System

Abstract

A system is provided.SOLUTION: This system includes a server means including a database for storing past weather data, disaster data and topographical data, a simulation means for executing simulation on the basis of a condition designated by a user, a result generation means for generating a simulation result and transmitting it to the terminal of the user, and a terminal means for receiving the input of the user and transmitting it to the server.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] Conventional hazard maps provided by local governments are useful for determining the possibility of disasters occurring, but they have the problem of not being able to provide a detailed understanding of the extent to which a disaster will occur under specific conditions. This means that when making important decisions such as choosing a place to live or opening a business, users are unable to fully understand the risks of future disasters, making it difficult for them to make accurate decisions. [Means for solving the problem]

[0005] The present invention provides a system including a server including a database storing past weather data, disaster data, and topographical data; a simulation unit that executes simulations based on user-specified conditions; a result generation unit that generates simulation results and transmits them to the user's terminal; and a terminal unit that receives user input and transmits them to the server. This system enables users to check in detail the disaster risk under specific conditions when choosing a place to live or start a business, enabling them to make accurate decisions. The system also includes a function for visually displaying the simulation results, allowing users to intuitively understand the results, as the user-specified conditions include rainfall, wind speed, and location.

[0006] The "server means" is a computer system that stores past weather data, disaster data, and topographical data and includes a database for executing simulations.

[0007] The "simulation means" is a function that simulates a disaster occurrence situation using data stored in the server means, based on conditions specified by the user.

[0008] The "result generation means" is a function that compiles the results generated by the simulation means and transmits them to the user's terminal.

[0009] "Terminal means" refers to a device or software that has the function of receiving input from a user and transmitting the input data to server means.

[0010] "User-specified conditions" are parameters such as rainfall, wind speed, and location that the user inputs to perform a simulation.

[0011] The "visual display means" is a function for visually displaying the simulation results on the terminal means using graphs, charts, etc. [Brief explanation of the drawings]

[0012] [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

[0013] 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.

[0014] First, the terms used in the following description will be explained.

[0015] 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).

[0016] 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.

[0017] 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.

[0018] 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.

[0019] 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."

[0020] [First embodiment]

[0021] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0022] 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.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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.

[0028] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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."

[0033] The present invention is a disaster simulation system that supports important decisions regarding residence or business start-up. This system operates in cooperation with three parties: a server, a terminal, and a user. A specific implementation method for this system is described below.

[0034] Data collection and storage

[0035] The server periodically retrieves past weather, disaster, and terrain data from reliable external data sources and stores it in a database, allowing simulations to always be based on the latest information.

[0036] Receiving user input

[0037] The user enters the desired conditions (e.g., rainfall, wind speed, location) into the application's input form. This input data is converted to JSON format on the user's device and sent to the server.

[0038] Running the simulation

[0039] Based on the request data received from the user, the server uses its internal simulation engine to simulate disaster risks under specified conditions, such as calculating the risk of flooding or landslides at a specified location under conditions of 100 mm of rainfall and 10 m / s wind speed.

[0040] Generate and send results

[0041] The server compiles the data generated as a result of the simulation (e.g., "Flood risk: high," "Landslide risk: medium," etc.) and sends it to the user's device in JSON format. The result generation means then properly formats and transmits this data.

[0042] Visual display of results

[0043] The terminal displays the simulation results received from the server to the user in a visually easy-to-understand format (for example, graphs or charts), allowing the user to intuitively understand the results and make decisions more easily.

[0044] Specific examples

[0045] For example, a user is considering a certain area in Tokyo as a new place to live and wants to investigate the disaster risk under the conditions of 100 mm of rainfall and 10 m / s wind speed based on past weather data.

[0046] 1. The user enters "Rainfall: 100 mm", "Wind speed: 10 m / s", and "Location: Tokyo" into the application form.

[0047] 2. The terminal sends the entered data to the server in JSON format.

[0048] 3. The server runs a simulation based on the received input data, resulting in "Flood risk: High" and "Landslide risk: Medium."

[0049] 4. The server sends the simulation results in JSON format to the user's device.

[0050] 5. The terminal visually displays the received simulation results.

[0051] This system allows users to gain a detailed understanding of disaster risks under specific conditions and make accurate decisions when choosing a place to live or starting a business.

[0052] The processing flow will be explained below.

[0053] Step 1:

[0054] The server periodically retrieves past weather, disaster, and terrain data from reliable external data sources and stores it in a database, enabling simulations to be performed based on the most up-to-date information.

[0055] Step 2:

[0056] The user opens the application and inputs the specified conditions, such as rainfall, wind speed, location, and other parameters, into an input form.

[0057] Step 3:

[0058] The terminal collects the parameters entered by the user and converts them into JSON format for sending to the server, which then sends the converted data to the server as an HTTP request.

[0059] Step 4:

[0060] The server receives the JSON-formatted data sent from the device, analyzes it, and passes it to the simulation engine, which then retrieves the necessary data from a past database and runs a disaster simulation under the specified conditions.

[0061] Step 5:

[0062] The server uses the results obtained from the simulation engine to generate detailed simulation results such as flood risk and landslide risk, and this data is compiled in JSON format.

[0063] Step 6:

[0064] The server sends the generated simulation results to the terminal, which are formatted in a way that is intuitively understandable to the user.

[0065] Step 7:

[0066] The terminal analyzes the simulation results received from the server and displays them to the user in a visually easy-to-understand format (e.g., graphs and charts), which helps the user intuitively understand the disaster risk under specific conditions.

[0067] As a specific example, a user is considering a certain area in Tokyo as a new place to live.

[0068] Step 1:

[0069] The server periodically retrieves weather data, disaster data, and terrain data from external reliable data sources and stores them in a database.

[0070] Step 2:

[0071] The user opens the application and enters the specified conditions: "Rainfall: 100 mm," "Wind speed: 10 m / s," and "Location: Tokyo."

[0072] Step 3:

[0073] The terminal collects the parameters entered by the user, converts them into JSON format, and sends them to the server as an HTTP request.

[0074] Step 4:

[0075] The server receives the data sent from the device and passes it to the simulation engine for analysis, using past weather data, disaster data, and topographical data.

[0076] Step 5:

[0077] The server generates the simulation results "Flood risk: high" and "Landslide risk: medium" and summarizes them in JSON format.

[0078] Step 6:

[0079] The server transmits the generated simulation results to the terminal.

[0080] Step 7:

[0081] The device analyzes the simulation results received from the server and displays them to the user in a visually easy-to-understand format, allowing the user to intuitively understand the disaster risk under specified conditions and make decisions about where to live.

[0082] Example 1

[0083] 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."

[0084] Currently, there is a lack of systems that can predict and visually present detailed risks based on past weather and disaster data when selecting a place to live or start a business. There is also a need for a method that can flexibly perform simulations based on specific conditions specified by the user and provide the results quickly and in an easy-to-understand manner.

[0085] 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.

[0086] In this invention, the server includes means for periodically obtaining past weather data, disaster data, and topographical data from an external reliable data source and storing the data in a database, means for inputting conditions specified by a user into an application input form, converting the data into JSON format, and sending the JSON format to the server, means for simulating disaster risk using an internal simulation engine based on the request data received from the user, means for sending the simulation results in JSON format to a terminal, and means for displaying the simulation results received from the server to the user in a visually easy-to-understand format. This allows the user to predict disaster risk under specific conditions in detail and obtain the results in a visually easy-to-understand format.

[0087] A "server" is a device or system that periodically retrieves data from external data sources and stores it in a database.

[0088] A "database" is a system for storing and managing acquired past weather data, disaster data, and topographical data.

[0089] A "terminal" is a device or system through which a user inputs simulation conditions and transmits data to a server.

[0090] "JSON format" is an abbreviation for JavaScript Object Notation, and is a lightweight data exchange format for structuring and representing data.

[0091] A "simulation engine" is a program or system for calculating and predicting disaster risks based on conditions received from a user.

[0092] "Simulation" is the process of calculating disaster risk under specified conditions and predicting the consequences.

[0093] The "result generation means" is a system for generating simulation results in JSON format and sending them to the user's terminal.

[0094] The "visual display means" is a system for displaying the received simulation results in the form of graphs, charts, etc. so that the user can intuitively understand them.

[0095] "Weather data" refers to past data related to weather, specifically including rainfall and wind speed.

[0096] "Disaster data" refers to data related to natural disasters that have occurred in the past, including floods and landslides.

[0097] "Topographic data" refers to data relating to geographical features, including information on land elevation, geology, etc.

[0098] The present invention is a disaster simulation system that supports important decisions regarding residence or business start-up. This system operates in cooperation with three parties: a server, a terminal, and a user. A specific implementation method for this system is described below.

[0099] Data collection and storage

[0100] The server periodically retrieves historical weather, disaster, and terrain data from external, reliable data sources and stores it in a database. The server is configured with software that executes API calls using Python or scripts. The database is a relational database such as PostgreSQL.

[0101] Receiving user input

[0102] The user enters the desired conditions (for example, rainfall, wind speed, and location) into the application's input form. This data is converted to JSON format on the user's device. The device uses HTML and JavaScript to collect the form data and send an HTTP POST request to the server. Conditions entered by the user include "rainfall: 100 mm," "wind speed: 10 m / s," and "location: Tokyo."

[0103] Running the simulation

[0104] Based on the request data received from the user, the server uses its internal simulation engine to simulate disaster risk under specified conditions. For example, a simulation script implemented in Python is used to calculate flood risk and landslide risk, utilizing numerical analysis libraries such as NumPy and SciPy. Information such as rainfall, wind speed, and location is used as simulation conditions.

[0105] Generate and send results

[0106] The server compiles the data generated as a result of the simulation (e.g., "Flood risk: high," "Landslide risk: medium," etc.) and sends it to the user's device in JSON format. The result generation means then properly formats and transmits this data.

[0107] Visual display of results

[0108] The terminal receives the simulation results from the server and displays them to the user in a visually easy-to-understand format (for example, graphs or charts). The visual display uses the JavaScript D3.js library, which allows the user to intuitively understand the results and make decisions more easily.

[0109] Specific examples

[0110] For example, a user is considering a certain area in Tokyo as a new place to live. He wants to investigate the disaster risk based on historical weather data under the condition of 100 mm of rainfall and 10 m / s wind speed. Here is a specific example:

[0111] 1. The user enters "Rainfall: 100 mm", "Wind speed: 10 m / s", and "Location: Tokyo" into the application form.

[0112] 2. The terminal sends the entered data to the server in JSON format.

[0113] 3. The server runs a simulation based on the received input data, resulting in "Flood risk: High" and "Landslide risk: Medium."

[0114] 4. The server sends the simulation results in JSON format to the user's device.

[0115] 5. The terminal visually displays the received simulation results.

[0116] This system allows users to gain a detailed understanding of disaster risks under specific conditions and make accurate decisions when choosing a place to live or starting a business.

[0117] An example of a prompt is "Simulate the disaster risk in Tokyo with 100 mm of rainfall and a wind speed of 10 m / s."

[0118] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0119] Step 1:

[0120] Data collection and storage

[0121] The server periodically retrieves historical weather, disaster, and terrain data from external, reliable data sources. Specifically, the server sends API requests to retrieve the data and stores it in a database. The API endpoint and authentication information are required as input, and the collected data is stored in the database as output. For example, a scheduled task using a Python script pulls the data from the API and stores it in PostgreSQL.

[0122] Step 2:

[0123] Receiving user input

[0124] The user enters the simulation conditions (rainfall, wind speed, location) into the application's input form. This input data is converted into JSON format on the user's device. Specifically, the user enters data into the form through a web browser, and JavaScript collects it and converts it into JSON format. The input includes the rainfall, wind speed, and location entered by the user, and JSON format data is generated as the output. For example, the user enters "rainfall: 100 mm," "wind speed: 10 m / s," and "location: Tokyo."

[0125] Step 3:

[0126] Running the simulation

[0127] The server uses a simulation engine to simulate disaster risk based on the request data received from the user. Specifically, it parses the received JSON data and passes it to the simulation engine for calculations. The inputs required are the JSON data sent by the user, the internal simulation engine, and past weather and topographical data. The output is the simulation results (e.g., "Flood risk: high," "Landslide risk: medium"). For example, a simulation script implemented in Python calculates risk using libraries such as NumPy and SciPy.

[0128] Step 4:

[0129] Generate and send results

[0130] The server sends the simulation results in JSON format to the user's device. Specifically, it formats the simulation results appropriately and sends them to the user's device as an HTTP response. The input is the simulation calculation result, and the output is formatted JSON data sent as an HTTP response. For example, the JSON data generated as a result of the simulation is sent to the user's device.

[0131] Step 5:

[0132] Visual display of results

[0133] The terminal displays the simulation results received from the server to the user in a visually easy-to-understand format. Specifically, it uses the JavaScript D3.js library to display the results in graphs and charts. The input is the simulation results in JSON format sent from the server, and the output is displayed in a visually easy-to-understand format. For example, a "high" flood risk or a "medium" landslide risk is displayed as a graph in the browser.

[0134] An example prompt based on a generative AI model is "Simulate the disaster risk in Tokyo with 100 mm of rainfall and a wind speed of 10 m / s."

[0135] (Application example 1)

[0136] 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."

[0137] Conventional disaster simulation systems mainly make predictions based on past data, making it difficult to adapt to changing disaster risks in real time. Furthermore, when users grasp the disaster risk under specific conditions, they do not provide enough information to take prompt and appropriate action. This makes it difficult for users to take appropriate evacuation actions in response to the predicted risks.

[0138] 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.

[0139] In this invention, the server includes database means for storing past weather data, disaster data, and topographical data, simulation means for executing simulations based on conditions specified by the user, result generation means for generating simulation results and sending them to the user's terminal, terminal means for receiving user input and sending them to the server, and monitoring means for monitoring disaster risks in real time and sending alerts. This allows the system to not only support users in making residential and business decisions, but also enable immediate responses to disaster risks that change in real time.

[0140] The "database means" is a system for storing past weather data, disaster data, and topographical data.

[0141] The "simulation means" is a function for predicting disaster risks based on conditions specified by the user and executing simulations.

[0142] The "result generation means" is a function for generating simulation results and transmitting the results to the user's terminal.

[0143] "Terminal means" refers to equipment or applications that receive user input and transmit that information to the server.

[0144] "Monitoring means" refers to the function of monitoring disaster risks in real time and sending alerts as necessary.

[0145] "Rainfall" refers to the amount of rain that falls in a particular location within a certain period of time.

[0146] "Wind speed" refers to the speed of wind recorded at a particular location over a given period of time.

[0147] "Location" refers to a particular geographic area for which a user specifies that they want to know the disaster risk.

[0148] "Visual display" refers to presenting the simulation results in a form such as a graph or chart in an easy-to-read format for the user.

[0149] "Server" refers to a central computing device that stores data, runs simulations, and generates and transmits results.

[0150] "User" refers to an individual or entity that uses the System to simulate disaster risks and receives the results.

[0151] This invention is a disaster risk monitoring system that simulates disaster risks using past weather data, disaster data, and topographical data, and provides the results to users in real time. This system operates in cooperation with a server, a terminal, and a user, and is configured as follows:

[0152] The server periodically retrieves past weather, disaster, and terrain data from external, reliable data sources and stores it in a database. This database serves as the basis for running accurate simulations based on the latest information. The server also includes a simulation tool that simulates disaster risk based on user-specified conditions (rainfall, wind speed, location, etc.).

[0153] Users input their desired conditions through an application on their device. This input data is converted to JSON format and sent to the server. The server then runs a simulation based on the received request data and calculates the disaster risk under the specified conditions. For example, it evaluates the risk of flooding or landslides under conditions of 100 mm of rainfall and 10 m / s wind speed at a specific location.

[0154] Simulation results are sent from the server to the terminal in JSON format. This result generation method allows the simulation results to be properly formatted and quickly sent to the user's terminal. The terminal then displays the results to the user in a visually easy-to-understand format. This allows the user to intuitively understand the results and respond quickly to various disaster risks.

[0155] Furthermore, the system includes a monitoring means that monitors disaster risks in real time and sends alerts to user devices as necessary, allowing users to immediately grasp the risks and take appropriate measures even in the event of a sudden disaster.

[0156] As a concrete example, if a user is considering an area for a new home and wants to explore the disaster risk in the presence of 100 mm of rainfall and 10 m / s wind speed, they can generate a prompt like this:

[0157] Example prompt:

[0158] What is the risk of disaster in Tokyo when rainfall is 100mm and wind speed is 10m / s?

[0159] Based on this prompt, the system monitors disaster risks in real time and provides users with simulation results, such as visually displaying high flood risk or medium landslide risk. In this way, users can gain a detailed understanding of disaster risks under specific conditions and make accurate decisions when choosing a place to live or start a business.

[0160] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0161] Step 1:

[0162] The user opens the application on their device and inputs the specified conditions (rainfall, wind speed, location). The input data is in the format of "rainfall: 100 mm," "wind speed: 10 m / s," "location: Tokyo," etc. This data is converted into JSON format and sent to the server.

[0163] Input: User-specified conditions (rainfall, wind speed, location)

[0164] Output: Request data in JSON format

[0165] Step 2:

[0166] The server analyzes the request data received from the terminal and extracts the relevant weather, disaster, and terrain data from the database, thereby preparing a simulation based on the specified conditions.

[0167] Input: Request data in JSON format

[0168] Output: Weather data, disaster data, and topographical data required for simulation

[0169] Step 3:

[0170] The server's simulation tool uses the extracted data to simulate disaster risk under specified conditions (rainfall of 100 mm, wind speed of 10 m / s). Specifically, it calculates the risk of flooding and landslides.

[0171] Input: Weather data, disaster data, and topographical data required for the simulation

[0172] Output: Disaster risk simulation results

[0173] Step 4:

[0174] The server generates the simulation results in an appropriate format (e.g., "Flood risk: high" or "Landslide risk: medium"), which are then converted back into JSON format and sent to the user's device.

[0175] Input: Disaster risk simulation results

[0176] Output: Simulation result data in JSON format

[0177] Step 5:

[0178] The terminal analyzes the simulation results received from the server and displays them to the user in a visually easy-to-understand format (for example, graphs or charts), allowing the user to intuitively understand the results.

[0179] Input: Simulation result data in JSON format

[0180] Output: Visually displayed simulation results

[0181] Step 6:

[0182] The server's monitoring means monitors disaster risks in real time and sends alerts to users' devices as needed. For example, if sudden rainfall or an increase in wind speed is predicted, a warning is sent to the user immediately.

[0183] Input: Real-time weather data

[0184] Output: Alert notification to user terminal

[0185] As a concrete example, a user considering a certain area of ​​Tokyo as a new residence can simulate disaster risk using the following prompts:

[0186] Example prompt:

[0187] What is the risk of disaster in Tokyo when rainfall is 100mm and wind speed is 10m / s?

[0188] This prompt allows the server to perform a simulation under the specified conditions and provide the results to the user.

[0189] 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.

[0190] This invention is a disaster simulation system that supports important decisions regarding residence and business start-up, and also combines an emotion engine that takes into account the user's emotional state. This system operates in cooperation with a server, a terminal, and a user. Specific implementation methods are described below.

[0191] Data collection and storage

[0192] The server periodically retrieves past weather, disaster, and terrain data from reliable external data sources and stores it in a database, allowing simulations to always be based on the latest information.

[0193] Recognizing user emotions and receiving input

[0194] The user enters the desired conditions (e.g., rainfall, wind speed, location) into the application's input form. This input data is converted to JSON format on the user's device and sent to the server. In addition, as the user enters data, the emotion engine analyzes the user's facial expressions and voice to recognize their emotional state.

[0195] Emotion Engine Operation

[0196] The device is equipped with an emotion engine that analyzes the user's facial expressions and voice to recognize their emotional state. This emotion data is sent to the server along with the user's specified conditions. The emotional states selected by the emotion engine are, for example, "relief," "worry," and "excitement."

[0197] Running the simulation

[0198] The server uses its internal simulation engine to simulate disaster risks under specified conditions based on the request data and emotion data received from the user. For example, it calculates the risk of flooding or landslides under conditions of 100 mm of rainfall and 10 m / s wind speed at a specified location.

[0199] Generate and send results

[0200] The server generates results based on the results obtained from the simulation engine, taking into account emotional data. This data is compiled in JSON format. For example, if the user's emotional state is "worried," detailed risk explanations and countermeasures can be added to the simulation results.

[0201] Visual display of results

[0202] The device receives the simulation results from the server and displays them to the user in a visually easy-to-understand format (e.g., graphs or charts). Furthermore, the display format can be customized according to the user's emotional state, making it easier for the user to intuitively understand the results. For example, a standard display format can be used in a "relieved" state, while more detailed information can be added in a "worried" state.

[0203] Specific examples

[0204] For example, if a user is considering a certain area in Tokyo as a new place to live and wants to investigate the disaster risk under the conditions of 100 mm of rainfall and a wind speed of 10 m / s, the following specific example will be explained.

[0205] 1. The user inputs "rainfall: 100 mm," "wind speed: 10 m / s," and "location: Tokyo" into the application form and requests a simulation.

[0206] 2. The device sends the input data in JSON format to the server. At the same time, the emotion engine analyzes the user's facial expressions and voice to recognize the emotional state of "worry."

[0207] 3. The server runs a simulation based on the received input data and emotion data, resulting in "Flood risk: High" and "Landslide risk: Medium."

[0208] 4. The server customizes the simulation results based on the emotional data and sends them to the user's device in JSON format. For example, for the "worried" emotional state, detailed explanations of the risks and countermeasures are added.

[0209] 5. The device analyzes the received simulation results and displays them to the user in a visually easy-to-understand format. Specifically, it displays graphs showing risk levels and lists of countermeasures, helping the user make decisions with confidence.

[0210] This system allows users to understand in detail the disaster risks under specific conditions when choosing a place to live or starting a business, and provides information that takes their emotional state into account, allowing them to make accurate and reassuring decisions.

[0211] The processing flow will be explained below.

[0212] Step 1:

[0213] The server periodically retrieves historical weather, disaster, and terrain data from external reliable data sources and stores it in a database. Collecting and storing this data is important for running simulations based on the most up-to-date information.

[0214] Step 2:

[0215] The user opens the application and inputs the specified conditions (e.g., rainfall, wind speed, location, etc.), which sets the specific simulation conditions.

[0216] Step 3:

[0217] The terminal collects the conditions entered by the user and converts them into JSON format, which is then sent to the server.

[0218] Step 4:

[0219] The terminal sends the entered data to the server using an HTTP request, which the server receives.

[0220] Step 5:

[0221] The device's onboard emotion engine analyzes the user's facial expressions and voice to recognize their emotional state, for example, using a webcam and microphone to determine whether the user is feeling safe or anxious.

[0222] Step 6:

[0223] The device also converts the recognized emotion data into JSON format and sends it to the server along with the simulation conditions collected earlier.

[0224] Step 7:

[0225] The server analyzes the simulation conditions and emotion data received from the device and passes this information to the simulation engine, which then retrieves the necessary data from a past database and simulates disaster risk under the specified conditions.

[0226] Step 8:

[0227] The server generates detailed simulation results, such as flood risk and landslide risk, based on the results obtained from the simulation engine. It also takes into account emotional data and customizes the results according to the user's emotional state.

[0228] Step 9:

[0229] The server compiles the generated simulation results in JSON format and sends them to the user's device. For example, if the user's emotional state is "worried," it adds a detailed explanation of the risk and countermeasures.

[0230] Step 10:

[0231] The device analyzes the simulation results received from the server and displays them to the user in a visually easy-to-understand format (e.g., graphs or charts). The display method is customized according to the user's emotional state, allowing for intuitive understanding. For example, a standard display format is used for a "relieved" state, while more detailed information is added for a "worried" state.

[0232] Specific examples

[0233] For example, if a user is considering a certain area in Tokyo as a new place to live and wants to investigate the disaster risk under the conditions of 100 mm of rainfall and 10 m / s wind speed:

[0234] Step 1:

[0235] The server periodically retrieves weather data, disaster data, and terrain data from external reliable data sources and stores them in a database.

[0236] Step 2:

[0237] The user opens the application and enters the specified conditions: "Rainfall: 100 mm," "Wind speed: 10 m / s," and "Location: Tokyo."

[0238] Step 3:

[0239] The terminal collects the conditions entered by the user and converts them into JSON format.

[0240] Step 4:

[0241] The terminal transmits this input data to the server.

[0242] Step 5:

[0243] The device's emotion engine analyzes the user's facial expressions and voice to recognize the emotional state of "worry."

[0244] Step 6:

[0245] The device converts the recognized emotion data into JSON format and sends it to the server along with the simulation conditions.

[0246] Step 7:

[0247] The server analyzes the received data and passes it to the simulation engine, which then simulates disaster risks under specified conditions.

[0248] Step 8:

[0249] The server generates simulation results such as "Flood risk: high" and "Landslide risk: medium" and customizes them taking into account emotional data.

[0250] Step 9:

[0251] The server sends the customized simulation results to the user's terminal.

[0252] Step 10:

[0253] The device analyzes the simulation results and visually displays detailed risk explanations and countermeasures in response to the "worry" emotional state, allowing users to intuitively understand the risks and make decisions with confidence.

[0254] Example 2

[0255] 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."

[0256] When assessing the risk of natural disasters, many systems perform simulations based solely on numerical data, making it difficult to provide results that take into account the user's emotional state. It is also important to provide simulation results in a format that is intuitively easy to understand, but this is not being done enough. This makes it difficult for users to make important decisions with confidence.

[0257] 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 including a database that stores past weather data, disaster data, and topographical data, simulation means that executes a simulation based on conditions specified by the user, result generation means that generates simulation results and transmits them to the user's terminal, terminal means that includes an emotion engine that analyzes the user's facial expressions and voice and recognizes the user's emotional state, and result generation means that customizes the simulation results taking the emotional state into consideration. This makes it possible to provide intuitive and easy-to-understand simulation results based on the user's emotional state.

[0258] The "server means" is a means including a database that stores past weather data, disaster data, and topographical data.

[0259] The "simulation means" is a means for executing a simulation based on conditions specified by the user.

[0260] The "result generation means" is a means for generating simulation results and transmitting them to the user's terminal.

[0261] The "terminal means" refers to a means including a means for receiving user input and transmitting it to a server, and an emotion engine for analyzing the user's facial expressions and voice to recognize the user's emotional state.

[0262] An "emotion engine" is a software or hardware function that analyzes a user's facial expressions and voice to recognize their emotional state.

[0263] This invention is a disaster simulation system to support important decisions such as residence and business start-up, and is combined with an emotion engine that takes into account the emotional state of the user. This system operates in cooperation with the server, terminals, and users.

[0264] Data collection and storage

[0265] The server periodically obtains past weather data, disaster data, and topographical data from external reliable data sources (e.g., the Japan Meteorological Agency, the Earthquake Research Institute) and stores it in a database. This allows simulations to always be based on the latest information. Data is obtained using API and FTP.

[0266] Receiving user input

[0267] The user enters the desired conditions (for example, rainfall, wind speed, location) into the application's input form. Input items include "rainfall," "wind speed," and "location." This input data is converted to JSON format on the device and sent to the server.

[0268] Emotion recognition by emotion engine

[0269] The device's built-in emotion engine analyzes the user's facial expressions and voice in real time to recognize their emotional state, and this emotional data is also sent to the server in JSON format.

[0270] Running the simulation

[0271] Based on the input data and emotion data received from the user, the server uses an internal simulation engine (e.g., an AI model implemented by the system) to simulate disaster risks under specified conditions.

[0272] Generate and send results

[0273] The server generates the final result based on the simulation results and emotion data. The result is compiled in JSON format and sent to the device. If the emotion data is "worried," the result includes additional risk explanations and countermeasures.

[0274] Visual display of results

[0275] The device analyzes the simulation results received from the server and displays them to the user in a visually easy-to-understand format, customizing the display format according to the user's emotional state.

[0276] Specific examples

[0277] If a user is considering a certain area in Tokyo as a new place to live and wants to investigate the disaster risk under conditions of 100 mm of rainfall and a wind speed of 10 m / s, the following would be done:

[0278] 1. The user inputs "rainfall: 100 mm," "wind speed: 10 m / s," and "location: Tokyo" into the application form and requests a simulation.

[0279] 2. The device sends the input data in JSON format to the server. At the same time, the emotion engine analyzes the user's facial expressions and voice to recognize the emotional state of "worry."

[0280] 3. The server runs a simulation based on the received input data and emotion data, resulting in "Flood risk: High" and "Landslide risk: Medium."

[0281] 4. The server customizes the simulation results based on the emotional data and sends them to the user's device in JSON format. For example, for the "worried" emotional state, detailed explanations of the risks and countermeasures are added.

[0282] 5. The device analyzes the received simulation results and displays them to the user in a visually easy-to-understand format. Specifically, it displays graphs showing risk levels and lists of countermeasures, helping the user make decisions with confidence.

[0283] Prompt Sentence Examples

[0284] "Based on the following inputs, simulate disaster risk at a specific location in Tokyo under conditions of 100 mm of rainfall and 10 m / s wind speed, and generate results including appropriate countermeasures for anxious users."

[0285] In this way, the system of the present invention can provide intuitive and easy-to-understand simulation results based on the user's emotional state.

[0286] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0287] Step 1: Collect and store data

[0288] The server periodically retrieves past weather data, disaster data, and topographical data from external reliable data sources. As a specific example, it retrieves rainfall data for the past week using the Japan Meteorological Agency's API. The retrieved data is then stored in a database on the server. The input here is the weather data retrieved from the API, and the output is the weather data stored in the database. Data retrieval and storage are performed periodically by an automated script.

[0289] Step 2: Accepting User Input

[0290] The user enters the simulation conditions (rainfall, wind speed, location) into the application's input form. The input data is converted to JSON format on the user's device. For example, if the user enters "rainfall: 100 mm", "wind speed: 10 m / s", and "location: Tokyo", the device generates the JSON {"rainfall": 100, "wind_speed": 10, "location": "Tokyo"} and sends it to the server. The input here is the simulation conditions entered by the user, and the output is JSON data.

[0291] Step 3: Emotion recognition by the emotion engine

[0292] An emotion engine installed on the device analyzes the user's facial expressions and voice in real time to recognize their emotional state. As a specific example, a camera and microphone are used to capture the user's facial expressions and voice, and this data is input into the emotion engine. The emotion engine generates emotion data such as "relief," "worry," and "excitement" as the analysis results, and sends it in JSON format to the server. For example, data such as {"emotion": "worried"} is generated. The input here is the user's facial expression and voice data, and the output is JSON data indicating their emotional state.

[0293] Step 4: Run the simulation

[0294] The server uses a simulation engine to simulate disaster risks based on the simulation condition data and emotion data received from the user. As a specific example, the data {"rainfall": 100, "wind_speed": 10, "location": "Tokyo", "emotion": "worried"} is input into the simulation engine, and based on that, the results "Flood risk: high" and "Landslide risk: medium" are generated. The input here is the simulation conditions and emotion data, and the output is the simulation results.

[0295] Step 5: Generate and submit results

[0296] The server generates the final result based on the simulation results and emotion data. If the emotion data is "worried," it creates a result including additional risk explanations and countermeasures, and sends it all together in JSON format to the terminal. As a specific example, it generates the following data: {"flood_risk": "high", "landslide_risk": "medium", "advice": "Take these measures if you are worried"}. The input here is the simulation results and emotion data, and the output is the customized simulation results.

[0297] Step 6: Visualizing the results

[0298] The terminal analyzes the simulation results received from the server and displays them to the user in a visually easy-to-understand format (for example, graphs or charts). The display format can be customized depending on the user's emotional state. For example, it can display a red graph indicating high flood risk, a yellow graph indicating medium landslide risk, and a list of appropriate countermeasures for a "worried" state. The input here is the JSON data of the simulation results, and the output is the visual display content.

[0299] (Application example 2)

[0300] 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."

[0301] Conventional simulation systems only simulate disaster risks based on user-specified conditions, but lack the functionality to provide information that takes into account the user's emotional state and psychological burden. As a result, users have difficulty understanding the simulation results and are unable to alleviate their anxiety and worries. Furthermore, when considering disaster risks in the establishment or renovation planning of physical stores, a flexible response based on emotions is required.

[0302] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0303] In this invention, the server includes means including a database for storing past weather data, disaster data, and topographical data, simulation means for executing a simulation based on conditions and emotional state specified by the user, result generation means for generating simulation results and generating results customized according to the user's emotional state, terminal means for receiving user input, transmitting it to the server, and further analyzing the user's facial expressions and voice to recognize the user's emotional state, and display means for visually displaying the simulation results and optimizing the display format according to the user's emotional state. This makes it easier for users to receive disaster risk information according to their emotional state, enabling them to make decisions more intuitively and with a sense of security.

[0304] A "database" is a storage device that stores past weather data, disaster data, and topographical data, and holds information for performing simulations based on user input.

[0305] A "server" is a computer system that processes data received from users, performs simulations, and generates and transmits the results.

[0306] The "simulation means" is a device or software that has the function of simulating disaster risks based on conditions and emotional states specified by a user and generating the results.

[0307] The "result generation means" is a device or software that has the function of generating simulation results and further generating customized information according to the user's emotional state.

[0308] The "terminal means" is a device that has the function of receiving user input, transmitting it to the server, and analyzing the user's facial expressions and voice to recognize the user's emotional state.

[0309] The "display means" is a device or software that visually displays the simulation results to the user and has the function of optimizing the display format according to the user's emotional state.

[0310] An "emotional state" is a psychological state or emotion that a user is in when providing input data, and may include, for example, "relieved," "worried," or "excited."

[0311] "Disaster risk" refers to the risk of disasters occurring under certain conditions.

[0312] This invention is based on a disaster simulation system, and by customizing the results taking into account the user's emotional state, it reduces the psychological burden on the user and enables them to make decisions more intuitively and with a sense of security. This system works in conjunction with a server and terminal, providing users with visual and emotionally sensitive disaster risk information.

[0313] Data collection and storage

[0314] The server periodically retrieves historical weather, disaster, and terrain data from reliable external data sources and stores it in a database. This ensures that the most up-to-date and accurate information is used during simulation. Specifically, the server retrieves data from an API using the Python requests library and stores it in a cloud database such as AWS RDS.

[0315] Recognizing user emotions and receiving input

[0316] The user uses the device to input conditions such as rainfall, wind speed, and location into the application's input form. This input data is converted into JSON format and sent to the server. At the same time, the device is equipped with an emotion engine that analyzes the user's facial expressions and voice to recognize their emotional state. This emotion data is also sent to the server. Apple's CoreML is used for emotion recognition, and Google's Speech-to-Text API is used for voice analysis.

[0317] Running the simulation

[0318] The server uses its internal simulation engine to simulate disaster risks based on the condition and emotion data received from users. This simulation engine uses MATLAB and the Python Scipy library. As a specific example, it calculates the risk of landslides and floods under conditions of 150 mm of rainfall and 20 m / s wind speed.

[0319] Generate and send results

[0320] The server generates customized results based on the simulation results, taking into account the user's emotional state. For example, if the user's emotional state is "worried," the server adds detailed risk explanations and countermeasures to the simulation results. This data is sent to the device in JSON format.

[0321] Visual display of results

[0322] The device receives the simulation results from the server and displays them to the user in a visually easy-to-understand format. It uses D3.js to draw graphs and charts, and customizes the display format depending on the user's emotional state. For example, a user in the "worried" emotional state might display detailed risk information, while a user in the "relieved" emotional state might display a concise summary.

[0323] Specific examples

[0324] For example, if a user is considering a certain area in Osaka as the location for a new store and wants to investigate the disaster risk under the conditions of "rainfall: 150 mm" and "wind speed: 20 m / s," the system will operate as follows:

[0325] 1. The user enters "rainfall: 150 mm," "wind speed: 20 m / s," and "location: Osaka" into the app form and requests a simulation.

[0326] 2. The device sends the input data in JSON format to the server, and at the same time the emotion engine recognizes the emotional state of "worry."

[0327] 3. The server runs a simulation based on the received data and obtains the results "Flood risk: Very high" and "Landslide risk: High."

[0328] 4. The server adds detailed risk explanations and countermeasures to the simulation results, taking into account emotional data, and sends them to the terminal in JSON format.

[0329] 5. The terminal processes the received results into a visually easy-to-understand format (graphs or charts) and displays them to the user.

[0330] Prompt Sentence Examples

[0331] For example, the prompt sentence "Use MATLAB to generate Python code that simulates disaster risk based on specified rainfall and wind speed data" is input to the generative AI model.

[0332] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0333] Step 1:

[0334] The server periodically retrieves historical weather data, disaster data, and terrain data from external reliable data sources. Data is retrieved from the API as input and stored in a database on the server. This processing is performed using the Python requests library, and data is retrieved in JSON format. AWS RDS is used to store the database. The data includes weather conditions, disaster history, and terrain information.

[0335] Step 2:

[0336] The user uses their device to input conditions such as rainfall, wind speed, and location into the application's input form. This input data is converted to JSON format on the device. The emotion engine then analyzes the user's facial expressions and voice to recognize their emotional state. Emotion recognition uses Apple's CoreML and Google's Speech-to-Text API. The user's input data and emotion data are sent from the device to the server.

[0337] Step 3:

[0338] The server uses its internal simulation engine to simulate disaster risks based on the received condition and emotion data. The simulation engine uses MATLAB and Python's Scipy library. Input data includes rainfall, wind speed, and location information, and outputs flood risk and landslide risk. This output data is used in the next step.

[0339] Step 4:

[0340] The server then customizes the simulation results by taking into account the user's emotional state. For example, if the user is in an "anxious" emotional state, the server adds detailed risk explanations and countermeasures to the simulation results. The customized result data generated by this process is sent to the device in JSON format.

[0341] Step 5:

[0342] The device receives the simulation results from the server and displays them to the user in a visually easy-to-understand format. Graphs and charts are drawn using D3.js. The display format can also be customized according to the user's emotional state. For example, if the user's emotional state is "worried," detailed risk information is displayed, while if the user's emotional state is "relieved," a concise summary is displayed.

[0343] Examples:

[0344] For example, if a user is considering a certain area in Osaka as the location for a new store and wants to investigate the disaster risk under the conditions of "rainfall: 150 mm" and "wind speed: 20 m / s," the system will operate as follows:

[0345] 1. The user enters "rainfall: 150 mm," "wind speed: 20 m / s," and "location: Osaka" into the app form and requests a simulation.

[0346] 2. The device sends the input data in JSON format to the server, and at the same time the emotion engine recognizes the emotional state of "worry."

[0347] 3. The server runs a simulation based on the received data and obtains the results "Flood risk: Very high" and "Landslide risk: High."

[0348] 4. The server adds detailed risk explanations and countermeasures to the simulation results, taking into account emotional data, and sends them to the terminal in JSON format.

[0349] 5. The terminal processes the received results into a visually easy-to-understand format (graphs or charts) and displays them to the user.

[0350] Example prompt sentence:

[0351] For example, the prompt sentence "Use MATLAB to generate Python code that simulates disaster risk based on specified rainfall and wind speed data" is input to the generative AI model.

[0352] 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.

[0353] 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.

[0354] 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.

[0355] [Second embodiment]

[0356] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0357] 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.

[0358] 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).

[0359] 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.

[0360] 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.

[0361] 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).

[0362] 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.

[0363] 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.

[0364] 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.

[0365] 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.

[0366] In the smart glasses 214, 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.

[0367] 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."

[0368] The present invention is a disaster simulation system that supports important decisions regarding residence or business start-up. This system operates in cooperation with three parties: a server, a terminal, and a user. A specific implementation method for this system is described below.

[0369] Data collection and storage

[0370] The server periodically retrieves past weather, disaster, and terrain data from reliable external data sources and stores it in a database, allowing simulations to always be based on the latest information.

[0371] Receiving user input

[0372] The user enters the desired conditions (e.g., rainfall, wind speed, location) into the application's input form. This input data is converted to JSON format on the user's device and sent to the server.

[0373] Running the simulation

[0374] Based on the request data received from the user, the server uses its internal simulation engine to simulate disaster risks under specified conditions, such as calculating the risk of flooding or landslides at a specified location under conditions of 100 mm of rainfall and 10 m / s wind speed.

[0375] Generate and send results

[0376] The server compiles the data generated as a result of the simulation (e.g., "Flood risk: high," "Landslide risk: medium," etc.) and sends it to the user's device in JSON format. The result generation means then properly formats and transmits this data.

[0377] Visual display of results

[0378] The terminal displays the simulation results received from the server to the user in a visually easy-to-understand format (for example, graphs or charts), allowing the user to intuitively understand the results and make decisions more easily.

[0379] Specific examples

[0380] For example, a user is considering a certain area in Tokyo as a new place to live and wants to investigate the disaster risk under the conditions of 100 mm of rainfall and 10 m / s wind speed based on past weather data.

[0381] 1. The user enters "Rainfall: 100 mm", "Wind speed: 10 m / s", and "Location: Tokyo" into the application form.

[0382] 2. The terminal sends the entered data to the server in JSON format.

[0383] 3. The server runs a simulation based on the received input data, resulting in "Flood risk: High" and "Landslide risk: Medium."

[0384] 4. The server sends the simulation results in JSON format to the user's device.

[0385] 5. The terminal visually displays the received simulation results.

[0386] This system allows users to gain a detailed understanding of disaster risks under specific conditions and make accurate decisions when choosing a place to live or starting a business.

[0387] The processing flow will be explained below.

[0388] Step 1:

[0389] The server periodically retrieves past weather, disaster, and terrain data from reliable external data sources and stores it in a database, enabling simulations to be performed based on the most up-to-date information.

[0390] Step 2:

[0391] The user opens the application and inputs the specified conditions, such as rainfall, wind speed, location, and other parameters, into an input form.

[0392] Step 3:

[0393] The terminal collects the parameters entered by the user and converts them into JSON format for sending to the server, which then sends the converted data to the server as an HTTP request.

[0394] Step 4:

[0395] The server receives the JSON-formatted data sent from the device, analyzes it, and passes it to the simulation engine, which then retrieves the necessary data from a past database and runs a disaster simulation under the specified conditions.

[0396] Step 5:

[0397] The server uses the results obtained from the simulation engine to generate detailed simulation results such as flood risk and landslide risk, and this data is compiled in JSON format.

[0398] Step 6:

[0399] The server sends the generated simulation results to the terminal, which are formatted in a way that is intuitively understandable to the user.

[0400] Step 7:

[0401] The terminal analyzes the simulation results received from the server and displays them to the user in a visually easy-to-understand format (e.g., graphs and charts), which helps the user intuitively understand the disaster risk under specific conditions.

[0402] As a specific example, a user is considering a certain area in Tokyo as a new place to live.

[0403] Step 1:

[0404] The server periodically retrieves weather data, disaster data, and terrain data from external reliable data sources and stores them in a database.

[0405] Step 2:

[0406] The user opens the application and enters the specified conditions: "Rainfall: 100 mm," "Wind speed: 10 m / s," and "Location: Tokyo."

[0407] Step 3:

[0408] The terminal collects the parameters entered by the user, converts them into JSON format, and sends them to the server as an HTTP request.

[0409] Step 4:

[0410] The server receives the data sent from the device and passes it to the simulation engine for analysis, using past weather data, disaster data, and topographical data.

[0411] Step 5:

[0412] The server generates the simulation results "Flood risk: high" and "Landslide risk: medium" and summarizes them in JSON format.

[0413] Step 6:

[0414] The server transmits the generated simulation results to the terminal.

[0415] Step 7:

[0416] The device analyzes the simulation results received from the server and displays them to the user in a visually easy-to-understand format, allowing the user to intuitively understand the disaster risk under specified conditions and make decisions about where to live.

[0417] Example 1

[0418] 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."

[0419] Currently, there is a lack of systems that can predict and visually present detailed risks based on past weather and disaster data when selecting a place to live or start a business. There is also a need for a method that can flexibly perform simulations based on specific conditions specified by the user and provide the results quickly and in an easy-to-understand manner.

[0420] 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.

[0421] In this invention, the server includes means for periodically obtaining past weather data, disaster data, and topographical data from an external reliable data source and storing the data in a database, means for inputting conditions specified by a user into an application input form, converting the data into JSON format, and sending the JSON format to the server, means for simulating disaster risk using an internal simulation engine based on the request data received from the user, means for sending the simulation results in JSON format to a terminal, and means for displaying the simulation results received from the server to the user in a visually easy-to-understand format. This allows the user to predict disaster risk under specific conditions in detail and obtain the results in a visually easy-to-understand format.

[0422] A "server" is a device or system that periodically retrieves data from external data sources and stores it in a database.

[0423] A "database" is a system for storing and managing acquired past weather data, disaster data, and topographical data.

[0424] A "terminal" is a device or system through which a user inputs simulation conditions and transmits data to a server.

[0425] "JSON format" is an abbreviation for JavaScript Object Notation, and is a lightweight data exchange format for structuring and representing data.

[0426] A "simulation engine" is a program or system for calculating and predicting disaster risks based on conditions received from a user.

[0427] "Simulation" is the process of calculating disaster risk under specified conditions and predicting the consequences.

[0428] The "result generation means" is a system for generating simulation results in JSON format and sending them to the user's terminal.

[0429] The "visual display means" is a system for displaying the received simulation results in the form of graphs, charts, etc. so that the user can intuitively understand them.

[0430] "Weather data" refers to past data related to weather, specifically including rainfall and wind speed.

[0431] "Disaster data" refers to data related to natural disasters that have occurred in the past, including floods and landslides.

[0432] "Topographic data" refers to data relating to geographical features, including information on land elevation, geology, etc.

[0433] The present invention is a disaster simulation system that supports important decisions regarding residence or business start-up. This system operates in cooperation with three parties: a server, a terminal, and a user. A specific implementation method for this system is described below.

[0434] Data collection and storage

[0435] The server periodically retrieves historical weather, disaster, and terrain data from external, reliable data sources and stores it in a database. The server is configured with software that executes API calls using Python or scripts. The database is a relational database such as PostgreSQL.

[0436] Receiving user input

[0437] The user enters the desired conditions (for example, rainfall, wind speed, and location) into the application's input form. This data is converted to JSON format on the user's device. The device uses HTML and JavaScript to collect the form data and send an HTTP POST request to the server. Conditions entered by the user include "rainfall: 100 mm," "wind speed: 10 m / s," and "location: Tokyo."

[0438] Running the simulation

[0439] Based on the request data received from the user, the server uses its internal simulation engine to simulate disaster risk under specified conditions. For example, a simulation script implemented in Python is used to calculate flood risk and landslide risk, utilizing numerical analysis libraries such as NumPy and SciPy. Information such as rainfall, wind speed, and location is used as simulation conditions.

[0440] Generate and send results

[0441] The server compiles the data generated as a result of the simulation (e.g., "Flood risk: high," "Landslide risk: medium," etc.) and sends it to the user's device in JSON format. The result generation means then properly formats and transmits this data.

[0442] Visual display of results

[0443] The terminal receives the simulation results from the server and displays them to the user in a visually easy-to-understand format (for example, graphs or charts). The visual display uses the JavaScript D3.js library, which allows the user to intuitively understand the results and make decisions more easily.

[0444] Specific examples

[0445] For example, a user is considering a certain area in Tokyo as a new place to live. He wants to investigate the disaster risk based on historical weather data under the condition of 100 mm of rainfall and 10 m / s wind speed. Here is a specific example:

[0446] 1. The user enters "Rainfall: 100 mm", "Wind speed: 10 m / s", and "Location: Tokyo" into the application form.

[0447] 2. The terminal sends the entered data to the server in JSON format.

[0448] 3. The server runs a simulation based on the received input data, resulting in "Flood risk: High" and "Landslide risk: Medium."

[0449] 4. The server sends the simulation results in JSON format to the user's device.

[0450] 5. The terminal visually displays the received simulation results.

[0451] This system allows users to gain a detailed understanding of disaster risks under specific conditions and make accurate decisions when choosing a place to live or starting a business.

[0452] An example of a prompt is "Simulate the disaster risk in Tokyo with 100 mm of rainfall and a wind speed of 10 m / s."

[0453] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0454] Step 1:

[0455] Data collection and storage

[0456] The server periodically retrieves historical weather, disaster, and terrain data from external, reliable data sources. Specifically, the server sends API requests to retrieve the data and stores it in a database. The API endpoint and authentication information are required as input, and the collected data is stored in the database as output. For example, a scheduled task using a Python script pulls the data from the API and stores it in PostgreSQL.

[0457] Step 2:

[0458] Receiving user input

[0459] The user enters the simulation conditions (rainfall, wind speed, location) into the application's input form. This input data is converted into JSON format on the user's device. Specifically, the user enters data into the form through a web browser, and JavaScript collects it and converts it into JSON format. The input includes the rainfall, wind speed, and location entered by the user, and JSON format data is generated as the output. For example, the user enters "rainfall: 100 mm," "wind speed: 10 m / s," and "location: Tokyo."

[0460] Step 3:

[0461] Running the simulation

[0462] The server uses a simulation engine to simulate disaster risk based on the request data received from the user. Specifically, it parses the received JSON data and passes it to the simulation engine for calculations. The inputs required are the JSON data sent by the user, the internal simulation engine, and past weather and topographical data. The output is the simulation results (e.g., "Flood risk: high," "Landslide risk: medium"). For example, a simulation script implemented in Python calculates risk using libraries such as NumPy and SciPy.

[0463] Step 4:

[0464] Generate and send results

[0465] The server sends the simulation results in JSON format to the user's device. Specifically, it formats the simulation results appropriately and sends them to the user's device as an HTTP response. The input is the simulation calculation result, and the output is formatted JSON data sent as an HTTP response. For example, the JSON data generated as a result of the simulation is sent to the user's device.

[0466] Step 5:

[0467] Visual display of results

[0468] The terminal displays the simulation results received from the server to the user in a visually easy-to-understand format. Specifically, it uses the JavaScript D3.js library to display the results in graphs and charts. The input is the simulation results in JSON format sent from the server, and the output is displayed in a visually easy-to-understand format. For example, a "high" flood risk or a "medium" landslide risk is displayed as a graph in the browser.

[0469] An example prompt based on a generative AI model is "Simulate the disaster risk in Tokyo with 100 mm of rainfall and a wind speed of 10 m / s."

[0470] (Application example 1)

[0471] 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."

[0472] Conventional disaster simulation systems mainly make predictions based on past data, making it difficult to adapt to changing disaster risks in real time. Furthermore, when users grasp the disaster risk under specific conditions, they do not provide enough information to take prompt and appropriate action. This makes it difficult for users to take appropriate evacuation actions in response to the predicted risks.

[0473] 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.

[0474] In this invention, the server includes database means for storing past weather data, disaster data, and topographical data, simulation means for executing simulations based on conditions specified by the user, result generation means for generating simulation results and sending them to the user's terminal, terminal means for receiving user input and sending them to the server, and monitoring means for monitoring disaster risks in real time and sending alerts. This allows the system to not only support users in making residential and business decisions, but also enable immediate responses to disaster risks that change in real time.

[0475] The "database means" is a system for storing past weather data, disaster data, and topographical data.

[0476] The "simulation means" is a function for predicting disaster risks based on conditions specified by the user and executing simulations.

[0477] The "result generation means" is a function for generating simulation results and transmitting the results to the user's terminal.

[0478] "Terminal means" refers to equipment or applications that receive user input and transmit that information to the server.

[0479] "Monitoring means" refers to the function of monitoring disaster risks in real time and sending alerts as necessary.

[0480] "Rainfall" refers to the amount of rain that falls in a particular location within a certain period of time.

[0481] "Wind speed" refers to the speed of wind recorded at a particular location over a given period of time.

[0482] "Location" refers to a particular geographic area for which a user specifies that they want to know the disaster risk.

[0483] "Visual display" refers to presenting the simulation results in a form such as a graph or chart in an easy-to-read format for the user.

[0484] "Server" refers to a central computing device that stores data, runs simulations, and generates and transmits results.

[0485] "User" refers to an individual or entity that uses the System to simulate disaster risks and receives the results.

[0486] This invention is a disaster risk monitoring system that simulates disaster risks using past weather data, disaster data, and topographical data, and provides the results to users in real time. This system operates in cooperation with a server, a terminal, and a user, and is configured as follows:

[0487] The server periodically retrieves past weather, disaster, and terrain data from external, reliable data sources and stores it in a database. This database serves as the basis for running accurate simulations based on the latest information. The server also includes a simulation tool that simulates disaster risk based on user-specified conditions (rainfall, wind speed, location, etc.).

[0488] Users input their desired conditions through an application on their device. This input data is converted to JSON format and sent to the server. The server then runs a simulation based on the received request data and calculates the disaster risk under the specified conditions. For example, it evaluates the risk of flooding or landslides under conditions of 100 mm of rainfall and 10 m / s wind speed at a specific location.

[0489] Simulation results are sent from the server to the terminal in JSON format. This result generation method allows the simulation results to be properly formatted and quickly sent to the user's terminal. The terminal then displays the results to the user in a visually easy-to-understand format. This allows the user to intuitively understand the results and respond quickly to various disaster risks.

[0490] Furthermore, the system includes a monitoring means that monitors disaster risks in real time and sends alerts to user devices as necessary, allowing users to immediately grasp the risks and take appropriate measures even in the event of a sudden disaster.

[0491] As a concrete example, if a user is considering an area for a new home and wants to explore the disaster risk in the presence of 100 mm of rainfall and 10 m / s wind speed, they can generate a prompt like this:

[0492] Example prompt:

[0493] What is the risk of disaster in Tokyo when rainfall is 100mm and wind speed is 10m / s?

[0494] Based on this prompt, the system monitors disaster risks in real time and provides users with simulation results, such as visually displaying high flood risk or medium landslide risk. In this way, users can gain a detailed understanding of disaster risks under specific conditions and make accurate decisions when choosing a place to live or start a business.

[0495] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0496] Step 1:

[0497] The user opens the application on their device and inputs the specified conditions (rainfall, wind speed, location). The input data is in the format of "rainfall: 100 mm," "wind speed: 10 m / s," "location: Tokyo," etc. This data is converted into JSON format and sent to the server.

[0498] Input: User-specified conditions (rainfall, wind speed, location)

[0499] Output: Request data in JSON format

[0500] Step 2:

[0501] The server analyzes the request data received from the terminal and extracts the relevant weather, disaster, and terrain data from the database, thereby preparing a simulation based on the specified conditions.

[0502] Input: Request data in JSON format

[0503] Output: Weather data, disaster data, and topographical data required for simulation

[0504] Step 3:

[0505] The server's simulation tool uses the extracted data to simulate disaster risk under specified conditions (rainfall of 100 mm, wind speed of 10 m / s). Specifically, it calculates the risk of flooding and landslides.

[0506] Input: Weather data, disaster data, and topographical data required for the simulation

[0507] Output: Disaster risk simulation results

[0508] Step 4:

[0509] The server generates the simulation results in an appropriate format (e.g., "Flood risk: high" or "Landslide risk: medium"), which are then converted back into JSON format and sent to the user's device.

[0510] Input: Disaster risk simulation results

[0511] Output: Simulation result data in JSON format

[0512] Step 5:

[0513] The terminal analyzes the simulation results received from the server and displays them to the user in a visually easy-to-understand format (for example, graphs or charts), allowing the user to intuitively understand the results.

[0514] Input: Simulation result data in JSON format

[0515] Output: Visually displayed simulation results

[0516] Step 6:

[0517] The server's monitoring means monitors disaster risks in real time and sends alerts to users' devices as needed. For example, if sudden rainfall or an increase in wind speed is predicted, a warning is sent to the user immediately.

[0518] Input: Real-time weather data

[0519] Output: Alert notification to user terminal

[0520] As a concrete example, a user considering a certain area of ​​Tokyo as a new residence can simulate disaster risk using the following prompts:

[0521] Example prompt:

[0522] What is the risk of disaster in Tokyo when rainfall is 100mm and wind speed is 10m / s?

[0523] This prompt allows the server to perform a simulation under the specified conditions and provide the results to the user.

[0524] 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.

[0525] This invention is a disaster simulation system that supports important decisions regarding residence and business start-up, and also combines an emotion engine that takes into account the user's emotional state. This system operates in cooperation with a server, a terminal, and a user. Specific implementation methods are described below.

[0526] Data collection and storage

[0527] The server periodically retrieves past weather, disaster, and terrain data from reliable external data sources and stores it in a database, allowing simulations to always be based on the latest information.

[0528] Recognizing user emotions and receiving input

[0529] The user enters the desired conditions (e.g., rainfall, wind speed, location) into the application's input form. This input data is converted to JSON format on the user's device and sent to the server. In addition, as the user enters data, the emotion engine analyzes the user's facial expressions and voice to recognize their emotional state.

[0530] Emotion Engine Operation

[0531] The device is equipped with an emotion engine that analyzes the user's facial expressions and voice to recognize their emotional state. This emotion data is sent to the server along with the user's specified conditions. The emotional states selected by the emotion engine are, for example, "relief," "worry," and "excitement."

[0532] Running the simulation

[0533] The server uses its internal simulation engine to simulate disaster risks under specified conditions based on the request data and emotion data received from the user. For example, it calculates the risk of flooding or landslides under conditions of 100 mm of rainfall and 10 m / s wind speed at a specified location.

[0534] Generate and send results

[0535] The server generates results based on the results obtained from the simulation engine, taking into account emotional data. This data is compiled in JSON format. For example, if the user's emotional state is "worried," detailed risk explanations and countermeasures can be added to the simulation results.

[0536] Visual display of results

[0537] The device receives the simulation results from the server and displays them to the user in a visually easy-to-understand format (e.g., graphs or charts). Furthermore, the display format can be customized according to the user's emotional state, making it easier for the user to intuitively understand the results. For example, a standard display format can be used in a "relieved" state, while more detailed information can be added in a "worried" state.

[0538] Specific examples

[0539] For example, if a user is considering a certain area in Tokyo as a new place to live and wants to investigate the disaster risk under the conditions of 100 mm of rainfall and a wind speed of 10 m / s, the following specific example will be explained.

[0540] 1. The user inputs "rainfall: 100 mm," "wind speed: 10 m / s," and "location: Tokyo" into the application form and requests a simulation.

[0541] 2. The device sends the input data in JSON format to the server. At the same time, the emotion engine analyzes the user's facial expressions and voice to recognize the emotional state of "worry."

[0542] 3. The server runs a simulation based on the received input data and emotion data, resulting in "Flood risk: High" and "Landslide risk: Medium."

[0543] 4. The server customizes the simulation results based on the emotional data and sends them to the user's device in JSON format. For example, for the "worried" emotional state, detailed explanations of the risks and countermeasures are added.

[0544] 5. The device analyzes the received simulation results and displays them to the user in a visually easy-to-understand format. Specifically, it displays graphs showing risk levels and lists of countermeasures, helping the user make decisions with confidence.

[0545] This system allows users to understand in detail the disaster risks under specific conditions when choosing a place to live or starting a business, and provides information that takes their emotional state into account, allowing them to make accurate and reassuring decisions.

[0546] The processing flow will be explained below.

[0547] Step 1:

[0548] The server periodically retrieves historical weather, disaster, and terrain data from external reliable data sources and stores it in a database. Collecting and storing this data is important for running simulations based on the most up-to-date information.

[0549] Step 2:

[0550] The user opens the application and inputs the specified conditions (e.g., rainfall, wind speed, location, etc.), which sets the specific simulation conditions.

[0551] Step 3:

[0552] The terminal collects the conditions entered by the user and converts them into JSON format, which is then sent to the server.

[0553] Step 4:

[0554] The terminal sends the entered data to the server using an HTTP request, which the server receives.

[0555] Step 5:

[0556] The device's onboard emotion engine analyzes the user's facial expressions and voice to recognize their emotional state, for example, using a webcam and microphone to determine whether the user is feeling safe or anxious.

[0557] Step 6:

[0558] The device also converts the recognized emotion data into JSON format and sends it to the server along with the simulation conditions collected earlier.

[0559] Step 7:

[0560] The server analyzes the simulation conditions and emotion data received from the device and passes this information to the simulation engine, which then retrieves the necessary data from a past database and simulates disaster risk under the specified conditions.

[0561] Step 8:

[0562] The server generates detailed simulation results, such as flood risk and landslide risk, based on the results obtained from the simulation engine. It also takes into account emotional data and customizes the results according to the user's emotional state.

[0563] Step 9:

[0564] The server compiles the generated simulation results in JSON format and sends them to the user's device. For example, if the user's emotional state is "worried," it adds a detailed explanation of the risk and countermeasures.

[0565] Step 10:

[0566] The device analyzes the simulation results received from the server and displays them to the user in a visually easy-to-understand format (e.g., graphs or charts). The display method is customized according to the user's emotional state, allowing for intuitive understanding. For example, a standard display format is used for a "relieved" state, while more detailed information is added for a "worried" state.

[0567] Specific examples

[0568] For example, if a user is considering a certain area in Tokyo as a new place to live and wants to investigate the disaster risk under the conditions of 100 mm of rainfall and 10 m / s wind speed:

[0569] Step 1:

[0570] The server periodically retrieves weather data, disaster data, and terrain data from external reliable data sources and stores them in a database.

[0571] Step 2:

[0572] The user opens the application and enters the specified conditions: "Rainfall: 100 mm," "Wind speed: 10 m / s," and "Location: Tokyo."

[0573] Step 3:

[0574] The terminal collects the conditions entered by the user and converts them into JSON format.

[0575] Step 4:

[0576] The terminal transmits this input data to the server.

[0577] Step 5:

[0578] The device's emotion engine analyzes the user's facial expressions and voice to recognize the emotional state of "worry."

[0579] Step 6:

[0580] The device converts the recognized emotion data into JSON format and sends it to the server along with the simulation conditions.

[0581] Step 7:

[0582] The server analyzes the received data and passes it to the simulation engine, which then simulates disaster risks under specified conditions.

[0583] Step 8:

[0584] The server generates simulation results such as "Flood risk: high" and "Landslide risk: medium" and customizes them taking into account emotional data.

[0585] Step 9:

[0586] The server sends the customized simulation results to the user's terminal.

[0587] Step 10:

[0588] The device analyzes the simulation results and visually displays detailed risk explanations and countermeasures in response to the "worry" emotional state, allowing users to intuitively understand the risks and make decisions with confidence.

[0589] Example 2

[0590] 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."

[0591] When assessing the risk of natural disasters, many systems perform simulations based solely on numerical data, making it difficult to provide results that take into account the user's emotional state. It is also important to provide simulation results in a format that is intuitively easy to understand, but this is not being done enough. This makes it difficult for users to make important decisions with confidence.

[0592] 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 including a database that stores past weather data, disaster data, and topographical data, simulation means that executes a simulation based on conditions specified by the user, result generation means that generates simulation results and transmits them to the user's terminal, terminal means that includes an emotion engine that analyzes the user's facial expressions and voice and recognizes the user's emotional state, and result generation means that customizes the simulation results taking the emotional state into consideration. This makes it possible to provide intuitive and easy-to-understand simulation results based on the user's emotional state.

[0593] The "server means" is a means including a database that stores past weather data, disaster data, and topographical data.

[0594] The "simulation means" is a means for executing a simulation based on conditions specified by the user.

[0595] The "result generation means" is a means for generating simulation results and transmitting them to the user's terminal.

[0596] The "terminal means" refers to a means including a means for receiving user input and transmitting it to a server, and an emotion engine for analyzing the user's facial expressions and voice to recognize the user's emotional state.

[0597] An "emotion engine" is a software or hardware function that analyzes a user's facial expressions and voice to recognize their emotional state.

[0598] This invention is a disaster simulation system to support important decisions such as residence and business start-up, and is combined with an emotion engine that takes into account the emotional state of the user. This system operates in cooperation with the server, terminals, and users.

[0599] Data collection and storage

[0600] The server periodically obtains past weather data, disaster data, and topographical data from external reliable data sources (e.g., the Japan Meteorological Agency, the Earthquake Research Institute) and stores it in a database. This allows simulations to always be based on the latest information. Data is obtained using API and FTP.

[0601] Receiving user input

[0602] The user enters the desired conditions (for example, rainfall, wind speed, location) into the application's input form. Input items include "rainfall," "wind speed," and "location." This input data is converted to JSON format on the device and sent to the server.

[0603] Emotion recognition by emotion engine

[0604] The device's built-in emotion engine analyzes the user's facial expressions and voice in real time to recognize their emotional state, and this emotional data is also sent to the server in JSON format.

[0605] Running the simulation

[0606] Based on the input data and emotion data received from the user, the server uses an internal simulation engine (e.g., an AI model implemented by the system) to simulate disaster risks under specified conditions.

[0607] Generate and send results

[0608] The server generates the final result based on the simulation results and emotion data. The result is compiled in JSON format and sent to the device. If the emotion data is "worried," the result includes additional risk explanations and countermeasures.

[0609] Visual display of results

[0610] The device analyzes the simulation results received from the server and displays them to the user in a visually easy-to-understand format, customizing the display format according to the user's emotional state.

[0611] Specific examples

[0612] If a user is considering a certain area in Tokyo as a new place to live and wants to investigate the disaster risk under conditions of 100 mm of rainfall and a wind speed of 10 m / s, the following would be done:

[0613] 1. The user inputs "rainfall: 100 mm," "wind speed: 10 m / s," and "location: Tokyo" into the application form and requests a simulation.

[0614] 2. The device sends the input data in JSON format to the server. At the same time, the emotion engine analyzes the user's facial expressions and voice to recognize the emotional state of "worry."

[0615] 3. The server runs a simulation based on the received input data and emotion data, resulting in "Flood risk: High" and "Landslide risk: Medium."

[0616] 4. The server customizes the simulation results based on the emotional data and sends them to the user's device in JSON format. For example, for the "worried" emotional state, detailed explanations of the risks and countermeasures are added.

[0617] 5. The device analyzes the received simulation results and displays them to the user in a visually easy-to-understand format. Specifically, it displays graphs showing risk levels and lists of countermeasures, helping the user make decisions with confidence.

[0618] Prompt Sentence Examples

[0619] "Based on the following inputs, simulate disaster risk at a specific location in Tokyo under conditions of 100 mm of rainfall and 10 m / s wind speed, and generate results including appropriate countermeasures for anxious users."

[0620] In this way, the system of the present invention can provide intuitive and easy-to-understand simulation results based on the user's emotional state.

[0621] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0622] Step 1: Collect and store data

[0623] The server periodically retrieves past weather data, disaster data, and topographical data from external reliable data sources. As a specific example, it retrieves rainfall data for the past week using the Japan Meteorological Agency's API. The retrieved data is then stored in a database on the server. The input here is the weather data retrieved from the API, and the output is the weather data stored in the database. Data retrieval and storage are performed periodically by an automated script.

[0624] Step 2: Accepting User Input

[0625] The user enters the simulation conditions (rainfall, wind speed, location) into the application's input form. The input data is converted to JSON format on the user's device. For example, if the user enters "rainfall: 100 mm", "wind speed: 10 m / s", and "location: Tokyo", the device generates the JSON {"rainfall": 100, "wind_speed": 10, "location": "Tokyo"} and sends it to the server. The input here is the simulation conditions entered by the user, and the output is JSON data.

[0626] Step 3: Emotion recognition by the emotion engine

[0627] An emotion engine installed on the device analyzes the user's facial expressions and voice in real time to recognize their emotional state. As a specific example, a camera and microphone are used to capture the user's facial expressions and voice, and this data is input into the emotion engine. The emotion engine generates emotion data such as "relief," "worry," and "excitement" as the analysis results, and sends it in JSON format to the server. For example, data such as {"emotion": "worried"} is generated. The input here is the user's facial expression and voice data, and the output is JSON data indicating their emotional state.

[0628] Step 4: Run the simulation

[0629] The server uses a simulation engine to simulate disaster risks based on the simulation condition data and emotion data received from the user. As a specific example, the data {"rainfall": 100, "wind_speed": 10, "location": "Tokyo", "emotion": "worried"} is input into the simulation engine, and based on that, the results "Flood risk: high" and "Landslide risk: medium" are generated. The input here is the simulation conditions and emotion data, and the output is the simulation results.

[0630] Step 5: Generate and submit results

[0631] The server generates the final result based on the simulation results and emotion data. If the emotion data is "worried," it creates a result including additional risk explanations and countermeasures, and sends it all together in JSON format to the terminal. As a specific example, it generates the following data: {"flood_risk": "high", "landslide_risk": "medium", "advice": "Take these measures if you are worried"}. The input here is the simulation results and emotion data, and the output is the customized simulation results.

[0632] Step 6: Visualizing the results

[0633] The terminal analyzes the simulation results received from the server and displays them to the user in a visually easy-to-understand format (for example, graphs or charts). The display format can be customized depending on the user's emotional state. For example, it can display a red graph indicating high flood risk, a yellow graph indicating medium landslide risk, and a list of appropriate countermeasures for a "worried" state. The input here is the JSON data of the simulation results, and the output is the visual display content.

[0634] (Application example 2)

[0635] 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."

[0636] Conventional simulation systems only simulate disaster risks based on user-specified conditions, but lack the functionality to provide information that takes into account the user's emotional state and psychological burden. As a result, users have difficulty understanding the simulation results and are unable to alleviate their anxiety and worries. Furthermore, when considering disaster risks in the establishment or renovation planning of physical stores, a flexible response based on emotions is required.

[0637] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0638] In this invention, the server includes means including a database for storing past weather data, disaster data, and topographical data, simulation means for executing a simulation based on conditions and emotional state specified by the user, result generation means for generating simulation results and generating results customized according to the user's emotional state, terminal means for receiving user input, transmitting it to the server, and further analyzing the user's facial expressions and voice to recognize the user's emotional state, and display means for visually displaying the simulation results and optimizing the display format according to the user's emotional state. This makes it easier for users to receive disaster risk information according to their emotional state, enabling them to make decisions more intuitively and with a sense of security.

[0639] A "database" is a storage device that stores past weather data, disaster data, and topographical data, and holds information for performing simulations based on user input.

[0640] A "server" is a computer system that processes data received from users, performs simulations, and generates and transmits the results.

[0641] The "simulation means" is a device or software that has the function of simulating disaster risks based on conditions and emotional states specified by a user and generating the results.

[0642] The "result generation means" is a device or software that has the function of generating simulation results and further generating customized information according to the user's emotional state.

[0643] The "terminal means" is a device that has the function of receiving user input, transmitting it to the server, and analyzing the user's facial expressions and voice to recognize the user's emotional state.

[0644] The "display means" is a device or software that visually displays the simulation results to the user and has the function of optimizing the display format according to the user's emotional state.

[0645] An "emotional state" is a psychological state or emotion that a user is in when providing input data, and may include, for example, "relieved," "worried," or "excited."

[0646] "Disaster risk" refers to the risk of disasters occurring under certain conditions.

[0647] This invention is based on a disaster simulation system, and by customizing the results taking into account the user's emotional state, it reduces the psychological burden on the user and enables them to make decisions more intuitively and with a sense of security. This system works in conjunction with a server and terminal, providing users with visual and emotionally sensitive disaster risk information.

[0648] Data collection and storage

[0649] The server periodically retrieves historical weather, disaster, and terrain data from reliable external data sources and stores it in a database. This ensures that the most up-to-date and accurate information is used during simulation. Specifically, the server retrieves data from an API using the Python requests library and stores it in a cloud database such as AWS RDS.

[0650] Recognizing user emotions and receiving input

[0651] The user uses the device to input conditions such as rainfall, wind speed, and location into the application's input form. This input data is converted into JSON format and sent to the server. At the same time, the device is equipped with an emotion engine that analyzes the user's facial expressions and voice to recognize their emotional state. This emotion data is also sent to the server. Apple's CoreML is used for emotion recognition, and Google's Speech-to-Text API is used for voice analysis.

[0652] Running the simulation

[0653] The server uses its internal simulation engine to simulate disaster risks based on the condition and emotion data received from users. This simulation engine uses MATLAB and the Python Scipy library. As a specific example, it calculates the risk of landslides and floods under conditions of 150 mm of rainfall and 20 m / s wind speed.

[0654] Generate and send results

[0655] The server generates customized results based on the simulation results, taking into account the user's emotional state. For example, if the user's emotional state is "worried," the server adds detailed risk explanations and countermeasures to the simulation results. This data is sent to the device in JSON format.

[0656] Visual display of results

[0657] The device receives the simulation results from the server and displays them to the user in a visually easy-to-understand format. It uses D3.js to draw graphs and charts, and customizes the display format depending on the user's emotional state. For example, a user in the "worried" emotional state might display detailed risk information, while a user in the "relieved" emotional state might display a concise summary.

[0658] Specific examples

[0659] For example, if a user is considering a certain area in Osaka as the location for a new store and wants to investigate the disaster risk under the conditions of "rainfall: 150 mm" and "wind speed: 20 m / s," the system will operate as follows:

[0660] 1. The user enters "rainfall: 150 mm," "wind speed: 20 m / s," and "location: Osaka" into the app form and requests a simulation.

[0661] 2. The device sends the input data in JSON format to the server, and at the same time the emotion engine recognizes the emotional state of "worry."

[0662] 3. The server runs a simulation based on the received data and obtains the results "Flood risk: Very high" and "Landslide risk: High."

[0663] 4. The server adds detailed risk explanations and countermeasures to the simulation results, taking into account emotional data, and sends them to the terminal in JSON format.

[0664] 5. The terminal processes the received results into a visually easy-to-understand format (graphs or charts) and displays them to the user.

[0665] Prompt Sentence Examples

[0666] For example, the prompt sentence "Use MATLAB to generate Python code that simulates disaster risk based on specified rainfall and wind speed data" is input to the generative AI model.

[0667] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0668] Step 1:

[0669] The server periodically retrieves historical weather data, disaster data, and terrain data from external reliable data sources. Data is retrieved from the API as input and stored in a database on the server. This processing is performed using the Python requests library, and data is retrieved in JSON format. AWS RDS is used to store the database. The data includes weather conditions, disaster history, and terrain information.

[0670] Step 2:

[0671] The user uses their device to input conditions such as rainfall, wind speed, and location into the application's input form. This input data is converted to JSON format on the device. The emotion engine then analyzes the user's facial expressions and voice to recognize their emotional state. Emotion recognition uses Apple's CoreML and Google's Speech-to-Text API. The user's input data and emotion data are sent from the device to the server.

[0672] Step 3:

[0673] The server uses its internal simulation engine to simulate disaster risks based on the received condition and emotion data. The simulation engine uses MATLAB and Python's Scipy library. Input data includes rainfall, wind speed, and location information, and outputs flood risk and landslide risk. This output data is used in the next step.

[0674] Step 4:

[0675] The server then customizes the simulation results by taking into account the user's emotional state. For example, if the user is in an "anxious" emotional state, the server adds detailed risk explanations and countermeasures to the simulation results. The customized result data generated by this process is sent to the device in JSON format.

[0676] Step 5:

[0677] The device receives the simulation results from the server and displays them to the user in a visually easy-to-understand format. Graphs and charts are drawn using D3.js. The display format can also be customized according to the user's emotional state. For example, if the user's emotional state is "worried," detailed risk information is displayed, while if the user's emotional state is "relieved," a concise summary is displayed.

[0678] Examples:

[0679] For example, if a user is considering a certain area in Osaka as the location for a new store and wants to investigate the disaster risk under the conditions of "rainfall: 150 mm" and "wind speed: 20 m / s," the system will operate as follows:

[0680] 1. The user enters "rainfall: 150 mm," "wind speed: 20 m / s," and "location: Osaka" into the app form and requests a simulation.

[0681] 2. The device sends the input data in JSON format to the server, and at the same time the emotion engine recognizes the emotional state of "worry."

[0682] 3. The server runs a simulation based on the received data and obtains the results "Flood risk: Very high" and "Landslide risk: High."

[0683] 4. The server adds detailed risk explanations and countermeasures to the simulation results, taking into account emotional data, and sends them to the terminal in JSON format.

[0684] 5. The terminal processes the received results into a visually easy-to-understand format (graphs or charts) and displays them to the user.

[0685] Example prompt sentence:

[0686] For example, the prompt sentence "Use MATLAB to generate Python code that simulates disaster risk based on specified rainfall and wind speed data" is input to the generative AI model.

[0687] 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.

[0688] 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.

[0689] 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.

[0690] [Third embodiment]

[0691] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0692] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0693] 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).

[0694] 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.

[0695] 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.

[0696] 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).

[0697] 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.

[0698] 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.

[0699] 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.

[0700] 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.

[0701] 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.

[0702] 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."

[0703] The present invention is a disaster simulation system that supports important decisions regarding residence or business start-up. This system operates in cooperation with three parties: a server, a terminal, and a user. A specific implementation method for this system is described below.

[0704] Data collection and storage

[0705] The server periodically retrieves past weather, disaster, and terrain data from reliable external data sources and stores it in a database, allowing simulations to always be based on the latest information.

[0706] Receiving user input

[0707] The user enters the desired conditions (e.g., rainfall, wind speed, location) into the application's input form. This input data is converted to JSON format on the user's device and sent to the server.

[0708] Running the simulation

[0709] Based on the request data received from the user, the server uses its internal simulation engine to simulate disaster risks under specified conditions, such as calculating the risk of flooding or landslides at a specified location under conditions of 100 mm of rainfall and 10 m / s wind speed.

[0710] Generate and send results

[0711] The server compiles the data generated as a result of the simulation (e.g., "Flood risk: high," "Landslide risk: medium," etc.) and sends it to the user's device in JSON format. The result generation means then properly formats and transmits this data.

[0712] Visual display of results

[0713] The terminal displays the simulation results received from the server to the user in a visually easy-to-understand format (for example, graphs or charts), allowing the user to intuitively understand the results and make decisions more easily.

[0714] Specific examples

[0715] For example, a user is considering a certain area in Tokyo as a new place to live and wants to investigate the disaster risk under the conditions of 100 mm of rainfall and 10 m / s wind speed based on past weather data.

[0716] 1. The user enters "Rainfall: 100 mm", "Wind speed: 10 m / s", and "Location: Tokyo" into the application form.

[0717] 2. The terminal sends the entered data to the server in JSON format.

[0718] 3. The server runs a simulation based on the received input data, resulting in "Flood risk: High" and "Landslide risk: Medium."

[0719] 4. The server sends the simulation results in JSON format to the user's device.

[0720] 5. The terminal visually displays the received simulation results.

[0721] This system allows users to gain a detailed understanding of disaster risks under specific conditions and make accurate decisions when choosing a place to live or starting a business.

[0722] The processing flow will be explained below.

[0723] Step 1:

[0724] The server periodically retrieves past weather, disaster, and terrain data from reliable external data sources and stores it in a database, enabling simulations to be performed based on the most up-to-date information.

[0725] Step 2:

[0726] The user opens the application and inputs the specified conditions, such as rainfall, wind speed, location, and other parameters, into an input form.

[0727] Step 3:

[0728] The terminal collects the parameters entered by the user and converts them into JSON format for sending to the server, which then sends the converted data to the server as an HTTP request.

[0729] Step 4:

[0730] The server receives the JSON-formatted data sent from the device, analyzes it, and passes it to the simulation engine, which then retrieves the necessary data from a past database and runs a disaster simulation under the specified conditions.

[0731] Step 5:

[0732] The server uses the results obtained from the simulation engine to generate detailed simulation results such as flood risk and landslide risk, and this data is compiled in JSON format.

[0733] Step 6:

[0734] The server sends the generated simulation results to the terminal, which are formatted in a way that is intuitively understandable to the user.

[0735] Step 7:

[0736] The terminal analyzes the simulation results received from the server and displays them to the user in a visually easy-to-understand format (e.g., graphs and charts), which helps the user intuitively understand the disaster risk under specific conditions.

[0737] As a specific example, a user is considering a certain area in Tokyo as a new place to live.

[0738] Step 1:

[0739] The server periodically retrieves weather data, disaster data, and terrain data from external reliable data sources and stores them in a database.

[0740] Step 2:

[0741] The user opens the application and enters the specified conditions: "Rainfall: 100 mm," "Wind speed: 10 m / s," and "Location: Tokyo."

[0742] Step 3:

[0743] The terminal collects the parameters entered by the user, converts them into JSON format, and sends them to the server as an HTTP request.

[0744] Step 4:

[0745] The server receives the data sent from the device and passes it to the simulation engine for analysis, using past weather data, disaster data, and topographical data.

[0746] Step 5:

[0747] The server generates the simulation results "Flood risk: high" and "Landslide risk: medium" and summarizes them in JSON format.

[0748] Step 6:

[0749] The server transmits the generated simulation results to the terminal.

[0750] Step 7:

[0751] The device analyzes the simulation results received from the server and displays them to the user in a visually easy-to-understand format, allowing the user to intuitively understand the disaster risk under specified conditions and make decisions about where to live.

[0752] Example 1

[0753] 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."

[0754] Currently, there is a lack of systems that can predict and visually present detailed risks based on past weather and disaster data when selecting a place to live or start a business. There is also a need for a method that can flexibly perform simulations based on specific conditions specified by the user and provide the results quickly and in an easy-to-understand manner.

[0755] 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.

[0756] In this invention, the server includes means for periodically obtaining past weather data, disaster data, and topographical data from an external reliable data source and storing the data in a database, means for inputting conditions specified by a user into an application input form, converting the data into JSON format, and sending the JSON format to the server, means for simulating disaster risk using an internal simulation engine based on the request data received from the user, means for sending the simulation results in JSON format to a terminal, and means for displaying the simulation results received from the server to the user in a visually easy-to-understand format. This allows the user to predict disaster risk under specific conditions in detail and obtain the results in a visually easy-to-understand format.

[0757] A "server" is a device or system that periodically retrieves data from external data sources and stores it in a database.

[0758] A "database" is a system for storing and managing acquired past weather data, disaster data, and topographical data.

[0759] A "terminal" is a device or system through which a user inputs simulation conditions and transmits data to a server.

[0760] "JSON format" is an abbreviation for JavaScript Object Notation, and is a lightweight data exchange format for structuring and representing data.

[0761] A "simulation engine" is a program or system for calculating and predicting disaster risks based on conditions received from a user.

[0762] "Simulation" is the process of calculating disaster risk under specified conditions and predicting the consequences.

[0763] The "result generation means" is a system for generating simulation results in JSON format and sending them to the user's terminal.

[0764] The "visual display means" is a system for displaying the received simulation results in the form of graphs, charts, etc. so that the user can intuitively understand them.

[0765] "Weather data" refers to past data related to weather, specifically including rainfall and wind speed.

[0766] "Disaster data" refers to data related to natural disasters that have occurred in the past, including floods and landslides.

[0767] "Topographic data" refers to data relating to geographical features, including information on land elevation, geology, etc.

[0768] The present invention is a disaster simulation system that supports important decisions regarding residence or business start-up. This system operates in cooperation with three parties: a server, a terminal, and a user. A specific implementation method for this system is described below.

[0769] Data collection and storage

[0770] The server periodically retrieves historical weather, disaster, and terrain data from external, reliable data sources and stores it in a database. The server is configured with software that executes API calls using Python or scripts. The database is a relational database such as PostgreSQL.

[0771] Receiving user input

[0772] The user enters the desired conditions (for example, rainfall, wind speed, and location) into the application's input form. This data is converted to JSON format on the user's device. The device uses HTML and JavaScript to collect the form data and send an HTTP POST request to the server. Conditions entered by the user include "rainfall: 100 mm," "wind speed: 10 m / s," and "location: Tokyo."

[0773] Running the simulation

[0774] Based on the request data received from the user, the server uses its internal simulation engine to simulate disaster risk under specified conditions. For example, a simulation script implemented in Python is used to calculate flood risk and landslide risk, utilizing numerical analysis libraries such as NumPy and SciPy. Information such as rainfall, wind speed, and location is used as simulation conditions.

[0775] Generate and send results

[0776] The server compiles the data generated as a result of the simulation (e.g., "Flood risk: high," "Landslide risk: medium," etc.) and sends it to the user's device in JSON format. The result generation means then properly formats and transmits this data.

[0777] Visual display of results

[0778] The terminal receives the simulation results from the server and displays them to the user in a visually easy-to-understand format (for example, graphs or charts). The visual display uses the JavaScript D3.js library, which allows the user to intuitively understand the results and make decisions more easily.

[0779] Specific examples

[0780] For example, a user is considering a certain area in Tokyo as a new place to live. He wants to investigate the disaster risk based on historical weather data under the condition of 100 mm of rainfall and 10 m / s wind speed. Here is a specific example:

[0781] 1. The user enters "Rainfall: 100 mm", "Wind speed: 10 m / s", and "Location: Tokyo" into the application form.

[0782] 2. The terminal sends the entered data to the server in JSON format.

[0783] 3. The server runs a simulation based on the received input data, resulting in "Flood risk: High" and "Landslide risk: Medium."

[0784] 4. The server sends the simulation results in JSON format to the user's device.

[0785] 5. The terminal visually displays the received simulation results.

[0786] This system allows users to gain a detailed understanding of disaster risks under specific conditions and make accurate decisions when choosing a place to live or starting a business.

[0787] An example of a prompt is "Simulate the disaster risk in Tokyo with 100 mm of rainfall and a wind speed of 10 m / s."

[0788] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0789] Step 1:

[0790] Data collection and storage

[0791] The server periodically retrieves historical weather, disaster, and terrain data from external, reliable data sources. Specifically, the server sends API requests to retrieve the data and stores it in a database. The API endpoint and authentication information are required as input, and the collected data is stored in the database as output. For example, a scheduled task using a Python script pulls the data from the API and stores it in PostgreSQL.

[0792] Step 2:

[0793] Receiving user input

[0794] The user enters the simulation conditions (rainfall, wind speed, location) into the application's input form. This input data is converted into JSON format on the user's device. Specifically, the user enters data into the form through a web browser, and JavaScript collects it and converts it into JSON format. The input includes the rainfall, wind speed, and location entered by the user, and JSON format data is generated as the output. For example, the user enters "rainfall: 100 mm," "wind speed: 10 m / s," and "location: Tokyo."

[0795] Step 3:

[0796] Running the simulation

[0797] The server uses a simulation engine to simulate disaster risk based on the request data received from the user. Specifically, it parses the received JSON data and passes it to the simulation engine for calculations. The inputs required are the JSON data sent by the user, the internal simulation engine, and past weather and topographical data. The output is the simulation results (e.g., "Flood risk: high," "Landslide risk: medium"). For example, a simulation script implemented in Python calculates risk using libraries such as NumPy and SciPy.

[0798] Step 4:

[0799] Generate and send results

[0800] The server sends the simulation results in JSON format to the user's device. Specifically, it formats the simulation results appropriately and sends them to the user's device as an HTTP response. The input is the simulation calculation result, and the output is formatted JSON data sent as an HTTP response. For example, the JSON data generated as a result of the simulation is sent to the user's device.

[0801] Step 5:

[0802] Visual display of results

[0803] The terminal displays the simulation results received from the server to the user in a visually easy-to-understand format. Specifically, it uses the JavaScript D3.js library to display the results in graphs and charts. The input is the simulation results in JSON format sent from the server, and the output is displayed in a visually easy-to-understand format. For example, a "high" flood risk or a "medium" landslide risk is displayed as a graph in the browser.

[0804] An example prompt based on a generative AI model is "Simulate the disaster risk in Tokyo with 100 mm of rainfall and a wind speed of 10 m / s."

[0805] (Application example 1)

[0806] 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."

[0807] Conventional disaster simulation systems mainly make predictions based on past data, making it difficult to adapt to changing disaster risks in real time. Furthermore, when users grasp the disaster risk under specific conditions, they do not provide enough information to take prompt and appropriate action. This makes it difficult for users to take appropriate evacuation actions in response to the predicted risks.

[0808] 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.

[0809] In this invention, the server includes database means for storing past weather data, disaster data, and topographical data, simulation means for executing simulations based on conditions specified by the user, result generation means for generating simulation results and sending them to the user's terminal, terminal means for receiving user input and sending them to the server, and monitoring means for monitoring disaster risks in real time and sending alerts. This allows the system to not only support users in making residential and business decisions, but also enable immediate responses to disaster risks that change in real time.

[0810] The "database means" is a system for storing past weather data, disaster data, and topographical data.

[0811] The "simulation means" is a function for predicting disaster risks based on conditions specified by the user and executing simulations.

[0812] The "result generation means" is a function for generating simulation results and transmitting the results to the user's terminal.

[0813] "Terminal means" refers to equipment or applications that receive user input and transmit that information to the server.

[0814] "Monitoring means" refers to the function of monitoring disaster risks in real time and sending alerts as necessary.

[0815] "Rainfall" refers to the amount of rain that falls in a particular location within a certain period of time.

[0816] "Wind speed" refers to the speed of wind recorded at a particular location over a given period of time.

[0817] "Location" refers to a particular geographic area for which a user specifies that they want to know the disaster risk.

[0818] "Visual display" refers to presenting the simulation results in a form such as a graph or chart in an easy-to-read format for the user.

[0819] "Server" refers to a central computing device that stores data, runs simulations, and generates and transmits results.

[0820] "User" refers to an individual or entity that uses the System to simulate disaster risks and receives the results.

[0821] This invention is a disaster risk monitoring system that simulates disaster risks using past weather data, disaster data, and topographical data, and provides the results to users in real time. This system operates in cooperation with a server, a terminal, and a user, and is configured as follows:

[0822] The server periodically retrieves past weather, disaster, and terrain data from external, reliable data sources and stores it in a database. This database serves as the basis for running accurate simulations based on the latest information. The server also includes a simulation tool that simulates disaster risk based on user-specified conditions (rainfall, wind speed, location, etc.).

[0823] Users input their desired conditions through an application on their device. This input data is converted to JSON format and sent to the server. The server then runs a simulation based on the received request data and calculates the disaster risk under the specified conditions. For example, it evaluates the risk of flooding or landslides under conditions of 100 mm of rainfall and 10 m / s wind speed at a specific location.

[0824] Simulation results are sent from the server to the terminal in JSON format. This result generation method allows the simulation results to be properly formatted and quickly sent to the user's terminal. The terminal then displays the results to the user in a visually easy-to-understand format. This allows the user to intuitively understand the results and respond quickly to various disaster risks.

[0825] Furthermore, the system includes a monitoring means that monitors disaster risks in real time and sends alerts to user devices as necessary, allowing users to immediately grasp the risks and take appropriate measures even in the event of a sudden disaster.

[0826] As a concrete example, if a user is considering an area for a new home and wants to explore the disaster risk in the presence of 100 mm of rainfall and 10 m / s wind speed, they can generate a prompt like this:

[0827] Example prompt:

[0828] What is the risk of disaster in Tokyo when rainfall is 100mm and wind speed is 10m / s?

[0829] Based on this prompt, the system monitors disaster risks in real time and provides users with simulation results, such as visually displaying high flood risk or medium landslide risk. In this way, users can gain a detailed understanding of disaster risks under specific conditions and make accurate decisions when choosing a place to live or start a business.

[0830] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0831] Step 1:

[0832] The user opens the application on their device and inputs the specified conditions (rainfall, wind speed, location). The input data is in the format of "rainfall: 100 mm," "wind speed: 10 m / s," "location: Tokyo," etc. This data is converted into JSON format and sent to the server.

[0833] Input: User-specified conditions (rainfall, wind speed, location)

[0834] Output: Request data in JSON format

[0835] Step 2:

[0836] The server analyzes the request data received from the terminal and extracts the relevant weather, disaster, and terrain data from the database, thereby preparing a simulation based on the specified conditions.

[0837] Input: Request data in JSON format

[0838] Output: Weather data, disaster data, and topographical data required for simulation

[0839] Step 3:

[0840] The server's simulation tool uses the extracted data to simulate disaster risk under specified conditions (rainfall of 100 mm, wind speed of 10 m / s). Specifically, it calculates the risk of flooding and landslides.

[0841] Input: Weather data, disaster data, and topographical data required for the simulation

[0842] Output: Disaster risk simulation results

[0843] Step 4:

[0844] The server generates the simulation results in an appropriate format (e.g., "Flood risk: high" or "Landslide risk: medium"), which are then converted back into JSON format and sent to the user's device.

[0845] Input: Disaster risk simulation results

[0846] Output: Simulation result data in JSON format

[0847] Step 5:

[0848] The terminal analyzes the simulation results received from the server and displays them to the user in a visually easy-to-understand format (for example, graphs or charts), allowing the user to intuitively understand the results.

[0849] Input: Simulation result data in JSON format

[0850] Output: Visually displayed simulation results

[0851] Step 6:

[0852] The server's monitoring means monitors disaster risks in real time and sends alerts to users' devices as needed. For example, if sudden rainfall or an increase in wind speed is predicted, a warning is sent to the user immediately.

[0853] Input: Real-time weather data

[0854] Output: Alert notification to user terminal

[0855] As a concrete example, a user considering a certain area of ​​Tokyo as a new residence can simulate disaster risk using the following prompts:

[0856] Example prompt:

[0857] What is the risk of disaster in Tokyo when rainfall is 100mm and wind speed is 10m / s?

[0858] This prompt allows the server to perform a simulation under the specified conditions and provide the results to the user.

[0859] 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.

[0860] This invention is a disaster simulation system that supports important decisions regarding residence and business start-up, and also combines an emotion engine that takes into account the user's emotional state. This system operates in cooperation with a server, a terminal, and a user. Specific implementation methods are described below.

[0861] Data collection and storage

[0862] The server periodically retrieves past weather, disaster, and terrain data from reliable external data sources and stores it in a database, allowing simulations to always be based on the latest information.

[0863] Recognizing user emotions and receiving input

[0864] The user enters the desired conditions (e.g., rainfall, wind speed, location) into the application's input form. This input data is converted to JSON format on the user's device and sent to the server. In addition, as the user enters data, the emotion engine analyzes the user's facial expressions and voice to recognize their emotional state.

[0865] Emotion Engine Operation

[0866] The device is equipped with an emotion engine that analyzes the user's facial expressions and voice to recognize their emotional state. This emotion data is sent to the server along with the user's specified conditions. The emotional states selected by the emotion engine are, for example, "relief," "worry," and "excitement."

[0867] Running the simulation

[0868] The server uses its internal simulation engine to simulate disaster risks under specified conditions based on the request data and emotion data received from the user. For example, it calculates the risk of flooding or landslides under conditions of 100 mm of rainfall and 10 m / s wind speed at a specified location.

[0869] Generate and send results

[0870] The server generates results based on the results obtained from the simulation engine, taking into account emotional data. This data is compiled in JSON format. For example, if the user's emotional state is "worried," detailed risk explanations and countermeasures can be added to the simulation results.

[0871] Visual display of results

[0872] The device receives the simulation results from the server and displays them to the user in a visually easy-to-understand format (e.g., graphs or charts). Furthermore, the display format can be customized according to the user's emotional state, making it easier for the user to intuitively understand the results. For example, a standard display format can be used in a "relieved" state, while more detailed information can be added in a "worried" state.

[0873] Specific examples

[0874] For example, if a user is considering a certain area in Tokyo as a new place to live and wants to investigate the disaster risk under the conditions of 100 mm of rainfall and a wind speed of 10 m / s, the following specific example will be explained.

[0875] 1. The user inputs "rainfall: 100 mm," "wind speed: 10 m / s," and "location: Tokyo" into the application form and requests a simulation.

[0876] 2. The device sends the input data in JSON format to the server. At the same time, the emotion engine analyzes the user's facial expressions and voice to recognize the emotional state of "worry."

[0877] 3. The server runs a simulation based on the received input data and emotion data, resulting in "Flood risk: High" and "Landslide risk: Medium."

[0878] 4. The server customizes the simulation results based on the emotional data and sends them to the user's device in JSON format. For example, for the "worried" emotional state, detailed explanations of the risks and countermeasures are added.

[0879] 5. The device analyzes the received simulation results and displays them to the user in a visually easy-to-understand format. Specifically, it displays graphs showing risk levels and lists of countermeasures, helping the user make decisions with confidence.

[0880] This system allows users to understand in detail the disaster risks under specific conditions when choosing a place to live or starting a business, and provides information that takes their emotional state into account, allowing them to make accurate and reassuring decisions.

[0881] The processing flow will be explained below.

[0882] Step 1:

[0883] The server periodically retrieves historical weather, disaster, and terrain data from external reliable data sources and stores it in a database. Collecting and storing this data is important for running simulations based on the most up-to-date information.

[0884] Step 2:

[0885] The user opens the application and inputs the specified conditions (e.g., rainfall, wind speed, location, etc.), which sets the specific simulation conditions.

[0886] Step 3:

[0887] The terminal collects the conditions entered by the user and converts them into JSON format, which is then sent to the server.

[0888] Step 4:

[0889] The terminal sends the entered data to the server using an HTTP request, which the server receives.

[0890] Step 5:

[0891] The device's onboard emotion engine analyzes the user's facial expressions and voice to recognize their emotional state, for example, using a webcam and microphone to determine whether the user is feeling safe or anxious.

[0892] Step 6:

[0893] The device also converts the recognized emotion data into JSON format and sends it to the server along with the simulation conditions collected earlier.

[0894] Step 7:

[0895] The server analyzes the simulation conditions and emotion data received from the device and passes this information to the simulation engine, which then retrieves the necessary data from a past database and simulates disaster risk under the specified conditions.

[0896] Step 8:

[0897] The server generates detailed simulation results, such as flood risk and landslide risk, based on the results obtained from the simulation engine. It also takes into account emotional data and customizes the results according to the user's emotional state.

[0898] Step 9:

[0899] The server compiles the generated simulation results in JSON format and sends them to the user's device. For example, if the user's emotional state is "worried," it adds a detailed explanation of the risk and countermeasures.

[0900] Step 10:

[0901] The device analyzes the simulation results received from the server and displays them to the user in a visually easy-to-understand format (e.g., graphs or charts). The display method is customized according to the user's emotional state, allowing for intuitive understanding. For example, a standard display format is used for a "relieved" state, while more detailed information is added for a "worried" state.

[0902] Specific examples

[0903] For example, if a user is considering a certain area in Tokyo as a new place to live and wants to investigate the disaster risk under the conditions of 100 mm of rainfall and 10 m / s wind speed:

[0904] Step 1:

[0905] The server periodically retrieves weather data, disaster data, and terrain data from external reliable data sources and stores them in a database.

[0906] Step 2:

[0907] The user opens the application and enters the specified conditions: "Rainfall: 100 mm," "Wind speed: 10 m / s," and "Location: Tokyo."

[0908] Step 3:

[0909] The terminal collects the conditions entered by the user and converts them into JSON format.

[0910] Step 4:

[0911] The terminal transmits this input data to the server.

[0912] Step 5:

[0913] The device's emotion engine analyzes the user's facial expressions and voice to recognize the emotional state of "worry."

[0914] Step 6:

[0915] The device converts the recognized emotion data into JSON format and sends it to the server along with the simulation conditions.

[0916] Step 7:

[0917] The server analyzes the received data and passes it to the simulation engine, which then simulates disaster risks under specified conditions.

[0918] Step 8:

[0919] The server generates simulation results such as "Flood risk: high" and "Landslide risk: medium" and customizes them taking into account emotional data.

[0920] Step 9:

[0921] The server sends the customized simulation results to the user's terminal.

[0922] Step 10:

[0923] The device analyzes the simulation results and visually displays detailed risk explanations and countermeasures in response to the "worry" emotional state, allowing users to intuitively understand the risks and make decisions with confidence.

[0924] Example 2

[0925] 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."

[0926] When assessing the risk of natural disasters, many systems perform simulations based solely on numerical data, making it difficult to provide results that take into account the user's emotional state. It is also important to provide simulation results in a format that is intuitively easy to understand, but this is not being done enough. This makes it difficult for users to make important decisions with confidence.

[0927] 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 including a database that stores past weather data, disaster data, and topographical data, simulation means that executes a simulation based on conditions specified by the user, result generation means that generates simulation results and transmits them to the user's terminal, terminal means that includes an emotion engine that analyzes the user's facial expressions and voice and recognizes the user's emotional state, and result generation means that customizes the simulation results taking the emotional state into consideration. This makes it possible to provide intuitive and easy-to-understand simulation results based on the user's emotional state.

[0928] The "server means" is a means including a database that stores past weather data, disaster data, and topographical data.

[0929] The "simulation means" is a means for executing a simulation based on conditions specified by the user.

[0930] The "result generation means" is a means for generating simulation results and transmitting them to the user's terminal.

[0931] The "terminal means" refers to a means including a means for receiving user input and transmitting it to a server, and an emotion engine for analyzing the user's facial expressions and voice to recognize the user's emotional state.

[0932] An "emotion engine" is a software or hardware function that analyzes a user's facial expressions and voice to recognize their emotional state.

[0933] This invention is a disaster simulation system to support important decisions such as residence and business start-up, and is combined with an emotion engine that takes into account the emotional state of the user. This system operates in cooperation with the server, terminals, and users.

[0934] Data collection and storage

[0935] The server periodically obtains past weather data, disaster data, and topographical data from external reliable data sources (e.g., the Japan Meteorological Agency, the Earthquake Research Institute) and stores it in a database. This allows simulations to always be based on the latest information. Data is obtained using API and FTP.

[0936] Receiving user input

[0937] The user enters the desired conditions (for example, rainfall, wind speed, location) into the application's input form. Input items include "rainfall," "wind speed," and "location." This input data is converted to JSON format on the device and sent to the server.

[0938] Emotion recognition by emotion engine

[0939] The device's built-in emotion engine analyzes the user's facial expressions and voice in real time to recognize their emotional state, and this emotional data is also sent to the server in JSON format.

[0940] Running the simulation

[0941] Based on the input data and emotion data received from the user, the server uses an internal simulation engine (e.g., an AI model implemented by the system) to simulate disaster risks under specified conditions.

[0942] Generate and send results

[0943] The server generates the final result based on the simulation results and emotion data. The result is compiled in JSON format and sent to the device. If the emotion data is "worried," the result includes additional risk explanations and countermeasures.

[0944] Visual display of results

[0945] The device analyzes the simulation results received from the server and displays them to the user in a visually easy-to-understand format, customizing the display format according to the user's emotional state.

[0946] Specific examples

[0947] If a user is considering a certain area in Tokyo as a new place to live and wants to investigate the disaster risk under conditions of 100 mm of rainfall and a wind speed of 10 m / s, the following would be done:

[0948] 1. The user inputs "rainfall: 100 mm," "wind speed: 10 m / s," and "location: Tokyo" into the application form and requests a simulation.

[0949] 2. The device sends the input data in JSON format to the server. At the same time, the emotion engine analyzes the user's facial expressions and voice to recognize the emotional state of "worry."

[0950] 3. The server runs a simulation based on the received input data and emotion data, resulting in "Flood risk: High" and "Landslide risk: Medium."

[0951] 4. The server customizes the simulation results based on the emotional data and sends them to the user's device in JSON format. For example, for the "worried" emotional state, detailed explanations of the risks and countermeasures are added.

[0952] 5. The device analyzes the received simulation results and displays them to the user in a visually easy-to-understand format. Specifically, it displays graphs showing risk levels and lists of countermeasures, helping the user make decisions with confidence.

[0953] Prompt Sentence Examples

[0954] "Based on the following inputs, simulate disaster risk at a specific location in Tokyo under conditions of 100 mm of rainfall and 10 m / s wind speed, and generate results including appropriate countermeasures for anxious users."

[0955] In this way, the system of the present invention can provide intuitive and easy-to-understand simulation results based on the user's emotional state.

[0956] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0957] Step 1: Collect and store data

[0958] The server periodically retrieves past weather data, disaster data, and topographical data from external reliable data sources. As a specific example, it retrieves rainfall data for the past week using the Japan Meteorological Agency's API. The retrieved data is then stored in a database on the server. The input here is the weather data retrieved from the API, and the output is the weather data stored in the database. Data retrieval and storage are performed periodically by an automated script.

[0959] Step 2: Accepting User Input

[0960] The user enters the simulation conditions (rainfall, wind speed, location) into the application's input form. The input data is converted to JSON format on the user's device. For example, if the user enters "rainfall: 100 mm", "wind speed: 10 m / s", and "location: Tokyo", the device generates the JSON {"rainfall": 100, "wind_speed": 10, "location": "Tokyo"} and sends it to the server. The input here is the simulation conditions entered by the user, and the output is JSON data.

[0961] Step 3: Emotion recognition by the emotion engine

[0962] An emotion engine installed on the device analyzes the user's facial expressions and voice in real time to recognize their emotional state. As a specific example, a camera and microphone are used to capture the user's facial expressions and voice, and this data is input into the emotion engine. The emotion engine generates emotion data such as "relief," "worry," and "excitement" as the analysis results, and sends it in JSON format to the server. For example, data such as {"emotion": "worried"} is generated. The input here is the user's facial expression and voice data, and the output is JSON data indicating their emotional state.

[0963] Step 4: Run the simulation

[0964] The server uses a simulation engine to simulate disaster risks based on the simulation condition data and emotion data received from the user. As a specific example, the data {"rainfall": 100, "wind_speed": 10, "location": "Tokyo", "emotion": "worried"} is input into the simulation engine, and based on that, the results "Flood risk: high" and "Landslide risk: medium" are generated. The input here is the simulation conditions and emotion data, and the output is the simulation results.

[0965] Step 5: Generate and submit results

[0966] The server generates the final result based on the simulation results and emotion data. If the emotion data is "worried," it creates a result including additional risk explanations and countermeasures, and sends it all together in JSON format to the terminal. As a specific example, it generates the following data: {"flood_risk": "high", "landslide_risk": "medium", "advice": "Take these measures if you are worried"}. The input here is the simulation results and emotion data, and the output is the customized simulation results.

[0967] Step 6: Visualizing the results

[0968] The terminal analyzes the simulation results received from the server and displays them to the user in a visually easy-to-understand format (for example, graphs or charts). The display format can be customized depending on the user's emotional state. For example, it can display a red graph indicating high flood risk, a yellow graph indicating medium landslide risk, and a list of appropriate countermeasures for a "worried" state. The input here is the JSON data of the simulation results, and the output is the visual display content.

[0969] (Application example 2)

[0970] 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."

[0971] Conventional simulation systems only simulate disaster risks based on user-specified conditions, but lack the functionality to provide information that takes into account the user's emotional state and psychological burden. As a result, users have difficulty understanding the simulation results and are unable to alleviate their anxiety and worries. Furthermore, when considering disaster risks in the establishment or renovation planning of physical stores, a flexible response based on emotions is required.

[0972] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0973] In this invention, the server includes means including a database for storing past weather data, disaster data, and topographical data, simulation means for executing a simulation based on conditions and emotional state specified by the user, result generation means for generating simulation results and generating results customized according to the user's emotional state, terminal means for receiving user input, transmitting it to the server, and further analyzing the user's facial expressions and voice to recognize the user's emotional state, and display means for visually displaying the simulation results and optimizing the display format according to the user's emotional state. This makes it easier for users to receive disaster risk information according to their emotional state, enabling them to make decisions more intuitively and with a sense of security.

[0974] A "database" is a storage device that stores past weather data, disaster data, and topographical data, and holds information for performing simulations based on user input.

[0975] A "server" is a computer system that processes data received from users, performs simulations, and generates and transmits the results.

[0976] The "simulation means" is a device or software that has the function of simulating disaster risks based on conditions and emotional states specified by a user and generating the results.

[0977] The "result generation means" is a device or software that has the function of generating simulation results and further generating customized information according to the user's emotional state.

[0978] The "terminal means" is a device that has the function of receiving user input, transmitting it to the server, and analyzing the user's facial expressions and voice to recognize the user's emotional state.

[0979] The "display means" is a device or software that visually displays the simulation results to the user and has the function of optimizing the display format according to the user's emotional state.

[0980] An "emotional state" is a psychological state or emotion that a user is in when providing input data, and may include, for example, "relieved," "worried," or "excited."

[0981] "Disaster risk" refers to the risk of disasters occurring under certain conditions.

[0982] This invention is based on a disaster simulation system, and by customizing the results taking into account the user's emotional state, it reduces the psychological burden on the user and enables them to make decisions more intuitively and with a sense of security. This system works in conjunction with a server and terminal, providing users with visual and emotionally sensitive disaster risk information.

[0983] Data collection and storage

[0984] The server periodically retrieves historical weather, disaster, and terrain data from reliable external data sources and stores it in a database. This ensures that the most up-to-date and accurate information is used during simulation. Specifically, the server retrieves data from an API using the Python requests library and stores it in a cloud database such as AWS RDS.

[0985] Recognizing user emotions and receiving input

[0986] The user uses the device to input conditions such as rainfall, wind speed, and location into the application's input form. This input data is converted into JSON format and sent to the server. At the same time, the device is equipped with an emotion engine that analyzes the user's facial expressions and voice to recognize their emotional state. This emotion data is also sent to the server. Apple's CoreML is used for emotion recognition, and Google's Speech-to-Text API is used for voice analysis.

[0987] Running the simulation

[0988] The server uses its internal simulation engine to simulate disaster risks based on the condition and emotion data received from users. This simulation engine uses MATLAB and the Python Scipy library. As a specific example, it calculates the risk of landslides and floods under conditions of 150 mm of rainfall and 20 m / s wind speed.

[0989] Generate and send results

[0990] The server generates customized results based on the simulation results, taking into account the user's emotional state. For example, if the user's emotional state is "worried," the server adds detailed risk explanations and countermeasures to the simulation results. This data is sent to the device in JSON format.

[0991] Visual display of results

[0992] The device receives the simulation results from the server and displays them to the user in a visually easy-to-understand format. It uses D3.js to draw graphs and charts, and customizes the display format depending on the user's emotional state. For example, a user in the "worried" emotional state might display detailed risk information, while a user in the "relieved" emotional state might display a concise summary.

[0993] Specific examples

[0994] For example, if a user is considering a certain area in Osaka as the location for a new store and wants to investigate the disaster risk under the conditions of "rainfall: 150 mm" and "wind speed: 20 m / s," the system will operate as follows:

[0995] 1. The user enters "rainfall: 150 mm," "wind speed: 20 m / s," and "location: Osaka" into the app form and requests a simulation.

[0996] 2. The device sends the input data in JSON format to the server, and at the same time the emotion engine recognizes the emotional state of "worry."

[0997] 3. The server runs a simulation based on the received data and obtains the results "Flood risk: Very high" and "Landslide risk: High."

[0998] 4. The server adds detailed risk explanations and countermeasures to the simulation results, taking into account emotional data, and sends them to the terminal in JSON format.

[0999] 5. The terminal processes the received results into a visually easy-to-understand format (graphs or charts) and displays them to the user.

[1000] Prompt Sentence Examples

[1001] For example, the prompt sentence "Use MATLAB to generate Python code that simulates disaster risk based on specified rainfall and wind speed data" is input to the generative AI model.

[1002] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1003] Step 1:

[1004] The server periodically retrieves historical weather data, disaster data, and terrain data from external reliable data sources. Data is retrieved from the API as input and stored in a database on the server. This processing is performed using the Python requests library, and data is retrieved in JSON format. AWS RDS is used to store the database. The data includes weather conditions, disaster history, and terrain information.

[1005] Step 2:

[1006] The user uses their device to input conditions such as rainfall, wind speed, and location into the application's input form. This input data is converted to JSON format on the device. The emotion engine then analyzes the user's facial expressions and voice to recognize their emotional state. Emotion recognition uses Apple's CoreML and Google's Speech-to-Text API. The user's input data and emotion data are sent from the device to the server.

[1007] Step 3:

[1008] The server uses its internal simulation engine to simulate disaster risks based on the received condition and emotion data. The simulation engine uses MATLAB and Python's Scipy library. Input data includes rainfall, wind speed, and location information, and outputs flood risk and landslide risk. This output data is used in the next step.

[1009] Step 4:

[1010] The server then customizes the simulation results by taking into account the user's emotional state. For example, if the user is in an "anxious" emotional state, the server adds detailed risk explanations and countermeasures to the simulation results. The customized result data generated by this process is sent to the device in JSON format.

[1011] Step 5:

[1012] The device receives the simulation results from the server and displays them to the user in a visually easy-to-understand format. Graphs and charts are drawn using D3.js. The display format can also be customized according to the user's emotional state. For example, if the user's emotional state is "worried," detailed risk information is displayed, while if the user's emotional state is "relieved," a concise summary is displayed.

[1013] Examples:

[1014] For example, if a user is considering a certain area in Osaka as the location for a new store and wants to investigate the disaster risk under the conditions of "rainfall: 150 mm" and "wind speed: 20 m / s," the system will operate as follows:

[1015] 1. The user enters "rainfall: 150 mm," "wind speed: 20 m / s," and "location: Osaka" into the app form and requests a simulation.

[1016] 2. The device sends the input data in JSON format to the server, and at the same time the emotion engine recognizes the emotional state of "worry."

[1017] 3. The server runs a simulation based on the received data and obtains the results "Flood risk: Very high" and "Landslide risk: High."

[1018] 4. The server adds detailed risk explanations and countermeasures to the simulation results, taking into account emotional data, and sends them to the terminal in JSON format.

[1019] 5. The terminal processes the received results into a visually easy-to-understand format (graphs or charts) and displays them to the user.

[1020] Example prompt sentence:

[1021] For example, the prompt sentence "Use MATLAB to generate Python code that simulates disaster risk based on specified rainfall and wind speed data" is input to the generative AI model.

[1022] 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.

[1023] 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.

[1024] 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.

[1025] [Fourth embodiment]

[1026] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1027] 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.

[1028] 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).

[1029] 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.

[1030] 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.

[1031] 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).

[1032] 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.

[1033] 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.

[1034] 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.

[1035] 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.

[1036] 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.

[1037] 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.

[1038] 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."

[1039] The present invention is a disaster simulation system that supports important decisions regarding residence or business start-up. This system operates in cooperation with three parties: a server, a terminal, and a user. A specific implementation method for this system is described below.

[1040] Data collection and storage

[1041] The server periodically retrieves past weather, disaster, and terrain data from reliable external data sources and stores it in a database, allowing simulations to always be based on the latest information.

[1042] Receiving user input

[1043] The user enters the desired conditions (e.g., rainfall, wind speed, location) into the application's input form. This input data is converted to JSON format on the user's device and sent to the server.

[1044] Running the simulation

[1045] Based on the request data received from the user, the server uses its internal simulation engine to simulate disaster risks under specified conditions, such as calculating the risk of flooding or landslides at a specified location under conditions of 100 mm of rainfall and 10 m / s wind speed.

[1046] Generate and send results

[1047] The server compiles the data generated as a result of the simulation (e.g., "Flood risk: high," "Landslide risk: medium," etc.) and sends it to the user's device in JSON format. The result generation means then properly formats and transmits this data.

[1048] Visual display of results

[1049] The terminal displays the simulation results received from the server to the user in a visually easy-to-understand format (for example, graphs or charts), allowing the user to intuitively understand the results and make decisions more easily.

[1050] Specific examples

[1051] For example, a user is considering a certain area in Tokyo as a new place to live and wants to investigate the disaster risk under the conditions of 100 mm of rainfall and 10 m / s wind speed based on past weather data.

[1052] 1. The user enters "Rainfall: 100 mm", "Wind speed: 10 m / s", and "Location: Tokyo" into the application form.

[1053] 2. The terminal sends the entered data to the server in JSON format.

[1054] 3. The server runs a simulation based on the received input data, resulting in "Flood risk: High" and "Landslide risk: Medium."

[1055] 4. The server sends the simulation results in JSON format to the user's device.

[1056] 5. The terminal visually displays the received simulation results.

[1057] This system allows users to gain a detailed understanding of disaster risks under specific conditions and make accurate decisions when choosing a place to live or starting a business.

[1058] The processing flow will be explained below.

[1059] Step 1:

[1060] The server periodically retrieves past weather, disaster, and terrain data from reliable external data sources and stores it in a database, enabling simulations to be performed based on the most up-to-date information.

[1061] Step 2:

[1062] The user opens the application and inputs the specified conditions, such as rainfall, wind speed, location, and other parameters, into an input form.

[1063] Step 3:

[1064] The terminal collects the parameters entered by the user and converts them into JSON format for sending to the server, which then sends the converted data to the server as an HTTP request.

[1065] Step 4:

[1066] The server receives the JSON-formatted data sent from the device, analyzes it, and passes it to the simulation engine, which then retrieves the necessary data from a past database and runs a disaster simulation under the specified conditions.

[1067] Step 5:

[1068] The server uses the results obtained from the simulation engine to generate detailed simulation results such as flood risk and landslide risk, and this data is compiled in JSON format.

[1069] Step 6:

[1070] The server sends the generated simulation results to the terminal, which are formatted in a way that is intuitively understandable to the user.

[1071] Step 7:

[1072] The terminal analyzes the simulation results received from the server and displays them to the user in a visually easy-to-understand format (e.g., graphs and charts), which helps the user intuitively understand the disaster risk under specific conditions.

[1073] As a specific example, a user is considering a certain area in Tokyo as a new place to live.

[1074] Step 1:

[1075] The server periodically retrieves weather data, disaster data, and terrain data from external reliable data sources and stores them in a database.

[1076] Step 2:

[1077] The user opens the application and enters the specified conditions: "Rainfall: 100 mm," "Wind speed: 10 m / s," and "Location: Tokyo."

[1078] Step 3:

[1079] The terminal collects the parameters entered by the user, converts them into JSON format, and sends them to the server as an HTTP request.

[1080] Step 4:

[1081] The server receives the data sent from the device and passes it to the simulation engine for analysis, using past weather data, disaster data, and topographical data.

[1082] Step 5:

[1083] The server generates the simulation results "Flood risk: high" and "Landslide risk: medium" and summarizes them in JSON format.

[1084] Step 6:

[1085] The server transmits the generated simulation results to the terminal.

[1086] Step 7:

[1087] The device analyzes the simulation results received from the server and displays them to the user in a visually easy-to-understand format, allowing the user to intuitively understand the disaster risk under specified conditions and make decisions about where to live.

[1088] Example 1

[1089] 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."

[1090] Currently, there is a lack of systems that can predict and visually present detailed risks based on past weather and disaster data when selecting a place to live or start a business. There is also a need for a method that can flexibly perform simulations based on specific conditions specified by the user and provide the results quickly and in an easy-to-understand manner.

[1091] 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.

[1092] In this invention, the server includes means for periodically obtaining past weather data, disaster data, and topographical data from an external reliable data source and storing the data in a database, means for inputting conditions specified by a user into an application input form, converting the data into JSON format, and sending the JSON format to the server, means for simulating disaster risk using an internal simulation engine based on the request data received from the user, means for sending the simulation results in JSON format to a terminal, and means for displaying the simulation results received from the server to the user in a visually easy-to-understand format. This allows the user to predict disaster risk under specific conditions in detail and obtain the results in a visually easy-to-understand format.

[1093] A "server" is a device or system that periodically retrieves data from external data sources and stores it in a database.

[1094] A "database" is a system for storing and managing acquired past weather data, disaster data, and topographical data.

[1095] A "terminal" is a device or system through which a user inputs simulation conditions and transmits data to a server.

[1096] "JSON format" is an abbreviation for JavaScript Object Notation, and is a lightweight data exchange format for structuring and representing data.

[1097] A "simulation engine" is a program or system for calculating and predicting disaster risks based on conditions received from a user.

[1098] "Simulation" is the process of calculating disaster risk under specified conditions and predicting the consequences.

[1099] The "result generation means" is a system for generating simulation results in JSON format and sending them to the user's terminal.

[1100] The "visual display means" is a system for displaying the received simulation results in the form of graphs, charts, etc. so that the user can intuitively understand them.

[1101] "Weather data" refers to past data related to weather, specifically including rainfall and wind speed.

[1102] "Disaster data" refers to data related to natural disasters that have occurred in the past, including floods and landslides.

[1103] "Topographic data" refers to data relating to geographical features, including information on land elevation, geology, etc.

[1104] The present invention is a disaster simulation system that supports important decisions regarding residence or business start-up. This system operates in cooperation with three parties: a server, a terminal, and a user. A specific implementation method for this system is described below.

[1105] Data collection and storage

[1106] The server periodically retrieves historical weather, disaster, and terrain data from external, reliable data sources and stores it in a database. The server is configured with software that executes API calls using Python or scripts. The database is a relational database such as PostgreSQL.

[1107] Receiving user input

[1108] The user enters the desired conditions (for example, rainfall, wind speed, and location) into the application's input form. This data is converted to JSON format on the user's device. The device uses HTML and JavaScript to collect the form data and send an HTTP POST request to the server. Conditions entered by the user include "rainfall: 100 mm," "wind speed: 10 m / s," and "location: Tokyo."

[1109] Running the simulation

[1110] Based on the request data received from the user, the server uses its internal simulation engine to simulate disaster risk under specified conditions. For example, a simulation script implemented in Python is used to calculate flood risk and landslide risk, utilizing numerical analysis libraries such as NumPy and SciPy. Information such as rainfall, wind speed, and location is used as simulation conditions.

[1111] Generate and send results

[1112] The server compiles the data generated as a result of the simulation (e.g., "Flood risk: high," "Landslide risk: medium," etc.) and sends it to the user's device in JSON format. The result generation means then properly formats and transmits this data.

[1113] Visual display of results

[1114] The terminal receives the simulation results from the server and displays them to the user in a visually easy-to-understand format (for example, graphs or charts). The visual display uses the JavaScript D3.js library, which allows the user to intuitively understand the results and make decisions more easily.

[1115] Specific examples

[1116] For example, a user is considering a certain area in Tokyo as a new place to live. He wants to investigate the disaster risk based on historical weather data under the condition of 100 mm of rainfall and 10 m / s wind speed. Here is a specific example:

[1117] 1. The user enters "Rainfall: 100 mm", "Wind speed: 10 m / s", and "Location: Tokyo" into the application form.

[1118] 2. The terminal sends the entered data to the server in JSON format.

[1119] 3. The server runs a simulation based on the received input data, resulting in "Flood risk: High" and "Landslide risk: Medium."

[1120] 4. The server sends the simulation results in JSON format to the user's device.

[1121] 5. The terminal visually displays the received simulation results.

[1122] This system allows users to gain a detailed understanding of disaster risks under specific conditions and make accurate decisions when choosing a place to live or starting a business.

[1123] An example of a prompt is "Simulate the disaster risk in Tokyo with 100 mm of rainfall and a wind speed of 10 m / s."

[1124] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1125] Step 1:

[1126] Data collection and storage

[1127] The server periodically retrieves historical weather, disaster, and terrain data from external, reliable data sources. Specifically, the server sends API requests to retrieve the data and stores it in a database. The API endpoint and authentication information are required as input, and the collected data is stored in the database as output. For example, a scheduled task using a Python script pulls the data from the API and stores it in PostgreSQL.

[1128] Step 2:

[1129] Receiving user input

[1130] The user enters the simulation conditions (rainfall, wind speed, location) into the application's input form. This input data is converted into JSON format on the user's device. Specifically, the user enters data into the form through a web browser, and JavaScript collects it and converts it into JSON format. The input includes the rainfall, wind speed, and location entered by the user, and JSON format data is generated as the output. For example, the user enters "rainfall: 100 mm," "wind speed: 10 m / s," and "location: Tokyo."

[1131] Step 3:

[1132] Running the simulation

[1133] The server uses a simulation engine to simulate disaster risk based on the request data received from the user. Specifically, it parses the received JSON data and passes it to the simulation engine for calculations. The inputs required are the JSON data sent by the user, the internal simulation engine, and past weather and topographical data. The output is the simulation results (e.g., "Flood risk: high," "Landslide risk: medium"). For example, a simulation script implemented in Python calculates risk using libraries such as NumPy and SciPy.

[1134] Step 4:

[1135] Generate and send results

[1136] The server sends the simulation results in JSON format to the user's device. Specifically, it formats the simulation results appropriately and sends them to the user's device as an HTTP response. The input is the simulation calculation result, and the output is formatted JSON data sent as an HTTP response. For example, the JSON data generated as a result of the simulation is sent to the user's device.

[1137] Step 5:

[1138] Visual display of results

[1139] The terminal displays the simulation results received from the server to the user in a visually easy-to-understand format. Specifically, it uses the JavaScript D3.js library to display the results in graphs and charts. The input is the simulation results in JSON format sent from the server, and the output is displayed in a visually easy-to-understand format. For example, a "high" flood risk or a "medium" landslide risk is displayed as a graph in the browser.

[1140] An example prompt based on a generative AI model is "Simulate the disaster risk in Tokyo with 100 mm of rainfall and a wind speed of 10 m / s."

[1141] (Application example 1)

[1142] 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."

[1143] Conventional disaster simulation systems mainly make predictions based on past data, making it difficult to adapt to changing disaster risks in real time. Furthermore, when users grasp the disaster risk under specific conditions, they do not provide enough information to take prompt and appropriate action. This makes it difficult for users to take appropriate evacuation actions in response to the predicted risks.

[1144] 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.

[1145] In this invention, the server includes database means for storing past weather data, disaster data, and topographical data, simulation means for executing simulations based on conditions specified by the user, result generation means for generating simulation results and sending them to the user's terminal, terminal means for receiving user input and sending them to the server, and monitoring means for monitoring disaster risks in real time and sending alerts. This allows the system to not only support users in making residential and business decisions, but also enable immediate responses to disaster risks that change in real time.

[1146] The "database means" is a system for storing past weather data, disaster data, and topographical data.

[1147] The "simulation means" is a function for predicting disaster risks based on conditions specified by the user and executing simulations.

[1148] The "result generation means" is a function for generating simulation results and transmitting the results to the user's terminal.

[1149] "Terminal means" refers to equipment or applications that receive user input and transmit that information to the server.

[1150] "Monitoring means" refers to the function of monitoring disaster risks in real time and sending alerts as necessary.

[1151] "Rainfall" refers to the amount of rain that falls in a particular location within a certain period of time.

[1152] "Wind speed" refers to the speed of wind recorded at a particular location over a given period of time.

[1153] "Location" refers to a particular geographic area for which a user specifies that they want to know the disaster risk.

[1154] "Visual display" refers to presenting the simulation results in a form such as a graph or chart in an easy-to-read format for the user.

[1155] "Server" refers to a central computing device that stores data, runs simulations, and generates and transmits results.

[1156] "User" refers to an individual or entity that uses the System to simulate disaster risks and receives the results.

[1157] This invention is a disaster risk monitoring system that simulates disaster risks using past weather data, disaster data, and topographical data, and provides the results to users in real time. This system operates in cooperation with a server, a terminal, and a user, and is configured as follows:

[1158] The server periodically retrieves past weather, disaster, and terrain data from external, reliable data sources and stores it in a database. This database serves as the basis for running accurate simulations based on the latest information. The server also includes a simulation tool that simulates disaster risk based on user-specified conditions (rainfall, wind speed, location, etc.).

[1159] Users input their desired conditions through an application on their device. This input data is converted to JSON format and sent to the server. The server then runs a simulation based on the received request data and calculates the disaster risk under the specified conditions. For example, it evaluates the risk of flooding or landslides under conditions of 100 mm of rainfall and 10 m / s wind speed at a specific location.

[1160] Simulation results are sent from the server to the terminal in JSON format. This result generation method allows the simulation results to be properly formatted and quickly sent to the user's terminal. The terminal then displays the results to the user in a visually easy-to-understand format. This allows the user to intuitively understand the results and respond quickly to various disaster risks.

[1161] Furthermore, the system includes a monitoring means that monitors disaster risks in real time and sends alerts to user devices as necessary, allowing users to immediately grasp the risks and take appropriate measures even in the event of a sudden disaster.

[1162] As a concrete example, if a user is considering an area for a new home and wants to explore the disaster risk in the presence of 100 mm of rainfall and 10 m / s wind speed, they can generate a prompt like this:

[1163] Example prompt:

[1164] What is the risk of disaster in Tokyo when rainfall is 100mm and wind speed is 10m / s?

[1165] Based on this prompt, the system monitors disaster risks in real time and provides users with simulation results, such as visually displaying high flood risk or medium landslide risk. In this way, users can gain a detailed understanding of disaster risks under specific conditions and make accurate decisions when choosing a place to live or start a business.

[1166] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1167] Step 1:

[1168] The user opens the application on their device and inputs the specified conditions (rainfall, wind speed, location). The input data is in the format of "rainfall: 100 mm," "wind speed: 10 m / s," "location: Tokyo," etc. This data is converted into JSON format and sent to the server.

[1169] Input: User-specified conditions (rainfall, wind speed, location)

[1170] Output: Request data in JSON format

[1171] Step 2:

[1172] The server analyzes the request data received from the terminal and extracts the relevant weather, disaster, and terrain data from the database, thereby preparing a simulation based on the specified conditions.

[1173] Input: Request data in JSON format

[1174] Output: Weather data, disaster data, and topographical data required for simulation

[1175] Step 3:

[1176] The server's simulation tool uses the extracted data to simulate disaster risk under specified conditions (rainfall of 100 mm, wind speed of 10 m / s). Specifically, it calculates the risk of flooding and landslides.

[1177] Input: Weather data, disaster data, and topographical data required for the simulation

[1178] Output: Disaster risk simulation results

[1179] Step 4:

[1180] The server generates the simulation results in an appropriate format (e.g., "Flood risk: high" or "Landslide risk: medium"), which are then converted back into JSON format and sent to the user's device.

[1181] Input: Disaster risk simulation results

[1182] Output: Simulation result data in JSON format

[1183] Step 5:

[1184] The terminal analyzes the simulation results received from the server and displays them to the user in a visually easy-to-understand format (for example, graphs or charts), allowing the user to intuitively understand the results.

[1185] Input: Simulation result data in JSON format

[1186] Output: Visually displayed simulation results

[1187] Step 6:

[1188] The server's monitoring means monitors disaster risks in real time and sends alerts to users' devices as needed. For example, if sudden rainfall or an increase in wind speed is predicted, a warning is sent to the user immediately.

[1189] Input: Real-time weather data

[1190] Output: Alert notification to user terminal

[1191] As a concrete example, a user considering a certain area of ​​Tokyo as a new residence can simulate disaster risk using the following prompts:

[1192] Example prompt:

[1193] What is the risk of disaster in Tokyo when rainfall is 100mm and wind speed is 10m / s?

[1194] This prompt allows the server to perform a simulation under the specified conditions and provide the results to the user.

[1195] 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.

[1196] This invention is a disaster simulation system that supports important decisions regarding residence and business start-up, and also combines an emotion engine that takes into account the user's emotional state. This system operates in cooperation with a server, a terminal, and a user. Specific implementation methods are described below.

[1197] Data collection and storage

[1198] The server periodically retrieves past weather, disaster, and terrain data from reliable external data sources and stores it in a database, allowing simulations to always be based on the latest information.

[1199] Recognizing user emotions and receiving input

[1200] The user enters the desired conditions (e.g., rainfall, wind speed, location) into the application's input form. This input data is converted to JSON format on the user's device and sent to the server. In addition, as the user enters data, the emotion engine analyzes the user's facial expressions and voice to recognize their emotional state.

[1201] Emotion Engine Operation

[1202] The device is equipped with an emotion engine that analyzes the user's facial expressions and voice to recognize their emotional state. This emotion data is sent to the server along with the user's specified conditions. The emotional states selected by the emotion engine are, for example, "relief," "worry," and "excitement."

[1203] Running the simulation

[1204] The server uses its internal simulation engine to simulate disaster risks under specified conditions based on the request data and emotion data received from the user. For example, it calculates the risk of flooding or landslides under conditions of 100 mm of rainfall and 10 m / s wind speed at a specified location.

[1205] Generate and send results

[1206] The server generates results based on the results obtained from the simulation engine, taking into account emotional data. This data is compiled in JSON format. For example, if the user's emotional state is "worried," detailed risk explanations and countermeasures can be added to the simulation results.

[1207] Visual display of results

[1208] The device receives the simulation results from the server and displays them to the user in a visually easy-to-understand format (e.g., graphs or charts). Furthermore, the display format can be customized according to the user's emotional state, making it easier for the user to intuitively understand the results. For example, a standard display format can be used in a "relieved" state, while more detailed information can be added in a "worried" state.

[1209] Specific examples

[1210] For example, if a user is considering a certain area in Tokyo as a new place to live and wants to investigate the disaster risk under the conditions of 100 mm of rainfall and a wind speed of 10 m / s, the following specific example will be explained.

[1211] 1. The user inputs "rainfall: 100 mm," "wind speed: 10 m / s," and "location: Tokyo" into the application form and requests a simulation.

[1212] 2. The device sends the input data in JSON format to the server. At the same time, the emotion engine analyzes the user's facial expressions and voice to recognize the emotional state of "worry."

[1213] 3. The server runs a simulation based on the received input data and emotion data, resulting in "Flood risk: High" and "Landslide risk: Medium."

[1214] 4. The server customizes the simulation results based on the emotional data and sends them to the user's device in JSON format. For example, for the "worried" emotional state, detailed explanations of the risks and countermeasures are added.

[1215] 5. The device analyzes the received simulation results and displays them to the user in a visually easy-to-understand format. Specifically, it displays graphs showing risk levels and lists of countermeasures, helping the user make decisions with confidence.

[1216] This system allows users to understand in detail the disaster risks under specific conditions when choosing a place to live or starting a business, and provides information that takes their emotional state into account, allowing them to make accurate and reassuring decisions.

[1217] The processing flow will be explained below.

[1218] Step 1:

[1219] The server periodically retrieves historical weather, disaster, and terrain data from external reliable data sources and stores it in a database. Collecting and storing this data is important for running simulations based on the most up-to-date information.

[1220] Step 2:

[1221] The user opens the application and inputs the specified conditions (e.g., rainfall, wind speed, location, etc.), which sets the specific simulation conditions.

[1222] Step 3:

[1223] The terminal collects the conditions entered by the user and converts them into JSON format, which is then sent to the server.

[1224] Step 4:

[1225] The terminal sends the entered data to the server using an HTTP request, which the server receives.

[1226] Step 5:

[1227] The device's onboard emotion engine analyzes the user's facial expressions and voice to recognize their emotional state, for example, using a webcam and microphone to determine whether the user is feeling safe or anxious.

[1228] Step 6:

[1229] The device also converts the recognized emotion data into JSON format and sends it to the server along with the simulation conditions collected earlier.

[1230] Step 7:

[1231] The server analyzes the simulation conditions and emotion data received from the device and passes this information to the simulation engine, which then retrieves the necessary data from a past database and simulates disaster risk under the specified conditions.

[1232] Step 8:

[1233] The server generates detailed simulation results, such as flood risk and landslide risk, based on the results obtained from the simulation engine. It also takes into account emotional data and customizes the results according to the user's emotional state.

[1234] Step 9:

[1235] The server compiles the generated simulation results in JSON format and sends them to the user's device. For example, if the user's emotional state is "worried," it adds a detailed explanation of the risk and countermeasures.

[1236] Step 10:

[1237] The device analyzes the simulation results received from the server and displays them to the user in a visually easy-to-understand format (e.g., graphs or charts). The display method is customized according to the user's emotional state, allowing for intuitive understanding. For example, a standard display format is used for a "relieved" state, while more detailed information is added for a "worried" state.

[1238] Specific examples

[1239] For example, if a user is considering a certain area in Tokyo as a new place to live and wants to investigate the disaster risk under the conditions of 100 mm of rainfall and 10 m / s wind speed:

[1240] Step 1:

[1241] The server periodically retrieves weather data, disaster data, and terrain data from external reliable data sources and stores them in a database.

[1242] Step 2:

[1243] The user opens the application and enters the specified conditions: "Rainfall: 100 mm," "Wind speed: 10 m / s," and "Location: Tokyo."

[1244] Step 3:

[1245] The terminal collects the conditions entered by the user and converts them into JSON format.

[1246] Step 4:

[1247] The terminal transmits this input data to the server.

[1248] Step 5:

[1249] The device's emotion engine analyzes the user's facial expressions and voice to recognize the emotional state of "worry."

[1250] Step 6:

[1251] The device converts the recognized emotion data into JSON format and sends it to the server along with the simulation conditions.

[1252] Step 7:

[1253] The server analyzes the received data and passes it to the simulation engine, which then simulates disaster risks under specified conditions.

[1254] Step 8:

[1255] The server generates simulation results such as "Flood risk: high" and "Landslide risk: medium" and customizes them taking into account emotional data.

[1256] Step 9:

[1257] The server sends the customized simulation results to the user's terminal.

[1258] Step 10:

[1259] The device analyzes the simulation results and visually displays detailed risk explanations and countermeasures in response to the "worry" emotional state, allowing users to intuitively understand the risks and make decisions with confidence.

[1260] Example 2

[1261] 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."

[1262] When assessing the risk of natural disasters, many systems perform simulations based solely on numerical data, making it difficult to provide results that take into account the user's emotional state. It is also important to provide simulation results in a format that is intuitively easy to understand, but this is not being done enough. This makes it difficult for users to make important decisions with confidence.

[1263] 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 including a database that stores past weather data, disaster data, and topographical data, simulation means that executes a simulation based on conditions specified by the user, result generation means that generates simulation results and transmits them to the user's terminal, terminal means that includes an emotion engine that analyzes the user's facial expressions and voice and recognizes the user's emotional state, and result generation means that customizes the simulation results taking the emotional state into consideration. This makes it possible to provide intuitive and easy-to-understand simulation results based on the user's emotional state.

[1264] The "server means" is a means including a database that stores past weather data, disaster data, and topographical data.

[1265] The "simulation means" is a means for executing a simulation based on conditions specified by the user.

[1266] The "result generation means" is a means for generating simulation results and transmitting them to the user's terminal.

[1267] The "terminal means" refers to a means including a means for receiving user input and transmitting it to a server, and an emotion engine for analyzing the user's facial expressions and voice to recognize the user's emotional state.

[1268] An "emotion engine" is a software or hardware function that analyzes a user's facial expressions and voice to recognize their emotional state.

[1269] This invention is a disaster simulation system to support important decisions such as residence and business start-up, and is combined with an emotion engine that takes into account the emotional state of the user. This system operates in cooperation with the server, terminals, and users.

[1270] Data collection and storage

[1271] The server periodically obtains past weather data, disaster data, and topographical data from external reliable data sources (e.g., the Japan Meteorological Agency, the Earthquake Research Institute) and stores it in a database. This allows simulations to always be based on the latest information. Data is obtained using API and FTP.

[1272] Receiving user input

[1273] The user enters the desired conditions (for example, rainfall, wind speed, location) into the application's input form. Input items include "rainfall," "wind speed," and "location." This input data is converted to JSON format on the device and sent to the server.

[1274] Emotion recognition by emotion engine

[1275] The device's built-in emotion engine analyzes the user's facial expressions and voice in real time to recognize their emotional state, and this emotional data is also sent to the server in JSON format.

[1276] Running the simulation

[1277] Based on the input data and emotion data received from the user, the server uses an internal simulation engine (e.g., an AI model implemented by the system) to simulate disaster risks under specified conditions.

[1278] Generate and send results

[1279] The server generates the final result based on the simulation results and emotion data. The result is compiled in JSON format and sent to the device. If the emotion data is "worried," the result includes additional risk explanations and countermeasures.

[1280] Visual display of results

[1281] The device analyzes the simulation results received from the server and displays them to the user in a visually easy-to-understand format, customizing the display format according to the user's emotional state.

[1282] Specific examples

[1283] If a user is considering a certain area in Tokyo as a new place to live and wants to investigate the disaster risk under conditions of 100 mm of rainfall and a wind speed of 10 m / s, the following would be done:

[1284] 1. The user inputs "rainfall: 100 mm," "wind speed: 10 m / s," and "location: Tokyo" into the application form and requests a simulation.

[1285] 2. The device sends the input data in JSON format to the server. At the same time, the emotion engine analyzes the user's facial expressions and voice to recognize the emotional state of "worry."

[1286] 3. The server runs a simulation based on the received input data and emotion data, resulting in "Flood risk: High" and "Landslide risk: Medium."

[1287] 4. The server customizes the simulation results based on the emotional data and sends them to the user's device in JSON format. For example, for the "worried" emotional state, detailed explanations of the risks and countermeasures are added.

[1288] 5. The device analyzes the received simulation results and displays them to the user in a visually easy-to-understand format. Specifically, it displays graphs showing risk levels and lists of countermeasures, helping the user make decisions with confidence.

[1289] Prompt Sentence Examples

[1290] "Based on the following inputs, simulate disaster risk at a specific location in Tokyo under conditions of 100 mm of rainfall and 10 m / s wind speed, and generate results including appropriate countermeasures for anxious users."

[1291] In this way, the system of the present invention can provide intuitive and easy-to-understand simulation results based on the user's emotional state.

[1292] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1293] Step 1: Collect and store data

[1294] The server periodically retrieves past weather data, disaster data, and topographical data from external reliable data sources. As a specific example, it retrieves rainfall data for the past week using the Japan Meteorological Agency's API. The retrieved data is then stored in a database on the server. The input here is the weather data retrieved from the API, and the output is the weather data stored in the database. Data retrieval and storage are performed periodically by an automated script.

[1295] Step 2: Accepting User Input

[1296] The user enters the simulation conditions (rainfall, wind speed, location) into the application's input form. The input data is converted to JSON format on the user's device. For example, if the user enters "rainfall: 100 mm", "wind speed: 10 m / s", and "location: Tokyo", the device generates the JSON {"rainfall": 100, "wind_speed": 10, "location": "Tokyo"} and sends it to the server. The input here is the simulation conditions entered by the user, and the output is JSON data.

[1297] Step 3: Emotion recognition by the emotion engine

[1298] An emotion engine installed on the device analyzes the user's facial expressions and voice in real time to recognize their emotional state. As a specific example, a camera and microphone are used to capture the user's facial expressions and voice, and this data is input into the emotion engine. The emotion engine generates emotion data such as "relief," "worry," and "excitement" as the analysis results, and sends it in JSON format to the server. For example, data such as {"emotion": "worried"} is generated. The input here is the user's facial expression and voice data, and the output is JSON data indicating their emotional state.

[1299] Step 4: Run the simulation

[1300] The server uses a simulation engine to simulate disaster risks based on the simulation condition data and emotion data received from the user. As a specific example, the data {"rainfall": 100, "wind_speed": 10, "location": "Tokyo", "emotion": "worried"} is input into the simulation engine, and based on that, the results "Flood risk: high" and "Landslide risk: medium" are generated. The input here is the simulation conditions and emotion data, and the output is the simulation results.

[1301] Step 5: Generate and submit results

[1302] The server generates the final result based on the simulation results and emotion data. If the emotion data is "worried," it creates a result including additional risk explanations and countermeasures, and sends it all together in JSON format to the terminal. As a specific example, it generates the following data: {"flood_risk": "high", "landslide_risk": "medium", "advice": "Take these measures if you are worried"}. The input here is the simulation results and emotion data, and the output is the customized simulation results.

[1303] Step 6: Visualizing the results

[1304] The terminal analyzes the simulation results received from the server and displays them to the user in a visually easy-to-understand format (for example, graphs or charts). The display format can be customized depending on the user's emotional state. For example, it can display a red graph indicating high flood risk, a yellow graph indicating medium landslide risk, and a list of appropriate countermeasures for a "worried" state. The input here is the JSON data of the simulation results, and the output is the visual display content.

[1305] (Application example 2)

[1306] 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."

[1307] Conventional simulation systems only simulate disaster risks based on user-specified conditions, but lack the functionality to provide information that takes into account the user's emotional state and psychological burden. As a result, users have difficulty understanding the simulation results and are unable to alleviate their anxiety and worries. Furthermore, when considering disaster risks in the establishment or renovation planning of physical stores, a flexible response based on emotions is required.

[1308] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1309] In this invention, the server includes means including a database for storing past weather data, disaster data, and topographical data, simulation means for executing a simulation based on conditions and emotional state specified by the user, result generation means for generating simulation results and generating results customized according to the user's emotional state, terminal means for receiving user input, transmitting it to the server, and further analyzing the user's facial expressions and voice to recognize the user's emotional state, and display means for visually displaying the simulation results and optimizing the display format according to the user's emotional state. This makes it easier for users to receive disaster risk information according to their emotional state, enabling them to make decisions more intuitively and with a sense of security.

[1310] A "database" is a storage device that stores past weather data, disaster data, and topographical data, and holds information for performing simulations based on user input.

[1311] A "server" is a computer system that processes data received from users, performs simulations, and generates and transmits the results.

[1312] The "simulation means" is a device or software that has the function of simulating disaster risks based on conditions and emotional states specified by a user and generating the results.

[1313] The "result generation means" is a device or software that has the function of generating simulation results and further generating customized information according to the user's emotional state.

[1314] The "terminal means" is a device that has the function of receiving user input, transmitting it to the server, and analyzing the user's facial expressions and voice to recognize the user's emotional state.

[1315] The "display means" is a device or software that visually displays the simulation results to the user and has the function of optimizing the display format according to the user's emotional state.

[1316] An "emotional state" is a psychological state or emotion that a user is in when providing input data, and may include, for example, "relieved," "worried," or "excited."

[1317] "Disaster risk" refers to the risk of disasters occurring under certain conditions.

[1318] This invention is based on a disaster simulation system, and by customizing the results taking into account the user's emotional state, it reduces the psychological burden on the user and enables them to make decisions more intuitively and with a sense of security. This system works in conjunction with a server and terminal, providing users with visual and emotionally sensitive disaster risk information.

[1319] Data collection and storage

[1320] The server periodically retrieves historical weather, disaster, and terrain data from reliable external data sources and stores it in a database. This ensures that the most up-to-date and accurate information is used during simulation. Specifically, the server retrieves data from an API using the Python requests library and stores it in a cloud database such as AWS RDS.

[1321] Recognizing user emotions and receiving input

[1322] The user uses the device to input conditions such as rainfall, wind speed, and location into the application's input form. This input data is converted into JSON format and sent to the server. At the same time, the device is equipped with an emotion engine that analyzes the user's facial expressions and voice to recognize their emotional state. This emotion data is also sent to the server. Apple's CoreML is used for emotion recognition, and Google's Speech-to-Text API is used for voice analysis.

[1323] Running the simulation

[1324] The server uses its internal simulation engine to simulate disaster risks based on the condition and emotion data received from users. This simulation engine uses MATLAB and the Python Scipy library. As a specific example, it calculates the risk of landslides and floods under conditions of 150 mm of rainfall and 20 m / s wind speed.

[1325] Generate and send results

[1326] The server generates customized results based on the simulation results, taking into account the user's emotional state. For example, if the user's emotional state is "worried," the server adds detailed risk explanations and countermeasures to the simulation results. This data is sent to the device in JSON format.

[1327] Visual display of results

[1328] The device receives the simulation results from the server and displays them to the user in a visually easy-to-understand format. It uses D3.js to draw graphs and charts, and customizes the display format depending on the user's emotional state. For example, a user in the "worried" emotional state might display detailed risk information, while a user in the "relieved" emotional state might display a concise summary.

[1329] Specific examples

[1330] For example, if a user is considering a certain area in Osaka as the location for a new store and wants to investigate the disaster risk under the conditions of "rainfall: 150 mm" and "wind speed: 20 m / s," the system will operate as follows:

[1331] 1. The user enters "rainfall: 150 mm," "wind speed: 20 m / s," and "location: Osaka" into the app form and requests a simulation.

[1332] 2. The device sends the input data in JSON format to the server, and at the same time the emotion engine recognizes the emotional state of "worry."

[1333] 3. The server runs a simulation based on the received data and obtains the results "Flood risk: Very high" and "Landslide risk: High."

[1334] 4. The server adds detailed risk explanations and countermeasures to the simulation results, taking into account emotional data, and sends them to the terminal in JSON format.

[1335] 5. The terminal processes the received results into a visually easy-to-understand format (graphs or charts) and displays them to the user.

[1336] Prompt Sentence Examples

[1337] For example, the prompt sentence "Use MATLAB to generate Python code that simulates disaster risk based on specified rainfall and wind speed data" is input to the generative AI model.

[1338] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1339] Step 1:

[1340] The server periodically retrieves historical weather data, disaster data, and terrain data from external reliable data sources. Data is retrieved from the API as input and stored in a database on the server. This processing is performed using the Python requests library, and data is retrieved in JSON format. AWS RDS is used to store the database. The data includes weather conditions, disaster history, and terrain information.

[1341] Step 2:

[1342] The user uses their device to input conditions such as rainfall, wind speed, and location into the application's input form. This input data is converted to JSON format on the device. The emotion engine then analyzes the user's facial expressions and voice to recognize their emotional state. Emotion recognition uses Apple's CoreML and Google's Speech-to-Text API. The user's input data and emotion data are sent from the device to the server.

[1343] Step 3:

[1344] The server uses its internal simulation engine to simulate disaster risks based on the received condition and emotion data. The simulation engine uses MATLAB and Python's Scipy library. Input data includes rainfall, wind speed, and location information, and outputs flood risk and landslide risk. This output data is used in the next step.

[1345] Step 4:

[1346] The server then customizes the simulation results by taking into account the user's emotional state. For example, if the user is in an "anxious" emotional state, the server adds detailed risk explanations and countermeasures to the simulation results. The customized result data generated by this process is sent to the device in JSON format.

[1347] Step 5:

[1348] The device receives the simulation results from the server and displays them to the user in a visually easy-to-understand format. Graphs and charts are drawn using D3.js. The display format can also be customized according to the user's emotional state. For example, if the user's emotional state is "worried," detailed risk information is displayed, while if the user's emotional state is "relieved," a concise summary is displayed.

[1349] Examples:

[1350] For example, if a user is considering a certain area in Osaka as the location for a new store and wants to investigate the disaster risk under the conditions of "rainfall: 150 mm" and "wind speed: 20 m / s," the system will operate as follows:

[1351] 1. The user enters "rainfall: 150 mm," "wind speed: 20 m / s," and "location: Osaka" into the app form and requests a simulation.

[1352] 2. The device sends the input data in JSON format to the server, and at the same time the emotion engine recognizes the emotional state of "worry."

[1353] 3. The server runs a simulation based on the received data and obtains the results "Flood risk: Very high" and "Landslide risk: High."

[1354] 4. The server adds detailed risk explanations and countermeasures to the simulation results, taking into account emotional data, and sends them to the terminal in JSON format.

[1355] 5. The terminal processes the received results into a visually easy-to-understand format (graphs or charts) and displays them to the user.

[1356] Example prompt sentence:

[1357] For example, the prompt sentence "Use MATLAB to generate Python code that simulates disaster risk based on specified rainfall and wind speed data" is input to the generative AI model.

[1358] 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.

[1359] 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.

[1360] 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.

[1361] 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.

[1362] 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.

[1363] 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.

[1364] 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).

[1365] 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.

[1366] 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."

[1367] 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.

[1368] 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).

[1369] 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.

[1370] 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.

[1371] 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.

[1372] 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.

[1373] 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.

[1374] 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.

[1375] 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.

[1376] 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.

[1377] 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 unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1378] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1379] The following is further disclosed regarding the above embodiment.

[1380] (Claim 1)

[1381] a server means including a database for storing past weather data, disaster data, and topographical data;

[1382] a simulation means for executing a simulation based on conditions specified by a user;

[1383] a result generating means for generating a simulation result and transmitting the result to a user's terminal;

[1384] terminal means for receiving user input and transmitting it to the server;

[1385] A system including:

[1386] (Claim 2)

[1387] 10. The system of claim 1, wherein the user-specified conditions include rainfall, wind speed, and location.

[1388] (Claim 3)

[1389] 10. The system according to claim 1, further comprising a display means for visually displaying the simulation results.

[1390] "Example 1"

[1391] (Claim 1)

[1392] a server means for periodically acquiring past weather data, disaster data, and topographical data from an external reliable data source and storing the data in a database;

[1393] A terminal means for a user to input conditions specified in an application input form, convert the input into JSON format, and send it to a server;

[1394] a simulation means for simulating disaster risks using an internal simulation engine based on request data received from a user;

[1395] a result generation means for transmitting the simulation results to the terminal in JSON format;

[1396] a display means for displaying the simulation results received from the server to the user in a visually easy-to-understand format;

[1397] A system including:

[1398] (Claim 2)

[1399] 10. The system of claim 1, wherein the user-specified conditions include rainfall, wind speed, and location.

[1400] (Claim 3)

[1401] 10. The system according to claim 1, further comprising a display means for visually displaying the simulation results.

[1402] "Application Example 1"

[1403] (Claim 1)

[1404] a server means including a database for storing past weather data, disaster data, and topographical data;

[1405] a simulation means for executing a simulation based on conditions specified by a user;

[1406] a result generating means for generating a simulation result and transmitting the result to a user's terminal;

[1407] terminal means for receiving user input and transmitting it to the server;

[1408] A monitoring method to monitor disaster risks in real time and send alerts;

[1409] A system including:

[1410] (Claim 2)

[1411] 10. The system of claim 1, wherein the user-specified conditions include rainfall, wind speed, and location.

[1412] (Claim 3)

[1413] 10. The system according to claim 1, further comprising a display means for visually displaying the simulation results.

[1414] "Example 2: Combining Emotion Engines"

[1415] (Claim 1)

[1416] a server means including a database for storing past weather data, disaster data, and topographical data;

[1417] a simulation means for executing a simulation based on conditions specified by a user;

[1418] a result generating means for generating a simulation result and transmitting the result to a user's terminal;

[1419] terminal means for receiving user input and transmitting it to the server;

[1420] a terminal means including an emotion engine that analyzes the user's facial expressions and voice to recognize the user's emotional state;

[1421] a result generation means for customizing the simulation results taking into account the emotional state;

[1422] A system including:

[1423] (Claim 2)

[1424] 10. The system of claim 1, wherein the user-specified conditions include rainfall, wind speed, and location.

[1425] (Claim 3)

[1426] 10. The system according to claim 1, further comprising a display means for visually displaying the simulation results.

[1427] "Application example 2 when combining emotion engines"

[1428] (Claim 1)

[1429] a server means including a database for storing past weather data, disaster data, and topographical data;

[1430] a simulation means for executing a simulation based on conditions and emotional states specified by a user;

[1431] a result generating means for generating a simulation result and generating a customized result according to the emotional state of the user;

[1432] a terminal means for receiving input from a user, transmitting the input to a server, and analyzing the user's facial expressions and voice to recognize the user's emotional state;

[1433] a display means for visually displaying the simulation results and optimizing the display format according to the emotional state;

[1434] A system including:

[1435] (Claim 2)

[1436] 10. The system of claim 1, wherein the user-specified conditions include rainfall, wind speed, and location.

[1437] (Claim 3)

[1438] 10. The system of claim 1, further comprising emotion recognition and analysis means for customizing the data based on emotional state. [Explanation of symbols]

[1439] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a server means including a database for storing past weather data, disaster data, and topographical data; a simulation means for executing a simulation based on conditions specified by a user; a result generating means for generating a simulation result and transmitting the result to a user's terminal; terminal means for receiving user input and transmitting it to the server; A system including:

2. 2. The system of claim 1, wherein the user-specified conditions include rainfall, wind speed, and location.

3. 2. The system according to claim 1, further comprising display means for visually displaying the simulation results.

Images