system

Abstract

We provide the system. [Solution] A means of acquiring information to identify the area where a disaster has occurred and to analyze the extent of damage in that area, An analysis means that analyzes the data obtained from the above information acquisition means to determine the location of disaster victims and the necessary relief supplies, Based on the results of the above analysis, a distribution method for distributing relief supplies to appropriate locations, 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 about a character of the chatbot; encoding the prompt; and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. 【Prior Art Documents】 【Patent Documents】 【0003】 【Patent Document 1】 Japanese Patent Application Laid-Open No. 2022-180282 【Summary of the Invention】 【Problems to be Solved by the Invention】 【0004】 Traditional manual rescue activities during natural disasters have problems such as slow initial response, uneven identification of disaster victims and material supply, and poor efficiency. As a result, there is a need for a rapid and comprehensive disaster response, such as many lives being at risk and unevenness in support occurring. 【Means for Solving the Problems】 【0005】 This invention includes means for acquiring information to identify areas where disasters have occurred and to analyze the extent of damage in those areas in detail. It also includes analytical means for analyzing the acquired data to quickly determine the location of victims and the relief supplies needed. Furthermore, it includes delivery means for efficiently distributing relief supplies using unmanned aerial vehicles and ground-based mobile equipment based on the analysis results, thereby aiming to expedite and streamline rescue operations during disasters. 【0006】 "Information acquisition means" refers to a device or method that includes a function for identifying disaster-affected areas and collecting data on the extent of damage in those areas. 【0007】 "Analysis means" refers to a device or method that analyzes data obtained from information acquisition means to determine the location of disaster victims and the supplies needed for rescue. 【0008】 "Distribution means" refers to a device or method that has the function of efficiently distributing relief supplies to appropriate locations based on information determined by analytical means. 【0009】 An "unmanned aircraft" is an aircraft that flies remotely or autonomously to collect information from the air or drop supplies. 【0010】 A "ground mobile device" is a mobile device that has the function of collecting information and providing supplies while moving on the ground. 【0011】 "Thermal imaging analysis technology" is a technology that detects infrared radiation emitted from objects or the human body and visualizes temperature information. 【0012】 "Speech recognition technology" is a technology that analyzes speech signals to identify and interpret intended information from speech. [Brief explanation of the drawing] 【0013】 [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It 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] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined. 【Mode for Carrying Out the Invention】 【0014】 Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings. 【0015】 First, the terms used in the following description will be explained. 【0016】 In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like. 【0017】 In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor. 【0018】 In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs 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. 【0019】 In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applicable 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. 【0020】 In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or." 【0021】 [First Embodiment] 【0022】 Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment. 【0023】 As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server. 【0024】 The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network). 【0025】 The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52. 【0026】 The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input. 【0027】 The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor. 【0028】 Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54. 【0029】 Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14. 【0030】 As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30. 【0031】 The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. 【0032】 In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48. 【0033】 Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal". 【0034】 The present invention is a disaster relief system having means for acquiring information, means for analyzing information, and means for distributing information. The system can be activated quickly when a disaster occurs, and with each of its functions, it can immediately grasp the situation in the affected area and rapidly deploy necessary relief activities. 【0035】 In this system, the server first defines disaster risk areas and monitors earthquake and weather data in real time. When a disaster occurs, the server identifies the coordinates of the affected area and sends scan commands to deployed drones and ground robots (hereinafter referred to as terminals). The terminals then quickly begin to move, collecting information using thermal imaging cameras from the air and voice recognition technology from the ground. 【0036】 This collected information is analyzed by a server. The server assesses the precise location of victims and the risk of secondary disasters, and develops optimal rescue routes and supply plans. For example, if victims isolated in a remote area are detected, the server will designate that area as a high priority and develop a plan to quickly deliver first-aid kits, drinking water, and other supplies. 【0037】 Next, the terminal flies or drives along a specific route based on this plan, precisely positioning the relief supplies at designated locations. The drone sends back high-resolution images from the air while dropping the supplies, and ground robots distribute them directly, enabling rapid delivery of aid to disaster victims. 【0038】 Furthermore, data from ongoing rescue operations is monitored by the server and continuously analyzed until the operation is completed. This allows the user to be provided with updates on the rescue progress and to flexibly adjust any necessary additional actions. For example, if the situation on the ground changes, the user can instruct the server to set new priorities, thereby immediately optimizing the rescue operation. 【0039】 Thus, the system of the present invention enables a flexible and rapid response tailored to the situation in the disaster area, and significantly improves the accuracy and efficiency of life-saving efforts. 【0040】 The following describes the processing flow. 【0041】 Step 1: 【0042】 The server monitors weather and earthquake data in real time to detect the occurrence of disasters. This allows for preparation in advance so that a rapid response can be made when a disaster occurs in a specific region. 【0043】 Step 2: 【0044】 When a disaster occurs, the server identifies its coordinates and issues scan commands to drones and ground robots (terminals) waiting nearby. This instructs them to move quickly to the area to be scanned. 【0045】 Step 3: 【0046】 Once the terminal (drone) reaches the airspace above the disaster area, it begins a wide-area scan. It uses a thermal imaging camera to detect the body temperature of victims and obtain their location information. 【0047】 Step 4: 【0048】 The terminal (ground robot) moves around on the ground using voice recognition technology to detect cries for help, collision sounds, and other sounds, and conducts a detailed scan. 【0049】 Step 5: 【0050】 The server receives data sent from the terminals and analyzes that information using AI. This allows it to determine the location of disaster victims, as well as their situation and priorities. 【0051】 Step 6: 【0052】 Based on the analysis results, the server develops an optimal rescue plan. This plan includes supplying materials to high-priority victims and setting specific routes. 【0053】 Step 7: 【0054】 The terminal (drone) follows instructions from the server and drops supplies to designated locations. Ground robots also follow those instructions and head towards the disaster area carrying supplies. 【0055】 Step 8: 【0056】 The server monitors the progress of the entire rescue operation and continuously provides users with activity reports. It receives new instructions as the situation changes and optimizes the plan again. 【0057】 (Example 1) 【0058】 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." 【0059】 In recent years, with the increasing frequency of natural disasters, there is a growing need for rapid and accurate assessment of the situation in disaster-stricken areas and efficient rescue operations. Current systems make it difficult to accurately assess the situation in disaster-stricken areas and plan appropriate rescue routes, sometimes hindering a swift response. To solve this problem, a system is needed for real-time data monitoring and analysis, as well as for efficient material distribution. 【0060】 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. 【0061】 In this invention, the server includes an information acquisition means for setting the area where a disaster has occurred and monitoring weather and seismic data in that area in real time to detect anomalies; an analysis means for analyzing the data obtained from the information acquisition means to identify the location of victims and formulate an optimal rescue route and supply plan; and a delivery means for distributing rescue supplies to appropriate locations using unmanned aircraft and ground mobility equipment based on the results of the analysis means. This makes it possible to quickly and accurately grasp the situation in the disaster area and carry out rescue operations efficiently. 【0062】 "Areas where a disaster has occurred" refers to areas identified by geographic information systems as being at risk of natural disasters or areas where disasters have actually occurred. 【0063】 "Information acquisition means" refers to technical devices or methods for collecting data related to disaster occurrence in real time, and specifically includes data from weather sensors and earthquake detection sensors. 【0064】 "Analysis means" refers to algorithms or software that process data obtained from information acquisition means to derive information necessary for identifying the location of disaster victims and planning rescue routes. 【0065】 "Delivery means" refers to a function or device intended to accurately distribute goods to target locations using unmanned aerial vehicles or ground-based mobile equipment, based on a plan determined by analytical means. 【0066】 An "unmanned aircraft" is an aircraft that can fly remotely or autonomously, and is primarily used for aerial data collection and material transport. 【0067】 "Ground-based mobile equipment" refers to robotic or vehicle-type devices used to collect data and deliver supplies while moving on the ground. 【0068】 A "thermal imaging camera" is a device that visualizes infrared radiation emitted from objects or the Earth's surface, and has the function of forming an image of the heat distribution. 【0069】 "Speech recognition technology" refers to the technology or algorithms used to analyze audio data and recognize specific sounds or human voices. 【0070】 A "machine learning algorithm" refers to a computer learning method that creates a model based on a large amount of data and uses it to make predictions and evaluations on unknown data. 【0071】 "Risk of secondary disasters" refers to the probability of a disaster or dangerous event occurring following the primary disaster, or the impact associated with such an event. 【0072】 This disaster relief system is designed to enable a rapid and efficient response in the event of a disaster. The following describes a specific implementation of this system. 【0073】 First, the server configures disaster risk areas and uses GIS (Geographic Information System) to identify affected areas. The server acquires data in real time through interfaces such as weather sensors and earthquake detection sensors, and monitors weather and earthquake data using a RESTful API. This process utilizes analytical algorithms to immediately detect anomalies. 【0074】 Regarding the operational aspects, unmanned aerial vehicles (drones) and ground-based mobile equipment (ground robots) will be used to collect information and deliver supplies. Drones will be equipped with high-precision sensors such as FLIR thermal imaging cameras to acquire thermal information from the air. Meanwhile, ground robots will use voice recognition technology to analyze audio data from the disaster area and detect human voices and other important sounds. 【0075】 The server integrates and analyzes this data. It implements machine learning algorithms to assess the precise location of victims and the risk of potential secondary disasters. Based on this analysis, it develops optimal rescue routes and supply plans. For example, if a victim isolated in a remote location is detected, the server designates that location as a high priority and creates a plan to quickly deliver necessary relief supplies. 【0076】 The terminals can deliver supplies along pre-configured flight or driving routes according to the server's plan. Drones can send real-time collected data back to the server and adjust their routes as needed. Ground robots are responsible for delivering supplies directly to disaster victims. 【0077】 As a concrete example related to this system, the following is an example of a prompt statement using a generative AI model: "Please analyze the current situation in the disaster area in detail and propose the optimal rescue route." By inputting this prompt statement, the system can quickly create a rescue plan based on the latest information. 【0078】 In this way, the system supports accurate assessment of the situation during a disaster and efficient rescue operations. 【0079】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0080】 Step 1: 【0081】 The server defines disaster risk areas using GIS data. The input to this process is historical disaster data and geographical condition data, and the output is a list of high-risk areas. The server receives real-time data from weather and earthquake sensors via APIs and uses it to detect anomalies and notify of disaster occurrences. 【0082】 Step 2: 【0083】 When the server detects a disaster, it immediately identifies the coordinates of the affected area and sends a scan command to the terminal. The input for this step is weather and earthquake data collected in real time, and the output is the coordinate information of the identified affected area. The server uses this coordinate information to instruct the terminal to take immediate action. 【0084】 Step 3: 【0085】 The terminal receives commands from the server and heads towards the area requiring attention. The drone begins gathering information from above, acquiring temperature distribution data using a thermal imaging camera. The ground robot collects surrounding audio data using voice recognition technology. The input for this step is command details from the server, and the output is environmental data acquired in real time. 【0086】 Step 4: 【0087】 The server analyzes data transmitted from terminals to identify the location of victims. Input consists of thermal images and audio data, and machine learning algorithms are used to assess the location of victims and the risk of secondary disasters. Output is the precise location of victims and a list of areas requiring top-priority response. 【0088】 Step 5: 【0089】 The server devises the optimal rescue route and supply plan based on the analysis results. Traffic conditions and topographical information are also considered in creating this plan. The input consists of the location information of victims and geographical data of the area, and the generated rescue plan is provided to the terminal as output. 【0090】 Step 6: 【0091】 The terminal delivers supplies along a designated route based on the received rescue plan. Drones accurately drop supplies, and ground robots travel to the necessary locations to distribute them. The input is the specific rescue plan, and the output is the status of the appropriate distribution of supplies in the disaster area. 【0092】 Step 7: 【0093】 Users can receive progress information on ongoing rescue operations from the server and issue additional instructions as needed. Input is progress data provided by the server, while output is new instructions and priority settings sent by the user to the server. This enables rapid, situation-driven responses. 【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】 In the event of a disaster, it is crucial to quickly and accurately grasp the situation in the affected area and to efficiently carry out rescue operations. Conventional systems have faced challenges in appropriately analyzing the situation across the entire affected area in real time, prioritizing tasks, and generating the optimal rescue routes. 【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: information acquisition means for identifying the area where a disaster has occurred and analyzing the damage situation in that area; analysis means for analyzing the data obtained from the information acquisition means to determine the location of victims and the necessary relief supplies; delivery means for distributing relief supplies to appropriate locations based on the results of the analysis means; and optimization means for analyzing location information to optimize rescue operations and prioritizing them using environmental data acquired in real time. This makes it possible to quickly grasp the situation in the affected area when a disaster occurs and to efficiently deploy optimal rescue operations. 【0099】 "Information acquisition means" refers to devices and systems used to identify areas where disasters have occurred and to collect information on the extent of damage in those areas in real time. 【0100】 "Analysis means" refers to devices and methods for analyzing data obtained from information acquisition means to determine the precise location of disaster victims and the necessary relief supplies. 【0101】 "Distribution means" refers to a system or method for distributing relief supplies to appropriate locations according to a route optimized based on the results of the analysis means. 【0102】 "Optimization methods" refer to techniques and technologies that use environmental data acquired in real time to analyze location information and prioritize rescue operations in order to optimize them. 【0103】 The system for realizing this application is centered around a server. The server is equipped with means for acquiring, analyzing, and distributing information. Specifically, the server is connected to sensors and data feeds for rapidly collecting local information in the event of a disaster. This makes it possible to monitor earthquake and weather data in real time. The server receives this data, identifies the coordinates of the affected area, and, if necessary, commands terminals such as unmanned aerial vehicles and ground robots. 【0104】 These devices are equipped with thermal imaging cameras and voice recognition technology, and they scan the disaster area in detail based on commands sent from the server. The data collected by the devices is sent back to the server and analyzed using AI algorithms, including deep learning (e.g., TENSORFLOW®). This analysis assesses the location of victims and the risk of secondary disasters, and helps to formulate the most efficient rescue routes and supply plans. 【0105】 As a delivery method, the server plans the drone's flight path and precisely drops supplies to designated locations. Data from ongoing rescue operations is also continuously monitored, providing users with progress updates. The server instantly optimizes rescue operations by allowing users to specify new priorities as needed. 【0106】 For example, if an earthquake occurs and isolated victims are detected in a remote area, the server will designate this area as a high priority and quickly plan to deliver first-aid kits and drinking water. The following prompt statements can be used in the generative AI model. 【0107】 Example of a prompt: 【0108】 "We have received new disaster information. An earthquake has occurred, there are victims at location A, and there is a risk of landslides at location B. Please develop the optimal relief routes and supply distribution plans." 【0109】 In this way, the system enables servers, terminals, and users to work together to carry out disaster relief activities quickly and efficiently. 【0110】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0111】 Step 1: 【0112】 The server acquires information about the disaster-stricken area. It receives input from various sensors and data feeds to collect real-time data such as earthquake, weather, and location information. Based on this data, it processes the data to identify the coordinates of the affected area. The output is the coordinate information of the disaster-stricken area. 【0113】 Step 2: 【0114】 The server sends scan commands to the terminals of unmanned aerial vehicles and ground robots based on the identified coordinates. The input here is coordinate information, and the output is the scan command received by the terminal. Specifically, the server communicates with the terminal via the network and sets the scan conditions. 【0115】 Step 3: 【0116】 The terminal scans the disaster area based on instructions from the server. It uses thermal imaging cameras and voice recognition technology to obtain information on the extent of the damage and the location of victims. The input is the scan command, and the output is the scan result data. Specifically, unmanned aerial vehicles take thermal images from the air, and ground robots record ambient sounds. 【0117】 Step 4: 【0118】 The scan results acquired by the terminal are sent to the server. The server receives this as input and performs data analysis using an AI algorithm. The output is the location of victims and other important disaster information. Generative AI models such as TensorFlow are used in this step. 【0119】 Step 5: 【0120】 The server formulates the optimal rescue route and supply plan based on the analysis results. The input is the analyzed data, and the output is a rescue plan. Specifically, the AI ​​optimizes the route and proposes resource allocation. 【0121】 Step 6: 【0122】 The server directs the drone's precise flight path based on the formulated plan. The input is the rescue plan, and the output is the drone's flight route information. The server uses this information to direct the distribution of supplies. 【0123】 Step 7: 【0124】 The user receives progress information on the rescue operation from the server. The server sends the progress to the user and accepts input for adjusting the priority if necessary. The output is the operation progress and the new rescue priority. Specifically, the server periodically sends notifications to the user and waits for instructions. 【0125】 These steps enable disaster relief operations to be carried out efficiently. 【0126】 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. 【0127】 This invention relates to a disaster relief system incorporating an emotion engine, developed to provide more effective support during disasters. The system includes information acquisition means, analysis means, delivery means, and an emotion engine. 【0128】 After a disaster occurs, the server monitors the situation in the affected area in real time, and drones and ground robots (terminals) quickly initiate initial response. Each terminal uses thermal imaging technology to scan a wide area and collect data to identify victims. This data is then sent to the server, where detailed analysis is performed using AI. This determines the location of victims, the presence or absence of obstacles, and priority. 【0129】 A distinctive feature of this system is the presence of an emotion engine. This emotion engine analyzes the emotions of disaster victims and users, and is equipped with an algorithm that evaluates stress levels and anxiety levels. For example, if a disaster victim is feeling anxious or fearful, the emotion engine recognizes this state and readjusts the priority of rescue operations accordingly. 【0130】 For example, if an isolated disaster victim sends a message urgently requesting rescue, the emotion engine will determine the level of stress and urgency from the voice and text contained in the message. This result will be reflected on the server, and a flexible rescue plan will be reconstructed to meet the needs of the entire disaster area. 【0131】 Furthermore, the emotion engine analyzes the emotional state of not only disaster victims but also users (for example, operators on rescue teams). If the server determines that a user's stress level is high, it will automatically recommend actions to reduce response times, supporting efficient operations. 【0132】 This entire system aims to enable swift and considerate rescue operations that also take into account the emotional well-being of disaster victims. The emotional engine's functionality allows for detailed responses tailored to the individual circumstances of each victim, contributing to saving more lives. 【0133】 The following describes the processing flow. 【0134】 Step 1: 【0135】 The server collects disaster precursor and occurrence information in real time and monitors data related to earthquakes and weather changes. Based on this information, it creates a list of potentially affected areas and enhances monitoring. 【0136】 Step 2: 【0137】 When a disaster is detected, the server immediately identifies the coordinates of the affected area and sends dispatch orders to nearby drones and ground robots (terminals). 【0138】 Step 3: 【0139】 Once the terminal (drone) reaches the airspace above the disaster area, it will begin scanning using a thermal imaging camera to identify high-priority responses. It will also collect body temperature information from victims and transmit it to a server. 【0140】 Step 4: 【0141】 The terminal (ground robot) patrols the disaster area, using voice recognition to detect cries for help and sounds made by people, and transmits that information to a server. 【0142】 Step 5: 【0143】 The server uses AI technology to analyze the vast amount of data collected from terminals and assess the precise location and condition of disaster victims. It then uses an emotion engine to measure the degree of stress and the level of urgency of their emotions, along with the level of danger to their lives. 【0144】 Step 6: 【0145】 The emotion engine analyzes the anxiety and fear of disaster victims based on emotional analysis data and provides the server with a priority list for rescue efforts that takes this into account. 【0146】 Step 7: 【0147】 The server develops an optimized rescue plan based on emotional data and analysis results. This plan includes precise distribution points and routes for supplies and takes into account the emotional state of the victims. 【0148】 Step 8: 【0149】 Based on instructions from the server, the terminal (drone) drops supplies, including first-aid kits and food, to designated locations, while ground robots also deliver supplies to specified target points. 【0150】 Step 9: 【0151】 The server monitors the progress of rescue operations in real time and provides users with status reports. It accepts new instructions as needed and can flexibly adjust the rescue plan. 【0152】 Step 10: 【0153】 Based on the analysis results of an emotion engine that supports on-site operations, users provide care-based responses as part of their communication with disaster victims. This makes it possible to provide psychological support during disasters. 【0154】 (Example 2) 【0155】 Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal". 【0156】 To ensure rapid and appropriate rescue operations during a disaster, prioritization must consider not only the location of victims but also their emotional state. However, conventional rescue systems lacked the means to appropriately assess victims' emotions and dynamically adjust rescue priorities based on those assessments. As a result, rescue operations sometimes fail to adequately meet the urgent needs of victims. 【0157】 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. 【0158】 In this invention, the server includes information gathering means for identifying and analyzing the area where a disaster has occurred, emotion analysis means for analyzing the emotions of disaster victims and users, and analysis means for optimizing rescue operations based on the collected data. This makes it possible to prioritize appropriate and rapid rescue operations while taking into account the emotional state of disaster victims. 【0159】 "Information gathering means" refers to technologies and devices used to identify areas where disasters have occurred and to monitor and analyze the extent of damage in those areas. 【0160】 "Analysis means" refers to the technology and equipment used to determine the location of disaster victims and the necessary relief supplies based on data obtained from information gathering means. 【0161】 "Emotional analysis means" refers to technologies and devices that analyze the emotional state of disaster victims and users, and optimize the priority of rescue operations based on that analysis. 【0162】 "Delivery means" refers to the technology and equipment used to distribute rescue supplies to appropriate locations based on the results of analytical means and sentiment analysis means. 【0163】 An "unmanned vehicle" is a remotely controlled or autonomously operating flying device used to efficiently scan disaster-stricken areas. 【0164】 "Ground equipment" refers to autonomous or remotely operated devices used on the ground to scan disaster-stricken areas and collect data. 【0165】 "Thermal radiation imaging technology" is a technique that visualizes the location of victims and the condition of objects by detecting infrared radiation emitted from them. 【0166】 "Speech recognition technology" is a technology that analyzes speech data to understand its meaning and emotions, and then processes the data accordingly. 【0167】 This invention is a system that enables rapid and appropriate rescue operations during disasters. Its main components include information gathering means, analysis means, sentiment analysis means, and delivery means. 【0168】 The server uses information gathering methods to identify the situation in disaster-stricken areas and monitors and analyzes damage data in real time. This information gathering utilizes unmanned vehicles (such as drones) and ground devices (such as robots). These devices employ thermal imaging technology and voice recognition technology to scan wide areas and precisely detect the location and condition of victims. 【0169】 The collected data is sent to a server, where detailed analysis is performed using AI-generated models. This makes it possible to accurately pinpoint the location of disaster victims and identify the necessary relief supplies. 【0170】 Furthermore, the server uses sentiment analysis tools to assess the emotional state of disaster victims and rescue users. This assessment is performed by converting voice messages and text data from the disaster area into prompts. For example, if it receives a message such as, "It's very dangerous here, and I'm anxious. I need help quickly," it analyzes the emotional content and reassessss the rescue priority. 【0171】 From the user's perspective, the rescue team operators are included in the scope of emotion analysis, and the server adjusts their workload appropriately to reduce their stress and psychological burden. 【0172】 Ultimately, a delivery system is established, and an optimized rescue plan is implemented based on the results of analytical and sentiment analysis, ensuring that necessary rescue supplies are distributed efficiently and appropriately. 【0173】 An example of a prompt message could be: "We have received an urgent message from the disaster area. The message reads: 'It is very dangerous here and I am anxious. I need help quickly.' Based on this information, analyze the emotional state of the victims and optimize the rescue plan." This allows the generative AI model to perform an emotional assessment and facilitate a rapid response. 【0174】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0175】 Step 1: 【0176】 The server begins collecting information after a disaster occurs. It controls unmanned robots and ground equipment to scan the affected area. Topographic information and disaster trigger information are used as input, and real-time scan data of the affected area is obtained as output. The server acquires this data and performs initial analysis. 【0177】 Step 2: 【0178】 The terminals, specifically drones and robots, analyze the collected scan data using thermal radiation imaging technology. The input is scan data, and the output is location information for areas with temperature anomalies and suspected victims. The terminals register this information into a map of the affected area. 【0179】 Step 3: 【0180】 The server receives location and temperature data collected from terminals and performs detailed analysis using a generating AI model. The input is location and temperature data, and the output is the precise location of the victims, the presence or absence of obstacles, and a rescue priority list. Furthermore, the AI ​​model predicts the condition of the victims and calculates the degree of urgency. 【0181】 Step 4: 【0182】 The server then uses emotion analysis tools to evaluate the emotional state of the victims and users. Inputs include voice and text messages from victims, and output is the result of the emotional state evaluation. For example, it processes messages such as "It's very dangerous here, and I'm anxious. I need help quickly" using prompts, and quantifies the level of anxiety and urgency. 【0183】 Step 5: 【0184】 The emotional state of the users, specifically the operators of the rescue team, is also evaluated. Inputs include user operation logs and audio data, while output is the user's stress level. Based on this information, the server adjusts the workload of the operation and optimizes the efficiency of each operator. 【0185】 Step 6: 【0186】 The server optimizes rescue operations based on the results of analytical and sentiment analysis. It plans the delivery routes for rescue supplies and ensures their rapid distribution to the necessary locations. The inputs are a rescue priority list and sentiment evaluation results, and the output is an optimized rescue plan. This enables efficient rescue operations. 【0187】 (Application Example 2) 【0188】 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". 【0189】 During disasters, swift and accurate rescue operations are crucial. However, while conventional disaster relief systems excel at identifying the physical location of victims and determining necessary supplies, they fail to prioritize rescue efforts based on the psychological state of both victims and rescuers. This can lead to delays in responding to victims, especially those who are emotionally unstable. Furthermore, it can increase the psychological burden on rescuers themselves, potentially reducing operational efficiency. Therefore, a more flexible rescue system that takes psychological states into consideration is needed. 【0190】 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. 【0191】 In this invention, the server includes data acquisition means for identifying the area where a disaster has occurred and analyzing the extent of the damage; data analysis means for determining the location of victims and the necessary relief supplies based on the information obtained from the data acquisition means; and sentiment analysis means for re-evaluating the priorities of rescue operations through sentiment analysis. This enables accurate and efficient rescue operations based on both the physical situation and the psychological state of the victims. 【0192】 "Area affected by a disaster" refers to a specific geographical area where physical damage or disruption has occurred due to a natural or man-made disaster. 【0193】 "Data acquisition means for analyzing damage status" refers to a group of devices that use sensors, drones, and ground-based mobile devices to collect information from the site and analyze the extent of damage caused by the disaster and the situation of the victims. 【0194】 "Information obtained from data acquisition methods" refers to disaster situation and environmental data collected from the field, and is the basic data used to conduct more detailed analysis based on this information. 【0195】 "Location of victims and necessary relief supplies" refers to information about the current location of victims and the types and quantities of supplies and materials they need for survival and recovery. 【0196】 "Data analysis tools" is a general term for software and hardware used to analyze collected data and generate specific information necessary for rescuing disaster victims. 【0197】 "An emotional analysis tool for re-evaluating rescue priorities through emotional analysis" refers to a device and method for analyzing the psychological state of disaster victims and rescue teams, and determining the optimal rescue priorities based on the results obtained. 【0198】 "Physical conditions and psychological states" refer to both the specific physical conditions of the damage at the disaster site (e.g., the location of victims, the extent of building collapse) and the psychological aspects such as the emotions and stress levels of victims and rescuers. 【0199】 This invention relates to a system for enabling rapid and accurate rescue operations during disasters. The server uses data acquisition means to identify the area where the disaster has occurred and efficiently collects information on the situation in the affected area using unmanned aerial vehicles and ground mobile devices. It is also equipped with data analysis means to determine the location of victims and the necessary relief supplies based on the information obtained from the data acquisition means. 【0200】 Furthermore, it includes an emotion analysis mechanism that re-evaluates the prioritization of rescue operations based on emotion analysis. This emotion analysis uses machine learning libraries such as TensorFlow to determine the psychological state of victims and rescue teams. For example, it analyzes voice and text data to measure the stress levels and anxiety levels of victims and determine the urgency of the rescue. 【0201】 In this system, the server processes the collected data and displays it on a dashboard in real time. Rescue team operators, who are the users, use this information to develop rescue plans. Furthermore, because the results of emotion analysis are reflected immediately, it becomes possible to prioritize the rescue of victims who are psychologically unstable. 【0202】 As a concrete example, consider a scenario where the system responds to a large-scale earthquake. In this case, the system performs real-time emotion analysis on text and voice messages sent from victims near the epicenter to identify victims who are experiencing fear or anxiety. The server then communicates the results to an operator and, if necessary, issues instructions to direct a drone towards the location of that victim. 【0203】 By utilizing a generative AI model and inputting prompts such as, "Please tell me what actions EmoRobo should take to reduce residents' stress during a disaster," it is possible to propose specific countermeasures tailored to the emotional state of disaster victims. In this way, the invention constructs an advanced rescue system that considers not only physical but also psychological aspects. 【0204】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0205】 Step 1: 【0206】 The server identifies the area affected by the disaster. It controls unmanned aerial vehicles and ground mobile devices to collect local data using sensors. The input is real-time geographic data, and the output is location information and initial damage information. Based on this information, an overview of the entire affected area is obtained. 【0207】 Step 2: 【0208】 The server further analyzes the collected data using data acquisition methods. Here, it analyzes the specific locations of disaster victims and the necessary relief supplies. Inputs include location information and damage information, while outputs include location data for disaster victims and a list of necessary supplies. 【0209】 Step 3: 【0210】 The user, a rescue team operator, verifies the location data of disaster victims provided by the server. Furthermore, they develop a transportation plan based on the necessary relief supplies information. Inputs are disaster victim location data and a list of necessary supplies; output is the transportation plan. This enables efficient supply delivery. 【0211】 Step 4: 【0212】 The terminal, either a ground mobile device or an unmanned aerial vehicle, actually distributes relief supplies according to the transport plan. Based on instructions from the server, it begins moving to the location of the affected people and delivers the supplies. The input is the transport plan, and the output is the delivery completion status of the supplies. 【0213】 Step 5: 【0214】 The server uses emotion analysis tools to monitor the psychological state of disaster victims and users. Using voice and text data from sensors as input, a generative AI model measures stress levels and anxiety levels, and outputs the results of the emotion state analysis. This analysis is used to revise the priorities of rescue operations. 【0215】 Step 6: 【0216】 The user adjusts rescue operations based on the results of the emotional state analysis. Specifically, they order a rapid response to victims with increased priority and reallocate the rescue team's tasks as needed. The input is the emotional state analysis results, and the output is the optimized rescue operation strategy. 【0217】 Step 7: 【0218】 The prompt "Please tell us what actions EmoRobo should take to reduce residents' stress during a disaster" is input to the generating AI model, which then proposes the optimal method of resident care. This result will be reflected in rescue operations. The input is the prompt to the generating AI, and the output is the method of reducing residents' stress. 【0219】 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. 【0220】 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. 【0221】 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. 【0222】 [Second Embodiment] 【0223】 Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment. 【0224】 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. 【0225】 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). 【0226】 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. 【0227】 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. 【0228】 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). 【0229】 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. 【0230】 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. 【0231】 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. 【0232】 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. 【0233】 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. 【0234】 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". 【0235】 The present invention is a disaster relief system having means for acquiring information, means for analyzing information, and means for distributing information. The system can be activated quickly when a disaster occurs, and with each of its functions, it can immediately grasp the situation in the affected area and rapidly deploy necessary relief activities. 【0236】 In this system, the server first defines disaster risk areas and monitors earthquake and weather data in real time. When a disaster occurs, the server identifies the coordinates of the affected area and sends scan commands to deployed drones and ground robots (hereinafter referred to as terminals). The terminals then quickly begin to move, collecting information using thermal imaging cameras from the air and voice recognition technology from the ground. 【0237】 This collected information is analyzed by a server. The server assesses the precise location of victims and the risk of secondary disasters, and develops optimal rescue routes and supply plans. For example, if victims isolated in a remote area are detected, the server will designate that area as a high priority and develop a plan to quickly deliver first-aid kits, drinking water, and other supplies. 【0238】 Next, the terminal flies or drives along a specific route based on this plan, precisely positioning the relief supplies at designated locations. The drone sends back high-resolution images from the air while dropping the supplies, and ground robots distribute them directly, enabling rapid delivery of aid to disaster victims. 【0239】 Furthermore, data from ongoing rescue operations is monitored by the server and continuously analyzed until the operation is completed. This allows the user to be provided with updates on the rescue progress and to flexibly adjust any necessary additional actions. For example, if the situation on the ground changes, the user can instruct the server to set new priorities, thereby immediately optimizing the rescue operation. 【0240】 Thus, the system of the present invention enables a flexible and rapid response tailored to the situation in the disaster area, and significantly improves the accuracy and efficiency of life-saving efforts. 【0241】 The following describes the processing flow. 【0242】 Step 1: 【0243】 The server monitors weather and earthquake data in real time to detect the occurrence of disasters. This allows for preparation in advance so that a rapid response can be made when a disaster occurs in a specific region. 【0244】 Step 2: 【0245】 When a disaster occurs, the server identifies its coordinates and issues scan commands to drones and ground robots (terminals) waiting nearby. This instructs them to move quickly to the area to be scanned. 【0246】 Step 3: 【0247】 Once the terminal (drone) reaches the airspace above the disaster area, it begins a wide-area scan. It uses a thermal imaging camera to detect the body temperature of victims and obtain their location information. 【0248】 Step 4: 【0249】 The terminal (ground robot) moves around on the ground using voice recognition technology to detect cries for help, collision sounds, and other sounds, and conducts a detailed scan. 【0250】 Step 5: 【0251】 The server receives data sent from the terminals and analyzes that information using AI. This allows it to determine the location of disaster victims, as well as their situation and priorities. 【0252】 Step 6: 【0253】 Based on the analysis results, the server develops an optimal rescue plan. This plan includes supplying materials to high-priority victims and setting specific routes. 【0254】 Step 7: 【0255】 The terminal (drone) follows instructions from the server and drops supplies to designated locations. Ground robots also follow those instructions and head towards the disaster area carrying supplies. 【0256】 Step 8: 【0257】 The server monitors the progress of the entire rescue operation and continuously provides users with activity reports. It receives new instructions as the situation changes and optimizes the plan again. 【0258】 (Example 1) 【0259】 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." 【0260】 In recent years, with the increasing frequency of natural disasters, there is a growing need for rapid and accurate assessment of the situation in disaster-stricken areas and efficient rescue operations. Current systems make it difficult to accurately assess the situation in disaster-stricken areas and plan appropriate rescue routes, sometimes hindering a swift response. To solve this problem, a system is needed for real-time data monitoring and analysis, as well as for efficient material distribution. 【0261】 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. 【0262】 In this invention, the server includes an information acquisition means for setting the area where a disaster has occurred and monitoring weather and seismic data in that area in real time to detect anomalies; an analysis means for analyzing the data obtained from the information acquisition means to identify the location of victims and formulate an optimal rescue route and supply plan; and a delivery means for distributing rescue supplies to appropriate locations using unmanned aircraft and ground mobility equipment based on the results of the analysis means. This makes it possible to quickly and accurately grasp the situation in the disaster area and carry out rescue operations efficiently. 【0263】 "Areas where a disaster has occurred" refers to areas identified by geographic information systems as being at risk of natural disasters or areas where disasters have actually occurred. 【0264】 "Information acquisition means" refers to technical devices or methods for collecting data related to disaster occurrence in real time, and specifically includes data from weather sensors and earthquake detection sensors. 【0265】 "Analysis means" refers to algorithms or software that process data obtained from information acquisition means to derive information necessary for identifying the location of disaster victims and planning rescue routes. 【0266】 "Delivery means" refers to a function or device intended to accurately distribute goods to target locations using unmanned aerial vehicles or ground-based mobile equipment, based on a plan determined by analytical means. 【0267】 An "unmanned aircraft" is an aircraft that can fly remotely or autonomously, and is primarily used for aerial data collection and material transport. 【0268】 "Ground-based mobile equipment" refers to robotic or vehicle-type devices used to collect data and deliver supplies while moving on the ground. 【0269】 A "thermal imaging camera" is a device that visualizes infrared radiation emitted from objects or the Earth's surface, and has the function of forming an image of the heat distribution. 【0270】 "Speech recognition technology" refers to the technology or algorithms used to analyze audio data and recognize specific sounds or human voices. 【0271】 A "machine learning algorithm" refers to a computer learning method that creates a model based on a large amount of data and uses it to make predictions and evaluations on unknown data. 【0272】 "Risk of secondary disasters" refers to the probability of a disaster or dangerous event occurring following the primary disaster, or the impact associated with such an event. 【0273】 This disaster relief system is designed to enable a rapid and efficient response in the event of a disaster. The following describes a specific implementation of this system. 【0274】 First, the server configures disaster risk areas and uses GIS (Geographic Information System) to identify affected areas. The server acquires data in real time through interfaces such as weather sensors and earthquake detection sensors, and monitors weather and earthquake data using a RESTful API. This process utilizes analytical algorithms to immediately detect anomalies. 【0275】 Regarding the operational aspects, unmanned aerial vehicles (drones) and ground-based mobile equipment (ground robots) will be used to collect information and deliver supplies. Drones will be equipped with high-precision sensors such as FLIR thermal imaging cameras to acquire thermal information from the air. Meanwhile, ground robots will use voice recognition technology to analyze audio data from the disaster area and detect human voices and other important sounds. 【0276】 The server integrates and analyzes this data. It implements machine learning algorithms to assess the precise location of victims and the risk of potential secondary disasters. Based on this analysis, it develops optimal rescue routes and supply plans. For example, if a victim isolated in a remote location is detected, the server designates that location as a high priority and creates a plan to quickly deliver necessary relief supplies. 【0277】 The terminals can deliver supplies along pre-configured flight or driving routes according to the server's plan. Drones can send real-time collected data back to the server and adjust their routes as needed. Ground robots are responsible for delivering supplies directly to disaster victims. 【0278】 As a specific example related to this system, an example of a prompt sentence using a generative AI model is shown below. "Please analyze the current situation of the disaster area in detail and propose the optimal rescue route." With the input of this prompt sentence, the system can quickly create a rescue plan based on the latest information. 【0279】 In this way, the system supports the accurate grasp of the situation during a disaster and the implementation of efficient rescue activities. 【0280】 The flow of the specific process in Example 1 will be described using FIG. 11. 【0281】 Step 1: 【0282】 The server sets the disaster risk area using GIS data. The input for this process is past disaster data and geographical condition data, and as its output, a list of high-risk areas is generated. The server receives real-time data from weather sensors and seismic sensors through an API and uses it to detect anomalies and notify of the occurrence of a disaster. 【0283】 Step 2: 【0284】 When the server detects the occurrence of a disaster, it immediately identifies the coordinates of the disaster area and sends a scan command to the terminal. The input for this step is the meteorological and seismic data collected in real time, and the output is the coordinate information of the identified disaster area. The server uses this coordinate information to instruct the terminal to take immediate action. 【0285】 Step 3: 【0286】 The terminal receives the command from the server and heads towards the area where action is required. The drone starts collecting information from the air and uses a thermal imaging camera to obtain temperature distribution data. The ground robot uses voice recognition technology to collect ambient voice data. The input for this step is the details of the command from the server, and the output is the environmental data obtained in real time. 【0287】 Step 4: 【0288】 The server analyzes data transmitted from terminals to identify the location of victims. Input consists of thermal images and audio data, and machine learning algorithms are used to assess the location of victims and the risk of secondary disasters. Output is the precise location of victims and a list of areas requiring top-priority response. 【0289】 Step 5: 【0290】 The server devises the optimal rescue route and supply plan based on the analysis results. Traffic conditions and topographical information are also considered in creating this plan. The input consists of the location information of victims and geographical data of the area, and the generated rescue plan is provided to the terminal as output. 【0291】 Step 6: 【0292】 The terminal delivers supplies along a designated route based on the received rescue plan. Drones accurately drop supplies, and ground robots travel to the necessary locations to distribute them. The input is the specific rescue plan, and the output is the status of the appropriate distribution of supplies in the disaster area. 【0293】 Step 7: 【0294】 Users can receive progress information on ongoing rescue operations from the server and issue additional instructions as needed. Input is progress data provided by the server, while output is new instructions and priority settings sent by the user to the server. This enables rapid, situation-driven responses. 【0295】 (Application Example 1) 【0296】 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." 【0297】 In the event of a disaster, it is crucial to quickly and accurately grasp the situation in the affected area and to efficiently carry out rescue operations. Conventional systems have faced challenges in appropriately analyzing the situation across the entire affected area in real time, prioritizing tasks, and generating the optimal rescue routes. 【0298】 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. 【0299】 In this invention, the server includes: information acquisition means for identifying the area where a disaster has occurred and analyzing the damage situation in that area; analysis means for analyzing the data obtained from the information acquisition means to determine the location of victims and the necessary relief supplies; delivery means for distributing relief supplies to appropriate locations based on the results of the analysis means; and optimization means for analyzing location information to optimize rescue operations and prioritizing them using environmental data acquired in real time. This makes it possible to quickly grasp the situation in the affected area when a disaster occurs and to efficiently deploy optimal rescue operations. 【0300】 "Information acquisition means" refers to devices and systems used to identify areas where disasters have occurred and to collect information on the extent of damage in those areas in real time. 【0301】 "Analysis means" refers to devices and methods for analyzing data obtained from information acquisition means to determine the precise location of disaster victims and the necessary relief supplies. 【0302】 "Distribution means" refers to a system or method for distributing relief supplies to appropriate locations according to a route optimized based on the results of the analysis means. 【0303】 "Optimization methods" refer to techniques and technologies that use environmental data acquired in real time to analyze location information and prioritize rescue operations in order to optimize them. 【0304】 The system for realizing this application example is centered around a server. The server is equipped with information acquisition means, analysis means, and delivery means. Specifically, sensors and data feeds for quickly collecting regional information in the event of a disaster are connected to the server. This enables real-time monitoring of earthquake and meteorological data. The server receives the data, identifies the coordinates of the disaster area, and commands terminals such as unmanned aircraft and ground robots as needed. 【0305】 These terminals are equipped with thermal imaging cameras and voice recognition technology, and scan the disaster area in detail based on commands sent from the server. The data collected by the terminals is sent back to the server again and analyzed using AI algorithms including deep learning (such as TensorFlow as an example). Through this analysis, the positions of the victims and the risk of secondary disasters are evaluated, and the most efficient rescue routes and material supply plans are formulated. 【0306】 As the delivery means, the server plans the flight path of drones and accurately drops supplies at the designated locations. Also, the data of ongoing rescue activities is continuously monitored and the progress status is provided to the user. By the user indicating a new priority as needed, the server immediately optimizes the rescue activities. 【0307】 As a specific example, when an earthquake occurs and victims isolated in a remote area are detected, the server designates this area as having a high priority and quickly formulates a plan to deliver emergency kits and drinking water. The following prompt sentences can be used in the generated AI model. 【0308】 Example of prompt sentences: 【0309】 "New disaster information has been received. An earthquake has occurred, there are victims at location A, and there is a risk of landslide at location B. Please formulate the optimal rescue route and material distribution plan." 【0310】 In this way, the system enables servers, terminals, and users to work together to carry out disaster relief activities quickly and efficiently. 【0311】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0312】 Step 1: 【0313】 The server acquires information about the disaster-stricken area. It receives input from various sensors and data feeds to collect real-time data such as earthquake, weather, and location information. Based on this data, it processes the data to identify the coordinates of the affected area. The output is the coordinate information of the disaster-stricken area. 【0314】 Step 2: 【0315】 The server sends scan commands to the terminals of unmanned aerial vehicles and ground robots based on the identified coordinates. The input here is coordinate information, and the output is the scan command received by the terminal. Specifically, the server communicates with the terminal via the network and sets the scan conditions. 【0316】 Step 3: 【0317】 The terminal scans the disaster area based on instructions from the server. It uses thermal imaging cameras and voice recognition technology to obtain information on the extent of the damage and the location of victims. The input is the scan command, and the output is the scan result data. Specifically, unmanned aerial vehicles take thermal images from the air, and ground robots record ambient sounds. 【0318】 Step 4: 【0319】 The scan results acquired by the terminal are sent to the server. The server receives this as input and performs data analysis using an AI algorithm. The output is the location of victims and other important disaster information. Generative AI models such as TensorFlow are used in this step. 【0320】 Step 5: 【0321】 The server formulates the optimal rescue route and supply plan based on the analysis results. The input is the analyzed data, and the output is a rescue plan. Specifically, the AI ​​optimizes the route and proposes resource allocation. 【0322】 Step 6: 【0323】 The server directs the drone's precise flight path based on the formulated plan. The input is the rescue plan, and the output is the drone's flight route information. The server uses this information to direct the distribution of supplies. 【0324】 Step 7: 【0325】 The user receives progress information on the rescue operation from the server. The server sends the progress to the user and accepts input for adjusting the priority if necessary. The output is the operation progress and the new rescue priority. Specifically, the server periodically sends notifications to the user and waits for instructions. 【0326】 These steps enable disaster relief operations to be carried out efficiently. 【0327】 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. 【0328】 This invention relates to a disaster relief system incorporating an emotion engine, developed to provide more effective support during disasters. The system includes information acquisition means, analysis means, delivery means, and an emotion engine. 【0329】 After a disaster occurs, the server monitors the situation in the affected area in real time, and drones and ground robots (terminals) quickly initiate initial response. Each terminal uses thermal imaging technology to scan a wide area and collect data to identify victims. This data is then sent to the server, where detailed analysis is performed using AI. This determines the location of victims, the presence or absence of obstacles, and priority. 【0330】 A distinctive feature of this system is the presence of an emotion engine. This emotion engine analyzes the emotions of disaster victims and users, and is equipped with an algorithm that evaluates stress levels and anxiety levels. For example, if a disaster victim is feeling anxious or fearful, the emotion engine recognizes this state and readjusts the priority of rescue operations accordingly. 【0331】 For example, if an isolated disaster victim sends a message urgently requesting rescue, the emotion engine will determine the level of stress and urgency from the voice and text contained in the message. This result will be reflected on the server, and a flexible rescue plan will be reconstructed to meet the needs of the entire disaster area. 【0332】 Furthermore, the emotion engine analyzes the emotional state of not only disaster victims but also users (for example, operators on rescue teams). If the server determines that a user's stress level is high, it will automatically recommend actions to reduce response times, supporting efficient operations. 【0333】 This entire system aims to enable swift and considerate rescue operations that also take into account the emotional well-being of disaster victims. The emotional engine's functionality allows for detailed responses tailored to the individual circumstances of each victim, contributing to saving more lives. 【0334】 The following describes the processing flow. 【0335】 Step 1: 【0336】 The server collects disaster precursor and occurrence information in real time and monitors data related to earthquakes and weather changes. Based on this information, it creates a list of potentially affected areas and enhances monitoring. 【0337】 Step 2: 【0338】 When a disaster is detected, the server immediately identifies the coordinates of the affected area and sends dispatch orders to nearby drones and ground robots (terminals). 【0339】 Step 3: 【0340】 Once the terminal (drone) reaches the airspace above the disaster area, it will begin scanning using a thermal imaging camera to identify high-priority responses. It will also collect body temperature information from victims and transmit it to a server. 【0341】 Step 4: 【0342】 The terminal (ground robot) patrols the disaster area, using voice recognition to detect cries for help and sounds made by people, and transmits that information to a server. 【0343】 Step 5: 【0344】 The server uses AI technology to analyze the vast amount of data collected from terminals and assess the precise location and condition of disaster victims. It then uses an emotion engine to measure the degree of stress and the level of urgency of their emotions, along with the level of danger to their lives. 【0345】 Step 6: 【0346】 The emotion engine analyzes the anxiety and fear of disaster victims based on emotional analysis data and provides the server with a priority list for rescue efforts that takes this into account. 【0347】 Step 7: 【0348】 The server develops an optimized rescue plan based on emotional data and analysis results. This plan includes precise distribution points and routes for supplies and takes into account the emotional state of the victims. 【0349】 Step 8: 【0350】 Based on instructions from the server, the terminal (drone) drops supplies, including first-aid kits and food, to designated locations, while ground robots also deliver supplies to specified target points. 【0351】 Step 9: 【0352】 The server monitors the progress of rescue operations in real time and provides users with status reports. It accepts new instructions as needed and can flexibly adjust the rescue plan. 【0353】 Step 10: 【0354】 Based on the analysis results of an emotion engine that supports on-site operations, users provide care-based responses as part of their communication with disaster victims. This makes it possible to provide psychological support during disasters. 【0355】 (Example 2) 【0356】 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". 【0357】 To ensure rapid and appropriate rescue operations during a disaster, prioritization must consider not only the location of victims but also their emotional state. However, conventional rescue systems lacked the means to appropriately assess victims' emotions and dynamically adjust rescue priorities based on those assessments. As a result, rescue operations sometimes fail to adequately meet the urgent needs of victims. 【0358】 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. 【0359】 In this invention, the server includes information gathering means for identifying and analyzing the area where a disaster has occurred, emotion analysis means for analyzing the emotions of disaster victims and users, and analysis means for optimizing rescue operations based on the collected data. This makes it possible to prioritize appropriate and rapid rescue operations while taking into account the emotional state of disaster victims. 【0360】 "Information gathering means" refers to technologies and devices used to identify areas where disasters have occurred and to monitor and analyze the extent of damage in those areas. 【0361】 "Analysis means" refers to the technology and equipment used to determine the location of disaster victims and the necessary relief supplies based on data obtained from information gathering means. 【0362】 "Emotional analysis means" refers to technologies and devices that analyze the emotional state of disaster victims and users, and optimize the priority of rescue operations based on that analysis. 【0363】 "Delivery means" refers to the technology and equipment used to distribute rescue supplies to appropriate locations based on the results of analytical means and sentiment analysis means. 【0364】 An "unmanned vehicle" is a remotely controlled or autonomously operating flying device used to efficiently scan disaster-stricken areas. 【0365】 "Ground equipment" refers to autonomous or remotely operated devices used on the ground to scan disaster-stricken areas and collect data. 【0366】 "Thermal radiation imaging technology" is a technique that visualizes the location of victims and the condition of objects by detecting infrared radiation emitted from them. 【0367】 "Speech recognition technology" is a technology that analyzes speech data to understand its meaning and emotions, and then processes the data accordingly. 【0368】 This invention is a system that enables rapid and appropriate rescue operations during disasters. Its main components include information gathering means, analysis means, sentiment analysis means, and delivery means. 【0369】 The server uses information gathering methods to identify the situation in disaster-stricken areas and monitors and analyzes damage data in real time. This information gathering utilizes unmanned vehicles (such as drones) and ground devices (such as robots). These devices employ thermal imaging technology and voice recognition technology to scan wide areas and precisely detect the location and condition of victims. 【0370】 The collected data is sent to a server, where detailed analysis is performed using AI-generated models. This makes it possible to accurately pinpoint the location of disaster victims and identify the necessary relief supplies. 【0371】 Furthermore, the server uses sentiment analysis tools to assess the emotional state of disaster victims and rescue users. This assessment is performed by converting voice messages and text data from the disaster area into prompts. For example, if it receives a message such as, "It's very dangerous here, and I'm anxious. I need help quickly," it analyzes the emotional content and reassessss the rescue priority. 【0372】 From the user's perspective, the rescue team operators are included in the scope of emotion analysis, and the server adjusts their workload appropriately to reduce their stress and psychological burden. 【0373】 Ultimately, a delivery system is established, and an optimized rescue plan is implemented based on the results of analytical and sentiment analysis, ensuring that necessary rescue supplies are distributed efficiently and appropriately. 【0374】 An example of a prompt message could be: "We have received an urgent message from the disaster area. The message reads: 'It is very dangerous here and I am anxious. I need help quickly.' Based on this information, analyze the emotional state of the victims and optimize the rescue plan." This allows the generative AI model to perform an emotional assessment and facilitate a rapid response. 【0375】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0376】 Step 1: 【0377】 The server begins collecting information after a disaster occurs. It controls unmanned robots and ground equipment to scan the affected area. Topographic information and disaster trigger information are used as input, and real-time scan data of the affected area is obtained as output. The server acquires this data and performs initial analysis. 【0378】 Step 2: 【0379】 The terminals, specifically drones and robots, analyze the collected scan data using thermal radiation imaging technology. The input is scan data, and the output is location information for areas with temperature anomalies and suspected victims. The terminals register this information into a map of the affected area. 【0380】 Step 3: 【0381】 The server receives location and temperature data collected from terminals and performs detailed analysis using a generating AI model. The input is location and temperature data, and the output is the precise location of the victims, the presence or absence of obstacles, and a rescue priority list. Furthermore, the AI ​​model predicts the condition of the victims and calculates the degree of urgency. 【0382】 Step 4: 【0383】 The server then uses emotion analysis tools to evaluate the emotional state of the victims and users. Inputs include voice and text messages from victims, and output is the result of the emotional state evaluation. For example, it processes messages such as "It's very dangerous here, and I'm anxious. I need help quickly" using prompts, and quantifies the level of anxiety and urgency. 【0384】 Step 5: 【0385】 The emotional state of the users, specifically the operators of the rescue team, is also evaluated. Inputs include user operation logs and audio data, while output is the user's stress level. Based on this information, the server adjusts the workload of the operation and optimizes the efficiency of each operator. 【0386】 Step 6: 【0387】 The server optimizes rescue operations based on the results of analytical and sentiment analysis. It plans the delivery routes for rescue supplies and ensures their rapid distribution to the necessary locations. The inputs are a rescue priority list and sentiment evaluation results, and the output is an optimized rescue plan. This enables efficient rescue operations. 【0388】 (Application Example 2) 【0389】 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." 【0390】 During disasters, swift and accurate rescue operations are crucial. However, while conventional disaster relief systems excel at identifying the physical location of victims and determining necessary supplies, they fail to prioritize rescue efforts based on the psychological state of both victims and rescuers. This can lead to delays in responding to victims, especially those who are emotionally unstable. Furthermore, it can increase the psychological burden on rescuers themselves, potentially reducing operational efficiency. Therefore, a more flexible rescue system that takes psychological states into consideration is needed. 【0391】 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. 【0392】 In this invention, the server includes data acquisition means for identifying the area where a disaster has occurred and analyzing the extent of the damage; data analysis means for determining the location of victims and the necessary relief supplies based on the information obtained from the data acquisition means; and sentiment analysis means for re-evaluating the priorities of rescue operations through sentiment analysis. This enables accurate and efficient rescue operations based on both the physical situation and the psychological state of the victims. 【0393】 "Area affected by a disaster" refers to a specific geographical area where physical damage or disruption has occurred due to a natural or man-made disaster. 【0394】 "Data acquisition means for analyzing damage status" refers to a group of devices that use sensors, drones, and ground-based mobile devices to collect information from the site and analyze the extent of damage caused by the disaster and the situation of the victims. 【0395】 "Information obtained from data acquisition methods" refers to disaster situation and environmental data collected from the field, and is the basic data used to conduct more detailed analysis based on this information. 【0396】 "Location of victims and necessary relief supplies" refers to information about the current location of victims and the types and quantities of supplies and materials they need for survival and recovery. 【0397】 "Data analysis tools" is a general term for software and hardware used to analyze collected data and generate specific information necessary for rescuing disaster victims. 【0398】 "An emotional analysis tool for re-evaluating rescue priorities through emotional analysis" refers to a device and method for analyzing the psychological state of disaster victims and rescue teams, and determining the optimal rescue priorities based on the results obtained. 【0399】 "Physical conditions and psychological states" refer to both the specific physical conditions of the damage at the disaster site (e.g., the location of victims, the extent of building collapse) and the psychological aspects such as the emotions and stress levels of victims and rescuers. 【0400】 This invention relates to a system for enabling rapid and accurate rescue operations during disasters. The server uses data acquisition means to identify the area where the disaster has occurred and efficiently collects information on the situation in the affected area using unmanned aerial vehicles and ground mobile devices. It is also equipped with data analysis means to determine the location of victims and the necessary relief supplies based on the information obtained from the data acquisition means. 【0401】 Furthermore, it includes an emotion analysis mechanism that re-evaluates the prioritization of rescue operations based on emotion analysis. This emotion analysis uses machine learning libraries such as TensorFlow to determine the psychological state of victims and rescue teams. For example, it analyzes voice and text data to measure the stress levels and anxiety levels of victims and determine the urgency of the rescue. 【0402】 In this system, the server processes the collected data and displays it on a dashboard in real time. Rescue team operators, who are the users, use this information to develop rescue plans. Furthermore, because the results of emotion analysis are reflected immediately, it becomes possible to prioritize the rescue of victims who are psychologically unstable. 【0403】 As a concrete example, consider a scenario where the system responds to a large-scale earthquake. In this case, the system performs real-time emotion analysis on text and voice messages sent from victims near the epicenter to identify victims who are experiencing fear or anxiety. The server then communicates the results to an operator and, if necessary, issues instructions to direct a drone towards the location of that victim. 【0404】 By utilizing a generative AI model and inputting prompts such as, "Please tell me what actions EmoRobo should take to reduce residents' stress during a disaster," it is possible to propose specific countermeasures tailored to the emotional state of disaster victims. In this way, the invention constructs an advanced rescue system that considers not only physical but also psychological aspects. 【0405】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0406】 Step 1: 【0407】 The server identifies the area affected by the disaster. It controls unmanned aerial vehicles and ground mobile devices to collect local data using sensors. The input is real-time geographic data, and the output is location information and initial damage information. Based on this information, an overview of the entire affected area is obtained. 【0408】 Step 2: 【0409】 The server further analyzes the collected data using data acquisition methods. Here, it analyzes the specific locations of disaster victims and the necessary relief supplies. Inputs include location information and damage information, while outputs include location data for disaster victims and a list of necessary supplies. 【0410】 Step 3: 【0411】 The user, a rescue team operator, verifies the location data of disaster victims provided by the server. Furthermore, they develop a transportation plan based on the necessary relief supplies information. Inputs are disaster victim location data and a list of necessary supplies; output is the transportation plan. This enables efficient supply delivery. 【0412】 Step 4: 【0413】 The terminal, either a ground mobile device or an unmanned aerial vehicle, actually distributes relief supplies according to the transport plan. Based on instructions from the server, it begins moving to the location of the affected people and delivers the supplies. The input is the transport plan, and the output is the delivery completion status of the supplies. 【0414】 Step 5: 【0415】 The server uses emotion analysis tools to monitor the psychological state of disaster victims and users. Using voice and text data from sensors as input, a generative AI model measures stress levels and anxiety levels, and outputs the results of the emotion state analysis. This analysis is used to revise the priorities of rescue operations. 【0416】 Step 6: 【0417】 The user adjusts rescue operations based on the results of the emotional state analysis. Specifically, they order a rapid response to victims with increased priority and reallocate the rescue team's tasks as needed. The input is the emotional state analysis results, and the output is the optimized rescue operation strategy. 【0418】 Step 7: 【0419】 The prompt "Please tell us what actions EmoRobo should take to reduce residents' stress during a disaster" is input to the generating AI model, which then proposes the optimal method of resident care. This result will be reflected in rescue operations. The input is the prompt to the generating AI, and the output is the method of reducing residents' stress. 【0420】 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. 【0421】 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. 【0422】 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. 【0423】 [Third Embodiment] 【0424】 Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment. 【0425】 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. 【0426】 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). 【0427】 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. 【0428】 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. 【0429】 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). 【0430】 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. 【0431】 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. 【0432】 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. 【0433】 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. 【0434】 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. 【0435】 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". 【0436】 The present invention is a disaster relief system having means for acquiring information, means for analyzing information, and means for distributing information. The system can be activated quickly when a disaster occurs, and with each of its functions, it can immediately grasp the situation in the affected area and rapidly deploy necessary relief activities. 【0437】 In this system, the server first defines disaster risk areas and monitors earthquake and weather data in real time. When a disaster occurs, the server identifies the coordinates of the affected area and sends scan commands to deployed drones and ground robots (hereinafter referred to as terminals). The terminals then quickly begin to move, collecting information using thermal imaging cameras from the air and voice recognition technology from the ground. 【0438】 This collected information is analyzed by a server. The server assesses the precise location of victims and the risk of secondary disasters, and develops optimal rescue routes and supply plans. For example, if victims isolated in a remote area are detected, the server will designate that area as a high priority and develop a plan to quickly deliver first-aid kits, drinking water, and other supplies. 【0439】 Next, the terminal flies or drives along a specific route based on this plan, precisely positioning the relief supplies at designated locations. The drone sends back high-resolution images from the air while dropping the supplies, and ground robots distribute them directly, enabling rapid delivery of aid to disaster victims. 【0440】 Furthermore, data from ongoing rescue operations is monitored by the server and continuously analyzed until the operation is completed. This allows the user to be provided with updates on the rescue progress and to flexibly adjust any necessary additional actions. For example, if the situation on the ground changes, the user can instruct the server to set new priorities, thereby immediately optimizing the rescue operation. 【0441】 Thus, the system of the present invention enables a flexible and rapid response tailored to the situation in the disaster area, and significantly improves the accuracy and efficiency of life-saving efforts. 【0442】 The following describes the processing flow. 【0443】 Step 1: 【0444】 The server monitors weather and earthquake data in real time to detect the occurrence of disasters. This allows for preparation in advance so that a rapid response can be made when a disaster occurs in a specific region. 【0445】 Step 2: 【0446】 When a disaster occurs, the server identifies its coordinates and issues scan commands to drones and ground robots (terminals) waiting nearby. This instructs them to move quickly to the area to be scanned. 【0447】 Step 3: 【0448】 Once the terminal (drone) reaches the airspace above the disaster area, it begins a wide-area scan. It uses a thermal imaging camera to detect the body temperature of victims and obtain their location information. 【0449】 Step 4: 【0450】 The terminal (ground robot) moves around on the ground using voice recognition technology to detect cries for help, collision sounds, and other sounds, and conducts a detailed scan. 【0451】 Step 5: 【0452】 The server receives data sent from the terminals and analyzes that information using AI. This allows it to determine the location of disaster victims, as well as their situation and priorities. 【0453】 Step 6: 【0454】 Based on the analysis results, the server develops an optimal rescue plan. This plan includes supplying materials to high-priority victims and setting specific routes. 【0455】 Step 7: 【0456】 The terminal (drone) follows instructions from the server and drops supplies to designated locations. Ground robots also follow those instructions and head towards the disaster area carrying supplies. 【0457】 Step 8: 【0458】 The server monitors the progress of the entire rescue operation and continuously provides users with activity reports. It receives new instructions as the situation changes and optimizes the plan again. 【0459】 (Example 1) 【0460】 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." 【0461】 In recent years, with the increasing frequency of natural disasters, there is a growing need for rapid and accurate assessment of the situation in disaster-stricken areas and efficient rescue operations. Current systems make it difficult to accurately assess the situation in disaster-stricken areas and plan appropriate rescue routes, sometimes hindering a swift response. To solve this problem, a system is needed for real-time data monitoring and analysis, as well as for efficient material distribution. 【0462】 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. 【0463】 In this invention, the server includes an information acquisition means for setting the area where a disaster has occurred and monitoring weather and seismic data in that area in real time to detect anomalies; an analysis means for analyzing the data obtained from the information acquisition means to identify the location of victims and formulate an optimal rescue route and supply plan; and a delivery means for distributing rescue supplies to appropriate locations using unmanned aircraft and ground mobility equipment based on the results of the analysis means. This makes it possible to quickly and accurately grasp the situation in the disaster area and carry out rescue operations efficiently. 【0464】 "Areas where a disaster has occurred" refers to areas identified by geographic information systems as being at risk of natural disasters or areas where disasters have actually occurred. 【0465】 "Information acquisition means" refers to technical devices or methods for collecting data related to disaster occurrence in real time, and specifically includes data from weather sensors and earthquake detection sensors. 【0466】 "Analysis means" refers to algorithms or software that process data obtained from information acquisition means to derive information necessary for identifying the location of disaster victims and planning rescue routes. 【0467】 "Delivery means" refers to a function or device intended to accurately distribute goods to target locations using unmanned aerial vehicles or ground-based mobile equipment, based on a plan determined by analytical means. 【0468】 An "unmanned aircraft" is an aircraft that can fly remotely or autonomously, and is primarily used for aerial data collection and material transport. 【0469】 "Ground-based mobile equipment" refers to robotic or vehicle-type devices used to collect data and deliver supplies while moving on the ground. 【0470】 A "thermal imaging camera" is a device that visualizes infrared radiation emitted from objects or the Earth's surface, and has the function of forming an image of the heat distribution. 【0471】 "Speech recognition technology" refers to the technology or algorithms used to analyze audio data and recognize specific sounds or human voices. 【0472】 A "machine learning algorithm" refers to a computer learning method that creates a model based on a large amount of data and uses it to make predictions and evaluations on unknown data. 【0473】 "Risk of secondary disasters" refers to the probability of a disaster or dangerous event occurring following the primary disaster, or the impact associated with such an event. 【0474】 This disaster relief system is designed to enable a rapid and efficient response in the event of a disaster. The following describes a specific implementation of this system. 【0475】 First, the server configures disaster risk areas and uses GIS (Geographic Information System) to identify affected areas. The server acquires data in real time through interfaces such as weather sensors and earthquake detection sensors, and monitors weather and earthquake data using a RESTful API. This process utilizes analytical algorithms to immediately detect anomalies. 【0476】 Regarding the operational aspects, unmanned aerial vehicles (drones) and ground-based mobile equipment (ground robots) will be used to collect information and deliver supplies. Drones will be equipped with high-precision sensors such as FLIR thermal imaging cameras to acquire thermal information from the air. Meanwhile, ground robots will use voice recognition technology to analyze audio data from the disaster area and detect human voices and other important sounds. 【0477】 The server integrates and analyzes this data. It implements machine learning algorithms to assess the precise location of victims and the risk of potential secondary disasters. Based on this analysis, it develops optimal rescue routes and supply plans. For example, if a victim isolated in a remote location is detected, the server designates that location as a high priority and creates a plan to quickly deliver necessary relief supplies. 【0478】 The terminals can deliver supplies along pre-configured flight or driving routes according to the server's plan. Drones can send real-time collected data back to the server and adjust their routes as needed. Ground robots are responsible for delivering supplies directly to disaster victims. 【0479】 As a concrete example related to this system, the following is an example of a prompt statement using a generative AI model: "Please analyze the current situation in the disaster area in detail and propose the optimal rescue route." By inputting this prompt statement, the system can quickly create a rescue plan based on the latest information. 【0480】 In this way, the system supports accurate assessment of the situation during a disaster and efficient rescue operations. 【0481】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0482】 Step 1: 【0483】 The server defines disaster risk areas using GIS data. The input to this process is historical disaster data and geographical condition data, and the output is a list of high-risk areas. The server receives real-time data from weather and earthquake sensors via APIs and uses it to detect anomalies and notify of disaster occurrences. 【0484】 Step 2: 【0485】 When the server detects a disaster, it immediately identifies the coordinates of the affected area and sends a scan command to the terminal. The input for this step is weather and earthquake data collected in real time, and the output is the coordinate information of the identified affected area. The server uses this coordinate information to instruct the terminal to take immediate action. 【0486】 Step 3: 【0487】 The terminal receives commands from the server and heads towards the area requiring attention. The drone begins gathering information from above, acquiring temperature distribution data using a thermal imaging camera. The ground robot collects surrounding audio data using voice recognition technology. The input for this step is command details from the server, and the output is environmental data acquired in real time. 【0488】 Step 4: 【0489】 The server analyzes data transmitted from terminals to identify the location of victims. Input consists of thermal images and audio data, and machine learning algorithms are used to assess the location of victims and the risk of secondary disasters. Output is the precise location of victims and a list of areas requiring top-priority response. 【0490】 Step 5: 【0491】 The server devises the optimal rescue route and supply plan based on the analysis results. Traffic conditions and topographical information are also considered in creating this plan. The input consists of the location information of victims and geographical data of the area, and the generated rescue plan is provided to the terminal as output. 【0492】 Step 6: 【0493】 The terminal delivers supplies along a designated route based on the received rescue plan. Drones accurately drop supplies, and ground robots travel to the necessary locations to distribute them. The input is the specific rescue plan, and the output is the status of the appropriate distribution of supplies in the disaster area. 【0494】 Step 7: 【0495】 Users can receive progress information on ongoing rescue operations from the server and issue additional instructions as needed. Input is progress data provided by the server, while output is new instructions and priority settings sent by the user to the server. This enables rapid, situation-driven responses. 【0496】 (Application Example 1) 【0497】 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." 【0498】 In the event of a disaster, it is crucial to quickly and accurately grasp the situation in the affected area and to efficiently carry out rescue operations. Conventional systems have faced challenges in appropriately analyzing the situation across the entire affected area in real time, prioritizing tasks, and generating the optimal rescue routes. 【0499】 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. 【0500】 In this invention, the server includes: information acquisition means for identifying the area where a disaster has occurred and analyzing the damage situation in that area; analysis means for analyzing the data obtained from the information acquisition means to determine the location of victims and the necessary relief supplies; delivery means for distributing relief supplies to appropriate locations based on the results of the analysis means; and optimization means for analyzing location information to optimize rescue operations and prioritizing them using environmental data acquired in real time. This makes it possible to quickly grasp the situation in the affected area when a disaster occurs and to efficiently deploy optimal rescue operations. 【0501】 "Information acquisition means" refers to devices and systems used to identify areas where disasters have occurred and to collect information on the extent of damage in those areas in real time. 【0502】 "Analysis means" refers to devices and methods for analyzing data obtained from information acquisition means to determine the precise location of disaster victims and the necessary relief supplies. 【0503】 "Distribution means" refers to a system or method for distributing relief supplies to appropriate locations according to a route optimized based on the results of the analysis means. 【0504】 "Optimization methods" refer to techniques and technologies that use environmental data acquired in real time to analyze location information and prioritize rescue operations in order to optimize them. 【0505】 The system for realizing this application is centered around a server. The server is equipped with means for acquiring, analyzing, and distributing information. Specifically, the server is connected to sensors and data feeds for rapidly collecting local information in the event of a disaster. This makes it possible to monitor earthquake and weather data in real time. The server receives this data, identifies the coordinates of the affected area, and, if necessary, commands terminals such as unmanned aerial vehicles and ground robots. 【0506】 These devices are equipped with thermal imaging cameras and voice recognition technology, and they scan the disaster area in detail based on commands sent from the server. The data collected by the devices is sent back to the server and analyzed using AI algorithms, including deep learning (e.g., TensorFlow). This analysis assesses the location of victims and the risk of secondary disasters, and helps to develop the most efficient rescue routes and supply plans. 【0507】 As a delivery method, the server plans the drone's flight path and precisely drops supplies to designated locations. Data from ongoing rescue operations is also continuously monitored, providing users with progress updates. The server instantly optimizes rescue operations by allowing users to specify new priorities as needed. 【0508】 For example, if an earthquake occurs and isolated victims are detected in a remote area, the server will designate this area as a high priority and quickly plan to deliver first-aid kits and drinking water. The following prompt statements can be used in the generative AI model. 【0509】 Example of a prompt: 【0510】 "We have received new disaster information. An earthquake has occurred, there are victims at location A, and there is a risk of landslides at location B. Please develop the optimal relief routes and supply distribution plans." 【0511】 In this way, the system enables servers, terminals, and users to work together to carry out disaster relief activities quickly and efficiently. 【0512】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0513】 Step 1: 【0514】 The server acquires information about the disaster-stricken area. It receives input from various sensors and data feeds to collect real-time data such as earthquake, weather, and location information. Based on this data, it processes the data to identify the coordinates of the affected area. The output is the coordinate information of the disaster-stricken area. 【0515】 Step 2: 【0516】 The server sends scan commands to the terminals of unmanned aerial vehicles and ground robots based on the identified coordinates. The input here is coordinate information, and the output is the scan command received by the terminal. Specifically, the server communicates with the terminal via the network and sets the scan conditions. 【0517】 Step 3: 【0518】 The terminal scans the disaster area based on instructions from the server. It uses thermal imaging cameras and voice recognition technology to obtain information on the extent of the damage and the location of victims. The input is the scan command, and the output is the scan result data. Specifically, unmanned aerial vehicles take thermal images from the air, and ground robots record ambient sounds. 【0519】 Step 4: 【0520】 The scan results acquired by the terminal are sent to the server. The server receives this as input and performs data analysis using an AI algorithm. The output is the location of victims and other important disaster information. Generative AI models such as TensorFlow are used in this step. 【0521】 Step 5: 【0522】 The server formulates the optimal rescue route and supply plan based on the analysis results. The input is the analyzed data, and the output is a rescue plan. Specifically, the AI ​​optimizes the route and proposes resource allocation. 【0523】 Step 6: 【0524】 The server directs the drone's precise flight path based on the formulated plan. The input is the rescue plan, and the output is the drone's flight route information. The server uses this information to direct the distribution of supplies. 【0525】 Step 7: 【0526】 The user receives progress information on the rescue operation from the server. The server sends the progress to the user and accepts input for adjusting the priority if necessary. The output is the operation progress and the new rescue priority. Specifically, the server periodically sends notifications to the user and waits for instructions. 【0527】 These steps enable disaster relief operations to be carried out efficiently. 【0528】 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. 【0529】 This invention relates to a disaster relief system incorporating an emotion engine, developed to provide more effective support during disasters. The system includes information acquisition means, analysis means, delivery means, and an emotion engine. 【0530】 After a disaster occurs, the server monitors the situation in the affected area in real time, and drones and ground robots (terminals) quickly initiate initial response. Each terminal uses thermal imaging technology to scan a wide area and collect data to identify victims. This data is then sent to the server, where detailed analysis is performed using AI. This determines the location of victims, the presence or absence of obstacles, and priority. 【0531】 A distinctive feature of this system is the presence of an emotion engine. This emotion engine analyzes the emotions of disaster victims and users, and is equipped with an algorithm that evaluates stress levels and anxiety levels. For example, if a disaster victim is feeling anxious or fearful, the emotion engine recognizes this state and readjusts the priority of rescue operations accordingly. 【0532】 For example, if an isolated disaster victim sends a message urgently requesting rescue, the emotion engine will determine the level of stress and urgency from the voice and text contained in the message. This result will be reflected on the server, and a flexible rescue plan will be reconstructed to meet the needs of the entire disaster area. 【0533】 Furthermore, the emotion engine analyzes the emotional state of not only disaster victims but also users (for example, operators on rescue teams). If the server determines that a user's stress level is high, it will automatically recommend actions to reduce response times, supporting efficient operations. 【0534】 This entire system aims to enable swift and considerate rescue operations that also take into account the emotional well-being of disaster victims. The emotional engine's functionality allows for detailed responses tailored to the individual circumstances of each victim, contributing to saving more lives. 【0535】 The following describes the processing flow. 【0536】 Step 1: 【0537】 The server collects disaster precursor and occurrence information in real time and monitors data related to earthquakes and weather changes. Based on this information, it creates a list of potentially affected areas and enhances monitoring. 【0538】 Step 2: 【0539】 When a disaster is detected, the server immediately identifies the coordinates of the affected area and sends dispatch orders to nearby drones and ground robots (terminals). 【0540】 Step 3: 【0541】 Once the terminal (drone) reaches the airspace above the disaster area, it will begin scanning using a thermal imaging camera to identify high-priority responses. It will also collect body temperature information from victims and transmit it to a server. 【0542】 Step 4: 【0543】 The terminal (ground robot) patrols the disaster area, using voice recognition to detect cries for help and sounds made by people, and transmits that information to a server. 【0544】 Step 5: 【0545】 The server uses AI technology to analyze the vast amount of data collected from terminals and assess the precise location and condition of disaster victims. It then uses an emotion engine to measure the degree of stress and the level of urgency of their emotions, along with the level of danger to their lives. 【0546】 Step 6: 【0547】 The emotion engine analyzes the anxiety and fear of disaster victims based on emotional analysis data and provides the server with a priority list for rescue efforts that takes this into account. 【0548】 Step 7: 【0549】 The server develops an optimized rescue plan based on emotional data and analysis results. This plan includes precise distribution points and routes for supplies and takes into account the emotional state of the victims. 【0550】 Step 8: 【0551】 Based on instructions from the server, the terminal (drone) drops supplies, including first-aid kits and food, to designated locations, while ground robots also deliver supplies to specified target points. 【0552】 Step 9: 【0553】 The server monitors the progress of rescue operations in real time and provides users with status reports. It accepts new instructions as needed and can flexibly adjust the rescue plan. 【0554】 Step 10: 【0555】 Based on the analysis results of an emotion engine that supports on-site operations, users provide care-based responses as part of their communication with disaster victims. This makes it possible to provide psychological support during disasters. 【0556】 (Example 2) 【0557】 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." 【0558】 To ensure rapid and appropriate rescue operations during a disaster, prioritization must consider not only the location of victims but also their emotional state. However, conventional rescue systems lacked the means to appropriately assess victims' emotions and dynamically adjust rescue priorities based on those assessments. As a result, rescue operations sometimes fail to adequately meet the urgent needs of victims. 【0559】 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. 【0560】 In this invention, the server includes information gathering means for identifying and analyzing the area where a disaster has occurred, emotion analysis means for analyzing the emotions of disaster victims and users, and analysis means for optimizing rescue operations based on the collected data. This makes it possible to prioritize appropriate and rapid rescue operations while taking into account the emotional state of disaster victims. 【0561】 "Information gathering means" refers to technologies and devices used to identify areas where disasters have occurred and to monitor and analyze the extent of damage in those areas. 【0562】 "Analysis means" refers to the technology and equipment used to determine the location of disaster victims and the necessary relief supplies based on data obtained from information gathering means. 【0563】 "Emotional analysis means" refers to technologies and devices that analyze the emotional state of disaster victims and users, and optimize the priority of rescue operations based on that analysis. 【0564】 "Delivery means" refers to the technology and equipment used to distribute rescue supplies to appropriate locations based on the results of analytical means and sentiment analysis means. 【0565】 An "unmanned vehicle" is a remotely controlled or autonomously operating flying device used to efficiently scan disaster-stricken areas. 【0566】 "Ground equipment" refers to autonomous or remotely operated devices used on the ground to scan disaster-stricken areas and collect data. 【0567】 "Thermal radiation imaging technology" is a technique that visualizes the location of victims and the condition of objects by detecting infrared radiation emitted from them. 【0568】 "Speech recognition technology" is a technology that analyzes speech data to understand its meaning and emotions, and then processes the data accordingly. 【0569】 This invention is a system that enables rapid and appropriate rescue operations during disasters. Its main components include information gathering means, analysis means, sentiment analysis means, and delivery means. 【0570】 The server uses information gathering methods to identify the situation in disaster-stricken areas and monitors and analyzes damage data in real time. This information gathering utilizes unmanned vehicles (such as drones) and ground devices (such as robots). These devices employ thermal imaging technology and voice recognition technology to scan wide areas and precisely detect the location and condition of victims. 【0571】 The collected data is sent to a server, where detailed analysis is performed using AI-generated models. This makes it possible to accurately pinpoint the location of disaster victims and identify the necessary relief supplies. 【0572】 Furthermore, the server uses sentiment analysis tools to assess the emotional state of disaster victims and rescue users. This assessment is performed by converting voice messages and text data from the disaster area into prompts. For example, if it receives a message such as, "It's very dangerous here, and I'm anxious. I need help quickly," it analyzes the emotional content and reassessss the rescue priority. 【0573】 From the user's perspective, the rescue team operators are included in the scope of emotion analysis, and the server adjusts their workload appropriately to reduce their stress and psychological burden. 【0574】 Ultimately, a delivery system is established, and an optimized rescue plan is implemented based on the results of analytical and sentiment analysis, ensuring that necessary rescue supplies are distributed efficiently and appropriately. 【0575】 An example of a prompt message could be: "We have received an urgent message from the disaster area. The message reads: 'It is very dangerous here and I am anxious. I need help quickly.' Based on this information, analyze the emotional state of the victims and optimize the rescue plan." This allows the generative AI model to perform an emotional assessment and facilitate a rapid response. 【0576】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0577】 Step 1: 【0578】 The server begins collecting information after a disaster occurs. It controls unmanned robots and ground equipment to scan the affected area. Topographic information and disaster trigger information are used as input, and real-time scan data of the affected area is obtained as output. The server acquires this data and performs initial analysis. 【0579】 Step 2: 【0580】 The terminals, specifically drones and robots, analyze the collected scan data using thermal radiation imaging technology. The input is scan data, and the output is location information for areas with temperature anomalies and suspected victims. The terminals register this information into a map of the affected area. 【0581】 Step 3: 【0582】 The server receives location and temperature data collected from terminals and performs detailed analysis using a generating AI model. The input is location and temperature data, and the output is the precise location of the victims, the presence or absence of obstacles, and a rescue priority list. Furthermore, the AI ​​model predicts the condition of the victims and calculates the degree of urgency. 【0583】 Step 4: 【0584】 The server then uses emotion analysis tools to evaluate the emotional state of the victims and users. Inputs include voice and text messages from victims, and output is the result of the emotional state evaluation. For example, it processes messages such as "It's very dangerous here, and I'm anxious. I need help quickly" using prompts, and quantifies the level of anxiety and urgency. 【0585】 Step 5: 【0586】 The emotional state of the users, specifically the operators of the rescue team, is also evaluated. Inputs include user operation logs and audio data, while output is the user's stress level. Based on this information, the server adjusts the workload of the operation and optimizes the efficiency of each operator. 【0587】 Step 6: 【0588】 The server optimizes rescue operations based on the results of analytical and sentiment analysis. It plans the delivery routes for rescue supplies and ensures their rapid distribution to the necessary locations. The inputs are a rescue priority list and sentiment evaluation results, and the output is an optimized rescue plan. This enables efficient rescue operations. 【0589】 (Application Example 2) 【0590】 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." 【0591】 During disasters, swift and accurate rescue operations are crucial. However, while conventional disaster relief systems excel at identifying the physical location of victims and determining necessary supplies, they fail to prioritize rescue efforts based on the psychological state of both victims and rescuers. This can lead to delays in responding to victims, especially those who are emotionally unstable. Furthermore, it can increase the psychological burden on rescuers themselves, potentially reducing operational efficiency. Therefore, a more flexible rescue system that takes psychological states into consideration is needed. 【0592】 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. 【0593】 In this invention, the server includes data acquisition means for identifying the area where a disaster has occurred and analyzing the extent of the damage; data analysis means for determining the location of victims and the necessary relief supplies based on the information obtained from the data acquisition means; and sentiment analysis means for re-evaluating the priorities of rescue operations through sentiment analysis. This enables accurate and efficient rescue operations based on both the physical situation and the psychological state of the victims. 【0594】 "Area affected by a disaster" refers to a specific geographical area where physical damage or disruption has occurred due to a natural or man-made disaster. 【0595】 "Data acquisition means for analyzing damage status" refers to a group of devices that use sensors, drones, and ground-based mobile devices to collect information from the site and analyze the extent of damage caused by the disaster and the situation of the victims. 【0596】 "Information obtained from data acquisition methods" refers to disaster situation and environmental data collected from the field, and is the basic data used to conduct more detailed analysis based on this information. 【0597】 "Location of victims and necessary relief supplies" refers to information about the current location of victims and the types and quantities of supplies and materials they need for survival and recovery. 【0598】 "Data analysis tools" is a general term for software and hardware used to analyze collected data and generate specific information necessary for rescuing disaster victims. 【0599】 "An emotional analysis tool for re-evaluating rescue priorities through emotional analysis" refers to a device and method for analyzing the psychological state of disaster victims and rescue teams, and determining the optimal rescue priorities based on the results obtained. 【0600】 "Physical conditions and psychological states" refer to both the specific physical conditions of the damage at the disaster site (e.g., the location of victims, the extent of building collapse) and the psychological aspects such as the emotions and stress levels of victims and rescuers. 【0601】 This invention relates to a system for enabling rapid and accurate rescue operations during disasters. The server uses data acquisition means to identify the area where the disaster has occurred and efficiently collects information on the situation in the affected area using unmanned aerial vehicles and ground mobile devices. It is also equipped with data analysis means to determine the location of victims and the necessary relief supplies based on the information obtained from the data acquisition means. 【0602】 Furthermore, it includes an emotion analysis mechanism that re-evaluates the prioritization of rescue operations based on emotion analysis. This emotion analysis uses machine learning libraries such as TensorFlow to determine the psychological state of victims and rescue teams. For example, it analyzes voice and text data to measure the stress levels and anxiety levels of victims and determine the urgency of the rescue. 【0603】 In this system, the server processes the collected data and displays it on a dashboard in real time. Rescue team operators, who are the users, use this information to develop rescue plans. Furthermore, because the results of emotion analysis are reflected immediately, it becomes possible to prioritize the rescue of victims who are psychologically unstable. 【0604】 As a concrete example, consider a scenario where the system responds to a large-scale earthquake. In this case, the system performs real-time emotion analysis on text and voice messages sent from victims near the epicenter to identify victims who are experiencing fear or anxiety. The server then communicates the results to an operator and, if necessary, issues instructions to direct a drone towards the location of that victim. 【0605】 By utilizing a generative AI model and inputting prompts such as, "Please tell me what actions EmoRobo should take to reduce residents' stress during a disaster," it is possible to propose specific countermeasures tailored to the emotional state of disaster victims. In this way, the invention constructs an advanced rescue system that considers not only physical but also psychological aspects. 【0606】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0607】 Step 1: 【0608】 The server identifies the area affected by the disaster. It controls unmanned aerial vehicles and ground mobile devices to collect local data using sensors. The input is real-time geographic data, and the output is location information and initial damage information. Based on this information, an overview of the entire affected area is obtained. 【0609】 Step 2: 【0610】 The server further analyzes the collected data using data acquisition methods. Here, it analyzes the specific locations of disaster victims and the necessary relief supplies. Inputs include location information and damage information, while outputs include location data for disaster victims and a list of necessary supplies. 【0611】 Step 3: 【0612】 The user, a rescue team operator, verifies the location data of disaster victims provided by the server. Furthermore, they develop a transportation plan based on the necessary relief supplies information. Inputs are disaster victim location data and a list of necessary supplies; output is the transportation plan. This enables efficient supply delivery. 【0613】 Step 4: 【0614】 The terminal, either a ground mobile device or an unmanned aerial vehicle, actually distributes relief supplies according to the transport plan. Based on instructions from the server, it begins moving to the location of the affected people and delivers the supplies. The input is the transport plan, and the output is the delivery completion status of the supplies. 【0615】 Step 5: 【0616】 The server uses emotion analysis tools to monitor the psychological state of disaster victims and users. Using voice and text data from sensors as input, a generative AI model measures stress levels and anxiety levels, and outputs the results of the emotion state analysis. This analysis is used to revise the priorities of rescue operations. 【0617】 Step 6: 【0618】 The user adjusts rescue operations based on the results of the emotional state analysis. Specifically, they order a rapid response to victims with increased priority and reallocate the rescue team's tasks as needed. The input is the emotional state analysis results, and the output is the optimized rescue operation strategy. 【0619】 Step 7: 【0620】 The prompt "Please tell us what actions EmoRobo should take to reduce residents' stress during a disaster" is input to the generating AI model, which then proposes the optimal method of resident care. This result will be reflected in rescue operations. The input is the prompt to the generating AI, and the output is the method of reducing residents' stress. 【0621】 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. 【0622】 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. 【0623】 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. 【0624】 [Fourth Embodiment] 【0625】 Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment. 【0626】 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. 【0627】 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). 【0628】 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. 【0629】 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. 【0630】 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). 【0631】 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. 【0632】 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. 【0633】 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. 【0634】 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. 【0635】 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. 【0636】 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. 【0637】 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". 【0638】 The present invention is a disaster relief system having means for acquiring information, means for analyzing information, and means for distributing information. The system can be activated quickly when a disaster occurs, and with each of its functions, it can immediately grasp the situation in the affected area and rapidly deploy necessary relief activities. 【0639】 In this system, the server first defines disaster risk areas and monitors earthquake and weather data in real time. When a disaster occurs, the server identifies the coordinates of the affected area and sends scan commands to deployed drones and ground robots (hereinafter referred to as terminals). The terminals then quickly begin to move, collecting information using thermal imaging cameras from the air and voice recognition technology from the ground. 【0640】 This collected information is analyzed by a server. The server assesses the precise location of victims and the risk of secondary disasters, and develops optimal rescue routes and supply plans. For example, if victims isolated in a remote area are detected, the server will designate that area as a high priority and develop a plan to quickly deliver first-aid kits, drinking water, and other supplies. 【0641】 Next, the terminal flies or drives along a specific route based on this plan, precisely positioning the relief supplies at designated locations. The drone sends back high-resolution images from the air while dropping the supplies, and ground robots distribute them directly, enabling rapid delivery of aid to disaster victims. 【0642】 Furthermore, data from ongoing rescue operations is monitored by the server and continuously analyzed until the operation is completed. This allows the user to be provided with updates on the rescue progress and to flexibly adjust any necessary additional actions. For example, if the situation on the ground changes, the user can instruct the server to set new priorities, thereby immediately optimizing the rescue operation. 【0643】 Thus, the system of the present invention enables a flexible and rapid response tailored to the situation in the disaster area, and significantly improves the accuracy and efficiency of life-saving efforts. 【0644】 The following describes the processing flow. 【0645】 Step 1: 【0646】 The server monitors weather and earthquake data in real time to detect the occurrence of disasters. This allows for preparation in advance so that a rapid response can be made when a disaster occurs in a specific region. 【0647】 Step 2: 【0648】 When a disaster occurs, the server identifies its coordinates and issues scan commands to drones and ground robots (terminals) waiting nearby. This instructs them to move quickly to the area to be scanned. 【0649】 Step 3: 【0650】 Once the terminal (drone) reaches the airspace above the disaster area, it begins a wide-area scan. It uses a thermal imaging camera to detect the body temperature of victims and obtain their location information. 【0651】 Step 4: 【0652】 The terminal (ground robot) moves around on the ground using voice recognition technology to detect cries for help, collision sounds, and other sounds, and conducts a detailed scan. 【0653】 Step 5: 【0654】 The server receives data sent from the terminals and analyzes that information using AI. This allows it to determine the location of disaster victims, as well as their situation and priorities. 【0655】 Step 6: 【0656】 Based on the analysis results, the server develops an optimal rescue plan. This plan includes supplying materials to high-priority victims and setting specific routes. 【0657】 Step 7: 【0658】 The terminal (drone) follows instructions from the server and drops supplies to designated locations. Ground robots also follow those instructions and head towards the disaster area carrying supplies. 【0659】 Step 8: 【0660】 The server monitors the progress of the entire rescue operation and continuously provides users with activity reports. It receives new instructions as the situation changes and optimizes the plan again. 【0661】 (Example 1) 【0662】 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". 【0663】 In recent years, with the increasing frequency of natural disasters, there is a growing need for rapid and accurate assessment of the situation in disaster-stricken areas and efficient rescue operations. Current systems make it difficult to accurately assess the situation in disaster-stricken areas and plan appropriate rescue routes, sometimes hindering a swift response. To solve this problem, a system is needed for real-time data monitoring and analysis, as well as for efficient material distribution. 【0664】 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. 【0665】 In this invention, the server includes an information acquisition means for setting the area where a disaster has occurred and monitoring weather and seismic data in that area in real time to detect anomalies; an analysis means for analyzing the data obtained from the information acquisition means to identify the location of victims and formulate an optimal rescue route and supply plan; and a delivery means for distributing rescue supplies to appropriate locations using unmanned aircraft and ground mobility equipment based on the results of the analysis means. This makes it possible to quickly and accurately grasp the situation in the disaster area and carry out rescue operations efficiently. 【0666】 "Areas where a disaster has occurred" refers to areas identified by geographic information systems as being at risk of natural disasters or areas where disasters have actually occurred. 【0667】 "Information acquisition means" refers to technical devices or methods for collecting data related to disaster occurrence in real time, and specifically includes data from weather sensors and earthquake detection sensors. 【0668】 "Analysis means" refers to algorithms or software that process data obtained from information acquisition means to derive information necessary for identifying the location of disaster victims and planning rescue routes. 【0669】 "Delivery means" refers to a function or device intended to accurately distribute goods to target locations using unmanned aerial vehicles or ground-based mobile equipment, based on a plan determined by analytical means. 【0670】 An "unmanned aircraft" is an aircraft that can fly remotely or autonomously, and is primarily used for aerial data collection and material transport. 【0671】 "Ground-based mobile equipment" refers to robotic or vehicle-type devices used to collect data and deliver supplies while moving on the ground. 【0672】 A "thermal imaging camera" is a device that visualizes infrared radiation emitted from objects or the Earth's surface, and has the function of forming an image of the heat distribution. 【0673】 "Speech recognition technology" refers to the technology or algorithms used to analyze audio data and recognize specific sounds or human voices. 【0674】 A "machine learning algorithm" refers to a computer learning method that creates a model based on a large amount of data and uses it to make predictions and evaluations on unknown data. 【0675】 "Risk of secondary disasters" refers to the probability of a disaster or dangerous event occurring following the primary disaster, or the impact associated with such an event. 【0676】 This disaster relief system is designed to enable a rapid and efficient response in the event of a disaster. The following describes a specific implementation of this system. 【0677】 First, the server configures disaster risk areas and uses GIS (Geographic Information System) to identify affected areas. The server acquires data in real time through interfaces such as weather sensors and earthquake detection sensors, and monitors weather and earthquake data using a RESTful API. This process utilizes analytical algorithms to immediately detect anomalies. 【0678】 Regarding the operational aspects, unmanned aerial vehicles (drones) and ground-based mobile equipment (ground robots) will be used to collect information and deliver supplies. Drones will be equipped with high-precision sensors such as FLIR thermal imaging cameras to acquire thermal information from the air. Meanwhile, ground robots will use voice recognition technology to analyze audio data from the disaster area and detect human voices and other important sounds. 【0679】 The server integrates and analyzes this data. It implements machine learning algorithms to assess the precise location of victims and the risk of potential secondary disasters. Based on this analysis, it develops optimal rescue routes and supply plans. For example, if a victim isolated in a remote location is detected, the server designates that location as a high priority and creates a plan to quickly deliver necessary relief supplies. 【0680】 The terminals can deliver supplies along pre-configured flight or driving routes according to the server's plan. Drones can send real-time collected data back to the server and adjust their routes as needed. Ground robots are responsible for delivering supplies directly to disaster victims. 【0681】 As a concrete example related to this system, the following is an example of a prompt statement using a generative AI model: "Please analyze the current situation in the disaster area in detail and propose the optimal rescue route." By inputting this prompt statement, the system can quickly create a rescue plan based on the latest information. 【0682】 In this way, the system supports accurate assessment of the situation during a disaster and efficient rescue operations. 【0683】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0684】 Step 1: 【0685】 The server defines disaster risk areas using GIS data. The input to this process is historical disaster data and geographical condition data, and the output is a list of high-risk areas. The server receives real-time data from weather and earthquake sensors via APIs and uses it to detect anomalies and notify of disaster occurrences. 【0686】 Step 2: 【0687】 When the server detects a disaster, it immediately identifies the coordinates of the affected area and sends a scan command to the terminal. The input for this step is weather and earthquake data collected in real time, and the output is the coordinate information of the identified affected area. The server uses this coordinate information to instruct the terminal to take immediate action. 【0688】 Step 3: 【0689】 The terminal receives commands from the server and heads towards the area requiring attention. The drone begins gathering information from above, acquiring temperature distribution data using a thermal imaging camera. The ground robot collects surrounding audio data using voice recognition technology. The input for this step is command details from the server, and the output is environmental data acquired in real time. 【0690】 Step 4: 【0691】 The server analyzes data transmitted from terminals to identify the location of victims. Input consists of thermal images and audio data, and machine learning algorithms are used to assess the location of victims and the risk of secondary disasters. Output is the precise location of victims and a list of areas requiring top-priority response. 【0692】 Step 5: 【0693】 The server devises the optimal rescue route and supply plan based on the analysis results. Traffic conditions and topographical information are also considered in creating this plan. The input consists of the location information of victims and geographical data of the area, and the generated rescue plan is provided to the terminal as output. 【0694】 Step 6: 【0695】 The terminal delivers supplies along a designated route based on the received rescue plan. Drones accurately drop supplies, and ground robots travel to the necessary locations to distribute them. The input is the specific rescue plan, and the output is the status of the appropriate distribution of supplies in the disaster area. 【0696】 Step 7: 【0697】 Users can receive progress information on ongoing rescue operations from the server and issue additional instructions as needed. Input is progress data provided by the server, while output is new instructions and priority settings sent by the user to the server. This enables rapid, situation-driven responses. 【0698】 (Application Example 1) 【0699】 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". 【0700】 In the event of a disaster, it is crucial to quickly and accurately grasp the situation in the affected area and to efficiently carry out rescue operations. Conventional systems have faced challenges in appropriately analyzing the situation across the entire affected area in real time, prioritizing tasks, and generating the optimal rescue routes. 【0701】 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. 【0702】 In this invention, the server includes: information acquisition means for identifying the area where a disaster has occurred and analyzing the damage situation in that area; analysis means for analyzing the data obtained from the information acquisition means to determine the location of victims and the necessary relief supplies; delivery means for distributing relief supplies to appropriate locations based on the results of the analysis means; and optimization means for analyzing location information to optimize rescue operations and prioritizing them using environmental data acquired in real time. This makes it possible to quickly grasp the situation in the affected area when a disaster occurs and to efficiently deploy optimal rescue operations. 【0703】 "Information acquisition means" refers to devices and systems used to identify areas where disasters have occurred and to collect information on the extent of damage in those areas in real time. 【0704】 "Analysis means" refers to devices and methods for analyzing data obtained from information acquisition means to determine the precise location of disaster victims and the necessary relief supplies. 【0705】 "Distribution means" refers to a system or method for distributing relief supplies to appropriate locations according to a route optimized based on the results of the analysis means. 【0706】 "Optimization methods" refer to techniques and technologies that use environmental data acquired in real time to analyze location information and prioritize rescue operations in order to optimize them. 【0707】 The system for realizing this application is centered around a server. The server is equipped with means for acquiring, analyzing, and distributing information. Specifically, the server is connected to sensors and data feeds for rapidly collecting local information in the event of a disaster. This makes it possible to monitor earthquake and weather data in real time. The server receives this data, identifies the coordinates of the affected area, and, if necessary, commands terminals such as unmanned aerial vehicles and ground robots. 【0708】 These devices are equipped with thermal imaging cameras and voice recognition technology, and they scan the disaster area in detail based on commands sent from the server. The data collected by the devices is sent back to the server and analyzed using AI algorithms, including deep learning (e.g., TensorFlow). This analysis assesses the location of victims and the risk of secondary disasters, and helps to develop the most efficient rescue routes and supply plans. 【0709】 As a delivery method, the server plans the drone's flight path and precisely drops supplies to designated locations. Data from ongoing rescue operations is also continuously monitored, providing users with progress updates. The server instantly optimizes rescue operations by allowing users to specify new priorities as needed. 【0710】 For example, if an earthquake occurs and isolated victims are detected in a remote area, the server will designate this area as a high priority and quickly plan to deliver first-aid kits and drinking water. The following prompt statements can be used in the generative AI model. 【0711】 Example of a prompt: 【0712】 "We have received new disaster information. An earthquake has occurred, there are victims at location A, and there is a risk of landslides at location B. Please develop the optimal relief routes and supply distribution plans." 【0713】 In this way, the system enables servers, terminals, and users to work together to carry out disaster relief activities quickly and efficiently. 【0714】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0715】 Step 1: 【0716】 The server acquires information about the disaster-stricken area. It receives input from various sensors and data feeds to collect real-time data such as earthquake, weather, and location information. Based on this data, it processes the data to identify the coordinates of the affected area. The output is the coordinate information of the disaster-stricken area. 【0717】 Step 2: 【0718】 The server sends scan commands to the terminals of unmanned aerial vehicles and ground robots based on the identified coordinates. The input here is coordinate information, and the output is the scan command received by the terminal. Specifically, the server communicates with the terminal via the network and sets the scan conditions. 【0719】 Step 3: 【0720】 The terminal scans the disaster area based on instructions from the server. It uses thermal imaging cameras and voice recognition technology to obtain information on the extent of the damage and the location of victims. The input is the scan command, and the output is the scan result data. Specifically, unmanned aerial vehicles take thermal images from the air, and ground robots record ambient sounds. 【0721】 Step 4: 【0722】 The scan results acquired by the terminal are sent to the server. The server receives this as input and performs data analysis using an AI algorithm. The output is the location of victims and other important disaster information. Generative AI models such as TensorFlow are used in this step. 【0723】 Step 5: 【0724】 The server formulates the optimal rescue route and supply plan based on the analysis results. The input is the analyzed data, and the output is a rescue plan. Specifically, the AI ​​optimizes the route and proposes resource allocation. 【0725】 Step 6: 【0726】 The server directs the drone's precise flight path based on the formulated plan. The input is the rescue plan, and the output is the drone's flight route information. The server uses this information to direct the distribution of supplies. 【0727】 Step 7: 【0728】 The user receives progress information on the rescue operation from the server. The server sends the progress to the user and accepts input for adjusting the priority if necessary. The output is the operation progress and the new rescue priority. Specifically, the server periodically sends notifications to the user and waits for instructions. 【0729】 These steps enable disaster relief operations to be carried out efficiently. 【0730】 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. 【0731】 This invention relates to a disaster relief system incorporating an emotion engine, developed to provide more effective support during disasters. The system includes information acquisition means, analysis means, delivery means, and an emotion engine. 【0732】 After a disaster occurs, the server monitors the situation in the affected area in real time, and drones and ground robots (terminals) quickly initiate initial response. Each terminal uses thermal imaging technology to scan a wide area and collect data to identify victims. This data is then sent to the server, where detailed analysis is performed using AI. This determines the location of victims, the presence or absence of obstacles, and priority. 【0733】 A distinctive feature of this system is the presence of an emotion engine. This emotion engine analyzes the emotions of disaster victims and users, and is equipped with an algorithm that evaluates stress levels and anxiety levels. For example, if a disaster victim is feeling anxious or fearful, the emotion engine recognizes this state and readjusts the priority of rescue operations accordingly. 【0734】 For example, if an isolated disaster victim sends a message urgently requesting rescue, the emotion engine will determine the level of stress and urgency from the voice and text contained in the message. This result will be reflected on the server, and a flexible rescue plan will be reconstructed to meet the needs of the entire disaster area. 【0735】 Furthermore, the emotion engine analyzes the emotional state of not only disaster victims but also users (for example, operators on rescue teams). If the server determines that a user's stress level is high, it will automatically recommend actions to reduce response times, supporting efficient operations. 【0736】 This entire system aims to enable swift and considerate rescue operations that also take into account the emotional well-being of disaster victims. The emotional engine's functionality allows for detailed responses tailored to the individual circumstances of each victim, contributing to saving more lives. 【0737】 The following describes the processing flow. 【0738】 Step 1: 【0739】 The server collects disaster precursor and occurrence information in real time and monitors data related to earthquakes and weather changes. Based on this information, it creates a list of potentially affected areas and enhances monitoring. 【0740】 Step 2: 【0741】 When a disaster is detected, the server immediately identifies the coordinates of the affected area and sends dispatch orders to nearby drones and ground robots (terminals). 【0742】 Step 3: 【0743】 Once the terminal (drone) reaches the airspace above the disaster area, it will begin scanning using a thermal imaging camera to identify high-priority responses. It will also collect body temperature information from victims and transmit it to a server. 【0744】 Step 4: 【0745】 The terminal (ground robot) patrols the disaster area, using voice recognition to detect cries for help and sounds made by people, and transmits that information to a server. 【0746】 Step 5: 【0747】 The server uses AI technology to analyze the vast amount of data collected from terminals and assess the precise location and condition of disaster victims. It then uses an emotion engine to measure the degree of stress and the level of urgency of their emotions, along with the level of danger to their lives. 【0748】 Step 6: 【0749】 The emotion engine analyzes the anxiety and fear of disaster victims based on emotional analysis data and provides the server with a priority list for rescue efforts that takes this into account. 【0750】 Step 7: 【0751】 The server develops an optimized rescue plan based on emotional data and analysis results. This plan includes precise distribution points and routes for supplies and takes into account the emotional state of the victims. 【0752】 Step 8: 【0753】 Based on instructions from the server, the terminal (drone) drops supplies, including first-aid kits and food, to designated locations, while ground robots also deliver supplies to specified target points. 【0754】 Step 9: 【0755】 The server monitors the progress of rescue operations in real time and provides users with status reports. It accepts new instructions as needed and can flexibly adjust the rescue plan. 【0756】 Step 10: 【0757】 Based on the analysis results of an emotion engine that supports on-site operations, users provide care-based responses as part of their communication with disaster victims. This makes it possible to provide psychological support during disasters. 【0758】 (Example 2) 【0759】 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". 【0760】 To ensure rapid and appropriate rescue operations during a disaster, prioritization must consider not only the location of victims but also their emotional state. However, conventional rescue systems lacked the means to appropriately assess victims' emotions and dynamically adjust rescue priorities based on those assessments. As a result, rescue operations sometimes fail to adequately meet the urgent needs of victims. 【0761】 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. 【0762】 In this invention, the server includes information gathering means for identifying and analyzing the area where a disaster has occurred, emotion analysis means for analyzing the emotions of disaster victims and users, and analysis means for optimizing rescue operations based on the collected data. This makes it possible to prioritize appropriate and rapid rescue operations while taking into account the emotional state of disaster victims. 【0763】 "Information gathering means" refers to technologies and devices used to identify areas where disasters have occurred and to monitor and analyze the extent of damage in those areas. 【0764】 "Analysis means" refers to the technology and equipment used to determine the location of disaster victims and the necessary relief supplies based on data obtained from information gathering means. 【0765】 "Emotional analysis means" refers to technologies and devices that analyze the emotional state of disaster victims and users, and optimize the priority of rescue operations based on that analysis. 【0766】 "Delivery means" refers to the technology and equipment used to distribute rescue supplies to appropriate locations based on the results of analytical means and sentiment analysis means. 【0767】 An "unmanned vehicle" is a remotely controlled or autonomously operating flying device used to efficiently scan disaster-stricken areas. 【0768】 "Ground equipment" refers to autonomous or remotely operated devices used on the ground to scan disaster-stricken areas and collect data. 【0769】 "Thermal radiation imaging technology" is a technique that visualizes the location of victims and the condition of objects by detecting infrared radiation emitted from them. 【0770】 "Speech recognition technology" is a technology that analyzes speech data to understand its meaning and emotions, and then processes the data accordingly. 【0771】 This invention is a system that enables rapid and appropriate rescue operations during disasters. Its main components include information gathering means, analysis means, sentiment analysis means, and delivery means. 【0772】 The server uses information gathering methods to identify the situation in disaster-stricken areas and monitors and analyzes damage data in real time. This information gathering utilizes unmanned vehicles (such as drones) and ground devices (such as robots). These devices employ thermal imaging technology and voice recognition technology to scan wide areas and precisely detect the location and condition of victims. 【0773】 The collected data is sent to a server, where detailed analysis is performed using AI-generated models. This makes it possible to accurately pinpoint the location of disaster victims and identify the necessary relief supplies. 【0774】 Furthermore, the server uses sentiment analysis tools to assess the emotional state of disaster victims and rescue users. This assessment is performed by converting voice messages and text data from the disaster area into prompts. For example, if it receives a message such as, "It's very dangerous here, and I'm anxious. I need help quickly," it analyzes the emotional content and reassessss the rescue priority. 【0775】 From the user's perspective, the rescue team operators are included in the scope of emotion analysis, and the server adjusts their workload appropriately to reduce their stress and psychological burden. 【0776】 Ultimately, a delivery system is established, and an optimized rescue plan is implemented based on the results of analytical and sentiment analysis, ensuring that necessary rescue supplies are distributed efficiently and appropriately. 【0777】 An example of a prompt message could be: "We have received an urgent message from the disaster area. The message reads: 'It is very dangerous here and I am anxious. I need help quickly.' Based on this information, analyze the emotional state of the victims and optimize the rescue plan." This allows the generative AI model to perform an emotional assessment and facilitate a rapid response. 【0778】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0779】 Step 1: 【0780】 The server begins collecting information after a disaster occurs. It controls unmanned robots and ground equipment to scan the affected area. Topographic information and disaster trigger information are used as input, and real-time scan data of the affected area is obtained as output. The server acquires this data and performs initial analysis. 【0781】 Step 2: 【0782】 The terminals, specifically drones and robots, analyze the collected scan data using thermal radiation imaging technology. The input is scan data, and the output is location information for areas with temperature anomalies and suspected victims. The terminals register this information into a map of the affected area. 【0783】 Step 3: 【0784】 The server receives location and temperature data collected from terminals and performs detailed analysis using a generating AI model. The input is location and temperature data, and the output is the precise location of the victims, the presence or absence of obstacles, and a rescue priority list. Furthermore, the AI ​​model predicts the condition of the victims and calculates the degree of urgency. 【0785】 Step 4: 【0786】 The server then uses emotion analysis tools to evaluate the emotional state of the victims and users. Inputs include voice and text messages from victims, and output is the result of the emotional state evaluation. For example, it processes messages such as "It's very dangerous here, and I'm anxious. I need help quickly" using prompts, and quantifies the level of anxiety and urgency. 【0787】 Step 5: 【0788】 The emotional state of the users, specifically the operators of the rescue team, is also evaluated. Inputs include user operation logs and audio data, while output is the user's stress level. Based on this information, the server adjusts the workload of the operation and optimizes the efficiency of each operator. 【0789】 Step 6: 【0790】 The server optimizes rescue operations based on the results of analytical and sentiment analysis. It plans the delivery routes for rescue supplies and ensures their rapid distribution to the necessary locations. The inputs are a rescue priority list and sentiment evaluation results, and the output is an optimized rescue plan. This enables efficient rescue operations. 【0791】 (Application Example 2) 【0792】 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". 【0793】 During disasters, swift and accurate rescue operations are crucial. However, while conventional disaster relief systems excel at identifying the physical location of victims and determining necessary supplies, they fail to prioritize rescue efforts based on the psychological state of both victims and rescuers. This can lead to delays in responding to victims, especially those who are emotionally unstable. Furthermore, it can increase the psychological burden on rescuers themselves, potentially reducing operational efficiency. Therefore, a more flexible rescue system that takes psychological states into consideration is needed. 【0794】 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. 【0795】 In this invention, the server includes data acquisition means for identifying the area where a disaster has occurred and analyzing the extent of the damage; data analysis means for determining the location of victims and the necessary relief supplies based on the information obtained from the data acquisition means; and sentiment analysis means for re-evaluating the priorities of rescue operations through sentiment analysis. This enables accurate and efficient rescue operations based on both the physical situation and the psychological state of the victims. 【0796】 "Area affected by a disaster" refers to a specific geographical area where physical damage or disruption has occurred due to a natural or man-made disaster. 【0797】 "Data acquisition means for analyzing damage status" refers to a group of devices that use sensors, drones, and ground-based mobile devices to collect information from the site and analyze the extent of damage caused by the disaster and the situation of the victims. 【0798】 "Information obtained from data acquisition methods" refers to disaster situation and environmental data collected from the field, and is the basic data used to conduct more detailed analysis based on this information. 【0799】 "Location of victims and necessary relief supplies" refers to information about the current location of victims and the types and quantities of supplies and materials they need for survival and recovery. 【0800】 "Data analysis tools" is a general term for software and hardware used to analyze collected data and generate specific information necessary for rescuing disaster victims. 【0801】 "An emotional analysis tool for re-evaluating rescue priorities through emotional analysis" refers to a device and method for analyzing the psychological state of disaster victims and rescue teams, and determining the optimal rescue priorities based on the results obtained. 【0802】 "Physical conditions and psychological states" refer to both the specific physical conditions of the damage at the disaster site (e.g., the location of victims, the extent of building collapse) and the psychological aspects such as the emotions and stress levels of victims and rescuers. 【0803】 This invention relates to a system for enabling rapid and accurate rescue operations during disasters. The server uses data acquisition means to identify the area where the disaster has occurred and efficiently collects information on the situation in the affected area using unmanned aerial vehicles and ground mobile devices. It is also equipped with data analysis means to determine the location of victims and the necessary relief supplies based on the information obtained from the data acquisition means. 【0804】 Furthermore, it includes an emotion analysis mechanism that re-evaluates the prioritization of rescue operations based on emotion analysis. This emotion analysis uses machine learning libraries such as TensorFlow to determine the psychological state of victims and rescue teams. For example, it analyzes voice and text data to measure the stress levels and anxiety levels of victims and determine the urgency of the rescue. 【0805】 In this system, the server processes the collected data and displays it on a dashboard in real time. Rescue team operators, who are the users, use this information to develop rescue plans. Furthermore, because the results of emotion analysis are reflected immediately, it becomes possible to prioritize the rescue of victims who are psychologically unstable. 【0806】 As a concrete example, consider a scenario where the system responds to a large-scale earthquake. In this case, the system performs real-time emotion analysis on text and voice messages sent from victims near the epicenter to identify victims who are experiencing fear or anxiety. The server then communicates the results to an operator and, if necessary, issues instructions to direct a drone towards the location of that victim. 【0807】 By utilizing a generative AI model and inputting prompts such as, "Please tell me what actions EmoRobo should take to reduce residents' stress during a disaster," it is possible to propose specific countermeasures tailored to the emotional state of disaster victims. In this way, the invention constructs an advanced rescue system that considers not only physical but also psychological aspects. 【0808】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0809】 Step 1: 【0810】 The server identifies the area affected by the disaster. It controls unmanned aerial vehicles and ground mobile devices to collect local data using sensors. The input is real-time geographic data, and the output is location information and initial damage information. Based on this information, an overview of the entire affected area is obtained. 【0811】 Step 2: 【0812】 The server further analyzes the collected data using data acquisition methods. Here, it analyzes the specific locations of disaster victims and the necessary relief supplies. Inputs include location information and damage information, while outputs include location data for disaster victims and a list of necessary supplies. 【0813】 Step 3: 【0814】 The user, a rescue team operator, verifies the location data of disaster victims provided by the server. Furthermore, they develop a transportation plan based on the necessary relief supplies information. Inputs are disaster victim location data and a list of necessary supplies; output is the transportation plan. This enables efficient supply delivery. 【0815】 Step 4: 【0816】 The terminal, either a ground mobile device or an unmanned aerial vehicle, actually distributes relief supplies according to the transport plan. Based on instructions from the server, it begins moving to the location of the affected people and delivers the supplies. The input is the transport plan, and the output is the delivery completion status of the supplies. 【0817】 Step 5: 【0818】 The server uses emotion analysis tools to monitor the psychological state of disaster victims and users. Using voice and text data from sensors as input, a generative AI model measures stress levels and anxiety levels, and outputs the results of the emotion state analysis. This analysis is used to revise the priorities of rescue operations. 【0819】 Step 6: 【0820】 The user adjusts rescue operations based on the results of the emotional state analysis. Specifically, they order a rapid response to victims with increased priority and reallocate the rescue team's tasks as needed. The input is the emotional state analysis results, and the output is the optimized rescue operation strategy. 【0821】 Step 7: 【0822】 The prompt "Please tell us what actions EmoRobo should take to reduce residents' stress during a disaster" is input to the generating AI model, which then proposes the optimal method of resident care. This result will be reflected in rescue operations. The input is the prompt to the generating AI, and the output is the method of reducing residents' stress. 【0823】 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. 【0824】 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. 【0825】 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. 【0826】 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. 【0827】 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. 【0828】 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. 【0829】 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. 【0830】 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. 【0831】 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." 【0832】 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. 【0833】 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. 【0834】 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. 【0835】 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. 【0836】 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. 【0837】 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. 【0838】 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. 【0839】 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. 【0840】 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. 【0841】 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. 【0842】 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. 【0843】 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 to be incorporated by reference. 【0844】 The following is further disclosed regarding the embodiments described above. 【0845】 (Claim 1) 【0846】 A means of acquiring information to identify the area where a disaster has occurred and to analyze the extent of damage in that area, 【0847】 An analysis means that analyzes the data obtained from the above information acquisition means to determine the location of disaster victims and the necessary relief supplies, 【0848】 Based on the results of the above analysis, a distribution method for distributing relief supplies to appropriate locations, 【0849】 A system that includes this. 【0850】 (Claim 2) 【0851】 The system according to claim 1, which efficiently scans a disaster-stricken area using an unmanned aerial vehicle and ground mobility equipment. 【0852】 (Claim 3) 【0853】 The system according to claim 1, which utilizes thermal image analysis technology and speech recognition technology when analyzing the collected data. 【0854】 "Example 1" 【0855】 (Claim 1) 【0856】 A means for acquiring information by setting a disaster area, monitoring weather data and earthquake data in that area in real time, and detecting anomalies, 【0857】 An analytical means that analyzes the data obtained from the above information acquisition means to identify the location of disaster victims and formulate the optimal rescue route and supply plan, 【0858】 Based on the results of the above analysis, a delivery means for distributing rescue supplies to appropriate locations using unmanned aircraft and ground mobility equipment, 【0859】 A system that includes this. 【0860】 (Claim 2) 【0861】 The system according to claim 1, wherein information about the disaster area is acquired using a thermal imaging camera by taking photographs from above, and ambient sounds are collected on the ground using voice recognition technology. 【0862】 (Claim 3) 【0863】 The system according to claim 1, which uses machine learning algorithms to evaluate the location information of disaster victims and the risk of secondary disasters when analyzing collected data. 【0864】 "Application Example 1" 【0865】 (Claim 1) 【0866】 A means of acquiring information to identify the area where a disaster has occurred and to analyze the extent of damage in that area, 【0867】 An analysis means that analyzes the data obtained from the above information acquisition means to determine the location of disaster victims and the necessary relief supplies, 【0868】 Based on the results of the above analysis, a distribution method for distributing relief supplies to appropriate locations, 【0869】 An optimization method that uses environmental data acquired in real time to analyze location information and prioritize rescue operations, 【0870】 A system that includes this. 【0871】 (Claim 2) 【0872】 The system according to claim 1, which uses unmanned aerial vehicles and ground mobility devices to efficiently scan a disaster-stricken area and determine the optimal route based on location information. 【0873】 (Claim 3) 【0874】 The system according to claim 1, which uses thermal image analysis technology and speech recognition technology to analyze collected data and generates the optimal rescue route in real time using an AI algorithm. 【0875】 "Example 2 of combining an emotion engine" 【0876】 (Claim 1) 【0877】 A means of collecting information to identify the area where the disaster occurred and to analyze the extent of the damage in that area, 【0878】 An analytical means for analyzing data obtained from the above information gathering means to determine the location of disaster victims and the necessary relief supplies, 【0879】 A server analyzes the emotions of disaster victims and users, and based on this, optimizes the priority of rescue operations using emotion analysis means. 【0880】 Based on the results of the above analysis and emotion analysis, a delivery means for distributing rescue supplies to appropriate locations is provided. 【0881】 A system that includes this. 【0882】 (Claim 2) 【0883】 The system according to claim 1, which efficiently scans a disaster-stricken area using an unmanned body and ground equipment. 【0884】 (Claim 3) 【0885】 The system according to claim 1, which utilizes thermal radiation imaging technology and speech recognition technology when analyzing the collected data. 【0886】 "Application example 2 when combining with an emotional engine" 【0887】 (Claim 1) 【0888】 A data acquisition means for identifying the area where a disaster has occurred and analyzing the damage situation in that area, 【0889】 A data analysis means that analyzes the information obtained from the above data acquisition means to determine the location of disaster victims and the necessary relief supplies, 【0890】 Based on the results of the above data analysis, a means of transporting relief supplies to an appropriate location, 【0891】 An emotion analysis tool that analyzes the emotions of disaster victims and users and re-evaluates the priorities of rescue operations based on their psychological state, 【0892】 A system that includes this. 【0893】 (Claim 2) 【0894】 The system according to claim 1, which effectively scans a disaster area using an unmanned aerial vehicle and a ground mobile device. 【0895】 (Claim 3) 【0896】 The system according to claim 1, which utilizes thermal image analysis technology, speech recognition technology, and psychological state analysis technology when analyzing collected information. [Explanation of symbols] 【0897】 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 means of acquiring information to identify the area where a disaster has occurred and to analyze the extent of damage in that area, An analysis means that analyzes the data obtained from the above information acquisition means to determine the location of disaster victims and the necessary relief supplies, Based on the results of the above analysis, a distribution method for distributing relief supplies to appropriate locations, A system that includes this. [Claim 2] The system according to claim 1, which efficiently scans a disaster-stricken area using an unmanned aerial vehicle and ground mobility equipment. [Claim 3] The system according to claim 1, which utilizes thermal image analysis technology and speech recognition technology when analyzing the collected data.

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