System

Abstract

A system is provided.SOLUTION: A system comprising: means for acquiring satellite and aerial photographic data; means for acquiring location information of shelters; means for acquiring location information of disaster victims; means for analyzing and generating a safe and optimal evacuation route based on the acquired data; means for transmitting the generated evacuation route information to a terminal; and means for displaying the transmitted evacuation route information.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 evacuation route guidance systems for use during disasters rely on regularly updated map data, making it difficult to update information in real time. This makes it difficult for them to respond to changes in the situation, such as road collapses or new obstacles, making it difficult for victims to evacuate safely. Furthermore, there is a lack of a mechanism for integrating the location information of evacuation shelters and the location information of disaster victims to generate safe and optimal routes. It is necessary to provide a system that solves these problems and enables disaster victims to evacuate to evacuation shelters quickly and safely. [Means for solving the problem]

[0005] The present invention provides a system that includes a means for acquiring satellite and aerial photograph data, a means for acquiring location information of evacuation shelters, and a means for acquiring location information of disaster victims, and a means for analyzing and generating safe and optimal evacuation routes based on the data collected in this manner. The system also includes a means for transmitting the generated evacuation route information to a terminal, a means for displaying the transmitted evacuation route information, and a function for notifying the user of the displayed information by voice and text. This system provides optimal evacuation routes that reflect real-time disaster information and topographical information, allowing disaster victims to evacuate safely based on the latest information.

[0006] "Satellite" means an instrument designed to orbit the Earth and observe the Earth's surface.

[0007] "Aerial photography" means a photograph or image of the earth's surface taken from an aircraft and used to obtain information about the terrain and environment over a wide area.

[0008] An "evacuation shelter" is a safe place or facility set up for local residents to temporarily evacuate to in the event of a disaster.

[0009] "Location information" is information that indicates the geographic location of a particular object or user.

[0010] "Disaster Victims" refers to people who face and are affected by disasters.

[0011] "Optimal evacuation route" means a route that allows disaster victims to reach evacuation shelters as safely and quickly as possible.

[0012] "Analysis" refers to the process of examining and calculating collected data to extract useful information.

[0013] "Generation" refers to the creation of new information or data based on the results of analysis.

[0014] "Server" means a computer system that processes and stores data and provides information to other devices and users.

[0015] "Terminal" refers to a device (e.g., smartphone, tablet) that is directly operated by a user and has the ability to receive and display information.

[0016] "Real-time" means that data is acquired and processed almost immediately.

[0017] "GPS" is the Global Positioning System used to obtain location information across the globe.

[0018] An "API" is an interface for linking data and functions between applications.

[0019] "Evacuation route information" refers to information about the directions and routes required for evacuation.

[0020] "Voice guide" is a function that uses voice to give instructions and guidance to the user. [Brief explanation of the drawings]

[0021] [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

[0022] 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.

[0023] First, the terms used in the following description will be explained.

[0024] 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).

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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."

[0029] [First embodiment]

[0030] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0031] 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.

[0032] 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).

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0038] 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.

[0039] 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.

[0040] 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.

[0041] 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."

[0042] The present invention relates to a system for generating a route that will allow disaster victims to reach an evacuation shelter in the safest way possible in the event of a disaster. The system of the present invention includes the following means.

[0043] 1. Data Collection Methods

[0044] The server acquires satellite and aerial photographic data. This data reflects changes in the terrain and damage caused by disasters in real time. The server also obtains the latest location information for evacuation shelters through local government databases and APIs. The server also collects location information from each victim's device. The information collected in this way becomes the basic data for generating evacuation routes.

[0045] 2. Data Analysis and Pathway Generation Procedures

[0046] The server uses AI technology and algorithms to analyze and generate safe and optimal evacuation routes in real time based on the collected data. This analysis includes the current terrain, passable roads, and the safety of evacuation shelters. For example, information on roads being flooded due to flooding or roads being destroyed by earthquakes is reflected in real time.

[0047] 3. Route information transmission and display means

[0048] The server sends the generated evacuation route information to the device. The sent information is displayed as a map on each victim's device. Specifically, the map application displays the route visually and provides audio and text guidance. This allows victims to evacuate safely while checking the route.

[0049] 4. Real-time update methods

[0050] As disaster situations change every moment, the server periodically collects new data and updates the generated evacuation routes as necessary. The device receives the updated information from the server and updates the route information displayed in real time, providing the most up-to-date evacuation routes at all times.

[0051] Specific example explanation

[0052] In the case of a city experiencing flood disasters

[0053] 1. Data Collection Methods

[0054] The server periodically retrieves the latest satellite images and aerial photographs.

[0055] The server accesses the local government's database and updates the location information of active evacuation shelters.

[0056] The server collects real-time GPS data from the device.

[0057] 2. Data Analysis and Pathway Generation Procedures

[0058] The server uses AI models to detect flooded areas and impassable roads.

[0059] The server calculates the route from the victim to the nearest safe evacuation shelter, taking into account real-time terrain and evacuation shelter information.

[0060] 3. Route information transmission and display means

[0061] The server sends the calculated safe path to the terminal.

[0062] The device displays the received route information on a map application and provides voice guidance and text instructions.

[0063] 4. Real-time update methods

[0064] The device uses GPS to periodically send the user's current location to the server.

[0065] The server takes in new situational data in real time, recalculates evacuation routes as needed, and sends updates to the device.

[0066] The device updates the map and audio guide based on the new information.

[0067] In this way, the system of the present invention assists disaster victims in obtaining the most up-to-date and optimal route information and evacuating safely in the event of a disaster.

[0068] The processing flow will be explained below.

[0069] Step 1:

[0070] The server acquires satellite and aerial image data. Specifically, the server utilizes existing APIs and data feeds to periodically download the latest satellite and aerial images. This allows the collection of basic data for understanding the progress of disasters and changes in the terrain in real time.

[0071] Step 2:

[0072] The server obtains location information for evacuation shelters. It accesses databases from local governments and related organizations to obtain the latest location information for currently operating evacuation shelters. This information is necessary for selecting safe evacuation destinations.

[0073] Step 3:

[0074] The server acquires the location information of the victims. It receives GPS data from the victims' devices and grasps their current location in real time. This makes it possible to constantly track the movements of the victims.

[0075] Step 4:

[0076] The server runs an AI model to analyze the environmental data and location information collected, and the AI ​​model detects collapsed roads and new obstacles, searching for the safest and most optimal route.

[0077] Step 5:

[0078] The server generates evacuation routes based on the analysis results. These routes are designed to minimize the impact of the disaster and allow victims to reach evacuation shelters in the safest and quickest way possible.

[0079] Step 6:

[0080] The server then sends the generated evacuation route information to the victim's device. The route information is packaged in JSON or XML format and sent in real time.

[0081] Step 7:

[0082] The device analyzes the route information it receives and displays it as a visual map on the user interface. It also simultaneously provides voice guidance and text instructions, navigating the user visually and audibly.

[0083] Step 8:

[0084] The user follows the route information displayed on the device and begins evacuation along a safe route. By following the instructions on the device, victims can evacuate safely.

[0085] Step 9:

[0086] The device periodically tracks the user's current location using GPS and sends it to the server, allowing the server to always have the most up-to-date location information.

[0087] Step 10:

[0088] The server collects new disaster information and terrain data in real time and recalculates evacuation routes as needed. For example, if a new landslide or road closure occurs, it will be updated immediately.

[0089] Step 11:

[0090] The device receives updated information from the server and notifies the user of the latest route information, allowing disaster victims to continue evacuating along the optimal evacuation route.

[0091] Through these steps, the system of the present invention realizes safe evacuation in the event of a disaster.

[0092] Example 1

[0093] 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."

[0094] In the event of a disaster, there is a need to provide route information to help victims reach evacuation shelters safely and quickly. However, conventional systems cannot reflect disaster information and topographical information that is updated in real time, making it difficult to provide safe evacuation routes. Furthermore, if evacuation route information is not properly communicated, there is a risk that the safety of victims will not be ensured.

[0095] 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.

[0096] In this invention, the server includes means for collecting satellite and aerial photograph data, means for acquiring location information of evacuation shelters, means for collecting location information of disaster victims, means for analyzing and generating a safe and optimal evacuation route based on the collected data, means for transmitting the generated evacuation route information to a terminal, means for displaying the transmitted evacuation route information, and means for updating the evacuation route information in real time, thereby making it possible to provide real-time route information that will allow disaster victims to reach evacuation shelters in the safest and fastest way possible in the event of a disaster.

[0097] "Satellite and aerial photographic data" refers to images and information obtained from satellites and aircraft to observe the Earth's surface, and is used to analyze terrain and disaster situations.

[0098] "Location information of evacuation shelters" refers to information on the geographic coordinates and addresses of facilities and locations for evacuation in the event of a disaster.

[0099] "Victim location information" is geographical data that indicates the current location of individuals or groups of people affected by a disaster.

[0100] "Collected data" is a general term that includes all information obtained by the system, such as satellite and aerial photograph data, location information of evacuation centers, and location information of disaster victims.

[0101] "Means of analysis and generation" refers to the techniques and methods for performing calculations and analyses to derive safe and optimal evacuation routes based on collected data.

[0102] "Generated evacuation route information" refers to information on safe and optimal evacuation routes derived using analytical and generating methods.

[0103] "Devices" refer to electronic devices such as smartphones and tablets carried by disaster victims, which are used to receive and display evacuation route information.

[0104] "Display means" refers to the technology or method for visually displaying the generated evacuation route information on a terminal so that the user can confirm it.

[0105] "Means of updating in real time" refers to mechanisms and technologies that instantly revise evacuation route information in response to changes in disaster conditions and topographical information, and provide users with the latest information.

[0106] The present invention relates to a system that generates routes that will allow disaster victims to reach evacuation shelters in the safest way possible. The system of the present invention is mainly composed of three main elements: a server, a terminal, and a user.

[0107] System configuration

[0108] The server has the means to acquire satellite and aerial photographic data. This data reflects changes in the terrain and damage caused by disasters in real time. The server also obtains the latest location information for evacuation shelters through local government databases and APIs. The server also collects location information from each victim's device. This collected information serves as the basic data for generating evacuation routes.

[0109] Specifically, the server uses the Python requests library to retrieve data from NASA and other commercial satellite companies via API, leverages data analysis libraries such as Pandas to format and analyze the data, and uses the Google Maps API to retrieve shelter location information.

[0110] The devices are electronic devices such as smartphones and tablets carried by disaster victims. A map application is installed on the devices, which visually displays evacuation route information sent from the server. Specifically, the devices were developed using React Native, and use the GPS function to send the user's current location to the server.

[0111] The user is a disaster victim, and the goal is to use evacuation route information to safely reach a shelter. The user checks real-time evacuation route information through a map application and evacuates by following voice and text guidance.

[0112] System action

[0113] As a data collection method, the server periodically collects satellite and aerial photograph data, location information of evacuation centers, and location information of disaster victims. This information is used to obtain basic data for generating evacuation routes.

[0114] As a means of data analysis and route generation, the server uses AI technology (TensorFlow and PyTorch, for example) and algorithms (such as Dijkstra's algorithm) based on the collected data to generate safe and optimal evacuation routes. Real-time disaster and terrain information is reflected, and the latest routes are always provided.

[0115] As a route information transmission and display means, the server transmits the generated evacuation route information to the terminal, which then visually displays it on a map application. The user can then check this information and evacuate.

[0116] As a real-time update method, the device periodically sends the user's current location to the server, and the server obtains new disaster information and regenerates evacuation routes as necessary. This updated information is immediately sent to the device, and the map and audio guide are updated in real time.

[0117] Specific examples

[0118] For example, in the case of a city experiencing a flood disaster, the server retrieves the latest satellite and aerial images, combines them with GPS data received from the devices of individual victims, and generates routes that avoid flooded areas and impassable roads. The generated evacuation route is sent to the device and displayed visually on the Google Maps application.

[0119] Example prompts for generative AI models

[0120] A generative AI model for use in disaster situations might be given prompts like the following:

[0121] "What is the best algorithm for generating safe evacuation routes in areas experiencing flooding?"

[0122] "Help us design an evacuation route generation system to respond to real-time changing disaster situations."

[0123] In this way, the system of the present invention assists disaster victims in obtaining the most up-to-date and optimal route information and evacuating safely in the event of a disaster.

[0124] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0125] Step 1: Data collection

[0126] The server collects the latest satellite and aerial image data. This involves the server sending API requests to retrieve image data from data providers such as NASA. The input is the API request, and the output is the retrieved image data. Specifically, it uses the Python requests library to retrieve the data and save it in a directory for image analysis.

[0127] Step 2: Obtaining evacuation shelter location information

[0128] The server retrieves the latest evacuation shelter location information from the local government database or API. The server sends an HTTP request and receives the evacuation shelter location information in JSON format. The input is the HTTP request, and the output is the retrieved evacuation shelter location information. Specifically, the server uses the request library to parse the received JSON data and store it in the database.

[0129] Step 3: Collect location information of victims

[0130] The device collects the user's current location information through its GPS function and sends it to the server. The input is the device's GPS data, and the output is the transmitted location data. Specifically, the device uses a React Native application to send the GPS data to the server as a POST request at regular intervals.

[0131] Step 4: Data analysis

[0132] The server receives and analyzes the collected satellite images, evacuation center location information, and disaster victim location information. The server cleanses and normalizes the data, and then inputs it into the AI ​​model. The input is all the data collected in the previous step, and the output is the cleansed data. Specifically, the data is preprocessed using Pandas.

[0133] Step 5: Generate evacuation routes

[0134] The server uses AI technology and algorithms to generate optimal evacuation routes based on the cleansed data. The server inputs and analyzes the data into an AI model and applies Dijkstra's algorithm and other route search algorithms. The input is preprocessed data, and the output is the generated evacuation route information. Specifically, the server uses a TensorFlow model to perform route analysis and saves the results.

[0135] Step 6: Send evacuation route information

[0136] The server sends the generated evacuation route information to the terminal. Data is pushed in real time via WebSocket. The input is the generated evacuation route information, and the output is the sent route data. Specifically, the WebSocket server is used to send the route data to the terminal.

[0137] Step 7: Display evacuation route information

[0138] The device receives evacuation route information and displays it on a map application, notifying the user by voice and text. The input is route information received from the server, and the output is a map display on the device and voice guidance. Specifically, the system uses the Google Maps API to display the route, and provides text and voice guidance in a React Native app.

[0139] Step 8: Real-time updates

[0140] The device continuously sends the user's current location to the server in real time, and the server regenerates evacuation routes based on updated disaster information and sends them back to the device. The input is newly collected location information and disaster information, and the output is updated evacuation route information. Specifically, the system uses the GPS data and newly acquired disaster data to perform analysis again using AI models and algorithms, and sends the results back to the device in real time.

[0141] By following the steps above, disaster victims can always obtain the latest evacuation route information and evacuate safely.

[0142] (Application example 1)

[0143] 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."

[0144] In the event of a disaster, providing a quick and optimal evacuation route is important to ensure the safety of employees and robots in a factory. However, with conventional systems, it has been difficult to issue appropriate evacuation instructions to robots and employees in a factory while reflecting constantly changing disaster information and terrain information in real time. The present invention aims to solve this problem.

[0145] 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.

[0146] In this invention, the server includes means for acquiring satellite and aerial photograph data, means for acquiring location information of evacuation shelters, means for acquiring location information of evacuation robots and employees, means for analyzing and generating a safe and optimal evacuation route based on the acquired data, means for transmitting the generated evacuation route information to a terminal, means for displaying the transmitted evacuation route information, and means for supporting the evacuation of factory robots and employees in the event of a disaster. This makes it possible to provide quick and appropriate evacuation instructions to robots and employees in the factory based on optimal evacuation route information updated in real time.

[0147] "Satellite and aerial photograph data" refers to image data taken from satellites or aircraft, and is used to understand the terrain and disaster conditions.

[0148] "Location information of evacuation shelters" refers to information about the locations of evacuation shelters designated to ensure safety in the event of a disaster.

[0149] "Location information of evacuation robots and employees" is data indicating the current locations of robots and employees operating within the factory.

[0150] "Means for analyzing and generating safe and optimal evacuation routes based on acquired data" refers to algorithms and programs that use various collected data to calculate the safest and most effective evacuation routes.

[0151] The "means for transmitting the generated evacuation route information to the terminal" is a method for communicating the calculated evacuation route to the terminal of the disaster victim or user in real time.

[0152] The "means for displaying the transmitted evacuation route information" refers to an application or device that displays the received evacuation route information visually or audibly on the terminal.

[0153] "Means to support the evacuation of factory robots and employees in the event of a disaster" refers to systems and technologies that provide instructions to robots and employees in a factory to evacuate safely when a disaster occurs.

[0154] The present invention is a system for supporting the safe evacuation of factory robots and employees in the event of a disaster, and includes the following means.

[0155] 1. Data Collection Methods

[0156] The server acquires satellite and aerial photographic data, which allows it to obtain the latest information on disasters and terrain in real time. The server also obtains the latest evacuation shelter location information through local government databases and APIs. The server also collects location information from each robot and employee's device. This data becomes the basic data for generating evacuation routes.

[0157] 2. Data Analysis and Pathway Generation Procedures

[0158] The server uses AI technology and algorithms to analyze and generate safe and optimal evacuation routes in real time based on the acquired data. This analysis includes the current terrain conditions, passable roads, and the safety of evacuation shelters. For example, it reflects information on areas blocked off due to fires or gas leaks, as well as impassable routes.

[0159] 3. Route information transmission and display means

[0160] The server then sends the generated evacuation route information to the device, which then uses the information to visually display the evacuation route on a map application and provide audio and text guidance, allowing the user to evacuate safely while checking the route.

[0161] 4. Real-time update methods

[0162] As disaster situations change every moment, the server periodically collects new data and updates the generated evacuation routes as necessary. The device receives the updated information from the server and updates the route information displayed in real time, providing the most up-to-date evacuation routes at all times.

[0163] As an example, if a fire breaks out in a factory during a disaster, the server will analyze and generate evacuation routes based on the latest data and send the route information in real time to all robots and employee terminals in the factory. The terminals will display a safe evacuation route based on the received information, and users can quickly begin evacuating by following the audio guidance.

[0164] As another example, consider the case of building damage caused by an earthquake. The server receives the latest information, immediately calculates evacuation routes that reflect the damage situation, and issues appropriate instructions to robots and employees. This allows all parties involved to quickly take the optimal evacuation route.

[0165] Below are some example input prompts for a generative AI model:

[0166] "Design an application that generates safe evacuation routes and provides instructions to robots and employees in the event of a fire in a factory. The evacuation routes need to be updated in real time."

[0167] Using this prompt, each means of the above system can be specifically implemented.

[0168] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0169] Step 1:

[0170] The server retrieves satellite and aerial photo data. The server uses the API to retrieve the latest topographical and disaster information and stores the data in an internal database. The input is the data retrieved from the API, and the output is the updated information stored in the database. This data is used by the analysis and generation tools.

[0171] Step 2:

[0172] The server obtains the location information of evacuation shelters from the local government database. The server periodically queries the latest information on evacuation shelters through the API and stores that information in the database. The input is the evacuation shelter information obtained from the local government database, and the output is the location information of the evacuation shelters stored in the server's database.

[0173] Step 3:

[0174] The server collects real-time location information from each robot and employee's device. The device periodically sends its GPS information to the server, which stores it in a database. The input is the location information sent from the robot and employee's device, and the output is the location information stored in the server's database.

[0175] Step 4:

[0176] The server analyzes and generates safe and optimal evacuation routes based on the collected data. The server uses an AI algorithm to analyze topographical data, disaster data, location information, etc., and calculates the optimal route. The input is the topographical information, evacuation shelter information, and location information stored on the server, and the output is the analyzed optimal evacuation route information.

[0177] Step 5:

[0178] The server sends the generated evacuation route information to the terminals. The server sends the calculated evacuation route information to the robots and employee terminals in real time. The input is the analyzed evacuation route information, and the output is the route information sent to each terminal.

[0179] Step 6:

[0180] The terminal displays the received evacuation route information. The terminal application displays the route information on a map and provides guidance by voice and text. The input is the evacuation route information sent from the server, and the output is route guidance provided to the user visually and audibly.

[0181] Step 7:

[0182] The server collects new data in real time and updates evacuation routes as necessary. The server periodically recollects and reanalyzes topographical, disaster, and location information. The input is the newly added topographical and location information to the database, and the output is updated evacuation route information. This ensures that the latest evacuation routes are always provided to the terminal.

[0183] Through the above processing steps, a system is realized that provides optimal routes in real time for factory robots and employees to evacuate quickly and safely in the event of a disaster.

[0184] 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.

[0185] The present invention relates to a system for generating a route that will allow disaster victims to reach a shelter most safely in the event of a disaster. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it is possible to respond flexibly according to the user's psychological state. The system of the present invention includes the following means.

[0186] 1. Data Collection Methods

[0187] The server acquires satellite and aerial photographic data. This data reflects changes in the terrain and damage caused by disasters in real time. The server also obtains the latest location information for evacuation shelters through local government databases and APIs. The server also collects location information from each victim's device. The information collected in this way becomes the basic data for generating evacuation routes.

[0188] 2. Data Analysis and Pathway Generation Procedures

[0189] The server uses AI technology and algorithms to analyze and generate safe and optimal evacuation routes in real time based on the collected data. This analysis includes the current terrain, passable roads, and the safety of evacuation shelters. For example, information on roads being flooded due to flooding or roads being destroyed by earthquakes is reflected in real time.

[0190] 3. Route information transmission and display means

[0191] The server sends the generated evacuation route information to the device. The sent information is displayed as a map on each victim's device. Specifically, the map application displays the route visually and provides audio and text guidance, allowing users to evacuate safely while checking the route.

[0192] 4. Emotion recognition means

[0193] One feature of the present invention is that it includes an emotion engine that recognizes the user's emotions. The device analyzes the user's facial expressions and tone of voice to understand their emotional state (e.g., stress, fear, relief). Based on this analysis, the emotion engine evaluates the user's psychological state.

[0194] 5. Emotion-based route adjustment measures

[0195] The server adjusts evacuation route information based on feedback from the emotion engine. For example, if the user is experiencing high stress levels, the server recalculates a simpler and safer evacuation route and sends it to the device. Voice and text notification methods are also adjusted according to the user's emotional state.

[0196] Specific example explanation

[0197] In the case of a city experiencing flood disasters

[0198] 1. Data Collection Methods

[0199] The server periodically retrieves the latest satellite images and aerial photographs.

[0200] The server accesses the local government's database and updates the location information of active evacuation shelters.

[0201] The server collects real-time GPS data from the device.

[0202] 2. Data Analysis and Pathway Generation Procedures

[0203] The server uses AI models to detect flooded areas and impassable roads.

[0204] The server calculates the route from the victim to the nearest safe evacuation shelter, taking into account real-time terrain and evacuation shelter information.

[0205] 3. Route information transmission and display means

[0206] The server sends the calculated safe path to the terminal.

[0207] The device displays the received route information on a map application and provides voice guidance and text instructions.

[0208] 4. Emotion recognition means

[0209] The device analyzes the user's facial expressions and tone of voice to assess their emotional state. For example, the user's facial expressions and tone of voice are analyzed via the camera and microphone of their smartphone.

[0210] 5. Emotion-based route adjustment measures

[0211] The server receives the stress level assessment results from the emotion engine and adjusts the route and notification method as needed. For example, if a high stress level is detected, it provides a voice prompt to encourage reassurance.

[0212] In this way, the system of the present invention not only supports the safe evacuation of disaster victims but also enables flexible responses according to the emotional state of the user.

[0213] The processing flow will be explained below.

[0214] Step 1:

[0215] The server acquires satellite and aerial image data. Specifically, the server utilizes existing APIs and data feeds to periodically download the latest satellite and aerial images. This provides the basic data for understanding the progress of disasters and changes in the terrain in real time.

[0216] Step 2:

[0217] The server obtains location information for evacuation shelters. It accesses databases from local governments and related organizations and downloads the latest location information for currently operating evacuation shelters. This information is necessary for selecting safe evacuation destinations.

[0218] Step 3:

[0219] The server acquires the location information of the victims. It receives GPS data from the victims' devices in real time to determine their current location. This makes it possible to constantly track the movements of the victims.

[0220] Step 4:

[0221] The server runs an AI model to analyze the environmental data and location information collected by the server, which then detects collapsed roads and new obstacles, and searches for the safest and most optimal route.

[0222] Step 5:

[0223] The server generates evacuation routes based on the analysis results. These routes are designed to minimize the impact of the disaster and allow victims to reach evacuation shelters in the safest and quickest way possible.

[0224] Step 6:

[0225] The server then sends the generated evacuation route information to the victim's device. The route information is packaged in JSON or XML format and sent in real time. Data security and privacy are ensured during this transmission process.

[0226] Step 7:

[0227] The device analyzes the route information it receives and displays it as a visual map in the user interface. In addition, it provides audio guidance and text instructions, guiding the user both visually and aurally.

[0228] Step 8:

[0229] The device analyzes the user's facial expressions and tone of voice and evaluates their emotional state using an emotion engine. For example, it uses the smartphone's camera and microphone to analyze the user's facial expressions and tone of voice in real time.

[0230] Step 9:

[0231] The server receives feedback from the emotion engine and adjusts evacuation route information based on the user's emotional state. If a high stress level is detected, the server recalculates and provides a simpler and more reassuring evacuation route and notification method.

[0232] Step 10:

[0233] The device displays new route information based on emotion recognition and notifies the user via voice and text. The emotion engine provides information that allows the user to evacuate calmly.

[0234] Step 11:

[0235] The user follows the route information displayed on the device and begins evacuation along a safe route. By following the instructions on the device, victims can continue evacuating safely.

[0236] Step 12:

[0237] The device periodically tracks the user's current location using GPS and sends it to the server, allowing the server to always have the most up-to-date location information.

[0238] Step 13:

[0239] The server collects new disaster information and terrain data in real time and recalculates evacuation routes as needed. For example, if a new landslide or road closure occurs, this is reflected immediately.

[0240] Step 14:

[0241] The device receives updated information from the server and reflects it in the user interface in real time. If necessary, audio guides and text instructions are also updated to inform the user of the latest information.

[0242] Through these steps, the system of the present invention supports the safe evacuation of disaster victims in real time. It also enables flexible responses according to the user's emotional state, providing psychological security.

[0243] Example 2

[0244] 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."

[0245] In the event of a disaster, it is important to provide victims with the safest and fastest route to an evacuation shelter. However, current systems have difficulty reflecting disaster and terrain information in real time, and do not take into account the emotional state of victims. Therefore, a system that allows victims to evacuate safely is needed.

[0246] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0247] In this invention, the server includes means for acquiring satellite and aerial photograph data, means for acquiring location information of evacuation shelters, means for acquiring location information of disaster victims, means for analyzing and generating a safe and optimal evacuation route based on the collected data, means for transmitting the generated evacuation route information to a terminal, means for displaying the transmitted evacuation route information, means for recognizing a user's emotion, and means for adjusting the evacuation route information based on the emotion recognition result. This makes it possible to provide real-time route information for disaster victims to evacuate safely and also to flexibly respond to the user's emotional state.

[0248] "Satellite Data" refers to imagery and terrain information obtained from satellites.

[0249] "Aerial photographic data" refers to images and terrain information taken from an aircraft.

[0250] "Evacuation shelter location information" refers to the geographic location information of safe and operational evacuation shelters in the event of a disaster.

[0251] "Location information of disaster victims" refers to the current geographical location information of each disaster victim in the area where the disaster has occurred.

[0252] An "evacuation route" refers to the route that disaster victims take to move from their current location to a safe evacuation shelter.

[0253] "Emotion recognition" refers to the technology of analyzing a user's facial expressions and tone of voice to assess their psychological and emotional state.

[0254] An "emotion engine" refers to software or algorithms that generate specific actions or feedback based on a user's emotional state derived from emotion recognition.

[0255] "Generated evacuation route information" refers to information on safe and optimal evacuation routes generated based on collected data and analysis results.

[0256] "Devices" refer to portable electronic devices such as smartphones and tablets carried by disaster victims.

[0257] "Server" refers to a computer system that acts as a central control system and collects and analyzes various data, creates routes, and transmits data.

[0258] The present invention relates to a system for generating a route that will allow disaster victims to reach a shelter most safely in the event of a disaster. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it is possible to respond flexibly according to the user's psychological state. The system of the present invention includes the following means.

[0259] 1. Data Collection Methods

[0260] The server acquires satellite and aerial photographic data. Specifically, it periodically acquires image data using APIs from NASA and local aircraft companies. The server also acquires the latest location information for evacuation shelters via local government databases and APIs. Specific examples include the Google Civic Information API and the National Evacuation Shelter Database. The server also collects real-time location information from each victim's device. By acquiring location information from the device's GPS sensor, the current location of the victim can be determined.

[0261] 2. Data analysis methods

[0262] The server uses machine learning algorithms (e.g., deep learning models) based on the collected data to identify areas affected by disasters such as floods and landslides. It also organizes the collected evacuation shelter information and lists safe, operational shelters. It also identifies the current locations of disaster victims based on real-time location information from devices.

[0263] 3. Route Generation Method

[0264] The server uses AI technology (e.g., generative AI models) and the Dijkstra algorithm to generate safe and optimal evacuation routes, calculating routes that avoid flooded areas and impassable roads.

[0265] 4. Route Information Transmission Method

[0266] The server sends the generated evacuation route information to the victim's terminal in real time.

[0267] 5. Route Information Display Method

[0268] The route information received by the device is displayed on a map application. Specifically, it is displayed visually using Google Maps API or OpenStreetMap. The device also provides audio guidance and text instructions using the device's built-in speaker and text display function.

[0269] 6. Emotion recognition means

[0270] The device uses a camera and microphone to analyze the user's facial expressions and tone of voice in real time, for example, by using an emotion recognition API (e.g., Microsoft Azure Emotion API). The analysis results are sent to an emotion engine, which evaluates the user's emotional state (e.g., stress, relief, etc.).

[0271] 7. Route Adjustment Methods

[0272] The server receives feedback from the emotion engine and adjusts evacuation routes and notification methods as needed. For example, if a high stress level is detected, the server resends a recalculated route to the device. The device then displays the new route information and provides reassuring audio guidance, using a calming voice tone and positive messages.

[0273] Specific example explanation

[0274] In the case of a city experiencing flood disasters

[0275] 1. Data Collection Methods

[0276] The server periodically retrieves the latest satellite images and aerial photographs.

[0277] The server accesses the local government's database and updates the location information of active evacuation shelters.

[0278] The server collects real-time GPS data from the device.

[0279] 2. Data analysis methods

[0280] The server uses AI models to detect flooded areas and impassable roads.

[0281] The server calculates the route from the victim to the nearest safe evacuation shelter, taking into account real-time terrain and evacuation shelter information.

[0282] 3. Route Information Transmission Method

[0283] The server sends the calculated safe path to the terminal.

[0284] The device displays the received route information on a map application and provides voice guidance and text instructions.

[0285] 4. Emotion recognition means

[0286] The device analyzes the user's facial expressions and tone of voice to assess their emotional state. For example, the user's facial expressions and tone of voice are analyzed via the camera and microphone of their smartphone.

[0287] 5. Route Adjustment Methods

[0288] The server receives the stress level assessment results from the emotion engine and adjusts the route and notification method as needed. For example, if a high stress level is detected, it provides a voice prompt to encourage reassurance.

[0289] In this way, the system of the present invention not only supports the safe evacuation of disaster victims but also enables flexible responses according to the emotional state of the user.

[0290] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0291] Step 1: Collect data

[0292] The server retrieves satellite and aerial image data. This data is retrieved periodically through an API. The input is the latest image data from satellites and aircraft, and the output is initial data on the area affected by the disaster. Specifically, the server downloads image data from APIs provided by NASA and local aircraft companies.

[0293] Step 2: Collecting evacuation shelter and victim location information

[0294] The server obtains evacuation shelter location information from the local government's database and API. The server also collects real-time location information from each victim's device. The input is the local government's latest evacuation shelter information and the victim's GPS data, and the output is location information data including the locations of the evacuation shelter and the victim. The server collects evacuation shelter data through the local government's API and receives GPS data from each device.

[0295] Step 3: Data analysis

[0296] The server analyzes the collected data. The inputs are satellite data, aerial photographs, evacuation shelter information, and victim location information. The output is a list of disaster-affected areas and evacuation shelters, as well as the current locations of the victims. Specifically, it uses machine learning algorithms to analyze the data and identify flooded areas and impassable roads.

[0297] Step 4: Generate evacuation routes

[0298] The server uses AI technology and algorithms to generate a safe and optimal evacuation route. The input is analyzed data, and the output is safe evacuation route information. Specifically, the server uses an AI model (e.g., a generative AI model) and the Dijkstra algorithm to perform route calculations.

[0299] Step 5: Sending route information

[0300] The server sends the generated evacuation route information to the terminals. The input is the generated evacuation route information, and the output is the transmission of evacuation route data to each terminal. Specifically, the server transfers data to the terminals in real time.

[0301] Step 6: View route information

[0302] The route information received by the device is displayed on a map application. The input is evacuation route information sent from the server, and the output is a route displayed on a map and audio and text guidance. Specifically, the device uses Google Maps API and OpenStreetMap to visually display the route, and provides guidance using the built-in speaker and text display function.

[0303] Step 7: Emotion Recognition

[0304] The device uses a camera and microphone to analyze the user's facial expressions and tone of voice. The input is the user's facial expressions and tone of voice, and the output is an evaluation of their emotional state. Specifically, the analysis is performed using an emotion recognition API (e.g., Microsoft Azure Emotion API).

[0305] Step 8: Route Adjustment

[0306] The server receives feedback from the emotion engine and adjusts the evacuation route. The input is the emotion recognition result, and the output is adjusted route information. Specifically, the server generates a recalculated route based on the emotion engine's evaluation results and sends it back to the device. The device then displays the new route information and provides audio guidance to encourage reassurance.

[0307] (Application example 2)

[0308] 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."

[0309] Conventional evacuation support systems can help disaster victims select the safest route, but because they do not take into account the user's emotional state, they are unable to respond appropriately in situations where the user feels stress or anxiety. Furthermore, there is a lack of technology to utilize autonomous vehicles for evacuation support, making it difficult to ensure the safety and efficiency of autonomously driven evacuations. Given this background, there is a need for a system that provides safe and optimal evacuation routes using autonomous vehicles while taking into account the user's emotional state during a disaster.

[0310] 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.

[0311] In this invention, the server includes means for acquiring satellite and aerial photograph data, means for acquiring location information of evacuation shelters, means for acquiring location information of disaster victims, means for analyzing and generating a safe and optimal evacuation route based on the acquired data, means for transmitting the generated evacuation route information to a terminal, means for displaying the transmitted evacuation route information, means for recognizing the emotional state of a user using a camera and a microphone, and means for adjusting the evacuation route based on the recognized emotional state. This enables flexible route selection according to the emotional state of a user even during a disaster, thereby realizing evacuation support using self-driving vehicles.

[0312] "Satellite data" refers to data that is photographed by satellites placed in space and provides information on the Earth's surface and topography.

[0313] "Aerial photograph data" refers to photographic data of the earth's surface taken from an aircraft, and provides detailed information on urban areas and topography.

[0314] "Location information of evacuation shelter" is information indicating the current location of an evacuation shelter where disaster victims can safely evacuate.

[0315] "Location information of disaster victims" is GPS data that indicates the current location of disaster victims at the time of the disaster.

[0316] An "evacuation route" refers to the safest and most optimal route for disaster victims to reach an evacuation shelter.

[0317] "AI technology" is a technology that uses artificial intelligence to analyze data and make decisions.

[0318] "Route information display means" refers to a device or interface for visually displaying the calculated evacuation route.

[0319] The "emotion recognition means" is a device that uses sensors such as a camera and a microphone to determine the user's emotional state (e.g., stress, fear, relief).

[0320] "Route adjustment means" refers to an algorithm or system for dynamically changing an evacuation route based on the user's emotional state obtained from the emotion recognition means.

[0321] An "autonomous vehicle" is a vehicle that can drive autonomously without requiring operation by a passenger.

[0322] An "emotion engine" is software or hardware that analyzes a user's emotional state and provides an appropriate response based on the results.

[0323] A "safe evacuation route" is a route that is least affected by a disaster and has the highest chance of allowing victims to reach a shelter safely.

[0324] "Real-time information" is information that instantly acquires the current situation and status and displays and processes it instantly.

[0325] The present invention relates to an evacuation support system in the event of a disaster using an autonomous driving vehicle, and specifically describes a method for implementing each of the following means.

[0326] First, the server acquires satellite and aerial photographic data. This data reflects changes in the terrain and damage caused by the disaster in real time. The server also obtains the latest location information for evacuation shelters through local government databases and APIs. The server also collects current GPS location information from victims' smartphones and other location-providing devices.

[0327] The server then uses AI technology and algorithms to analyze and generate safe and optimal evacuation routes in real time based on the collected data. This analysis includes the current terrain, passable roads, and the safety of evacuation shelters. For example, it reflects information in real time about roads that are inundated by floods or damaged by earthquakes.

[0328] The generated evacuation route information is sent from the server to the autonomous vehicle's navigation system, which then displays the received information on a map application and provides voice guidance and text instructions, allowing the user to evacuate safely while checking the evacuation route.

[0329] The emotion recognition function uses cameras and microphones installed in autonomous vehicles to analyze the user's emotional state from their facial expressions and tone of voice. For example, if the user is feeling stressed, the system will adjust the evacuation route and notification method accordingly. This is done by the emotion engine, which evaluates the user's stress level and, if necessary, recalculates and provides a safer route and gentler voice guidance.

[0330] As a concrete example, when this system is applied to a city experiencing a flood disaster, the server first obtains the latest satellite and aerial images and accesses the local government's database to update the location information of evacuation shelters. It also collects real-time GPS data from the user's smartphone. The server uses an AI model to detect flooded areas and impassable roads and calculates the optimal route from the victim to the nearest safe evacuation shelter. The calculated safe route is sent to the autonomous vehicle, where it is displayed on the map application and audio and text instructions are provided.

[0331] The system operates based on the following example prompt:

[0332] "Generate an evacuation route based on the following data.

[0333] data:

[0334] Satellite imagery: latest flood-affected areas

[0335] Municipal database: Location information of currently operating evacuation shelters

[0336] GPS data: Current location of victim (latitude: 35.6895, longitude: 139.6917)

[0337] In this way, the present invention supports the safe evacuation of disaster victims and enables flexible responses according to the emotional state of the user. Furthermore, the use of autonomous vehicles improves the efficiency and safety of evacuation.

[0338] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0339] Step 1:

[0340] The server acquires satellite and aerial image data. This involves periodically downloading the latest satellite and aerial images from the Internet. The input is image data from satellites and aircraft, and the output is the latest image data of the Earth's surface. This allows the server to maintain the latest map information.

[0341] Step 2:

[0342] The server obtains the location information of evacuation shelters. This involves accessing the local government's database or API to obtain the latest list of evacuation shelters. The input is the local government's database, and the output is a dataset containing the location information of evacuation shelters. This allows the server to collect the latest evacuation shelter information.

[0343] Step 3:

[0344] The server obtains the location information of the victim. This involves obtaining real-time location data from the victim's smartphone or GPS device. The input is the GPS data from the victim's smartphone, and the output is the victim's current location information. This allows the server to accurately determine the victim's location.

[0345] Step 4:

[0346] The server analyzes and generates a safe and optimal evacuation route based on the acquired data. This process involves using an AI algorithm to analyze the collected map data, evacuation shelter information, and location information of disaster victims, and calculate the optimal route. The input is satellite data, evacuation shelter information, and location information of disaster victims, and the output is the optimal evacuation route. This allows the server to provide the optimal evacuation route.

[0347] Step 5:

[0348] The server transmits the generated evacuation route information to the terminal, which includes transmitting the evacuation route to the autonomous vehicle's navigation system using an appropriate protocol. The input is the evacuation route information, and the output is the transmitted route information. This provides the route information to the autonomous vehicle.

[0349] Step 6:

[0350] The device displays the transmitted evacuation route information. This involves the navigation system visually displaying the route information on a map application and providing audio and text instructions. The input is the route information, and the output is the displayed map and audio and text instructions. This allows the user to confirm a safe evacuation route.

[0351] Step 7:

[0352] The device recognizes the user's emotional state using a camera and microphone. This involves using sensor data to analyze the user's facial expressions and tone of voice to evaluate the emotional state. The input is sensor data from the camera and microphone, and the output is the recognized emotional state. This allows the device to understand the user's psychological state.

[0353] Step 8:

[0354] The server adjusts the evacuation route based on the recognized emotional state. This involves receiving feedback from the emotion engine and modifying the route information and audio guidance as necessary. The input is the user's emotional state, and the output is the adjusted route information. This allows for appropriate responses according to the user's psychological state.

[0355] 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.

[0356] 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.

[0357] 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.

[0358] [Second embodiment]

[0359] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0360] 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.

[0361] 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).

[0362] 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.

[0363] 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.

[0364] 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).

[0365] 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.

[0366] 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.

[0367] 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.

[0368] 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.

[0369] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0370] 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."

[0371] The present invention relates to a system for generating a route that will allow disaster victims to reach an evacuation shelter in the safest way possible in the event of a disaster. The system of the present invention includes the following means.

[0372] 1. Data Collection Methods

[0373] The server acquires satellite and aerial photographic data. This data reflects changes in the terrain and damage caused by disasters in real time. The server also obtains the latest location information for evacuation shelters through local government databases and APIs. The server also collects location information from each victim's device. The information collected in this way becomes the basic data for generating evacuation routes.

[0374] 2. Data Analysis and Pathway Generation Procedures

[0375] The server uses AI technology and algorithms to analyze and generate safe and optimal evacuation routes in real time based on the collected data. This analysis includes the current terrain, passable roads, and the safety of evacuation shelters. For example, information on roads being flooded due to flooding or roads being destroyed by earthquakes is reflected in real time.

[0376] 3. Route information transmission and display means

[0377] The server sends the generated evacuation route information to the device. The sent information is displayed as a map on each victim's device. Specifically, the map application displays the route visually and provides audio and text guidance. This allows victims to evacuate safely while checking the route.

[0378] 4. Real-time update methods

[0379] As disaster situations change every moment, the server periodically collects new data and updates the generated evacuation routes as necessary. The device receives the updated information from the server and updates the route information displayed in real time, providing the most up-to-date evacuation routes at all times.

[0380] Specific example explanation

[0381] In the case of a city experiencing flood disasters

[0382] 1. Data Collection Methods

[0383] The server periodically retrieves the latest satellite images and aerial photographs.

[0384] The server accesses the local government's database and updates the location information of active evacuation shelters.

[0385] The server collects real-time GPS data from the device.

[0386] 2. Data Analysis and Pathway Generation Procedures

[0387] The server uses AI models to detect flooded areas and impassable roads.

[0388] The server calculates the route from the victim to the nearest safe evacuation shelter, taking into account real-time terrain and evacuation shelter information.

[0389] 3. Route information transmission and display means

[0390] The server sends the calculated safe path to the terminal.

[0391] The device displays the received route information on a map application and provides voice guidance and text instructions.

[0392] 4. Real-time update methods

[0393] The device uses GPS to periodically send the user's current location to the server.

[0394] The server takes in new situational data in real time, recalculates evacuation routes as needed, and sends updates to the device.

[0395] The device updates the map and audio guide based on the new information.

[0396] In this way, the system of the present invention assists disaster victims in obtaining the most up-to-date and optimal route information and evacuating safely in the event of a disaster.

[0397] The processing flow will be explained below.

[0398] Step 1:

[0399] The server acquires satellite and aerial image data. Specifically, the server utilizes existing APIs and data feeds to periodically download the latest satellite and aerial images. This allows the collection of basic data for understanding the progress of disasters and changes in the terrain in real time.

[0400] Step 2:

[0401] The server obtains location information for evacuation shelters. It accesses databases from local governments and related organizations to obtain the latest location information for currently operating evacuation shelters. This information is necessary for selecting safe evacuation destinations.

[0402] Step 3:

[0403] The server acquires the location information of the victims. It receives GPS data from the victims' devices and grasps their current location in real time. This makes it possible to constantly track the movements of the victims.

[0404] Step 4:

[0405] The server runs an AI model to analyze the environmental data and location information collected, and the AI ​​model detects collapsed roads and new obstacles, searching for the safest and most optimal route.

[0406] Step 5:

[0407] The server generates evacuation routes based on the analysis results. These routes are designed to minimize the impact of the disaster and allow victims to reach evacuation shelters in the safest and quickest way possible.

[0408] Step 6:

[0409] The server then sends the generated evacuation route information to the victim's device. The route information is packaged in JSON or XML format and sent in real time.

[0410] Step 7:

[0411] The device analyzes the route information it receives and displays it as a visual map on the user interface. It also simultaneously provides voice guidance and text instructions, navigating the user visually and audibly.

[0412] Step 8:

[0413] The user follows the route information displayed on the device and begins evacuation along a safe route. By following the instructions on the device, victims can evacuate safely.

[0414] Step 9:

[0415] The device periodically tracks the user's current location using GPS and sends it to the server, allowing the server to always have the most up-to-date location information.

[0416] Step 10:

[0417] The server collects new disaster information and terrain data in real time and recalculates evacuation routes as needed. For example, if a new landslide or road closure occurs, it will be updated immediately.

[0418] Step 11:

[0419] The device receives updated information from the server and notifies the user of the latest route information, allowing disaster victims to continue evacuating along the optimal evacuation route.

[0420] Through these steps, the system of the present invention realizes safe evacuation in the event of a disaster.

[0421] Example 1

[0422] 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."

[0423] In the event of a disaster, there is a need to provide route information to help victims reach evacuation shelters safely and quickly. However, conventional systems cannot reflect disaster information and topographical information that is updated in real time, making it difficult to provide safe evacuation routes. Furthermore, if evacuation route information is not properly communicated, there is a risk that the safety of victims will not be ensured.

[0424] 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.

[0425] In this invention, the server includes means for collecting satellite and aerial photograph data, means for acquiring location information of evacuation shelters, means for collecting location information of disaster victims, means for analyzing and generating a safe and optimal evacuation route based on the collected data, means for transmitting the generated evacuation route information to a terminal, means for displaying the transmitted evacuation route information, and means for updating the evacuation route information in real time, thereby making it possible to provide real-time route information that will allow disaster victims to reach evacuation shelters in the safest and fastest way possible in the event of a disaster.

[0426] "Satellite and aerial photographic data" refers to images and information obtained from satellites and aircraft to observe the Earth's surface, and is used to analyze terrain and disaster situations.

[0427] "Location information of evacuation shelters" refers to information on the geographic coordinates and addresses of facilities and locations for evacuation in the event of a disaster.

[0428] "Victim location information" is geographical data that indicates the current location of individuals or groups of people affected by a disaster.

[0429] "Collected data" is a general term that includes all information obtained by the system, such as satellite and aerial photograph data, location information of evacuation centers, and location information of disaster victims.

[0430] "Means of analysis and generation" refers to the techniques and methods for performing calculations and analyses to derive safe and optimal evacuation routes based on collected data.

[0431] "Generated evacuation route information" refers to information on safe and optimal evacuation routes derived using analytical and generating methods.

[0432] "Devices" refer to electronic devices such as smartphones and tablets carried by disaster victims, which are used to receive and display evacuation route information.

[0433] "Display means" refers to the technology or method for visually displaying the generated evacuation route information on a terminal so that the user can confirm it.

[0434] "Means of updating in real time" refers to mechanisms and technologies that instantly revise evacuation route information in response to changes in disaster conditions and topographical information, and provide users with the latest information.

[0435] The present invention relates to a system that generates routes that will allow disaster victims to reach evacuation shelters in the safest way possible. The system of the present invention is mainly composed of three main elements: a server, a terminal, and a user.

[0436] System configuration

[0437] The server has the means to acquire satellite and aerial photographic data. This data reflects changes in the terrain and damage caused by disasters in real time. The server also obtains the latest location information for evacuation shelters through local government databases and APIs. The server also collects location information from each victim's device. This collected information serves as the basic data for generating evacuation routes.

[0438] Specifically, the server uses the Python requests library to retrieve data from NASA and other commercial satellite companies via API, leverages data analysis libraries such as Pandas to format and analyze the data, and uses the Google Maps API to retrieve shelter location information.

[0439] The devices are electronic devices such as smartphones and tablets carried by disaster victims. A map application is installed on the devices, which visually displays evacuation route information sent from the server. Specifically, the devices were developed using React Native, and use the GPS function to send the user's current location to the server.

[0440] The user is a disaster victim, and the goal is to use evacuation route information to safely reach a shelter. The user checks real-time evacuation route information through a map application and evacuates by following voice and text guidance.

[0441] System action

[0442] As a data collection method, the server periodically collects satellite and aerial photograph data, location information of evacuation centers, and location information of disaster victims. This information is used to obtain basic data for generating evacuation routes.

[0443] As a means of data analysis and route generation, the server uses AI technology (TensorFlow and PyTorch, for example) and algorithms (such as Dijkstra's algorithm) based on the collected data to generate safe and optimal evacuation routes. Real-time disaster and terrain information is reflected, and the latest routes are always provided.

[0444] As a route information transmission and display means, the server transmits the generated evacuation route information to the terminal, which then visually displays it on a map application. The user can then check this information and evacuate.

[0445] As a real-time update method, the device periodically sends the user's current location to the server, and the server obtains new disaster information and regenerates evacuation routes as necessary. This updated information is immediately sent to the device, and the map and audio guide are updated in real time.

[0446] Specific examples

[0447] For example, in the case of a city experiencing a flood disaster, the server retrieves the latest satellite and aerial images, combines them with GPS data received from the devices of individual victims, and generates routes that avoid flooded areas and impassable roads. The generated evacuation route is sent to the device and displayed visually on the Google Maps application.

[0448] Example prompts for generative AI models

[0449] A generative AI model for use in disaster situations might be given prompts like the following:

[0450] "What is the best algorithm for generating safe evacuation routes in areas experiencing flooding?"

[0451] "Help us design an evacuation route generation system to respond to real-time changing disaster situations."

[0452] In this way, the system of the present invention assists disaster victims in obtaining the most up-to-date and optimal route information and evacuating safely in the event of a disaster.

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

[0454] Step 1: Data collection

[0455] The server collects the latest satellite and aerial image data. This involves the server sending API requests to retrieve image data from data providers such as NASA. The input is the API request, and the output is the retrieved image data. Specifically, it uses the Python requests library to retrieve the data and save it in a directory for image analysis.

[0456] Step 2: Obtaining evacuation shelter location information

[0457] The server retrieves the latest evacuation shelter location information from the local government database or API. The server sends an HTTP request and receives the evacuation shelter location information in JSON format. The input is the HTTP request, and the output is the retrieved evacuation shelter location information. Specifically, the server uses the request library to parse the received JSON data and store it in the database.

[0458] Step 3: Collect location information of victims

[0459] The device collects the user's current location information through its GPS function and sends it to the server. The input is the device's GPS data, and the output is the transmitted location data. Specifically, the device uses a React Native application to send the GPS data to the server as a POST request at regular intervals.

[0460] Step 4: Data analysis

[0461] The server receives and analyzes the collected satellite images, evacuation center location information, and disaster victim location information. The server cleanses and normalizes the data, and then inputs it into the AI ​​model. The input is all the data collected in the previous step, and the output is the cleansed data. Specifically, the data is preprocessed using Pandas.

[0462] Step 5: Generate evacuation routes

[0463] The server uses AI technology and algorithms to generate optimal evacuation routes based on the cleansed data. The server inputs and analyzes the data into an AI model and applies Dijkstra's algorithm and other route search algorithms. The input is preprocessed data, and the output is the generated evacuation route information. Specifically, the server uses a TensorFlow model to perform route analysis and saves the results.

[0464] Step 6: Send evacuation route information

[0465] The server sends the generated evacuation route information to the terminal. Data is pushed in real time via WebSocket. The input is the generated evacuation route information, and the output is the sent route data. Specifically, the WebSocket server is used to send the route data to the terminal.

[0466] Step 7: Display evacuation route information

[0467] The device receives evacuation route information and displays it on a map application, notifying the user by voice and text. The input is route information received from the server, and the output is a map display on the device and voice guidance. Specifically, the system uses the Google Maps API to display the route, and provides text and voice guidance in a React Native app.

[0468] Step 8: Real-time updates

[0469] The device continuously sends the user's current location to the server in real time, and the server regenerates evacuation routes based on updated disaster information and sends them back to the device. The input is newly collected location information and disaster information, and the output is updated evacuation route information. Specifically, the system uses the GPS data and newly acquired disaster data to perform analysis again using AI models and algorithms, and sends the results back to the device in real time.

[0470] By following the steps above, disaster victims can always obtain the latest evacuation route information and evacuate safely.

[0471] (Application example 1)

[0472] 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."

[0473] In the event of a disaster, providing a quick and optimal evacuation route is important to ensure the safety of employees and robots in a factory. However, with conventional systems, it has been difficult to issue appropriate evacuation instructions to robots and employees in a factory while reflecting constantly changing disaster information and terrain information in real time. The present invention aims to solve this problem.

[0474] 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.

[0475] In this invention, the server includes means for acquiring satellite and aerial photograph data, means for acquiring location information of evacuation shelters, means for acquiring location information of evacuation robots and employees, means for analyzing and generating a safe and optimal evacuation route based on the acquired data, means for transmitting the generated evacuation route information to a terminal, means for displaying the transmitted evacuation route information, and means for supporting the evacuation of factory robots and employees in the event of a disaster. This makes it possible to provide quick and appropriate evacuation instructions to robots and employees in the factory based on optimal evacuation route information updated in real time.

[0476] "Satellite and aerial photograph data" refers to image data taken from satellites or aircraft, and is used to understand the terrain and disaster conditions.

[0477] "Location information of evacuation shelters" refers to information about the locations of evacuation shelters designated to ensure safety in the event of a disaster.

[0478] "Location information of evacuation robots and employees" is data indicating the current locations of robots and employees operating within the factory.

[0479] "Means for analyzing and generating safe and optimal evacuation routes based on acquired data" refers to algorithms and programs that use various collected data to calculate the safest and most effective evacuation routes.

[0480] The "means for transmitting the generated evacuation route information to the terminal" is a method for communicating the calculated evacuation route to the terminal of the disaster victim or user in real time.

[0481] The "means for displaying the transmitted evacuation route information" refers to an application or device that displays the received evacuation route information visually or audibly on the terminal.

[0482] "Means to support the evacuation of factory robots and employees in the event of a disaster" refers to systems and technologies that provide instructions to robots and employees in a factory to evacuate safely when a disaster occurs.

[0483] The present invention is a system for supporting the safe evacuation of factory robots and employees in the event of a disaster, and includes the following means.

[0484] 1. Data Collection Methods

[0485] The server acquires satellite and aerial photographic data, which allows it to obtain the latest information on disasters and terrain in real time. The server also obtains the latest evacuation shelter location information through local government databases and APIs. The server also collects location information from each robot and employee's device. This data becomes the basic data for generating evacuation routes.

[0486] 2. Data Analysis and Pathway Generation Procedures

[0487] The server uses AI technology and algorithms to analyze and generate safe and optimal evacuation routes in real time based on the acquired data. This analysis includes the current terrain conditions, passable roads, and the safety of evacuation shelters. For example, it reflects information on areas blocked off due to fires or gas leaks, as well as impassable routes.

[0488] 3. Route information transmission and display means

[0489] The server then sends the generated evacuation route information to the device, which then uses the information to visually display the evacuation route on a map application and provide audio and text guidance, allowing the user to evacuate safely while checking the route.

[0490] 4. Real-time update methods

[0491] As disaster situations change every moment, the server periodically collects new data and updates the generated evacuation routes as necessary. The device receives the updated information from the server and updates the route information displayed in real time, providing the most up-to-date evacuation routes at all times.

[0492] As an example, if a fire breaks out in a factory during a disaster, the server will analyze and generate evacuation routes based on the latest data and send the route information in real time to all robots and employee terminals in the factory. The terminals will display a safe evacuation route based on the received information, and users can quickly begin evacuating by following the audio guidance.

[0493] As another example, consider the case of building damage caused by an earthquake. The server receives the latest information, immediately calculates evacuation routes that reflect the damage situation, and issues appropriate instructions to robots and employees. This allows all parties involved to quickly take the optimal evacuation route.

[0494] Below are some example input prompts for a generative AI model:

[0495] "Design an application that generates safe evacuation routes and provides instructions to robots and employees in the event of a fire in a factory. The evacuation routes need to be updated in real time."

[0496] Using this prompt, each means of the above system can be specifically implemented.

[0497] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0498] Step 1:

[0499] The server retrieves satellite and aerial photo data. The server uses the API to retrieve the latest topographical and disaster information and stores the data in an internal database. The input is the data retrieved from the API, and the output is the updated information stored in the database. This data is used by the analysis and generation tools.

[0500] Step 2:

[0501] The server obtains the location information of evacuation shelters from the local government database. The server periodically queries the latest information on evacuation shelters through the API and stores that information in the database. The input is the evacuation shelter information obtained from the local government database, and the output is the location information of the evacuation shelters stored in the server's database.

[0502] Step 3:

[0503] The server collects real-time location information from each robot and employee's device. The device periodically sends its GPS information to the server, which stores it in a database. The input is the location information sent from the robot and employee's device, and the output is the location information stored in the server's database.

[0504] Step 4:

[0505] The server analyzes and generates safe and optimal evacuation routes based on the collected data. The server uses an AI algorithm to analyze topographical data, disaster data, location information, etc., and calculates the optimal route. The input is the topographical information, evacuation shelter information, and location information stored on the server, and the output is the analyzed optimal evacuation route information.

[0506] Step 5:

[0507] The server sends the generated evacuation route information to the terminals. The server sends the calculated evacuation route information to the robots and employee terminals in real time. The input is the analyzed evacuation route information, and the output is the route information sent to each terminal.

[0508] Step 6:

[0509] The terminal displays the received evacuation route information. The terminal application displays the route information on a map and provides guidance by voice and text. The input is the evacuation route information sent from the server, and the output is route guidance provided to the user visually and audibly.

[0510] Step 7:

[0511] The server collects new data in real time and updates evacuation routes as necessary. The server periodically recollects and reanalyzes topographical, disaster, and location information. The input is the newly added topographical and location information to the database, and the output is updated evacuation route information. This ensures that the latest evacuation routes are always provided to the terminal.

[0512] Through the above processing steps, a system is realized that provides optimal routes in real time for factory robots and employees to evacuate quickly and safely in the event of a disaster.

[0513] 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.

[0514] The present invention relates to a system for generating a route that will allow disaster victims to reach a shelter most safely in the event of a disaster. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it is possible to respond flexibly according to the user's psychological state. The system of the present invention includes the following means.

[0515] 1. Data Collection Methods

[0516] The server acquires satellite and aerial photographic data. This data reflects changes in the terrain and damage caused by disasters in real time. The server also obtains the latest location information for evacuation shelters through local government databases and APIs. The server also collects location information from each victim's device. The information collected in this way becomes the basic data for generating evacuation routes.

[0517] 2. Data Analysis and Pathway Generation Procedures

[0518] The server uses AI technology and algorithms to analyze and generate safe and optimal evacuation routes in real time based on the collected data. This analysis includes the current terrain, passable roads, and the safety of evacuation shelters. For example, information on roads being flooded due to flooding or roads being destroyed by earthquakes is reflected in real time.

[0519] 3. Route information transmission and display means

[0520] The server sends the generated evacuation route information to the device. The sent information is displayed as a map on each victim's device. Specifically, the map application displays the route visually and provides audio and text guidance, allowing users to evacuate safely while checking the route.

[0521] 4. Emotion recognition means

[0522] One feature of the present invention is that it includes an emotion engine that recognizes the user's emotions. The device analyzes the user's facial expressions and tone of voice to understand their emotional state (e.g., stress, fear, relief). Based on this analysis, the emotion engine evaluates the user's psychological state.

[0523] 5. Emotion-based route adjustment measures

[0524] The server adjusts evacuation route information based on feedback from the emotion engine. For example, if the user is experiencing high stress levels, the server recalculates a simpler and safer evacuation route and sends it to the device. Voice and text notification methods are also adjusted according to the user's emotional state.

[0525] Specific example explanation

[0526] In the case of a city experiencing flood disasters

[0527] 1. Data Collection Methods

[0528] The server periodically retrieves the latest satellite images and aerial photographs.

[0529] The server accesses the local government's database and updates the location information of active evacuation shelters.

[0530] The server collects real-time GPS data from the device.

[0531] 2. Data Analysis and Pathway Generation Procedures

[0532] The server uses AI models to detect flooded areas and impassable roads.

[0533] The server calculates the route from the victim to the nearest safe evacuation shelter, taking into account real-time terrain and evacuation shelter information.

[0534] 3. Route information transmission and display means

[0535] The server sends the calculated safe path to the terminal.

[0536] The device displays the received route information on a map application and provides voice guidance and text instructions.

[0537] 4. Emotion recognition means

[0538] The device analyzes the user's facial expressions and tone of voice to assess their emotional state. For example, the user's facial expressions and tone of voice are analyzed via the camera and microphone of their smartphone.

[0539] 5. Emotion-based route adjustment measures

[0540] The server receives the stress level assessment results from the emotion engine and adjusts the route and notification method as needed. For example, if a high stress level is detected, it provides a voice prompt to encourage reassurance.

[0541] In this way, the system of the present invention not only supports the safe evacuation of disaster victims but also enables flexible responses according to the emotional state of the user.

[0542] The processing flow will be explained below.

[0543] Step 1:

[0544] The server acquires satellite and aerial image data. Specifically, the server utilizes existing APIs and data feeds to periodically download the latest satellite and aerial images. This provides the basic data for understanding the progress of disasters and changes in the terrain in real time.

[0545] Step 2:

[0546] The server obtains location information for evacuation shelters. It accesses databases from local governments and related organizations and downloads the latest location information for currently operating evacuation shelters. This information is necessary for selecting safe evacuation destinations.

[0547] Step 3:

[0548] The server acquires the location information of the victims. It receives GPS data from the victims' devices in real time to determine their current location. This makes it possible to constantly track the movements of the victims.

[0549] Step 4:

[0550] The server runs an AI model to analyze the environmental data and location information collected by the server, which then detects collapsed roads and new obstacles, and searches for the safest and most optimal route.

[0551] Step 5:

[0552] The server generates evacuation routes based on the analysis results. These routes are designed to minimize the impact of the disaster and allow victims to reach evacuation shelters in the safest and quickest way possible.

[0553] Step 6:

[0554] The server then sends the generated evacuation route information to the victim's device. The route information is packaged in JSON or XML format and sent in real time. Data security and privacy are ensured during this transmission process.

[0555] Step 7:

[0556] The device analyzes the route information it receives and displays it as a visual map in the user interface. In addition, it provides audio guidance and text instructions, guiding the user both visually and aurally.

[0557] Step 8:

[0558] The device analyzes the user's facial expressions and tone of voice and evaluates their emotional state using an emotion engine. For example, it uses the smartphone's camera and microphone to analyze the user's facial expressions and tone of voice in real time.

[0559] Step 9:

[0560] The server receives feedback from the emotion engine and adjusts evacuation route information based on the user's emotional state. If a high stress level is detected, the server recalculates and provides a simpler and more reassuring evacuation route and notification method.

[0561] Step 10:

[0562] The device displays new route information based on emotion recognition and notifies the user via voice and text. The emotion engine provides information that allows the user to evacuate calmly.

[0563] Step 11:

[0564] The user follows the route information displayed on the device and begins evacuation along a safe route. By following the instructions on the device, victims can continue evacuating safely.

[0565] Step 12:

[0566] The device periodically tracks the user's current location using GPS and sends it to the server, allowing the server to always have the most up-to-date location information.

[0567] Step 13:

[0568] The server collects new disaster information and terrain data in real time and recalculates evacuation routes as needed. For example, if a new landslide or road closure occurs, this is reflected immediately.

[0569] Step 14:

[0570] The device receives updated information from the server and reflects it in the user interface in real time. If necessary, audio guides and text instructions are also updated to inform the user of the latest information.

[0571] Through these steps, the system of the present invention supports the safe evacuation of disaster victims in real time. It also enables flexible responses according to the user's emotional state, providing psychological security.

[0572] Example 2

[0573] 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."

[0574] In the event of a disaster, it is important to provide victims with the safest and fastest route to an evacuation shelter. However, current systems have difficulty reflecting disaster and terrain information in real time, and do not take into account the emotional state of victims. Therefore, a system that allows victims to evacuate safely is needed.

[0575] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0576] In this invention, the server includes means for acquiring satellite and aerial photograph data, means for acquiring location information of evacuation shelters, means for acquiring location information of disaster victims, means for analyzing and generating a safe and optimal evacuation route based on the collected data, means for transmitting the generated evacuation route information to a terminal, means for displaying the transmitted evacuation route information, means for recognizing a user's emotion, and means for adjusting the evacuation route information based on the emotion recognition result. This makes it possible to provide real-time route information for disaster victims to evacuate safely and also to flexibly respond to the user's emotional state.

[0577] "Satellite Data" refers to imagery and terrain information obtained from satellites.

[0578] "Aerial photographic data" refers to images and terrain information taken from an aircraft.

[0579] "Evacuation shelter location information" refers to the geographic location information of safe and operational evacuation shelters in the event of a disaster.

[0580] "Location information of disaster victims" refers to the current geographical location information of each disaster victim in the area where the disaster has occurred.

[0581] An "evacuation route" refers to the route that disaster victims take to move from their current location to a safe evacuation shelter.

[0582] "Emotion recognition" refers to the technology of analyzing a user's facial expressions and tone of voice to assess their psychological and emotional state.

[0583] An "emotion engine" refers to software or algorithms that generate specific actions or feedback based on a user's emotional state derived from emotion recognition.

[0584] "Generated evacuation route information" refers to information on safe and optimal evacuation routes generated based on collected data and analysis results.

[0585] "Devices" refer to portable electronic devices such as smartphones and tablets carried by disaster victims.

[0586] "Server" refers to a computer system that acts as a central control system and collects and analyzes various data, creates routes, and transmits data.

[0587] The present invention relates to a system for generating a route that will allow disaster victims to reach a shelter most safely in the event of a disaster. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it is possible to respond flexibly according to the user's psychological state. The system of the present invention includes the following means.

[0588] 1. Data Collection Methods

[0589] The server acquires satellite and aerial photographic data. Specifically, it periodically acquires image data using APIs from NASA and local aircraft companies. The server also acquires the latest location information for evacuation shelters via local government databases and APIs. Specific examples include the Google Civic Information API and the National Evacuation Shelter Database. The server also collects real-time location information from each victim's device. By acquiring location information from the device's GPS sensor, the current location of the victim can be determined.

[0590] 2. Data analysis methods

[0591] The server uses machine learning algorithms (e.g., deep learning models) based on the collected data to identify areas affected by disasters such as floods and landslides. It also organizes the collected evacuation shelter information and lists safe, operational shelters. It also identifies the current locations of disaster victims based on real-time location information from devices.

[0592] 3. Route Generation Method

[0593] The server uses AI technology (e.g., generative AI models) and the Dijkstra algorithm to generate safe and optimal evacuation routes, calculating routes that avoid flooded areas and impassable roads.

[0594] 4. Route Information Transmission Method

[0595] The server sends the generated evacuation route information to the victim's terminal in real time.

[0596] 5. Route Information Display Method

[0597] The route information received by the device is displayed on a map application. Specifically, it is displayed visually using Google Maps API or OpenStreetMap. The device also provides audio guidance and text instructions using the device's built-in speaker and text display function.

[0598] 6. Emotion recognition means

[0599] The device uses a camera and microphone to analyze the user's facial expressions and tone of voice in real time, for example, by using an emotion recognition API (e.g., Microsoft Azure Emotion API). The analysis results are sent to an emotion engine, which evaluates the user's emotional state (e.g., stress, relief, etc.).

[0600] 7. Route Adjustment Methods

[0601] The server receives feedback from the emotion engine and adjusts evacuation routes and notification methods as needed. For example, if a high stress level is detected, the server resends a recalculated route to the device. The device then displays the new route information and provides reassuring audio guidance, using a calming voice tone and positive messages.

[0602] Specific example explanation

[0603] In the case of a city experiencing flood disasters

[0604] 1. Data Collection Methods

[0605] The server periodically retrieves the latest satellite images and aerial photographs.

[0606] The server accesses the local government's database and updates the location information of active evacuation shelters.

[0607] The server collects real-time GPS data from the device.

[0608] 2. Data analysis methods

[0609] The server uses AI models to detect flooded areas and impassable roads.

[0610] The server calculates the route from the victim to the nearest safe evacuation shelter, taking into account real-time terrain and evacuation shelter information.

[0611] 3. Route Information Transmission Method

[0612] The server sends the calculated safe path to the terminal.

[0613] The device displays the received route information on a map application and provides voice guidance and text instructions.

[0614] 4. Emotion recognition means

[0615] The device analyzes the user's facial expressions and tone of voice to assess their emotional state. For example, the user's facial expressions and tone of voice are analyzed via the camera and microphone of their smartphone.

[0616] 5. Route Adjustment Methods

[0617] The server receives the stress level assessment results from the emotion engine and adjusts the route and notification method as needed. For example, if a high stress level is detected, it provides a voice prompt to encourage reassurance.

[0618] In this way, the system of the present invention not only supports the safe evacuation of disaster victims but also enables flexible responses according to the emotional state of the user.

[0619] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0620] Step 1: Collect data

[0621] The server retrieves satellite and aerial image data. This data is retrieved periodically through an API. The input is the latest image data from satellites and aircraft, and the output is initial data on the area affected by the disaster. Specifically, the server downloads image data from APIs provided by NASA and local aircraft companies.

[0622] Step 2: Collecting evacuation shelter and victim location information

[0623] The server obtains evacuation shelter location information from the local government's database and API. The server also collects real-time location information from each victim's device. The input is the local government's latest evacuation shelter information and the victim's GPS data, and the output is location information data including the locations of the evacuation shelter and the victim. The server collects evacuation shelter data through the local government's API and receives GPS data from each device.

[0624] Step 3: Data analysis

[0625] The server analyzes the collected data. The inputs are satellite data, aerial photographs, evacuation shelter information, and victim location information. The output is a list of disaster-affected areas and evacuation shelters, as well as the current locations of the victims. Specifically, it uses machine learning algorithms to analyze the data and identify flooded areas and impassable roads.

[0626] Step 4: Generate evacuation routes

[0627] The server uses AI technology and algorithms to generate a safe and optimal evacuation route. The input is analyzed data, and the output is safe evacuation route information. Specifically, the server uses an AI model (e.g., a generative AI model) and the Dijkstra algorithm to perform route calculations.

[0628] Step 5: Sending route information

[0629] The server sends the generated evacuation route information to the terminals. The input is the generated evacuation route information, and the output is the transmission of evacuation route data to each terminal. Specifically, the server transfers data to the terminals in real time.

[0630] Step 6: View route information

[0631] The route information received by the device is displayed on a map application. The input is evacuation route information sent from the server, and the output is a route displayed on a map and audio and text guidance. Specifically, the device uses Google Maps API and OpenStreetMap to visually display the route, and provides guidance using the built-in speaker and text display function.

[0632] Step 7: Emotion Recognition

[0633] The device uses a camera and microphone to analyze the user's facial expressions and tone of voice. The input is the user's facial expressions and tone of voice, and the output is an evaluation of their emotional state. Specifically, the analysis is performed using an emotion recognition API (e.g., Microsoft Azure Emotion API).

[0634] Step 8: Route Adjustment

[0635] The server receives feedback from the emotion engine and adjusts the evacuation route. The input is the emotion recognition result, and the output is adjusted route information. Specifically, the server generates a recalculated route based on the emotion engine's evaluation results and sends it back to the device. The device then displays the new route information and provides audio guidance to encourage reassurance.

[0636] (Application example 2)

[0637] 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."

[0638] Conventional evacuation support systems can help disaster victims select the safest route, but because they do not take into account the user's emotional state, they are unable to respond appropriately in situations where the user feels stress or anxiety. Furthermore, there is a lack of technology to utilize autonomous vehicles for evacuation support, making it difficult to ensure the safety and efficiency of autonomously driven evacuations. Given this background, there is a need for a system that provides safe and optimal evacuation routes using autonomous vehicles while taking into account the user's emotional state during a disaster.

[0639] 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.

[0640] In this invention, the server includes means for acquiring satellite and aerial photograph data, means for acquiring location information of evacuation shelters, means for acquiring location information of disaster victims, means for analyzing and generating a safe and optimal evacuation route based on the acquired data, means for transmitting the generated evacuation route information to a terminal, means for displaying the transmitted evacuation route information, means for recognizing the emotional state of a user using a camera and a microphone, and means for adjusting the evacuation route based on the recognized emotional state. This enables flexible route selection according to the emotional state of a user even during a disaster, thereby realizing evacuation support using self-driving vehicles.

[0641] "Satellite data" refers to data that is photographed by satellites placed in space and provides information on the Earth's surface and topography.

[0642] "Aerial photograph data" refers to photographic data of the earth's surface taken from an aircraft, and provides detailed information on urban areas and topography.

[0643] "Location information of evacuation shelter" is information indicating the current location of an evacuation shelter where disaster victims can safely evacuate.

[0644] "Location information of disaster victims" is GPS data that indicates the current location of disaster victims at the time of the disaster.

[0645] An "evacuation route" refers to the safest and most optimal route for disaster victims to reach an evacuation shelter.

[0646] "AI technology" is a technology that uses artificial intelligence to analyze data and make decisions.

[0647] "Route information display means" refers to a device or interface for visually displaying the calculated evacuation route.

[0648] The "emotion recognition means" is a device that uses sensors such as a camera and a microphone to determine the user's emotional state (e.g., stress, fear, relief).

[0649] "Route adjustment means" refers to an algorithm or system for dynamically changing an evacuation route based on the user's emotional state obtained from the emotion recognition means.

[0650] An "autonomous vehicle" is a vehicle that can drive autonomously without requiring operation by a passenger.

[0651] An "emotion engine" is software or hardware that analyzes a user's emotional state and provides an appropriate response based on the results.

[0652] A "safe evacuation route" is a route that is least affected by a disaster and has the highest chance of allowing victims to reach a shelter safely.

[0653] "Real-time information" is information that instantly acquires the current situation and status and displays and processes it instantly.

[0654] The present invention relates to an evacuation support system in the event of a disaster using an autonomous driving vehicle, and specifically describes a method for implementing each of the following means.

[0655] First, the server acquires satellite and aerial photographic data. This data reflects changes in the terrain and damage caused by the disaster in real time. The server also obtains the latest location information for evacuation shelters through local government databases and APIs. The server also collects current GPS location information from victims' smartphones and other location-providing devices.

[0656] The server then uses AI technology and algorithms to analyze and generate safe and optimal evacuation routes in real time based on the collected data. This analysis includes the current terrain, passable roads, and the safety of evacuation shelters. For example, it reflects information in real time about roads that are inundated by floods or damaged by earthquakes.

[0657] The generated evacuation route information is sent from the server to the autonomous vehicle's navigation system, which then displays the received information on a map application and provides voice guidance and text instructions, allowing the user to evacuate safely while checking the evacuation route.

[0658] The emotion recognition function uses cameras and microphones installed in autonomous vehicles to analyze the user's emotional state from their facial expressions and tone of voice. For example, if the user is feeling stressed, the system will adjust the evacuation route and notification method accordingly. This is done by the emotion engine, which evaluates the user's stress level and, if necessary, recalculates and provides a safer route and gentler voice guidance.

[0659] As a concrete example, when this system is applied to a city experiencing a flood disaster, the server first obtains the latest satellite and aerial images and accesses the local government's database to update the location information of evacuation shelters. It also collects real-time GPS data from the user's smartphone. The server uses an AI model to detect flooded areas and impassable roads and calculates the optimal route from the victim to the nearest safe evacuation shelter. The calculated safe route is sent to the autonomous vehicle, where it is displayed on the map application and audio and text instructions are provided.

[0660] The system operates based on the following example prompt:

[0661] "Generate an evacuation route based on the following data.

[0662] data:

[0663] Satellite imagery: latest flood-affected areas

[0664] Municipal database: Location information of currently operating evacuation shelters

[0665] GPS data: Current location of victim (latitude: 35.6895, longitude: 139.6917)

[0666] In this way, the present invention supports the safe evacuation of disaster victims and enables flexible responses according to the emotional state of the user. Furthermore, the use of autonomous vehicles improves the efficiency and safety of evacuation.

[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 acquires satellite and aerial image data. This involves periodically downloading the latest satellite and aerial images from the Internet. The input is image data from satellites and aircraft, and the output is the latest image data of the Earth's surface. This allows the server to maintain the latest map information.

[0670] Step 2:

[0671] The server obtains the location information of evacuation shelters. This involves accessing the local government's database or API to obtain the latest list of evacuation shelters. The input is the local government's database, and the output is a dataset containing the location information of evacuation shelters. This allows the server to collect the latest evacuation shelter information.

[0672] Step 3:

[0673] The server obtains the location information of the victim. This involves obtaining real-time location data from the victim's smartphone or GPS device. The input is the GPS data from the victim's smartphone, and the output is the victim's current location information. This allows the server to accurately determine the victim's location.

[0674] Step 4:

[0675] The server analyzes and generates a safe and optimal evacuation route based on the acquired data. This process involves using an AI algorithm to analyze the collected map data, evacuation shelter information, and location information of disaster victims, and calculate the optimal route. The input is satellite data, evacuation shelter information, and location information of disaster victims, and the output is the optimal evacuation route. This allows the server to provide the optimal evacuation route.

[0676] Step 5:

[0677] The server transmits the generated evacuation route information to the terminal, which includes transmitting the evacuation route to the autonomous vehicle's navigation system using an appropriate protocol. The input is the evacuation route information, and the output is the transmitted route information. This provides the route information to the autonomous vehicle.

[0678] Step 6:

[0679] The device displays the transmitted evacuation route information. This involves the navigation system visually displaying the route information on a map application and providing audio and text instructions. The input is the route information, and the output is the displayed map and audio and text instructions. This allows the user to confirm a safe evacuation route.

[0680] Step 7:

[0681] The device recognizes the user's emotional state using a camera and microphone. This involves using sensor data to analyze the user's facial expressions and tone of voice to evaluate the emotional state. The input is sensor data from the camera and microphone, and the output is the recognized emotional state. This allows the device to understand the user's psychological state.

[0682] Step 8:

[0683] The server adjusts the evacuation route based on the recognized emotional state. This involves receiving feedback from the emotion engine and modifying the route information and audio guidance as necessary. The input is the user's emotional state, and the output is the adjusted route information. This allows for appropriate responses according to the user's psychological state.

[0684] 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.

[0685] 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.

[0686] 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.

[0687] [Third embodiment]

[0688] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0689] 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.

[0690] 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).

[0691] 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.

[0692] 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.

[0693] 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).

[0694] 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.

[0695] 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.

[0696] 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.

[0697] 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.

[0698] 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.

[0699] 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."

[0700] The present invention relates to a system for generating a route that will allow disaster victims to reach an evacuation shelter in the safest way possible in the event of a disaster. The system of the present invention includes the following means.

[0701] 1. Data Collection Methods

[0702] The server acquires satellite and aerial photographic data. This data reflects changes in the terrain and damage caused by disasters in real time. The server also obtains the latest location information for evacuation shelters through local government databases and APIs. The server also collects location information from each victim's device. The information collected in this way becomes the basic data for generating evacuation routes.

[0703] 2. Data Analysis and Pathway Generation Procedures

[0704] The server uses AI technology and algorithms to analyze and generate safe and optimal evacuation routes in real time based on the collected data. This analysis includes the current terrain, passable roads, and the safety of evacuation shelters. For example, information on roads being flooded due to flooding or roads being destroyed by earthquakes is reflected in real time.

[0705] 3. Route information transmission and display means

[0706] The server sends the generated evacuation route information to the device. The sent information is displayed as a map on each victim's device. Specifically, the map application displays the route visually and provides audio and text guidance. This allows victims to evacuate safely while checking the route.

[0707] 4. Real-time update methods

[0708] As disaster situations change every moment, the server periodically collects new data and updates the generated evacuation routes as necessary. The device receives the updated information from the server and updates the route information displayed in real time, providing the most up-to-date evacuation routes at all times.

[0709] Specific example explanation

[0710] In the case of a city experiencing flood disasters

[0711] 1. Data Collection Methods

[0712] The server periodically retrieves the latest satellite images and aerial photographs.

[0713] The server accesses the local government's database and updates the location information of active evacuation shelters.

[0714] The server collects real-time GPS data from the device.

[0715] 2. Data Analysis and Pathway Generation Procedures

[0716] The server uses AI models to detect flooded areas and impassable roads.

[0717] The server calculates the route from the victim to the nearest safe evacuation shelter, taking into account real-time terrain and evacuation shelter information.

[0718] 3. Route information transmission and display means

[0719] The server sends the calculated safe path to the terminal.

[0720] The device displays the received route information on a map application and provides voice guidance and text instructions.

[0721] 4. Real-time update methods

[0722] The device uses GPS to periodically send the user's current location to the server.

[0723] The server takes in new situational data in real time, recalculates evacuation routes as needed, and sends updates to the device.

[0724] The device updates the map and audio guide based on the new information.

[0725] In this way, the system of the present invention assists disaster victims in obtaining the most up-to-date and optimal route information and evacuating safely in the event of a disaster.

[0726] The processing flow will be explained below.

[0727] Step 1:

[0728] The server acquires satellite and aerial image data. Specifically, the server utilizes existing APIs and data feeds to periodically download the latest satellite and aerial images. This allows the collection of basic data for understanding the progress of disasters and changes in the terrain in real time.

[0729] Step 2:

[0730] The server obtains location information for evacuation shelters. It accesses databases from local governments and related organizations to obtain the latest location information for currently operating evacuation shelters. This information is necessary for selecting safe evacuation destinations.

[0731] Step 3:

[0732] The server acquires the location information of the victims. It receives GPS data from the victims' devices and grasps their current location in real time. This makes it possible to constantly track the movements of the victims.

[0733] Step 4:

[0734] The server runs an AI model to analyze the environmental data and location information collected, and the AI ​​model detects collapsed roads and new obstacles, searching for the safest and most optimal route.

[0735] Step 5:

[0736] The server generates evacuation routes based on the analysis results. These routes are designed to minimize the impact of the disaster and allow victims to reach evacuation shelters in the safest and quickest way possible.

[0737] Step 6:

[0738] The server then sends the generated evacuation route information to the victim's device. The route information is packaged in JSON or XML format and sent in real time.

[0739] Step 7:

[0740] The device analyzes the route information it receives and displays it as a visual map on the user interface. It also simultaneously provides voice guidance and text instructions, navigating the user visually and audibly.

[0741] Step 8:

[0742] The user follows the route information displayed on the device and begins evacuation along a safe route. By following the instructions on the device, victims can evacuate safely.

[0743] Step 9:

[0744] The device periodically tracks the user's current location using GPS and sends it to the server, allowing the server to always have the most up-to-date location information.

[0745] Step 10:

[0746] The server collects new disaster information and terrain data in real time and recalculates evacuation routes as needed. For example, if a new landslide or road closure occurs, it will be updated immediately.

[0747] Step 11:

[0748] The device receives updated information from the server and notifies the user of the latest route information, allowing disaster victims to continue evacuating along the optimal evacuation route.

[0749] Through these steps, the system of the present invention realizes safe evacuation in the event of a disaster.

[0750] Example 1

[0751] 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."

[0752] In the event of a disaster, there is a need to provide route information to help victims reach evacuation shelters safely and quickly. However, conventional systems cannot reflect disaster information and topographical information that is updated in real time, making it difficult to provide safe evacuation routes. Furthermore, if evacuation route information is not properly communicated, there is a risk that the safety of victims will not be ensured.

[0753] 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.

[0754] In this invention, the server includes means for collecting satellite and aerial photograph data, means for acquiring location information of evacuation shelters, means for collecting location information of disaster victims, means for analyzing and generating a safe and optimal evacuation route based on the collected data, means for transmitting the generated evacuation route information to a terminal, means for displaying the transmitted evacuation route information, and means for updating the evacuation route information in real time, thereby making it possible to provide real-time route information that will allow disaster victims to reach evacuation shelters in the safest and fastest way possible in the event of a disaster.

[0755] "Satellite and aerial photographic data" refers to images and information obtained from satellites and aircraft to observe the Earth's surface, and is used to analyze terrain and disaster situations.

[0756] "Location information of evacuation shelters" refers to information on the geographic coordinates and addresses of facilities and locations for evacuation in the event of a disaster.

[0757] "Victim location information" is geographical data that indicates the current location of individuals or groups of people affected by a disaster.

[0758] "Collected data" is a general term that includes all information obtained by the system, such as satellite and aerial photograph data, location information of evacuation centers, and location information of disaster victims.

[0759] "Means of analysis and generation" refers to the techniques and methods for performing calculations and analyses to derive safe and optimal evacuation routes based on collected data.

[0760] "Generated evacuation route information" refers to information on safe and optimal evacuation routes derived using analytical and generating methods.

[0761] "Devices" refer to electronic devices such as smartphones and tablets carried by disaster victims, which are used to receive and display evacuation route information.

[0762] "Display means" refers to the technology or method for visually displaying the generated evacuation route information on a terminal so that the user can confirm it.

[0763] "Means of updating in real time" refers to mechanisms and technologies that instantly revise evacuation route information in response to changes in disaster conditions and topographical information, and provide users with the latest information.

[0764] The present invention relates to a system that generates routes that will allow disaster victims to reach evacuation shelters in the safest way possible. The system of the present invention is mainly composed of three main elements: a server, a terminal, and a user.

[0765] System configuration

[0766] The server has the means to acquire satellite and aerial photographic data. This data reflects changes in the terrain and damage caused by disasters in real time. The server also obtains the latest location information for evacuation shelters through local government databases and APIs. The server also collects location information from each victim's device. This collected information serves as the basic data for generating evacuation routes.

[0767] Specifically, the server uses the Python requests library to retrieve data from NASA and other commercial satellite companies via API, leverages data analysis libraries such as Pandas to format and analyze the data, and uses the Google Maps API to retrieve shelter location information.

[0768] The devices are electronic devices such as smartphones and tablets carried by disaster victims. A map application is installed on the devices, which visually displays evacuation route information sent from the server. Specifically, the devices were developed using React Native, and use the GPS function to send the user's current location to the server.

[0769] The user is a disaster victim, and the goal is to use evacuation route information to safely reach a shelter. The user checks real-time evacuation route information through a map application and evacuates by following voice and text guidance.

[0770] System action

[0771] As a data collection method, the server periodically collects satellite and aerial photograph data, location information of evacuation centers, and location information of disaster victims. This information is used to obtain basic data for generating evacuation routes.

[0772] As a means of data analysis and route generation, the server uses AI technology (TensorFlow and PyTorch, for example) and algorithms (such as Dijkstra's algorithm) based on the collected data to generate safe and optimal evacuation routes. Real-time disaster and terrain information is reflected, and the latest routes are always provided.

[0773] As a route information transmission and display means, the server transmits the generated evacuation route information to the terminal, which then visually displays it on a map application. The user can then check this information and evacuate.

[0774] As a real-time update method, the device periodically sends the user's current location to the server, and the server obtains new disaster information and regenerates evacuation routes as necessary. This updated information is immediately sent to the device, and the map and audio guide are updated in real time.

[0775] Specific examples

[0776] For example, in the case of a city experiencing a flood disaster, the server retrieves the latest satellite and aerial images, combines them with GPS data received from the devices of individual victims, and generates routes that avoid flooded areas and impassable roads. The generated evacuation route is sent to the device and displayed visually on the Google Maps application.

[0777] Example prompts for generative AI models

[0778] A generative AI model for use in disaster situations might be given prompts like the following:

[0779] "What is the best algorithm for generating safe evacuation routes in areas experiencing flooding?"

[0780] "Help us design an evacuation route generation system to respond to real-time changing disaster situations."

[0781] In this way, the system of the present invention assists disaster victims in obtaining the most up-to-date and optimal route information and evacuating safely in the event of a disaster.

[0782] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0783] Step 1: Data collection

[0784] The server collects the latest satellite and aerial image data. This involves the server sending API requests to retrieve image data from data providers such as NASA. The input is the API request, and the output is the retrieved image data. Specifically, it uses the Python requests library to retrieve the data and save it in a directory for image analysis.

[0785] Step 2: Obtaining evacuation shelter location information

[0786] The server retrieves the latest evacuation shelter location information from the local government database or API. The server sends an HTTP request and receives the evacuation shelter location information in JSON format. The input is the HTTP request, and the output is the retrieved evacuation shelter location information. Specifically, the server uses the request library to parse the received JSON data and store it in the database.

[0787] Step 3: Collect location information of victims

[0788] The device collects the user's current location information through its GPS function and sends it to the server. The input is the device's GPS data, and the output is the transmitted location data. Specifically, the device uses a React Native application to send the GPS data to the server as a POST request at regular intervals.

[0789] Step 4: Data analysis

[0790] The server receives and analyzes the collected satellite images, evacuation center location information, and disaster victim location information. The server cleanses and normalizes the data, and then inputs it into the AI ​​model. The input is all the data collected in the previous step, and the output is the cleansed data. Specifically, the data is preprocessed using Pandas.

[0791] Step 5: Generate evacuation routes

[0792] The server uses AI technology and algorithms to generate optimal evacuation routes based on the cleansed data. The server inputs and analyzes the data into an AI model and applies Dijkstra's algorithm and other route search algorithms. The input is preprocessed data, and the output is the generated evacuation route information. Specifically, the server uses a TensorFlow model to perform route analysis and saves the results.

[0793] Step 6: Send evacuation route information

[0794] The server sends the generated evacuation route information to the terminal. Data is pushed in real time via WebSocket. The input is the generated evacuation route information, and the output is the sent route data. Specifically, the WebSocket server is used to send the route data to the terminal.

[0795] Step 7: Display evacuation route information

[0796] The device receives evacuation route information and displays it on a map application, notifying the user by voice and text. The input is route information received from the server, and the output is a map display on the device and voice guidance. Specifically, the system uses the Google Maps API to display the route, and provides text and voice guidance in a React Native app.

[0797] Step 8: Real-time updates

[0798] The device continuously sends the user's current location to the server in real time, and the server regenerates evacuation routes based on updated disaster information and sends them back to the device. The input is newly collected location information and disaster information, and the output is updated evacuation route information. Specifically, the system uses the GPS data and newly acquired disaster data to perform analysis again using AI models and algorithms, and sends the results back to the device in real time.

[0799] By following the steps above, disaster victims can always obtain the latest evacuation route information and evacuate safely.

[0800] (Application example 1)

[0801] 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."

[0802] In the event of a disaster, providing a quick and optimal evacuation route is important to ensure the safety of employees and robots in a factory. However, with conventional systems, it has been difficult to issue appropriate evacuation instructions to robots and employees in a factory while reflecting constantly changing disaster information and terrain information in real time. The present invention aims to solve this problem.

[0803] 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.

[0804] In this invention, the server includes means for acquiring satellite and aerial photograph data, means for acquiring location information of evacuation shelters, means for acquiring location information of evacuation robots and employees, means for analyzing and generating a safe and optimal evacuation route based on the acquired data, means for transmitting the generated evacuation route information to a terminal, means for displaying the transmitted evacuation route information, and means for supporting the evacuation of factory robots and employees in the event of a disaster. This makes it possible to provide quick and appropriate evacuation instructions to robots and employees in the factory based on optimal evacuation route information updated in real time.

[0805] "Satellite and aerial photograph data" refers to image data taken from satellites or aircraft, and is used to understand the terrain and disaster conditions.

[0806] "Location information of evacuation shelters" refers to information about the locations of evacuation shelters designated to ensure safety in the event of a disaster.

[0807] "Location information of evacuation robots and employees" is data indicating the current locations of robots and employees operating within the factory.

[0808] "Means for analyzing and generating safe and optimal evacuation routes based on acquired data" refers to algorithms and programs that use various collected data to calculate the safest and most effective evacuation routes.

[0809] The "means for transmitting the generated evacuation route information to the terminal" is a method for communicating the calculated evacuation route to the terminal of the disaster victim or user in real time.

[0810] The "means for displaying the transmitted evacuation route information" refers to an application or device that displays the received evacuation route information visually or audibly on the terminal.

[0811] "Means to support the evacuation of factory robots and employees in the event of a disaster" refers to systems and technologies that provide instructions to robots and employees in a factory to evacuate safely when a disaster occurs.

[0812] The present invention is a system for supporting the safe evacuation of factory robots and employees in the event of a disaster, and includes the following means.

[0813] 1. Data Collection Methods

[0814] The server acquires satellite and aerial photographic data, which allows it to obtain the latest information on disasters and terrain in real time. The server also obtains the latest evacuation shelter location information through local government databases and APIs. The server also collects location information from each robot and employee's device. This data becomes the basic data for generating evacuation routes.

[0815] 2. Data Analysis and Pathway Generation Procedures

[0816] The server uses AI technology and algorithms to analyze and generate safe and optimal evacuation routes in real time based on the acquired data. This analysis includes the current terrain conditions, passable roads, and the safety of evacuation shelters. For example, it reflects information on areas blocked off due to fires or gas leaks, as well as impassable routes.

[0817] 3. Route information transmission and display means

[0818] The server then sends the generated evacuation route information to the device, which then uses the information to visually display the evacuation route on a map application and provide audio and text guidance, allowing the user to evacuate safely while checking the route.

[0819] 4. Real-time update methods

[0820] As disaster situations change every moment, the server periodically collects new data and updates the generated evacuation routes as necessary. The device receives the updated information from the server and updates the route information displayed in real time, providing the most up-to-date evacuation routes at all times.

[0821] As an example, if a fire breaks out in a factory during a disaster, the server will analyze and generate evacuation routes based on the latest data and send the route information in real time to all robots and employee terminals in the factory. The terminals will display a safe evacuation route based on the received information, and users can quickly begin evacuating by following the audio guidance.

[0822] As another example, consider the case of building damage caused by an earthquake. The server receives the latest information, immediately calculates evacuation routes that reflect the damage situation, and issues appropriate instructions to robots and employees. This allows all parties involved to quickly take the optimal evacuation route.

[0823] Below are some example input prompts for a generative AI model:

[0824] "Design an application that generates safe evacuation routes and provides instructions to robots and employees in the event of a fire in a factory. The evacuation routes need to be updated in real time."

[0825] Using this prompt, each means of the above system can be specifically implemented.

[0826] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0827] Step 1:

[0828] The server retrieves satellite and aerial photo data. The server uses the API to retrieve the latest topographical and disaster information and stores the data in an internal database. The input is the data retrieved from the API, and the output is the updated information stored in the database. This data is used by the analysis and generation tools.

[0829] Step 2:

[0830] The server obtains the location information of evacuation shelters from the local government database. The server periodically queries the latest information on evacuation shelters through the API and stores that information in the database. The input is the evacuation shelter information obtained from the local government database, and the output is the location information of the evacuation shelters stored in the server's database.

[0831] Step 3:

[0832] The server collects real-time location information from each robot and employee's device. The device periodically sends its GPS information to the server, which stores it in a database. The input is the location information sent from the robot and employee's device, and the output is the location information stored in the server's database.

[0833] Step 4:

[0834] The server analyzes and generates safe and optimal evacuation routes based on the collected data. The server uses an AI algorithm to analyze topographical data, disaster data, location information, etc., and calculates the optimal route. The input is the topographical information, evacuation shelter information, and location information stored on the server, and the output is the analyzed optimal evacuation route information.

[0835] Step 5:

[0836] The server sends the generated evacuation route information to the terminals. The server sends the calculated evacuation route information to the robots and employee terminals in real time. The input is the analyzed evacuation route information, and the output is the route information sent to each terminal.

[0837] Step 6:

[0838] The terminal displays the received evacuation route information. The terminal application displays the route information on a map and provides guidance by voice and text. The input is the evacuation route information sent from the server, and the output is route guidance provided to the user visually and audibly.

[0839] Step 7:

[0840] The server collects new data in real time and updates evacuation routes as necessary. The server periodically recollects and reanalyzes topographical, disaster, and location information. The input is the newly added topographical and location information to the database, and the output is updated evacuation route information. This ensures that the latest evacuation routes are always provided to the terminal.

[0841] Through the above processing steps, a system is realized that provides optimal routes in real time for factory robots and employees to evacuate quickly and safely in the event of a disaster.

[0842] 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.

[0843] The present invention relates to a system for generating a route that will allow disaster victims to reach a shelter most safely in the event of a disaster. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it is possible to respond flexibly according to the user's psychological state. The system of the present invention includes the following means.

[0844] 1. Data Collection Methods

[0845] The server acquires satellite and aerial photographic data. This data reflects changes in the terrain and damage caused by disasters in real time. The server also obtains the latest location information for evacuation shelters through local government databases and APIs. The server also collects location information from each victim's device. The information collected in this way becomes the basic data for generating evacuation routes.

[0846] 2. Data Analysis and Pathway Generation Procedures

[0847] The server uses AI technology and algorithms to analyze and generate safe and optimal evacuation routes in real time based on the collected data. This analysis includes the current terrain, passable roads, and the safety of evacuation shelters. For example, information on roads being flooded due to flooding or roads being destroyed by earthquakes is reflected in real time.

[0848] 3. Route information transmission and display means

[0849] The server sends the generated evacuation route information to the device. The sent information is displayed as a map on each victim's device. Specifically, the map application displays the route visually and provides audio and text guidance, allowing users to evacuate safely while checking the route.

[0850] 4. Emotion recognition means

[0851] One feature of the present invention is that it includes an emotion engine that recognizes the user's emotions. The device analyzes the user's facial expressions and tone of voice to understand their emotional state (e.g., stress, fear, relief). Based on this analysis, the emotion engine evaluates the user's psychological state.

[0852] 5. Emotion-based route adjustment measures

[0853] The server adjusts evacuation route information based on feedback from the emotion engine. For example, if the user is experiencing high stress levels, the server recalculates a simpler and safer evacuation route and sends it to the device. Voice and text notification methods are also adjusted according to the user's emotional state.

[0854] Specific example explanation

[0855] In the case of a city experiencing flood disasters

[0856] 1. Data Collection Methods

[0857] The server periodically retrieves the latest satellite images and aerial photographs.

[0858] The server accesses the local government's database and updates the location information of active evacuation shelters.

[0859] The server collects real-time GPS data from the device.

[0860] 2. Data Analysis and Pathway Generation Procedures

[0861] The server uses AI models to detect flooded areas and impassable roads.

[0862] The server calculates the route from the victim to the nearest safe evacuation shelter, taking into account real-time terrain and evacuation shelter information.

[0863] 3. Route information transmission and display means

[0864] The server sends the calculated safe path to the terminal.

[0865] The device displays the received route information on a map application and provides voice guidance and text instructions.

[0866] 4. Emotion recognition means

[0867] The device analyzes the user's facial expressions and tone of voice to assess their emotional state. For example, the user's facial expressions and tone of voice are analyzed via the camera and microphone of their smartphone.

[0868] 5. Emotion-based route adjustment measures

[0869] The server receives the stress level assessment results from the emotion engine and adjusts the route and notification method as needed. For example, if a high stress level is detected, it provides a voice prompt to encourage reassurance.

[0870] In this way, the system of the present invention not only supports the safe evacuation of disaster victims but also enables flexible responses according to the emotional state of the user.

[0871] The processing flow will be explained below.

[0872] Step 1:

[0873] The server acquires satellite and aerial image data. Specifically, the server utilizes existing APIs and data feeds to periodically download the latest satellite and aerial images. This provides the basic data for understanding the progress of disasters and changes in the terrain in real time.

[0874] Step 2:

[0875] The server obtains location information for evacuation shelters. It accesses databases from local governments and related organizations and downloads the latest location information for currently operating evacuation shelters. This information is necessary for selecting safe evacuation destinations.

[0876] Step 3:

[0877] The server acquires the location information of the victims. It receives GPS data from the victims' devices in real time to determine their current location. This makes it possible to constantly track the movements of the victims.

[0878] Step 4:

[0879] The server runs an AI model to analyze the environmental data and location information collected by the server, which then detects collapsed roads and new obstacles, and searches for the safest and most optimal route.

[0880] Step 5:

[0881] The server generates evacuation routes based on the analysis results. These routes are designed to minimize the impact of the disaster and allow victims to reach evacuation shelters in the safest and quickest way possible.

[0882] Step 6:

[0883] The server then sends the generated evacuation route information to the victim's device. The route information is packaged in JSON or XML format and sent in real time. Data security and privacy are ensured during this transmission process.

[0884] Step 7:

[0885] The device analyzes the route information it receives and displays it as a visual map in the user interface. In addition, it provides audio guidance and text instructions, guiding the user both visually and aurally.

[0886] Step 8:

[0887] The device analyzes the user's facial expressions and tone of voice and evaluates their emotional state using an emotion engine. For example, it uses the smartphone's camera and microphone to analyze the user's facial expressions and tone of voice in real time.

[0888] Step 9:

[0889] The server receives feedback from the emotion engine and adjusts evacuation route information based on the user's emotional state. If a high stress level is detected, the server recalculates and provides a simpler and more reassuring evacuation route and notification method.

[0890] Step 10:

[0891] The device displays new route information based on emotion recognition and notifies the user via voice and text. The emotion engine provides information that allows the user to evacuate calmly.

[0892] Step 11:

[0893] The user follows the route information displayed on the device and begins evacuation along a safe route. By following the instructions on the device, victims can continue evacuating safely.

[0894] Step 12:

[0895] The device periodically tracks the user's current location using GPS and sends it to the server, allowing the server to always have the most up-to-date location information.

[0896] Step 13:

[0897] The server collects new disaster information and terrain data in real time and recalculates evacuation routes as needed. For example, if a new landslide or road closure occurs, this is reflected immediately.

[0898] Step 14:

[0899] The device receives updated information from the server and reflects it in the user interface in real time. If necessary, audio guides and text instructions are also updated to inform the user of the latest information.

[0900] Through these steps, the system of the present invention supports the safe evacuation of disaster victims in real time. It also enables flexible responses according to the user's emotional state, providing psychological security.

[0901] Example 2

[0902] 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."

[0903] In the event of a disaster, it is important to provide victims with the safest and fastest route to an evacuation shelter. However, current systems have difficulty reflecting disaster and terrain information in real time, and do not take into account the emotional state of victims. Therefore, a system that allows victims to evacuate safely is needed.

[0904] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0905] In this invention, the server includes means for acquiring satellite and aerial photograph data, means for acquiring location information of evacuation shelters, means for acquiring location information of disaster victims, means for analyzing and generating a safe and optimal evacuation route based on the collected data, means for transmitting the generated evacuation route information to a terminal, means for displaying the transmitted evacuation route information, means for recognizing a user's emotion, and means for adjusting the evacuation route information based on the emotion recognition result. This makes it possible to provide real-time route information for disaster victims to evacuate safely and also to flexibly respond to the user's emotional state.

[0906] "Satellite Data" refers to imagery and terrain information obtained from satellites.

[0907] "Aerial photographic data" refers to images and terrain information taken from an aircraft.

[0908] "Evacuation shelter location information" refers to the geographic location information of safe and operational evacuation shelters in the event of a disaster.

[0909] "Location information of disaster victims" refers to the current geographical location information of each disaster victim in the area where the disaster has occurred.

[0910] An "evacuation route" refers to the route that disaster victims take to move from their current location to a safe evacuation shelter.

[0911] "Emotion recognition" refers to the technology of analyzing a user's facial expressions and tone of voice to assess their psychological and emotional state.

[0912] An "emotion engine" refers to software or algorithms that generate specific actions or feedback based on a user's emotional state derived from emotion recognition.

[0913] "Generated evacuation route information" refers to information on safe and optimal evacuation routes generated based on collected data and analysis results.

[0914] "Devices" refer to portable electronic devices such as smartphones and tablets carried by disaster victims.

[0915] "Server" refers to a computer system that acts as a central control system and collects and analyzes various data, creates routes, and transmits data.

[0916] The present invention relates to a system for generating a route that will allow disaster victims to reach a shelter most safely in the event of a disaster. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it is possible to respond flexibly according to the user's psychological state. The system of the present invention includes the following means.

[0917] 1. Data Collection Methods

[0918] The server acquires satellite and aerial photographic data. Specifically, it periodically acquires image data using APIs from NASA and local aircraft companies. The server also acquires the latest location information for evacuation shelters via local government databases and APIs. Specific examples include the Google Civic Information API and the National Evacuation Shelter Database. The server also collects real-time location information from each victim's device. By acquiring location information from the device's GPS sensor, the current location of the victim can be determined.

[0919] 2. Data analysis methods

[0920] The server uses machine learning algorithms (e.g., deep learning models) based on the collected data to identify areas affected by disasters such as floods and landslides. It also organizes the collected evacuation shelter information and lists safe, operational shelters. It also identifies the current locations of disaster victims based on real-time location information from devices.

[0921] 3. Route Generation Method

[0922] The server uses AI technology (e.g., generative AI models) and the Dijkstra algorithm to generate safe and optimal evacuation routes, calculating routes that avoid flooded areas and impassable roads.

[0923] 4. Route Information Transmission Method

[0924] The server sends the generated evacuation route information to the victim's terminal in real time.

[0925] 5. Route Information Display Method

[0926] The route information received by the device is displayed on a map application. Specifically, it is displayed visually using Google Maps API or OpenStreetMap. The device also provides audio guidance and text instructions using the device's built-in speaker and text display function.

[0927] 6. Emotion recognition means

[0928] The device uses a camera and microphone to analyze the user's facial expressions and tone of voice in real time, for example, by using an emotion recognition API (e.g., Microsoft Azure Emotion API). The analysis results are sent to an emotion engine, which evaluates the user's emotional state (e.g., stress, relief, etc.).

[0929] 7. Route Adjustment Methods

[0930] The server receives feedback from the emotion engine and adjusts evacuation routes and notification methods as needed. For example, if a high stress level is detected, the server resends a recalculated route to the device. The device then displays the new route information and provides reassuring audio guidance, using a calming voice tone and positive messages.

[0931] Specific example explanation

[0932] In the case of a city experiencing flood disasters

[0933] 1. Data Collection Methods

[0934] The server periodically retrieves the latest satellite images and aerial photographs.

[0935] The server accesses the local government's database and updates the location information of active evacuation shelters.

[0936] The server collects real-time GPS data from the device.

[0937] 2. Data analysis methods

[0938] The server uses AI models to detect flooded areas and impassable roads.

[0939] The server calculates the route from the victim to the nearest safe evacuation shelter, taking into account real-time terrain and evacuation shelter information.

[0940] 3. Route Information Transmission Method

[0941] The server sends the calculated safe path to the terminal.

[0942] The device displays the received route information on a map application and provides voice guidance and text instructions.

[0943] 4. Emotion recognition means

[0944] The device analyzes the user's facial expressions and tone of voice to assess their emotional state. For example, the user's facial expressions and tone of voice are analyzed via the camera and microphone of their smartphone.

[0945] 5. Route Adjustment Methods

[0946] The server receives the stress level assessment results from the emotion engine and adjusts the route and notification method as needed. For example, if a high stress level is detected, it provides a voice prompt to encourage reassurance.

[0947] In this way, the system of the present invention not only supports the safe evacuation of disaster victims but also enables flexible responses according to the emotional state of the user.

[0948] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0949] Step 1: Collect data

[0950] The server retrieves satellite and aerial image data. This data is retrieved periodically through an API. The input is the latest image data from satellites and aircraft, and the output is initial data on the area affected by the disaster. Specifically, the server downloads image data from APIs provided by NASA and local aircraft companies.

[0951] Step 2: Collecting evacuation shelter and victim location information

[0952] The server obtains evacuation shelter location information from the local government's database and API. The server also collects real-time location information from each victim's device. The input is the local government's latest evacuation shelter information and the victim's GPS data, and the output is location information data including the locations of the evacuation shelter and the victim. The server collects evacuation shelter data through the local government's API and receives GPS data from each device.

[0953] Step 3: Data analysis

[0954] The server analyzes the collected data. The inputs are satellite data, aerial photographs, evacuation shelter information, and victim location information. The output is a list of disaster-affected areas and evacuation shelters, as well as the current locations of the victims. Specifically, it uses machine learning algorithms to analyze the data and identify flooded areas and impassable roads.

[0955] Step 4: Generate evacuation routes

[0956] The server uses AI technology and algorithms to generate a safe and optimal evacuation route. The input is analyzed data, and the output is safe evacuation route information. Specifically, the server uses an AI model (e.g., a generative AI model) and the Dijkstra algorithm to perform route calculations.

[0957] Step 5: Sending route information

[0958] The server sends the generated evacuation route information to the terminals. The input is the generated evacuation route information, and the output is the transmission of evacuation route data to each terminal. Specifically, the server transfers data to the terminals in real time.

[0959] Step 6: View route information

[0960] The route information received by the device is displayed on a map application. The input is evacuation route information sent from the server, and the output is a route displayed on a map and audio and text guidance. Specifically, the device uses Google Maps API and OpenStreetMap to visually display the route, and provides guidance using the built-in speaker and text display function.

[0961] Step 7: Emotion Recognition

[0962] The device uses a camera and microphone to analyze the user's facial expressions and tone of voice. The input is the user's facial expressions and tone of voice, and the output is an evaluation of their emotional state. Specifically, the analysis is performed using an emotion recognition API (e.g., Microsoft Azure Emotion API).

[0963] Step 8: Route Adjustment

[0964] The server receives feedback from the emotion engine and adjusts the evacuation route. The input is the emotion recognition result, and the output is adjusted route information. Specifically, the server generates a recalculated route based on the emotion engine's evaluation results and sends it back to the device. The device then displays the new route information and provides audio guidance to encourage reassurance.

[0965] (Application example 2)

[0966] 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."

[0967] Conventional evacuation support systems can help disaster victims select the safest route, but because they do not take into account the user's emotional state, they are unable to respond appropriately in situations where the user feels stress or anxiety. Furthermore, there is a lack of technology to utilize autonomous vehicles for evacuation support, making it difficult to ensure the safety and efficiency of autonomously driven evacuations. Given this background, there is a need for a system that provides safe and optimal evacuation routes using autonomous vehicles while taking into account the user's emotional state during a disaster.

[0968] 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.

[0969] In this invention, the server includes means for acquiring satellite and aerial photograph data, means for acquiring location information of evacuation shelters, means for acquiring location information of disaster victims, means for analyzing and generating a safe and optimal evacuation route based on the acquired data, means for transmitting the generated evacuation route information to a terminal, means for displaying the transmitted evacuation route information, means for recognizing the emotional state of a user using a camera and a microphone, and means for adjusting the evacuation route based on the recognized emotional state. This enables flexible route selection according to the emotional state of a user even during a disaster, thereby realizing evacuation support using self-driving vehicles.

[0970] "Satellite data" refers to data that is photographed by satellites placed in space and provides information on the Earth's surface and topography.

[0971] "Aerial photograph data" refers to photographic data of the earth's surface taken from an aircraft, and provides detailed information on urban areas and topography.

[0972] "Location information of evacuation shelter" is information indicating the current location of an evacuation shelter where disaster victims can safely evacuate.

[0973] "Location information of disaster victims" is GPS data that indicates the current location of disaster victims at the time of the disaster.

[0974] An "evacuation route" refers to the safest and most optimal route for disaster victims to reach an evacuation shelter.

[0975] "AI technology" is a technology that uses artificial intelligence to analyze data and make decisions.

[0976] "Route information display means" refers to a device or interface for visually displaying the calculated evacuation route.

[0977] The "emotion recognition means" is a device that uses sensors such as a camera and a microphone to determine the user's emotional state (e.g., stress, fear, relief).

[0978] "Route adjustment means" refers to an algorithm or system for dynamically changing an evacuation route based on the user's emotional state obtained from the emotion recognition means.

[0979] An "autonomous vehicle" is a vehicle that can drive autonomously without requiring operation by a passenger.

[0980] An "emotion engine" is software or hardware that analyzes a user's emotional state and provides an appropriate response based on the results.

[0981] A "safe evacuation route" is a route that is least affected by a disaster and has the highest chance of allowing victims to reach a shelter safely.

[0982] "Real-time information" is information that instantly acquires the current situation and status and displays and processes it instantly.

[0983] The present invention relates to an evacuation support system in the event of a disaster using an autonomous driving vehicle, and specifically describes a method for implementing each of the following means.

[0984] First, the server acquires satellite and aerial photographic data. This data reflects changes in the terrain and damage caused by the disaster in real time. The server also obtains the latest location information for evacuation shelters through local government databases and APIs. The server also collects current GPS location information from victims' smartphones and other location-providing devices.

[0985] The server then uses AI technology and algorithms to analyze and generate safe and optimal evacuation routes in real time based on the collected data. This analysis includes the current terrain, passable roads, and the safety of evacuation shelters. For example, it reflects information in real time about roads that are inundated by floods or damaged by earthquakes.

[0986] The generated evacuation route information is sent from the server to the autonomous vehicle's navigation system, which then displays the received information on a map application and provides voice guidance and text instructions, allowing the user to evacuate safely while checking the evacuation route.

[0987] The emotion recognition function uses cameras and microphones installed in autonomous vehicles to analyze the user's emotional state from their facial expressions and tone of voice. For example, if the user is feeling stressed, the system will adjust the evacuation route and notification method accordingly. This is done by the emotion engine, which evaluates the user's stress level and, if necessary, recalculates and provides a safer route and gentler voice guidance.

[0988] As a concrete example, when this system is applied to a city experiencing a flood disaster, the server first obtains the latest satellite and aerial images and accesses the local government's database to update the location information of evacuation shelters. It also collects real-time GPS data from the user's smartphone. The server uses an AI model to detect flooded areas and impassable roads and calculates the optimal route from the victim to the nearest safe evacuation shelter. The calculated safe route is sent to the autonomous vehicle, where it is displayed on the map application and audio and text instructions are provided.

[0989] The system operates based on the following example prompt:

[0990] "Generate an evacuation route based on the following data.

[0991] data:

[0992] Satellite imagery: latest flood-affected areas

[0993] Municipal database: Location information of currently operating evacuation shelters

[0994] GPS data: Current location of victim (latitude: 35.6895, longitude: 139.6917)

[0995] In this way, the present invention supports the safe evacuation of disaster victims and enables flexible responses according to the emotional state of the user. Furthermore, the use of autonomous vehicles improves the efficiency and safety of evacuation.

[0996] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0997] Step 1:

[0998] The server acquires satellite and aerial image data. This involves periodically downloading the latest satellite and aerial images from the Internet. The input is image data from satellites and aircraft, and the output is the latest image data of the Earth's surface. This allows the server to maintain the latest map information.

[0999] Step 2:

[1000] The server obtains the location information of evacuation shelters. This involves accessing the local government's database or API to obtain the latest list of evacuation shelters. The input is the local government's database, and the output is a dataset containing the location information of evacuation shelters. This allows the server to collect the latest evacuation shelter information.

[1001] Step 3:

[1002] The server obtains the location information of the victim. This involves obtaining real-time location data from the victim's smartphone or GPS device. The input is the GPS data from the victim's smartphone, and the output is the victim's current location information. This allows the server to accurately determine the victim's location.

[1003] Step 4:

[1004] The server analyzes and generates a safe and optimal evacuation route based on the acquired data. This process involves using an AI algorithm to analyze the collected map data, evacuation shelter information, and location information of disaster victims, and calculate the optimal route. The input is satellite data, evacuation shelter information, and location information of disaster victims, and the output is the optimal evacuation route. This allows the server to provide the optimal evacuation route.

[1005] Step 5:

[1006] The server transmits the generated evacuation route information to the terminal, which includes transmitting the evacuation route to the autonomous vehicle's navigation system using an appropriate protocol. The input is the evacuation route information, and the output is the transmitted route information. This provides the route information to the autonomous vehicle.

[1007] Step 6:

[1008] The device displays the transmitted evacuation route information. This involves the navigation system visually displaying the route information on a map application and providing audio and text instructions. The input is the route information, and the output is the displayed map and audio and text instructions. This allows the user to confirm a safe evacuation route.

[1009] Step 7:

[1010] The device recognizes the user's emotional state using a camera and microphone. This involves using sensor data to analyze the user's facial expressions and tone of voice to evaluate the emotional state. The input is sensor data from the camera and microphone, and the output is the recognized emotional state. This allows the device to understand the user's psychological state.

[1011] Step 8:

[1012] The server adjusts the evacuation route based on the recognized emotional state. This involves receiving feedback from the emotion engine and modifying the route information and audio guidance as necessary. The input is the user's emotional state, and the output is the adjusted route information. This allows for appropriate responses according to the user's psychological state.

[1013] 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.

[1014] 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.

[1015] 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.

[1016] [Fourth embodiment]

[1017] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1018] 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.

[1019] 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).

[1020] 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.

[1021] 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.

[1022] 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).

[1023] 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.

[1024] 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.

[1025] 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.

[1026] 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.

[1027] 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.

[1028] 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.

[1029] 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."

[1030] The present invention relates to a system for generating a route that will allow disaster victims to reach an evacuation shelter in the safest way possible in the event of a disaster. The system of the present invention includes the following means.

[1031] 1. Data Collection Methods

[1032] The server acquires satellite and aerial photographic data. This data reflects changes in the terrain and damage caused by disasters in real time. The server also obtains the latest location information for evacuation shelters through local government databases and APIs. The server also collects location information from each victim's device. The information collected in this way becomes the basic data for generating evacuation routes.

[1033] 2. Data Analysis and Pathway Generation Procedures

[1034] The server uses AI technology and algorithms to analyze and generate safe and optimal evacuation routes in real time based on the collected data. This analysis includes the current terrain, passable roads, and the safety of evacuation shelters. For example, information on roads being flooded due to flooding or roads being destroyed by earthquakes is reflected in real time.

[1035] 3. Route information transmission and display means

[1036] The server sends the generated evacuation route information to the device. The sent information is displayed as a map on each victim's device. Specifically, the map application displays the route visually and provides audio and text guidance. This allows victims to evacuate safely while checking the route.

[1037] 4. Real-time update methods

[1038] As disaster situations change every moment, the server periodically collects new data and updates the generated evacuation routes as necessary. The device receives the updated information from the server and updates the route information displayed in real time, providing the most up-to-date evacuation routes at all times.

[1039] Specific example explanation

[1040] In the case of a city experiencing flood disasters

[1041] 1. Data Collection Methods

[1042] The server periodically retrieves the latest satellite images and aerial photographs.

[1043] The server accesses the local government's database and updates the location information of active evacuation shelters.

[1044] The server collects real-time GPS data from the device.

[1045] 2. Data Analysis and Pathway Generation Procedures

[1046] The server uses AI models to detect flooded areas and impassable roads.

[1047] The server calculates the route from the victim to the nearest safe evacuation shelter, taking into account real-time terrain and evacuation shelter information.

[1048] 3. Route information transmission and display means

[1049] The server sends the calculated safe path to the terminal.

[1050] The device displays the received route information on a map application and provides voice guidance and text instructions.

[1051] 4. Real-time update methods

[1052] The device uses GPS to periodically send the user's current location to the server.

[1053] The server takes in new situational data in real time, recalculates evacuation routes as needed, and sends updates to the device.

[1054] The device updates the map and audio guide based on the new information.

[1055] In this way, the system of the present invention assists disaster victims in obtaining the most up-to-date and optimal route information and evacuating safely in the event of a disaster.

[1056] The processing flow will be explained below.

[1057] Step 1:

[1058] The server acquires satellite and aerial image data. Specifically, the server utilizes existing APIs and data feeds to periodically download the latest satellite and aerial images. This allows the collection of basic data for understanding the progress of disasters and changes in the terrain in real time.

[1059] Step 2:

[1060] The server obtains location information for evacuation shelters. It accesses databases from local governments and related organizations to obtain the latest location information for currently operating evacuation shelters. This information is necessary for selecting safe evacuation destinations.

[1061] Step 3:

[1062] The server acquires the location information of the victims. It receives GPS data from the victims' devices and grasps their current location in real time. This makes it possible to constantly track the movements of the victims.

[1063] Step 4:

[1064] The server runs an AI model to analyze the environmental data and location information collected, and the AI ​​model detects collapsed roads and new obstacles, searching for the safest and most optimal route.

[1065] Step 5:

[1066] The server generates evacuation routes based on the analysis results. These routes are designed to minimize the impact of the disaster and allow victims to reach evacuation shelters in the safest and quickest way possible.

[1067] Step 6:

[1068] The server then sends the generated evacuation route information to the victim's device. The route information is packaged in JSON or XML format and sent in real time.

[1069] Step 7:

[1070] The device analyzes the route information it receives and displays it as a visual map on the user interface. It also simultaneously provides voice guidance and text instructions, navigating the user visually and audibly.

[1071] Step 8:

[1072] The user follows the route information displayed on the device and begins evacuation along a safe route. By following the instructions on the device, victims can evacuate safely.

[1073] Step 9:

[1074] The device periodically tracks the user's current location using GPS and sends it to the server, allowing the server to always have the most up-to-date location information.

[1075] Step 10:

[1076] The server collects new disaster information and terrain data in real time and recalculates evacuation routes as needed. For example, if a new landslide or road closure occurs, it will be updated immediately.

[1077] Step 11:

[1078] The device receives updated information from the server and notifies the user of the latest route information, allowing disaster victims to continue evacuating along the optimal evacuation route.

[1079] Through these steps, the system of the present invention realizes safe evacuation in the event of a disaster.

[1080] Example 1

[1081] 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."

[1082] In the event of a disaster, there is a need to provide route information to help victims reach evacuation shelters safely and quickly. However, conventional systems cannot reflect disaster information and topographical information that is updated in real time, making it difficult to provide safe evacuation routes. Furthermore, if evacuation route information is not properly communicated, there is a risk that the safety of victims will not be ensured.

[1083] 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.

[1084] In this invention, the server includes means for collecting satellite and aerial photograph data, means for acquiring location information of evacuation shelters, means for collecting location information of disaster victims, means for analyzing and generating a safe and optimal evacuation route based on the collected data, means for transmitting the generated evacuation route information to a terminal, means for displaying the transmitted evacuation route information, and means for updating the evacuation route information in real time, thereby making it possible to provide real-time route information that will allow disaster victims to reach evacuation shelters in the safest and fastest way possible in the event of a disaster.

[1085] "Satellite and aerial photographic data" refers to images and information obtained from satellites and aircraft to observe the Earth's surface, and is used to analyze terrain and disaster situations.

[1086] "Location information of evacuation shelters" refers to information on the geographic coordinates and addresses of facilities and locations for evacuation in the event of a disaster.

[1087] "Victim location information" is geographical data that indicates the current location of individuals or groups of people affected by a disaster.

[1088] "Collected data" is a general term that includes all information obtained by the system, such as satellite and aerial photograph data, location information of evacuation centers, and location information of disaster victims.

[1089] "Means of analysis and generation" refers to the techniques and methods for performing calculations and analyses to derive safe and optimal evacuation routes based on collected data.

[1090] "Generated evacuation route information" refers to information on safe and optimal evacuation routes derived using analytical and generating methods.

[1091] "Devices" refer to electronic devices such as smartphones and tablets carried by disaster victims, which are used to receive and display evacuation route information.

[1092] "Display means" refers to the technology or method for visually displaying the generated evacuation route information on a terminal so that the user can confirm it.

[1093] "Means of updating in real time" refers to mechanisms and technologies that instantly revise evacuation route information in response to changes in disaster conditions and topographical information, and provide users with the latest information.

[1094] The present invention relates to a system that generates routes that will allow disaster victims to reach evacuation shelters in the safest way possible. The system of the present invention is mainly composed of three main elements: a server, a terminal, and a user.

[1095] System configuration

[1096] The server has the means to acquire satellite and aerial photographic data. This data reflects changes in the terrain and damage caused by disasters in real time. The server also obtains the latest location information for evacuation shelters through local government databases and APIs. The server also collects location information from each victim's device. This collected information serves as the basic data for generating evacuation routes.

[1097] Specifically, the server uses the Python requests library to retrieve data from NASA and other commercial satellite companies via API, leverages data analysis libraries such as Pandas to format and analyze the data, and uses the Google Maps API to retrieve shelter location information.

[1098] The devices are electronic devices such as smartphones and tablets carried by disaster victims. A map application is installed on the devices, which visually displays evacuation route information sent from the server. Specifically, the devices were developed using React Native, and use the GPS function to send the user's current location to the server.

[1099] The user is a disaster victim, and the goal is to use evacuation route information to safely reach a shelter. The user checks real-time evacuation route information through a map application and evacuates by following voice and text guidance.

[1100] System action

[1101] As a data collection method, the server periodically collects satellite and aerial photograph data, location information of evacuation centers, and location information of disaster victims. This information is used to obtain basic data for generating evacuation routes.

[1102] As a means of data analysis and route generation, the server uses AI technology (TensorFlow and PyTorch, for example) and algorithms (such as Dijkstra's algorithm) based on the collected data to generate safe and optimal evacuation routes. Real-time disaster and terrain information is reflected, and the latest routes are always provided.

[1103] As a route information transmission and display means, the server transmits the generated evacuation route information to the terminal, which then visually displays it on a map application. The user can then check this information and evacuate.

[1104] As a real-time update method, the device periodically sends the user's current location to the server, and the server obtains new disaster information and regenerates evacuation routes as necessary. This updated information is immediately sent to the device, and the map and audio guide are updated in real time.

[1105] Specific examples

[1106] For example, in the case of a city experiencing a flood disaster, the server retrieves the latest satellite and aerial images, combines them with GPS data received from the devices of individual victims, and generates routes that avoid flooded areas and impassable roads. The generated evacuation route is sent to the device and displayed visually on the Google Maps application.

[1107] Example prompts for generative AI models

[1108] A generative AI model for use in disaster situations might be given prompts like the following:

[1109] "What is the best algorithm for generating safe evacuation routes in areas experiencing flooding?"

[1110] "Help us design an evacuation route generation system to respond to real-time changing disaster situations."

[1111] In this way, the system of the present invention assists disaster victims in obtaining the most up-to-date and optimal route information and evacuating safely in the event of a disaster.

[1112] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1113] Step 1: Data collection

[1114] The server collects the latest satellite and aerial image data. This involves the server sending API requests to retrieve image data from data providers such as NASA. The input is the API request, and the output is the retrieved image data. Specifically, it uses the Python requests library to retrieve the data and save it in a directory for image analysis.

[1115] Step 2: Obtaining evacuation shelter location information

[1116] The server retrieves the latest evacuation shelter location information from the local government database or API. The server sends an HTTP request and receives the evacuation shelter location information in JSON format. The input is the HTTP request, and the output is the retrieved evacuation shelter location information. Specifically, the server uses the request library to parse the received JSON data and store it in the database.

[1117] Step 3: Collect location information of victims

[1118] The device collects the user's current location information through its GPS function and sends it to the server. The input is the device's GPS data, and the output is the transmitted location data. Specifically, the device uses a React Native application to send the GPS data to the server as a POST request at regular intervals.

[1119] Step 4: Data analysis

[1120] The server receives and analyzes the collected satellite images, evacuation center location information, and disaster victim location information. The server cleanses and normalizes the data, and then inputs it into the AI ​​model. The input is all the data collected in the previous step, and the output is the cleansed data. Specifically, the data is preprocessed using Pandas.

[1121] Step 5: Generate evacuation routes

[1122] The server uses AI technology and algorithms to generate optimal evacuation routes based on the cleansed data. The server inputs and analyzes the data into an AI model and applies Dijkstra's algorithm and other route search algorithms. The input is preprocessed data, and the output is the generated evacuation route information. Specifically, the server uses a TensorFlow model to perform route analysis and saves the results.

[1123] Step 6: Send evacuation route information

[1124] The server sends the generated evacuation route information to the terminal. Data is pushed in real time via WebSocket. The input is the generated evacuation route information, and the output is the sent route data. Specifically, the WebSocket server is used to send the route data to the terminal.

[1125] Step 7: Display evacuation route information

[1126] The device receives evacuation route information and displays it on a map application, notifying the user by voice and text. The input is route information received from the server, and the output is a map display on the device and voice guidance. Specifically, the system uses the Google Maps API to display the route, and provides text and voice guidance in a React Native app.

[1127] Step 8: Real-time updates

[1128] The device continuously sends the user's current location to the server in real time, and the server regenerates evacuation routes based on updated disaster information and sends them back to the device. The input is newly collected location information and disaster information, and the output is updated evacuation route information. Specifically, the system uses the GPS data and newly acquired disaster data to perform analysis again using AI models and algorithms, and sends the results back to the device in real time.

[1129] By following the steps above, disaster victims can always obtain the latest evacuation route information and evacuate safely.

[1130] (Application example 1)

[1131] 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."

[1132] In the event of a disaster, providing a quick and optimal evacuation route is important to ensure the safety of employees and robots in a factory. However, with conventional systems, it has been difficult to issue appropriate evacuation instructions to robots and employees in a factory while reflecting constantly changing disaster information and terrain information in real time. The present invention aims to solve this problem.

[1133] 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.

[1134] In this invention, the server includes means for acquiring satellite and aerial photograph data, means for acquiring location information of evacuation shelters, means for acquiring location information of evacuation robots and employees, means for analyzing and generating a safe and optimal evacuation route based on the acquired data, means for transmitting the generated evacuation route information to a terminal, means for displaying the transmitted evacuation route information, and means for supporting the evacuation of factory robots and employees in the event of a disaster. This makes it possible to provide quick and appropriate evacuation instructions to robots and employees in the factory based on optimal evacuation route information updated in real time.

[1135] "Satellite and aerial photograph data" refers to image data taken from satellites or aircraft, and is used to understand the terrain and disaster conditions.

[1136] "Location information of evacuation shelters" refers to information about the locations of evacuation shelters designated to ensure safety in the event of a disaster.

[1137] "Location information of evacuation robots and employees" is data indicating the current locations of robots and employees operating within the factory.

[1138] "Means for analyzing and generating safe and optimal evacuation routes based on acquired data" refers to algorithms and programs that use various collected data to calculate the safest and most effective evacuation routes.

[1139] The "means for transmitting the generated evacuation route information to the terminal" is a method for communicating the calculated evacuation route to the terminal of the disaster victim or user in real time.

[1140] The "means for displaying the transmitted evacuation route information" refers to an application or device that displays the received evacuation route information visually or audibly on the terminal.

[1141] "Means to support the evacuation of factory robots and employees in the event of a disaster" refers to systems and technologies that provide instructions to robots and employees in a factory to evacuate safely when a disaster occurs.

[1142] The present invention is a system for supporting the safe evacuation of factory robots and employees in the event of a disaster, and includes the following means.

[1143] 1. Data Collection Methods

[1144] The server acquires satellite and aerial photographic data, which allows it to obtain the latest information on disasters and terrain in real time. The server also obtains the latest evacuation shelter location information through local government databases and APIs. The server also collects location information from each robot and employee's device. This data becomes the basic data for generating evacuation routes.

[1145] 2. Data Analysis and Pathway Generation Procedures

[1146] The server uses AI technology and algorithms to analyze and generate safe and optimal evacuation routes in real time based on the acquired data. This analysis includes the current terrain conditions, passable roads, and the safety of evacuation shelters. For example, it reflects information on areas blocked off due to fires or gas leaks, as well as impassable routes.

[1147] 3. Route information transmission and display means

[1148] The server then sends the generated evacuation route information to the device, which then uses the information to visually display the evacuation route on a map application and provide audio and text guidance, allowing the user to evacuate safely while checking the route.

[1149] 4. Real-time update methods

[1150] As disaster situations change every moment, the server periodically collects new data and updates the generated evacuation routes as necessary. The device receives the updated information from the server and updates the route information displayed in real time, providing the most up-to-date evacuation routes at all times.

[1151] As an example, if a fire breaks out in a factory during a disaster, the server will analyze and generate evacuation routes based on the latest data and send the route information in real time to all robots and employee terminals in the factory. The terminals will display a safe evacuation route based on the received information, and users can quickly begin evacuating by following the audio guidance.

[1152] As another example, consider the case of building damage caused by an earthquake. The server receives the latest information, immediately calculates evacuation routes that reflect the damage situation, and issues appropriate instructions to robots and employees. This allows all parties involved to quickly take the optimal evacuation route.

[1153] Below are some example input prompts for a generative AI model:

[1154] "Design an application that generates safe evacuation routes and provides instructions to robots and employees in the event of a fire in a factory. The evacuation routes need to be updated in real time."

[1155] Using this prompt, each means of the above system can be specifically implemented.

[1156] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1157] Step 1:

[1158] The server retrieves satellite and aerial photo data. The server uses the API to retrieve the latest topographical and disaster information and stores the data in an internal database. The input is the data retrieved from the API, and the output is the updated information stored in the database. This data is used by the analysis and generation tools.

[1159] Step 2:

[1160] The server obtains the location information of evacuation shelters from the local government database. The server periodically queries the latest information on evacuation shelters through the API and stores that information in the database. The input is the evacuation shelter information obtained from the local government database, and the output is the location information of the evacuation shelters stored in the server's database.

[1161] Step 3:

[1162] The server collects real-time location information from each robot and employee's device. The device periodically sends its GPS information to the server, which stores it in a database. The input is the location information sent from the robot and employee's device, and the output is the location information stored in the server's database.

[1163] Step 4:

[1164] The server analyzes and generates safe and optimal evacuation routes based on the collected data. The server uses an AI algorithm to analyze topographical data, disaster data, location information, etc., and calculates the optimal route. The input is the topographical information, evacuation shelter information, and location information stored on the server, and the output is the analyzed optimal evacuation route information.

[1165] Step 5:

[1166] The server sends the generated evacuation route information to the terminals. The server sends the calculated evacuation route information to the robots and employee terminals in real time. The input is the analyzed evacuation route information, and the output is the route information sent to each terminal.

[1167] Step 6:

[1168] The terminal displays the received evacuation route information. The terminal application displays the route information on a map and provides guidance by voice and text. The input is the evacuation route information sent from the server, and the output is route guidance provided to the user visually and audibly.

[1169] Step 7:

[1170] The server collects new data in real time and updates evacuation routes as necessary. The server periodically recollects and reanalyzes topographical, disaster, and location information. The input is the newly added topographical and location information to the database, and the output is updated evacuation route information. This ensures that the latest evacuation routes are always provided to the terminal.

[1171] Through the above processing steps, a system is realized that provides optimal routes in real time for factory robots and employees to evacuate quickly and safely in the event of a disaster.

[1172] 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.

[1173] The present invention relates to a system for generating a route that will allow disaster victims to reach a shelter most safely in the event of a disaster. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it is possible to respond flexibly according to the user's psychological state. The system of the present invention includes the following means.

[1174] 1. Data Collection Methods

[1175] The server acquires satellite and aerial photographic data. This data reflects changes in the terrain and damage caused by disasters in real time. The server also obtains the latest location information for evacuation shelters through local government databases and APIs. The server also collects location information from each victim's device. The information collected in this way becomes the basic data for generating evacuation routes.

[1176] 2. Data Analysis and Pathway Generation Procedures

[1177] The server uses AI technology and algorithms to analyze and generate safe and optimal evacuation routes in real time based on the collected data. This analysis includes the current terrain, passable roads, and the safety of evacuation shelters. For example, information on roads being flooded due to flooding or roads being destroyed by earthquakes is reflected in real time.

[1178] 3. Route information transmission and display means

[1179] The server sends the generated evacuation route information to the device. The sent information is displayed as a map on each victim's device. Specifically, the map application displays the route visually and provides audio and text guidance, allowing users to evacuate safely while checking the route.

[1180] 4. Emotion recognition means

[1181] One feature of the present invention is that it includes an emotion engine that recognizes the user's emotions. The device analyzes the user's facial expressions and tone of voice to understand their emotional state (e.g., stress, fear, relief). Based on this analysis, the emotion engine evaluates the user's psychological state.

[1182] 5. Emotion-based route adjustment measures

[1183] The server adjusts evacuation route information based on feedback from the emotion engine. For example, if the user is experiencing high stress levels, the server recalculates a simpler and safer evacuation route and sends it to the device. Voice and text notification methods are also adjusted according to the user's emotional state.

[1184] Specific example explanation

[1185] In the case of a city experiencing flood disasters

[1186] 1. Data Collection Methods

[1187] The server periodically retrieves the latest satellite images and aerial photographs.

[1188] The server accesses the local government's database and updates the location information of active evacuation shelters.

[1189] The server collects real-time GPS data from the device.

[1190] 2. Data Analysis and Pathway Generation Procedures

[1191] The server uses AI models to detect flooded areas and impassable roads.

[1192] The server calculates the route from the victim to the nearest safe evacuation shelter, taking into account real-time terrain and evacuation shelter information.

[1193] 3. Route information transmission and display means

[1194] The server sends the calculated safe path to the terminal.

[1195] The device displays the received route information on a map application and provides voice guidance and text instructions.

[1196] 4. Emotion recognition means

[1197] The device analyzes the user's facial expressions and tone of voice to assess their emotional state. For example, the user's facial expressions and tone of voice are analyzed via the camera and microphone of their smartphone.

[1198] 5. Emotion-based route adjustment measures

[1199] The server receives the stress level assessment results from the emotion engine and adjusts the route and notification method as needed. For example, if a high stress level is detected, it provides a voice prompt to encourage reassurance.

[1200] In this way, the system of the present invention not only supports the safe evacuation of disaster victims but also enables flexible responses according to the emotional state of the user.

[1201] The processing flow will be explained below.

[1202] Step 1:

[1203] The server acquires satellite and aerial image data. Specifically, the server utilizes existing APIs and data feeds to periodically download the latest satellite and aerial images. This provides the basic data for understanding the progress of disasters and changes in the terrain in real time.

[1204] Step 2:

[1205] The server obtains location information for evacuation shelters. It accesses databases from local governments and related organizations and downloads the latest location information for currently operating evacuation shelters. This information is necessary for selecting safe evacuation destinations.

[1206] Step 3:

[1207] The server acquires the location information of the victims. It receives GPS data from the victims' devices in real time to determine their current location. This makes it possible to constantly track the movements of the victims.

[1208] Step 4:

[1209] The server runs an AI model to analyze the environmental data and location information collected by the server, which then detects collapsed roads and new obstacles, and searches for the safest and most optimal route.

[1210] Step 5:

[1211] The server generates evacuation routes based on the analysis results. These routes are designed to minimize the impact of the disaster and allow victims to reach evacuation shelters in the safest and quickest way possible.

[1212] Step 6:

[1213] The server then sends the generated evacuation route information to the victim's device. The route information is packaged in JSON or XML format and sent in real time. Data security and privacy are ensured during this transmission process.

[1214] Step 7:

[1215] The device analyzes the route information it receives and displays it as a visual map in the user interface. In addition, it provides audio guidance and text instructions, guiding the user both visually and aurally.

[1216] Step 8:

[1217] The device analyzes the user's facial expressions and tone of voice and evaluates their emotional state using an emotion engine. For example, it uses the smartphone's camera and microphone to analyze the user's facial expressions and tone of voice in real time.

[1218] Step 9:

[1219] The server receives feedback from the emotion engine and adjusts evacuation route information based on the user's emotional state. If a high stress level is detected, the server recalculates and provides a simpler and more reassuring evacuation route and notification method.

[1220] Step 10:

[1221] The device displays new route information based on emotion recognition and notifies the user via voice and text. The emotion engine provides information that allows the user to evacuate calmly.

[1222] Step 11:

[1223] The user follows the route information displayed on the device and begins evacuation along a safe route. By following the instructions on the device, victims can continue evacuating safely.

[1224] Step 12:

[1225] The device periodically tracks the user's current location using GPS and sends it to the server, allowing the server to always have the most up-to-date location information.

[1226] Step 13:

[1227] The server collects new disaster information and terrain data in real time and recalculates evacuation routes as needed. For example, if a new landslide or road closure occurs, this is reflected immediately.

[1228] Step 14:

[1229] The device receives updated information from the server and reflects it in the user interface in real time. If necessary, audio guides and text instructions are also updated to inform the user of the latest information.

[1230] Through these steps, the system of the present invention supports the safe evacuation of disaster victims in real time. It also enables flexible responses according to the user's emotional state, providing psychological security.

[1231] Example 2

[1232] 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."

[1233] In the event of a disaster, it is important to provide victims with the safest and fastest route to an evacuation shelter. However, current systems have difficulty reflecting disaster and terrain information in real time, and do not take into account the emotional state of victims. Therefore, a system that allows victims to evacuate safely is needed.

[1234] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1235] In this invention, the server includes means for acquiring satellite and aerial photograph data, means for acquiring location information of evacuation shelters, means for acquiring location information of disaster victims, means for analyzing and generating a safe and optimal evacuation route based on the collected data, means for transmitting the generated evacuation route information to a terminal, means for displaying the transmitted evacuation route information, means for recognizing a user's emotion, and means for adjusting the evacuation route information based on the emotion recognition result. This makes it possible to provide real-time route information for disaster victims to evacuate safely and also to flexibly respond to the user's emotional state.

[1236] "Satellite Data" refers to imagery and terrain information obtained from satellites.

[1237] "Aerial photographic data" refers to images and terrain information taken from an aircraft.

[1238] "Evacuation shelter location information" refers to the geographic location information of safe and operational evacuation shelters in the event of a disaster.

[1239] "Location information of disaster victims" refers to the current geographical location information of each disaster victim in the area where the disaster has occurred.

[1240] An "evacuation route" refers to the route that disaster victims take to move from their current location to a safe evacuation shelter.

[1241] "Emotion recognition" refers to the technology of analyzing a user's facial expressions and tone of voice to assess their psychological and emotional state.

[1242] An "emotion engine" refers to software or algorithms that generate specific actions or feedback based on a user's emotional state derived from emotion recognition.

[1243] "Generated evacuation route information" refers to information on safe and optimal evacuation routes generated based on collected data and analysis results.

[1244] "Devices" refer to portable electronic devices such as smartphones and tablets carried by disaster victims.

[1245] "Server" refers to a computer system that acts as a central control system and collects and analyzes various data, creates routes, and transmits data.

[1246] The present invention relates to a system for generating a route that will allow disaster victims to reach a shelter most safely in the event of a disaster. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it is possible to respond flexibly according to the user's psychological state. The system of the present invention includes the following means.

[1247] 1. Data Collection Methods

[1248] The server acquires satellite and aerial photographic data. Specifically, it periodically acquires image data using APIs from NASA and local aircraft companies. The server also acquires the latest location information for evacuation shelters via local government databases and APIs. Specific examples include the Google Civic Information API and the National Evacuation Shelter Database. The server also collects real-time location information from each victim's device. By acquiring location information from the device's GPS sensor, the current location of the victim can be determined.

[1249] 2. Data analysis methods

[1250] The server uses machine learning algorithms (e.g., deep learning models) based on the collected data to identify areas affected by disasters such as floods and landslides. It also organizes the collected evacuation shelter information and lists safe, operational shelters. It also identifies the current locations of disaster victims based on real-time location information from devices.

[1251] 3. Route Generation Method

[1252] The server uses AI technology (e.g., generative AI models) and the Dijkstra algorithm to generate safe and optimal evacuation routes, calculating routes that avoid flooded areas and impassable roads.

[1253] 4. Route Information Transmission Method

[1254] The server sends the generated evacuation route information to the victim's terminal in real time.

[1255] 5. Route Information Display Method

[1256] The route information received by the device is displayed on a map application. Specifically, it is displayed visually using Google Maps API or OpenStreetMap. The device also provides audio guidance and text instructions using the device's built-in speaker and text display function.

[1257] 6. Emotion recognition means

[1258] The device uses a camera and microphone to analyze the user's facial expressions and tone of voice in real time, for example, by using an emotion recognition API (e.g., Microsoft Azure Emotion API). The analysis results are sent to an emotion engine, which evaluates the user's emotional state (e.g., stress, relief, etc.).

[1259] 7. Route Adjustment Methods

[1260] The server receives feedback from the emotion engine and adjusts evacuation routes and notification methods as needed. For example, if a high stress level is detected, the server resends a recalculated route to the device. The device then displays the new route information and provides reassuring audio guidance, using a calming voice tone and positive messages.

[1261] Specific example explanation

[1262] In the case of a city experiencing flood disasters

[1263] 1. Data Collection Methods

[1264] The server periodically retrieves the latest satellite images and aerial photographs.

[1265] The server accesses the local government's database and updates the location information of active evacuation shelters.

[1266] The server collects real-time GPS data from the device.

[1267] 2. Data analysis methods

[1268] The server uses AI models to detect flooded areas and impassable roads.

[1269] The server calculates the route from the victim to the nearest safe evacuation shelter, taking into account real-time terrain and evacuation shelter information.

[1270] 3. Route Information Transmission Method

[1271] The server sends the calculated safe path to the terminal.

[1272] The device displays the received route information on a map application and provides voice guidance and text instructions.

[1273] 4. Emotion recognition means

[1274] The device analyzes the user's facial expressions and tone of voice to assess their emotional state. For example, the user's facial expressions and tone of voice are analyzed via the camera and microphone of their smartphone.

[1275] 5. Route Adjustment Methods

[1276] The server receives the stress level assessment results from the emotion engine and adjusts the route and notification method as needed. For example, if a high stress level is detected, it provides a voice prompt to encourage reassurance.

[1277] In this way, the system of the present invention not only supports the safe evacuation of disaster victims but also enables flexible responses according to the emotional state of the user.

[1278] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1279] Step 1: Collect data

[1280] The server retrieves satellite and aerial image data. This data is retrieved periodically through an API. The input is the latest image data from satellites and aircraft, and the output is initial data on the area affected by the disaster. Specifically, the server downloads image data from APIs provided by NASA and local aircraft companies.

[1281] Step 2: Collecting evacuation shelter and victim location information

[1282] The server obtains evacuation shelter location information from the local government's database and API. The server also collects real-time location information from each victim's device. The input is the local government's latest evacuation shelter information and the victim's GPS data, and the output is location information data including the locations of the evacuation shelter and the victim. The server collects evacuation shelter data through the local government's API and receives GPS data from each device.

[1283] Step 3: Data analysis

[1284] The server analyzes the collected data. The inputs are satellite data, aerial photographs, evacuation shelter information, and victim location information. The output is a list of disaster-affected areas and evacuation shelters, as well as the current locations of the victims. Specifically, it uses machine learning algorithms to analyze the data and identify flooded areas and impassable roads.

[1285] Step 4: Generate evacuation routes

[1286] The server uses AI technology and algorithms to generate a safe and optimal evacuation route. The input is analyzed data, and the output is safe evacuation route information. Specifically, the server uses an AI model (e.g., a generative AI model) and the Dijkstra algorithm to perform route calculations.

[1287] Step 5: Sending route information

[1288] The server sends the generated evacuation route information to the terminals. The input is the generated evacuation route information, and the output is the transmission of evacuation route data to each terminal. Specifically, the server transfers data to the terminals in real time.

[1289] Step 6: View route information

[1290] The route information received by the device is displayed on a map application. The input is evacuation route information sent from the server, and the output is a route displayed on a map and audio and text guidance. Specifically, the device uses Google Maps API and OpenStreetMap to visually display the route, and provides guidance using the built-in speaker and text display function.

[1291] Step 7: Emotion Recognition

[1292] The device uses a camera and microphone to analyze the user's facial expressions and tone of voice. The input is the user's facial expressions and tone of voice, and the output is an evaluation of their emotional state. Specifically, the analysis is performed using an emotion recognition API (e.g., Microsoft Azure Emotion API).

[1293] Step 8: Route Adjustment

[1294] The server receives feedback from the emotion engine and adjusts the evacuation route. The input is the emotion recognition result, and the output is adjusted route information. Specifically, the server generates a recalculated route based on the emotion engine's evaluation results and sends it back to the device. The device then displays the new route information and provides audio guidance to encourage reassurance.

[1295] (Application example 2)

[1296] 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."

[1297] Conventional evacuation support systems can help disaster victims select the safest route, but because they do not take into account the user's emotional state, they are unable to respond appropriately in situations where the user feels stress or anxiety. Furthermore, there is a lack of technology to utilize autonomous vehicles for evacuation support, making it difficult to ensure the safety and efficiency of autonomously driven evacuations. Given this background, there is a need for a system that provides safe and optimal evacuation routes using autonomous vehicles while taking into account the user's emotional state during a disaster.

[1298] 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.

[1299] In this invention, the server includes means for acquiring satellite and aerial photograph data, means for acquiring location information of evacuation shelters, means for acquiring location information of disaster victims, means for analyzing and generating a safe and optimal evacuation route based on the acquired data, means for transmitting the generated evacuation route information to a terminal, means for displaying the transmitted evacuation route information, means for recognizing the emotional state of a user using a camera and a microphone, and means for adjusting the evacuation route based on the recognized emotional state. This enables flexible route selection according to the emotional state of a user even during a disaster, thereby realizing evacuation support using self-driving vehicles.

[1300] "Satellite data" refers to data that is photographed by satellites placed in space and provides information on the Earth's surface and topography.

[1301] "Aerial photograph data" refers to photographic data of the earth's surface taken from an aircraft, and provides detailed information on urban areas and topography.

[1302] "Location information of evacuation shelter" is information indicating the current location of an evacuation shelter where disaster victims can safely evacuate.

[1303] "Location information of disaster victims" is GPS data that indicates the current location of disaster victims at the time of the disaster.

[1304] An "evacuation route" refers to the safest and most optimal route for disaster victims to reach an evacuation shelter.

[1305] "AI technology" is a technology that uses artificial intelligence to analyze data and make decisions.

[1306] "Route information display means" refers to a device or interface for visually displaying the calculated evacuation route.

[1307] The "emotion recognition means" is a device that uses sensors such as a camera and a microphone to determine the user's emotional state (e.g., stress, fear, relief).

[1308] "Route adjustment means" refers to an algorithm or system for dynamically changing an evacuation route based on the user's emotional state obtained from the emotion recognition means.

[1309] An "autonomous vehicle" is a vehicle that can drive autonomously without requiring operation by a passenger.

[1310] An "emotion engine" is software or hardware that analyzes a user's emotional state and provides an appropriate response based on the results.

[1311] A "safe evacuation route" is a route that is least affected by a disaster and has the highest chance of allowing victims to reach a shelter safely.

[1312] "Real-time information" is information that instantly acquires the current situation and status and displays and processes it instantly.

[1313] The present invention relates to an evacuation support system in the event of a disaster using an autonomous driving vehicle, and specifically describes a method for implementing each of the following means.

[1314] First, the server acquires satellite and aerial photographic data. This data reflects changes in the terrain and damage caused by the disaster in real time. The server also obtains the latest location information for evacuation shelters through local government databases and APIs. The server also collects current GPS location information from victims' smartphones and other location-providing devices.

[1315] The server then uses AI technology and algorithms to analyze and generate safe and optimal evacuation routes in real time based on the collected data. This analysis includes the current terrain, passable roads, and the safety of evacuation shelters. For example, it reflects information in real time about roads that are inundated by floods or damaged by earthquakes.

[1316] The generated evacuation route information is sent from the server to the autonomous vehicle's navigation system, which then displays the received information on a map application and provides voice guidance and text instructions, allowing the user to evacuate safely while checking the evacuation route.

[1317] The emotion recognition function uses cameras and microphones installed in autonomous vehicles to analyze the user's emotional state from their facial expressions and tone of voice. For example, if the user is feeling stressed, the system will adjust the evacuation route and notification method accordingly. This is done by the emotion engine, which evaluates the user's stress level and, if necessary, recalculates and provides a safer route and gentler voice guidance.

[1318] As a concrete example, when this system is applied to a city experiencing a flood disaster, the server first obtains the latest satellite and aerial images and accesses the local government's database to update the location information of evacuation shelters. It also collects real-time GPS data from the user's smartphone. The server uses an AI model to detect flooded areas and impassable roads and calculates the optimal route from the victim to the nearest safe evacuation shelter. The calculated safe route is sent to the autonomous vehicle, where it is displayed on the map application and audio and text instructions are provided.

[1319] The system operates based on the following example prompt:

[1320] "Generate an evacuation route based on the following data.

[1321] data:

[1322] Satellite imagery: latest flood-affected areas

[1323] Municipal database: Location information of currently operating evacuation shelters

[1324] GPS data: Current location of victim (latitude: 35.6895, longitude: 139.6917)

[1325] In this way, the present invention supports the safe evacuation of disaster victims and enables flexible responses according to the emotional state of the user. Furthermore, the use of autonomous vehicles improves the efficiency and safety of evacuation.

[1326] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1327] Step 1:

[1328] The server acquires satellite and aerial image data. This involves periodically downloading the latest satellite and aerial images from the Internet. The input is image data from satellites and aircraft, and the output is the latest image data of the Earth's surface. This allows the server to maintain the latest map information.

[1329] Step 2:

[1330] The server obtains the location information of evacuation shelters. This involves accessing the local government's database or API to obtain the latest list of evacuation shelters. The input is the local government's database, and the output is a dataset containing the location information of evacuation shelters. This allows the server to collect the latest evacuation shelter information.

[1331] Step 3:

[1332] The server obtains the location information of the victim. This involves obtaining real-time location data from the victim's smartphone or GPS device. The input is the GPS data from the victim's smartphone, and the output is the victim's current location information. This allows the server to accurately determine the victim's location.

[1333] Step 4:

[1334] The server analyzes and generates a safe and optimal evacuation route based on the acquired data. This process involves using an AI algorithm to analyze the collected map data, evacuation shelter information, and location information of disaster victims, and calculate the optimal route. The input is satellite data, evacuation shelter information, and location information of disaster victims, and the output is the optimal evacuation route. This allows the server to provide the optimal evacuation route.

[1335] Step 5:

[1336] The server transmits the generated evacuation route information to the terminal, which includes transmitting the evacuation route to the autonomous vehicle's navigation system using an appropriate protocol. The input is the evacuation route information, and the output is the transmitted route information. This provides the route information to the autonomous vehicle.

[1337] Step 6:

[1338] The device displays the transmitted evacuation route information. This involves the navigation system visually displaying the route information on a map application and providing audio and text instructions. The input is the route information, and the output is the displayed map and audio and text instructions. This allows the user to confirm a safe evacuation route.

[1339] Step 7:

[1340] The device recognizes the user's emotional state using a camera and microphone. This involves using sensor data to analyze the user's facial expressions and tone of voice to evaluate the emotional state. The input is sensor data from the camera and microphone, and the output is the recognized emotional state. This allows the device to understand the user's psychological state.

[1341] Step 8:

[1342] The server adjusts the evacuation route based on the recognized emotional state. This involves receiving feedback from the emotion engine and modifying the route information and audio guidance as necessary. The input is the user's emotional state, and the output is the adjusted route information. This allows for appropriate responses according to the user's psychological state.

[1343] 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.

[1344] 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.

[1345] 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 robot 414.

[1346] 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.

[1347] 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.

[1348] 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.

[1349] 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).

[1350] 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.

[1351] 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."

[1352] 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.

[1353] 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).

[1354] 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.

[1355] 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.

[1356] 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.

[1357] 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.

[1358] 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.

[1359] 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.

[1360] 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.

[1361] 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.

[1362] 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.

[1363] 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.

[1364] The following is further disclosed regarding the above embodiment.

[1365] (Claim 1)

[1366] means for acquiring satellite and aerial image data;

[1367] A means for obtaining location information of a shelter;

[1368] A means for acquiring location information of the disaster victim;

[1369] A means of analyzing and generating safe and optimal evacuation routes based on the acquired data,

[1370] means for transmitting the generated evacuation route information to a terminal;

[1371] means for displaying the transmitted evacuation route information;

[1372] A system including:

[1373] (Claim 2)

[1374] The system according to claim 1, wherein the analyzing and generating means has a function of reflecting disaster information and topographical information in real time.

[1375] (Claim 3)

[1376] 2. The system according to claim 1, having a function of notifying the user of the transmitted evacuation route information by voice and text.

[1377] "Example 1"

[1378] (Claim 1)

[1379] means for collecting satellite and aerial imagery data;

[1380] A means for obtaining location information of a shelter;

[1381] a means for collecting location information of victims;

[1382] A method for analyzing and generating safe and optimal evacuation routes based on collected data,

[1383] means for transmitting the generated evacuation route information to a terminal;

[1384] means for displaying the transmitted evacuation route information;

[1385] A means of updating evacuation route information in real time;

[1386] A system including:

[1387] (Claim 2)

[1388] The system according to claim 1, wherein the analyzing and generating means has a function of reflecting disaster information and topographical information in real time.

[1389] (Claim 3)

[1390] 2. The system according to claim 1, having a function of notifying the user of the transmitted evacuation route information by voice and text.

[1391] "Application Example 1"

[1392] (Claim 1)

[1393] means for acquiring satellite and aerial image data;

[1394] A means for obtaining location information of a shelter;

[1395] A means for acquiring location information of the evacuation robot and employees;

[1396] A means of analyzing and generating safe and optimal evacuation routes based on the acquired data,

[1397] means for transmitting the generated evacuation route information to a terminal;

[1398] means for displaying the transmitted evacuation route information;

[1399] A means for supporting the evacuation of factory robots and employees in the event of a disaster;

[1400] A system including:

[1401] (Claim 2)

[1402] The system according to claim 1, wherein the analyzing and generating means has a function of reflecting disaster information and topographical information in real time.

[1403] (Claim 3)

[1404] 2. The system according to claim 1, having a function of notifying the user of the transmitted evacuation route information by voice and text.

[1405] "Example 2: Combining Emotion Engines"

[1406] (Claim 1)

[1407] means for acquiring satellite and aerial image data;

[1408] A means for obtaining location information of a shelter;

[1409] A means for acquiring location information of the disaster victim;

[1410] A means of analyzing and generating safe and optimal evacuation routes based on the acquired data,

[1411] means for transmitting the generated evacuation route information to a terminal;

[1412] means for displaying the transmitted evacuation route information;

[1413] means for recognizing a user's emotion;

[1414] a means for adjusting evacuation route information based on the emotion recognition result;

[1415] A system including:

[1416] (Claim 2)

[1417] The system according to claim 1, wherein the analyzing and generating means has a function of reflecting disaster information and topographical information in real time, and includes a function of reflecting emotion recognition results.

[1418] (Claim 3)

[1419] 2. The system according to claim 1, having a function of notifying the user of the transmitted evacuation route information by voice and text, and adjusting the notification method based on the emotion recognition result.

[1420] "Application example 2 when combining emotion engines"

[1421] (Claim 1)

[1422] means for acquiring satellite and aerial image data;

[1423] A means for obtaining location information of a shelter;

[1424] A means for acquiring location information of the disaster victim;

[1425] A means of analyzing and generating safe and optimal evacuation routes based on the acquired data,

[1426] means for transmitting the generated evacuation route information to a terminal;

[1427] means for displaying the transmitted evacuation route information;

[1428] A means for recognizing the emotional state of a user using a camera or microphone;

[1429] means for adjusting an evacuation route based on the perceived emotional state;

[1430] A system including:

[1431] (Claim 2)

[1432] The system according to claim 1, wherein the analyzing and generating means has a function of reflecting disaster information and topographical information in real time, and a function of recognizing the user's emotional state using a camera and microphone.

[1433] (Claim 3)

[1434] 2. The system according to claim 1, further comprising a function of notifying the user of the transmitted evacuation route information by voice and text, and a function of changing the notification method based on the recognized emotional state. [Explanation of symbols]

[1435] 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. means for acquiring satellite and aerial image data; A means for obtaining location information of a shelter; A means for acquiring location information of the disaster victim; A means for analyzing and generating safe and optimal evacuation routes based on the acquired data; means for transmitting the generated evacuation route information to a terminal; means for displaying the transmitted evacuation route information; A system including:

2. 2. The system according to claim 1, wherein the analyzing and generating means has a function of reflecting disaster information and topographical information in real time.

3. The system according to claim 1, further comprising a function of notifying the user of the transmitted evacuation route information by voice and text.

Images