system

Abstract

We provide the system. [Solution] A method that uses generative models to predict future natural disasters by analyzing environmental data collected from diverse sources, A means for providing an appropriate evacuation route to a user terminal based on the predictions of the generation model, A means of sharing community information among users in real time and promoting its use in disaster prevention, A method for using unmanned aerial vehicles to acquire information on the situation in disaster-stricken areas and then re-proposing the optimal evacuation route based on that information, A system that includes this.

Description

Technical Field

[0005] ,

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In recent years, the frequency and scale of natural disasters have been increasing, and prompt and effective disaster response is required. However, it is difficult for conventional systems to accurately predict disasters and give evacuation instructions in real time, and damage reduction and appropriate evacuation guidance are not sufficiently carried out. In addition, information sharing within the community is insufficient, and psychological care after the disaster is also limited. Against this background, a new system that enables comprehensive and efficient response during disasters is needed.

Means for Solving the Problems

[0005] This invention relates to a system that analyzes environmental data collected from diverse sources and predicts natural disasters using a generative model. It provides appropriate evacuation routes to user terminals and facilitates real-time community information sharing. It also has the function of using unmanned aerial vehicles to grasp the situation in disaster areas and propose optimal evacuation routes again. Furthermore, it enables comprehensive disaster response by managing a decentralized energy supply that does not depend on external power sources within the community and providing post-disaster psychological care resources using a generative model that has learned from past disaster data.

[0006] A "generative model" is an artificial intelligence technology that learns patterns from diverse input data to predict future events and situations.

[0007] An "unmanned aerial vehicle" is an aerial system that flies by remote control or an automated program and can perform tasks in the air without receiving instructions from the ground.

[0008] "Decentralized energy supply" refers to a system in which each region or household independently generates and manages energy, without relying on a central power grid.

[0009] "Community information sharing" is a process of enhancing overall disaster preparedness by collecting information within a specific region or group and sharing it with each other.

[0010] "Psychological care resources" refer to counseling and support tools provided for the purpose of stress management and trauma care in the aftermath of a disaster.

[0011] "Environmental data" refers to various types of data related to the natural environment, such as weather information, geographical information, and input from sensors.

[0012] "Real-time" is a term that describes a state in which data collection, processing, and information distribution occur immediately. [Brief explanation of the drawing]

[0013] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0014] An example of an embodiment of the system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0017] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like. [[ID=##]] [[ID=##]]

[0019] [[ID=##]] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0020] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0024] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0025] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0026] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0027] 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 perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0028] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0031] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0032] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0033] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0034] This invention provides a system for predicting natural disasters and taking appropriate responses accordingly. This system achieves advanced disaster prevention functions through the collaborative work of servers, terminals, and users.

[0035] First, the server collects environmental data from various sources. This data includes weather information, geographical information, and sensor inputs, and is collected in real time. The server feeds this data into a generative model to predict weather conditions and the likelihood of natural disasters. This predictive information is transmitted from the server to each terminal, providing warnings to users.

[0036] Furthermore, the device displays appropriate information to the user in real time based on evacuation route information received from the server. This is achieved by determining the user's current location via GPS and displaying a safe evacuation route. Based on this information, the user can take swift evacuation action.

[0037] Unmanned aerial vehicles (UAVs) are used to gather detailed information about the situation in disaster-stricken areas. Servers analyze the video data transmitted from these aircraft and update evacuation routes and safety information. This allows users to take safe actions based on the latest situation.

[0038] Furthermore, this system is designed to allow users to share information within the community. The server aggregates the posted information and distributes information deemed reliable to other users, thereby supporting the improvement of disaster preparedness across the entire community.

[0039] Furthermore, this system also includes resources for providing psychological care after a disaster. Based on a generative model that has learned from the impacts of past disasters, the server suggests appropriate psychological care and resources to the user. In this way, it is possible to address stress and trauma after a disaster.

[0040] For example, if the server predicts heavy rainfall, the terminal immediately notifies the user of the need to evacuate. Then, if information from unmanned aerial vehicles confirms a safe route in the area, the user can proceed with evacuation based on that information. This process allows users to protect themselves from disasters quickly and effectively.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] The server collects environmental data in real time from weather agencies and sensor equipment. This data includes elements such as temperature, precipitation, wind speed, and seismic waves.

[0044] Step 2:

[0045] The server feeds collected environmental data into a generative model to predict the likelihood of natural disasters. This process involves analyzing historical disaster data in combination with real-time conditions.

[0046] Step 3:

[0047] The server identifies potential disasters that may occur in a specific region based on prediction results obtained from the generative model, and transmits that information to the terminal.

[0048] Step 4:

[0049] The terminal uses disaster prediction information received from the server and the user's current location information to display appropriate evacuation warnings to the user. The indicated evacuation routes are visually provided through a map application.

[0050] Step 5:

[0051] Unmanned aerial vehicles fly over disaster-affected areas and capture images of the current situation with their cameras. This video data is transmitted to a server in real time.

[0052] Step 6:

[0053] The server analyzes video data transmitted from the unmanned aerial vehicle and re-evaluates realistic evacuation route options. It updates the information on the terminal as needed to encourage users to evacuate efficiently.

[0054] Step 7:

[0055] Users follow the evacuation instructions displayed on their devices and evacuate using safe routes. They can also exchange useful information with other users using the community's information sharing function.

[0056] Step 8:

[0057] After the disaster subsides, the server provides users with psychological care resources. The generative model analyzes the support information needed after the disaster and selects and recommends appropriate support.

[0058] (Example 1)

[0059] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0060] In recent years, natural disasters have become frequent in many regions, necessitating prediction and appropriate response. However, conventional disaster prediction systems often suffer from low prediction accuracy and difficulty in providing timely and appropriate information. Furthermore, a lack of support for information sharing and psychological care after a disaster creates many challenges in post-disaster recovery. This invention aims to provide a comprehensive system to solve these problems.

[0061] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0062] This invention includes a server that uses a generation algorithm to analyze environmental information collected from various sources and predict weather conditions and the likelihood of natural disasters; a server that provides appropriate evacuation routes to user devices based on the predictions of the generation algorithm; a server that facilitates the real-time sharing of community information among users and promotes its use in disaster prevention; and a server that uses an automated flight simulator to acquire information on the disaster area and proposes the optimal evacuation route based on that information. This improves the accuracy of natural disaster predictions, enables the rapid and accurate provision of evacuation information, and further enables post-disaster information sharing and psychological support.

[0063] "Environmental information" refers to a collection of data gathered from diverse sources, such as weather conditions, geographical information, and sensor data.

[0064] A "generative algorithm" refers to a computational method that learns from past data and predicts future events based on specific conditions.

[0065] "User device" refers to a terminal owned by a user that displays information received from the server and provides instructions to the user.

[0066] "Community information" refers to various pieces of information shared by multiple users, including useful knowledge and information on local conditions related to disaster prevention.

[0067] An "automatic aircraft" refers to a device that flies unmanned and can acquire video and images from the air.

[0068] "Psychological care resources" refer to support methods and services for dealing with stress and trauma after a disaster.

[0069] This invention is an advanced disaster prevention system aimed at predicting and responding to natural disasters. The system consists of the interaction of servers, terminals, and users.

[0070] The server collects environmental information from diverse sources. This information includes weather conditions, geographical information, and sensor data. The server stores this data using a database management system. APIs are typically used to obtain weather information, and GIS software is used for geographic information. The server uses a generative AI model to process the collected information. This generative AI model predicts the likelihood of future natural disasters through learning from historical data. For example, when the server predicts heavy rainfall, the model analyzes similar historical data to calculate the probability of precipitation and the affected area.

[0071] The terminal receives forecast information transmitted from the server. The terminal is equipped with GPS functionality to determine the user's current location. This allows the user's device to display the optimal evacuation route in the event of a disaster. A standard map application is used for the map information. For example, if a heavy rain forecast is issued, the terminal will show a safe route from the current location to the evacuation site.

[0072] Users can share information within the community via their devices, exchanging real-time insights into local conditions and disaster response. The server filters the received community information and distributes reliable content to other users.

[0073] Furthermore, the automated aircraft is used to acquire information about the disaster-stricken area. The server processes the video data obtained from the aircraft and can update information on the disaster situation and evacuation procedures. This ensures that evacuation routes are always up-to-date.

[0074] Following a disaster, the server uses a generative AI model to learn from past disaster impacts and provides users with psychological care resources. Specifically, this includes recommending stress management applications and counseling services.

[0075] (Example of a prompt message)

[0076] "Please assess the weather forecast for the next 24 hours in this region and the resulting disaster risk."

[0077] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0078] Step 1:

[0079] The server collects environmental information from diverse sources. It obtains weather data using weather information APIs, geographic data from geographic information systems, and real-time data from various sensors. This input data is stored in a database management system. Specifically, the server automates the process of periodically making API calls to update the data with new information.

[0080] Step 2:

[0081] The server feeds the collected data into a generative AI model. The generative AI model has learned from past disaster patterns and analyzes current data to predict the risk of natural disasters. The input data consists of weather conditions and geographical features, and the output provides the probability of disaster occurrence and the affected area for each region. Specifically, the model combines changes in weather conditions and geographical conditions to calculate the likelihood of floods and heavy rainfall.

[0082] Step 3:

[0083] The server organizes the prediction results of the generated AI model and sends them to the terminal. The prediction results are output in text format or as map data, and evacuation advisory information corresponding to the warning level is added. Specifically, the server sends warnings to the user via email or notification services.

[0084] Step 4:

[0085] The terminal receives warning information from the server and displays it on the user's device. Using GPS functionality, it determines the user's current location and displays the optimal evacuation route on a map. Inputs are GPS location and evacuation information, and output is visual evacuation route guidance. Specifically, the terminal calculates and displays the safest route from the user's current location to the evacuation shelter.

[0086] Step 5:

[0087] Users can post and share community information using their devices. The server receives the posted information, evaluates the reliability of the data, and distributes it to other users. The input is text and images posted by users, and the output is filtered, reliable information. Specifically, the server uses natural language processing algorithms to analyze the posts and filter out misinformation.

[0088] Step 6:

[0089] The server receives video data from the automated aircraft and analyzes the information. The captured video is analyzed by image processing software to understand the latest situation in the disaster area. The input is video data from the aircraft, and the output is evacuation routes and safety information as a result of the analysis. Specifically, the server executes a video analysis algorithm to identify obstacles and dangerous areas.

[0090] Step 7:

[0091] The server uses a generative AI model to select resources for providing psychological care after a disaster. Based on past disaster data, it proposes support services suitable for the user. The input is past disaster data and user profiles, and the output is a proposal for psychological care services. Specifically, the server uses a recommendation algorithm to select appropriate care measures and notifies the user.

[0092] (Application Example 1)

[0093] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0094] With the increasing frequency of natural disasters, there is a need for effective means to enable individuals and communities to evacuate quickly and safely. However, current systems are insufficient in providing real-time information, appropriately indicating evacuation routes, and offering adequate psychological support after a disaster. Therefore, more comprehensive disaster prevention measures are necessary.

[0095] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0096] This invention includes a server that uses a generative model to analyze environmental data collected from various sources and predict future natural disasters; a server that provides appropriate evacuation routes to user terminals based on the predictions of the generative model, and visually presents the routes using map information and location information technology; and a server that provides psychological support to users after a disaster based on past disaster data. This enables the rapid provision of necessary information, ensures the safety of users, and reduces psychological burden through post-disaster care.

[0097] "Diverse information sources" refers to multiple different information sources, such as weather data, geographical data, and sensor data, which are used to collect various environmental data necessary for disaster prediction.

[0098] A "generative model" is a mathematical model that uses machine learning and AI to predict the probability of natural disasters occurring from past data, and is used to provide real-time predictive information.

[0099] A "user terminal" refers to a device that a user can carry with them, such as a smartphone or smart glasses, and has the role of receiving information from the server and notifying the user during a disaster.

[0100] "Evacuation routes" are route information provided to ensure safe movement for users evacuating from natural disasters, and are updated in real time.

[0101] "Map information" refers to geographical information displayed on the user's terminal, and is data that allows users to visually confirm evacuation routes.

[0102] "Location-based technology" refers to technologies that use GPS and other methods to determine a user's current location and provide the optimal route.

[0103] An "unmanned aerial vehicle" is an autonomously flying device used to acquire detailed images and data of disaster areas, and is useful for situation assessment and information sharing.

[0104] "Psychological support" refers to care and support resources provided to alleviate the psychological stress and trauma that arise after a disaster, and is an activity to support the mental health of users.

[0105] To implement this invention, a server plays a central role. The server processes environmental data collected from various sources and uses a generative AI model to predict the occurrence of natural disasters. This generative model learns from historical data and analyzes weather conditions and local sensor information in real time to make predictions that increase the likelihood of disaster occurrence.

[0106] Based on the prediction results, the server provides appropriate evacuation routes to the user's device. In doing so, the server utilizes map information and location technology to visualize the route clearly on smartphones and smart glasses. Users can then safely evacuate while confirming the route displayed on their device.

[0107] Furthermore, unmanned aerial vehicles (UAVs) are used to acquire detailed information about the disaster area, and this information is analyzed on a server. The analyzed information is used to propose revised evacuation routes, and users are presented with safer routes based on the latest data. The video footage acquired by the UAVs is analyzed using software such as OpenCV.

[0108] Information sharing among users is also crucial. The server aggregates community-based information provided by users and distributes it to other users as reliable data. This real-time information sharing improves disaster preparedness.

[0109] The server also provides psychological support after disasters. Based on a generative model trained on past disaster data, it offers users appropriate psychological support measures. This includes everything from simple care using chatbots to arranging detailed counseling in collaboration with specialists. It uses prompts such as, "In past typhoons like this, what message would you send to someone you were worried about?" to make suggestions using the generative AI model.

[0110] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0111] Step 1:

[0112] The server collects environmental data such as weather data, geographical data, and sensor information from diverse sources. Inputs include data from various APIs and local sensors, and output is an integrated environmental dataset. This dataset is used for predictions by a generative AI model. The server continuously updates this data in real time.

[0113] Step 2:

[0114] The server feeds the collected environmental data into a generative AI model to predict natural disasters. The input is the environmental dataset integrated in Step 1, and the output generates information on the likelihood of disaster occurrence and the predicted impact. The generative AI model uses an algorithm trained on historical disaster data. The server generates a detailed prediction report and prepares for the next step.

[0115] Step 3:

[0116] The server provides appropriate evacuation routes to user terminals based on the generated disaster prediction information. Inputs include prediction results, map information, and the user's current location; output is the evacuation route information sent to the user terminal. Using map information and location technology, users can visually confirm the evacuation route. The terminal then uses this information to send notifications to the user.

[0117] Step 4:

[0118] The unmanned aerial vehicle transmits aerial footage of the disaster area to a server. The server receives the aerial footage as input and performs video analysis using OpenCV. The output of the analysis is current terrain conditions and obstacle information, which is used to propose the next evacuation route. The server generates an optimized evacuation route and sends the updated information to the user's terminal.

[0119] Step 5:

[0120] Users share community information with other users through the application. The server aggregates this real-time information, evaluates its reliability, and then distributes it to other users. The input is local information obtained from users, and the output is a notification to other users as reliable disaster prevention information.

[0121] Step 6:

[0122] After a disaster, the server provides psychological support to users using a generative model that has learned from past disaster data. The input is user information determined to be in need of psychological support, and the output is a generated psychological care plan and support resources. Users can access support via chatbots or links.

[0123] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0124] This invention relates to a system that analyzes diverse environmental data to predict natural disasters and recognizes the emotional state of users to provide appropriate support. This system integrates environmental data analysis, emotion recognition, real-time information sharing, and data collection using unmanned aerial vehicles.

[0125] The server first aggregates weather information, geographical data, and sensor data, and then uses a generative model to predict the occurrence of natural disasters. This prediction information is sent to the terminal, and users can receive evacuation instructions based on it. The terminal displays guidance on safe evacuation routes to help users take action quickly.

[0126] On the other hand, the emotion recognition engine has the function of identifying the user's emotional state by analyzing the user's facial expression data and voice input. This allows the server to evaluate the user's psychological burden in real time and provide appropriate psychological support as needed. For example, if anxiety or fear is detected during a disaster, information on relaxation methods and counseling services will be displayed on the terminal.

[0127] After a disaster occurs, unmanned aerial vehicles (UAVs) assess the detailed situation on-site and transmit data to a server. The server analyzes this data, reassesssing the safety of the site and updating the optimal evacuation routes. In addition, users can share their status and useful information in real time within the community, enabling them to cooperate and overcome the crisis.

[0128] For example, if the server predicts an earthquake, the terminal immediately displays an alert to the user and shows them the route to the nearest evacuation center. Meanwhile, if the emotion engine detects the user's distressed facial expressions or voice, the terminal offers options such as a safety check alert or counseling support. In this way, it can support both physical safety and psychological reassurance.

[0129] This embodiment provides rapid and appropriate evacuation support during disasters, as well as psychological care during and after evacuation, thereby more reliably ensuring the overall safety of users.

[0130] The following describes the processing flow.

[0131] Step 1:

[0132] The server collects weather data, geographical data, and sensor data in real time. This data is then input into a generative model to predict natural disasters.

[0133] Step 2:

[0134] The server analyzes disaster prediction information generated by a generative model and creates evacuation orders for the affected areas. This information is then distributed to the user's terminal.

[0135] Step 3:

[0136] The terminal displays warnings to the user based on disaster prediction information and evacuation orders received from the server. It also uses GPS functionality to determine the user's current location and displays the nearest safe evacuation route on a map.

[0137] Step 4:

[0138] Using an emotion recognition engine, the device collects and analyzes the user's facial expressions and voice data. From this information, it identifies the user's emotional state and sends it to the server.

[0139] Step 5:

[0140] The server analyzes the user's emotional state and determines the necessary psychological support information. It provides the user's device with suggestions for relaxation methods and links to online counseling.

[0141] Step 6:

[0142] The unmanned aerial vehicle flies around the disaster area and collects video data in real time. The video is then transmitted to a server.

[0143] Step 7:

[0144] The server analyzes video data obtained from unmanned aerial vehicles to re-evaluate the local situation and existing evacuation routes. It then updates the evacuation route information to reflect the new situation and distributes it to terminals.

[0145] Step 8:

[0146] Users can move according to the latest evacuation route information provided by their devices. They can also continue to exchange information with other users in the community and work together to ensure their collective safety.

[0147] Step 9:

[0148] After the disaster subsides, the server will continue to provide psychological support to users by offering mental health resources based on information obtained through the emotion engine, helping users regain their sense of security.

[0149] (Example 2)

[0150] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0151] In modern society, with the increasing frequency of natural disasters, people are required to take swift and appropriate evacuation actions. However, existing systems suffer from delays in disaster prediction and information provision, failing to adequately ensure user safety. Furthermore, a lack of psychological support increases the mental burden during disasters. Therefore, there is a need for a system that allows users to respond quickly to disasters and provides psychological reassurance.

[0152] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0153] In this invention, the server includes means for aggregating environmental data obtained from various data sources and predicting the occurrence of natural disasters using a generative AI model; means for providing safe evacuation routes to communication devices based on the predicted disaster information and supporting the user's evacuation; and means for analyzing the user's biometric information, identifying their emotional state, and providing psychological support. This enables the user to take swift and safe evacuation actions and maintain emotional stability during disasters.

[0154] "Diverse data sources" refers to a collection of data of different types and origins, such as weather information, geographical information, and sensor data.

[0155] A "generative AI model" refers to artificial intelligence technology that learns patterns from collected data and predicts future events.

[0156] "Communication equipment" refers to devices or interfaces used to provide information to users and transmit instructions.

[0157] "Biometric information" refers to data that indicates an individual's physical or emotional state, such as a user's facial expressions or voice.

[0158] An "unmanned aerial vehicle" refers to an aircraft that is remotely or autonomously operated without a human on board.

[0159] "Psychological support" refers to services and methods aimed at stabilizing a user's emotional state and providing a sense of security.

[0160] A "community" refers to a group of users organized to share information and act collaboratively.

[0161] This invention relates to a system that analyzes environmental data to predict natural disasters and provides rapid psychological support by recognizing the emotional state of users. This system mainly consists of a server, terminals, an unmanned aerial vehicle, and a generative AI model.

[0162] The server interfaces with an advanced database management system to aggregate environmental data from diverse data sources, including weather information, geographical data, and sensor data. Cloud computing technology is used to run data analysis software. The collected data is analyzed by a generative AI model to predict future natural disasters. For example, known weather patterns are input into the AI ​​model using the prompt "Predict natural disasters that may occur within the next 24 hours," and the results are analyzed.

[0163] The terminal provides users with disaster prediction information transmitted from a server. Communication devices such as smartphones and tablets use map application software (for example, a digital map service, a general term) to display safe evacuation routes. Furthermore, the terminal is equipped with a camera and microphone, which are used to acquire the user's biometric information and transmit it to an engine for emotion recognition. If the terminal determines that the user's emotional state is unstable, it displays information on relaxation methods and online counseling services.

[0164] Unmanned aerial vehicles (UAVs) are used to assess the situation on the ground in disaster areas, transmitting high-resolution image data and sensor data to a server. These devices have GPS-based autonomous flight capabilities, enabling real-time information collection. Based on this on-site data, the server re-evaluates evacuation routes and safety, and provides updated information to users.

[0165] Information sharing features within the community are also a crucial element. Users can share their situation and useful information with other users via their devices and collaborate. This allows users to support each other and overcome crises together.

[0166] In this form, the invention provides users with both physical safety and psychological support, enabling a rapid and effective response during disasters.

[0167] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0168] Step 1:

[0169] The server collects environmental data from various data sources. Weather information, geographical data, and sensor data are input to the server via APIs. This data is stored in a database on the server and serves as the foundation for subsequent analysis.

[0170] Step 2:

[0171] The server inputs the aggregated data into an AI model to predict natural disasters. The data input here is analyzed by the AI ​​model, and the likelihood of a disaster occurring in 30 minutes or 1 hour is output. Specifically, the prompt message "Predict the natural disasters that may occur in the next hour" is used in the AI ​​model.

[0172] Step 3:

[0173] The server sends disaster prediction information generated by the AI ​​model to the terminal. The outputted prediction information is transmitted to the terminal via the communication network. The server includes detailed information such as the location of the disaster, the predicted time, and the scope of impact.

[0174] Step 4:

[0175] The device displays alerts to the user based on predictive information received from the server. These alerts visually and audibly alert the user and are displayed on the smartphone or tablet screen. Specific route guidance is also provided using Google Maps or other map applications.

[0176] Step 5:

[0177] The device uses its built-in camera and microphone to collect the user's facial expressions and voice. This data is input into the device's emotion recognition engine, which analyzes it to identify the user's current emotional state. For example, if an anxious facial expression or a trembling voice is detected, the system will output "anxiety."

[0178] Step 6:

[0179] The server receives emotional data transmitted from the terminal and determines the necessary psychological support. Based on the outputted emotional state, a guide to relaxation methods and information about counseling services are created and sent back to the terminal.

[0180] Step 7:

[0181] Unmanned aerial vehicles (UAVs) are dispatched to disaster areas to assess the detailed situation on the ground. High-resolution images and environmental data measured by sensors are collected and transmitted to a server. The input data is analyzed on the server, and updated evacuation route information is generated based on the actual situation on the ground.

[0182] Step 8:

[0183] The server sends the latest evacuation information to the terminal based on newly analyzed data. Evacuation routes and safety conditions are updated, allowing users to always act based on the most up-to-date information.

[0184] Step 9:

[0185] Users share their situation and useful information they discover within the community via their devices. Text messages and location information are sent to other users in real time, enabling them to support each other.

[0186] (Application Example 2)

[0187] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0188] There is a need for technology that can improve the accuracy of natural disaster predictions, provide swift and accurate evacuation instructions during disasters, and further grasp the psychological state of individual users in real time and provide appropriate psychological support. The challenge is to comprehensively ensure user safety from both the physical safety of evacuation and psychological care perspectives.

[0189] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0190] In this invention, the server includes means for using a generative model that analyzes environmental data collected from various information sources to predict future natural disasters; means for providing appropriate evacuation routes to user terminals based on the predictions of the generative model; and means for identifying the user's psychological state by analyzing the user's voice and video signals and providing appropriate psychological support. This makes it possible to respond quickly to natural disasters and to ensure the psychological stability of evacuees.

[0191] A "generative model" is a machine learning model used to analyze environmental data collected from diverse sources and predict the occurrence of natural disasters.

[0192] "Evacuation routes" refer to recommended routes for users to quickly move to a safe location during natural disasters, and are provided based on predictions from generative models.

[0193] A "user terminal" is an electronic device used to present information to individual users, and its role is to display information such as evacuation routes and psychological support.

[0194] "Psychological support" is the process of analyzing a user's voice and video signals to identify their psychological state and provide appropriate care and support for emotions such as anxiety and fear.

[0195] An "unmanned aerial vehicle" is an autonomous aircraft used to acquire information about the situation in disaster-stricken areas and transmit important information to a server during a disaster.

[0196] "Disaster-stricken area conditions" refers to on-site information about areas affected by natural disasters, and the data is acquired by unmanned aerial vehicles.

[0197] "Community information" refers to information shared in real time among users during a disaster, contributing to the promotion of disaster prevention activities in local communities.

[0198] The system implementing this invention mainly consists of a server, a user terminal, and an unmanned aerial vehicle. The server collects environmental data from various sources and predicts the occurrence of natural disasters using a generative AI model. This data includes weather information, geographical information, and information from various sensors. Based on this prediction, the server provides the user terminal with appropriate evacuation routes in real time.

[0199] The user terminal is an electronic device such as a smartphone or head-mounted display, equipped with an emotion recognition engine that analyzes the user's voice and video signals. This allows the terminal to identify the user's psychological state, and if anxiety or fear is detected, it suggests relaxation methods or counseling services through on-screen displays and audio guidance.

[0200] Furthermore, unmanned aerial vehicles (UAVs) acquire information about the situation in disaster-stricken areas and transmit that information to a server. Based on this information, the server optimizes evacuation routes and sends updated information to user terminals. Through this process, users are provided not only with physical safety but also with a sense of psychological security.

[0201] For example, if the server predicts heavy rain, an evacuation warning will be displayed directly on the user's terminal, and navigation information to the nearest evacuation center will be provided. At the same time, if the emotion recognition engine detects anxiety from the user's voice, advice such as "Take a deep breath and relax" will be played in voice. In this way, the system ensures the overall safety of the user.

[0202] For example, a possible prompt might read: "Analyze the audio clip and identify the user's emotions. Also, consider whether evacuation is necessary and, if appropriate, provide a route."

[0203] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0204] Step 1:

[0205] The server collects environmental data from diverse sources. Input data includes weather information, geographical information, and sensor data. This data is fed into a generative AI model to predict the occurrence of natural disasters, and the output is risk assessment information. Based on this assessment information, the likelihood of a disaster occurring is determined.

[0206] Step 2:

[0207] The server provides evacuation route information to the user terminal based on risk assessments performed by a generated AI model. The inputs are the user's location information and the output of the generated AI model. Based on this, the server calculates the nearest safe evacuation route and sends navigation information to the user terminal as output.

[0208] Step 3:

[0209] The device collects the user's voice and video signals and analyzes their psychological state using an emotion recognition engine. Voice and video are used as input data. Emotions are identified from these signals, and the results are output. If the emotion is anxiety or fear, information on relaxation methods and counseling services is presented.

[0210] Step 4:

[0211] Unmanned aerial vehicles (UAVs) fly to disaster areas to assess the situation and collect data. Input comes from information obtained through the aircraft's built-in cameras and sensors. This on-site information is then transmitted to a server as output. Based on this data, the server updates evacuation routes.

[0212] Step 5:

[0213] The user refers to the evacuation route displayed on the device and moves to a safe location. Navigation information from the device is used as input. When the device receives updated information, it outputs the latest evacuation instructions to the user in real time.

[0214] Through this series of steps, a swift and accurate response to natural disasters and the provision of psychological reassurance to users can be achieved.

[0215] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0216] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0217] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0218] [Second Embodiment]

[0219] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0220] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0221] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0222] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0223] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0224] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0225] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0226] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0227] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0228] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0229] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0230] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. 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".

[0231] This invention provides a system for predicting natural disasters and taking appropriate responses accordingly. This system achieves advanced disaster prevention functions through the collaborative work of servers, terminals, and users.

[0232] First, the server collects environmental data from various sources. This data includes weather information, geographical information, and sensor inputs, and is collected in real time. The server feeds this data into a generative model to predict weather conditions and the likelihood of natural disasters. This predictive information is transmitted from the server to each terminal, providing warnings to users.

[0233] Furthermore, the device displays appropriate information to the user in real time based on evacuation route information received from the server. This is achieved by determining the user's current location via GPS and displaying a safe evacuation route. Based on this information, the user can take swift evacuation action.

[0234] Unmanned aerial vehicles (UAVs) are used to gather detailed information about the situation in disaster-stricken areas. Servers analyze the video data transmitted from these aircraft and update evacuation routes and safety information. This allows users to take safe actions based on the latest situation.

[0235] Furthermore, this system is designed to allow users to share information within the community. The server aggregates the posted information and distributes information deemed reliable to other users, thereby supporting the improvement of disaster preparedness across the entire community.

[0236] Furthermore, this system also includes resources for providing psychological care after a disaster. Based on a generative model that has learned from the impacts of past disasters, the server suggests appropriate psychological care and resources to the user. In this way, it is possible to address stress and trauma after a disaster.

[0237] For example, if the server predicts heavy rainfall, the terminal immediately notifies the user of the need to evacuate. Then, if information from unmanned aerial vehicles confirms a safe route in the area, the user can proceed with evacuation based on that information. This process allows users to protect themselves from disasters quickly and effectively.

[0238] The following describes the processing flow.

[0239] Step 1:

[0240] The server collects environmental data in real time from weather agencies and sensor equipment. This data includes elements such as temperature, precipitation, wind speed, and seismic waves.

[0241] Step 2:

[0242] The server feeds collected environmental data into a generative model to predict the likelihood of natural disasters. This process involves analyzing historical disaster data in combination with real-time conditions.

[0243] Step 3:

[0244] The server identifies potential disasters that may occur in a specific region based on prediction results obtained from the generative model, and transmits that information to the terminal.

[0245] Step 4:

[0246] The terminal uses disaster prediction information received from the server and the user's current location information to display appropriate evacuation warnings to the user. The indicated evacuation routes are visually provided through a map application.

[0247] Step 5:

[0248] The unmanned aerial vehicle (UAV) flies over the disaster-affected area and captures images of the current situation with its camera. This video data is transmitted to a server in real time.

[0249] Step 6:

[0250] The server analyzes video data transmitted from the unmanned aerial vehicle and re-evaluates realistic evacuation route options. It updates the information on the terminal as needed to encourage users to evacuate efficiently.

[0251] Step 7:

[0252] Users follow the evacuation instructions displayed on their devices and evacuate using safe routes. They can also exchange useful information with other users using the community's information sharing function.

[0253] Step 8:

[0254] After the disaster subsides, the server provides users with psychological care resources. The generative model analyzes the support information needed after the disaster and selects and recommends appropriate support.

[0255] (Example 1)

[0256] Next, we will describe Example 1. 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."

[0257] In recent years, natural disasters have become frequent in many regions, necessitating prediction and appropriate response. However, conventional disaster prediction systems often suffer from low prediction accuracy and difficulty in providing timely and appropriate information. Furthermore, a lack of support for information sharing and psychological care after a disaster creates many challenges in post-disaster recovery. This invention aims to provide a comprehensive system to solve these problems.

[0258] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0259] This invention includes a server that uses a generation algorithm to analyze environmental information collected from various sources and predict weather conditions and the likelihood of natural disasters; a server that provides appropriate evacuation routes to user devices based on the predictions of the generation algorithm; a server that facilitates the real-time sharing of community information among users and promotes its use in disaster prevention; and a server that uses an automated flight simulator to acquire information on the disaster area and proposes the optimal evacuation route based on that information. This improves the accuracy of natural disaster predictions, enables the rapid and accurate provision of evacuation information, and further enables post-disaster information sharing and psychological support.

[0260] "Environmental information" refers to a collection of data gathered from diverse sources, such as weather conditions, geographical information, and sensor data.

[0261] A "generative algorithm" refers to a computational method that learns from past data and predicts future events based on specific conditions.

[0262] "User device" refers to a terminal owned by a user that displays information received from the server and provides instructions to the user.

[0263] "Community information" refers to various pieces of information shared by multiple users, including useful knowledge and information on local conditions related to disaster prevention.

[0264] An "automatic aircraft" refers to a device that flies unmanned and can acquire video and images from the air.

[0265] "Psychological care resources" refer to support methods and services for dealing with stress and trauma after a disaster.

[0266] This invention is an advanced disaster prevention system aimed at predicting and responding to natural disasters. The system consists of the interaction of servers, terminals, and users.

[0267] The server collects environmental information from diverse sources. This information includes weather conditions, geographical information, and data from sensors. The server stores this data using a database management system. APIs are typically used to obtain weather information, and GIS software is commonly used for geographical information. The server uses a generative AI model to process the collected information. This generative AI model predicts the likelihood of future natural disasters through learning from historical data. For example, when the server predicts heavy rainfall, the model analyzes similar historical data to calculate the probability of precipitation and the affected area.

[0268] The terminal receives forecast information transmitted from the server. The terminal is equipped with GPS functionality to determine the user's current location. This allows the user's device to display the optimal evacuation route in the event of a disaster. A standard map application is used for the map information. For example, if a heavy rain forecast is issued, the terminal will show a safe route from the current location to the evacuation site.

[0269] Users can share information within the community via their devices, exchanging real-time insights into local conditions and disaster response. The server filters the received community information and distributes reliable content to other users.

[0270] Furthermore, the automated aircraft is used to acquire information about the disaster-stricken area. The server processes the video data obtained from the aircraft and can update information on the disaster situation and evacuation procedures. This ensures that evacuation routes are always up-to-date.

[0271] After a disaster, the server uses a generative AI model to learn from past disaster impacts and provides users with psychological care resources. Specifically, this includes recommending stress management applications and counseling services.

[0272] (Example of a prompt message)

[0273] "Please assess the weather forecast for the next 24 hours in this region and the resulting disaster risk."

[0274] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0275] Step 1:

[0276] The server collects environmental information from diverse sources. It obtains weather data using weather information APIs, geographic data from geographic information systems, and real-time data from various sensors. This input data is stored in a database management system. Specifically, the server automates the process of periodically making API calls to update the data with new information.

[0277] Step 2:

[0278] The server feeds the collected data into a generative AI model. The generative AI model has learned from past disaster patterns and analyzes current data to predict the risk of natural disasters. The input data consists of weather conditions and geographical features, and the output provides the probability of disaster occurrence and the affected area for each region. Specifically, the model combines changes in weather conditions and geographical conditions to calculate the likelihood of floods and heavy rainfall.

[0279] Step 3:

[0280] The server organizes the prediction results of the generated AI model and sends them to the terminal. The prediction results are output in text format or as map data, and evacuation advisory information corresponding to the warning level is added. Specifically, the server sends warnings to the user via email or notification services.

[0281] Step 4:

[0282] The terminal receives warning information from the server and displays it on the user's device. Using GPS functionality, it determines the user's current location and displays the optimal evacuation route on a map. Inputs are GPS location and evacuation information, and output is visual evacuation route guidance. Specifically, the terminal calculates and displays the safest route from the user's current location to the evacuation shelter.

[0283] Step 5:

[0284] Users can post and share community information using a terminal. The server receives the posted information, evaluates the reliability of the data, and distributes it to other users. The input is the text and image posts by users, and the output is the filtered and reliable information. As a specific operation, the server uses natural language processing algorithms to analyze the posts and filter out misinformation.

[0285] Step 6:

[0286] The server receives video data from an unmanned aircraft device and analyzes the information. The captured video is analyzed by image processing software to grasp the latest situation in the disaster area. The input is the video data by the aircraft, and the output is the evacuation route and safety information as the analysis result. As a specific operation, the server executes a video analysis algorithm to identify obstacles and dangerous areas.

[0287] Step 7:

[0288] The server uses a generative AI model to select resources for providing psychological care after a disaster. Based on past disaster data, it proposes support services suitable for users. The input is past disaster data and user profiles, and the output is the proposal of psychological care services. As a specific operation, the server uses a recommendation algorithm to select appropriate care measures and notify the users.

[0289] (Application Example 1)

[0290] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0291] With the increasing frequency of natural disasters, effective means for individuals and communities to evacuate quickly and safely are required. However, in the current system, real-time information provision is insufficient, and the appropriate presentation of evacuation routes and psychological support after disasters is insufficient. Therefore, more comprehensive disaster prevention measures are necessary.

[0292] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0293] This invention includes a server that uses a generative model to analyze environmental data collected from various sources and predict future natural disasters; a server that provides appropriate evacuation routes to user terminals based on the predictions of the generative model, and visually presents the routes using map information and location information technology; and a server that provides psychological support to users after a disaster based on past disaster data. This enables the rapid provision of necessary information, ensures the safety of users, and reduces psychological burden through post-disaster care.

[0294] "Diverse information sources" refers to multiple different information sources, such as weather data, geographical data, and sensor data, which are used to collect various environmental data necessary for disaster prediction.

[0295] A "generative model" is a mathematical model that uses machine learning and AI to predict the probability of natural disasters occurring from past data, and is used to provide real-time predictive information.

[0296] A "user terminal" refers to a device that a user can carry with them, such as a smartphone or smart glasses, and is responsible for receiving information from the server and notifying the user during a disaster.

[0297] "Evacuation routes" are route information provided to ensure safe movement for users evacuating from natural disasters, and are updated in real time.

[0298] "Map information" refers to geographical information displayed on the user's terminal, and is data that allows users to visually confirm evacuation routes.

[0299] "Location-based technology" refers to technologies that use GPS and other methods to determine a user's current location and provide the optimal route.

[0300] An "unmanned aerial vehicle" is an autonomously flying device used to acquire detailed images and data of disaster areas, and is useful for situation assessment and information sharing.

[0301] "Psychological support" refers to care and support resources provided to alleviate the psychological stress and trauma that arise after a disaster, and is an activity to support the mental health of users.

[0302] To implement this invention, a server plays a central role. The server processes environmental data collected from various sources and uses a generative AI model to predict the occurrence of natural disasters. This generative model learns from historical data and analyzes weather conditions and local sensor information in real time to make predictions that increase the likelihood of disaster occurrence.

[0303] Based on the prediction results, the server provides appropriate evacuation routes to the user's device. In doing so, the server utilizes map information and location technology to visualize the route clearly on smartphones and smart glasses. Users can then safely evacuate while confirming the route displayed on their device.

[0304] Furthermore, unmanned aerial vehicles (UAVs) are used to acquire detailed information about the disaster area, and this information is analyzed on a server. The analyzed information is used to propose revised evacuation routes, and users are presented with safer routes based on the latest data. The video footage acquired by the UAVs is analyzed using software such as OpenCV.

[0305] Information sharing among users is also crucial. The server aggregates community-based information provided by users and distributes it to other users as reliable data. This real-time information sharing improves disaster preparedness.

[0306] The server also supports psychological care after disasters. Based on a generative model trained with past disaster data, it provides appropriate psychological support measures to users. This includes simple care using chatbots and arranging detailed counseling through cooperation with experts. By using prompts such as "What messages could be sent to worried people during a typhoon like this in the past?", proposals using the generative AI model are made.

[0307] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0308] Step 1:

[0309] The server collects environmental data such as meteorological data, geographical data, and sensor information from various information sources. The input is data from various APIs and local sensors, and an integrated environmental data set is generated as the output. This data set is used for predictions by the generative AI model. The server continuously updates this data in real time.

[0310] Step 2:

[0311] The server inputs the collected environmental data into the generative AI model to predict natural disasters. The input is the environmental data set integrated in Step 1, and information on the likelihood of disaster occurrence and predicted impacts is generated as the output. The generative AI model uses an algorithm trained based on past disaster data. The server generates a detailed prediction report in preparation for the next step.

[0312] Step 3:

[0313] Based on the generated disaster prediction information, the server provides an appropriate evacuation route to the user terminal. The input is the prediction result, map information, and the user's current location information, and the evacuation route information is transmitted to the user terminal as the output. With map information and location information technology, the user can visually confirm the evacuation route. The terminal notifies the user based on this.

[0314] Step 4:

[0315] The unmanned aerial vehicle transmits aerial footage of the disaster area to a server. The server receives the aerial footage as input and performs video analysis using OpenCV. The output of the analysis is current terrain conditions and obstacle information, which is used to propose new evacuation routes. The server generates an optimized evacuation route and sends updated information to the user's terminal.

[0316] Step 5:

[0317] Users share community information with other users through the application. The server aggregates this real-time information, evaluates its reliability, and then distributes it to other users. The input is local information obtained from users, and the output is a notification to other users as reliable disaster prevention information.

[0318] Step 6:

[0319] After a disaster, the server provides psychological support to users using a generative model that has learned from past disaster data. The input is user information determined to be in need of psychological support, and the output is a generated psychological care plan and support resources. Users can access support via chatbots or links.

[0320] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0321] This invention relates to a system that analyzes diverse environmental data to predict natural disasters and recognizes the emotional state of users to provide appropriate support. This system integrates environmental data analysis, emotion recognition, real-time information sharing, and data collection using unmanned aerial vehicles.

[0322] The server first aggregates weather information, geographical data, and sensor data, and then uses a generative model to predict the occurrence of natural disasters. This prediction information is sent to the terminal, and users can receive evacuation instructions based on it. The terminal displays guidance on safe evacuation routes to help users take action quickly.

[0323] On the other hand, the emotion recognition engine has the function of identifying the user's emotional state by analyzing the user's facial expression data and voice input. This allows the server to evaluate the user's psychological burden in real time and provide appropriate psychological support as needed. For example, if anxiety or fear is detected during a disaster, information on relaxation methods and counseling services will be displayed on the terminal.

[0324] After a disaster occurs, unmanned aerial vehicles (UAVs) assess the detailed situation on-site and transmit data to a server. The server analyzes this data, reassesssing the safety of the site and updating the optimal evacuation routes. In addition, users can share their status and useful information in real time within the community, enabling them to cooperate and overcome the crisis.

[0325] For example, if the server predicts an earthquake, the terminal immediately displays an alert to the user and shows them the route to the nearest evacuation center. Meanwhile, if the emotion engine detects the user's distressed facial expressions or voice, the terminal offers options such as a safety check alert or counseling support. In this way, it can support both physical safety and psychological reassurance.

[0326] This embodiment provides rapid and appropriate evacuation support during disasters, as well as psychological care during and after evacuation, thereby more reliably ensuring the overall safety of users.

[0327] The following describes the processing flow.

[0328] Step 1:

[0329] The server collects weather data, geographical data, and sensor data in real time. This data is then input into a generative model to predict natural disasters.

[0330] Step 2:

[0331] The server analyzes disaster prediction information generated by a generative model and creates evacuation orders for the affected areas. This information is then distributed to the user's terminal.

[0332] Step 3:

[0333] The terminal displays warnings to the user based on disaster prediction information and evacuation orders received from the server. It also uses GPS functionality to determine the user's current location and displays the nearest safe evacuation route on a map.

[0334] Step 4:

[0335] Using an emotion recognition engine, the device collects and analyzes the user's facial expressions and voice data. From this information, it identifies the user's emotional state and sends it to the server.

[0336] Step 5:

[0337] The server analyzes the user's emotional state and determines the necessary psychological support information. It provides the user's device with suggestions for relaxation methods and links to online counseling.

[0338] Step 6:

[0339] The unmanned aerial vehicle flies around the disaster area and collects video data in real time. The video is then transmitted to a server.

[0340] Step 7:

[0341] The server analyzes video data obtained from unmanned aerial vehicles to re-evaluate the local situation and existing evacuation routes. It then updates the evacuation route information to reflect the new situation and distributes it to terminals.

[0342] Step 8:

[0343] Users can move according to the latest evacuation route information provided by their devices. They can also continue to exchange information with other users in the community and work together to ensure their collective safety.

[0344] Step 9:

[0345] After the disaster subsides, the server will continue to provide psychological support to users by offering mental health resources based on information obtained through the emotion engine, helping users regain their sense of security.

[0346] (Example 2)

[0347] Next, we will describe Example 2. 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".

[0348] In modern society, with the increasing frequency of natural disasters, people are required to take swift and appropriate evacuation actions. However, existing systems suffer from delays in disaster prediction and information provision, failing to adequately ensure user safety. Furthermore, a lack of psychological support increases the mental burden during disasters. Therefore, there is a need for a system that allows users to respond quickly to disasters and provides psychological reassurance.

[0349] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0350] In this invention, the server includes means for aggregating environmental data obtained from various data sources and predicting the occurrence of natural disasters using a generative AI model; means for providing safe evacuation routes to communication devices based on the predicted disaster information and supporting the user's evacuation; and means for analyzing the user's biometric information, identifying their emotional state, and providing psychological support. This enables the user to take swift and safe evacuation actions and maintain emotional stability during disasters.

[0351] "Diverse data sources" refers to a collection of data of different types and origins, such as weather information, geographical information, and sensor data.

[0352] A "generative AI model" refers to artificial intelligence technology that learns patterns from collected data and predicts future events.

[0353] "Communication equipment" refers to devices or interfaces used to provide information to users and transmit instructions.

[0354] "Biometric information" refers to data that indicates an individual's physical or emotional state, such as a user's facial expressions or voice.

[0355] An "unmanned aerial vehicle" refers to an aircraft that is remotely or autonomously operated without a human on board.

[0356] "Psychological support" refers to services and methods aimed at stabilizing a user's emotional state and providing a sense of security.

[0357] A "community" refers to a group of users organized to share information and act collaboratively.

[0358] This invention relates to a system that analyzes environmental data to predict natural disasters and recognizes the emotional state of users to provide rapid psychological support. This system mainly consists of a server, terminals, an unmanned aerial vehicle, and a generative AI model.

[0359] The server interfaces with an advanced database management system to aggregate environmental data from diverse data sources, including weather information, geographic data, and sensor data. Cloud computing technology is used to run data analysis software. The collected data is analyzed by a generative AI model to predict future natural disasters. For example, known weather patterns are input into the AI ​​model using the prompt "Predict natural disasters that may occur within the next 24 hours," and the results are analyzed.

[0360] The terminal provides users with disaster prediction information transmitted from a server. Communication devices such as smartphones and tablets use map application software (for example, a digital map service, a general term) to display safe evacuation routes. Furthermore, the terminal is equipped with a camera and microphone, which are used to acquire the user's biometric information and transmit it to an engine for emotion recognition. If the terminal determines that the user's emotional state is unstable, it displays information on relaxation methods and online counseling services.

[0361] Unmanned aerial vehicles (UAVs) are used to assess the situation on the ground in disaster areas, transmitting high-resolution image data and sensor data to a server. These devices have GPS-based autonomous flight capabilities, enabling real-time information collection. Based on this on-site data, the server re-evaluates evacuation routes and safety, and provides updated information to users.

[0362] Information sharing features within the community are also a crucial element. Users can share their situation and useful information with other users via their devices and collaborate. This allows users to support each other and overcome crises together.

[0363] In this form, the invention provides users with both physical safety and psychological support, enabling a rapid and effective response during disasters.

[0364] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0365] Step 1:

[0366] The server collects environmental data from various data sources. Weather information, geographical data, and sensor data are input to the server via APIs. This data is stored in a database on the server and serves as the foundation for subsequent analysis.

[0367] Step 2:

[0368] The server inputs the aggregated data into an AI model to predict natural disasters. The data input here is analyzed by the AI ​​model, and the likelihood of disasters occurring in 30 minutes or 1 hour is output. Specifically, the prompt message "Predict the natural disasters that may occur in the next hour" is used in the AI ​​model.

[0369] Step 3:

[0370] The server sends disaster prediction information generated by the AI ​​model to the terminal. The outputted prediction information is transmitted to the terminal via the communication network. The server includes detailed information such as the location of the disaster, the predicted time, and the scope of impact.

[0371] Step 4:

[0372] The device displays alerts to the user based on predictive information received from the server. These alerts visually and audibly alert the user and are displayed on the smartphone or tablet screen. Specific route guidance is also provided using Google Maps or other map applications.

[0373] Step 5:

[0374] The device uses its built-in camera and microphone to collect the user's facial expressions and voice. This data is input into the device's emotion recognition engine, which analyzes it to identify the user's current emotional state. For example, if an anxious facial expression or a trembling voice is detected, the system will output "anxiety."

[0375] Step 6:

[0376] The server receives emotional data transmitted from the terminal and determines the necessary psychological support. Based on the outputted emotional state, a guide to relaxation methods and information about counseling services are created and sent back to the terminal.

[0377] Step 7:

[0378] Unmanned aerial vehicles (UAVs) are dispatched to disaster areas to assess the detailed situation on the ground. High-resolution images and environmental data measured by sensors are collected and transmitted to a server. The input data is analyzed on the server, and updated evacuation route information is generated based on the actual situation on the ground.

[0379] Step 8:

[0380] The server sends the latest evacuation information to the terminal based on newly analyzed data. Evacuation routes and safety conditions are updated, allowing users to always act based on the most up-to-date information.

[0381] Step 9:

[0382] Users share their situation and useful information they discover within the community via their devices. Text messages and location information are sent to other users in real time, enabling them to support each other.

[0383] (Application Example 2)

[0384] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0385] There is a need for technology that can improve the accuracy of natural disaster predictions, provide swift and accurate evacuation instructions during disasters, and further grasp the psychological state of individual users in real time and provide appropriate psychological support. The challenge is to comprehensively ensure user safety from both the physical safety of evacuation and psychological care perspectives.

[0386] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0387] In this invention, the server includes means for using a generative model that analyzes environmental data collected from various information sources to predict future natural disasters; means for providing appropriate evacuation routes to user terminals based on the predictions of the generative model; and means for identifying the user's psychological state by analyzing the user's voice and video signals and providing appropriate psychological support. This makes it possible to respond quickly to natural disasters and to ensure the psychological stability of evacuees.

[0388] A "generative model" is a machine learning model used to analyze environmental data collected from diverse sources and predict the occurrence of natural disasters.

[0389] "Evacuation routes" refer to recommended routes for users to quickly move to a safe location during natural disasters, and are provided based on predictions from generative models.

[0390] A "user terminal" is an electronic device used to present information to individual users, and its role is to display information such as evacuation routes and psychological support.

[0391] "Psychological support" is the process of analyzing a user's voice and video signals to identify their psychological state and provide appropriate care and support for emotions such as anxiety and fear.

[0392] An "unmanned aerial vehicle" is an autonomous aircraft used to acquire information about the situation in disaster-stricken areas and transmit important information to a server during a disaster.

[0393] "Disaster-stricken area conditions" refers to on-site information about areas affected by natural disasters, and the data is acquired by unmanned aerial vehicles.

[0394] "Community information" refers to information shared in real time among users during a disaster, contributing to the promotion of disaster prevention activities in local communities.

[0395] The system implementing this invention mainly consists of a server, a user terminal, and an unmanned aerial vehicle. The server collects environmental data from various sources and predicts the occurrence of natural disasters using a generative AI model. This data includes weather information, geographical information, and information from various sensors. Based on this prediction, the server provides the user terminal with appropriate evacuation routes in real time.

[0396] The user terminal is an electronic device such as a smartphone or head-mounted display, equipped with an emotion recognition engine that analyzes the user's voice and video signals. This allows the terminal to identify the user's psychological state, and if anxiety or fear is detected, it suggests relaxation methods or counseling services through on-screen displays and audio guidance.

[0397] Furthermore, unmanned aerial vehicles (UAVs) acquire information about the situation in disaster-stricken areas and transmit that information to a server. Based on this information, the server optimizes evacuation routes and sends updated information to user terminals. Through this process, users are provided not only with physical safety but also with a sense of psychological security.

[0398] For example, if the server predicts heavy rain, an evacuation warning will be displayed directly on the user's terminal, and navigation information to the nearest evacuation center will be provided. At the same time, if the emotion recognition engine detects anxiety from the user's voice, advice such as "Take a deep breath and relax" will be played in voice. In this way, the system ensures the overall safety of the user.

[0399] For example, a possible prompt message might be: "Analyze the audio clip and identify the user's emotions. Also, consider whether evacuation is necessary and, if appropriate, provide a route."

[0400] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0401] Step 1:

[0402] The server collects environmental data from diverse sources. Input data includes weather information, geographical information, and sensor data. This data is fed into a generative AI model to predict the occurrence of natural disasters, and the output is risk assessment information. Based on this assessment information, the likelihood of a disaster occurring is determined.

[0403] Step 2:

[0404] The server provides evacuation route information to the user terminal based on risk assessments performed by a generated AI model. The inputs are the user's location information and the output of the generated AI model. Based on this, the server calculates the nearest safe evacuation route and sends navigation information to the user terminal as output.

[0405] Step 3:

[0406] The device collects the user's voice and video signals and analyzes their psychological state using an emotion recognition engine. Voice and video are used as input data. Emotions are identified from these signals, and the results are output. If the emotion is anxiety or fear, information on relaxation methods and counseling services is presented.

[0407] Step 4:

[0408] Unmanned aerial vehicles (UAVs) fly to disaster areas to assess the situation and collect data. Input comes from information obtained through the aircraft's built-in cameras and sensors. This on-site information is then transmitted to a server as output. Based on this data, the server updates evacuation routes.

[0409] Step 5:

[0410] The user refers to the evacuation route displayed on the device and moves to a safe location. Navigation information from the device is used as input. When the device receives updated information, it outputs the latest evacuation instructions to the user in real time.

[0411] Through this series of steps, a swift and accurate response to natural disasters and the provision of psychological reassurance to users can be achieved.

[0412] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0413] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0414] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0415] [Third Embodiment]

[0416] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0417] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0418] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0419] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0420] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0421] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0422] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0423] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0424] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0425] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0426] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0427] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0428] This invention provides a system for predicting natural disasters and taking appropriate responses accordingly. This system achieves advanced disaster prevention functions through the collaborative work of servers, terminals, and users.

[0429] First, the server collects environmental data from various sources. This data includes weather information, geographical information, and sensor inputs, and is collected in real time. The server feeds this data into a generative model to predict weather conditions and the likelihood of natural disasters. This predictive information is transmitted from the server to each terminal, providing warnings to users.

[0430] Furthermore, the device displays appropriate information to the user in real time based on evacuation route information received from the server. This is achieved by determining the user's current location via GPS and displaying a safe evacuation route. Based on this information, the user can take swift evacuation action.

[0431] Unmanned aerial vehicles (UAVs) are used to gather detailed information about the situation in disaster-stricken areas. Servers analyze the video data transmitted from these aircraft and update evacuation routes and safety information. This allows users to take safe actions based on the latest situation.

[0432] Furthermore, this system is designed to allow users to share information within the community. The server aggregates the posted information and distributes information deemed reliable to other users, thereby supporting the improvement of disaster preparedness across the entire community.

[0433] Furthermore, this system also includes resources for providing psychological care after a disaster. Based on a generative model that has learned from the impacts of past disasters, the server suggests appropriate psychological care and resources to the user. In this way, it is possible to address stress and trauma after a disaster.

[0434] For example, if the server predicts heavy rainfall, the terminal immediately notifies the user of the need to evacuate. Then, if information from unmanned aerial vehicles confirms a safe route in the area, the user can proceed with evacuation based on that information. This process allows users to protect themselves from disasters quickly and effectively.

[0435] The following describes the processing flow.

[0436] Step 1:

[0437] The server collects environmental data in real time from weather agencies and sensor equipment. This data includes elements such as temperature, precipitation, wind speed, and seismic waves.

[0438] Step 2:

[0439] The server feeds collected environmental data into a generative model to predict the likelihood of natural disasters. This process involves analyzing historical disaster data in combination with real-time conditions.

[0440] Step 3:

[0441] The server identifies potential disasters that may occur in a specific region based on prediction results obtained from the generative model, and transmits that information to the terminal.

[0442] Step 4:

[0443] The terminal uses disaster prediction information received from the server and the user's current location information to display appropriate evacuation warnings to the user. The indicated evacuation routes are visually provided through a map application.

[0444] Step 5:

[0445] The unmanned aerial vehicle (UAV) flies over the disaster-affected area and captures images of the current situation with its camera. This video data is transmitted to a server in real time.

[0446] Step 6:

[0447] The server analyzes video data transmitted from the unmanned aerial vehicle and re-evaluates realistic evacuation route options. It updates the information on the terminal as needed to encourage users to evacuate efficiently.

[0448] Step 7:

[0449] Users follow the evacuation instructions displayed on their devices and evacuate using safe routes. They can also exchange useful information with other users using the community's information sharing function.

[0450] Step 8:

[0451] After the disaster subsides, the server provides users with psychological care resources. The generative model analyzes the support information needed after the disaster and selects and recommends appropriate support.

[0452] (Example 1)

[0453] Next, we will describe Example 1. 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."

[0454] In recent years, natural disasters have become frequent in many regions, necessitating prediction and appropriate response. However, conventional disaster prediction systems often suffer from low prediction accuracy and difficulty in providing timely and appropriate information. Furthermore, a lack of support for information sharing and psychological care after a disaster creates many challenges in post-disaster recovery. This invention aims to provide a comprehensive system to solve these problems.

[0455] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0456] This invention includes a server that uses a generation algorithm to analyze environmental information collected from various sources and predict weather conditions and the likelihood of natural disasters; a server that provides appropriate evacuation routes to user devices based on the predictions of the generation algorithm; a server that facilitates the real-time sharing of community information among users and promotes its use in disaster prevention; and a server that uses an automated flight simulator to acquire information on the disaster area and proposes the optimal evacuation route based on that information. This improves the accuracy of natural disaster predictions, enables the rapid and accurate provision of evacuation information, and further enables post-disaster information sharing and psychological support.

[0457] "Environmental information" refers to a collection of data gathered from diverse sources, such as weather conditions, geographical information, and sensor data.

[0458] A "generative algorithm" refers to a computational method that learns from past data and predicts future events based on specific conditions.

[0459] "User device" refers to a terminal owned by a user that displays information received from the server and provides instructions to the user.

[0460] "Community information" refers to various pieces of information shared by multiple users, including useful knowledge and information on local conditions related to disaster prevention.

[0461] An "automatic aircraft" refers to a device that flies unmanned and can acquire video and images from the air.

[0462] "Psychological care resources" refer to support methods and services for dealing with stress and trauma after a disaster.

[0463] This invention is an advanced disaster prevention system aimed at predicting and responding to natural disasters. The system consists of the interaction of servers, terminals, and users.

[0464] The server collects environmental information from diverse sources. This information includes weather conditions, geographical information, and data from sensors. The server stores this data using a database management system. APIs are typically used to obtain weather information, and GIS software is commonly used for geographical information. The server uses a generative AI model to process the collected information. This generative AI model predicts the likelihood of future natural disasters through learning from historical data. For example, when the server predicts heavy rainfall, the model analyzes similar historical data to calculate the probability of precipitation and the affected area.

[0465] The terminal receives forecast information transmitted from the server. The terminal is equipped with GPS functionality to determine the user's current location. This allows the user's device to display the optimal evacuation route in the event of a disaster. A standard map application is used for the map information. For example, if a heavy rain forecast is issued, the terminal will show a safe route from the current location to the evacuation site.

[0466] Users can share information within the community via their devices, exchanging real-time insights into local conditions and disaster response. The server filters the received community information and distributes reliable content to other users.

[0467] Furthermore, the automated aircraft is used to acquire information about the disaster-stricken area. The server processes the video data obtained from the aircraft and can update information on the disaster situation and evacuation procedures. This ensures that evacuation routes are always up-to-date.

[0468] After a disaster, the server uses a generative AI model to learn from past disaster impacts and provides users with psychological care resources. Specifically, this includes recommending stress management applications and counseling services.

[0469] (Example of a prompt message)

[0470] "Please assess the weather forecast for the next 24 hours in this region and the resulting disaster risk."

[0471] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0472] Step 1:

[0473] The server collects environmental information from diverse sources. It obtains weather data using weather information APIs, geographic data from geographic information systems, and real-time data from various sensors. This input data is stored in a database management system. Specifically, the server automates the process of periodically making API calls to update the data with new information.

[0474] Step 2:

[0475] The server feeds the collected data into a generative AI model. The generative AI model has learned from past disaster patterns and analyzes current data to predict the risk of natural disasters. The input data consists of weather conditions and geographical features, and the output provides the probability of disaster occurrence and the affected area for each region. Specifically, the model combines changes in weather conditions and geographical conditions to calculate the likelihood of floods and heavy rainfall.

[0476] Step 3:

[0477] The server organizes the prediction results of the generated AI model and sends them to the terminal. The prediction results are output in text format or as map data, and evacuation advisory information corresponding to the warning level is added. Specifically, the server sends warnings to the user via email or notification services.

[0478] Step 4:

[0479] The terminal receives warning information from the server and displays it on the user's device. Using GPS functionality, it determines the user's current location and displays the optimal evacuation route on a map. Inputs are GPS location and evacuation information, and output is visual evacuation route guidance. Specifically, the terminal calculates and displays the safest route from the user's current location to the evacuation shelter.

[0480] Step 5:

[0481] Users can post and share community information using their devices. The server receives the posted information, evaluates the reliability of the data, and distributes it to other users. The input is text and images posted by users, and the output is filtered, reliable information. Specifically, the server uses natural language processing algorithms to analyze the posts and filter out misinformation.

[0482] Step 6:

[0483] The server receives video data from the automated aircraft and analyzes the information. The captured video is analyzed by image processing software to understand the latest situation in the disaster area. The input is video data from the aircraft, and the output is evacuation routes and safety information as a result of the analysis. Specifically, the server executes a video analysis algorithm to identify obstacles and hazardous areas.

[0484] Step 7:

[0485] The server uses a generative AI model to select resources for providing psychological care after a disaster. Based on past disaster data, it proposes support services suitable for the user. The input is past disaster data and user profiles, and the output is a proposal for psychological care services. Specifically, the server uses a recommendation algorithm to select appropriate care measures and notifies the user.

[0486] (Application Example 1)

[0487] Next, we will explain Application Example 1. In the following explanation, 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."

[0488] With the increasing frequency of natural disasters, there is a need for effective means to enable individuals and communities to evacuate quickly and safely. However, current systems are insufficient in providing real-time information, appropriately indicating evacuation routes, and providing adequate psychological support after a disaster. Therefore, more comprehensive disaster prevention measures are necessary.

[0489] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0490] This invention includes a server that uses a generative model to analyze environmental data collected from various sources and predict future natural disasters; a server that provides appropriate evacuation routes to user terminals based on the predictions of the generative model, and visually presents the routes using map information and location information technology; and a server that provides psychological support to users after a disaster based on past disaster data. This enables the rapid provision of necessary information, ensures the safety of users, and reduces psychological burden through post-disaster care.

[0491] "Diverse information sources" refers to multiple different information sources, such as weather data, geographical data, and sensor data, which are used to collect various environmental data necessary for disaster prediction.

[0492] A "generative model" is a mathematical model that uses machine learning and AI to predict the probability of natural disasters occurring from past data, and is used to provide real-time predictive information.

[0493] A "user terminal" refers to a device that a user can carry with them, such as a smartphone or smart glasses, and is responsible for receiving information from the server and notifying the user during a disaster.

[0494] "Evacuation routes" are route information provided to ensure safe movement for users evacuating from natural disasters, and are updated in real time.

[0495] "Map information" refers to geographical information displayed on the user's terminal, and is data that allows users to visually confirm evacuation routes.

[0496] "Location-based technology" refers to technologies that use GPS and other methods to determine a user's current location and provide the optimal route.

[0497] An "unmanned aerial vehicle" is an autonomously flying device used to acquire detailed images and data of disaster areas, and is useful for situation assessment and information sharing.

[0498] "Psychological support" refers to care and support resources provided to alleviate the psychological stress and trauma that arise after a disaster, and is an activity to support the mental health of users.

[0499] To implement this invention, a server plays a central role. The server processes environmental data collected from various sources and uses a generative AI model to predict the occurrence of natural disasters. This generative model learns from historical data and analyzes weather conditions and local sensor information in real time to make predictions that increase the likelihood of disaster occurrence.

[0500] Based on the prediction results, the server provides appropriate evacuation routes to the user's device. In doing so, the server utilizes map information and location technology to visualize the route clearly on smartphones and smart glasses. Users can then safely evacuate while confirming the route displayed on their device.

[0501] Furthermore, unmanned aerial vehicles (UAVs) are used to acquire detailed information about the disaster area, and this information is analyzed on a server. The analyzed information is used to propose revised evacuation routes, and users are presented with safer routes based on the latest data. The video footage acquired by the UAVs is analyzed using software such as OpenCV.

[0502] Information sharing among users is also crucial. The server aggregates community-based information provided by users and distributes it to other users as reliable data. This real-time information sharing improves disaster preparedness.

[0503] The server also provides psychological support after disasters. Based on a generative model trained on past disaster data, it offers users appropriate psychological support measures. This includes simple care using chatbots, as well as arranging detailed counseling in collaboration with experts. It uses prompts such as, "In past typhoons like this, what message would you send to someone you were worried about?" to make suggestions using the generative AI model.

[0504] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0505] Step 1:

[0506] The server collects environmental data such as weather data, geographical data, and sensor information from diverse sources. Inputs include data from various APIs and local sensors, and output is an integrated environmental dataset. This dataset is used for predictions by a generative AI model. The server continuously updates this data in real time.

[0507] Step 2:

[0508] The server feeds the collected environmental data into a generative AI model to predict natural disasters. The input is the environmental dataset integrated in Step 1, and the output generates information on the likelihood of disaster occurrence and the predicted impact. The generative AI model uses an algorithm trained on historical disaster data. The server generates a detailed prediction report and prepares for the next step.

[0509] Step 3:

[0510] The server provides appropriate evacuation routes to user terminals based on the generated disaster prediction information. Inputs include prediction results, map information, and the user's current location; output is the evacuation route information sent to the user terminal. Using map information and location technology, users can visually confirm the evacuation route. The terminal then uses this information to send notifications to the user.

[0511] Step 4:

[0512] The unmanned aerial vehicle transmits aerial footage of the disaster area to a server. The server receives the aerial footage as input and performs video analysis using OpenCV. The output of the analysis is current terrain conditions and obstacle information, which is used to propose new evacuation routes. The server generates an optimized evacuation route and sends updated information to the user's terminal.

[0513] Step 5:

[0514] Users share community information with other users through the application. The server aggregates this real-time information, evaluates its reliability, and then distributes it to other users. The input is local information obtained from users, and the output is a notification to other users as reliable disaster prevention information.

[0515] Step 6:

[0516] After a disaster, the server provides psychological support to users using a generative model that has learned from past disaster data. The input is user information determined to be in need of psychological support, and the output is a generated psychological care plan and support resources. Users can access support via chatbots or links.

[0517] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0518] This invention relates to a system that analyzes diverse environmental data to predict natural disasters and recognizes the emotional state of users to provide appropriate support. This system integrates environmental data analysis, emotion recognition, real-time information sharing, and data collection using unmanned aerial vehicles.

[0519] The server first aggregates weather information, geographical data, and sensor data, and then uses a generative model to predict the occurrence of natural disasters. This prediction information is sent to the terminal, and users can receive evacuation instructions based on it. The terminal displays guidance on safe evacuation routes to help users take action quickly.

[0520] On the other hand, the emotion recognition engine has the function of identifying the user's emotional state by analyzing the user's facial expression data and voice input. This allows the server to evaluate the user's psychological burden in real time and provide appropriate psychological support as needed. For example, if anxiety or fear is detected during a disaster, information on relaxation methods and counseling services will be displayed on the terminal.

[0521] After a disaster occurs, unmanned aerial vehicles (UAVs) assess the detailed situation on-site and transmit data to a server. The server analyzes this data, reassesssing the safety of the site and updating the optimal evacuation routes. In addition, users can share their status and useful information in real time within the community, enabling them to cooperate and overcome the crisis.

[0522] For example, if the server predicts an earthquake, the terminal immediately displays an alert to the user and shows them the route to the nearest evacuation center. Meanwhile, if the emotion engine detects the user's distressed facial expressions or voice, the terminal offers options such as a safety check alert or counseling support. In this way, it can support both physical safety and psychological reassurance.

[0523] This embodiment provides rapid and appropriate evacuation support during disasters, as well as psychological care during and after evacuation, thereby more reliably ensuring the overall safety of users.

[0524] The following describes the processing flow.

[0525] Step 1:

[0526] The server collects weather data, geographical data, and sensor data in real time. This data is then input into a generative model to predict natural disasters.

[0527] Step 2:

[0528] The server analyzes disaster prediction information generated by a generative model and creates evacuation orders for the affected areas. This information is then distributed to the user's terminal.

[0529] Step 3:

[0530] The terminal displays warnings to the user based on disaster prediction information and evacuation orders received from the server. It also uses GPS functionality to determine the user's current location and displays the nearest safe evacuation route on a map.

[0531] Step 4:

[0532] Using an emotion recognition engine, the device collects and analyzes the user's facial expressions and voice data. From this information, it identifies the user's emotional state and sends it to the server.

[0533] Step 5:

[0534] The server analyzes the user's emotional state and determines the necessary psychological support information. It provides the user's device with suggestions for relaxation methods and links to online counseling.

[0535] Step 6:

[0536] The unmanned aerial vehicle flies around the disaster area and collects video data in real time. The video is then transmitted to a server.

[0537] Step 7:

[0538] The server analyzes video data obtained from unmanned aerial vehicles to re-evaluate the local situation and existing evacuation routes. It then updates the evacuation route information to reflect the new situation and distributes it to terminals.

[0539] Step 8:

[0540] Users can move according to the latest evacuation route information provided by their devices. They can also continue to exchange information with other users in the community and work together to ensure their collective safety.

[0541] Step 9:

[0542] After the disaster subsides, the server will continue to provide psychological support to users by offering mental health resources based on information obtained through the emotion engine, helping users regain their sense of security.

[0543] (Example 2)

[0544] Next, we will describe Example 2. 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."

[0545] In modern society, with the increasing frequency of natural disasters, people are required to take swift and appropriate evacuation actions. However, existing systems suffer from delays in disaster prediction and information provision, failing to adequately ensure user safety. Furthermore, a lack of psychological support increases the mental burden during disasters. Therefore, there is a need for a system that allows users to respond quickly to disasters and provides psychological reassurance.

[0546] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0547] In this invention, the server includes means for aggregating environmental data obtained from various data sources and predicting the occurrence of natural disasters using a generative AI model; means for providing safe evacuation routes to communication devices based on the predicted disaster information and supporting the user's evacuation; and means for analyzing the user's biometric information, identifying their emotional state, and providing psychological support. This enables the user to take swift and safe evacuation actions and maintain emotional stability during disasters.

[0548] "Diverse data sources" refers to a collection of data of different types and origins, such as weather information, geographical information, and sensor data.

[0549] A "generative AI model" refers to artificial intelligence technology that learns patterns from collected data and predicts future events.

[0550] "Communication equipment" refers to devices or interfaces used to provide information to users and transmit instructions.

[0551] "Biometric information" refers to data that indicates an individual's physical or emotional state, such as a user's facial expressions or voice.

[0552] An "unmanned aerial vehicle" refers to an aircraft that is remotely or autonomously operated without a human on board.

[0553] "Psychological support" refers to services and methods aimed at stabilizing a user's emotional state and providing a sense of security.

[0554] A "community" refers to a group of users organized to share information and act collaboratively.

[0555] This invention relates to a system that analyzes environmental data to predict natural disasters and recognizes the emotional state of users to provide rapid psychological support. This system mainly consists of a server, terminals, an unmanned aerial vehicle, and a generative AI model.

[0556] The server interfaces with an advanced database management system to aggregate environmental data from diverse data sources, including weather information, geographic data, and sensor data. Cloud computing technology is used to run data analysis software. The collected data is analyzed by a generative AI model to predict future natural disasters. For example, known weather patterns are input into the AI ​​model using the prompt "Predict natural disasters that may occur within the next 24 hours," and the results are analyzed.

[0557] The terminal provides users with disaster prediction information transmitted from a server. Communication devices such as smartphones and tablets use map application software (for example, a digital map service, a general term) to display safe evacuation routes. Furthermore, the terminal is equipped with a camera and microphone, which are used to acquire the user's biometric information and transmit it to an engine for emotion recognition. If the terminal determines that the user's emotional state is unstable, it displays information on relaxation methods and online counseling services.

[0558] Unmanned aerial vehicles (UAVs) are used to assess the situation on the ground in disaster areas, transmitting high-resolution image data and sensor data to a server. These devices have GPS-based autonomous flight capabilities, enabling real-time information collection. Based on this on-site data, the server re-evaluates evacuation routes and safety, and provides updated information to users.

[0559] Information sharing features within the community are also a crucial element. Users can share their situation and useful information with other users via their devices and collaborate. This allows users to support each other and overcome crises together.

[0560] In this form, the invention provides users with both physical safety and psychological support, enabling a rapid and effective response during disasters.

[0561] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0562] Step 1:

[0563] The server collects environmental data from various data sources. Weather information, geographical data, and sensor data are input to the server via APIs. This data is stored in a database on the server and serves as the foundation for subsequent analysis.

[0564] Step 2:

[0565] The server inputs the aggregated data into an AI model to predict natural disasters. The data input here is analyzed by the AI ​​model, and the likelihood of disasters occurring in 30 minutes or 1 hour is output. Specifically, the prompt message "Predict the natural disasters that may occur in the next hour" is used in the AI ​​model.

[0566] Step 3:

[0567] The server sends disaster prediction information generated by the AI ​​model to the terminal. The outputted prediction information is transmitted to the terminal via the communication network. The server includes detailed information such as the location of the disaster, the predicted time, and the scope of impact.

[0568] Step 4:

[0569] The device displays alerts to the user based on predictive information received from the server. These alerts visually and audibly alert the user and are displayed on the smartphone or tablet screen. Specific route guidance is also provided using Google Maps or other map applications.

[0570] Step 5:

[0571] The device uses its built-in camera and microphone to collect the user's facial expressions and voice. This data is input into the device's emotion recognition engine, which analyzes it to identify the user's current emotional state. For example, if an anxious facial expression or a trembling voice is detected, the system will output "anxiety."

[0572] Step 6:

[0573] The server receives emotional data transmitted from the terminal and determines the necessary psychological support. Based on the outputted emotional state, a guide to relaxation methods and information about counseling services are created and sent back to the terminal.

[0574] Step 7:

[0575] Unmanned aerial vehicles (UAVs) are dispatched to disaster areas to assess the detailed situation on the ground. High-resolution images and environmental data measured by sensors are collected and transmitted to a server. The input data is analyzed on the server, and updated evacuation route information is generated based on the actual situation on the ground.

[0576] Step 8:

[0577] The server sends the latest evacuation information to the terminal based on newly analyzed data. Evacuation routes and safety conditions are updated, allowing users to always act based on the most up-to-date information.

[0578] Step 9:

[0579] Users share their situation and useful information they discover within the community via their devices. Text messages and location information are sent to other users in real time, enabling them to support each other.

[0580] (Application Example 2)

[0581] Next, we will explain application example 2. In the following explanation, 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."

[0582] There is a need for technology that can improve the accuracy of natural disaster predictions, provide swift and accurate evacuation instructions during disasters, and further grasp the psychological state of individual users in real time and provide appropriate psychological support. The challenge is to comprehensively ensure user safety from both the physical safety of evacuation and psychological care perspectives.

[0583] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0584] In this invention, the server includes means for using a generative model that analyzes environmental data collected from various information sources to predict future natural disasters; means for providing appropriate evacuation routes to user terminals based on the predictions of the generative model; and means for identifying the user's psychological state by analyzing the user's voice and video signals and providing appropriate psychological support. This makes it possible to respond quickly to natural disasters and to ensure the psychological stability of evacuees.

[0585] A "generative model" is a machine learning model used to analyze environmental data collected from diverse sources and predict the occurrence of natural disasters.

[0586] "Evacuation routes" refer to recommended routes for users to quickly move to a safe location during natural disasters, and are provided based on predictions from generative models.

[0587] A "user terminal" is an electronic device used to present information to individual users, and its role is to display information such as evacuation routes and psychological support.

[0588] "Psychological support" is the process of analyzing a user's voice and video signals to identify their psychological state and provide appropriate care and support for emotions such as anxiety and fear.

[0589] An "unmanned aerial vehicle" is an autonomous aircraft used to acquire information about the situation in disaster-stricken areas and transmit important information to a server during a disaster.

[0590] "Disaster-stricken area conditions" refers to on-site information about areas affected by natural disasters, and the data is acquired by unmanned aerial vehicles.

[0591] "Community information" refers to information shared in real time among users during a disaster, contributing to the promotion of disaster prevention activities in local communities.

[0592] The system implementing this invention mainly consists of a server, a user terminal, and an unmanned aerial vehicle. The server collects environmental data from various sources and predicts the occurrence of natural disasters using a generative AI model. This data includes weather information, geographical information, and information from various sensors. Based on this prediction, the server provides the user terminal with appropriate evacuation routes in real time.

[0593] The user terminal is an electronic device such as a smartphone or head-mounted display, equipped with an emotion recognition engine that analyzes the user's voice and video signals. This allows the terminal to identify the user's psychological state, and if anxiety or fear is detected, it suggests relaxation methods or counseling services through on-screen displays and audio guidance.

[0594] Furthermore, unmanned aerial vehicles (UAVs) acquire information about the situation in disaster-stricken areas and transmit that information to a server. Based on this information, the server optimizes evacuation routes and sends updated information to user terminals. Through this process, users are provided not only with physical safety but also with a sense of psychological security.

[0595] For example, if the server predicts heavy rain, an evacuation warning will be displayed directly on the user's terminal, and navigation information to the nearest evacuation center will be provided. At the same time, if the emotion recognition engine detects anxiety from the user's voice, advice such as "Take a deep breath and relax" will be played in voice. In this way, the system ensures the overall safety of the user.

[0596] For example, a possible prompt message might be: "Analyze the audio clip and identify the user's emotions. Also, consider whether evacuation is necessary and, if appropriate, provide a route."

[0597] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0598] Step 1:

[0599] The server collects environmental data from diverse sources. Input data includes weather information, geographical information, and sensor data. This data is fed into a generative AI model to predict the occurrence of natural disasters, and the output is risk assessment information. Based on this assessment information, the likelihood of a disaster occurring is determined.

[0600] Step 2:

[0601] The server provides evacuation route information to the user terminal based on risk assessments performed by a generated AI model. The inputs are the user's location information and the output of the generated AI model. Based on this, the server calculates the nearest safe evacuation route and sends navigation information to the user terminal as output.

[0602] Step 3:

[0603] The device collects the user's voice and video signals and analyzes their psychological state using an emotion recognition engine. Voice and video are used as input data. Emotions are identified from these signals, and the results are output. If the emotion is anxiety or fear, information on relaxation methods and counseling services is presented.

[0604] Step 4:

[0605] Unmanned aerial vehicles (UAVs) fly to disaster areas to assess the situation and collect data. Input comes from information obtained through the aircraft's built-in cameras and sensors. This on-site information is then transmitted to a server as output. Based on this data, the server updates evacuation routes.

[0606] Step 5:

[0607] The user refers to the evacuation route displayed on the device and moves to a safe location. Navigation information from the device is used as input. When the device receives updated information, it outputs the latest evacuation instructions to the user in real time.

[0608] Through this series of steps, a swift and accurate response to natural disasters and the provision of psychological reassurance to users can be achieved.

[0609] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0610] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0611] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0612] [Fourth Embodiment]

[0613] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0614] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0615] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0616] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0617] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0618] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0619] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0620] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0621] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0622] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0623] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0624] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0625] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0626] This invention provides a system for predicting natural disasters and taking appropriate responses accordingly. This system achieves advanced disaster prevention functions through the collaborative work of servers, terminals, and users.

[0627] First, the server collects environmental data from various sources. This data includes weather information, geographical information, and sensor inputs, and is collected in real time. The server feeds this data into a generative model to predict weather conditions and the likelihood of natural disasters. This predictive information is transmitted from the server to each terminal, providing warnings to users.

[0628] Furthermore, the device displays appropriate information to the user in real time based on evacuation route information received from the server. This is achieved by determining the user's current location via GPS and displaying a safe evacuation route. Based on this information, the user can take swift evacuation action.

[0629] Unmanned aerial vehicles (UAVs) are used to gather detailed information about the situation in disaster-stricken areas. Servers analyze the video data transmitted from these aircraft and update evacuation routes and safety information. This allows users to take safe actions based on the latest situation.

[0630] Furthermore, this system is designed to allow users to share information within the community. The server aggregates the posted information and distributes information deemed reliable to other users, thereby supporting the improvement of disaster preparedness across the entire community.

[0631] Furthermore, this system also includes resources for providing psychological care after a disaster. Based on a generative model that has learned from the impacts of past disasters, the server suggests appropriate psychological care and resources to the user. In this way, it is possible to address stress and trauma after a disaster.

[0632] For example, if the server predicts heavy rainfall, the terminal immediately notifies the user of the need to evacuate. Then, if information from unmanned aerial vehicles confirms a safe route in the area, the user can proceed with evacuation based on that information. This process allows users to protect themselves from disasters quickly and effectively.

[0633] The following describes the processing flow.

[0634] Step 1:

[0635] The server collects environmental data in real time from weather agencies and sensor equipment. This data includes elements such as temperature, precipitation, wind speed, and seismic waves.

[0636] Step 2:

[0637] The server feeds collected environmental data into a generative model to predict the likelihood of natural disasters. This process involves analyzing historical disaster data in combination with real-time conditions.

[0638] Step 3:

[0639] The server identifies potential disasters that may occur in a specific region based on prediction results obtained from the generative model, and transmits that information to the terminal.

[0640] Step 4:

[0641] The terminal uses disaster prediction information received from the server and the user's current location information to display appropriate evacuation warnings to the user. The indicated evacuation routes are visually provided through a map application.

[0642] Step 5:

[0643] The unmanned aerial vehicle (UAV) flies over the disaster-affected area and captures images of the current situation with its camera. This video data is transmitted to a server in real time.

[0644] Step 6:

[0645] The server analyzes video data transmitted from the unmanned aerial vehicle and re-evaluates realistic evacuation route options. It updates the information on the terminal as needed to encourage users to evacuate efficiently.

[0646] Step 7:

[0647] Users follow the evacuation instructions displayed on their devices and evacuate using safe routes. They can also exchange useful information with other users using the community's information sharing function.

[0648] Step 8:

[0649] After the disaster subsides, the server provides users with psychological care resources. The generative model analyzes the support information needed after the disaster and selects and recommends appropriate support.

[0650] (Example 1)

[0651] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0652] In recent years, natural disasters have become frequent in many regions, necessitating prediction and appropriate response. However, conventional disaster prediction systems often suffer from low prediction accuracy and difficulty in providing timely and appropriate information. Furthermore, a lack of support for information sharing and psychological care after a disaster creates many challenges in post-disaster recovery. This invention aims to provide a comprehensive system to solve these problems.

[0653] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0654] This invention includes a server that uses a generation algorithm to analyze environmental information collected from various sources and predict weather conditions and the likelihood of natural disasters; a server that provides appropriate evacuation routes to user devices based on the predictions of the generation algorithm; a server that facilitates the real-time sharing of community information among users and promotes its use in disaster prevention; and a server that uses an automated flight simulator to acquire information on the disaster area and proposes the optimal evacuation route based on that information. This improves the accuracy of natural disaster predictions, enables the rapid and accurate provision of evacuation information, and further enables post-disaster information sharing and psychological support.

[0655] "Environmental information" refers to a collection of data gathered from diverse sources, such as weather conditions, geographical information, and sensor data.

[0656] A "generative algorithm" refers to a computational method that learns from past data and predicts future events based on specific conditions.

[0657] "User device" refers to a terminal owned by a user that displays information received from the server and provides instructions to the user.

[0658] "Community information" refers to various pieces of information shared by multiple users, including useful knowledge and information on local conditions related to disaster prevention.

[0659] An "automatic aircraft" refers to a device that flies unmanned and can acquire video and images from the air.

[0660] "Psychological care resources" refer to support methods and services for dealing with stress and trauma after a disaster.

[0661] This invention is an advanced disaster prevention system aimed at predicting and responding to natural disasters. The system consists of the interaction of servers, terminals, and users.

[0662] The server collects environmental information from diverse sources. This information includes weather conditions, geographical information, and data from sensors. The server stores this data using a database management system. APIs are typically used to obtain weather information, and GIS software is commonly used for geographical information. The server uses a generative AI model to process the collected information. This generative AI model predicts the likelihood of future natural disasters through learning from historical data. For example, when the server predicts heavy rainfall, the model analyzes similar historical data to calculate the probability of precipitation and the affected area.

[0663] The terminal receives forecast information transmitted from the server. The terminal is equipped with GPS functionality to determine the user's current location. This allows the user's device to display the optimal evacuation route in the event of a disaster. A standard map application is used for the map information. For example, if a heavy rain forecast is issued, the terminal will show a safe route from the current location to the evacuation site.

[0664] Users can share information within the community via their devices, exchanging real-time insights into local conditions and disaster response. The server filters the received community information and distributes reliable content to other users.

[0665] Furthermore, the automated aircraft is used to acquire information about the disaster-stricken area. The server processes the video data obtained from the aircraft and can update information on the disaster situation and evacuation procedures. This ensures that evacuation routes are always up-to-date.

[0666] After a disaster, the server uses a generative AI model to learn from past disaster impacts and provides users with psychological care resources. Specifically, this includes recommending stress management applications and counseling services.

[0667] (Example of a prompt message)

[0668] "Please assess the weather forecast for the next 24 hours in this region and the resulting disaster risk."

[0669] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0670] Step 1:

[0671] The server collects environmental information from diverse sources. It obtains weather data using weather information APIs, geographic data from geographic information systems, and real-time data from various sensors. This input data is stored in a database management system. Specifically, the server automates the process of periodically making API calls to update the data with new information.

[0672] Step 2:

[0673] The server feeds the collected data into a generative AI model. The generative AI model has learned from past disaster patterns and analyzes current data to predict the risk of natural disasters. The input data consists of weather conditions and geographical features, and the output provides the probability of disaster occurrence and the affected area for each region. Specifically, the model combines changes in weather conditions and geographical conditions to calculate the likelihood of floods and heavy rainfall.

[0674] Step 3:

[0675] The server organizes the prediction results of the generated AI model and sends them to the terminal. The prediction results are output in text format or as map data, and evacuation advisory information corresponding to the warning level is added. Specifically, the server sends warnings to the user via email or notification services.

[0676] Step 4:

[0677] The terminal receives warning information from the server and displays it on the user's device. Using GPS functionality, it determines the user's current location and displays the optimal evacuation route on a map. Inputs are GPS location and evacuation information, and output is visual evacuation route guidance. Specifically, the terminal calculates and displays the safest route from the user's current location to the evacuation shelter.

[0678] Step 5:

[0679] Users can post and share community information using their devices. The server receives the posted information, evaluates the reliability of the data, and distributes it to other users. The input is text and images posted by users, and the output is filtered, reliable information. Specifically, the server uses natural language processing algorithms to analyze the posts and filter out misinformation.

[0680] Step 6:

[0681] The server receives video data from the automated aircraft and analyzes the information. The captured video is analyzed by image processing software to understand the latest situation in the disaster area. The input is video data from the aircraft, and the output is evacuation routes and safety information as a result of the analysis. Specifically, the server executes a video analysis algorithm to identify obstacles and hazardous areas.

[0682] Step 7:

[0683] The server uses a generative AI model to select resources for providing psychological care after a disaster. Based on past disaster data, it proposes support services suitable for the user. The input is past disaster data and user profiles, and the output is a proposal for psychological care services. Specifically, the server uses a recommendation algorithm to select appropriate care measures and notifies the user.

[0684] (Application Example 1)

[0685] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0686] With the increasing frequency of natural disasters, there is a need for effective means to enable individuals and communities to evacuate quickly and safely. However, current systems are insufficient in providing real-time information, appropriately indicating evacuation routes, and providing adequate psychological support after a disaster. Therefore, more comprehensive disaster prevention measures are necessary.

[0687] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0688] This invention includes a server that uses a generative model to analyze environmental data collected from various sources and predict future natural disasters; a server that provides appropriate evacuation routes to user terminals based on the predictions of the generative model, and visually presents the routes using map information and location information technology; and a server that provides psychological support to users after a disaster based on past disaster data. This enables the rapid provision of necessary information, ensures the safety of users, and reduces psychological burden through post-disaster care.

[0689] "Diverse information sources" refers to multiple different information sources, such as weather data, geographical data, and sensor data, which are used to collect various environmental data necessary for disaster prediction.

[0690] A "generative model" is a mathematical model that uses machine learning and AI to predict the probability of natural disasters occurring from past data, and is used to provide real-time predictive information.

[0691] A "user terminal" refers to a device that a user can carry with them, such as a smartphone or smart glasses, and is responsible for receiving information from the server and notifying the user during a disaster.

[0692] "Evacuation routes" are route information provided to ensure safe movement for users evacuating from natural disasters, and are updated in real time.

[0693] "Map information" refers to geographical information displayed on the user's terminal, and is data that allows users to visually confirm evacuation routes.

[0694] "Location-based technology" refers to technologies that use GPS and other methods to determine a user's current location and provide the optimal route.

[0695] An "unmanned aerial vehicle" is an autonomously flying device used to acquire detailed images and data of disaster areas, and is useful for situation assessment and information sharing.

[0696] "Psychological support" refers to care and support resources provided to alleviate the psychological stress and trauma that arise after a disaster, and is an activity to support the mental health of users.

[0697] To implement this invention, a server plays a central role. The server processes environmental data collected from various sources and uses a generative AI model to predict the occurrence of natural disasters. This generative model learns from historical data and analyzes weather conditions and local sensor information in real time to make predictions that increase the likelihood of disaster occurrence.

[0698] Based on the prediction results, the server provides appropriate evacuation routes to the user's device. In doing so, the server utilizes map information and location technology to visualize the route clearly on smartphones and smart glasses. Users can then safely evacuate while confirming the route displayed on their device.

[0699] Furthermore, unmanned aerial vehicles (UAVs) are used to acquire detailed information about the disaster area, and this information is analyzed on a server. The analyzed information is used to propose revised evacuation routes, and users are presented with safer routes based on the latest data. The video footage acquired by the UAVs is analyzed using software such as OpenCV.

[0700] Information sharing among users is also crucial. The server aggregates community-based information provided by users and distributes it to other users as reliable data. This real-time information sharing improves disaster preparedness.

[0701] The server also provides psychological support after disasters. Based on a generative model trained on past disaster data, it offers users appropriate psychological support measures. This includes simple care using chatbots, as well as arranging detailed counseling in collaboration with experts. It uses prompts such as, "In past typhoons like this, what message would you send to someone you were worried about?" to make suggestions using the generative AI model.

[0702] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0703] Step 1:

[0704] The server collects environmental data such as weather data, geographical data, and sensor information from diverse sources. Inputs include data from various APIs and local sensors, and output is an integrated environmental dataset. This dataset is used for predictions by a generative AI model. The server continuously updates this data in real time.

[0705] Step 2:

[0706] The server feeds the collected environmental data into a generative AI model to predict natural disasters. The input is the environmental dataset integrated in Step 1, and the output generates information on the likelihood of disaster occurrence and the predicted impact. The generative AI model uses an algorithm trained on historical disaster data. The server generates a detailed prediction report and prepares for the next step.

[0707] Step 3:

[0708] The server provides appropriate evacuation routes to user terminals based on the generated disaster prediction information. Inputs include prediction results, map information, and the user's current location; output is the evacuation route information sent to the user terminal. Using map information and location technology, users can visually confirm the evacuation route. The terminal then uses this information to send notifications to the user.

[0709] Step 4:

[0710] The unmanned aerial vehicle transmits aerial footage of the disaster area to a server. The server receives the aerial footage as input and performs video analysis using OpenCV. The output of the analysis is current terrain conditions and obstacle information, which is used to propose new evacuation routes. The server generates an optimized evacuation route and sends updated information to the user's terminal.

[0711] Step 5:

[0712] Users share community information with other users through the application. The server aggregates this real-time information, evaluates its reliability, and then distributes it to other users. The input is local information obtained from users, and the output is a notification to other users as reliable disaster prevention information.

[0713] Step 6:

[0714] After a disaster, the server provides psychological support to users using a generative model that has learned from past disaster data. The input is user information determined to be in need of psychological support, and the output is a generated psychological care plan and support resources. Users can access support via chatbots or links.

[0715] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0716] This invention relates to a system that analyzes diverse environmental data to predict natural disasters and recognizes the emotional state of users to provide appropriate support. This system integrates environmental data analysis, emotion recognition, real-time information sharing, and data collection using unmanned aerial vehicles.

[0717] The server first aggregates weather information, geographical data, and sensor data, and then uses a generative model to predict the occurrence of natural disasters. This prediction information is sent to the terminal, and users can receive evacuation instructions based on it. The terminal displays guidance on safe evacuation routes to help users take action quickly.

[0718] On the other hand, the emotion recognition engine has the function of identifying the user's emotional state by analyzing the user's facial expression data and voice input. This allows the server to evaluate the user's psychological burden in real time and provide appropriate psychological support as needed. For example, if anxiety or fear is detected during a disaster, information on relaxation methods and counseling services will be displayed on the terminal.

[0719] After a disaster occurs, unmanned aerial vehicles (UAVs) assess the detailed situation on-site and transmit data to a server. The server analyzes this data, reassesssing the safety of the site and updating the optimal evacuation routes. In addition, users can share their status and useful information in real time within the community, enabling them to cooperate and overcome the crisis.

[0720] For example, if the server predicts an earthquake, the terminal immediately displays an alert to the user and shows them the route to the nearest evacuation center. Meanwhile, if the emotion engine detects the user's distressed facial expressions or voice, the terminal offers options such as a safety check alert or counseling support. In this way, it can support both physical safety and psychological reassurance.

[0721] This embodiment provides rapid and appropriate evacuation support during disasters, as well as psychological care during and after evacuation, thereby more reliably ensuring the overall safety of users.

[0722] The following describes the processing flow.

[0723] Step 1:

[0724] The server collects weather data, geographical data, and sensor data in real time. This data is then input into a generative model to predict natural disasters.

[0725] Step 2:

[0726] The server analyzes disaster prediction information generated by a generative model and creates evacuation orders for the affected areas. This information is then distributed to the user's terminal.

[0727] Step 3:

[0728] The terminal displays warnings to the user based on disaster prediction information and evacuation orders received from the server. It also uses GPS functionality to determine the user's current location and displays the nearest safe evacuation route on a map.

[0729] Step 4:

[0730] Using an emotion recognition engine, the device collects and analyzes the user's facial expressions and voice data. From this information, it identifies the user's emotional state and sends it to the server.

[0731] Step 5:

[0732] The server analyzes the user's emotional state and determines the necessary psychological support information. It provides the user's device with suggestions for relaxation methods and links to online counseling.

[0733] Step 6:

[0734] The unmanned aerial vehicle flies around the disaster area and collects video data in real time. The video is then transmitted to a server.

[0735] Step 7:

[0736] The server analyzes video data obtained from unmanned aerial vehicles to re-evaluate the local situation and existing evacuation routes. It then updates the evacuation route information to reflect the new situation and distributes it to terminals.

[0737] Step 8:

[0738] Users can move according to the latest evacuation route information provided by their devices. They can also continue to exchange information with other users in the community and work together to ensure their collective safety.

[0739] Step 9:

[0740] After the disaster subsides, the server will continue to provide psychological support to users by offering mental health resources based on information obtained through the emotion engine, helping users regain their sense of security.

[0741] (Example 2)

[0742] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0743] In modern society, with the increasing frequency of natural disasters, people are required to take swift and appropriate evacuation actions. However, existing systems suffer from delays in disaster prediction and information provision, failing to adequately ensure user safety. Furthermore, a lack of psychological support increases the mental burden during disasters. Therefore, there is a need for a system that allows users to respond quickly to disasters and provides psychological reassurance.

[0744] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0745] In this invention, the server includes means for aggregating environmental data obtained from various data sources and predicting the occurrence of natural disasters using a generative AI model; means for providing safe evacuation routes to communication devices based on the predicted disaster information and supporting the user's evacuation; and means for analyzing the user's biometric information, identifying their emotional state, and providing psychological support. This enables the user to take swift and safe evacuation actions and maintain emotional stability during disasters.

[0746] "Diverse data sources" refers to a collection of data of different types and origins, such as weather information, geographical information, and sensor data.

[0747] A "generative AI model" refers to artificial intelligence technology that learns patterns from collected data and predicts future events.

[0748] "Communication equipment" refers to devices or interfaces used to provide information to users and transmit instructions.

[0749] "Biometric information" refers to data that indicates an individual's physical or emotional state, such as a user's facial expressions or voice.

[0750] An "unmanned aerial vehicle" refers to an aircraft that is remotely or autonomously operated without a human on board.

[0751] "Psychological support" refers to services and methods aimed at stabilizing a user's emotional state and providing a sense of security.

[0752] A "community" refers to a group of users organized to share information and act collaboratively.

[0753] This invention relates to a system that analyzes environmental data to predict natural disasters and recognizes the emotional state of users to provide rapid psychological support. This system mainly consists of a server, terminals, an unmanned aerial vehicle, and a generative AI model.

[0754] The server interfaces with an advanced database management system to aggregate environmental data from diverse data sources, including weather information, geographic data, and sensor data. Cloud computing technology is used to run data analysis software. The collected data is analyzed by a generative AI model to predict future natural disasters. For example, known weather patterns are input into the AI ​​model using the prompt "Predict natural disasters that may occur within the next 24 hours," and the results are analyzed.

[0755] The terminal provides users with disaster prediction information transmitted from a server. Communication devices such as smartphones and tablets use map application software (for example, a digital map service, a general term) to display safe evacuation routes. Furthermore, the terminal is equipped with a camera and microphone, which are used to acquire the user's biometric information and transmit it to an engine for emotion recognition. If the terminal determines that the user's emotional state is unstable, it displays information on relaxation methods and online counseling services.

[0756] Unmanned aerial vehicles (UAVs) are used to assess the situation on the ground in disaster areas, transmitting high-resolution image data and sensor data to a server. These devices have GPS-based autonomous flight capabilities, enabling real-time information collection. Based on this on-site data, the server re-evaluates evacuation routes and safety, and provides updated information to users.

[0757] Information sharing features within the community are also a crucial element. Users can share their situation and useful information with other users via their devices and collaborate. This allows users to support each other and overcome crises together.

[0758] In this form, the invention provides users with both physical safety and psychological support, enabling a rapid and effective response during disasters.

[0759] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0760] Step 1:

[0761] The server collects environmental data from various data sources. Weather information, geographical data, and sensor data are input to the server via APIs. This data is stored in a database on the server and serves as the foundation for subsequent analysis.

[0762] Step 2:

[0763] The server inputs the aggregated data into an AI model to predict natural disasters. The data input here is analyzed by the AI ​​model, and the likelihood of disasters occurring in 30 minutes or 1 hour is output. Specifically, the prompt message "Predict the natural disasters that may occur in the next hour" is used in the AI ​​model.

[0764] Step 3:

[0765] The server sends disaster prediction information generated by the AI ​​model to the terminal. The outputted prediction information is transmitted to the terminal via the communication network. The server includes detailed information such as the location of the disaster, the predicted time, and the scope of impact.

[0766] Step 4:

[0767] The device displays alerts to the user based on predictive information received from the server. These alerts visually and audibly alert the user and are displayed on the smartphone or tablet screen. Specific route guidance is also provided using Google Maps or other map applications.

[0768] Step 5:

[0769] The device uses its built-in camera and microphone to collect the user's facial expressions and voice. This data is input into the device's emotion recognition engine, which analyzes it to identify the user's current emotional state. For example, if an anxious facial expression or a trembling voice is detected, the system will output "anxiety."

[0770] Step 6:

[0771] The server receives emotional data transmitted from the terminal and determines the necessary psychological support. Based on the outputted emotional state, a guide to relaxation methods and information about counseling services are created and sent back to the terminal.

[0772] Step 7:

[0773] Unmanned aerial vehicles (UAVs) are dispatched to disaster areas to assess the detailed situation on the ground. High-resolution images and environmental data measured by sensors are collected and transmitted to a server. The input data is analyzed on the server, and updated evacuation route information is generated based on the actual situation on the ground.

[0774] Step 8:

[0775] The server sends the latest evacuation information to the terminal based on newly analyzed data. Evacuation routes and safety conditions are updated, allowing users to always act based on the most up-to-date information.

[0776] Step 9:

[0777] Users share their situation and useful information they discover within the community via their devices. Text messages and location information are sent to other users in real time, enabling them to support each other.

[0778] (Application Example 2)

[0779] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0780] There is a need for technology that can improve the accuracy of natural disaster predictions, provide swift and accurate evacuation instructions during disasters, and further grasp the psychological state of individual users in real time and provide appropriate psychological support. The challenge is to comprehensively ensure user safety from both the physical safety of evacuation and psychological care perspectives.

[0781] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0782] In this invention, the server includes means for using a generative model that analyzes environmental data collected from various information sources to predict future natural disasters; means for providing appropriate evacuation routes to user terminals based on the predictions of the generative model; and means for identifying the user's psychological state by analyzing the user's voice and video signals and providing appropriate psychological support. This makes it possible to respond quickly to natural disasters and to ensure the psychological stability of evacuees.

[0783] A "generative model" is a machine learning model used to analyze environmental data collected from diverse sources and predict the occurrence of natural disasters.

[0784] "Evacuation routes" refer to recommended routes for users to quickly move to a safe location during natural disasters, and are provided based on predictions from generative models.

[0785] A "user terminal" is an electronic device used to present information to individual users, and its role is to display information such as evacuation routes and psychological support.

[0786] "Psychological support" is the process of analyzing a user's voice and video signals to identify their psychological state and provide appropriate care and support for emotions such as anxiety and fear.

[0787] An "unmanned aerial vehicle" is an autonomous aircraft used to acquire information about the situation in disaster-stricken areas and transmit important information to a server during a disaster.

[0788] "Disaster-stricken area conditions" refers to on-site information about areas affected by natural disasters, and the data is acquired by unmanned aerial vehicles.

[0789] "Community information" refers to information shared in real time among users during a disaster, contributing to the promotion of disaster prevention activities in local communities.

[0790] The system implementing this invention mainly consists of a server, a user terminal, and an unmanned aerial vehicle. The server collects environmental data from various sources and predicts the occurrence of natural disasters using a generative AI model. This data includes weather information, geographical information, and information from various sensors. Based on this prediction, the server provides the user terminal with appropriate evacuation routes in real time.

[0791] The user terminal is an electronic device such as a smartphone or head-mounted display, equipped with an emotion recognition engine that analyzes the user's voice and video signals. This allows the terminal to identify the user's psychological state, and if anxiety or fear is detected, it suggests relaxation methods or counseling services through on-screen displays and audio guidance.

[0792] Furthermore, unmanned aerial vehicles (UAVs) acquire information about the situation in disaster-stricken areas and transmit that information to a server. Based on this information, the server optimizes evacuation routes and sends updated information to user terminals. Through this process, users are provided not only with physical safety but also with a sense of psychological security.

[0793] For example, if the server predicts heavy rain, an evacuation warning will be displayed directly on the user's terminal, and navigation information to the nearest evacuation center will be provided. At the same time, if the emotion recognition engine detects anxiety from the user's voice, advice such as "Take a deep breath and relax" will be played in voice. In this way, the system ensures the overall safety of the user.

[0794] For example, a possible prompt message might be: "Analyze the audio clip and identify the user's emotions. Also, consider whether evacuation is necessary and, if appropriate, provide a route."

[0795] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0796] Step 1:

[0797] The server collects environmental data from diverse sources. Input data includes weather information, geographical information, and sensor data. This data is fed into a generative AI model to predict the occurrence of natural disasters, and the output is risk assessment information. Based on this assessment information, the likelihood of a disaster occurring is determined.

[0798] Step 2:

[0799] The server provides evacuation route information to the user terminal based on risk assessments performed by a generated AI model. The inputs are the user's location information and the output of the generated AI model. Based on this, the server calculates the nearest safe evacuation route and sends navigation information to the user terminal as output.

[0800] Step 3:

[0801] The device collects the user's voice and video signals and analyzes their psychological state using an emotion recognition engine. Voice and video are used as input data. Emotions are identified from these signals, and the results are output. If the emotion is anxiety or fear, information on relaxation methods and counseling services is presented.

[0802] Step 4:

[0803] Unmanned aerial vehicles (UAVs) fly to disaster areas to assess the situation and collect data. Input comes from information obtained through the aircraft's built-in cameras and sensors. This on-site information is then transmitted to a server as output. Based on this data, the server updates evacuation routes.

[0804] Step 5:

[0805] The user refers to the evacuation route displayed on the device and moves to a safe location. Navigation information from the device is used as input. When the device receives updated information, it outputs the latest evacuation instructions to the user in real time.

[0806] Through this series of steps, a swift and accurate response to natural disasters and the provision of psychological reassurance to users can be achieved.

[0807] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0808] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0809] 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 this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0810] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0811] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0812] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0813] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0814] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0815] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0816] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0817] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0818] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0819] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0820] 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.

[0821] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0822] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0823] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0824] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0825] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0826] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0827] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[0828] The following is further disclosed regarding the embodiments described above.

[0829] (Claim 1)

[0830] A method that uses generative models to predict future natural disasters by analyzing environmental data collected from diverse sources,

[0831] A means for providing an appropriate evacuation route to a user terminal based on the predictions of the generation model,

[0832] A means of sharing community information among users in real time and promoting its use in disaster prevention,

[0833] A method for using unmanned aerial vehicles to acquire information on the situation in disaster-stricken areas and then re-proposing the optimal evacuation route based on that information,

[0834] A system that includes this.

[0835] (Claim 2)

[0836] The system according to claim 1, which does not rely on an external power source and manages a decentralized energy supply within a community.

[0837] (Claim 3)

[0838] The system according to claim 1, which provides post-disaster psychological care resources using a generative model that has learned the effects of past disasters.

[0839] "Example 1"

[0840] (Claim 1)

[0841] A method that uses a generative algorithm to analyze environmental information collected from diverse sources and predict weather conditions and the likelihood of natural disasters,

[0842] A means for providing an appropriate evacuation route to the user device based on the prediction of the generation algorithm,

[0843] A means of sharing community information among users in real time and promoting its use in disaster prevention,

[0844] A means of acquiring disaster area conditions using an automated flight system and re-proposing the optimal evacuation route based on that information,

[0845] A system that includes this.

[0846] (Claim 2)

[0847] The system according to claim 1, which manages a decentralized energy supply locally without relying on an external power source.

[0848] (Claim 3)

[0849] The system according to claim 1, which provides post-disaster psychological care resources using a generation algorithm that has learned the effects of past disasters.

[0850] "Application Example 1"

[0851] (Claim 1)

[0852] A method that uses generative models to predict future natural disasters by analyzing environmental data collected from diverse sources,

[0853] Based on the predictions of the generation model, the system provides an appropriate evacuation route to the user terminal and presents the route visually using map information and location information technology.

[0854] A means of sharing community information among users in real time and promoting its use in disaster prevention,

[0855] A means of using unmanned aerial vehicles to acquire information on the situation in disaster-stricken areas and, based on that information, re-proposing the optimal evacuation route,

[0856] A means of providing psychological support to users after a disaster, based on past disaster data,

[0857] A system that includes this.

[0858] (Claim 2)

[0859] The system according to claim 1, which does not rely on an external power source, manages a decentralized energy supply within the community, and provides a means of energy supply that can be sustainably used even in the event of a disaster.

[0860] (Claim 3)

[0861] The system according to claim 1, which provides post-disaster psychological care resources using a generative model that has learned the effects of past disasters.

[0862] "Example 2 of combining an emotion engine"

[0863] (Claim 1)

[0864] A method for predicting the occurrence of natural disasters by aggregating environmental data obtained from diverse data sources and using a generative AI model,

[0865] Based on predicted disaster information, a means of providing safe evacuation routes to communication devices and assisting users in their evacuation,

[0866] A means of analyzing a user's biometric information, identifying their emotional state, and providing psychological support,

[0867] A means of using unmanned aerial vehicles to collect information on disaster areas and updating evacuation routes based on that information,

[0868] A means that enables users within a community to share information and cooperate to overcome disasters,

[0869] A system that includes this.

[0870] (Claim 2)

[0871] The system according to claim 1, which reduces dependence on an external power source by using a distributed energy supply.

[0872] (Claim 3)

[0873] The system according to claim 1, which learns from data of past disasters and provides resources to provide mental health care to users after a disaster.

[0874] "Application example 2 when combining with an emotional engine"

[0875] (Claim 1)

[0876] A method that uses generative models to predict future natural disasters by analyzing environmental data collected from diverse sources,

[0877] A means for providing an appropriate evacuation route to a user terminal based on the predictions of the generation model,

[0878] A means of analyzing a user's audio and video signals to identify their psychological state and provide appropriate psychological support,

[0879] A means of sharing community information among users in real time and promoting its use in disaster prevention,

[0880] A method for using unmanned aerial vehicles to acquire information on the situation in disaster-stricken areas and then re-proposing the optimal evacuation route based on that information,

[0881] A system that includes this.

[0882] (Claim 2)

[0883] The system according to claim 1, which does not rely on an external power source and manages a decentralized energy supply within a community.

[0884] (Claim 3)

[0885] The system according to claim 1, which provides post-disaster psychological care resources using a generative model that has learned the effects of past disasters. [Explanation of symbols]

[0886] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A method that uses generative models to predict future natural disasters by analyzing environmental data collected from diverse sources, A means for providing an appropriate evacuation route to a user terminal based on the predictions of the generation model, A means of sharing community information among users in real time and promoting its use in disaster prevention, A method for using unmanned aerial vehicles to acquire information on the situation in disaster-stricken areas and then re-proposing the optimal evacuation route based on that information, A system that includes this.

2. The system according to claim 1, which does not rely on an external power source and manages a decentralized energy supply within a community.

3. The system according to claim 1, which provides post-disaster psychological care resources using a generative model that has learned the effects of past disasters.

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