA evacuation shelter, and the best evacuation route is BBB. Please evacuate immediately."

[1489] By feeding this prompt into a generative AI model, a message providing evacuation instructions in natural language is generated.

[1490] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1491] Step 1: Obtaining earthquake information

[1492] The server connects with multiple sensors and public APIs to obtain real-time earthquake information. This information includes the epicenter, seismic intensity, and time of occurrence. The input is data from the sensors and APIs, and the output is stored internally as earthquake information. This data is used in subsequent processing.

[1493] Step 2: Collect terrain and weather data

[1494] The server collects the latest topographic and meteorological data from crowdsourcing and public datasets. The input is data from external databases, and the output is stored internally as topographic and meteorological data. This data is used for tsunami simulation.

[1495] Step 3: Run the tsunami simulation

[1496] The server runs a tsunami simulation based on the collected earthquake and topographical information. The input is earthquake information and topographical information, and through calculations, the progress, wave height, arrival time, and affected area of ​​the tsunami are simulated. The output is saved in a database as the simulation results.

[1497] Step 4: User Risk Assessment

[1498] The server performs risk assessment based on the user's current location and simulation data. The input is the user's location information and the simulation results, and a risk score is calculated by comparing these. The output is the risk assessment result for each user, and notification content is determined based on this.

[1499] Step 5: Generate and send notifications

[1500] The server generates notifications for users based on the risk assessment results. These notifications include evacuation instructions, recommended evacuation routes, and information on the nearest evacuation shelters. The input is the risk assessment results, and the output is the notification message sent to the user. The notification content is personalized and sent to the user's smart device.

[1501] Step 6: Evacuation route guidance using AR display

[1502] The user's smart device (smart glasses or smartphone) displays an AR evacuation route based on the received notification. The input is the notification data from the server, and the output is the evacuation route displayed on the device. Using augmented reality technology, users can visually confirm the evacuation route and evacuate quickly.

[1503] The above process enables users to take swift and appropriate evacuation action in the event of an earthquake.

[1504] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1505] This invention relates to a hazard map service for minimizing damage caused by tsunamis in the event of an earthquake. In particular, it focuses on a system that combines methods for recognizing users' emotions to optimize the content and notification method of evacuation instructions. This service acquires earthquake information, topographical information, and meteorological data, and performs tsunami simulations based on this information. Furthermore, it uses an emotion engine to recognize the user's emotional state and personalize evacuation instructions.

[1506] System Configuration

[1507] The system of the present invention consists of the following main components:

[1508] 1. Earthquake detection subsystem (server)

[1509] 2. Data collection subsystem (server)

[1510] 3. Tsunami Simulation Subsystem (Server)

[1511] 4. Risk Assessment Subsystem (Server)

[1512] 5. Notification sending subsystem (server)

[1513] 6. Personalized Advice Generation Subsystem (Server)

[1514] 7. Emotion engine (server)

[1515] 8. User terminal (terminal)

[1516] Earthquake detection and data collection

[1517] The server connects with multiple earthquake detection sensors and public institution APIs to obtain earthquake occurrence information in real time. For example, it uses the Japan Meteorological Agency's API to collect data such as earthquake intensity, epicenter, and occurrence time. In addition, the device periodically sends the user's location information to the server. The server uses crowdsourcing and public datasets to obtain and store the latest coastline and seabed topography data. Weather data is also obtained in the same way and used in simulations.

[1518] Tsunami Simulation

[1519] Based on the earthquake information and the collected topographical information, the server runs an advanced tsunami simulation, which calculates the tsunami's progress, wave height, arrival time, and impact area, and stores the results in a database. The simulation is performed quickly and updated in real time.

[1520] Risk Assessment

[1521] Based on the results of the simulation, the server assesses the risk to the user's current location. Users' location information is compared with the simulation data, and users who are determined to be at high risk are required to take appropriate action based on the risk assessment. This risk assessment is updated regularly and is based on the latest information.

[1522] Emotion Recognition and Personalized Advice

[1523] The emotion engine analyzes data (e.g., voice, facial expressions, typing speed) obtained from the user's smartphone or other mobile device to recognize the user's emotional state in real time. The server takes the results of the emotion engine into consideration and optimizes the content and notification method of evacuation instructions based on the user's emotional state. For example, it provides simple and intuitive instructions to a panicked user and detailed information to a calm user.

[1524] Notifications and personalized advice

[1525] Based on the evaluation results, the server generates and sends notifications to high-risk users, including evacuation instructions, recommended evacuation routes, and information on the nearest evacuation shelters. In particular, high-risk users are provided with personalized evacuation instructions that reflect their emotional state, allowing them to take optimal evacuation actions based on their current location and surrounding environment.

[1526] Specific examples

[1527] 1. Earthquake occurs

[1528] The server receives information from a seismograph about a magnitude 6 earthquake and verifies that it occurred near the coast. The earthquake information includes the epicenter, time of occurrence, and seismic intensity.

[1529] 2. Data Collection

[1530] The server retrieves the latest coastline and bathymetry data from crowdsourcing and public datasets and updates the database.

[1531] 3. Run the simulation

[1532] Based on the acquired earthquake and topographical information, the server starts a tsunami simulation, which calculates the tsunami's progress, wave height, arrival time, and affected area.

[1533] 4. Risk Assessment

[1534] The server obtains the location information of users who are deemed high risk and evaluates whether the area will be affected by a tsunami. Users who are deemed high risk are given a high risk score.

[1535] 5. Emotion recognition

[1536] The device collects the user's voice and facial expression data, which is then analyzed by an emotion engine to assess the user's emotional state.

[1537] 6. Notice and Advice

[1538] The server generates evacuation instructions that take into account User A's emotional state and sends a personalized notification containing the evacuation instructions, including the nearest evacuation shelter and the specific evacuation route.

[1539] Users will receive a notification and can begin evacuation immediately.

[1540] This system will help users take prompt and appropriate evacuation actions in the event of an earthquake, minimizing damage from tsunamis. Furthermore, emotion recognition will enable responses based on the user's mental state, further improving the effectiveness of evacuation actions.

[1541] The processing flow will be explained below.

[1542] Step 1:

[1543] The server polls earthquake detectors and APIs of public institutions to obtain earthquake information. Specifically, it monitors the seismic intensity, epicenter, and occurrence time in real time, and triggers an earthquake detection event when an earthquake occurs that exceeds a threshold (for example, seismic intensity 5 or higher).

[1544] Step 2:

[1545] The device acquires the user's current location information using GPS and periodically sends it to the server. The user's location information must be highly accurate and is usually updated every few minutes.

[1546] Step 3:

[1547] The server collects up-to-date coastline and bathymetry data from crowdsourcing and public datasets, storing the data in a database that is updated in real time.

[1548] Step 4:

[1549] The server obtains meteorological data (e.g., tides, wind speed, air pressure, etc.) from the API of a public institution. This data is also stored and updated in the database and used during simulation.

[1550] Step 5:

[1551] The server receives a trigger from the earthquake detection subsystem and starts a tsunami simulation. It uses earthquake information and collected topographical and meteorological data to calculate the tsunami's progress, arrival time, wave height, and affected area. The simulation results are stored in a database.

[1552] Step 6:

[1553] The server retrieves the user's current location information from the database and compares it with the results of the tsunami simulation. If a specific user is in a high-risk area, it calculates the user's risk score and generates an evaluation result for each user.

[1554] Step 7:

[1555] The device collects the user's voice and facial expression data and sends it to the emotion engine. Voice data is obtained using a microphone, and facial expression data is obtained using a camera.

[1556] Step 8:

[1557] The emotion engine analyzes the received voice and facial expression data to recognize the user's emotional state. For example, it analyzes the tone of voice and facial features to determine whether the user is panicked or calm.

[1558] Step 9:

[1559] The server receives the results of the emotion engine and optimizes the content and notification method of evacuation instructions based on the user's emotional state, for example, providing simple and intuitive instructions to a panicked user and detailed information to a calm user.

[1560] Step 10:

[1561] The server generates evacuation instructions that take into account the emotional state of the user and sends them to the devices of high-risk users. The server quickly delivers information using push notifications and SMS. It also logs whether the transmission was successful.

[1562] Step 11:

[1563] The device receives the notification and displays an alert to the user, which is visually and audibly communicated to the user so that the user can see it immediately.

[1564] Step 12:

[1565] The user checks the notification and follows the instructions to begin evacuation. The user heads to the nearest evacuation shelter, selects a safe route, and moves quickly. During evacuation, the user can check updates and additional instructions on their smartphone.

[1566] This is how the system's process progresses, supporting users from the moment an earthquake occurs to the moment they decide to evacuate safely. In addition to distributed data collection and advanced simulation, emotion recognition enables optimal responses for each user.

[1567] Example 2

[1568] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1569] In order to minimize damage caused by tsunamis when an earthquake occurs, quick and accurate evacuation instructions are necessary. However, conventional systems have difficulty providing appropriate evacuation instructions that take into account the individual situation and emotional state of the user. This has led to problems such as users panicking and delaying evacuation behavior.

[1570] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for acquiring earthquake information, means for acquiring topographical information and meteorological data, means for executing a tsunami simulation based on the earthquake information and topographical information, means for assessing a user's risk based on the simulation results, means for sending a notification to the user based on the assessment results, means for recognizing the user's emotional state, and means for generating personalized evacuation instructions based on the recognized emotional state. This makes it possible to quickly provide appropriate evacuation instructions according to the user's emotional state in the event of an earthquake, and support the user's evacuation behavior.

[1571] "Earthquake information" is detailed data about earthquakes, including the magnitude, epicenter, and time of occurrence of the earthquake.

[1572] "Topographic information" is detailed data about the terrain, including coastline and undersea topography data.

[1573] "Weather data" refers to data related to weather, such as precipitation, wind speed, and temperature.

[1574] The "means for performing a tsunami simulation" is a means for calculating the progress, wave height, arrival time, and range of impact of a tsunami based on earthquake information and topographical information.

[1575] The "means for assessing the user's risk" is a means for comparing the results of a tsunami simulation with the user's current location to assess the possibility that the user will be affected by a tsunami.

[1576] The "means for sending notifications to users based on the assessment results" refers to the means for sending evacuation instructions or warnings to users based on the users' risk assessment.

[1577] The "means for recognizing the user's emotional state" refers to a means for analyzing data such as the user's voice, facial expression, typing speed, etc., and recognizing the user's emotional state in real time.

[1578] The "means for generating personalized evacuation instructions" is a means for creating evacuation instructions that are best suited to the user's situation based on the recognized emotional state.

[1579] "Crowdsourcing" is a method of collecting information from a large number of Internet users.

[1580] A "public dataset" is a collection of highly reliable data provided by governments, public institutions, etc.

[1581] "Real-time" refers to information being updated immediately without delay.

[1582] This invention is a system for providing hazard maps to minimize damage caused by tsunamis during earthquakes. The system acquires earthquake, topographical, and meteorological data, and performs tsunami simulations based on this information. It also recognizes the user's emotional state and provides personalized evacuation instructions.

[1583] System Configuration

[1584] The system of the present invention consists of the following main components:

[1585] 1. Earthquake detection subsystem (server)

[1586] 2. Data collection subsystem (server)

[1587] 3. Tsunami Simulation Subsystem (Server)

[1588] 4. Risk Assessment Subsystem (Server)

[1589] 5. Notification sending subsystem (server)

[1590] 6. Personalized Advice Generation Subsystem (Server)

[1591] 7. Emotion engine (server)

[1592] 8. User terminal (terminal)

[1593] Earthquake detection and data collection

[1594] The server connects with multiple sensors and public institution APIs to detect earthquakes and obtains earthquake occurrence information (e.g., seismic intensity, epicenter, and time of occurrence) in real time. For example, data is collected using the Japan Meteorological Agency's API. The device periodically sends the user's location information to the server. The server uses crowdsourcing and public datasets to obtain and store the latest coastline and seabed topography data. Weather data is also obtained in the same way and used in simulations.

[1595] Tsunami Simulation

[1596] The server runs an advanced tsunami simulation based on earthquake information and collected topographical information. This simulation calculates the tsunami's progress, wave height, arrival time, and impact area. The simulation results are stored in a database and are continuously updated. For example, GEOWAVE may be used as the tsunami simulation software.

[1597] Risk Assessment

[1598] The server evaluates the risk to the user's current location based on the simulation results. By comparing the user's location information with the simulation data, users who are deemed to be at high risk will be given special evacuation instructions. This risk assessment is updated in real time.

[1599] Emotion Recognition and Personalized Advice

[1600] The emotion engine analyzes data such as the user's voice, facial expressions, and typing speed to recognize their emotional state in real time. The server then uses the results of this emotion engine to personalize the evacuation instructions and notification method based on the user's emotional state. For example, it provides simple and intuitive instructions to a panicked user and detailed information to a calm user.

[1601] Notification and Action Support

[1602] The server generates optimal evacuation instructions based on the risk assessment and emotion recognition results, and sends a notification to the user's device. This notification includes evacuation instructions, recommended evacuation routes, and information on the nearest evacuation shelter. The user receives this notification and promptly begins appropriate evacuation actions.

[1603] Specific examples

[1604] 1. Earthquake occurs

[1605] The server receives information from a seismograph about a magnitude 6 earthquake and verifies that it occurred near the coast. The earthquake information includes the epicenter, time of occurrence, and seismic intensity.

[1606] 2. Data Collection

[1607] The server retrieves the latest coastline and bathymetry data from crowdsourcing and public datasets and updates the database.

[1608] 3. Run the simulation

[1609] Based on the acquired earthquake and topographical information, the server starts a tsunami simulation, which calculates the tsunami's progress, wave height, arrival time, and affected area.

[1610] 4. Risk Assessment

[1611] The server obtains the location information of users who are deemed high risk and evaluates whether the area will be affected by a tsunami. Users who are deemed high risk are given a high risk score.

[1612] 5. Emotion recognition

[1613] The device collects the user's voice and facial expression data, which is then analyzed by an emotion engine to assess the user's emotional state.

[1614] 6. Notice and Advice

[1615] The server generates evacuation instructions that take into account User A's emotional state and sends a personalized notification containing the evacuation instructions, including the nearest evacuation shelter and the specific evacuation route.

[1616] Users will receive a notification and can begin evacuation immediately.

[1617] Prompt Sentence Examples

[1618] "If you live in a coastal area, please suggest multiple scenarios for what evacuation route you should take in the event of a magnitude 6 earthquake, depending on your emotional state."

[1619] This system enables users to take prompt and appropriate evacuation actions in the event of an earthquake, minimizing damage from tsunamis. Furthermore, emotion recognition makes it possible to respond according to the user's mental state, further improving the effectiveness of evacuation actions.

[1620] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1621] Step 1: Earthquake detection

[1622] The server collects earthquake information from seismometers and public institutions (e.g., Japan Meteorological Agency API) to detect earthquakes.

[1623] Input: Seismic intensity, epicenter, time of occurrence

[1624] How it works: The server polls data from the seismometer and generates an alert if an earthquake of magnitude 5 or greater is detected.

[1625] Output: Earthquake alerts and data on seismic intensity, epicenter, and occurrence time

[1626] Step 2: Data collection

[1627] The device periodically transmits the user's location information to the server.

[1628] Input: GPS data

[1629] How it works: The device receives a GPS signal and sends location information to the server at specified intervals.

[1630] Output: Current location of the user

[1631] Step 3: Update terrain and weather data

[1632] The server uses crowdsourcing and public datasets to obtain the latest terrain and weather data and update the database.

[1633] Input: Crowdsourced data, public datasets

[1634] How it works: The server periodically checks for updates to the dataset and automatically updates the database if new data is found.

[1635] Output: Latest terrain and weather data

[1636] Step 4: Run the tsunami simulation

[1637] The server runs a tsunami simulation based on the collected earthquake and topographical information.

[1638] Input: Earthquake information, topographical information

[1639] How it works: The server launches simulation software (e.g., GEOWAVE), inputs data, and calculates the tsunami's progress, wave height, arrival time, and area of ​​impact.

[1640] Output: Tsunami simulation results (wave height, arrival time, affected area)

[1641] Step 5: Risk assessment

[1642] The server evaluates the risk to the user's current location based on the simulation results.

[1643] Input: User location information, tsunami simulation results

[1644] How it works: The server compares the user's location with simulated data to identify users in high-risk areas and calculate a risk score.

[1645] Output: Risk assessment result (risk score)

[1646] Step 6: Collect and analyze emotion data

[1647] The device collects the user's voice and facial expression data, which is analyzed by an emotion engine to recognize the user's emotional state.

[1648] Input: Voice data, facial expression data

[1649] How it works: The device uses a microphone and camera to collect voice and facial expression data and sends it to a server. The emotion engine analyzes the data and evaluates the user's emotional state.

[1650] Output: Emotion evaluation result (e.g., panicked, calm)

[1651] Step 7: Generate personalized evacuation instructions

[1652] The server takes into account the results of the emotion engine to generate evacuation instructions based on the user's emotional state.

[1653] Input: Emotion assessment results, risk assessment results

[1654] How it works: Based on the analysis results of the emotion engine, the server selects different evacuation instruction templates and customizes evacuation instructions that best suit the user's situation.

[1655] Output: Personalized evacuation instructions

[1656] Step 8: Send notification

[1657] The server notifies the user of the generated evacuation instructions.

[1658] Input: personalized evacuation instructions

[1659] What it does: The server generates the notification and pushes it to the user's device. It also saves it in the notification history so the user can review it later.

[1660] Output: Evacuation notice sent to user

[1661] Step 9: User Actions

[1662] Users will receive a notification and will be able to quickly take action to evacuate.

[1663] Input: Evacuation order notice

[1664] Action: The user confirms the notification and follows the evacuation route provided. During the evacuation, the device automatically updates its location and sends it to the server.

[1665] Output: Evacuation action start and progress

[1666] In this way, the system can provide users with quick and accurate evacuation instructions, minimizing damage during disasters. Furthermore, personalized instructions based on emotion recognition can respond according to the user's mental state.

[1667] (Application example 2)

[1668] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1669] Conventional earthquake and tsunami evacuation systems do not take into account the user's psychological state when issuing evacuation instructions, which can lead to panic and confusion. It is particularly difficult for users to take appropriate evacuation actions when an earthquake occurs while they are shopping in a physical store. Therefore, it is necessary to optimize personalized evacuation instructions and notification methods according to each user's emotional state so that they can evacuate quickly and effectively.

[1670] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for acquiring earthquake information, means for acquiring topographical information and meteorological data, means for running a tsunami simulation based on the earthquake information and topographical information, means for assessing the user's risk based on the simulation results, means for sending a notification to the user based on the assessment results, means for generating personalized evacuation instructions based on the user's location information, means for recognizing the user's emotional state using an emotion engine, and means for optimizing the content of the evacuation instructions and the notification method based on the user's emotional state. This enables the user to take evacuation action quickly and effectively when they encounter an earthquake while shopping in a physical store.

[1671] "Means for acquiring earthquake information" refers to a system or device for detecting and collecting information on earthquake occurrence.

[1672] The "means for acquiring topographical information and meteorological data" is a system or device for collecting topographical data and meteorological information.

[1673] "Means for performing tsunami simulations based on earthquake information and topographical information" refers to a system or device for predicting and calculating the occurrence and progression of tsunamis using earthquake and topographical data.

[1674] "Means for assessing user risk based on simulation results" refers to a system or device that determines the risk level of the area where the user is located based on the results of a tsunami simulation.

[1675] "Means for sending a notification to a user based on the assessment result" refers to a system or device that sends an appropriate notification to a user depending on the result of the risk assessment.

[1676] "Means for generating personalized evacuation instructions based on the user's location information" refers to a system or device that generates individual evacuation instructions tailored to each situation based on the user's current location.

[1677] "Means for recognizing a user's emotional state using an emotion engine" refers to a system or device that uses emotion recognition technology to analyze and determine a user's current emotional state.

[1678] The "means for optimizing the content and notification method of evacuation instructions based on the emotional state of the user" is a system or device that generates evacuation instructions in the optimal format and content, taking into account the emotional state of the user.

[1679] This invention is a hazard map service for minimizing damage caused by tsunamis when an earthquake occurs. The system acquires earthquake information, topographical information, and meteorological data, and based on this information, performs tsunami simulations to assess risk and provides optimal evacuation instructions to users. Furthermore, it uses an emotion engine to recognize the user's emotional state and optimizes notification content according to that emotional state, helping users take quick and effective evacuation actions.

[1680] System Configuration

[1681] The system consists of the following main components:

[1682] 1. Earthquake detection subsystem (server): Detects earthquakes and collects earthquake information in real time.

[1683] 2. Data collection subsystem (server): Collects and updates terrain and meteorological data.

[1684] 3. Tsunami simulation subsystem (server): Simulates tsunami progression, wave height, arrival time, and impact area based on earthquake and topographical data.

[1685] 4. Risk assessment subsystem (server): Evaluates the user's risk based on the results of the tsunami simulation.

[1686] 5. Notification sending subsystem (server): Sends appropriate notifications to users based on the results of risk assessment.

[1687] 6. Personalized advice generation subsystem (server): Generates individual evacuation instructions based on the user's location and emotional state.

[1688] 7. Emotion Engine (Server): Recognizes the user's emotional state in real time.

[1689] 8. User terminal (terminal): Transmits the user's location information and emotional state data to the server and receives evacuation instructions.

[1690] Earthquake detection and data collection

[1691] The server connects with multiple sensors and public institution APIs to detect earthquakes and obtains earthquake occurrence information in real time. For example, it uses the Japan Meteorological Agency's API to collect data such as earthquake intensity, epicenter, and occurrence time. Furthermore, the device periodically sends location information to the server, allowing the server to determine the user's current location. It also uses crowdsourcing and public datasets to collect and store the latest topographical and meteorological data.

[1692] Tsunami Simulation

[1693] Based on the earthquake information and the collected topographical information, the server runs an advanced tsunami simulation, which calculates the tsunami's progress, wave height, arrival time and impact area, and stores the results in a database. The simulation is performed quickly and updated in real time.

[1694] Risk Assessment

[1695] Based on the results of the simulation, the server assesses the risk to the user's current location. The server compares the user's location information with the simulation data, and if a user is determined to be at high risk, they will be required to take appropriate action based on the risk assessment. This risk assessment is updated regularly and is based on the latest information.

[1696] Emotion Recognition and Personalized Advice

[1697] The emotion engine analyzes data (e.g., voice, facial expressions, typing speed) obtained from the user's smartphone or other mobile device to recognize the user's emotional state in real time. The server takes the results of the emotion engine into consideration and optimizes the content and notification method of evacuation instructions based on the user's emotional state. For example, it provides simple and intuitive instructions to a panicked user and detailed information to a calm user.

[1698] Notifications and personalized advice

[1699] Based on the risk assessment results, the server generates and sends notifications to high-risk users, including evacuation instructions, recommended evacuation routes, and information on the nearest evacuation shelters. In particular, high-risk users are provided with personalized evacuation instructions that reflect their emotional state, allowing them to take optimal evacuation actions based on their current location and surrounding environment.

[1700] Specific examples

[1701] If an earthquake occurs while a user is shopping in a physical store, real-time earthquake information is acquired to confirm that the user is in a coastal area. At the same time, a tsunami simulation is run to identify the user's area as high-risk. The emotion engine analyzes the user's facial expression data and determines that the user is in a state of panic.

[1702] When this happens, the server will send a notification to the user:

[1703] "High-risk area. Evacuate immediately to the nearest evacuation center. Please remain calm and follow simple instructions."

[1704] Example prompt sentence:

[1705] "Create a notification for tsunami evacuation instructions and personalized evacuation routes using emotion recognition. Earthquake information is as follows: Epicenter, Magnitude 6, Occurrence time 18:00. User is in a panic and in a high-risk area."

[1706] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1707] Step 1:

[1708] The server acquires earthquake information. First, the server acquires real-time earthquake occurrence information through the API of a public institution (e.g., the Japan Meteorological Agency API), collecting data such as seismic intensity, epicenter, and time of occurrence. The input data is data distributed by earthquake sensor networks and public institutions, and the output is detailed information about the earthquake.

[1709] Step 2:

[1710] The server obtains terrain information and weather data. The server obtains and stores the latest coastline and bathymetry data, as well as weather data, using crowdsourcing or public datasets. The input data is data from the terrain database and weather information API, and the output is updated terrain and weather data.

[1711] Step 3:

[1712] The server runs the tsunami simulation. It performs an advanced tsunami simulation based on earthquake information and collected topographical information. Specifically, it simulates the tsunami's progress, wave height, arrival time, and affected area, and stores the results in a database. The input data are earthquake information and topographical information, and the output is the simulation results.

[1713] Step 4:

[1714] The server performs risk assessment. Based on the simulation results, the server assesses the risk for the user's current location. Specifically, it compares the user's location information with the simulation data and determines whether the area is high-risk. The input data is the user's location information and the tsunami simulation results, and the output is the risk assessment results.

[1715] Step 5:

[1716] The device collects emotional state data. Emotional data such as the user's voice, facial expression, and typing speed are collected from smartphones and other mobile devices and sent to a server. The input data is the user's emotional expression data, and the output is the data sent to the server.

[1717] Step 6:

[1718] The server recognizes the emotional state using an emotion engine. Based on the received emotional state data, the server uses the emotion engine to analyze and determine the user's emotional state (e.g., panic, calm, etc.) in real time. The input data is emotional expression data, and the output is the determined emotional state.

[1719] Step 7:

[1720] The server generates personalized evacuation instructions. It integrates the emotion engine's judgment results and risk assessment results to generate optimal evacuation instructions according to the user's emotional state and risk level. As a specific example, it provides simple and intuitive instructions to a panicked user, and a detailed evacuation route to a calm user. The input data are the emotional state judgment results and risk assessment results, and the output is optimized evacuation instructions.

[1721] Step 8:

[1722] The server sends a notification to the user. Based on the optimized evacuation instructions, the server sends a notification to the user's device. The notification content includes specific evacuation instructions, recommended evacuation routes, and information about the nearest evacuation shelter. The input data is the optimized evacuation instructions, and the output is a notification to the user.

[1723] Specific examples

[1724] If an earthquake occurs while a user is shopping in a physical store, earthquake information is obtained in real time. At the same time, a tsunami simulation is run and the user's area is identified as high-risk. The emotion engine analyzes the user's facial expression data and recognizes that the user is in a state of panic. In this case, the server sends the following notification to the user:

[1725] "High-risk area. Evacuate immediately to the nearest evacuation center. Please remain calm and follow simple instructions."

[1726] Example prompt sentence:

[1727] "Create a notification for tsunami evacuation instructions and personalized evacuation routes using emotion recognition. Earthquake information is as follows: Epicenter, Magnitude 6, Occurrence time 18:00. User is in a panic and in a high-risk area."

[1728] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1729] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1730] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1731] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1732] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1733] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1734] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1735] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1736] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1737] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1738] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1739] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1740] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1741] 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.

[1742] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1743] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1744] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific processing may be a single processor.

[1745] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1746] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1747] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that un...

Claims

1. A means for acquiring earthquake information; means for acquiring topographical information and meteorological data; means for performing a tsunami simulation based on earthquake information and topographical information; A means of assessing user risk based on simulation results; a means for sending a notification to the user based on the evaluation results; a means for generating personalized evacuation instructions based on the user's location; A system including:

2. The system of claim 1 , further comprising means for updating the terrain information using crowdsourcing or public datasets.

3. 10. The system of claim 1, further comprising means for periodically updating the user's location information and providing real-time risk assessment.

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