system

Abstract

We provide the system. [Solution] A data processing means for collecting and analyzing information during a disaster, A display means that generates evacuation orders based on the analysis results and displays them on the user interface, A voice processing means that recognizes voice input from the user and provides necessary information, An external data analysis means that receives and analyzes real-time video and image data from an external device, A rescue resource management system that dynamically allocates and optimizes rescue resources, A system that includes this.

Description

【Technical Field】 【0001】 The technology of the present disclosure relates to a system. 【Background Art】 【0002】 Patent Document 1 discloses a persona chatbot control method performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance. 【Prior Art Documents】 【Patent Documents】 【0003】 【Patent Document 1】 Japanese Unexamined Patent Application Publication No. 2022-180282 【Summary of the Invention】 【Problems to be Solved by the Invention】 【0004】 It is necessary to solve the problems that excessive or insufficient information, further reduced judgment due to panic, and language barriers during disasters prevent appropriate actions. Also, in order for rescue activities to be carried out quickly and efficiently, real-time grasping of the disaster situation and optimal allocation of rescue resources are required. In such a situation, a system that realizes effective evacuation instructions and support for rescue activities is necessary. 【Means for Solving the Problems】 【0005】 This invention provides a system that collects and analyzes information in real time during a disaster, and generates and provides evacuation orders based on the results. Data processing means predict damage and evaluate risk areas, and displays evacuation routes optimized for individual users using augmented reality (AR) technology. Furthermore, voice processing means processes voice input from users and provides necessary information. By analyzing real-time data from external devices and dynamically allocating and optimizing rescue resources based on that analysis, rapid and efficient rescue operations become possible. This minimizes human casualties during disasters and creates an environment where everyone can evacuate safely. 【0006】 "Data processing means" refers to technologies or devices for collecting various types of data during a disaster and analyzing them in real time. 【0007】 "Display means" refers to technology or devices for generating evacuation orders based on analyzed information and providing them visually to the user. 【0008】 "Voice processing means" refers to technology or equipment for recognizing voice input from a user and providing necessary information based on that input. 【0009】 "External data analysis means" refers to a technology or device that analyzes video and image data received from drones or other external devices in real time. 【0010】 "Rescue resource management means" refers to technologies or devices for dynamically allocating rescue resources during a disaster and optimizing their efficiency. 【0011】 "AR technology" is a technology that overlays virtual information onto real-world visual information and is particularly used to display evacuation routes. [Brief explanation of the drawing] 【0012】 [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] 【0013】 Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings. 【0014】 First, the terms used in the following description will be explained. 【0015】 In the following embodiments, a processor with a reference number (hereinafter simply referred to as "processor") may be one arithmetic unit or a combination of a plurality of arithmetic units. Also, the processor may be one type of arithmetic unit or a combination of a plurality of 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. 【0016】 In the following embodiments, a RAM (Random Access Memory) with a reference number is a memory in which information is temporarily stored and is used as a work memory by the processor. 【0017】 In the following embodiments, a storage with a reference number is one or more non-volatile storage devices that store various programs and various parameters, etc. 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. 【0018】 In the following embodiments, a communication I / F (Interface) with a reference number is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), and the like. 【0019】 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." 【0020】 [First Embodiment] 【0021】 Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment. 【0022】 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. 【0023】 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). 【0024】 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. 【0025】 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. 【0026】 The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form 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. 【0027】 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. 【0028】 Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14. 【0029】 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. 【0030】 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. 【0031】 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. 【0032】 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". 【0033】 The present invention will now be described in terms of embodiments for carrying out the present invention. The present invention is a system that supports rapid and efficient evacuation instructions and rescue activities during disasters. This system combines data processing means, display means, voice processing means, external data analysis means, and rescue resource management means, each of which performs a different function. 【0034】 The server collects various types of data in real time during a disaster and analyzes that data using data processing tools. For example, during an earthquake, it collects seismic waveform data transmitted from sensors and various organizations, weather data, and geographical information of the affected area. This makes it possible to predict damage and identify risk areas. 【0035】 The server generates evacuation instructions based on the analysis results. These instructions are provided individually to each user terminal. Using a display device, the user terminal visually displays the evacuation route to the user using augmented reality (AR) technology. For example, when a user turns on the camera via their smartphone, the evacuation route is virtually displayed on the screen along with the real-world scenery. This route information is transmitted from the server and is intended to guide the user so that they can quickly evacuate to a safe place. 【0036】 If a user requests voice instructions, the device will use voice processing to convey those instructions verbally. For example, if a user asks, "Where is the nearest evacuation shelter?", the device will use information from the server to provide voice guidance on the direction and distance to the nearest evacuation shelter. 【0037】 Furthermore, the server uses external data analysis tools to analyze real-time video and image data transmitted from drones and autonomous robots. This information is used to identify new dangers in disaster areas and dynamically allocate resources necessary for rescue operations. For example, it can identify areas with particularly severe damage and deliver necessary supplies via drones. 【0038】 Ultimately, the server can automatically compile a report of the post-disaster situation and provide it to local governments and rescue teams. This report will be useful for evaluating disaster response and developing future countermeasures. 【0039】 Thus, the present invention realizes a system that enables real-time situation assessment and rapid countermeasures by integrating the collection, analysis, and provision of information during disasters. 【0040】 The following describes the processing flow. 【0041】 Step 1: 【0042】 When the server detects a disaster, it aggregates real-time earthquake data, weather data, and geographical information from various sensors and data providers. This data is stored in a centralized database. 【0043】 Step 2: 【0044】 The server processes the aggregated data using an analysis algorithm. It evaluates the magnitude and extent of the earthquake's impact and identifies risk areas. Using a damage prediction model, it calculates areas where damage is likely to be concentrated and regions where evacuation is necessary. 【0045】 Step 3: 【0046】 The server generates individual evacuation instructions for each user terminal based on the analysis results. It utilizes the user's current location information to identify safe evacuation routes in their vicinity and prepares data for visualization. 【0047】 Step 4: 【0048】 The device receives evacuation instructions and route information from the server. Using the received information, the device's AR function overlays the evacuation route onto a real-world video feed. This allows the user to visually understand which route is safe from their current location. 【0049】 Step 5: 【0050】 Users can obtain further information about evacuation by asking questions via voice to their device. For example, they can use voice input to confirm the location of the nearest evacuation shelter. 【0051】 Step 6: 【0052】 The terminal recognizes the user's voice input using voice processing equipment and requests appropriate information from the server. The server processes that information and sends the data to the terminal for the user to be provided via voice. The user can then follow this voice guidance to evacuate appropriately. 【0053】 Step 7: 【0054】 The server receives and analyzes video and image data transmitted from drones and autonomous robots using external data analysis tools. This allows for real-time monitoring of newly emerging risks and the escalation of damage, and provides feedback to local governments and rescue teams. 【0055】 Step 8: 【0056】 The server reassesss risk areas and dynamically allocates rescue resources. It identifies areas with particularly heavy damage and optimizes the deployment of drones for supply deliveries and additional rescue operations. 【0057】 Step 9: 【0058】 After the disaster subsides, the server uses its automated report generation function to generate a disaster situation report. This report is provided to local governments and related organizations and is used to summarize the disaster response and to develop future countermeasures. 【0059】 (Example 1) 【0060】 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." 【0061】 During disasters, rapid and accurate information gathering, analysis, and evacuation order provision are crucial. However, conventional technologies are insufficient for providing appropriate responses to individual users and optimizing rescue resources. Furthermore, there are challenges in dynamically generating evacuation orders according to the type of disaster, and in automatically grasping the situation on-site in real time and generating reports. 【0062】 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. 【0063】 In this invention, the server includes means for collecting information during a disaster and analyzing the data using an analysis device, a display device for generating evacuation orders based on the analysis results and displaying them on a user interface, and a voice processing device for recognizing voice input from the user and providing necessary information. This enables the provision of individually optimized evacuation routes, efficient allocation of rescue resources, and rapid assessment of the damage situation. 【0064】 "Collecting information" refers to obtaining various types of data, such as seismic waveform data and weather information, from sensors and agencies during disasters. 【0065】 "Analyzing the data using an analysis device" means using the collected data to predict damage and identify risk areas using machine learning algorithms and the like. 【0066】 "Generating evacuation orders" means outputting appropriate evacuation routes and action guidelines tailored to the individual user's situation, based on the analysis results. 【0067】 "Displaying on the user interface" refers to a technology that visually communicates the generated evacuation instructions to the user's terminal, allowing the user to confirm the information. 【0068】 A "voice processing device" is a device that recognizes voice input from a user and provides appropriate information in voice based on that input. 【0069】 "Receiving and analyzing real-time video and image data" means instantly capturing video and images transmitted from external devices, analyzing them, and understanding the situation. 【0070】 "Dynamically allocating and optimizing rescue resources" refers to efficiently allocating resources such as personnel and supplies for rescue operations and distributing them appropriately to where they are needed. 【0071】 A "generative model" refers to algorithms and tools used to dynamically create different evacuation orders depending on the type and impact of the disaster. 【0072】 Augmented reality technology is a technique that overlays virtual information onto real-world scenery, enabling users to intuitively understand evacuation routes. 【0073】 A "report generation device" is a device that has the function of automatically organizing the damage situation during a disaster and creating statistical data and map information for reporting to relevant organizations. 【0074】 This invention is a system for supporting rapid and accurate evacuation instructions and rescue operations during disasters. This system includes multiple means, primarily involving servers, terminals, and users. 【0075】 The server collects various types of data in real time during a disaster. Specifically, it collects seismic waveform data from sensors, weather information from meteorological agencies, and map data from geographic information systems. Hardware and software such as sensor networks and APIs are utilized in this process. The collected data is analyzed on an analysis device using machine learning algorithms. This enables damage prediction and identification of risk areas, forming the basis for the information provided to users. The server uses a generative AI model to dynamically create different evacuation orders depending on the type and impact of the disaster. 【0076】 The terminal is responsible for visually providing evacuation instructions received from the server to the user. For example, it overlays route information onto the smartphone's camera screen using augmented reality (AR) technology through the user interface. This technology utilizes ARKit or ARCore to realize augmented reality. Furthermore, the terminal can recognize voice input and respond to user questions using speech synthesis technology. For example, in response to the question, "Where is the nearest evacuation shelter?", it can provide appropriate guidance via voice. 【0077】 Based on the information provided through this system, users can take safe evacuation actions. For example, in the event of a flood, users can use their smartphones to check evacuation routes via augmented reality and safely head to shelters while receiving voice guidance. In this way, users can act intuitively without having to spend time understanding the information. 【0078】 An example of a prompt message is, "Create a system that provides evacuation instructions in real time during disasters. It will collect seismic waveform data and weather data, visualize evacuation routes using AR technology, and provide voice guidance." In this way, the generated AI model can be utilized. 【0079】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0080】 Step 1: 【0081】 The server collects various types of data in real time during disasters. Inputs include seismic waveform data, weather information, and geographical information obtained from sensors and organizations via APIs. Since this data cannot be used in its raw format, it undergoes standardization and formatting to create datasets for analysis. Specifically, it aggregates information from each data source and stores it in a centrally managed database. 【0082】 Step 2: 【0083】 The server uses the data formatted in Step 1 to apply machine learning algorithms and perform data analysis. It uses the input standardized data to predict damage and identify risk areas. The output includes the expected earthquake impact area and the identification of areas that should be evacuated. Specifically, it runs analysis models using libraries such as TENSORFLOW® and PyTorch, and then compiles the inference results. 【0084】 Step 3: 【0085】 The server uses a generative AI model based on the analysis results to generate evacuation instructions. It uses the analysis results obtained in step 2 as input to generate instructions tailored to the user. The output includes individual evacuation routes and action guidelines. Specifically, it uses a sentence generation model to automatically create instructions for the user in natural language. 【0086】 Step 4: 【0087】 The terminal visually provides the user with evacuation instructions received from the server. It receives evacuation routes and instruction information from the server as input. Output includes augmented reality (AR) displays on the terminal screen. Specifically, it uses ARKit or ARCore to overlay evacuation routes onto the smartphone screen, providing realistic guidance. 【0088】 Step 5: 【0089】 The device receives voice input from the user and returns a response using speech synthesis. Input includes questions and requests from the user via voice. Output is voice-based navigation and guidance information. Specifically, it utilizes voice technologies such as Google Assistant and Amazon Alexa to provide information in real time. 【0090】 Step 6: 【0091】 The server receives real-time video data transmitted from external devices and performs additional analysis. It acquires video and image data from drones and autonomous robots as input. The output is a detailed analysis of the danger situation in the disaster area. Specifically, it uses a deep learning model to perform image analysis and identify new dangerous areas. 【0092】 Step 7: 【0093】 The server automatically compiles reports on the post-disaster situation and provides them to relevant organizations. Its inputs include aggregating all analytical data and observational information. Its output includes detailed reports useful for evaluating disaster response. Specifically, it extracts necessary information from the database and generates visualized reports. 【0094】 (Application Example 1) 【0095】 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." 【0096】 When a disaster strikes, there is a need for an efficient system to issue swift and accurate evacuation orders and safely guide victims. In particular, challenges include providing appropriate evacuation routes based on the complex urban environment and individual user location information, voice guidance, and real-time situation monitoring to ensure safety. Furthermore, it is necessary to accurately grasp the situation after a disaster and to respond and report quickly. 【0097】 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. 【0098】 In this invention, the server includes data processing means for collecting and analyzing information during a disaster, display means for generating evacuation instructions based on the analysis results and displaying them on a user interface, voice processing means for recognizing voice input from the user and providing necessary information, augmented reality display means for integrating and displaying the real environment and virtual information, and voice guidance means for providing evacuation routes via voice instructions. This makes it possible to provide users with safe evacuation routes in real time. 【0099】 "Data processing means" refers to devices or software used to collect various types of information during a disaster and to analyze that information. 【0100】 "Display means" refers to a device or interface that visually displays evacuation instructions, generated based on analyzed information, to the user. 【0101】 "Voice processing means" refers to a device or program that recognizes voice input from a user and provides corresponding information as voice. 【0102】 "External data analysis means" refers to means for receiving and analyzing real-time video and image data transmitted from an external device. 【0103】 A "rescue resource management system" is a device or system that performs operations to efficiently allocate and optimize the rescue resources needed during a disaster. 【0104】 "Augmented reality display means" refers to a system or technology for integrating virtual information into a real environment and displaying it visually. 【0105】 "Voice guidance means" refers to a means of guiding users to evacuation routes and necessary information via voice. 【0106】 The system for implementing this invention is capable of providing rapid and accurate evacuation instructions during disasters and ensuring the safety of residents in smart cities. The system mainly consists of a server, user terminals, and related external devices. 【0107】 The server collects data transmitted in real time from various sensors and related organizations during disasters. For example, during earthquakes or heavy rain and storms, it integrates and analyzes location information, weather data, and sensor information from the surrounding environment. High-performance data processing algorithms are used for the analysis, and big data analysis technology is utilized to create damage prediction models. The server also generates evacuation orders and sends them to users' terminals. 【0108】 The user's device is a mobile information terminal such as a smartphone or tablet, and an application for displaying evacuation instructions is installed. Based on the evacuation instructions received from the server, the device uses augmented reality technology to overlay evacuation routes onto real-world images. This is achieved using augmented reality technologies such as ARKit for iOS and ARCore for Android®. In addition, the Google Maps API is used to provide even more detailed geographical information. Furthermore, by using a speech recognition API (such as the Google Speech-to-Text API), it enables voice input from the user and has a function to provide specific evacuation instructions via voice guidance. 【0109】 For example, if an earthquake occurs while a user is at home in the city, activating their smartphone will display the optimal evacuation route as an arrow on a map. Voice guidance will provide instructions such as, "Go 100 meters and turn left." If the user asks, "How much further is it to the evacuation center?", the device will recalculate the distance from their current location and provide voice guidance. 【0110】 To implement the system described above, you can use prompts such as, "Design an evacuation support system for disasters occurring in smart cities. Explain, with specific examples, the technologies, data flows, and interface improvements necessary to improve the user experience." 【0111】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0112】 Step 1: 【0113】 The server collects data in real time from various sensors and related organizations. Inputs include seismic waveform data, weather data, and location information from sensors, and an integrated dataset is generated as output. Data processing involves data normalization and integration, and data calculations include risk assessment based on damage prediction models. Specifically, the data is collected in a database on the server and then processed by an analysis algorithm. 【0114】 Step 2: 【0115】 The server generates evacuation instructions based on the analysis results. The input is the analysis results obtained in step 1, and the output is the evacuation instructions to be sent to the user. Data processing includes customization based on each user's location information, and the optimal route is calculated as a data calculation. Specifically, the server calculates the safest route based on the user's current location and generates instruction data. 【0116】 Step 3: 【0117】 The terminal displays evacuation instructions received from the server. The input is the evacuation instructions sent from the server, and the output is visual information presented to the user using augmented reality technology. In data processing, the received data is rendered to match the terminal's display, and mapping using AR technology is performed as a data calculation. Specifically, the evacuation route is overlaid on the camera image and displayed to the user. 【0118】 Step 4: 【0119】 When a user provides voice input, the terminal receives the instructions using speech recognition. The input is the user's voice, and the output is the text conversion result of that voice. As data processing, the voice is converted into text format, and as data calculation, the appropriate answer to the question is selected based on information from the server. Specifically, the question is analyzed and the voice guide is prepared. 【0120】 Step 5: 【0121】 The terminal provides voice guidance to the user. Input consists of information obtained from the server and analysis results based on the user's voice input, and output is voice guidance. Data processing includes formatting the server's response into natural language, and data calculations include optimizing route guidance. Specifically, it generates and plays voice data based on the user's current location and destination. 【0122】 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. 【0123】 Embodiments of the present invention describe a system that collects and analyzes information, generates evacuation orders, recognizes emotions, and provides adaptive information based on those emotions during a disaster. 【0124】 The server aggregates real-time data related to disasters and analyzes it using data processing tools. The aggregated data includes seismic waveforms, weather, and geographical information. Based on this data, the server identifies risk areas and predicts damage. 【0125】 The server then activates an emotion engine to analyze the user's voice and video data and recognize their emotional state. This analysis identifies emotions such as stress and anxiety. Specifically, it can evaluate the user's emotions by analyzing voice tone and speech rate, and detecting facial expressions from video data. 【0126】 If the emotion engine assesses the user's anxiety level, the server adjusts how evacuation instructions are delivered. This may include using a gentler voice guidance, simplifying on-screen graphics, and changing to a more calming color scheme. It may also play a more emphasized warning sound to grab the user's attention. 【0127】 The terminal provides users with evacuation instructions received from the server in real time. Using AR technology, it guides users to the optimal evacuation route while they check their surroundings through their smart device. For example, if a user wants to know the evacuation route, the terminal overlays key landmarks and arrows onto the camera view. 【0128】 Furthermore, users can request additional information through voice input. In stressful situations, emotion-optimized responses are provided to ensure users can obtain information with peace of mind. For example, if the emotion engine detects anxiety, it will explain the specific location and situation of the evacuation site in a calm and gentle voice. 【0129】 Finally, the server analyzes real-time data from external devices, provides feedback to local governments and rescue teams, and allocates rescue resources optimally. Disaster situation reports are also automatically generated and used for future disaster response. 【0130】 This system enables evacuation support that takes users' emotions into consideration during disasters, allowing for a swift and appropriate response. 【0131】 The following describes the processing flow. 【0132】 Step 1: 【0133】 The server collects information in real time from various sensors and data sources when a disaster occurs. This information includes seismic waveform data, weather information, and geographical information of the affected area. The aggregated data is stored in a central database. 【0134】 Step 2: 【0135】 The server analyzes the aggregated information using data processing tools. Based on the analysis results, it evaluates the magnitude and extent of the earthquake and identifies risk areas. It uses a damage prediction model to identify areas where damage is predicted. 【0136】 Step 3: 【0137】 The server activates an emotion engine to determine the user's emotions. It analyzes audio and video data collected from the user to measure stress levels and anxiety. For example, it identifies emotions from factors such as voice tone, speech speed, and facial expressions. 【0138】 Step 4: 【0139】 Based on the results of the emotion engine, the server generates customized evacuation instructions for each user. If stress or anxiety levels are detected as high, the tone and display method of the instructions are adjusted and provided in a format that is easy for the user to understand. 【0140】 Step 5: 【0141】 The terminal displays evacuation instructions received from the server. Using AR technology, it visually shows evacuation routes through the user's smart device. Arrows and landmarks are overlaid on the real-world scenery to guide the user to a safe evacuation. 【0142】 Step 6: 【0143】 Users can use voice input to request additional information during an evacuation. For example, they can ask about the location of nearby shelters. 【0144】 Step 7: 【0145】 The terminal recognizes the user's voice input using voice processing equipment and sends the request to the server. The server generates the necessary information and returns it to the terminal in an adaptively adjusted format. The terminal then provides this information to the user via voice. 【0146】 Step 8: 【0147】 The server monitors the situation by analyzing real-time video and image data transmitted by external devices such as drones. This data is fed back to local governments and rescue teams and used for the dynamic allocation of rescue resources. 【0148】 Step 9: 【0149】 Once the disaster response is complete, the server automatically generates a detailed report of the damage. This report is provided to relevant organizations and used to improve future disaster preparedness measures. 【0150】 (Example 2) 【0151】 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 will be referred to as the "terminal." 【0152】 Providing rapid and accurate information and evacuation support during disasters requires real-time information gathering and analysis. However, conventional systems lack the flexibility to provide information tailored to the user's psychological state using sentiment analysis, resulting in insufficient evacuation support that is optimal for the user. 【0153】 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. 【0154】 In this invention, the server includes processing means for collecting and analyzing information during a disaster, presentation means for generating evacuation orders based on the analysis results and displaying them on a human-machine interface, and emotion analysis means for performing emotion analysis and providing adaptive information based on the user's emotional state. This enables rapid and appropriate evacuation support that takes into account the user's emotional state during a disaster. 【0155】 "Information during disasters" refers to data such as earthquake, weather, and geographical information acquired when a natural disaster occurs, which is necessary for assessing its impact. 【0156】 "Processing means" refers to the general term for devices and software used to collect, analyze, and interpret data. 【0157】 A "human-machine interface" refers to the point of contact between devices and systems that allows users to receive and provide information. 【0158】 "Presentation means" refers to devices or software that have the function of communicating analysis results or instructions to the user visually or audibly. 【0159】 "Audio input" refers to audio data provided by the user, which is sound information used as instructions, questions, etc. 【0160】 "Acoustic processing means" refers to a technology or device for analyzing audio input, understanding its content, or generating a response. 【0161】 An "external device" is a hardware device outside the system that is connected for the purpose of acquiring data or providing information. 【0162】 "Real-time video and image data" refers to digital information that visually records and transmits ongoing events and situations. 【0163】 "External data analysis means" refers to technologies and devices for processing and analyzing data acquired from external devices and extracting necessary information. 【0164】 "Emotional analysis" is a method or process that analyzes audio and video data to evaluate a user's emotional state. 【0165】 "Adaptive information delivery" means dynamically optimizing and providing information and instructions according to the user's current situation and emotions. 【0166】 "Emotion analysis means" refers to a device or software that processes audio and video data for evaluating a user's emotions. 【0167】 "Resource management means" refers to technologies and systems for planning and implementing the efficient distribution and use of resources necessary for relief and assistance. 【0168】 This invention aims to construct a system in which servers, terminals, and users work together to provide effective information and evacuation support during disasters. 【0169】 The server plays a central role in collecting and analyzing real-time information during disasters. Specifically, it acquires data from external devices such as seismometers, weather sensors, and GPS devices. This data is aggregated in a cloud database and analyzed using specialized data analysis software (e.g., Python's pandas and SciPy). Based on the analysis results, risk areas and evacuation orders are generated. 【0170】 Furthermore, the server performs sentiment analysis. For this purpose, it uses a general speech recognition API for voice analysis and a video processing library for video analysis. Sentiment analysis identifies the user's stress and anxiety levels, enabling adaptive information delivery. This adaptive delivery includes flexible information presentation and voice guidance. 【0171】 The terminal is responsible for directly providing evacuation instructions and information received from the server to the user. Applications running on the terminal use augmented reality technology to overlay important information onto the camera view, making it intuitively understandable to the user. For example, by displaying landmarks and arrows in evacuation route guidance, it supports smooth evacuation even in crowded environments. 【0172】 Users can request additional information through acoustic input. Utilizing speech recognition technology, the system responds to user inquiries in a way that reflects their emotional state. For example, if anxiety is detected, it can provide detailed evacuation information in a calm tone. 【0173】 Finally, as a concrete example of simulating the operation of this system, the prompt phrase "emotional response to evacuation route guidance" can be input into the generating AI model to verify dynamic support tailored to the user's situation. Through such concrete applications, a system that enables rapid and appropriate responses during disasters can be provided. 【0174】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0175】 Step 1: 【0176】 The server collects real-time data from external devices such as seismometers, weather sensors, and GPS devices. Inputs include seismic waveform data, weather data, and location information. This data is aggregated into a cloud database and organized into a reliable dataset. The output is a consistent dataset necessary for analysis. 【0177】 Step 2: 【0178】 The server analyzes the collected data using data analysis software (e.g., Python's pandas or SciPy). The input is aggregated real-time data. Statistical analysis and machine learning algorithms are applied to this data to identify high-risk areas and potential risks. The output consists of a risk assessment report and specific evacuation orders. 【0179】 Step 3: 【0180】 The server activates an emotion analysis engine and analyzes the user's audio and video data. Input includes audio and video clips obtained from the user. This data is processed using speech recognition technology and video analysis libraries to identify the user's emotional state. Specifically, it assesses stress and anxiety levels based on voice tone and facial expressions. The output is a user emotional status report. 【0181】 Step 4: 【0182】 The server generates evacuation instructions optimized for the user based on their emotional status. Inputs include a risk assessment report and an emotional status report. Based on this, it adjusts the voice guidance to a gentle tone and the interface to a simple one before sending it to the terminal. The output is a personalized evacuation instruction tailored to the user. 【0183】 Step 5: 【0184】 The terminal displays individual evacuation instructions provided by the server and uses AR technology as needed. The input is the evacuation instructions sent from the server. When the user uses the terminal's camera view, landmarks and arrows are overlaid on the evacuation route. The output is a visually easy-to-understand evacuation route guide. 【0185】 Step 6: 【0186】 Users can request additional information through a voice interface. Input consists of questions or instructions expressed in the user's voice. The server analyzes this voice, generates an appropriate response, and returns it to the user. The output is a customized response tailored to the user's question. 【0187】 Step 7: 【0188】 The server uses analysis results and evacuation data to provide feedback to local governments and relief teams. Inputs include overall disaster data and real-time evacuation reports. Based on this, it generates an optimal resource allocation plan and sends it to the relevant organizations. Outputs include feedback and allocation plans to maximize the effectiveness of relief efforts. 【0189】 (Application Example 2) 【0190】 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". 【0191】 During a disaster, it is crucial to collect information quickly and accurately and provide evacuation instructions tailored to diverse user needs. However, providing appropriate information that addresses the emotional state of individual users is a challenge. Furthermore, real-time allocation of optimal rescue resources and the presentation of safe evacuation routes are also critical challenges. 【0192】 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. 【0193】 In this invention, the server includes a data processing device that aggregates and analyzes information, a display device that creates evacuation orders based on the analysis results, and an emotion analysis device that provides information according to the user's mental state. This makes it possible to provide accurate evacuation orders that are tailored to the individual needs and emotions of the user. 【0194】 A "data processing device for aggregating and analyzing information" is a device that collects various types of data during a disaster and analyzes them quickly, thereby contributing to the generation of appropriate evacuation orders. 【0195】 A "display device" is a device that visualizes evacuation orders generated based on analysis and conveys information intuitively to users. 【0196】 A "voice processing device" is a device that recognizes voice information from a user and provides necessary information according to its content. 【0197】 An "external information analysis device" is a device that analyzes real-time video and image information received from external devices and generates information useful for evacuation support. 【0198】 A "resource management device" is a device designed to efficiently allocate rescue resources in disaster-stricken areas and support rapid rescue operations. 【0199】 An "emotion analysis device" is a device that analyzes a user's emotions from their voice and facial expressions and provides information tailored to their mental state. 【0200】 Augmented reality technology is a technology that overlays computer-generated visual information onto the real environment, providing users with an enhanced sense of reality. 【0201】 The system implementing the present invention is based on a digital platform for real-time information collection and analysis during disasters. This system includes a data processing device for aggregating and analyzing information, a display device for displaying information to users, a voice processing device for processing voice information, an external information analysis device for analyzing real-time data from external sources, a resource management device for managing resource allocation, and an emotion analysis device for analyzing emotions. 【0202】 The server collects geographical information, weather data, and seismic waveforms related to disasters in real time, and analyzes them using a data processing device. This makes it possible to predict damage and identify risk areas. The results of this analysis are provided to users as evacuation orders via a display device. The evacuation orders are displayed on the user's smart device using augmented reality technology, making them easy to understand by overlaying them onto the real-world scenery. 【0203】 Furthermore, the voice processing device receives information requests through voice input from the user and provides the information in response. For example, when a user asks about evacuation routes, it provides that information in a gentle voice. An emotion analysis device also operates simultaneously, analyzing the user's voice tone and facial expressions to assess the user's stress and anxiety. Based on this assessment, it is possible to provide information that is appropriate to the user's emotional state. 【0204】 As a concrete example, the server assumes a user is walking around Tokyo Station, and the system provides a mechanism to update and display congestion and safety information for that location in real time. Furthermore, if the user feels uneasy, the system will initiate voice guidance such as, "This area is safe; you can walk around with peace of mind." 【0205】 An example of a prompt message would be: "You are a user traveling within Tokyo. Use real-time data from smart cities to provide emotionally appropriate navigation to ensure the user's safe movement." 【0206】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0207】 Step 1: 【0208】 The server collects real-time data such as geographic information related to disasters, weather data, and seismic waveforms from external data sources. The data obtained as input is integrated by a data processing unit, and data cleansing is performed as a preparatory step for analysis. This results in the output of integrated data in a format suitable for analysis. 【0209】 Step 2: 【0210】 The server analyzes the cleansed, integrated data to predict damage and identify risk areas. The data processing unit uses statistical models and machine learning algorithms to further the analysis. The output includes a list of high-risk areas and numerical results regarding the probability of damage occurring. This generates the basic information needed to issue evacuation orders to users. 【0211】 Step 3: 【0212】 The server generates appropriate evacuation instructions based on the generated risk information and transmits them to the terminal via a display device. The evacuation instructions are displayed on the terminal using augmented reality technology, overlaid with the user's actual location information. Here, the input is risk information and location information, and the output is a specific evacuation route displayed in AR. Guidance is provided using dynamically updated graphics in a way that is easy for the user to understand. 【0213】 Step 4: 【0214】 The terminal receives voice input from the user and analyzes the information request using a voice processing device. For example, if it receives the voice input "Tell me where the evacuation site is," it generates detailed information accordingly. The input is voice data, and the information is converted into text data using speech recognition technology and output, before proceeding to the next information provision step. 【0215】 Step 5: 【0216】 The server performs emotion analysis based on voice analysis. The emotion analysis device analyzes the user's voice tone and patterns to evaluate their emotional state. The input is the feature quantities of the voice data, and the output is an indicator of the degree to which the user is experiencing stress or anxiety. This allows for adjustments to provide information in a way that is appropriate to the user's emotions. 【0217】 Step 6: 【0218】 The device provides information tailored to the user based on the emotion analysis results. If the emotion is assessed as anxious, it provides a gentle voice guidance and information emphasizing the low level of danger. The input here is the emotion analysis result, and the output is information delivered to the user in an adjusted tone and content. At this time, the device uses speech synthesis technology to generate realistic voices that help the user feel at ease. 【0219】 Step 7: 【0220】 The server receives real-time video data from external sources and performs rapid analysis. An external information analysis device analyzes this video data to estimate the extent of building damage and detect damaged areas. The input is video data, and the output is estimated damage data. This optimizes the allocation of rescue resources. 【0221】 Step 8: 【0222】 The server collects all analysis results and generates reports for local governments and rescue organizations. The report generation device integrates data from the entire system and automatically creates damage reports. Inputs are various sensor data and analysis data, and outputs are detailed reports to support decision-making. This allows for the planning and implementation of optimal rescue operations. 【0223】 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. 【0224】 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. 【0225】 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. 【0226】 [Second Embodiment] 【0227】 Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment. 【0228】 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. 【0229】 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). 【0230】 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. 【0231】 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. 【0232】 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). 【0233】 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. 【0234】 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. 【0235】 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. 【0236】 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. 【0237】 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. 【0238】 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". 【0239】 The present invention will now be described in terms of embodiments for carrying out the present invention. The present invention is a system that supports rapid and efficient evacuation instructions and rescue activities during disasters. This system combines data processing means, display means, voice processing means, external data analysis means, and rescue resource management means, each of which performs a different function. 【0240】 The server collects various types of data in real time during a disaster and analyzes that data using data processing tools. For example, during an earthquake, it collects seismic waveform data transmitted from sensors and various organizations, weather data, and geographical information of the affected area. This makes it possible to predict damage and identify risk areas. 【0241】 The server generates evacuation instructions based on the analysis results. These instructions are provided individually to each user terminal. Using a display device, the user terminal visually displays the evacuation route to the user using augmented reality (AR) technology. For example, when a user turns on the camera via their smartphone, the evacuation route is virtually displayed on the screen along with the real-world scenery. This route information is transmitted from the server and is intended to guide the user so that they can quickly evacuate to a safe place. 【0242】 If a user requests voice instructions, the device will use voice processing to convey those instructions verbally. For example, if a user asks, "Where is the nearest evacuation shelter?", the device will use information from the server to provide voice guidance on the direction and distance to the nearest evacuation shelter. 【0243】 Furthermore, the server uses external data analysis tools to analyze real-time video and image data transmitted from drones and autonomous robots. This information is used to identify new dangers in disaster areas and dynamically allocate resources necessary for rescue operations. For example, it can identify areas with particularly severe damage and deliver necessary supplies via drones. 【0244】 Ultimately, the server can automatically compile a report of the post-disaster situation and provide it to local governments and rescue teams. This report will be useful for evaluating disaster response and developing future countermeasures. 【0245】 Thus, the present invention realizes a system that enables real-time situation assessment and rapid countermeasures by integrating the collection, analysis, and provision of information during disasters. 【0246】 The following describes the processing flow. 【0247】 Step 1: 【0248】 When the server detects a disaster, it aggregates real-time earthquake data, weather data, and geographical information from various sensors and data providers. This data is stored in a centralized database. 【0249】 Step 2: 【0250】 The server processes the aggregated data using an analysis algorithm. It evaluates the magnitude and extent of the earthquake's impact and identifies risk areas. Using a damage prediction model, it calculates areas where damage is likely to be concentrated and regions where evacuation is necessary. 【0251】 Step 3: 【0252】 The server generates individual evacuation instructions for each user terminal based on the analysis results. It utilizes the user's current location information to identify safe evacuation routes in their vicinity and prepares data for visualization. 【0253】 Step 4: 【0254】 The device receives evacuation instructions and route information from the server. Using the received information, the device's AR function overlays the evacuation route onto a real-world video feed. This allows the user to visually understand which route is safe from their current location. 【0255】 Step 5: 【0256】 Users can obtain further information about evacuation by asking questions via voice to their device. For example, they can use voice input to confirm the location of the nearest evacuation shelter. 【0257】 Step 6: 【0258】 The terminal recognizes the user's voice input using voice processing equipment and requests appropriate information from the server. The server processes that information and sends the data to the terminal for the user to be provided via voice. The user can then follow this voice guidance to evacuate appropriately. 【0259】 Step 7: 【0260】 The server receives and analyzes video and image data transmitted from drones and autonomous robots using external data analysis tools. This allows for real-time monitoring of newly emerging risks and the escalation of damage, and provides feedback to local governments and rescue teams. 【0261】 Step 8: 【0262】 The server reassesss risk areas and dynamically allocates rescue resources. It identifies areas with particularly heavy damage and optimizes the deployment of drones for supply deliveries and additional rescue operations. 【0263】 Step 9: 【0264】 After the disaster subsides, the server uses its automated report generation function to generate a disaster situation report. This report is provided to local governments and related organizations and is used to summarize the disaster response and to develop future countermeasures. 【0265】 (Example 1) 【0266】 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." 【0267】 During disasters, rapid and accurate information gathering, analysis, and evacuation order provision are crucial. However, conventional technologies are insufficient for providing appropriate responses to individual users and optimizing rescue resources. Furthermore, there are challenges in dynamically generating evacuation orders according to the type of disaster, and in automatically grasping the situation on-site in real time and generating reports. 【0268】 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. 【0269】 In this invention, the server includes means for collecting information during a disaster and analyzing the data using an analysis device, a display device for generating evacuation orders based on the analysis results and displaying them on a user interface, and a voice processing device for recognizing voice input from the user and providing necessary information. This enables the provision of individually optimized evacuation routes, efficient allocation of rescue resources, and rapid assessment of the damage situation. 【0270】 "Collecting information" refers to obtaining various types of data, such as seismic waveform data and weather information, from sensors and agencies during disasters. 【0271】 "Analyzing the data using an analysis device" means using the collected data to predict damage and identify risk areas using machine learning algorithms and the like. 【0272】 "Generating evacuation orders" means outputting appropriate evacuation routes and action guidelines tailored to the individual user's situation, based on the analysis results. 【0273】 "Displaying on the user interface" refers to a technology that visually communicates the generated evacuation instructions to the user's terminal, allowing the user to confirm the information. 【0274】 A "voice processing device" is a device that recognizes voice input from a user and provides appropriate information in voice based on that input. 【0275】 "Receiving and analyzing real-time video and image data" means instantly capturing video and images transmitted from external devices, analyzing them, and understanding the situation. 【0276】 "Dynamically allocating and optimizing rescue resources" refers to efficiently allocating resources such as personnel and supplies for rescue operations and distributing them appropriately to where they are needed. 【0277】 A "generative model" refers to algorithms and tools used to dynamically create different evacuation orders depending on the type and impact of the disaster. 【0278】 Augmented reality technology is a technique that overlays virtual information onto real-world scenery, enabling users to intuitively understand evacuation routes. 【0279】 A "report generation device" is a device that has the function of automatically organizing the damage situation during a disaster and creating statistical data and map information for reporting to relevant organizations. 【0280】 This invention is a system for supporting rapid and accurate evacuation instructions and rescue operations during disasters. This system includes multiple means, primarily involving servers, terminals, and users. 【0281】 The server collects various types of data in real time during a disaster. Specifically, it collects seismic waveform data from sensors, weather information from meteorological agencies, and map data from geographic information systems. Hardware and software such as sensor networks and APIs are utilized in this process. The collected data is analyzed on an analysis device using machine learning algorithms. This enables damage prediction and identification of risk areas, forming the basis for the information provided to users. The server uses a generative AI model to dynamically create different evacuation orders depending on the type and impact of the disaster. 【0282】 The terminal is responsible for visually providing evacuation instructions received from the server to the user. For example, it overlays route information onto the smartphone's camera screen using augmented reality (AR) technology through the user interface. This technology utilizes ARKit or ARCore to realize augmented reality. Furthermore, the terminal can recognize voice input and respond to user questions using speech synthesis technology. For example, in response to the question, "Where is the nearest evacuation shelter?", it can provide appropriate guidance via voice. 【0283】 Based on the information provided through this system, users can safely carry out evacuation actions. As a specific example, in the event of a flood, users can use their smartphones to check the evacuation route through AR and head towards the evacuation shelter safely while receiving voice guidance. In this way, users can act intuitively without spending time understanding the information. 【0284】 As an example of a prompt sentence, the generative AI model can be utilized in the form of "Please create a system that provides real-time evacuation instructions during disasters. It collects earthquake waveform data and weather data, visualizes the evacuation route using AR technology, and provides voice guidance." Thus, this invention integrates information management in disaster response and enables practical and effective evacuation support. 【0285】 The flow of the specific process in Example 1 will be described using FIG. 11. 【0286】 Step 1: 【0287】 The server collects various data in real time during a disaster. As inputs, earthquake waveform data, weather information, geographical information, etc. are obtained from sensors and institutions via APIs. Since these data cannot be utilized in their original form, they are standardized and formatted to construct a dataset for analysis. As a specific operation, the information from each data source is aggregated and stored in a database for unified management. 【0288】 Step 2: 【0289】 The server applies a machine learning algorithm using the data formatted in Step 1 to perform data analysis. From the input standardized data, damage prediction and identification of risk areas are carried out. As outputs, the predicted affected area of the earthquake and the identification results of the areas to be evacuated are obtained. As a specific operation, an analysis model is executed using libraries such as TensorFlow and PyTorch, and the inference results are summarized. 【0290】 Step 3: 【0291】 The server uses a generative AI model based on the analysis results to generate evacuation instructions. It uses the analysis results obtained in step 2 as input to generate instructions tailored to the user. The output includes individual evacuation routes and action guidelines. Specifically, it uses a sentence generation model to automatically create instructions for the user in natural language. 【0292】 Step 4: 【0293】 The terminal visually provides the user with evacuation instructions received from the server. It receives evacuation routes and instruction information from the server as input. Output includes augmented reality (AR) displays on the terminal screen. Specifically, it uses ARKit or ARCore to overlay evacuation routes onto the smartphone screen, providing realistic guidance. 【0294】 Step 5: 【0295】 The device receives voice input from the user and responds using speech synthesis. Input includes questions and requests from the user via voice. Output is voice-based navigation and guidance information. Specifically, it utilizes voice technologies such as Google Assistant and Amazon Alexa to provide information in real time. 【0296】 Step 6: 【0297】 The server receives real-time video data transmitted from external devices and performs additional analysis. It acquires video and image data from drones and autonomous robots as input. The output is a detailed analysis of the danger situation in the disaster area. Specifically, it uses a deep learning model to perform image analysis and identify new dangerous areas. 【0298】 Step 7: 【0299】 The server automatically compiles reports on the post-disaster situation and provides them to relevant organizations. Its inputs include aggregating all analytical data and observational information. Its output includes detailed reports useful for evaluating disaster response. Specifically, it extracts necessary information from the database and generates visualized reports. 【0300】 (Application Example 1) 【0301】 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 glasses 214 will be referred to as the "terminal." 【0302】 When a disaster strikes, there is a need for an efficient system to issue swift and accurate evacuation orders and safely guide victims. In particular, challenges include providing appropriate evacuation routes based on the complex urban environment and individual user location information, voice guidance, and real-time situation monitoring to ensure safety. Furthermore, it is necessary to accurately grasp the situation after a disaster and to respond and report quickly. 【0303】 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. 【0304】 In this invention, the server includes data processing means for collecting and analyzing information during a disaster, display means for generating evacuation instructions based on the analysis results and displaying them on a user interface, voice processing means for recognizing voice input from the user and providing necessary information, augmented reality display means for integrating and displaying the real environment and virtual information, and voice guidance means for providing evacuation routes via voice instructions. This makes it possible to provide users with safe evacuation routes in real time. 【0305】 "Data processing means" refers to devices or software used to collect various types of information during a disaster and to analyze that information. 【0306】 The "display means" is a device or interface that visually displays the evacuation instructions generated based on the analyzed information to the user. 【0307】 The "voice processing means" is a device or program that recognizes voice input from the user and provides corresponding information as voice. 【0308】 The "external data analysis means" is a means for receiving and analyzing real-time video and image data transmitted from an external device. 【0309】 The "rescue resource management means" is a device or system that efficiently distributes and optimizes the rescue resources required during a disaster. 【0310】 The "augmented reality display means" is a system or technology for integrating virtual information into the real environment and visually displaying it. 【0311】 The "voice guidance means" is a means for guiding the evacuation route and necessary information to the user by voice. 【0312】 The system for implementing this invention can provide rapid and accurate evacuation instructions during a disaster, and is for ensuring the safety of residents in a smart city. The system mainly consists of a server, the user's terminal, and related external devices. 【0313】 The server collects data transmitted from various sensors and related institutions in real time during a disaster. For example, during an earthquake or large-scale wind and rain, it integrates and analyzes location information, meteorological data, and sensor information of the surrounding environment. High-performance data processing algorithms are used for the analysis, and big data analysis technology is utilized to create a damage prediction model. The server further generates evacuation instructions and transmits them to the user's terminal. 【0314】 The user's device is a mobile information terminal such as a smartphone or tablet, and an application for displaying evacuation instructions is installed. Based on the evacuation instructions received from the server, the device uses augmented reality technology to overlay evacuation routes onto real-world images. This is achieved using augmented reality technologies such as ARKit for iOS and ARCore for Android. In addition, the Google Maps API is used to provide even more detailed geographical information. Furthermore, by using a speech recognition API (such as the Google Speech-to-Text API), it enables voice input from the user and has a function to provide specific evacuation instructions via voice guidance. 【0315】 For example, if an earthquake occurs while a user is at home in the city, activating their smartphone will display the optimal evacuation route as an arrow on a map. Voice guidance will provide instructions such as, "Go 100 meters and turn left." If the user asks, "How much further is it to the evacuation center?", the device will recalculate the distance from their current location and provide voice guidance. 【0316】 To implement the system described above, you can use prompts such as, "Design an evacuation support system for disasters occurring in smart cities. Explain, with specific examples, the technologies, data flows, and interface improvements necessary to improve the user experience." 【0317】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0318】 Step 1: 【0319】 The server collects data in real time from various sensors and related organizations. Inputs include seismic waveform data, weather data, and location information from sensors, and an integrated dataset is generated as output. Data processing involves data normalization and integration, and data calculations include risk assessment based on damage prediction models. Specifically, the data is collected in a database on the server and then processed by an analysis algorithm. 【0320】 Step 2: 【0321】 The server generates evacuation instructions based on the analysis results. The input is the analysis results obtained in step 1, and the output is the evacuation instructions to be sent to the user. Data processing includes customization based on each user's location information, and the optimal route is calculated as a data calculation. Specifically, the server calculates the safest route based on the user's current location and generates instruction data. 【0322】 Step 3: 【0323】 The terminal displays evacuation instructions received from the server. The input is the evacuation instructions sent from the server, and the output is visual information presented to the user using augmented reality technology. In data processing, the received data is rendered to match the terminal's display, and mapping using AR technology is performed as a data calculation. Specifically, the evacuation route is overlaid on the camera image and displayed to the user. 【0324】 Step 4: 【0325】 When a user provides voice input, the terminal receives the instructions using speech recognition. The input is the user's voice, and the output is the text conversion result of that voice. As data processing, the voice is converted into text format, and as data calculation, the appropriate answer to the question is selected based on information from the server. Specifically, the question is analyzed and the voice guide is prepared. 【0326】 Step 5: 【0327】 The terminal provides voice guidance to the user. Input consists of information obtained from the server and analysis results based on the user's voice input, and output is voice guidance. Data processing includes formatting the server's response into natural language, and data calculations include optimizing route guidance. Specifically, it generates and plays voice data based on the user's current location and destination. 【0328】 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. 【0329】 Embodiments of the present invention describe a system that collects and analyzes information, generates evacuation orders, recognizes emotions, and provides adaptive information based on those emotions during a disaster. 【0330】 The server aggregates real-time data related to disasters and analyzes it using data processing tools. The aggregated data includes seismic waveforms, weather, and geographical information. Based on this data, the server identifies risk areas and predicts damage. 【0331】 The server then activates an emotion engine to analyze the user's voice and video data and recognize their emotional state. This analysis identifies emotions such as stress and anxiety. Specifically, it can evaluate the user's emotions by analyzing voice tone and speech rate, and detecting facial expressions from video data. 【0332】 If the emotion engine assesses the user's anxiety level, the server adjusts how evacuation instructions are delivered. This may include using a gentler voice guidance, simplifying on-screen graphics, and changing to a more calming color scheme. It may also play a more emphasized warning sound to grab the user's attention. 【0333】 The terminal provides users with evacuation instructions received from the server in real time. Using AR technology, it guides users to the optimal evacuation route while they check their surroundings through their smart device. For example, if a user wants to know the evacuation route, the terminal overlays key landmarks and arrows onto the camera view. 【0334】 Furthermore, users can request additional information through voice input. In stressful situations, emotion-optimized responses are provided to ensure users can obtain information with peace of mind. For example, if the emotion engine detects anxiety, it will explain the specific location and situation of the evacuation site in a calm and gentle voice. 【0335】 Finally, the server analyzes real-time data from external devices, provides feedback to local governments and rescue teams, and allocates rescue resources optimally. Disaster situation reports are also automatically generated and used for future disaster response. 【0336】 This system enables evacuation support that takes users' emotions into consideration during disasters, allowing for a swift and appropriate response. 【0337】 The following describes the processing flow. 【0338】 Step 1: 【0339】 The server collects information in real time from various sensors and data sources when a disaster occurs. This information includes seismic waveform data, weather information, and geographical information of the affected area. The aggregated data is stored in a central database. 【0340】 Step 2: 【0341】 The server analyzes the aggregated information using data processing tools. Based on the analysis results, it evaluates the magnitude and extent of the earthquake and identifies risk areas. It uses a damage prediction model to identify areas where damage is predicted. 【0342】 Step 3: 【0343】 The server activates an emotion engine to determine the user's emotions. It analyzes audio and video data collected from the user to measure stress levels and anxiety. For example, it identifies emotions from factors such as voice tone, speech speed, and facial expressions. 【0344】 Step 4: 【0345】 Based on the results of the emotion engine, the server generates customized evacuation instructions for each user. If stress or anxiety levels are detected as high, the tone and display method of the instructions are adjusted and provided in a format that is easy for the user to understand. 【0346】 Step 5: 【0347】 The terminal displays evacuation instructions received from the server. Using AR technology, it visually shows evacuation routes through the user's smart device. Arrows and landmarks are overlaid on the real-world scenery to guide the user to a safe evacuation. 【0348】 Step 6: 【0349】 Users can use voice input to request additional information during an evacuation. For example, they can ask about the location of nearby shelters. 【0350】 Step 7: 【0351】 The terminal recognizes the user's voice input using voice processing equipment and sends the request to the server. The server generates the necessary information and returns it to the terminal in an adaptively adjusted format. The terminal then provides this information to the user via voice. 【0352】 Step 8: 【0353】 The server monitors the situation by analyzing real-time video and image data transmitted by external devices such as drones. This data is fed back to local governments and rescue teams and used for the dynamic allocation of rescue resources. 【0354】 Step 9: 【0355】 Once the disaster response is complete, the server automatically generates a detailed report of the damage. This report is provided to relevant organizations and used to improve future disaster preparedness measures. 【0356】 (Example 2) 【0357】 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". 【0358】 Providing rapid and accurate information and evacuation support during disasters requires real-time information gathering and analysis. However, conventional systems lack the flexibility to provide information tailored to the user's psychological state using sentiment analysis, resulting in insufficient evacuation support that is optimal for the user. 【0359】 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. 【0360】 In this invention, the server includes processing means for collecting and analyzing information during a disaster, presentation means for generating evacuation orders based on the analysis results and displaying them on a human-machine interface, and emotion analysis means for performing emotion analysis and providing adaptive information based on the user's emotional state. This enables rapid and appropriate evacuation support that takes into account the user's emotional state during a disaster. 【0361】 "Information during disasters" refers to data such as earthquake, weather, and geographical information acquired when a natural disaster occurs, which is necessary for assessing its impact. 【0362】 "Processing means" refers to the general term for devices and software used to collect, analyze, and interpret data. 【0363】 A "human-machine interface" refers to the point of contact between devices and systems that allows users to receive and provide information. 【0364】 "Presentation means" refers to devices or software that have the function of communicating analysis results or instructions to the user visually or audibly. 【0365】 "Audio input" refers to audio data provided by the user, which is sound information used as instructions, questions, etc. 【0366】 "Acoustic processing means" refers to a technology or device for analyzing audio input, understanding its content, or generating a response. 【0367】 An "external device" is a hardware device outside the system that is connected for the purpose of acquiring data or providing information. 【0368】 "Real-time video and image data" refers to digital information that visually records and transmits ongoing events and situations. 【0369】 "External data analysis means" refers to technologies and devices for processing and analyzing data acquired from external devices and extracting necessary information. 【0370】 "Emotional analysis" is a method or process that analyzes audio and video data to evaluate a user's emotional state. 【0371】 "Adaptive information delivery" means dynamically optimizing and providing information and instructions according to the user's current situation and emotions. 【0372】 "Emotion analysis means" refers to a device or software that processes audio and video data for evaluating a user's emotions. 【0373】 "Resource management means" refers to technologies and systems for planning and implementing the efficient distribution and use of resources necessary for relief and assistance. 【0374】 This invention aims to construct a system in which servers, terminals, and users work together to provide effective information and evacuation support during disasters. 【0375】 The server plays a central role in collecting and analyzing real-time information during disasters. Specifically, it acquires data from external devices such as seismometers, weather sensors, and GPS devices. This data is aggregated in a cloud database and analyzed using specialized data analysis software (e.g., Python's pandas and SciPy). Based on the analysis results, risk areas and evacuation orders are generated. 【0376】 Furthermore, the server performs sentiment analysis. For this purpose, it uses a general speech recognition API for voice analysis and a video processing library for video analysis. Sentiment analysis identifies the user's stress and anxiety levels, enabling adaptive information delivery. This adaptive delivery includes flexible information presentation and voice guidance. 【0377】 The terminal is responsible for directly providing evacuation instructions and information received from the server to the user. Applications running on the terminal use augmented reality technology to overlay important information onto the camera view, making it intuitively understandable to the user. For example, by displaying landmarks and arrows in evacuation route guidance, it supports smooth evacuation even in crowded environments. 【0378】 Users can request additional information through acoustic input. Utilizing speech recognition technology, the system responds to user inquiries in a way that reflects their emotional state. For example, if anxiety is detected, it can provide detailed evacuation information in a calm tone. 【0379】 Finally, as a concrete example of simulating the operation of this system, the prompt phrase "emotional response to evacuation route guidance" can be input into the generating AI model to verify dynamic support tailored to the user's situation. Through such concrete applications, a system that enables rapid and appropriate responses during disasters can be provided. 【0380】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0381】 Step 1: 【0382】 The server collects real-time data from external devices such as seismometers, weather sensors, and GPS devices. Inputs include seismic waveform data, weather data, and location information. This data is aggregated into a cloud database and organized into a reliable dataset. The output is a consistent dataset necessary for analysis. 【0383】 Step 2: 【0384】 The server analyzes the collected data using data analysis software (e.g., Python's pandas or SciPy). The input is aggregated real-time data. Statistical analysis and machine learning algorithms are applied to this data to identify high-risk areas and potential risks. The output consists of a risk assessment report and specific evacuation orders. 【0385】 Step 3: 【0386】 The server activates an emotion analysis engine and analyzes the user's audio and video data. Input includes audio and video clips obtained from the user. This data is processed using speech recognition technology and video analysis libraries to identify the user's emotional state. Specifically, it assesses stress and anxiety levels based on voice tone and facial expressions. The output is a user emotional status report. 【0387】 Step 4: 【0388】 The server generates evacuation instructions optimized for the user based on their emotional status. Inputs include a risk assessment report and an emotional status report. Based on this, it adjusts the voice guidance to a gentle tone and the interface to a simple one before sending it to the terminal. The output is a personalized evacuation instruction tailored to the user. 【0389】 Step 5: 【0390】 The terminal displays individual evacuation instructions provided by the server and uses AR technology as needed. The input is the evacuation instructions sent from the server. When the user uses the terminal's camera view, landmarks and arrows are overlaid on the evacuation route. The output is a visually easy-to-understand evacuation route guide. 【0391】 Step 6: 【0392】 Users can request additional information through a voice interface. Input consists of questions or instructions expressed in the user's voice. The server analyzes this voice, generates an appropriate response, and returns it to the user. The output is a customized response tailored to the user's question. 【0393】 Step 7: 【0394】 The server uses analysis results and evacuation data to provide feedback to local governments and relief teams. Inputs include overall disaster data and real-time evacuation reports. Based on this, it generates an optimal resource allocation plan and sends it to the relevant organizations. Outputs include feedback and allocation plans to maximize the effectiveness of relief efforts. 【0395】 (Application Example 2) 【0396】 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." 【0397】 During a disaster, it is crucial to collect information quickly and accurately and provide evacuation instructions tailored to diverse user needs. However, providing appropriate information that addresses the emotional state of individual users is a challenge. Furthermore, real-time allocation of optimal rescue resources and the presentation of safe evacuation routes are also critical challenges. 【0398】 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. 【0399】 In this invention, the server includes a data processing device that aggregates and analyzes information, a display device that creates evacuation orders based on the analysis results, and an emotion analysis device that provides information according to the user's mental state. This makes it possible to provide accurate evacuation orders that are tailored to the individual needs and emotions of the user. 【0400】 A "data processing device for aggregating and analyzing information" is a device that collects various types of data during a disaster and analyzes them quickly, thereby contributing to the generation of appropriate evacuation orders. 【0401】 A "display device" is a device that visualizes evacuation orders generated based on analysis and conveys information intuitively to users. 【0402】 A "voice processing device" is a device that recognizes voice information from a user and provides necessary information according to its content. 【0403】 An "external information analysis device" is a device that analyzes real-time video and image information received from external devices and generates information useful for evacuation support. 【0404】 A "resource management device" is a device designed to efficiently allocate rescue resources in disaster-stricken areas and support rapid rescue operations. 【0405】 An "emotion analysis device" is a device that analyzes a user's emotions from their voice and facial expressions and provides information tailored to their mental state. 【0406】 Augmented reality technology is a technology that overlays computer-generated visual information onto the real environment, providing users with an enhanced sense of reality. 【0407】 The system implementing the present invention is based on a digital platform for real-time information collection and analysis during disasters. This system includes a data processing device for aggregating and analyzing information, a display device for displaying information to users, a voice processing device for processing voice information, an external information analysis device for analyzing real-time data from external sources, a resource management device for managing resource allocation, and an emotion analysis device for analyzing emotions. 【0408】 The server collects geographical information, weather data, and seismic waveforms related to disasters in real time, and analyzes them using a data processing device. This makes it possible to predict damage and identify risk areas. The results of this analysis are provided to users as evacuation orders via a display device. The evacuation orders are displayed on the user's smart device using augmented reality technology, making them easy to understand by overlaying them onto the real-world scenery. 【0409】 Furthermore, the voice processing device receives information requests through voice input from the user and provides the information in response. For example, when a user asks about evacuation routes, it provides that information in a gentle voice. An emotion analysis device also operates simultaneously, analyzing the user's voice tone and facial expressions to assess the user's stress and anxiety. Based on this assessment, it is possible to provide information that is appropriate to the user's emotional state. 【0410】 As a concrete example, the server assumes a user is walking around Tokyo Station, and the system provides a mechanism to update and display congestion and safety information for that location in real time. Furthermore, if the user feels uneasy, the system will initiate voice guidance such as, "This area is safe; you can walk around with peace of mind." 【0411】 An example of a prompt message would be: "You are a user traveling within Tokyo. Use real-time data from smart cities to provide emotionally appropriate navigation to ensure the user's safe movement." 【0412】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0413】 Step 1: 【0414】 The server collects real-time data such as geographic information related to disasters, weather data, and seismic waveforms from external data sources. The data obtained as input is integrated by a data processing unit, and data cleansing is performed as a preparatory step for analysis. This results in the output of integrated data in a format suitable for analysis. 【0415】 Step 2: 【0416】 The server analyzes the cleansed, integrated data to predict damage and identify risk areas. The data processing unit uses statistical models and machine learning algorithms to further the analysis. The output includes a list of high-risk areas and numerical results regarding the probability of damage occurring. This generates the basic information needed to issue evacuation orders to users. 【0417】 Step 3: 【0418】 The server generates appropriate evacuation instructions based on the generated risk information and transmits them to the terminal via a display device. The evacuation instructions are displayed on the terminal using augmented reality technology, overlaid with the user's actual location information. Here, the input is risk information and location information, and the output is a specific evacuation route displayed in AR. Guidance is provided using dynamically updated graphics in a way that is easy for the user to understand. 【0419】 Step 4: 【0420】 The terminal receives voice input from the user and analyzes the information request using a voice processing device. For example, if it receives the voice input "Tell me where the evacuation site is," it generates detailed information accordingly. The input is voice data, and the information is converted into text data using speech recognition technology and output, before proceeding to the next information provision step. 【0421】 Step 5: 【0422】 The server performs emotion analysis based on voice analysis. The emotion analysis device analyzes the user's voice tone and patterns to evaluate their emotional state. The input is the feature quantities of the voice data, and the output is an indicator of the degree to which the user is experiencing stress or anxiety. This allows for adjustments to provide information in a way that is appropriate to the user's emotions. 【0423】 Step 6: 【0424】 The device provides information tailored to the user based on the emotion analysis results. If the emotion is assessed as anxious, it provides a gentle voice guidance and information emphasizing the low level of danger. The input here is the emotion analysis result, and the output is information delivered to the user in an adjusted tone and content. At this time, the device uses speech synthesis technology to generate realistic voices that help the user feel at ease. 【0425】 Step 7: 【0426】 The server receives real-time video data from external sources and performs rapid analysis. An external information analysis device analyzes this video data to estimate the extent of building damage and detect damaged areas. The input is video data, and the output is estimated damage data. This optimizes the allocation of rescue resources. 【0427】 Step 8: 【0428】 The server collects all analysis results and generates reports for local governments and rescue organizations. The report generation device integrates data from the entire system and automatically creates damage reports. Inputs are various sensor data and analysis data, and outputs are detailed reports to support decision-making. This allows for the planning and implementation of optimal rescue operations. 【0429】 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. 【0430】 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. 【0431】 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. 【0432】 [Third Embodiment] 【0433】 Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment. 【0434】 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. 【0435】 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). 【0436】 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. 【0437】 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. 【0438】 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). 【0439】 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. 【0440】 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. 【0441】 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. 【0442】 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. 【0443】 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. 【0444】 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". 【0445】 The present invention will now be described in terms of embodiments for carrying out the present invention. The present invention is a system that supports rapid and efficient evacuation instructions and rescue activities during disasters. This system combines data processing means, display means, voice processing means, external data analysis means, and rescue resource management means, each of which performs a different function. 【0446】 The server collects various types of data in real time during a disaster and analyzes that data using data processing tools. For example, during an earthquake, it collects seismic waveform data transmitted from sensors and various organizations, weather data, and geographical information of the affected area. This makes it possible to predict damage and identify risk areas. 【0447】 The server generates evacuation instructions based on the analysis results. These instructions are provided individually to each user terminal. Using a display device, the user terminal visually displays the evacuation route to the user using augmented reality (AR) technology. For example, when a user turns on the camera via their smartphone, the evacuation route is virtually displayed on the screen along with the real-world scenery. This route information is transmitted from the server and is intended to guide the user so that they can quickly evacuate to a safe place. 【0448】 If a user requests voice instructions, the device will use voice processing to convey those instructions verbally. For example, if a user asks, "Where is the nearest evacuation shelter?", the device will use information from the server to provide voice guidance on the direction and distance to the nearest evacuation shelter. 【0449】 Furthermore, the server uses external data analysis tools to analyze real-time video and image data transmitted from drones and autonomous robots. This information is used to identify new dangers in disaster areas and dynamically allocate resources necessary for rescue operations. For example, it can identify areas with particularly severe damage and deliver necessary supplies via drones. 【0450】 Ultimately, the server can automatically compile a report of the post-disaster situation and provide it to local governments and rescue teams. This report will be useful for evaluating disaster response and developing future countermeasures. 【0451】 Thus, the present invention realizes a system that enables real-time situation assessment and rapid countermeasures by integrating the collection, analysis, and provision of information during disasters. 【0452】 The following describes the processing flow. 【0453】 Step 1: 【0454】 When the server detects a disaster, it aggregates real-time earthquake data, weather data, and geographical information from various sensors and data providers. This data is stored in a centralized database. 【0455】 Step 2: 【0456】 The server processes the aggregated data using an analysis algorithm. It evaluates the magnitude and extent of the earthquake's impact and identifies risk areas. Using a damage prediction model, it calculates areas where damage is likely to be concentrated and regions where evacuation is necessary. 【0457】 Step 3: 【0458】 The server generates individual evacuation instructions for each user terminal based on the analysis results. It utilizes the user's current location information to identify safe evacuation routes in their vicinity and prepares data for visualization. 【0459】 Step 4: 【0460】 The device receives evacuation instructions and route information from the server. Using the received information, the device's AR function overlays the evacuation route onto a real-world video feed. This allows the user to visually understand which route is safe from their current location. 【0461】 Step 5: 【0462】 Users can obtain further information about evacuation by asking questions via voice to their device. For example, they can use voice input to confirm the location of the nearest evacuation shelter. 【0463】 Step 6: 【0464】 The terminal recognizes the user's voice input using voice processing equipment and requests appropriate information from the server. The server processes that information and sends the data to the terminal for the user to be provided via voice. The user can then follow this voice guidance to evacuate appropriately. 【0465】 Step 7: 【0466】 The server receives and analyzes video and image data transmitted from drones and autonomous robots using external data analysis tools. This allows for real-time monitoring of newly emerging risks and the escalation of damage, and provides feedback to local governments and rescue teams. 【0467】 Step 8: 【0468】 The server reassesss risk areas and dynamically allocates rescue resources. It identifies areas with particularly heavy damage and optimizes the deployment of drones for supply deliveries and additional rescue operations. 【0469】 Step 9: 【0470】 After the disaster subsides, the server uses its automated report generation function to generate a disaster situation report. This report is provided to local governments and related organizations and is used to summarize the disaster response and to develop future countermeasures. 【0471】 (Example 1) 【0472】 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." 【0473】 During disasters, rapid and accurate information gathering, analysis, and evacuation order provision are crucial. However, conventional technologies are insufficient for providing appropriate responses to individual users and optimizing rescue resources. Furthermore, there are challenges in dynamically generating evacuation orders according to the type of disaster, and in automatically grasping the situation on-site in real time and generating reports. 【0474】 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. 【0475】 In this invention, the server includes means for collecting information during a disaster and analyzing the data using an analysis device, a display device for generating evacuation orders based on the analysis results and displaying them on a user interface, and a voice processing device for recognizing voice input from the user and providing necessary information. This enables the provision of individually optimized evacuation routes, efficient allocation of rescue resources, and rapid assessment of the damage situation. 【0476】 "Collecting information" refers to obtaining various types of data, such as seismic waveform data and weather information, from sensors and agencies during disasters. 【0477】 "Analyzing the data using an analysis device" means using the collected data to predict damage and identify risk areas using machine learning algorithms and the like. 【0478】 "Generating evacuation orders" means outputting appropriate evacuation routes and action guidelines tailored to the individual user's situation, based on the analysis results. 【0479】 "Displaying on the user interface" refers to a technology that visually communicates the generated evacuation instructions to the user's terminal, allowing the user to confirm the information. 【0480】 A "voice processing device" is a device that recognizes voice input from a user and provides appropriate information in voice based on that input. 【0481】 "Receiving and analyzing real-time video and image data" means instantly capturing video and images transmitted from external devices, analyzing them, and understanding the situation. 【0482】 "Dynamically allocating and optimizing rescue resources" refers to efficiently allocating resources such as personnel and supplies for rescue operations and distributing them appropriately to where they are needed. 【0483】 A "generative model" refers to algorithms and tools used to dynamically create different evacuation orders depending on the type and impact of the disaster. 【0484】 Augmented reality technology is a technique that overlays virtual information onto real-world scenery, enabling users to intuitively understand evacuation routes. 【0485】 A "report generation device" is a device that has the function of automatically organizing the damage situation during a disaster and creating statistical data and map information for reporting to relevant organizations. 【0486】 This invention is a system for supporting rapid and accurate evacuation instructions and rescue operations during disasters. This system includes multiple means, primarily involving servers, terminals, and users. 【0487】 The server collects various types of data in real time during a disaster. Specifically, it collects seismic waveform data from sensors, weather information from meteorological agencies, and map data from geographic information systems. Hardware and software such as sensor networks and APIs are utilized in this process. The collected data is analyzed on an analysis device using machine learning algorithms. This enables damage prediction and identification of risk areas, forming the basis for the information provided to users. The server uses a generative AI model to dynamically create different evacuation orders depending on the type and impact of the disaster. 【0488】 The terminal is responsible for visually providing evacuation instructions received from the server to the user. For example, it overlays route information onto the smartphone's camera screen using augmented reality (AR) technology through the user interface. This technology utilizes ARKit or ARCore to realize augmented reality. Furthermore, the terminal can recognize voice input and respond to user questions using speech synthesis technology. For example, in response to the question, "Where is the nearest evacuation shelter?", it can provide appropriate guidance via voice. 【0489】 Based on the information provided through this system, users can take safe evacuation actions. For example, in the event of a flood, users can use their smartphones to check evacuation routes via augmented reality and safely head to shelters while receiving voice guidance. In this way, users can act intuitively without having to spend time understanding the information. 【0490】 An example of a prompt message is, "Create a system that provides evacuation instructions in real time during disasters. It will collect seismic waveform data and weather data, visualize evacuation routes using AR technology, and provide voice guidance." In this way, the generated AI model can be utilized. 【0491】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0492】 Step 1: 【0493】 The server collects various types of data in real time during disasters. Inputs include seismic waveform data, weather information, and geographical information obtained from sensors and organizations via APIs. Since this data cannot be used in its raw format, it undergoes standardization and formatting to create datasets for analysis. Specifically, it aggregates information from each data source and stores it in a centrally managed database. 【0494】 Step 2: 【0495】 The server uses the data formatted in Step 1 to apply machine learning algorithms and perform data analysis. It uses the input standardized data to predict damage and identify risk areas. The output includes the expected earthquake impact area and the identification of areas that should be evacuated. Specifically, it runs the analysis model using libraries such as TensorFlow and PyTorch and compiles the inference results. 【0496】 Step 3: 【0497】 The server uses a generative AI model based on the analysis results to generate evacuation instructions. It uses the analysis results obtained in step 2 as input to generate instructions tailored to the user. The output includes individual evacuation routes and action guidelines. Specifically, it uses a sentence generation model to automatically create instructions for the user in natural language. 【0498】 Step 4: 【0499】 The terminal visually provides the user with evacuation instructions received from the server. It receives evacuation routes and instruction information from the server as input. Output includes augmented reality (AR) displays on the terminal screen. Specifically, it uses ARKit or ARCore to overlay evacuation routes onto the smartphone screen, providing realistic guidance. 【0500】 Step 5: 【0501】 The device receives voice input from the user and responds using speech synthesis. Input includes questions and requests from the user via voice. Output is voice-based navigation and guidance information. Specifically, it utilizes voice technologies such as Google Assistant and Amazon Alexa to provide information in real time. 【0502】 Step 6: 【0503】 The server receives real-time video data transmitted from external devices and performs additional analysis. It acquires video and image data from drones and autonomous robots as input. The output is a detailed analysis of the danger situation in the disaster area. Specifically, it uses a deep learning model to perform image analysis and identify new dangerous areas. 【0504】 Step 7: 【0505】 The server automatically compiles reports on the post-disaster situation and provides them to relevant organizations. Its inputs include aggregating all analytical data and observational information. Its output includes detailed reports useful for evaluating disaster response. Specifically, it extracts necessary information from the database and generates visualized reports. 【0506】 (Application Example 1) 【0507】 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." 【0508】 When a disaster strikes, there is a need for an efficient system to issue swift and accurate evacuation orders and safely guide victims. In particular, challenges include providing appropriate evacuation routes based on the complex urban environment and individual user location information, voice guidance, and real-time situation monitoring to ensure safety. Furthermore, it is necessary to accurately grasp the situation after a disaster and to respond and report quickly. 【0509】 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. 【0510】 In this invention, the server includes data processing means for collecting and analyzing information during a disaster, display means for generating evacuation instructions based on the analysis results and displaying them on a user interface, voice processing means for recognizing voice input from the user and providing necessary information, augmented reality display means for integrating and displaying the real environment and virtual information, and voice guidance means for providing evacuation routes via voice instructions. This makes it possible to provide users with safe evacuation routes in real time. 【0511】 "Data processing means" refers to devices or software used to collect various types of information during a disaster and to analyze that information. 【0512】 "Display means" refers to a device or interface that visually displays evacuation instructions, generated based on analyzed information, to the user. 【0513】 "Voice processing means" refers to a device or program that recognizes voice input from a user and provides corresponding information as voice. 【0514】 "External data analysis means" refers to means for receiving and analyzing real-time video and image data transmitted from an external device. 【0515】 A "rescue resource management system" is a device or system that performs operations to efficiently allocate and optimize the rescue resources needed during a disaster. 【0516】 "Augmented reality display means" refers to a system or technology for integrating virtual information into a real environment and displaying it visually. 【0517】 "Voice guidance means" refers to a means of guiding users to evacuation routes and necessary information via voice. 【0518】 The system for implementing this invention is capable of providing rapid and accurate evacuation instructions during disasters and ensuring the safety of residents in smart cities. The system mainly consists of a server, user terminals, and related external devices. 【0519】 The server collects data transmitted in real time from various sensors and related organizations during disasters. For example, during earthquakes or heavy rain and storms, it integrates and analyzes location information, weather data, and sensor information from the surrounding environment. High-performance data processing algorithms are used for the analysis, and big data analysis technology is utilized to create damage prediction models. The server also generates evacuation orders and sends them to users' terminals. 【0520】 The user's device is a mobile information terminal such as a smartphone or tablet, and an application for displaying evacuation instructions is installed. Based on the evacuation instructions received from the server, the device uses augmented reality technology to overlay evacuation routes onto real-world images. This is achieved using augmented reality technologies such as ARKit for iOS and ARCore for Android. In addition, the Google Maps API is used to provide even more detailed geographical information. Furthermore, by using a speech recognition API (such as the Google Speech-to-Text API), it enables voice input from the user and has a function to provide specific evacuation instructions via voice guidance. 【0521】 For example, if an earthquake occurs while a user is at home in the city, activating their smartphone will display the optimal evacuation route as an arrow on a map. Voice guidance will provide instructions such as, "Go 100 meters and turn left." If the user asks, "How much further is it to the evacuation center?", the device will recalculate the distance from their current location and provide voice guidance. 【0522】 To implement the system described above, you can use prompts such as, "Design an evacuation support system for disasters occurring in smart cities. Explain, with specific examples, the technologies, data flows, and interface improvements necessary to improve the user experience." 【0523】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0524】 Step 1: 【0525】 The server collects data in real time from various sensors and related organizations. Inputs include seismic waveform data, weather data, and location information from sensors, and an integrated dataset is generated as output. Data processing involves data normalization and integration, and data calculations include risk assessment based on damage prediction models. Specifically, the data is collected in a database on the server and then processed by an analysis algorithm. 【0526】 Step 2: 【0527】 The server generates evacuation instructions based on the analysis results. The input is the analysis results obtained in step 1, and the output is the evacuation instructions to be sent to the user. Data processing includes customization based on each user's location information, and the optimal route is calculated as a data calculation. Specifically, the server calculates the safest route based on the user's current location and generates instruction data. 【0528】 Step 3: 【0529】 The terminal displays evacuation instructions received from the server. The input is the evacuation instructions sent from the server, and the output is visual information presented to the user using augmented reality technology. In data processing, the received data is rendered to match the terminal's display, and mapping using AR technology is performed as a data calculation. Specifically, the evacuation route is overlaid on the camera image and displayed to the user. 【0530】 Step 4: 【0531】 When a user provides voice input, the terminal receives the instructions using speech recognition. The input is the user's voice, and the output is the text conversion result of that voice. As data processing, the voice is converted into text format, and as data calculation, the appropriate answer to the question is selected based on information from the server. Specifically, the question is analyzed and the voice guide is prepared. 【0532】 Step 5: 【0533】 The terminal provides voice guidance to the user. Input consists of information obtained from the server and analysis results based on the user's voice input, and output is voice guidance. Data processing includes formatting the server's response into natural language, and data calculations include optimizing route guidance. Specifically, it generates and plays voice data based on the user's current location and destination. 【0534】 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. 【0535】 Embodiments of the present invention describe a system that collects and analyzes information, generates evacuation orders, recognizes emotions, and provides adaptive information based on those emotions during a disaster. 【0536】 The server aggregates real-time data related to disasters and analyzes it using data processing tools. The aggregated data includes seismic waveforms, weather, and geographical information. Based on this data, the server identifies risk areas and predicts damage. 【0537】 The server then activates an emotion engine to analyze the user's voice and video data and recognize their emotional state. This analysis identifies emotions such as stress and anxiety. Specifically, it can evaluate the user's emotions by analyzing voice tone and speech rate, and detecting facial expressions from video data. 【0538】 If the emotion engine assesses the user's anxiety level, the server adjusts how evacuation instructions are delivered. This may include using a gentler voice guidance, simplifying on-screen graphics, and changing to a more calming color scheme. It may also play a more emphasized warning sound to grab the user's attention. 【0539】 The terminal provides users with evacuation instructions received from the server in real time. Using AR technology, it guides users to the optimal evacuation route while they check their surroundings through their smart device. For example, if a user wants to know the evacuation route, the terminal overlays key landmarks and arrows onto the camera view. 【0540】 Furthermore, users can request additional information through voice input. In stressful situations, emotion-optimized responses are provided to ensure users can obtain information with peace of mind. For example, if the emotion engine detects anxiety, it will explain the specific location and situation of the evacuation site in a calm and gentle voice. 【0541】 Finally, the server analyzes real-time data from external devices, provides feedback to local governments and rescue teams, and allocates rescue resources optimally. Disaster situation reports are also automatically generated and used for future disaster response. 【0542】 This system enables evacuation support that takes users' emotions into consideration during disasters, allowing for a swift and appropriate response. 【0543】 The following describes the processing flow. 【0544】 Step 1: 【0545】 The server collects information in real time from various sensors and data sources when a disaster occurs. This information includes seismic waveform data, weather information, and geographical information of the affected area. The aggregated data is stored in a central database. 【0546】 Step 2: 【0547】 The server analyzes the aggregated information using data processing tools. Based on the analysis results, it evaluates the magnitude and extent of the earthquake and identifies risk areas. It uses a damage prediction model to identify areas where damage is predicted. 【0548】 Step 3: 【0549】 The server activates an emotion engine to determine the user's emotions. It analyzes audio and video data collected from the user to measure stress levels and anxiety. For example, it identifies emotions from factors such as voice tone, speech speed, and facial expressions. 【0550】 Step 4: 【0551】 Based on the results of the emotion engine, the server generates customized evacuation instructions for each user. If stress or anxiety levels are detected as high, the tone and display method of the instructions are adjusted and provided in a format that is easy for the user to understand. 【0552】 Step 5: 【0553】 The terminal displays evacuation instructions received from the server. Using AR technology, it visually shows evacuation routes through the user's smart device. Arrows and landmarks are overlaid on the real-world scenery to guide the user to a safe evacuation. 【0554】 Step 6: 【0555】 Users can use voice input to request additional information during an evacuation. For example, they can ask about the location of nearby shelters. 【0556】 Step 7: 【0557】 The terminal recognizes the user's voice input using voice processing equipment and sends the request to the server. The server generates the necessary information and returns it to the terminal in an adaptively adjusted format. The terminal then provides this information to the user via voice. 【0558】 Step 8: 【0559】 The server monitors the situation by analyzing real-time video and image data transmitted by external devices such as drones. This data is fed back to local governments and rescue teams and used for the dynamic allocation of rescue resources. 【0560】 Step 9: 【0561】 Once the disaster response is complete, the server automatically generates a detailed report of the damage. This report is provided to relevant organizations and used to improve future disaster preparedness measures. 【0562】 (Example 2) 【0563】 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." 【0564】 Providing rapid and accurate information and evacuation support during disasters requires real-time information gathering and analysis. However, conventional systems lack the flexibility to provide information tailored to the user's psychological state using sentiment analysis, resulting in insufficient evacuation support that is optimal for the user. 【0565】 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. 【0566】 In this invention, the server includes processing means for collecting and analyzing information during a disaster, presentation means for generating evacuation orders based on the analysis results and displaying them on a human-machine interface, and emotion analysis means for performing emotion analysis and providing adaptive information based on the user's emotional state. This enables rapid and appropriate evacuation support that takes into account the user's emotional state during a disaster. 【0567】 "Information during disasters" refers to data such as earthquake, weather, and geographical information acquired when a natural disaster occurs, which is necessary for assessing its impact. 【0568】 "Processing means" refers to the general term for devices and software used to collect, analyze, and interpret data. 【0569】 A "human-machine interface" refers to the point of contact between devices and systems that allows users to receive and provide information. 【0570】 "Presentation means" refers to devices or software that have the function of communicating analysis results or instructions to the user visually or audibly. 【0571】 "Audio input" refers to audio data provided by the user, which is sound information used as instructions, questions, etc. 【0572】 "Acoustic processing means" refers to a technology or device for analyzing audio input, understanding its content, or generating a response. 【0573】 An "external device" is a hardware device outside the system that is connected for the purpose of acquiring data or providing information. 【0574】 "Real-time video and image data" refers to digital information that visually records and transmits ongoing events and situations. 【0575】 "External data analysis means" refers to technologies and devices for processing and analyzing data acquired from external devices and extracting necessary information. 【0576】 "Emotional analysis" is a method or process that analyzes audio and video data to evaluate a user's emotional state. 【0577】 "Adaptive information delivery" means dynamically optimizing and providing information and instructions according to the user's current situation and emotions. 【0578】 "Emotion analysis means" refers to a device or software that processes audio and video data for evaluating a user's emotions. 【0579】 "Resource management means" refers to technologies and systems for planning and implementing the efficient distribution and use of resources necessary for relief and assistance. 【0580】 This invention aims to construct a system in which servers, terminals, and users work together to provide effective information and evacuation support during disasters. 【0581】 The server plays a central role in collecting and analyzing real-time information during disasters. Specifically, it acquires data from external devices such as seismometers, weather sensors, and GPS devices. This data is aggregated in a cloud database and analyzed using specialized data analysis software (e.g., Python's pandas and SciPy). Based on the analysis results, risk areas and evacuation orders are generated. 【0582】 Furthermore, the server performs sentiment analysis. For this purpose, it uses a general speech recognition API for voice analysis and a video processing library for video analysis. Sentiment analysis identifies the user's stress and anxiety levels, enabling adaptive information delivery. This adaptive delivery includes flexible information presentation and voice guidance. 【0583】 The terminal is responsible for directly providing evacuation instructions and information received from the server to the user. Applications running on the terminal use augmented reality technology to overlay important information onto the camera view, making it intuitively understandable to the user. For example, by displaying landmarks and arrows in evacuation route guidance, it supports smooth evacuation even in crowded environments. 【0584】 Users can request additional information through acoustic input. Utilizing speech recognition technology, the system responds to user inquiries in a way that reflects their emotional state. For example, if anxiety is detected, it can provide detailed evacuation information in a calm tone. 【0585】 Finally, as a concrete example of simulating the operation of this system, the prompt phrase "emotional response to evacuation route guidance" can be input into the generating AI model to verify dynamic support tailored to the user's situation. Through such concrete applications, a system that enables rapid and appropriate responses during disasters can be provided. 【0586】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0587】 Step 1: 【0588】 The server collects real-time data from external devices such as seismometers, weather sensors, and GPS devices. Inputs include seismic waveform data, weather data, and location information. This data is aggregated into a cloud database and organized into a reliable dataset. The output is a consistent dataset necessary for analysis. 【0589】 Step 2: 【0590】 The server analyzes the collected data using data analysis software (e.g., Python's pandas or SciPy). The input is aggregated real-time data. Statistical analysis and machine learning algorithms are applied to this data to identify high-risk areas and potential risks. The output consists of a risk assessment report and specific evacuation orders. 【0591】 Step 3: 【0592】 The server activates an emotion analysis engine and analyzes the user's audio and video data. Input includes audio and video clips obtained from the user. This data is processed using speech recognition technology and video analysis libraries to identify the user's emotional state. Specifically, it assesses stress and anxiety levels based on voice tone and facial expressions. The output is a user emotional status report. 【0593】 Step 4: 【0594】 The server generates evacuation instructions optimized for the user based on their emotional status. Inputs include a risk assessment report and an emotional status report. Based on this, it adjusts the voice guidance to a gentle tone and the interface to a simple one before sending it to the terminal. The output is a personalized evacuation instruction tailored to the user. 【0595】 Step 5: 【0596】 The terminal displays individual evacuation instructions provided by the server and uses AR technology as needed. The input is the evacuation instructions sent from the server. When the user uses the terminal's camera view, landmarks and arrows are overlaid on the evacuation route. The output is a visually easy-to-understand evacuation route guide. 【0597】 Step 6: 【0598】 Users can request additional information through a voice interface. Input consists of questions or instructions expressed in the user's voice. The server analyzes this voice, generates an appropriate response, and returns it to the user. The output is a customized response tailored to the user's question. 【0599】 Step 7: 【0600】 The server uses analysis results and evacuation data to provide feedback to local governments and relief teams. Inputs include overall disaster data and real-time evacuation reports. Based on this, it generates an optimal resource allocation plan and sends it to the relevant organizations. Outputs include feedback and allocation plans to maximize the effectiveness of relief efforts. 【0601】 (Application Example 2) 【0602】 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." 【0603】 During a disaster, it is crucial to collect information quickly and accurately and provide evacuation instructions tailored to diverse user needs. However, providing appropriate information that addresses the emotional state of individual users is a challenge. Furthermore, real-time allocation of optimal rescue resources and the presentation of safe evacuation routes are also critical challenges. 【0604】 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. 【0605】 In this invention, the server includes a data processing device that aggregates and analyzes information, a display device that creates evacuation orders based on the analysis results, and an emotion analysis device that provides information according to the user's mental state. This makes it possible to provide accurate evacuation orders that are tailored to the individual needs and emotions of the user. 【0606】 A "data processing device for aggregating and analyzing information" is a device that collects various types of data during a disaster and analyzes them quickly, thereby contributing to the generation of appropriate evacuation orders. 【0607】 A "display device" is a device that visualizes evacuation orders generated based on analysis and conveys information intuitively to users. 【0608】 A "voice processing device" is a device that recognizes voice information from a user and provides necessary information according to its content. 【0609】 An "external information analysis device" is a device that analyzes real-time video and image information received from external devices and generates information useful for evacuation support. 【0610】 A "resource management device" is a device designed to efficiently allocate rescue resources in disaster-stricken areas and support rapid rescue operations. 【0611】 An "emotion analysis device" is a device that analyzes a user's emotions from their voice and facial expressions and provides information tailored to their mental state. 【0612】 Augmented reality technology is a technology that overlays computer-generated visual information onto the real environment, providing users with an enhanced sense of reality. 【0613】 The system implementing the present invention is based on a digital platform for real-time information collection and analysis during disasters. This system includes a data processing device for aggregating and analyzing information, a display device for displaying information to users, a voice processing device for processing voice information, an external information analysis device for analyzing real-time data from external sources, a resource management device for managing resource allocation, and an emotion analysis device for analyzing emotions. 【0614】 The server collects geographical information, weather data, and seismic waveforms related to disasters in real time, and analyzes them using a data processing device. This makes it possible to predict damage and identify risk areas. The results of this analysis are provided to users as evacuation orders via a display device. The evacuation orders are displayed on the user's smart device using augmented reality technology, making them easy to understand by overlaying them onto the real-world scenery. 【0615】 Furthermore, the voice processing device receives information requests through voice input from the user and provides the information in response. For example, when a user asks about evacuation routes, it provides that information in a gentle voice. An emotion analysis device also operates simultaneously, analyzing the user's voice tone and facial expressions to assess the user's stress and anxiety. Based on this assessment, it is possible to provide information that is appropriate to the user's emotional state. 【0616】 As a concrete example, the server assumes a user is walking around Tokyo Station, and the system provides a mechanism to update and display congestion and safety information for that location in real time. Furthermore, if the user feels uneasy, the system will initiate voice guidance such as, "This area is safe; you can walk around with peace of mind." 【0617】 An example of a prompt message would be: "You are a user traveling within Tokyo. Use real-time data from smart cities to provide emotionally appropriate navigation to ensure the user's safe movement." 【0618】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0619】 Step 1: 【0620】 The server collects real-time data such as geographic information related to disasters, weather data, and seismic waveforms from external data sources. The data obtained as input is integrated by a data processing unit, and data cleansing is performed as a preparatory step for analysis. This results in the output of integrated data in a format suitable for analysis. 【0621】 Step 2: 【0622】 The server analyzes the cleansed, integrated data to predict damage and identify risk areas. The data processing unit uses statistical models and machine learning algorithms to further the analysis. The output includes a list of high-risk areas and numerical results regarding the probability of damage occurring. This generates the basic information needed to issue evacuation orders to users. 【0623】 Step 3: 【0624】 The server generates appropriate evacuation instructions based on the generated risk information and transmits them to the terminal via a display device. The evacuation instructions are displayed on the terminal using augmented reality technology, overlaid with the user's actual location information. Here, the input is risk information and location information, and the output is a specific evacuation route displayed in AR. Guidance is provided using dynamically updated graphics in a way that is easy for the user to understand. 【0625】 Step 4: 【0626】 The terminal receives voice input from the user and analyzes the information request using a voice processing device. For example, if it receives the voice input "Tell me where the evacuation site is," it generates detailed information accordingly. The input is voice data, and the information is converted into text data using speech recognition technology and output, before proceeding to the next information provision step. 【0627】 Step 5: 【0628】 The server performs emotion analysis based on voice analysis. The emotion analysis device analyzes the user's voice tone and patterns to evaluate their emotional state. The input is the feature quantities of the voice data, and the output is an indicator of the degree to which the user is experiencing stress or anxiety. This allows for adjustments to provide information in a way that is appropriate to the user's emotions. 【0629】 Step 6: 【0630】 The device provides information tailored to the user based on the emotion analysis results. If the emotion is assessed as anxious, it provides a gentle voice guidance and information emphasizing the low level of danger. The input here is the emotion analysis result, and the output is information delivered to the user in an adjusted tone and content. At this time, the device uses speech synthesis technology to generate realistic voices that help the user feel at ease. 【0631】 Step 7: 【0632】 The server receives real-time video data from external sources and performs rapid analysis. An external information analysis device analyzes this video data to estimate the extent of building damage and detect damaged areas. The input is video data, and the output is estimated damage data. This optimizes the allocation of rescue resources. 【0633】 Step 8: 【0634】 The server collects all analysis results and generates reports for local governments and rescue organizations. The report generation device integrates data from the entire system and automatically creates damage reports. Inputs are various sensor data and analysis data, and outputs are detailed reports to support decision-making. This allows for the planning and implementation of optimal rescue operations. 【0635】 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. 【0636】 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. 【0637】 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. 【0638】 [Fourth Embodiment] 【0639】 Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment. 【0640】 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. 【0641】 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). 【0642】 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. 【0643】 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. 【0644】 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). 【0645】 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. 【0646】 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. 【0647】 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. 【0648】 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. 【0649】 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. 【0650】 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. 【0651】 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". 【0652】 The present invention will now be described in terms of embodiments for carrying out the present invention. The present invention is a system that supports rapid and efficient evacuation instructions and rescue activities during disasters. This system combines data processing means, display means, voice processing means, external data analysis means, and rescue resource management means, each of which performs a different function. 【0653】 The server collects various types of data in real time during a disaster and analyzes that data using data processing tools. For example, during an earthquake, it collects seismic waveform data transmitted from sensors and various organizations, weather data, and geographical information of the affected area. This makes it possible to predict damage and identify risk areas. 【0654】 The server generates evacuation instructions based on the analysis results. These instructions are provided individually to each user terminal. Using a display device, the user terminal visually displays the evacuation route to the user using augmented reality (AR) technology. For example, when a user turns on the camera via their smartphone, the evacuation route is virtually displayed on the screen along with the real-world scenery. This route information is transmitted from the server and is intended to guide the user so that they can quickly evacuate to a safe place. 【0655】 If a user requests voice instructions, the device will use voice processing to convey those instructions verbally. For example, if a user asks, "Where is the nearest evacuation shelter?", the device will use information from the server to provide voice guidance on the direction and distance to the nearest evacuation shelter. 【0656】 Furthermore, the server uses external data analysis tools to analyze real-time video and image data transmitted from drones and autonomous robots. This information is used to identify new dangers in disaster areas and dynamically allocate resources necessary for rescue operations. For example, it can identify areas with particularly severe damage and deliver necessary supplies via drones. 【0657】 Ultimately, the server can automatically compile a report of the post-disaster situation and provide it to local governments and rescue teams. This report will be useful for evaluating disaster response and developing future countermeasures. 【0658】 Thus, the present invention realizes a system that enables real-time situation assessment and rapid countermeasures by integrating the collection, analysis, and provision of information during disasters. 【0659】 The following describes the processing flow. 【0660】 Step 1: 【0661】 When the server detects a disaster, it aggregates real-time earthquake data, weather data, and geographical information from various sensors and data providers. This data is stored in a centralized database. 【0662】 Step 2: 【0663】 The server processes the aggregated data using an analysis algorithm. It evaluates the magnitude and extent of the earthquake's impact and identifies risk areas. Using a damage prediction model, it calculates areas where damage is likely to be concentrated and regions where evacuation is necessary. 【0664】 Step 3: 【0665】 The server generates individual evacuation instructions for each user terminal based on the analysis results. It utilizes the user's current location information to identify safe evacuation routes in their vicinity and prepares data for visualization. 【0666】 Step 4: 【0667】 The device receives evacuation instructions and route information from the server. Using the received information, the device's AR function overlays the evacuation route onto a real-world video feed. This allows the user to visually understand which route is safe from their current location. 【0668】 Step 5: 【0669】 Users can obtain further information about evacuation by asking questions via voice to their device. For example, they can use voice input to confirm the location of the nearest evacuation shelter. 【0670】 Step 6: 【0671】 The terminal recognizes the user's voice input using voice processing equipment and requests appropriate information from the server. The server processes that information and sends the data to the terminal for the user to be provided via voice. The user can then follow this voice guidance to evacuate appropriately. 【0672】 Step 7: 【0673】 The server receives and analyzes video and image data transmitted from drones and autonomous robots using external data analysis tools. This allows for real-time monitoring of newly emerging risks and the escalation of damage, and provides feedback to local governments and rescue teams. 【0674】 Step 8: 【0675】 The server reassesss risk areas and dynamically allocates rescue resources. It identifies areas with particularly heavy damage and optimizes the deployment of drones for supply deliveries and additional rescue operations. 【0676】 Step 9: 【0677】 After the disaster subsides, the server uses its automated report generation function to generate a disaster situation report. This report is provided to local governments and related organizations and is used to summarize the disaster response and to develop future countermeasures. 【0678】 (Example 1) 【0679】 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". 【0680】 During disasters, rapid and accurate information gathering, analysis, and evacuation order provision are crucial. However, conventional technologies are insufficient for providing appropriate responses to individual users and optimizing rescue resources. Furthermore, there are challenges in dynamically generating evacuation orders according to the type of disaster, and in automatically grasping the situation on-site in real time and generating reports. 【0681】 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. 【0682】 In this invention, the server includes means for collecting information during a disaster and analyzing the data using an analysis device, a display device for generating evacuation orders based on the analysis results and displaying them on a user interface, and a voice processing device for recognizing voice input from the user and providing necessary information. This enables the provision of individually optimized evacuation routes, efficient allocation of rescue resources, and rapid assessment of the damage situation. 【0683】 "Collecting information" refers to obtaining various types of data, such as seismic waveform data and weather information, from sensors and agencies during disasters. 【0684】 "Analyzing the data using an analysis device" means using the collected data to predict damage and identify risk areas using machine learning algorithms and the like. 【0685】 "Generating evacuation orders" means outputting appropriate evacuation routes and action guidelines tailored to the individual user's situation, based on the analysis results. 【0686】 "Displaying on the user interface" refers to a technology that visually communicates the generated evacuation instructions to the user's terminal, allowing the user to confirm the information. 【0687】 A "voice processing device" is a device that recognizes voice input from a user and provides appropriate information in voice based on that input. 【0688】 "Receiving and analyzing real-time video and image data" means instantly capturing video and images transmitted from external devices, analyzing them, and understanding the situation. 【0689】 "Dynamically allocating and optimizing rescue resources" refers to efficiently allocating resources such as personnel and supplies for rescue operations and distributing them appropriately to where they are needed. 【0690】 A "generative model" refers to algorithms and tools used to dynamically create different evacuation orders depending on the type and impact of the disaster. 【0691】 Augmented reality technology is a technique that overlays virtual information onto real-world scenery, enabling users to intuitively understand evacuation routes. 【0692】 A "report generation device" is a device that has the function of automatically organizing the damage situation during a disaster and creating statistical data and map information for reporting to relevant organizations. 【0693】 This invention is a system for supporting rapid and accurate evacuation instructions and rescue operations during disasters. This system includes multiple means, primarily involving servers, terminals, and users. 【0694】 The server collects various types of data in real time during a disaster. Specifically, it collects seismic waveform data from sensors, weather information from meteorological agencies, and map data from geographic information systems. Hardware and software such as sensor networks and APIs are utilized in this process. The collected data is analyzed on an analysis device using machine learning algorithms. This enables damage prediction and identification of risk areas, forming the basis for the information provided to users. The server uses a generative AI model to dynamically create different evacuation orders depending on the type and impact of the disaster. 【0695】 The terminal is responsible for visually providing evacuation instructions received from the server to the user. For example, it overlays route information onto the smartphone's camera screen using augmented reality (AR) technology through the user interface. This technology utilizes ARKit or ARCore to realize augmented reality. Furthermore, the terminal can recognize voice input and respond to user questions using speech synthesis technology. For example, in response to the question, "Where is the nearest evacuation shelter?", it can provide appropriate guidance via voice. 【0696】 Based on the information provided through this system, users can take safe evacuation actions. For example, in the event of a flood, users can use their smartphones to check evacuation routes via augmented reality and safely head to shelters while receiving voice guidance. In this way, users can act intuitively without having to spend time understanding the information. 【0697】 An example of a prompt message is, "Create a system that provides evacuation instructions in real time during disasters. It will collect seismic waveform data and weather data, visualize evacuation routes using AR technology, and provide voice guidance." In this way, the generated AI model can be utilized. 【0698】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0699】 Step 1: 【0700】 The server collects various types of data in real time during disasters. Inputs include seismic waveform data, weather information, and geographical information obtained from sensors and organizations via APIs. Since this data cannot be used in its raw format, it undergoes standardization and formatting to create datasets for analysis. Specifically, it aggregates information from each data source and stores it in a centrally managed database. 【0701】 Step 2: 【0702】 The server uses the data formatted in Step 1 to apply machine learning algorithms and perform data analysis. It uses the input standardized data to predict damage and identify risk areas. The output includes the expected earthquake impact area and the identification of areas that should be evacuated. Specifically, it runs the analysis model using libraries such as TensorFlow and PyTorch and compiles the inference results. 【0703】 Step 3: 【0704】 The server uses a generative AI model based on the analysis results to generate evacuation instructions. It uses the analysis results obtained in step 2 as input to generate instructions tailored to the user. The output includes individual evacuation routes and action guidelines. Specifically, it uses a sentence generation model to automatically create instructions for the user in natural language. 【0705】 Step 4: 【0706】 The terminal visually provides the user with evacuation instructions received from the server. It receives evacuation routes and instruction information from the server as input. Output includes augmented reality (AR) displays on the terminal screen. Specifically, it uses ARKit or ARCore to overlay evacuation routes onto the smartphone screen, providing realistic guidance. 【0707】 Step 5: 【0708】 The device receives voice input from the user and responds using speech synthesis. Input includes questions and requests from the user via voice. Output is voice-based navigation and guidance information. Specifically, it utilizes voice technologies such as Google Assistant and Amazon Alexa to provide information in real time. 【0709】 Step 6: 【0710】 The server receives real-time video data transmitted from external devices and performs additional analysis. It acquires video and image data from drones and autonomous robots as input. The output is a detailed analysis of the danger situation in the disaster area. Specifically, it uses a deep learning model to perform image analysis and identify new dangerous areas. 【0711】 Step 7: 【0712】 The server automatically compiles reports on the post-disaster situation and provides them to relevant organizations. Its inputs include aggregating all analytical data and observational information. Its output includes detailed reports useful for evaluating disaster response. Specifically, it extracts necessary information from the database and generates visualized reports. 【0713】 (Application Example 1) 【0714】 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". 【0715】 When a disaster strikes, there is a need for an efficient system to issue swift and accurate evacuation orders and safely guide victims. In particular, challenges include providing appropriate evacuation routes based on the complex urban environment and individual user location information, voice guidance, and real-time situation monitoring to ensure safety. Furthermore, it is necessary to accurately grasp the situation after a disaster and to respond and report quickly. 【0716】 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. 【0717】 In this invention, the server includes data processing means for collecting and analyzing information during a disaster, display means for generating evacuation instructions based on the analysis results and displaying them on a user interface, voice processing means for recognizing voice input from the user and providing necessary information, augmented reality display means for integrating and displaying the real environment and virtual information, and voice guidance means for providing evacuation routes via voice instructions. This makes it possible to provide users with safe evacuation routes in real time. 【0718】 "Data processing means" refers to devices or software used to collect various types of information during a disaster and to analyze that information. 【0719】 "Display means" refers to a device or interface that visually displays evacuation instructions, generated based on analyzed information, to the user. 【0720】 "Voice processing means" refers to a device or program that recognizes voice input from a user and provides corresponding information as voice. 【0721】 "External data analysis means" refers to means for receiving and analyzing real-time video and image data transmitted from an external device. 【0722】 A "rescue resource management system" is a device or system that performs operations to efficiently allocate and optimize the rescue resources needed during a disaster. 【0723】 "Augmented reality display means" refers to a system or technology for integrating virtual information into a real environment and displaying it visually. 【0724】 "Voice guidance means" refers to a means of guiding users to evacuation routes and necessary information via voice. 【0725】 The system for implementing this invention is capable of providing rapid and accurate evacuation instructions during disasters and ensuring the safety of residents in smart cities. The system mainly consists of a server, user terminals, and related external devices. 【0726】 The server collects data transmitted in real time from various sensors and related organizations during disasters. For example, during earthquakes or heavy rain and storms, it integrates and analyzes location information, weather data, and sensor information from the surrounding environment. High-performance data processing algorithms are used for the analysis, and big data analysis technology is utilized to create damage prediction models. The server also generates evacuation orders and sends them to users' terminals. 【0727】 The user's device is a mobile information terminal such as a smartphone or tablet, and an application for displaying evacuation instructions is installed. Based on the evacuation instructions received from the server, the device uses augmented reality technology to overlay evacuation routes onto real-world images. This is achieved using augmented reality technologies such as ARKit for iOS and ARCore for Android. In addition, the Google Maps API is used to provide even more detailed geographical information. Furthermore, by using a speech recognition API (such as the Google Speech-to-Text API), it enables voice input from the user and has a function to provide specific evacuation instructions via voice guidance. 【0728】 For example, if an earthquake occurs while a user is at home in the city, activating their smartphone will display the optimal evacuation route as an arrow on a map. Voice guidance will provide instructions such as, "Go 100 meters and turn left." If the user asks, "How much further is it to the evacuation center?", the device will recalculate the distance from their current location and provide voice guidance. 【0729】 To implement the system described above, you can use prompts such as, "Design an evacuation support system for disasters occurring in smart cities. Explain, with specific examples, the technologies, data flows, and interface improvements necessary to improve the user experience." 【0730】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0731】 Step 1: 【0732】 The server collects data in real time from various sensors and related organizations. Inputs include seismic waveform data, weather data, and location information from sensors, and an integrated dataset is generated as output. Data processing involves data normalization and integration, and data calculations include risk assessment based on damage prediction models. Specifically, the data is collected in a database on the server and then processed by an analysis algorithm. 【0733】 Step 2: 【0734】 The server generates evacuation instructions based on the analysis results. The input is the analysis results obtained in step 1, and the output is the evacuation instructions to be sent to the user. Data processing includes customization based on each user's location information, and the optimal route is calculated as a data calculation. Specifically, the server calculates the safest route based on the user's current location and generates instruction data. 【0735】 Step 3: 【0736】 The terminal displays evacuation instructions received from the server. The input is the evacuation instructions sent from the server, and the output is visual information presented to the user using augmented reality technology. In data processing, the received data is rendered to match the terminal's display, and mapping using AR technology is performed as a data calculation. Specifically, the evacuation route is overlaid on the camera image and displayed to the user. 【0737】 Step 4: 【0738】 When a user provides voice input, the terminal receives the instructions using speech recognition. The input is the user's voice, and the output is the text conversion result of that voice. As data processing, the voice is converted into text format, and as data calculation, the appropriate answer to the question is selected based on information from the server. Specifically, the question is analyzed and the voice guide is prepared. 【0739】 Step 5: 【0740】 The terminal provides voice guidance to the user. Input consists of information obtained from the server and analysis results based on the user's voice input, and output is voice guidance. Data processing includes formatting the server's response into natural language, and data calculations include optimizing route guidance. Specifically, it generates and plays voice data based on the user's current location and destination. 【0741】 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. 【0742】 Embodiments of the present invention describe a system that collects and analyzes information, generates evacuation orders, recognizes emotions, and provides adaptive information based on those emotions during a disaster. 【0743】 The server aggregates real-time data related to disasters and analyzes it using data processing tools. The aggregated data includes seismic waveforms, weather, and geographical information. Based on this data, the server identifies risk areas and predicts damage. 【0744】 The server then activates an emotion engine to analyze the user's voice and video data and recognize their emotional state. This analysis identifies emotions such as stress and anxiety. Specifically, it can evaluate the user's emotions by analyzing voice tone and speech rate, and detecting facial expressions from video data. 【0745】 If the emotion engine assesses the user's anxiety level, the server adjusts how evacuation instructions are delivered. This may include using a gentler voice guidance, simplifying on-screen graphics, and changing to a more calming color scheme. It may also play a more emphasized warning sound to grab the user's attention. 【0746】 The terminal provides users with evacuation instructions received from the server in real time. Using AR technology, it guides users to the optimal evacuation route while they check their surroundings through their smart device. For example, if a user wants to know the evacuation route, the terminal overlays key landmarks and arrows onto the camera view. 【0747】 Furthermore, users can request additional information through voice input. In stressful situations, emotion-optimized responses are provided to ensure users can obtain information with peace of mind. For example, if the emotion engine detects anxiety, it will explain the specific location and situation of the evacuation site in a calm and gentle voice. 【0748】 Finally, the server analyzes real-time data from external devices, provides feedback to local governments and rescue teams, and allocates rescue resources optimally. Disaster situation reports are also automatically generated and used for future disaster response. 【0749】 This system enables evacuation support that takes users' emotions into consideration during disasters, allowing for a swift and appropriate response. 【0750】 The following describes the processing flow. 【0751】 Step 1: 【0752】 The server collects information in real time from various sensors and data sources when a disaster occurs. This information includes seismic waveform data, weather information, and geographical information of the affected area. The aggregated data is stored in a central database. 【0753】 Step 2: 【0754】 The server analyzes the aggregated information using data processing tools. Based on the analysis results, it evaluates the magnitude and extent of the earthquake and identifies risk areas. It uses a damage prediction model to identify areas where damage is predicted. 【0755】 Step 3: 【0756】 The server activates an emotion engine to determine the user's emotions. It analyzes audio and video data collected from the user to measure stress levels and anxiety. For example, it identifies emotions from factors such as voice tone, speech speed, and facial expressions. 【0757】 Step 4: 【0758】 Based on the results of the emotion engine, the server generates customized evacuation instructions for each user. If stress or anxiety levels are detected as high, the tone and display method of the instructions are adjusted and provided in a format that is easy for the user to understand. 【0759】 Step 5: 【0760】 The terminal displays evacuation instructions received from the server. Using AR technology, it visually shows evacuation routes through the user's smart device. Arrows and landmarks are overlaid on the real-world scenery to guide the user to a safe evacuation. 【0761】 Step 6: 【0762】 Users can use voice input to request additional information during an evacuation. For example, they can ask about the location of nearby shelters. 【0763】 Step 7: 【0764】 The terminal recognizes the user's voice input using voice processing equipment and sends the request to the server. The server generates the necessary information and returns it to the terminal in an adaptively adjusted format. The terminal then provides this information to the user via voice. 【0765】 Step 8: 【0766】 The server monitors the situation by analyzing real-time video and image data transmitted by external devices such as drones. This data is fed back to local governments and rescue teams and used for the dynamic allocation of rescue resources. 【0767】 Step 9: 【0768】 Once the disaster response is complete, the server automatically generates a detailed report of the damage. This report is provided to relevant organizations and used to improve future disaster preparedness measures. 【0769】 (Example 2) 【0770】 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". 【0771】 Providing rapid and accurate information and evacuation support during disasters requires real-time information gathering and analysis. However, conventional systems lack the flexibility to provide information tailored to the user's psychological state using sentiment analysis, resulting in insufficient evacuation support that is optimal for the user. 【0772】 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. 【0773】 In this invention, the server includes processing means for collecting and analyzing information during a disaster, presentation means for generating evacuation orders based on the analysis results and displaying them on a human-machine interface, and emotion analysis means for performing emotion analysis and providing adaptive information based on the user's emotional state. This enables rapid and appropriate evacuation support that takes into account the user's emotional state during a disaster. 【0774】 "Information during disasters" refers to data such as earthquake, weather, and geographical information acquired when a natural disaster occurs, which is necessary for assessing its impact. 【0775】 "Processing means" refers to the general term for devices and software used to collect, analyze, and interpret data. 【0776】 A "human-machine interface" refers to the point of contact between devices and systems that allows users to receive and provide information. 【0777】 "Presentation means" refers to devices or software that have the function of communicating analysis results or instructions to the user visually or audibly. 【0778】 "Audio input" refers to audio data provided by the user, which is sound information used as instructions, questions, etc. 【0779】 "Acoustic processing means" refers to a technology or device for analyzing audio input, understanding its content, or generating a response. 【0780】 An "external device" is a hardware device outside the system that is connected for the purpose of acquiring data or providing information. 【0781】 "Real-time video and image data" refers to digital information that visually records and transmits ongoing events and situations. 【0782】 "External data analysis means" refers to technologies and devices for processing and analyzing data acquired from external devices and extracting necessary information. 【0783】 "Emotional analysis" is a method or process that analyzes audio and video data to evaluate a user's emotional state. 【0784】 "Adaptive information delivery" means dynamically optimizing and providing information and instructions according to the user's current situation and emotions. 【0785】 "Emotion analysis means" refers to a device or software that processes audio and video data for evaluating a user's emotions. 【0786】 "Resource management means" refers to technologies and systems for planning and implementing the efficient distribution and use of resources necessary for relief and assistance. 【0787】 This invention aims to construct a system in which servers, terminals, and users work together to provide effective information and evacuation support during disasters. 【0788】 The server plays a central role in collecting and analyzing real-time information during disasters. Specifically, it acquires data from external devices such as seismometers, weather sensors, and GPS devices. This data is aggregated in a cloud database and analyzed using specialized data analysis software (e.g., Python's pandas and SciPy). Based on the analysis results, risk areas and evacuation orders are generated. 【0789】 Furthermore, the server performs sentiment analysis. For this purpose, it uses a general speech recognition API for voice analysis and a video processing library for video analysis. Sentiment analysis identifies the user's stress and anxiety levels, enabling adaptive information delivery. This adaptive delivery includes flexible information presentation and voice guidance. 【0790】 The terminal is responsible for directly providing evacuation instructions and information received from the server to the user. Applications running on the terminal use augmented reality technology to overlay important information onto the camera view, making it intuitively understandable to the user. For example, by displaying landmarks and arrows in evacuation route guidance, it supports smooth evacuation even in crowded environments. 【0791】 Users can request additional information through acoustic input. Utilizing speech recognition technology, the system responds to user inquiries in a way that reflects their emotional state. For example, if anxiety is detected, it can provide detailed evacuation information in a calm tone. 【0792】 Finally, as a concrete example of simulating the operation of this system, the prompt phrase "emotional response to evacuation route guidance" can be input into the generating AI model to verify dynamic support tailored to the user's situation. Through such concrete applications, a system that enables rapid and appropriate responses during disasters can be provided. 【0793】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0794】 Step 1: 【0795】 The server collects real-time data from external devices such as seismometers, weather sensors, and GPS devices. Inputs include seismic waveform data, weather data, and location information. This data is aggregated into a cloud database and organized into a reliable dataset. The output is a consistent dataset necessary for analysis. 【0796】 Step 2: 【0797】 The server analyzes the collected data using data analysis software (e.g., Python's pandas or SciPy). The input is aggregated real-time data. Statistical analysis and machine learning algorithms are applied to this data to identify high-risk areas and potential risks. The output consists of a risk assessment report and specific evacuation orders. 【0798】 Step 3: 【0799】 The server activates an emotion analysis engine and analyzes the user's audio and video data. Input includes audio and video clips obtained from the user. This data is processed using speech recognition technology and video analysis libraries to identify the user's emotional state. Specifically, it assesses stress and anxiety levels based on voice tone and facial expressions. The output is a user emotional status report. 【0800】 Step 4: 【0801】 The server generates evacuation instructions optimized for the user based on their emotional status. Inputs include a risk assessment report and an emotional status report. Based on this, it adjusts the voice guidance to a gentle tone and the interface to a simple one before sending it to the terminal. The output is a personalized evacuation instruction tailored to the user. 【0802】 Step 5: 【0803】 The terminal displays individual evacuation instructions provided by the server and uses AR technology as needed. The input is the evacuation instructions sent from the server. When the user uses the terminal's camera view, landmarks and arrows are overlaid on the evacuation route. The output is a visually easy-to-understand evacuation route guide. 【0804】 Step 6: 【0805】 Users can request additional information through a voice interface. Input consists of questions or instructions expressed in the user's voice. The server analyzes this voice, generates an appropriate response, and returns it to the user. The output is a customized response tailored to the user's question. 【0806】 Step 7: 【0807】 The server uses analysis results and evacuation data to provide feedback to local governments and relief teams. Inputs include overall disaster data and real-time evacuation reports. Based on this, it generates an optimal resource allocation plan and sends it to the relevant organizations. Outputs include feedback and allocation plans to maximize the effectiveness of relief efforts. 【0808】 (Application Example 2) 【0809】 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". 【0810】 During a disaster, it is crucial to collect information quickly and accurately and provide evacuation instructions tailored to diverse user needs. However, providing appropriate information that addresses the emotional state of individual users is a challenge. Furthermore, real-time allocation of optimal rescue resources and the presentation of safe evacuation routes are also critical challenges. 【0811】 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. 【0812】 In this invention, the server includes a data processing device that aggregates and analyzes information, a display device that creates evacuation orders based on the analysis results, and an emotion analysis device that provides information according to the user's mental state. This makes it possible to provide accurate evacuation orders that are tailored to the individual needs and emotions of the user. 【0813】 A "data processing device for aggregating and analyzing information" is a device that collects various types of data during a disaster and analyzes them quickly, thereby contributing to the generation of appropriate evacuation orders. 【0814】 A "display device" is a device that visualizes evacuation orders generated based on analysis and conveys information intuitively to users. 【0815】 A "voice processing device" is a device that recognizes voice information from a user and provides necessary information according to its content. 【0816】 An "external information analysis device" is a device that analyzes real-time video and image information received from external devices and generates information useful for evacuation support. 【0817】 A "resource management device" is a device designed to efficiently allocate rescue resources in disaster-stricken areas and support rapid rescue operations. 【0818】 An "emotion analysis device" is a device that analyzes a user's emotions from their voice and facial expressions and provides information tailored to their mental state. 【0819】 Augmented reality technology is a technology that overlays computer-generated visual information onto the real environment, providing users with an enhanced sense of reality. 【0820】 The system implementing the present invention is based on a digital platform for real-time information collection and analysis during disasters. This system includes a data processing device for aggregating and analyzing information, a display device for displaying information to users, a voice processing device for processing voice information, an external information analysis device for analyzing real-time data from external sources, a resource management device for managing resource allocation, and an emotion analysis device for analyzing emotions. 【0821】 The server collects geographical information, weather data, and seismic waveforms related to disasters in real time, and analyzes them using a data processing device. This makes it possible to predict damage and identify risk areas. The results of this analysis are provided to users as evacuation orders via a display device. The evacuation orders are displayed on the user's smart device using augmented reality technology, making them easy to understand by overlaying them onto the real-world scenery. 【0822】 Furthermore, the voice processing device receives information requests through voice input from the user and provides the information in response. For example, when a user asks about evacuation routes, it provides that information in a gentle voice. An emotion analysis device also operates simultaneously, analyzing the user's voice tone and facial expressions to assess the user's stress and anxiety. Based on this assessment, it is possible to provide information that is appropriate to the user's emotional state. 【0823】 As a concrete example, the server assumes a user is walking around Tokyo Station, and the system provides a mechanism to update and display congestion and safety information for that location in real time. Furthermore, if the user feels uneasy, the system will initiate voice guidance such as, "This area is safe; you can walk around with peace of mind." 【0824】 An example of a prompt message would be: "You are a user traveling within Tokyo. Use real-time data from smart cities to provide emotionally appropriate navigation to ensure the user's safe movement." 【0825】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0826】 Step 1: 【0827】 The server collects real-time data such as geographic information related to disasters, weather data, and seismic waveforms from external data sources. The data obtained as input is integrated by a data processing unit, and data cleansing is performed as a preparatory step for analysis. This results in the output of integrated data in a format suitable for analysis. 【0828】 Step 2: 【0829】 The server analyzes the cleansed, integrated data to predict damage and identify risk areas. The data processing unit uses statistical models and machine learning algorithms to further the analysis. The output includes a list of high-risk areas and numerical results regarding the probability of damage occurring. This generates the basic information needed to issue evacuation orders to users. 【0830】 Step 3: 【0831】 The server generates appropriate evacuation instructions based on the generated risk information and transmits them to the terminal via a display device. The evacuation instructions are displayed on the terminal using augmented reality technology, overlaid with the user's actual location information. Here, the input is risk information and location information, and the output is a specific evacuation route displayed in AR. Guidance is provided using dynamically updated graphics in a way that is easy for the user to understand. 【0832】 Step 4: 【0833】 The terminal receives voice input from the user and analyzes the information request using a voice processing device. For example, if it receives the voice input "Tell me where the evacuation site is," it generates detailed information accordingly. The input is voice data, and the information is converted into text data using speech recognition technology and output, before proceeding to the next information provision step. 【0834】 Step 5: 【0835】 The server performs emotion analysis based on voice analysis. The emotion analysis device analyzes the user's voice tone and patterns to evaluate their emotional state. The input is the feature quantities of the voice data, and the output is an indicator of the degree to which the user is experiencing stress or anxiety. This allows for adjustments to provide information in a way that is appropriate to the user's emotions. 【0836】 Step 6: 【0837】 The device provides information tailored to the user based on the emotion analysis results. If the emotion is assessed as anxious, it provides a gentle voice guidance and information emphasizing the low level of danger. The input here is the emotion analysis result, and the output is information delivered to the user in an adjusted tone and content. At this time, the device uses speech synthesis technology to generate realistic voices that help the user feel at ease. 【0838】 Step 7: 【0839】 The server receives real-time video data from external sources and performs rapid analysis. An external information analysis device analyzes this video data to estimate the extent of building damage and detect damaged areas. The input is video data, and the output is estimated damage data. This optimizes the allocation of rescue resources. 【0840】 Step 8: 【0841】 The server collects all analysis results and generates reports for local governments and rescue organizations. The report generation device integrates data from the entire system and automatically creates damage reports. Inputs are various sensor data and analysis data, and outputs are detailed reports to support decision-making. This allows for the planning and implementation of optimal rescue operations. 【0842】 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. 【0843】 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. 【0844】 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. 【0845】 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. 【0846】 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. 【0847】 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. 【0848】 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. 【0849】 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. 【0850】 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." 【0851】 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. 【0852】 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. 【0853】 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. 【0854】 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. 【0855】 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. 【0856】 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. 【0857】 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. 【0858】 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. 【0859】 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. 【0860】 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. 【0861】 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. 【0862】 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. 【0863】 The following is further disclosed regarding the embodiments described above. 【0864】 (Claim 1) 【0865】 A data processing means for collecting and analyzing information during a disaster, 【0866】 A display means that generates evacuation orders based on the analysis results and displays them on the user interface, 【0867】 A voice processing means that recognizes voice input from the user and provides necessary information, 【0868】 An external data analysis means that receives and analyzes real-time video and image data from an external device, 【0869】 A rescue resource management system that dynamically allocates and optimizes rescue resources, 【0870】 A system that includes this. 【0871】 (Claim 2) 【0872】 The system according to claim 1, comprising means for displaying individually optimized evacuation routes using AR technology, taking into account the user's location information. 【0873】 (Claim 3) 【0874】 The system according to claim 1, comprising a report generation means for automatically generating a report on the extent of damage during a disaster and providing the report to relevant organizations. 【0875】 "Example 1" 【0876】 (Claim 1) 【0877】 A means of collecting information during a disaster and analyzing that data using an analysis device, 【0878】 A display device that generates evacuation orders based on analysis results and displays them on the user interface, 【0879】 A voice processing device that recognizes voice input from the user and provides necessary information, 【0880】 An external data analysis device that receives and analyzes real-time video and image data from an external device, 【0881】 A rescue resource management device that dynamically allocates and optimizes rescue resources, 【0882】 A means for generating different evacuation orders depending on the type and impact of the disaster, 【0883】 A system that includes this. 【0884】 (Claim 2) 【0885】 The system according to claim 1, comprising means for displaying individually optimized evacuation routes using augmented reality technology, taking into account the user's location information. 【0886】 (Claim 3) 【0887】 The system according to claim 1, comprising a report generation device that automatically generates a report on the extent of damage during a disaster and provides the report to relevant organizations. 【0888】 "Application Example 1" 【0889】 (Claim 1) 【0890】 A data processing means for collecting and analyzing information during a disaster, 【0891】 A display means that generates evacuation orders based on the analysis results and displays them on the user interface, 【0892】 A voice processing means that recognizes voice input from the user and provides necessary information, 【0893】 An external data analysis means that receives and analyzes real-time video and image data from an external device, 【0894】 A rescue resource management system that dynamically allocates and optimizes rescue resources, 【0895】 Augmented reality display means that integrates and displays the real environment and virtual information, 【0896】 A voice guidance system that provides evacuation routes via voice instructions, 【0897】 A system that includes this. 【0898】 (Claim 2) 【0899】 The system according to claim 1, comprising means for displaying individually optimized evacuation routes using augmented reality technology, taking into account the user's location information. 【0900】 (Claim 3) 【0901】 The system according to claim 1, comprising means for generating an automatic report based on the situation during and after a disaster, and providing the report to the relevant authorities. 【0902】 "Example 2 of combining an emotion engine" 【0903】 (Claim 1) 【0904】 A processing means for collecting and analyzing information during a disaster, 【0905】 A presentation means that generates evacuation orders based on the analysis results and displays them on a human-machine interface, 【0906】 An acoustic processing means that recognizes acoustic input from a human and provides necessary information, 【0907】 An external data analysis means that receives and analyzes real-time video and image data from an external device, 【0908】 An emotion analysis means that performs emotion analysis and provides adaptive information based on the user's emotional state, 【0909】 Resource management means for dynamically allocating and optimizing relief resources, 【0910】 A system that includes this. 【0911】 (Claim 2) 【0912】 The system according to claim 1, comprising means for displaying individually optimized evacuation routes using augmented reality technology, taking into account the user's location information. 【0913】 (Claim 3) 【0914】 The system according to claim 1, comprising a report generation means for automatically generating a report on the extent of damage during a disaster and providing the report to the relevant organization. 【0915】 "Application example 2 when combining with an emotional engine" 【0916】 (Claim 1) 【0917】 A data processing device for aggregating and analyzing information during a disaster, 【0918】 Based on the analysis results, an evacuation order is created and displayed on the operation screen using a display device. 【0919】 A voice processing device that recognizes voice information from the user and provides necessary information, 【0920】 An external information analysis device that receives and analyzes real-time video and image information from an external device, 【0921】 A resource management device that efficiently allocates rescue resources, 【0922】 An emotion analysis device that performs emotional analysis and provides information tailored to the user's mental state, 【0923】 A system that includes this. 【0924】 (Claim 2) 【0925】 The system according to claim 1, comprising means for displaying an optimized evacuation route based on the user's location information using augmented reality technology. 【0926】 (Claim 3) 【0927】 The system according to claim 1, comprising a report generation device that automatically generates a report on the extent of damage during a disaster and provides the report to the relevant organizations. [Explanation of symbols] 【0928】 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

[Claim 1] A data processing means for collecting and analyzing information during a disaster, A display means that generates evacuation orders based on the analysis results and displays them on the user interface, A voice processing means that recognizes voice input from the user and provides necessary information, An external data analysis means that receives and analyzes real-time video and image data from an external device, A rescue resource management system that dynamically allocates and optimizes rescue resources, A system that includes this. [Claim 2] The system according to claim 1, comprising means for displaying individually optimized evacuation routes using AR technology, taking into account the user's location information. [Claim 3] The system according to claim 1, comprising a report generation means for automatically generating a report on the extent of damage during a disaster and providing the report to relevant organizations.

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