system

Abstract

We provide the system. [Solution] A means for collecting unmanned aircraft data, satellite data, sensor data, and social network data during a disaster, A means of analyzing the collected data to assess the extent of the damage, A means of setting priorities and formulating plans for rescue operations and infrastructure restoration based on assessments, A means to determine the necessary relief supplies and personnel allocation, and to set the optimal delivery route, A system that includes means for providing plans and instructions to an external system.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In order to immediately collect information during a natural disaster and achieve effective rescue of lives and restoration of infrastructure, there is a current situation where rapid and accurate information processing and decision-making are required. In conventional systems, information collection has been fragmentary, resource allocation has not been optimized, and confusion may have occurred. Therefore, there is a need for a method to solve these problems and automatically provide optimal support to disaster-stricken areas.

Means for Solving the Problems

[0005] This invention provides a means to accurately assess the extent of a disaster by integrating and analyzing data collected from unmanned aerial vehicles, satellites, sensors, and social network data. It also sets priorities based on the assessment results and automatically formulates rescue operations and infrastructure restoration plans. Furthermore, it includes a function to determine the optimal allocation of necessary relief supplies and personnel, and to update instructions and plans in real time in conjunction with external systems, thereby enabling efficient and rapid disaster response.

[0006] "Unmanned aircraft data" refers to remote sensing data and image information acquired using unmanned aerial vehicles.

[0007] "Satellite data" refers to Earth observation data and communication information acquired from artificial satellites placed in orbit.

[0008] "Sensor data" refers to information collected by various sensors used to detect environmental information.

[0009] "Social network data" refers to user-generated content such as posts, comments, and images obtained from social networking services (SNS) platforms on the internet.

[0010] "Analysis" refers to the technical methods and processes used to process acquired data and extract useful information.

[0011] "Disaster situation" refers to the extent and impact of damage to a region caused by a disaster.

[0012] "Priority" refers to the criteria used to rank and allocate limited resources to the most optimal locations and activities.

[0013] "Rescue operations" refer to a series of actions taken to protect the lives of disaster victims and guide them to a safe state.

[0014] "Infrastructure restoration" refers to activities for repairing backbone facilities and systems damaged by disasters and restoring their original functions.

[0015] "Relief supplies" refer to necessary items such as food, water, clothing, and medical supplies supplied to the disaster-stricken area.

[0016] "Personnel allocation" refers to appropriately allocating the required number of people to specific locations or activities in order to execute specific roles or tasks.

[0017] "External system" refers to other independent information systems or device groups with which the system of the present invention cooperates and issues instructions.

Brief Description of the Drawings

[0018] [Figure 1] It 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 multiple emotions are mapped. [Figure 10]Shows an emotion map to which multiple emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 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 Embodiment 2 when the 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 the emotion engine is combined.

Mode for Carrying Out the Invention

[0019] 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.

[0020] First, the language used in the following description will be explained.

[0021] 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.

[0022] 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.

[0023] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0024] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0025] 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."

[0026] [First Embodiment]

[0027] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0028] 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.

[0029] 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).

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0035] 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.

[0036] 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.

[0037] 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.

[0038] 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".

[0039] This invention provides an advanced information gathering and analysis system for automating disaster response. The main embodiment provides a means for aggregating information during a disaster, enabling rapid and precise rescue operations, and supporting the effective distribution of relief supplies.

[0040] Feature Overview

[0041] The servers begin operating immediately upon the reporting of a disaster. They collect video footage and environmental sensor data from the affected area via drones and satellites, and also gather information from social networks. This allows for a comprehensive understanding of the disaster's impact.

[0042] The server processes the collected data and evaluates the extent of the damage using analysis algorithms. This analysis utilizes technologies such as image recognition, data mining, and natural language processing.

[0043] Based on the assessment, the server determines priority areas and activities for rescue operations and infrastructure restoration, and then develops plans accordingly. These plans are designed to enable efficient rescue operations and provide streamlined support.

[0044] Users (e.g., local government officials) begin taking action based on plans generated by the server. Users utilize the instructions from the server to distribute relief supplies, provide evacuation routes, and provide information to local residents.

[0045] The terminals are devices deployed for volunteer staff working on-site, receiving instructions from the server and automatically recommending the optimal course of action. This includes determining the best evacuation routes and narrowing down who to assist in emergencies.

[0046] Specific example

[0047] Let's assume a large-scale earthquake occurs. In this case, the server immediately receives information about the earthquake through its monitoring system. Drones fly over the affected area, recording detailed topographic data and damage in video. Simultaneously, the server identifies areas where potential damage is expected to be significant, based on the observed earthquake magnitude.

[0048] Subsequently, the server analyzes social network data, including posts from residents in the affected areas, compares the impact of the disaster with known data, and identifies areas where rescue should be prioritized. This process enables the rapid issuance of evacuation orders.

[0049] Users concentrate relief supplies in designated high-priority areas to address the situation. They prepare for rapid delivery of supplies by following planned logistics routes.

[0050] Thus, by combining multifaceted information gathering and analysis, this invention enables rapid and appropriate disaster response and optimal allocation of resources in disaster-stricken areas. Furthermore, it is expected that this will significantly improve the efficiency of life-saving operations and infrastructure restoration.

[0051] The following describes the processing flow.

[0052] Step 1:

[0053] Upon receiving notification of a disaster, the server activates the control systems for drones and satellites and begins collecting data from the affected area. The drones fly over the affected area, acquiring video and environmental sensor data in real time. This data is then transmitted to the server.

[0054] Step 2:

[0055] The server collects disaster information from social networks. It uses SNS APIs to search for posts related to specific keywords and aggregates that data.

[0056] Step 3:

[0057] The server analyzes collected drone data, satellite data, sensor data, and social network data. Video data is processed using image recognition technology to detect collapsed buildings and impassable areas. Social media data is processed using natural language processing to extract important information.

[0058] Step 4:

[0059] The server prioritizes rescue operations and infrastructure restoration based on data analysis. It generates priority lists according to the extent of the damage and identifies areas that should receive priority relief.

[0060] Step 5:

[0061] The server develops optimal plans for rescue operations and supply transport. Based on a priority list, it determines the optimal allocation of personnel and supplies and establishes logistics routes.

[0062] Step 6:

[0063] Users execute local disaster response based on plans and instructions provided by the server. They issue instructions for transporting supplies and disseminate information to areas requiring priority evacuation.

[0064] Step 7:

[0065] The terminals receive instructions from the server on the devices of volunteers and local support staff, and initiate appropriate support activities. They respond quickly to emergency evacuation orders and personnel changes when necessary.

[0066] Step 8:

[0067] The server continuously monitors the situation and analyzes additional data. Based on the new information, it updates the plan and incorporates necessary adjustments and instructions in real time.

[0068] (Example 1)

[0069] 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."

[0070] There is a need to build systems that enable rapid and appropriate information gathering and analysis during disasters, effective rescue operation planning, efficient distribution of relief supplies, and provision of safe evacuation routes. Conventional technologies often rely heavily on manual information aggregation and analysis, leading to delayed responses and inefficient support. Therefore, real-time situation assessment and the establishment of a rapid response support system are essential.

[0071] 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.

[0072] In this invention, the server includes means for detecting disaster situations and aggregating information, means for collecting observational data using unmanned aerial vehicles, spacecraft, and high-altitude environmental detectors, and means for combining and comprehensively analyzing data obtained from information sharing services. This enables rapid and precise information gathering and analysis in the event of a disaster, supporting effective rescue operations and the rapid restoration of infrastructure.

[0073] "Disaster situation" refers to the state of events in an area where a natural disaster has occurred, and includes the scale of the damage and the scope of its impact.

[0074] "Means of aggregating information" refers to functions that integrate data obtained from different sources into one place and process it efficiently.

[0075] An "unmanned aircraft" is an aircraft that can be remotely controlled or fly autonomously, and its role is to collect data from the air above disaster areas.

[0076] A "spacecraft" is an artificial satellite or other device in space that orbits the Earth and provides a wide range of observational data.

[0077] An "advanced environmental detector" refers to a sensor device that detects environmental changes and anomalies with high precision, and collects information that is useful in the event of a disaster.

[0078] An "information sharing service" refers to a platform that collects data transmitted by users through social networks such as social media and news feeds provided on online platforms.

[0079] "Machine learning technology" is a general term for technologies that enable computers to automatically construct patterns and rules based on large amounts of data, and it plays an important role in data analysis.

[0080] "Infrastructure restoration" refers to activities that repair infrastructure damaged by a disaster and restore it to normal function.

[0081] A "transportation route" refers to a route used to efficiently deliver goods or personnel to a specific destination, and is an important element in optimizing planning.

[0082] "External devices" refer to devices or software that are connected to a system and extend its functionality by exchanging information.

[0083] "Devices that support on-site activities" refer to mobile devices used by volunteers and rescue workers working at disaster sites, which are used to provide information and issue instructions.

[0084] This invention provides a system that enables rapid and highly accurate information gathering and analysis during disasters, facilitating efficient rescue operations and the distribution of relief supplies. This system primarily consists of a server, terminals, and users.

[0085] The server receives information from the disaster monitoring system and collects observational data from the affected area using unmanned aerial vehicles (UAVs), spacecraft, and high-altitude environmental detectors. UAVs fly autonomously over the affected area, taking high-resolution video and photographs. Spacecraft monitor the situation over a wide area and provide weather and topographic data. High-altitude environmental detectors detect local environmental signals with high precision and acquire data such as earthquakes and wind speed.

[0086] The server aggregates this data and combines it with data obtained from information sharing services for comprehensive analysis. Using technologies such as image recognition, data mining, and natural language processing, it assesses the extent of disaster damage and determines priority areas for rescue and infrastructure restoration. Machine learning techniques are applied to streamline these analyses and improve their accuracy.

[0087] Users utilize server-generated plans to ensure the effective distribution of relief supplies and direct rescue routes. They are also responsible for directing necessary personnel deployments and coordinating on-site operations. The plans include transport routes and optimal resource utilization, reducing waste and enabling a rapid response.

[0088] The terminal functions as a device to support on-site activities, receiving information from the server and providing users with detailed instructions. These instructions aim to suggest safe evacuation routes and facilitate effective relief activities. As an example of prompts generated by the AI ​​model, the terminal can display specific instructions such as, "Report the current earthquake damage situation in the affected area and identify priority rescue areas."

[0089] As described above, through the cooperation of servers, users, and terminals, this system enables rapid and accurate disaster response, supporting life-saving efforts and the optimal allocation of resources in disaster-stricken areas.

[0090] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0091] Step 1:

[0092] The server receives information about earthquakes and typhoons from the disaster monitoring system. This input information includes the magnitude of the disaster, the time of occurrence, and the location. The server uses this as a trigger to initiate the relevant data collection process and outputs instructions to other data collection methods.

[0093] Step 2:

[0094] The server controls unmanned aerial vehicles (UAVs) and spacecraft to collect observational data from the disaster area. The UAVs fly over the affected region, taking video and photographic images using cameras. This data is high-resolution and records the terrain and damage in detail. Images and environmental data from the UAVs and spacecraft are obtained as input data. The server receives this data and outputs it as initial geographic information.

[0095] Step 3:

[0096] The server also integrates data from altitude environmental detectors, including information on wind speed, temperature, and humidity from weather sensors. This input data is crucial for understanding local weather conditions. The server analyzes the weather data and outputs the results to models that predict the progression and impact of disasters.

[0097] Step 4:

[0098] The server combines data from unmanned aerial vehicles, spacecraft, and altitude environmental detectors with social network data collected from information sharing services. This data includes posts and reports from disaster victims, providing raw information from the field. Input data includes text data, image data, and location information. The server processes this data with analysis software and outputs a map that identifies the extent of the disaster's impact.

[0099] Step 5:

[0100] The server uses a generative AI model to analyze data and assess the extent of the damage. This includes image recognition algorithms and natural language processing to identify the scale of the damage and priority areas for response. Based on the input observational data, the analysis results output a disaster situation map and a list of affected areas.

[0101] Step 6:

[0102] The server generates a rescue plan based on the analysis results. It determines the optimal deployment of rescue teams and supplies to high-priority areas and plans transportation routes. The server's input includes a map of the affected area and data on priority areas, and its output provides specific action guidelines and support routes.

[0103] Step 7:

[0104] The user receives plans and instructions from the server. Based on the optimized rescue plan, they prepare supplies and deploy support teams. Input data includes the rescue plan and transport routes, and the output is rapid response and supply delivery.

[0105] Step 8:

[0106] The terminal displays instructions provided by the server to volunteers and work staff conducting field activities. The terminal receives instruction information from the server and displays safe evacuation routes and specific support activities in the field to the user. The input is server instruction data provided to the terminal, and the output is to facilitate efficient activities in the field.

[0107] (Application Example 1)

[0108] 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."

[0109] In recent years, the frequency and scale of natural disasters have increased, creating a need for rapid and efficient disaster response. Conventional disaster response systems have often been slow to prioritize information gathering and rescue operations, leading to delays in providing support to affected areas. Furthermore, the lack of effective approaches to managing residents' evacuation behavior in urban environments has frequently resulted in confusion during disasters. This invention aims to solve these problems and realize a rapid and effective disaster response.

[0110] 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.

[0111] In this invention, the server includes means for collecting drone data, satellite data, sensor data, and social network data during a disaster; means for analyzing the collected data to evaluate the extent of the damage; means for providing residents with appropriate evacuation routes using mobile communication devices; and means for instructing volunteer personnel to carry out effective support activities. This enables residents and support organizations to take action based on accurate information in real time through rapid information gathering and analysis.

[0112] "Unmanned aircraft data" refers to data on terrain and damage conditions collected by unmanned aircraft in the air.

[0113] "Satellite data" refers to data that includes images and observational information taken from satellites, and is used to understand the extent of damage over a wide area.

[0114] "Sensor data" refers to data obtained from various sensors that detect environmental information such as temperature, humidity, and vibration.

[0115] "Social network data" refers to posts and information collected from social media and network platforms on the internet, and is data that reflects the voices and circumstances of residents.

[0116] A "mobile communication device" refers to a mobile device such as a cell phone or smartphone, which is used to send and receive information in real time.

[0117] "Means of providing evacuation routes" refer to methods and technologies for guiding residents to the optimal route so that they can safely evacuate from disaster areas.

[0118] "Volunteer personnel" refers to individuals or groups who work to provide support to disaster victims and distribute supplies during a disaster.

[0119] "Means of directing effective support activities" refer to methods and techniques for planning support activities and providing instructions for their implementation in an appropriate manner and timing.

[0120] This invention is an information gathering and analysis system for streamlining disaster response in smart cities. The server comprehensively collects and analyzes drone data, satellite data, sensor data, and social network data. The hardware used is a cloud server with high computing power, and a program written in Python is implemented for data processing. Apache® Kafka is used for real-time data processing, and backend computing is performed on AWS® Lambda.

[0121] Based on the analyzed data, the server provides optimal evacuation routes to mobile communication devices to support residents' evacuation actions. This information is delivered to residents in real time through a dedicated app built with React Native. It also directs volunteers to carry out effective support activities, ensuring that the distribution of supplies and relief efforts proceed efficiently.

[0122] For example, during a large-scale flood in a specific area, the server quickly identifies dangerous areas and displays safe evacuation routes to residents' mobile devices. At the same time, volunteers receive instructions, including prioritizing disaster relief efforts, ensuring that support activities are carried out appropriately. This speeds up and improves the accuracy of disaster response, making it possible to ensure the safety of many people.

[0123] An example of a prompt for a generated AI model is, "Please provide the information needed to develop an application that provides optimal evacuation routes in real time when a typhoon is approaching." By using this prompt, necessary technical knowledge can be quickly obtained and utilized in implementation.

[0124] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0125] Step 1:

[0126] The server collects drone data, satellite data, sensor data, and social network data on the cloud. To enable real-time acquisition of this data, it aggregates it using the data streaming platform Apache Kafka. Inputs include raw data from various sensors, drone video data, and social media feeds, which are then processed as structured data and output.

[0127] Step 2:

[0128] The server performs data analysis on the collected data to assess the extent of the damage. This analysis uses AWS Lambda, which runs a Python program, and utilizes image recognition and natural language processing algorithms. The input is the structured data obtained in step 1, and the output generates map information that visualizes the risk assessment and damage situation of the affected areas.

[0129] Step 3:

[0130] The server sets evacuation routes based on the assessment of the disaster situation and provides information to mobile communication devices. It calculates the optimal evacuation route based on the assessment results and notifies residents through an application built with React Native. The input is map information based on the assessment results, and the output is real-time evacuation route guidance for mobile devices.

[0131] Step 4:

[0132] The terminal provides evacuation information to residents through an application and updates evacuation routes in real time. It receives notifications from the server and uses residents' location data to send personalized evacuation information. Inputs include guidance information from the server and residents' current location data, while outputs include updated evacuation routes and safety confirmation notifications.

[0133] Step 5:

[0134] The server directs volunteers to carry out effective support activities and updates information as needed. It sends prioritized relief operation instructions to volunteer terminals to support on-site activities. Inputs include analysis results of the disaster situation and inventory information of relief supplies, while outputs include volunteer action plans and supply distribution plans.

[0135] 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.

[0136] This invention combines an emotion engine with a disaster response system to automatically propose support activities that take into account the psychological state of disaster victims, enabling more appropriate resource allocation. The system has a configuration that combines information gathering, data analysis, emotion recognition, and support plan development.

[0137] Feature Overview

[0138] In addition to normal data collection (from drones, satellites, sensors, social networks, etc.), the server operates an emotion engine. The emotion engine acquires emotion data through social networks and direct interaction with users. This is done through text analysis and speech analysis.

[0139] The server integrates emotional data with other information to conduct a comprehensive assessment of the disaster situation. This assessment identifies the degree of psychological stress and specific psychological states (e.g., anxiety, fear), and then plans appropriate responses.

[0140] Users (local government officials and rescue teams) conduct disaster relief activities based on detailed assessments and suggestions provided by the server. Suggestions based on sentiment data indicate priorities for specific supplies and situations requiring special care.

[0141] The device receives feedback from the emotional engine on-site, enabling more flexible and humane responses. For example, in emergency shelter care, it can propose and implement activities to reduce psychological burden.

[0142] Specific example

[0143] For example, consider a scenario involving a large-scale flood. The server collects information from the affected area in real time while analyzing the emotional state of the residents. This involves extracting text indicating anxiety and confusion from social media posts and detecting tension and fear during voice calls through emotional analysis.

[0144] Based on the analysis results, the server identifies areas where psychological support is particularly needed and proposes specific action plans to local government officials. These plans include not only the distribution of relief supplies but also the dispatch of specialists to provide psychological care. This information is then communicated to the field by users and immediately implemented.

[0145] Furthermore, if disaster victims are experiencing anxiety in evacuation shelters, the device will inform shelter staff of the situation and, if necessary, suggest counseling services or measures to enhance their sense of security. These measures will reduce the psychological burden on disaster victims and enable efficient and humane disaster response.

[0146] Thus, the present invention aims to provide a comprehensive support system that takes into account the psychological state of disaster victims, and to maximize the use of limited resources.

[0147] The following describes the processing flow.

[0148] Step 1:

[0149] Upon receiving notification of a disaster, the server begins collecting data from drones, satellites, sensors, and social network data. Furthermore, it activates an emotion engine to collect user emotion data, which is extracted from social media posts and audio data.

[0150] Step 2:

[0151] The server performs a series of processes to analyze the collected drone data, satellite data, sensor data, and social network data. The analysis extracts topographic information from the video footage and maps the damage situation.

[0152] Step 3:

[0153] The server integrates emotional data analyzed by the emotion engine to assess the psychological state of disaster victims. This makes it possible to identify areas where people are experiencing anxiety or fear.

[0154] Step 4:

[0155] The server prioritizes rescue operations and infrastructure restoration based on disaster situation and sentiment data. Detailed support plans are then created based on the high-priority areas and activities.

[0156] Step 5:

[0157] Users receive plans and instructions from the server and begin on-site support activities. This includes distributing supplies and providing psychological care.

[0158] Step 6:

[0159] The terminals receive instructions from the server on the devices of local staff and volunteers, facilitating real-time action. If psychological care is needed, they support immediate implementation of specific support measures.

[0160] Step 7:

[0161] The server continuously collects and analyzes new data, updating plans based on changing circumstances. Sentimental data is also re-evaluated, and instructions are adjusted as needed. This ensures optimal disaster response at all times.

[0162] (Example 2)

[0163] 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".

[0164] In disaster relief efforts, it is necessary not only to provide physical rescue and restore infrastructure, but also to appropriately understand and alleviate the psychological stress and anxiety of disaster victims. However, conventional systems have made it difficult to formulate support plans that take psychological conditions into account, often resulting in insufficient emotional care. Therefore, there is a need to collect and analyze emotional data of disaster victims and provide a comprehensive and dynamic support system based on this data.

[0165] 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.

[0166] In this invention, the server includes means for collecting drone data, satellite data, sensor data, and social network data; means for analyzing the collected text and voice data using natural language processing technology to evaluate emotional states; and means for integrating emotional data and environmental data to evaluate the overall situation in the disaster area. This makes it possible to automatically generate support plans that take into account the psychological state of disaster victims.

[0167] "Unmanned aircraft data" refers to observational data acquired by aircraft or ground vehicles that operate without a driver.

[0168] "Satellite data" refers to observational data acquired from artificial satellites orbiting the Earth.

[0169] "Sensor data" refers to data collected by sensor devices to measure various environmental conditions.

[0170] "Social network data" refers to information such as posts, comments, and responses generated by users via the internet.

[0171] "Natural language processing technology" refers to the technology that enables computers to understand, interpret, and generate human natural language.

[0172] "Emotional state" refers to the mental state of an individual or group at a given point in time, and includes emotions such as joy, anxiety, anger, and fear.

[0173] "Comprehensive situation assessment" is an evaluation that integrates information from multiple data sources to grasp the overall state of the disaster site.

[0174] "Dynamic updating" means modifying and adjusting information and plans in real time as circumstances change.

[0175] A "safe evacuation route" is a route recommended to allow disaster victims to evacuate while avoiding danger.

[0176] "Psychological care" refers to the support and activities provided to maintain or improve an individual's mental health.

[0177] This invention implements a system designed to optimize support activities during disasters.

[0178] The system consists of the following components:

[0179] Data collection

[0180] The server collects data from drones, satellites, ground sensors, and social networks. Drones and satellites provide real-time images and video data to visualize local conditions. Ground sensors are used to collect weather data and environmental conditions. Data from social networks, including user-generated posts and comments, is collected via APIs developed in Python and Java®.

[0181] Emotion analysis

[0182] The server analyzes collected text and audio data using natural language processing techniques to assess the emotional state of residents. This process utilizes natural language processing libraries (e.g., NLTK and spaCy). The analysis results enable the quantification of residents' emotions, such as anxiety and fear, and visualize the psychological state of disaster victims.

[0183] Data Integration and Evaluation

[0184] The server integrates emotional data with other environmental data to assess the overall situation in the affected area. This process utilizes a database system (e.g., PostgreSQL) and data visualization tools (e.g., Tableau). The assessed information is visually displayed to administrators to support the development of support plans.

[0185] Generating a support plan

[0186] Based on assessment information, the server automatically generates support plans that take into account the psychological health of disaster victims. These plans include the distribution of necessary supplies to specific areas and the dispatch of specialists to provide psychological care. The generated plans are then provided to local government officials and rescue teams.

[0187] Examples of specific cases and prompt statements

[0188] For example, in areas affected by large-scale flooding, the server analyzes social media posts to extract information indicating residents' anxiety and fear. Based on this sentiment analysis, areas requiring particular psychological care are identified, and countermeasures are formulated.

[0189] Example prompt: "In areas affected by large-scale flooding, use social media to understand the emotional state of residents and extract posts indicating anxiety or fear. Then, identify areas where psychological support is particularly needed and develop specific support plans for local government officials."

[0190] This system enables comprehensive support activities in disaster response that take psychological factors into consideration.

[0191] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0192] Step 1:

[0193] The server collects data from drones, satellites, sensors, and social networks. Its inputs include video data from drones and satellites, environmental data from sensors, and text data from social media. This data is retrieved in real time using APIs developed in Python and Java. The output is observational data in raw dataset form.

[0194] Step 2:

[0195] The server analyzes the collected text and audio data using natural language processing techniques to evaluate emotional states. The raw dataset from Step 1 is used as input. For data processing, the text data is analyzed, and positive or negative emotions are identified using an emotion analysis algorithm. The output is numerical data representing emotions. Natural language processing libraries such as NLTK and spaCy are used for this analysis.

[0196] Step 3:

[0197] The server integrates emotional and environmental data to assess the overall situation in the disaster-stricken area. It uses emotional data from Step 2 and other environmental data from Step 1 as input. For data calculation, an evaluation model is used to quantify the psychological and physical stress in the disaster-stricken area and visualize it on a map. The output is a comprehensive report of the disaster-stricken area. A database system and data visualization tools are used for this assessment.

[0198] Step 4:

[0199] The server generates a support plan that takes into account the psychological health of disaster victims, based on the evaluated information. The comprehensive situation report from Step 3 is used as input. As data calculation, a support prioritization algorithm is used to identify areas with the highest urgency and determine appropriate support measures. The output is a specific support plan, which includes details such as distribution routes for supplies and the deployment of psychological support specialists.

[0200] Step 5:

[0201] Users (local government officials and rescue workers) carry out activities on-site based on support plans provided by the server. The support plan generated in step 4 is used as input. Specific actions include transporting supplies and providing psychological care to disaster victims on-site, following the plan's instructions. The output is an improved on-site situation and stabilization of the residents' psychological state.

[0202] Step 6:

[0203] The terminal transmits feedback obtained on-site to the server in real time and adjusts the support plan as needed. It uses feedback data obtained from users on-site as input. The output is an updated support plan. This enables dynamic optimization of support activities.

[0204] (Application Example 2)

[0205] 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".

[0206] In disaster relief and infrastructure restoration, optimal allocation of supplies and personnel requires planning that considers not only the physical condition of victims but also their psychological state. Current systems place too much emphasis on assessing physical damage, and lack sufficient activities to alleviate the psychological burden on victims. Therefore, developing flexible plans that reflect psychological states is a key challenge.

[0207] 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.

[0208] This invention includes a server that analyzes collected data and evaluates the psychological state of disaster victims through sentiment analysis; a server that sets priorities for rescue operations and infrastructure restoration based on the evaluation and formulates a plan that includes psychological care for disaster victims; and a server that provides the plan and instructions to an external system and links with real-time public sentiment. This enables the implementation of efficient and effective activities that take into account the psychological state of disaster victims.

[0209] "Unmanned aircraft data" refers to video and image information acquired from the air during disasters, which is used to grasp the situation in disaster-stricken areas in real time.

[0210] "Satellite data" refers to images and sensor information acquired from outer space, which is used to understand the situation in disaster-stricken areas over a wide area.

[0211] "Sensor data" refers to data collected from various sensors installed on the ground, and is used to detect physical phenomena such as weather, earthquakes, and floods.

[0212] "Social network data" refers to text and image data collected from social media and other online communication platforms, which is used to understand the emotions and psychological state of disaster victims.

[0213] "Sentiment analysis" is a process of evaluating an individual's emotions and psychological state by analyzing text and audio data.

[0214] "Psychological care" refers to support activities aimed at reducing the emotional and psychological burden on disaster victims and maintaining and restoring their mental health.

[0215] "Real-time" refers to a state where information and data are acquired instantly and processed without any time lag.

[0216] "Public sentiment" is a general term for the psychological and emotional reactions of citizens during a disaster, and it is an important factor in determining the priorities of relief activities.

[0217] This invention is a system designed to alleviate the psychological burden on disaster-stricken areas and enable effective support activities. The system consists of a server, terminals, and users.

[0218] The server collects drone data, satellite data, sensor data, and social network data. Next, it analyzes this data and performs sentiment analysis to assess the psychological state of disaster victims. This process uses Python, employing libraries such as NLTK and spaCy for analysis.

[0219] The terminals formulate plans for specific rescue operations and psychological care based on analysis results from the server, and receive the instructions provided therein at the scene. For example, a dedicated application installed on a smartphone can immediately identify areas in need of assistance and respond based on those priorities.

[0220] Users, such as local government officials and rescue teams, will carry out disaster relief activities according to the detailed assessments and suggestions provided by the server. They are required to have a system in place to respond quickly, especially when psychological support is needed.

[0221] As a concrete example, in the event of a large-scale disaster, this system uses data collected from social media posts to detect emotions such as "tension" and "anxiety" through text analysis, and proposes emergency support plans to local governments accordingly. To ensure a rapid response, the system utilizes a generative AI model, and prompts such as "Please analyze the emotional situation in the designated area and assess the need for psychological care" are used.

[0222] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0223] Step 1:

[0224] The server collects data from drones, satellites, sensors, and social networks. Its purpose is to receive real-time images, weather data, environmental sensor information, and social media posts as input, and to centrally manage all of this data. This allows for the accumulation of information from diverse data sources.

[0225] Step 2:

[0226] The server analyzes the collected data to assess the basic damage situation in the affected areas. The input is the data set collected in step 1, which is used for natural language processing and image analysis. The output provides an initial assessment of the level of damage and geographical distribution information. Specifically, it detects flood areas using satellite imagery and identifies areas with particularly high posting rates from social media text.

[0227] Step 3:

[0228] The server performs sentiment analysis and evaluates the psychological state of disaster victims from social network data. The input is SNS post data, which is processed using text analysis tools (e.g., NLTK, spaCy). The output is a quantitative evaluation of psychological states such as anxiety, fear, and tension. Specifically, it calculates sentiment scores from the content of posts and generates a map of psychological burden for each region.

[0229] Step 4:

[0230] The server integrates the above data and develops a plan that includes rescue and psychological care activities. The input is the physical and psychological assessment data obtained in steps 2 and 3. The output generates a comprehensive action plan, including priority allocation of resources. Specifically, it plans to concentrate personnel on areas where the psychological state is deteriorating.

[0231] Step 5:

[0232] The terminal transmits plans and instructions from the server to users (rescue teams and local government officials) on-site. The input is the plan formulated in Step 4, which is then reflected in the on-site response. The output records the status and progress of ongoing activities in real time. Specific actions include explaining the plan to those receiving assistance and on-site monitoring.

[0233] 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.

[0234] 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.

[0235] 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.

[0236] [Second Embodiment]

[0237] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0238] 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.

[0239] 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).

[0240] 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.

[0241] 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.

[0242] 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).

[0243] 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.

[0244] 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.

[0245] 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.

[0246] 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.

[0247] 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.

[0248] 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".

[0249] This invention provides an advanced information gathering and analysis system for automating disaster response. The main embodiment provides a means for aggregating information during a disaster, enabling rapid and precise rescue operations, and supporting the effective distribution of relief supplies.

[0250] Feature Overview

[0251] The servers begin operating immediately upon the reporting of a disaster. They collect video footage and environmental sensor data from the affected area via drones and satellites, and also gather information from social networks. This allows for a comprehensive understanding of the disaster's impact.

[0252] The server processes the collected data and evaluates the extent of the damage using analysis algorithms. This analysis utilizes technologies such as image recognition, data mining, and natural language processing.

[0253] Based on the assessment, the server determines priority areas and activities for rescue operations and infrastructure restoration, and then develops plans accordingly. These plans are designed to enable efficient rescue operations and provide streamlined support.

[0254] Users (e.g., local government officials) begin taking action based on plans generated by the server. Users utilize the instructions from the server to distribute relief supplies, provide evacuation routes, and provide information to local residents.

[0255] The terminals are devices deployed for volunteer staff working on-site, receiving instructions from the server and automatically recommending the optimal course of action. This includes determining the best evacuation routes and narrowing down who to assist in emergencies.

[0256] Specific example

[0257] Let's assume a large-scale earthquake occurs. In this case, the server immediately receives information about the earthquake through its monitoring system. Drones fly over the affected area, recording detailed topographic data and damage in video. Simultaneously, the server identifies areas where potential damage is expected to be significant, based on the observed earthquake magnitude.

[0258] Subsequently, the server analyzes social network data, including posts from residents in the affected areas, compares the impact of the disaster with known data, and identifies areas where rescue should be prioritized. This process enables the rapid issuance of evacuation orders.

[0259] Users concentrate relief supplies in designated high-priority areas to address the situation. They prepare for rapid delivery of supplies by following planned logistics routes.

[0260] Thus, by combining multifaceted information gathering and analysis, this invention enables rapid and appropriate disaster response and optimal allocation of resources in disaster-stricken areas. Furthermore, it is expected that this will significantly improve the efficiency of life-saving operations and infrastructure restoration.

[0261] The following describes the processing flow.

[0262] Step 1:

[0263] Upon receiving notification of a disaster, the server activates the control systems for drones and satellites and begins collecting data from the affected area. The drones fly over the affected area, acquiring video and environmental sensor data in real time. This data is then transmitted to the server.

[0264] Step 2:

[0265] The server collects disaster information from social networks. It uses SNS APIs to search for posts related to specific keywords and aggregates that data.

[0266] Step 3:

[0267] The server analyzes collected drone data, satellite data, sensor data, and social network data. Video data is processed using image recognition technology to detect collapsed buildings and impassable areas. Social media data is processed using natural language processing to extract important information.

[0268] Step 4:

[0269] The server prioritizes rescue operations and infrastructure restoration based on data analysis. It generates priority lists according to the extent of the damage and identifies areas that should receive priority relief.

[0270] Step 5:

[0271] The server develops optimal plans for rescue operations and supply transport. Based on a priority list, it determines the optimal allocation of personnel and supplies and establishes logistics routes.

[0272] Step 6:

[0273] Users execute local disaster response based on plans and instructions provided by the server. They issue instructions for transporting supplies and disseminate information to areas requiring priority evacuation.

[0274] Step 7:

[0275] The terminals receive instructions from the server on the devices of volunteers and local support staff, and initiate appropriate support activities. They respond quickly to emergency evacuation orders and personnel changes when necessary.

[0276] Step 8:

[0277] The server continuously monitors the situation and analyzes additional data. Based on the new information, it updates the plan and incorporates necessary adjustments and instructions in real time.

[0278] (Example 1)

[0279] 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."

[0280] There is a need to build systems that enable rapid and appropriate information gathering and analysis during disasters, effective rescue operation planning, efficient distribution of relief supplies, and provision of safe evacuation routes. Conventional technologies often rely heavily on manual information aggregation and analysis, leading to delayed responses and inefficient support. Therefore, real-time situation assessment and the establishment of a rapid response support system are essential.

[0281] 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.

[0282] In this invention, the server includes means for detecting a disaster situation and aggregating information, means for collecting observation data using a drone, a spacecraft, and a high-altitude environment detector, and means for combining and comprehensively analyzing the data obtained from an information sharing service. As a result, rapid and detailed information collection and analysis during a disaster can be achieved, and effective rescue activities and rapid restoration of infrastructure can be supported.

[0283] The "disaster situation" refers to the state of events in the area where a natural disaster has occurred, including the scale of damage and the scope of influence.

[0284] The "means for aggregating information" refers to a function for integrating data obtained from different sources into one place and processing it efficiently.

[0285] A "drone" is an aircraft capable of remote control or autonomous flight, and is responsible for collecting data from above the disaster area.

[0286] A "spacecraft" is an artificial satellite orbiting the Earth or a device in space, and provides a wide range of observation data.

[0287] A "high-altitude environment detector" refers to a sensor device for detecting environmental changes and abnormalities with high precision, and collects information useful during a disaster.

[0288] An "information sharing service" refers to a platform for collecting data transmitted by users through social networks such as social media and news feeds provided on an online platform.

[0289] "Machine learning technology" is a general term for technologies in which a computer automatically constructs patterns and rules based on a large amount of data, and plays an important role in data analysis.

[0290] "Infrastructure restoration" refers to activities that repair infrastructure damaged by a disaster and restore it to normal function.

[0291] A "transportation route" refers to a route used to efficiently deliver goods or personnel to a specific destination, and is an important element in optimizing planning.

[0292] "External devices" refer to devices or software that are connected to a system and extend its functionality by exchanging information.

[0293] "Devices that support on-site activities" refer to mobile devices used by volunteers and rescue workers working at disaster sites, which are used to provide information and issue instructions.

[0294] This invention provides a system that enables rapid and highly accurate information gathering and analysis during disasters, facilitating efficient rescue operations and the distribution of relief supplies. This system primarily consists of a server, terminals, and users.

[0295] The server receives information from the disaster monitoring system and collects observational data from the affected area using unmanned aerial vehicles (UAVs), spacecraft, and high-altitude environmental detectors. UAVs fly autonomously over the affected area, taking high-resolution video and photographs. Spacecraft monitor the situation over a wide area and provide weather and topographic data. High-altitude environmental detectors detect local environmental signals with high precision and acquire data such as earthquakes and wind speed.

[0296] The server aggregates this data and combines it with data obtained from information sharing services for comprehensive analysis. Using technologies such as image recognition, data mining, and natural language processing, it assesses the extent of disaster damage and determines priority areas for rescue and infrastructure restoration. Machine learning techniques are applied to streamline these analyses and improve their accuracy.

[0297] Users utilize server-generated plans to ensure the effective distribution of relief supplies and direct rescue routes. They are also responsible for directing necessary personnel deployments and coordinating on-site operations. The plans include transport routes and optimal resource utilization, reducing waste and enabling a rapid response.

[0298] The terminal functions as a device to support on-site activities, receiving information from the server and providing users with detailed instructions. These instructions aim to suggest safe evacuation routes and facilitate effective relief activities. As an example of prompts generated by the AI ​​model, the terminal can display specific instructions such as, "Report the current earthquake damage situation in the affected area and identify priority rescue areas."

[0299] As described above, through the cooperation of servers, users, and terminals, this system enables rapid and accurate disaster response, supporting life-saving efforts and the optimal allocation of resources in disaster-stricken areas.

[0300] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0301] Step 1:

[0302] The server receives information about earthquakes and typhoons from the disaster monitoring system. This input information includes the magnitude of the disaster, the time of occurrence, and the location. The server uses this as a trigger to initiate the relevant data collection process and outputs instructions to other data collection methods.

[0303] Step 2:

[0304] The server controls unmanned aerial vehicles (UAVs) and spacecraft to collect observational data from the disaster area. The UAVs fly over the affected region, taking video and photographic images using cameras. This data is high-resolution and records the terrain and damage in detail. Images and environmental data from the UAVs and spacecraft are obtained as input data. The server receives this data and outputs it as initial geographic information.

[0305] Step 3:

[0306] The server also integrates data from the high-altitude environment detector. This includes information on wind speed, temperature, and humidity from weather sensors. These input data are important for understanding the local weather conditions. The server analyzes the weather data and outputs it to a model for predicting the progress and impact of disasters.

[0307] Step 4:

[0308] The server combines data from unmanned aircraft, spacecraft, and high-altitude environment detectors with social network data collected from information sharing services. This data includes posts and reports from disaster victims, providing real-time information on the ground. The input data includes text data, image data, and location information. The server processes this with analysis software and outputs a map identifying the affected areas of the disaster.

[0309] Step 5:

[0310] The server uses a generative AI model to analyze the data and evaluate the disaster situation. This includes image recognition algorithms and natural language processing to identify the scale of the damage and priority areas for response. Based on the input observed data, a disaster situation map and a list of affected areas are output as analysis results.

[0311] Step 6:

[0312] The server generates a rescue plan based on the analysis results. It determines the optimal allocation of rescue teams and supplies for high-priority areas and plans transportation routes. The server's input includes data on the affected area map and priority areas, and specific action guidelines and support routes are obtained as output.

[0313] Step 7:

[0314] The user receives plans and instructions from the server. Based on the optimized rescue plan, they prepare supplies and deploy support teams. Input data includes the rescue plan and transport routes, and the output is rapid response and supply delivery.

[0315] Step 8:

[0316] The terminal displays instructions provided by the server to volunteers and work staff conducting field activities. The terminal receives instruction information from the server and displays safe evacuation routes and specific support activities in the field to the user. The input is server instruction data provided to the terminal, and the output is to facilitate efficient activities in the field.

[0317] (Application Example 1)

[0318] 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."

[0319] In recent years, the frequency and scale of natural disasters have increased, creating a need for rapid and efficient disaster response. Conventional disaster response systems have often been slow to prioritize information gathering and rescue operations, leading to delays in providing support to affected areas. Furthermore, the lack of effective approaches to managing residents' evacuation behavior in urban environments has frequently resulted in confusion during disasters. This invention aims to solve these problems and realize a rapid and effective disaster response.

[0320] 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.

[0321] In this invention, the server includes means for collecting drone data, satellite data, sensor data, and social network data during a disaster; means for analyzing the collected data to evaluate the extent of the damage; means for providing residents with appropriate evacuation routes using mobile communication devices; and means for instructing volunteer personnel to carry out effective support activities. This enables residents and support organizations to take action based on accurate information in real time through rapid information gathering and analysis.

[0322] "Unmanned aircraft data" refers to data on terrain and damage conditions collected by unmanned aircraft in the air.

[0323] "Satellite data" refers to data that includes images and observational information taken from satellites, and is used to understand the extent of damage over a wide area.

[0324] "Sensor data" refers to data obtained from various sensors that detect environmental information such as temperature, humidity, and vibration.

[0325] "Social network data" refers to posts and information collected from social media and network platforms on the internet, and is data that reflects the voices and circumstances of residents.

[0326] A "mobile communication device" refers to a mobile device such as a cell phone or smartphone, which is used to send and receive information in real time.

[0327] "Means of providing evacuation routes" refer to methods and technologies for guiding residents to the optimal route so that they can safely evacuate from disaster areas.

[0328] "Volunteer personnel" refers to individuals or groups who work to provide support to disaster victims and distribute supplies during a disaster.

[0329] "Means of directing effective support activities" refer to methods and techniques for planning support activities and providing instructions for their implementation in an appropriate manner and timing.

[0330] This invention is an information gathering and analysis system for streamlining disaster response in smart cities. The server comprehensively collects and analyzes drone data, satellite data, sensor data, and social network data. The hardware used is a cloud server with high computing power, and a program written in Python is implemented for data processing. Apache Kafka is used for real-time data processing, and backend computing is performed on AWS Lambda.

[0331] Based on the analyzed data, the server provides optimal evacuation routes to mobile communication devices to support residents' evacuation actions. This information is delivered to residents in real time through a dedicated app built with React Native. It also directs volunteers to carry out effective support activities, ensuring that the distribution of supplies and relief efforts proceed efficiently.

[0332] For example, during a large-scale flood in a specific area, the server quickly identifies dangerous areas and displays safe evacuation routes to residents' mobile devices. At the same time, volunteers receive instructions, including prioritizing disaster relief efforts, ensuring that support activities are carried out appropriately. This speeds up and improves the accuracy of disaster response, making it possible to ensure the safety of many people.

[0333] An example of a prompt for a generated AI model is, "Please provide the information needed to develop an application that provides optimal evacuation routes in real time when a typhoon is approaching." By using this prompt, necessary technical knowledge can be quickly obtained and utilized in implementation.

[0334] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0335] Step 1:

[0336] The server collects drone data, satellite data, sensor data, and social network data on the cloud. To enable real-time acquisition of this data, it aggregates it using the data streaming platform Apache Kafka. Inputs include raw data from various sensors, drone video data, and social media feeds, which are then processed as structured data and output.

[0337] Step 2:

[0338] The server performs data analysis on the collected data to assess the extent of the damage. This analysis uses AWS Lambda, which runs a Python program, and utilizes image recognition and natural language processing algorithms. The input is the structured data obtained in step 1, and the output generates map information that visualizes the risk assessment and damage situation of the affected areas.

[0339] Step 3:

[0340] The server sets evacuation routes based on the assessment of the disaster situation and provides information to mobile communication devices. It calculates the optimal evacuation route based on the assessment results and notifies residents through an application built with React Native. The input is map information based on the assessment results, and the output is real-time evacuation route guidance for mobile devices.

[0341] Step 4:

[0342] The terminal provides evacuation information to residents through an application and updates evacuation routes in real time. It receives notifications from the server and uses residents' location data to send personalized evacuation information. Inputs include guidance information from the server and residents' current location data, while outputs include updated evacuation routes and safety confirmation notifications.

[0343] Step 5:

[0344] The server directs volunteers to carry out effective support activities and updates information as needed. It sends prioritized relief operation instructions to volunteer terminals to support on-site activities. Inputs include analysis results of the disaster situation and inventory information of relief supplies, while outputs include volunteer action plans and supply distribution plans.

[0345] 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.

[0346] This invention combines an emotion engine with a disaster response system to automatically propose support activities that take into account the psychological state of disaster victims, enabling more appropriate resource allocation. The system has a configuration that combines information gathering, data analysis, emotion recognition, and support plan development.

[0347] Feature Overview

[0348] In addition to normal data collection (from drones, satellites, sensors, social networks, etc.), the server operates an emotion engine. The emotion engine acquires emotion data through social networks and direct interaction with users. This is done through text analysis and speech analysis.

[0349] The server integrates emotional data with other information to conduct a comprehensive assessment of the disaster situation. This assessment identifies the degree of psychological stress and specific psychological states (e.g., anxiety, fear), and then plans appropriate responses.

[0350] Users (local government officials and rescue teams) conduct disaster relief activities based on detailed assessments and suggestions provided by the server. Suggestions based on sentiment data indicate priorities for specific supplies and situations requiring special care.

[0351] The device receives feedback from the emotional engine on-site, enabling more flexible and humane responses. For example, in emergency shelter care, it can propose and implement activities to reduce psychological burden.

[0352] Specific example

[0353] For example, consider a scenario involving a large-scale flood. The server collects information from the affected area in real time while analyzing the emotional state of the residents. This involves extracting text indicating anxiety and confusion from social media posts and detecting tension and fear during voice calls through emotional analysis.

[0354] Based on the analysis results, the server identifies areas where psychological support is particularly needed and proposes specific action plans to local government officials. These plans include not only the distribution of relief supplies but also the dispatch of specialists to provide psychological care. This information is then communicated to the field by users and immediately implemented.

[0355] Furthermore, if disaster victims are experiencing anxiety in evacuation shelters, the device will inform shelter staff of the situation and, if necessary, suggest counseling services or measures to enhance their sense of security. These measures will reduce the psychological burden on disaster victims and enable efficient and humane disaster response.

[0356] Thus, the present invention aims to provide a comprehensive support system that takes into account the psychological state of disaster victims, and to maximize the use of limited resources.

[0357] The following describes the processing flow.

[0358] Step 1:

[0359] Upon receiving notification of a disaster, the server begins collecting data from drones, satellites, sensors, and social network data. Furthermore, it activates an emotion engine to collect user emotion data, which is extracted from social media posts and audio data.

[0360] Step 2:

[0361] The server performs a series of processes to analyze the collected drone data, satellite data, sensor data, and social network data. The analysis extracts topographic information from the video footage and maps the damage situation.

[0362] Step 3:

[0363] The server integrates emotional data analyzed by the emotion engine to assess the psychological state of disaster victims. This makes it possible to identify areas where people are experiencing anxiety or fear.

[0364] Step 4:

[0365] The server prioritizes rescue operations and infrastructure restoration based on disaster situation and sentiment data. Detailed support plans are then created based on the high-priority areas and activities.

[0366] Step 5:

[0367] Users receive plans and instructions from the server and begin on-site support activities. This includes distributing supplies and providing psychological care.

[0368] Step 6:

[0369] The terminals receive instructions from the server on the devices of local staff and volunteers, facilitating real-time action. If psychological care is needed, they support immediate implementation of specific support measures.

[0370] Step 7:

[0371] The server continuously collects and analyzes new data, updating plans based on changing circumstances. Sentimental data is also re-evaluated, and instructions are adjusted as needed. This ensures optimal disaster response at all times.

[0372] (Example 2)

[0373] 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".

[0374] In disaster relief efforts, it is necessary not only to provide physical rescue and restore infrastructure, but also to appropriately understand and alleviate the psychological stress and anxiety of disaster victims. However, conventional systems have made it difficult to formulate support plans that take psychological conditions into account, often resulting in insufficient emotional care. Therefore, there is a need to collect and analyze emotional data of disaster victims and provide a comprehensive and dynamic support system based on this data.

[0375] 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.

[0376] In this invention, the server includes means for collecting drone data, satellite data, sensor data, and social network data; means for analyzing the collected text and voice data using natural language processing technology to evaluate emotional states; and means for integrating emotional data and environmental data to evaluate the overall situation in the disaster area. This makes it possible to automatically generate support plans that take into account the psychological state of disaster victims.

[0377] "Unmanned aircraft data" refers to observational data acquired by aircraft or ground vehicles that operate without a driver.

[0378] "Satellite data" refers to observational data acquired from artificial satellites orbiting the Earth.

[0379] "Sensor data" refers to data collected by sensor devices to measure various environmental conditions.

[0380] "Social network data" refers to information such as posts, comments, and responses generated by users via the internet.

[0381] "Natural language processing technology" refers to the technology that enables computers to understand, interpret, and generate human natural language.

[0382] "Emotional state" refers to the mental state of an individual or group at a given point in time, and includes emotions such as joy, anxiety, anger, and fear.

[0383] "Comprehensive situation assessment" is an evaluation that integrates information from multiple data sources to grasp the overall state of the disaster site.

[0384] "Dynamic updating" means modifying and adjusting information and plans in real time as circumstances change.

[0385] A "safe evacuation route" is a route recommended to allow disaster victims to evacuate while avoiding danger.

[0386] "Psychological care" refers to the support and activities provided to maintain or improve an individual's mental health.

[0387] This invention implements a system designed to optimize support activities during disasters.

[0388] The system consists of the following components:

[0389] Data collection

[0390] The server collects data from drones, satellites, ground sensors, and social networks. Drones and satellites provide real-time images and video data to visualize local conditions. Ground sensors are used to collect weather data and environmental conditions. Data from social networks, including user-generated posts and comments, is collected via APIs developed in Python and Java.

[0391] Emotion analysis

[0392] The server analyzes collected text and audio data using natural language processing techniques to assess the emotional state of residents. This process utilizes natural language processing libraries (e.g., NLTK and spaCy). The analysis results enable the quantification of residents' emotions, such as anxiety and fear, and visualize the psychological state of disaster victims.

[0393] Data Integration and Evaluation

[0394] The server integrates emotional data with other environmental data to assess the overall situation in the affected area. This process utilizes a database system (e.g., PostgreSQL) and data visualization tools (e.g., Tableau). The assessed information is visually displayed to administrators to support the development of support plans.

[0395] Generating a support plan

[0396] Based on assessment information, the server automatically generates support plans that take into account the psychological health of disaster victims. These plans include the distribution of necessary supplies to specific areas and the dispatch of specialists to provide psychological care. The generated plans are then provided to local government officials and rescue teams.

[0397] Examples of specific cases and prompt statements

[0398] For example, in areas affected by large-scale flooding, the server analyzes social media posts to extract information indicating residents' anxiety and fear. Based on this sentiment analysis, areas requiring particular psychological care are identified, and countermeasures are formulated.

[0399] Example prompt: "In areas affected by large-scale flooding, use social media to understand the emotional state of residents and extract posts indicating anxiety or fear. Then, identify areas where psychological support is particularly needed and develop specific support plans for local government officials."

[0400] This system enables comprehensive support activities in disaster response that take psychological factors into consideration.

[0401] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0402] Step 1:

[0403] The server collects data from drones, satellites, sensors, and social networks. Its inputs include video data from drones and satellites, environmental data from sensors, and text data from social media. This data is retrieved in real time using APIs developed in Python and Java. The output is observational data in raw dataset form.

[0404] Step 2:

[0405] The server analyzes the collected text and audio data using natural language processing techniques to evaluate emotional states. The raw dataset from Step 1 is used as input. For data processing, the text data is analyzed, and positive or negative emotions are identified using an emotion analysis algorithm. The output is numerical data representing emotions. Natural language processing libraries such as NLTK and spaCy are used for this analysis.

[0406] Step 3:

[0407] The server integrates emotional and environmental data to assess the overall situation in the disaster-stricken area. It uses emotional data from Step 2 and other environmental data from Step 1 as input. For data calculation, an evaluation model is used to quantify the psychological and physical stress in the disaster-stricken area and visualize it on a map. The output is a comprehensive report of the disaster-stricken area. A database system and data visualization tools are used for this assessment.

[0408] Step 4:

[0409] The server generates a support plan that takes into account the psychological health of disaster victims, based on the evaluated information. The comprehensive situation report from Step 3 is used as input. As data calculation, a support prioritization algorithm is used to identify areas with the highest urgency and determine appropriate support measures. The output is a specific support plan, which includes details such as distribution routes for supplies and the deployment of psychological support specialists.

[0410] Step 5:

[0411] Users (local government officials and rescue workers) carry out activities on-site based on support plans provided by the server. The support plan generated in step 4 is used as input. Specific actions include transporting supplies and providing psychological care to disaster victims on-site, following the plan's instructions. The output is an improved on-site situation and stabilization of the residents' psychological state.

[0412] Step 6:

[0413] The terminal transmits feedback obtained on-site to the server in real time and adjusts the support plan as needed. It uses feedback data obtained from users on-site as input. The output is an updated support plan. This enables dynamic optimization of support activities.

[0414] (Application Example 2)

[0415] 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."

[0416] In disaster relief and infrastructure restoration, optimal allocation of supplies and personnel requires planning that considers not only the physical condition of victims but also their psychological state. Current systems place too much emphasis on assessing physical damage, and lack sufficient activities to alleviate the psychological burden on victims. Therefore, developing flexible plans that reflect psychological states is a key challenge.

[0417] 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.

[0418] This invention includes a server that analyzes collected data and evaluates the psychological state of disaster victims through sentiment analysis; a server that sets priorities for rescue operations and infrastructure restoration based on the evaluation and formulates a plan that includes psychological care for disaster victims; and a server that provides the plan and instructions to an external system and links with real-time public sentiment. This enables the implementation of efficient and effective activities that take into account the psychological state of disaster victims.

[0419] "Unmanned aircraft data" refers to video and image information acquired from the air during disasters, which is used to grasp the situation in disaster-stricken areas in real time.

[0420] "Satellite data" refers to images and sensor information acquired from outer space, which is used to understand the situation in disaster-stricken areas over a wide area.

[0421] "Sensor data" refers to data collected from various sensors installed on the ground, and is used to detect physical phenomena such as weather, earthquakes, and floods.

[0422] "Social network data" refers to text and image data collected from social media and other online communication platforms, which is used to understand the emotions and psychological state of disaster victims.

[0423] "Sentiment analysis" is a process of evaluating an individual's emotions and psychological state by analyzing text and audio data.

[0424] "Psychological care" refers to support activities aimed at reducing the emotional and psychological burden on disaster victims and maintaining and restoring their mental health.

[0425] "Real-time" refers to a state where information and data are acquired instantly and processed without any time lag.

[0426] "Public sentiment" is a general term for the psychological and emotional reactions of citizens during a disaster, and it is an important factor in determining the priorities of relief activities.

[0427] This invention is a system designed to alleviate the psychological burden on disaster-stricken areas and enable effective support activities. The system consists of a server, terminals, and users.

[0428] The server collects drone data, satellite data, sensor data, and social network data. Next, it analyzes this data and performs sentiment analysis to assess the psychological state of disaster victims. This process uses Python, employing libraries such as NLTK and spaCy for analysis.

[0429] The terminals formulate plans for specific rescue operations and psychological care based on analysis results from the server, and receive the instructions provided therein at the scene. For example, a dedicated application installed on a smartphone can immediately identify areas in need of assistance and respond based on those priorities.

[0430] Users, such as local government officials and rescue teams, will carry out disaster relief activities according to the detailed assessments and suggestions provided by the server. They are required to have a system in place to respond quickly, especially when psychological support is needed.

[0431] As a concrete example, in the event of a large-scale disaster, this system uses data collected from social media posts to detect emotions such as "tension" and "anxiety" through text analysis, and proposes emergency support plans to local governments accordingly. To ensure a rapid response, the system utilizes a generative AI model, and prompts such as "Please analyze the emotional situation in the designated area and assess the need for psychological care" are used.

[0432] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0433] Step 1:

[0434] The server collects data from drones, satellites, sensors, and social networks. Its purpose is to receive real-time images, weather data, environmental sensor information, and social media posts as input, and to centrally manage all of this data. This allows for the accumulation of information from diverse data sources.

[0435] Step 2:

[0436] The server analyzes the collected data to assess the basic damage situation in the affected areas. The input is the data set collected in step 1, which is used for natural language processing and image analysis. The output provides an initial assessment of the level of damage and geographical distribution information. Specifically, it detects flood areas using satellite imagery and identifies areas with particularly high posting rates from social media text.

[0437] Step 3:

[0438] The server performs sentiment analysis and evaluates the psychological state of disaster victims from social network data. The input is SNS post data, which is processed using text analysis tools (e.g., NLTK, spaCy). The output is a quantitative evaluation of psychological states such as anxiety, fear, and tension. Specifically, it calculates sentiment scores from the content of posts and generates a map of psychological burden for each region.

[0439] Step 4:

[0440] The server integrates the above data and develops a plan that includes rescue and psychological care activities. The input is the physical and psychological assessment data obtained in steps 2 and 3. The output generates a comprehensive action plan, including priority allocation of resources. Specifically, it plans to concentrate personnel on areas where the psychological state is deteriorating.

[0441] Step 5:

[0442] The terminal transmits plans and instructions from the server to users (rescue teams and local government officials) on-site. The input is the plan formulated in Step 4, which is then reflected in the on-site response. The output records the status and progress of ongoing activities in real time. Specific actions include explaining the plan to those receiving assistance and on-site monitoring.

[0443] 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.

[0444] 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.

[0445] 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.

[0446] [Third Embodiment]

[0447] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0448] 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.

[0449] 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).

[0450] 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.

[0451] 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.

[0452] 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).

[0453] 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.

[0454] 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.

[0455] 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.

[0456] 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.

[0457] 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.

[0458] 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".

[0459] This invention provides an advanced information gathering and analysis system for automating disaster response. The main embodiment provides a means for aggregating information during a disaster, enabling rapid and precise rescue operations, and supporting the effective distribution of relief supplies.

[0460] Feature Overview

[0461] The servers begin operating immediately upon the reporting of a disaster. They collect video footage and environmental sensor data from the affected area via drones and satellites, and also gather information from social networks. This allows for a comprehensive understanding of the disaster's impact.

[0462] The server processes the collected data and evaluates the extent of the damage using analysis algorithms. This analysis utilizes technologies such as image recognition, data mining, and natural language processing.

[0463] Based on the assessment, the server determines priority areas and activities for rescue operations and infrastructure restoration, and then develops plans accordingly. These plans are designed to enable efficient rescue operations and provide streamlined support.

[0464] Users (e.g., local government officials) begin taking action based on plans generated by the server. Users utilize the instructions from the server to distribute relief supplies, provide evacuation routes, and provide information to local residents.

[0465] The terminals are devices deployed for volunteer staff working on-site, receiving instructions from the server and automatically recommending the optimal course of action. This includes determining the best evacuation routes and narrowing down who to assist in emergencies.

[0466] Specific example

[0467] Let's assume a large-scale earthquake occurs. In this case, the server immediately receives information about the earthquake through its monitoring system. Drones fly over the affected area, recording detailed topographic data and damage in video. Simultaneously, the server identifies areas where potential damage is expected to be significant, based on the observed earthquake magnitude.

[0468] Subsequently, the server analyzes social network data, including posts from residents in the affected areas, compares the impact of the disaster with known data, and identifies areas where rescue should be prioritized. This process enables the rapid issuance of evacuation orders.

[0469] Users concentrate relief supplies in designated high-priority areas to address the situation. They prepare for rapid delivery of supplies by following planned logistics routes.

[0470] Thus, by combining multifaceted information gathering and analysis, this invention enables rapid and appropriate disaster response and optimal allocation of resources in disaster-stricken areas. Furthermore, it is expected that this will significantly improve the efficiency of life-saving operations and infrastructure restoration.

[0471] The following describes the processing flow.

[0472] Step 1:

[0473] Upon receiving notification of a disaster, the server activates the control systems for drones and satellites and begins collecting data from the affected area. The drones fly over the affected area, acquiring video and environmental sensor data in real time. This data is then transmitted to the server.

[0474] Step 2:

[0475] The server collects disaster information from social networks. It uses SNS APIs to search for posts related to specific keywords and aggregates that data.

[0476] Step 3:

[0477] The server analyzes collected drone data, satellite data, sensor data, and social network data. Video data is processed using image recognition technology to detect collapsed buildings and impassable areas. Social media data is processed using natural language processing to extract important information.

[0478] Step 4:

[0479] The server prioritizes rescue operations and infrastructure restoration based on data analysis. It generates priority lists according to the extent of the damage and identifies areas that should receive priority relief.

[0480] Step 5:

[0481] The server develops optimal plans for rescue operations and supply transport. Based on a priority list, it determines the optimal allocation of personnel and supplies and establishes logistics routes.

[0482] Step 6:

[0483] Users execute local disaster response based on plans and instructions provided by the server. They issue instructions for transporting supplies and disseminate information to areas requiring priority evacuation.

[0484] Step 7:

[0485] The terminals receive instructions from the server on the devices of volunteers and local support staff, and initiate appropriate support activities. They respond quickly to emergency evacuation orders and personnel changes when necessary.

[0486] Step 8:

[0487] The server continuously monitors the situation and analyzes additional data. Based on the new information, it updates the plan and incorporates necessary adjustments and instructions in real time.

[0488] (Example 1)

[0489] 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."

[0490] There is a need to build systems that enable rapid and appropriate information gathering and analysis during disasters, effective rescue operation planning, efficient distribution of relief supplies, and provision of safe evacuation routes. Conventional technologies often rely heavily on manual information aggregation and analysis, leading to delayed responses and inefficient support. Therefore, real-time situation assessment and the establishment of a rapid response support system are essential.

[0491] 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.

[0492] In this invention, the server includes means for detecting disaster situations and aggregating information, means for collecting observational data using unmanned aerial vehicles, spacecraft, and high-altitude environmental detectors, and means for combining and comprehensively analyzing data obtained from information sharing services. This enables rapid and precise information gathering and analysis in the event of a disaster, supporting effective rescue operations and the rapid restoration of infrastructure.

[0493] "Disaster situation" refers to the state of events in an area where a natural disaster has occurred, and includes the scale of the damage and the scope of its impact.

[0494] "Means of aggregating information" refers to functions that integrate data obtained from different sources into one place and process it efficiently.

[0495] An "unmanned aircraft" is an aircraft that can be remotely controlled or fly autonomously, and its role is to collect data from the air above disaster areas.

[0496] A "spacecraft" is an artificial satellite or other device in space that orbits the Earth and provides a wide range of observational data.

[0497] An "advanced environmental detector" refers to a sensor device that detects environmental changes and anomalies with high precision, and collects information that is useful in the event of a disaster.

[0498] An "information sharing service" refers to a platform that collects data transmitted by users through social networks such as social media and news feeds provided on online platforms.

[0499] "Machine learning technology" is a general term for technologies that enable computers to automatically construct patterns and rules based on large amounts of data, and it plays an important role in data analysis.

[0500] "Infrastructure restoration" refers to activities that repair infrastructure damaged by a disaster and restore it to normal function.

[0501] A "transportation route" refers to a route used to efficiently deliver goods or personnel to a specific destination, and is an important element in optimizing planning.

[0502] "External devices" refer to devices or software that are connected to a system and extend its functionality by exchanging information.

[0503] "Devices that support on-site activities" refer to mobile devices used by volunteers and rescue workers working at disaster sites, which are used to provide information and issue instructions.

[0504] This invention provides a system that enables rapid and highly accurate information gathering and analysis during disasters, facilitating efficient rescue operations and the distribution of relief supplies. This system primarily consists of a server, terminals, and users.

[0505] The server receives information from the disaster monitoring system and collects observational data from the affected area using unmanned aerial vehicles (UAVs), spacecraft, and high-altitude environmental detectors. UAVs fly autonomously over the affected area, taking high-resolution video and photographs. Spacecraft monitor the situation over a wide area and provide weather and topographic data. High-altitude environmental detectors detect local environmental signals with high precision and acquire data such as earthquakes and wind speed.

[0506] The server aggregates this data and combines it with data obtained from information sharing services for comprehensive analysis. Using technologies such as image recognition, data mining, and natural language processing, it assesses the extent of disaster damage and determines priority areas for rescue and infrastructure restoration. Machine learning techniques are applied to streamline these analyses and improve their accuracy.

[0507] Users utilize server-generated plans to ensure the effective distribution of relief supplies and direct rescue routes. They are also responsible for directing necessary personnel deployments and coordinating on-site operations. The plans include transport routes and optimal resource utilization, reducing waste and enabling a rapid response.

[0508] The terminal functions as a device to support on-site activities, receiving information from the server and providing users with detailed instructions. These instructions aim to suggest safe evacuation routes and facilitate effective relief activities. As an example of prompts generated by the AI ​​model, the terminal can display specific instructions such as, "Report the current earthquake damage situation in the affected area and identify priority rescue areas."

[0509] As described above, through the cooperation of servers, users, and terminals, this system enables rapid and accurate disaster response, supporting life-saving efforts and the optimal allocation of resources in disaster-stricken areas.

[0510] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0511] Step 1:

[0512] The server receives information about earthquakes and typhoons from the disaster monitoring system. This input information includes the magnitude of the disaster, the time of occurrence, and the location. The server uses this as a trigger to initiate the relevant data collection process and outputs instructions to other data collection methods.

[0513] Step 2:

[0514] The server controls unmanned aerial vehicles (UAVs) and spacecraft to collect observational data from the disaster area. The UAVs fly over the affected region, taking video and photographic images using cameras. This data is high-resolution and records the terrain and damage in detail. Images and environmental data from the UAVs and spacecraft are obtained as input data. The server receives this data and outputs it as initial geographic information.

[0515] Step 3:

[0516] The server also integrates data from altitude environmental detectors, including information on wind speed, temperature, and humidity from weather sensors. This input data is crucial for understanding local weather conditions. The server analyzes the weather data and outputs the results to models that predict the progression and impact of disasters.

[0517] Step 4:

[0518] The server combines data from unmanned aerial vehicles, spacecraft, and altitude environmental detectors with social network data collected from information sharing services. This data includes posts and reports from disaster victims, providing raw information from the field. Input data includes text data, image data, and location information. The server processes this data with analysis software and outputs a map that identifies the extent of the disaster's impact.

[0519] Step 5:

[0520] The server uses a generative AI model to analyze data and assess the extent of the damage. This includes image recognition algorithms and natural language processing to identify the scale of the damage and priority areas for response. Based on the input observational data, the analysis results output a disaster situation map and a list of affected areas.

[0521] Step 6:

[0522] The server generates a rescue plan based on the analysis results. It determines the optimal deployment of rescue teams and supplies to high-priority areas and plans transportation routes. The server's input includes a map of the affected area and data on priority areas, and its output provides specific action guidelines and support routes.

[0523] Step 7:

[0524] The user receives plans and instructions from the server. Based on the optimized rescue plan, they prepare supplies and deploy support teams. Input data includes the rescue plan and transport routes, and the output is rapid response and supply delivery.

[0525] Step 8:

[0526] The terminal displays instructions provided by the server to volunteers and work staff conducting field activities. The terminal receives instruction information from the server and displays safe evacuation routes and specific support activities in the field to the user. The input is server instruction data provided to the terminal, and the output is to facilitate efficient activities in the field.

[0527] (Application Example 1)

[0528] 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."

[0529] In recent years, the frequency and scale of natural disasters have increased, creating a need for rapid and efficient disaster response. Conventional disaster response systems have often been slow to prioritize information gathering and rescue operations, leading to delays in providing support to affected areas. Furthermore, the lack of effective approaches to managing residents' evacuation behavior in urban environments has frequently resulted in confusion during disasters. This invention aims to solve these problems and realize a rapid and effective disaster response.

[0530] 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.

[0531] In this invention, the server includes means for collecting drone data, satellite data, sensor data, and social network data during a disaster; means for analyzing the collected data to evaluate the extent of the damage; means for providing residents with appropriate evacuation routes using mobile communication devices; and means for instructing volunteer personnel to carry out effective support activities. This enables residents and support organizations to take action based on accurate information in real time through rapid information gathering and analysis.

[0532] "Unmanned aircraft data" refers to data on terrain and damage conditions collected by unmanned aircraft in the air.

[0533] "Satellite data" refers to data that includes images and observational information taken from satellites, and is used to understand the extent of damage over a wide area.

[0534] "Sensor data" refers to data obtained from various sensors that detect environmental information such as temperature, humidity, and vibration.

[0535] "Social network data" refers to posts and information collected from social media and network platforms on the internet, and is data that reflects the voices and circumstances of residents.

[0536] A "mobile communication device" refers to a mobile device such as a cell phone or smartphone, which is used to send and receive information in real time.

[0537] "Means of providing evacuation routes" refer to methods and technologies for guiding residents to the optimal route so that they can safely evacuate from disaster areas.

[0538] "Volunteer personnel" refers to individuals or groups who work to provide support to disaster victims and distribute supplies during a disaster.

[0539] "Means of directing effective support activities" refer to methods and techniques for planning support activities and providing instructions for their implementation in an appropriate manner and timing.

[0540] This invention is an information gathering and analysis system for streamlining disaster response in smart cities. The server comprehensively collects and analyzes drone data, satellite data, sensor data, and social network data. The hardware used is a cloud server with high computing power, and a program written in Python is implemented for data processing. Apache Kafka is used for real-time data processing, and backend computing is performed on AWS Lambda.

[0541] Based on the analyzed data, the server provides optimal evacuation routes to mobile communication devices to support residents' evacuation actions. This information is delivered to residents in real time through a dedicated app built with React Native. It also directs volunteers to carry out effective support activities, ensuring that the distribution of supplies and relief efforts proceed efficiently.

[0542] For example, during a large-scale flood in a specific area, the server quickly identifies dangerous areas and displays safe evacuation routes to residents' mobile devices. At the same time, volunteers receive instructions, including prioritizing disaster relief efforts, ensuring that support activities are carried out appropriately. This speeds up and improves the accuracy of disaster response, making it possible to ensure the safety of many people.

[0543] An example of a prompt for a generated AI model is, "Please provide the information needed to develop an application that provides optimal evacuation routes in real time when a typhoon is approaching." By using this prompt, necessary technical knowledge can be quickly obtained and utilized in implementation.

[0544] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0545] Step 1:

[0546] The server collects drone data, satellite data, sensor data, and social network data on the cloud. To enable real-time acquisition of this data, it aggregates it using the data streaming platform Apache Kafka. Inputs include raw data from various sensors, drone video data, and social media feeds, which are then processed as structured data and output.

[0547] Step 2:

[0548] The server performs data analysis on the collected data to assess the extent of the damage. This analysis uses AWS Lambda, which runs a Python program, and utilizes image recognition and natural language processing algorithms. The input is the structured data obtained in step 1, and the output generates map information that visualizes the risk assessment and damage situation of the affected areas.

[0549] Step 3:

[0550] The server sets evacuation routes based on the assessment of the disaster situation and provides information to mobile communication devices. It calculates the optimal evacuation route based on the assessment results and notifies residents through an application built with React Native. The input is map information based on the assessment results, and the output is real-time evacuation route guidance for mobile devices.

[0551] Step 4:

[0552] The terminal provides evacuation information to residents through an application and updates evacuation routes in real time. It receives notifications from the server and uses residents' location data to send personalized evacuation information. Inputs include guidance information from the server and residents' current location data, while outputs include updated evacuation routes and safety confirmation notifications.

[0553] Step 5:

[0554] The server directs volunteers to carry out effective support activities and updates information as needed. It sends prioritized relief operation instructions to volunteer terminals to support on-site activities. Inputs include analysis results of the disaster situation and inventory information of relief supplies, while outputs include volunteer action plans and supply distribution plans.

[0555] 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.

[0556] This invention combines an emotion engine with a disaster response system to automatically propose support activities that take into account the psychological state of disaster victims, enabling more appropriate resource allocation. The system has a configuration that combines information gathering, data analysis, emotion recognition, and support plan development.

[0557] Feature Overview

[0558] In addition to normal data collection (from drones, satellites, sensors, social networks, etc.), the server operates an emotion engine. The emotion engine acquires emotion data through social networks and direct interaction with users. This is done through text analysis and speech analysis.

[0559] The server integrates emotional data with other information to conduct a comprehensive assessment of the disaster situation. This assessment identifies the degree of psychological stress and specific psychological states (e.g., anxiety, fear), and then plans appropriate responses.

[0560] Users (local government officials and rescue teams) conduct disaster relief activities based on detailed assessments and suggestions provided by the server. Suggestions based on sentiment data indicate priorities for specific supplies and situations requiring special care.

[0561] The device receives feedback from the emotional engine on-site, enabling more flexible and humane responses. For example, in emergency shelter care, it can propose and implement activities to reduce psychological burden.

[0562] Specific example

[0563] For example, consider a scenario involving a large-scale flood. The server collects information from the affected area in real time while analyzing the emotional state of the residents. This involves extracting text indicating anxiety and confusion from social media posts and detecting tension and fear during voice calls through emotional analysis.

[0564] Based on the analysis results, the server identifies areas where psychological support is particularly needed and proposes specific action plans to local government officials. These plans include not only the distribution of relief supplies but also the dispatch of specialists to provide psychological care. This information is then communicated to the field by users and immediately implemented.

[0565] Furthermore, if disaster victims are experiencing anxiety in evacuation shelters, the device will inform shelter staff of the situation and, if necessary, suggest counseling services or measures to enhance their sense of security. These measures will reduce the psychological burden on disaster victims and enable efficient and humane disaster response.

[0566] Thus, the present invention aims to provide a comprehensive support system that takes into account the psychological state of disaster victims, and to maximize the use of limited resources.

[0567] The following describes the processing flow.

[0568] Step 1:

[0569] Upon receiving notification of a disaster, the server begins collecting data from drones, satellites, sensors, and social network data. Furthermore, it activates an emotion engine to collect user emotion data, which is extracted from social media posts and audio data.

[0570] Step 2:

[0571] The server performs a series of processes to analyze the collected drone data, satellite data, sensor data, and social network data. The analysis extracts topographic information from the video footage and maps the damage situation.

[0572] Step 3:

[0573] The server integrates emotional data analyzed by the emotion engine to assess the psychological state of disaster victims. This makes it possible to identify areas where people are experiencing anxiety or fear.

[0574] Step 4:

[0575] The server prioritizes rescue operations and infrastructure restoration based on disaster situation and sentiment data. Detailed support plans are then created based on the high-priority areas and activities.

[0576] Step 5:

[0577] Users receive plans and instructions from the server and begin on-site support activities. This includes distributing supplies and providing psychological care.

[0578] Step 6:

[0579] The terminals receive instructions from the server on the devices of local staff and volunteers, facilitating real-time action. If psychological care is needed, they support immediate implementation of specific support measures.

[0580] Step 7:

[0581] The server continuously collects and analyzes new data, updating plans based on changing circumstances. Sentimental data is also re-evaluated, and instructions are adjusted as needed. This ensures optimal disaster response at all times.

[0582] (Example 2)

[0583] 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."

[0584] In disaster relief efforts, it is necessary not only to provide physical rescue and restore infrastructure, but also to appropriately understand and alleviate the psychological stress and anxiety of disaster victims. However, conventional systems have made it difficult to formulate support plans that take psychological conditions into account, often resulting in insufficient emotional care. Therefore, there is a need to collect and analyze emotional data of disaster victims and provide a comprehensive and dynamic support system based on this data.

[0585] 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.

[0586] In this invention, the server includes means for collecting drone data, satellite data, sensor data, and social network data; means for analyzing the collected text and voice data using natural language processing technology to evaluate emotional states; and means for integrating emotional data and environmental data to evaluate the overall situation in the disaster area. This makes it possible to automatically generate support plans that take into account the psychological state of disaster victims.

[0587] "Unmanned aircraft data" refers to observational data acquired by aircraft or ground vehicles that operate without a driver.

[0588] "Satellite data" refers to observational data acquired from artificial satellites orbiting the Earth.

[0589] "Sensor data" refers to data collected by sensor devices to measure various environmental conditions.

[0590] "Social network data" refers to information such as posts, comments, and responses generated by users via the internet.

[0591] "Natural language processing technology" refers to the technology that enables computers to understand, interpret, and generate human natural language.

[0592] "Emotional state" refers to the mental state of an individual or group at a given point in time, and includes emotions such as joy, anxiety, anger, and fear.

[0593] "Comprehensive situation assessment" is an evaluation that integrates information from multiple data sources to grasp the overall state of the disaster site.

[0594] "Dynamic updating" means modifying and adjusting information and plans in real time as circumstances change.

[0595] A "safe evacuation route" is a route recommended to allow disaster victims to evacuate while avoiding danger.

[0596] "Psychological care" refers to the support and activities provided to maintain or improve an individual's mental health.

[0597] This invention implements a system designed to optimize support activities during disasters.

[0598] The system consists of the following components:

[0599] Data collection

[0600] The server collects data from drones, satellites, ground sensors, and social networks. Drones and satellites provide real-time images and video data to visualize local conditions. Ground sensors are used to collect weather data and environmental conditions. Data from social networks, including user-generated posts and comments, is collected via APIs developed in Python and Java.

[0601] Emotion analysis

[0602] The server analyzes collected text and audio data using natural language processing techniques to assess the emotional state of residents. This process utilizes natural language processing libraries (e.g., NLTK and spaCy). The analysis results enable the quantification of residents' emotions, such as anxiety and fear, and visualize the psychological state of disaster victims.

[0603] Data Integration and Evaluation

[0604] The server integrates emotional data with other environmental data to assess the overall situation in the affected area. This process utilizes a database system (e.g., PostgreSQL) and data visualization tools (e.g., Tableau). The assessed information is visually displayed to administrators to support the development of support plans.

[0605] Generating a support plan

[0606] Based on assessment information, the server automatically generates support plans that take into account the psychological health of disaster victims. These plans include the distribution of necessary supplies to specific areas and the dispatch of specialists to provide psychological care. The generated plans are then provided to local government officials and rescue teams.

[0607] Examples of specific cases and prompt statements

[0608] For example, in areas affected by large-scale flooding, the server analyzes social media posts to extract information indicating residents' anxiety and fear. Based on this sentiment analysis, areas requiring particular psychological care are identified, and countermeasures are formulated.

[0609] Example prompt: "In areas affected by large-scale flooding, use social media to understand the emotional state of residents and extract posts indicating anxiety or fear. Then, identify areas where psychological support is particularly needed and develop specific support plans for local government officials."

[0610] This system enables comprehensive support activities in disaster response that take psychological factors into consideration.

[0611] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0612] Step 1:

[0613] The server collects data from drones, satellites, sensors, and social networks. Its inputs include video data from drones and satellites, environmental data from sensors, and text data from social media. This data is retrieved in real time using APIs developed in Python and Java. The output is observational data in raw dataset form.

[0614] Step 2:

[0615] The server analyzes the collected text and audio data using natural language processing techniques to evaluate emotional states. The raw dataset from Step 1 is used as input. For data processing, the text data is analyzed, and positive or negative emotions are identified using an emotion analysis algorithm. The output is numerical data representing emotions. Natural language processing libraries such as NLTK and spaCy are used for this analysis.

[0616] Step 3:

[0617] The server integrates emotional and environmental data to assess the overall situation in the disaster-stricken area. It uses emotional data from Step 2 and other environmental data from Step 1 as input. For data calculation, an evaluation model is used to quantify the psychological and physical stress in the disaster-stricken area and visualize it on a map. The output is a comprehensive report of the disaster-stricken area. A database system and data visualization tools are used for this assessment.

[0618] Step 4:

[0619] The server generates a support plan that takes into account the psychological health of disaster victims, based on the evaluated information. The comprehensive situation report from Step 3 is used as input. As data calculation, a support prioritization algorithm is used to identify areas with the highest urgency and determine appropriate support measures. The output is a specific support plan, which includes details such as distribution routes for supplies and the deployment of psychological support specialists.

[0620] Step 5:

[0621] Users (local government officials and rescue workers) carry out activities on-site based on support plans provided by the server. The support plan generated in step 4 is used as input. Specific actions include transporting supplies and providing psychological care to disaster victims on-site, following the plan's instructions. The output is an improved on-site situation and stabilization of the residents' psychological state.

[0622] Step 6:

[0623] The terminal transmits feedback obtained on-site to the server in real time and adjusts the support plan as needed. It uses feedback data obtained from users on-site as input. The output is an updated support plan. This enables dynamic optimization of support activities.

[0624] (Application Example 2)

[0625] 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."

[0626] In disaster relief and infrastructure restoration, optimal allocation of supplies and personnel requires planning that considers not only the physical condition of victims but also their psychological state. Current systems place too much emphasis on assessing physical damage, and lack sufficient activities to alleviate the psychological burden on victims. Therefore, developing flexible plans that reflect psychological states is a key challenge.

[0627] 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.

[0628] This invention includes a server that analyzes collected data and evaluates the psychological state of disaster victims through sentiment analysis; a server that sets priorities for rescue operations and infrastructure restoration based on the evaluation and formulates a plan that includes psychological care for disaster victims; and a server that provides the plan and instructions to an external system and links with real-time public sentiment. This enables the implementation of efficient and effective activities that take into account the psychological state of disaster victims.

[0629] "Unmanned aircraft data" refers to video and image information acquired from the air during disasters, which is used to grasp the situation in disaster-stricken areas in real time.

[0630] "Satellite data" refers to images and sensor information acquired from outer space, which is used to understand the situation in disaster-stricken areas over a wide area.

[0631] "Sensor data" refers to data collected from various sensors installed on the ground, and is used to detect physical phenomena such as weather, earthquakes, and floods.

[0632] "Social network data" refers to text and image data collected from social media and other online communication platforms, which is used to understand the emotions and psychological state of disaster victims.

[0633] "Sentiment analysis" is a process of evaluating an individual's emotions and psychological state by analyzing text and audio data.

[0634] "Psychological care" refers to support activities aimed at reducing the emotional and psychological burden on disaster victims and maintaining and restoring their mental health.

[0635] "Real-time" refers to a state where information and data are acquired instantly and processed without any time lag.

[0636] "Public sentiment" is a general term for the psychological and emotional reactions of citizens during a disaster, and it is an important factor in determining the priorities of relief activities.

[0637] This invention is a system designed to alleviate the psychological burden on disaster-stricken areas and enable effective support activities. The system consists of a server, terminals, and users.

[0638] The server collects drone data, satellite data, sensor data, and social network data. Next, it analyzes this data and performs sentiment analysis to assess the psychological state of disaster victims. This process uses Python, employing libraries such as NLTK and spaCy for analysis.

[0639] The terminals formulate plans for specific rescue operations and psychological care based on analysis results from the server, and receive the instructions provided therein at the scene. For example, a dedicated application installed on a smartphone can immediately identify areas in need of assistance and respond based on those priorities.

[0640] Users, such as local government officials and rescue teams, will carry out disaster relief activities according to the detailed assessments and suggestions provided by the server. They are required to have a system in place to respond quickly, especially when psychological support is needed.

[0641] As a concrete example, in the event of a large-scale disaster, this system uses data collected from social media posts to detect emotions such as "tension" and "anxiety" through text analysis, and proposes emergency support plans to local governments accordingly. To ensure a rapid response, the system utilizes a generative AI model, and prompts such as "Please analyze the emotional situation in the designated area and assess the need for psychological care" are used.

[0642] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0643] Step 1:

[0644] The server collects data from drones, satellites, sensors, and social networks. Its purpose is to receive real-time images, weather data, environmental sensor information, and social media posts as input, and to centrally manage all of this data. This allows for the accumulation of information from diverse data sources.

[0645] Step 2:

[0646] The server analyzes the collected data to assess the basic damage situation in the affected areas. The input is the data set collected in step 1, which is used for natural language processing and image analysis. The output provides an initial assessment of the level of damage and geographical distribution information. Specifically, it detects flood areas using satellite imagery and identifies areas with particularly high posting rates from social media text.

[0647] Step 3:

[0648] The server performs sentiment analysis and evaluates the psychological state of disaster victims from social network data. The input is SNS post data, which is processed using text analysis tools (e.g., NLTK, spaCy). The output is a quantitative evaluation of psychological states such as anxiety, fear, and tension. Specifically, it calculates sentiment scores from the content of posts and generates a map of psychological burden for each region.

[0649] Step 4:

[0650] The server integrates the above data and develops a plan that includes rescue and psychological care activities. The input is the physical and psychological assessment data obtained in steps 2 and 3. The output generates a comprehensive action plan, including priority allocation of resources. Specifically, it plans to concentrate personnel on areas where the psychological state is deteriorating.

[0651] Step 5:

[0652] The terminal transmits plans and instructions from the server to users (rescue teams and local government officials) on-site. The input is the plan formulated in Step 4, which is then reflected in the on-site response. The output records the status and progress of ongoing activities in real time. Specific actions include explaining the plan to those receiving assistance and on-site monitoring.

[0653] 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.

[0654] 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.

[0655] 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.

[0656] [Fourth Embodiment]

[0657] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0658] 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.

[0659] 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).

[0660] 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.

[0661] 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.

[0662] 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).

[0663] 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.

[0664] 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.

[0665] 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.

[0666] 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.

[0667] 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.

[0668] 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.

[0669] 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".

[0670] This invention provides an advanced information gathering and analysis system for automating disaster response. The main embodiment provides a means for aggregating information during a disaster, enabling rapid and precise rescue operations, and supporting the effective distribution of relief supplies.

[0671] Feature Overview

[0672] The servers begin operating immediately upon the reporting of a disaster. They collect video footage and environmental sensor data from the affected area via drones and satellites, and also gather information from social networks. This allows for a comprehensive understanding of the disaster's impact.

[0673] The server processes the collected data and evaluates the extent of the damage using analysis algorithms. This analysis utilizes technologies such as image recognition, data mining, and natural language processing.

[0674] Based on the assessment, the server determines priority areas and activities for rescue operations and infrastructure restoration, and then develops plans accordingly. These plans are designed to enable efficient rescue operations and provide streamlined support.

[0675] Users (e.g., local government officials) begin taking action based on plans generated by the server. Users utilize the instructions from the server to distribute relief supplies, provide evacuation routes, and provide information to local residents.

[0676] The terminals are devices deployed for volunteer staff working on-site, receiving instructions from the server and automatically recommending the optimal course of action. This includes determining the best evacuation routes and narrowing down who to assist in emergencies.

[0677] Specific example

[0678] Let's assume a large-scale earthquake occurs. In this case, the server immediately receives information about the earthquake through its monitoring system. Drones fly over the affected area, recording detailed topographic data and damage in video. Simultaneously, the server identifies areas where potential damage is expected to be significant, based on the observed earthquake magnitude.

[0679] Subsequently, the server analyzes social network data, including posts from residents in the affected areas, compares the impact of the disaster with known data, and identifies areas where rescue should be prioritized. This process enables the rapid issuance of evacuation orders.

[0680] Users concentrate relief supplies in designated high-priority areas to address the situation. They prepare for rapid delivery of supplies by following planned logistics routes.

[0681] Thus, by combining multifaceted information gathering and analysis, this invention enables rapid and appropriate disaster response and optimal allocation of resources in disaster-stricken areas. Furthermore, it is expected that this will significantly improve the efficiency of life-saving operations and infrastructure restoration.

[0682] The following describes the processing flow.

[0683] Step 1:

[0684] Upon receiving notification of a disaster, the server activates the control systems for drones and satellites and begins collecting data from the affected area. The drones fly over the affected area, acquiring video and environmental sensor data in real time. This data is then transmitted to the server.

[0685] Step 2:

[0686] The server collects disaster information from social networks. It uses SNS APIs to search for posts related to specific keywords and aggregates that data.

[0687] Step 3:

[0688] The server analyzes collected drone data, satellite data, sensor data, and social network data. Video data is processed using image recognition technology to detect collapsed buildings and impassable areas. Social media data is processed using natural language processing to extract important information.

[0689] Step 4:

[0690] The server prioritizes rescue operations and infrastructure restoration based on data analysis. It generates priority lists according to the extent of the damage and identifies areas that should receive priority relief.

[0691] Step 5:

[0692] The server develops optimal plans for rescue operations and supply transport. Based on a priority list, it determines the optimal allocation of personnel and supplies and establishes logistics routes.

[0693] Step 6:

[0694] Users execute local disaster response based on plans and instructions provided by the server. They issue instructions for transporting supplies and disseminate information to areas requiring priority evacuation.

[0695] Step 7:

[0696] The terminals receive instructions from the server on the devices of volunteers and local support staff, and initiate appropriate support activities. They respond quickly to emergency evacuation orders and personnel changes when necessary.

[0697] Step 8:

[0698] The server continuously monitors the situation and analyzes additional data. Based on the new information, it updates the plan and incorporates necessary adjustments and instructions in real time.

[0699] (Example 1)

[0700] 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".

[0701] There is a need to build systems that enable rapid and appropriate information gathering and analysis during disasters, effective rescue operation planning, efficient distribution of relief supplies, and provision of safe evacuation routes. Conventional technologies often rely heavily on manual information aggregation and analysis, leading to delayed responses and inefficient support. Therefore, real-time situation assessment and the establishment of a rapid response support system are essential.

[0702] 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.

[0703] In this invention, the server includes means for detecting disaster situations and aggregating information, means for collecting observational data using unmanned aerial vehicles, spacecraft, and high-altitude environmental detectors, and means for combining and comprehensively analyzing data obtained from information sharing services. This enables rapid and precise information gathering and analysis in the event of a disaster, supporting effective rescue operations and the rapid restoration of infrastructure.

[0704] "Disaster situation" refers to the state of events in an area where a natural disaster has occurred, and includes the scale of the damage and the scope of its impact.

[0705] "Means of aggregating information" refers to functions that integrate data obtained from different sources into one place and process it efficiently.

[0706] An "unmanned aircraft" is an aircraft that can be remotely controlled or fly autonomously, and its role is to collect data from the air above disaster areas.

[0707] A "spacecraft" is an artificial satellite or other device in space that orbits the Earth and provides a wide range of observational data.

[0708] An "advanced environmental detector" refers to a sensor device that detects environmental changes and anomalies with high precision, and collects information that is useful in the event of a disaster.

[0709] An "information sharing service" refers to a platform that collects data transmitted by users through social networks such as social media and news feeds provided on online platforms.

[0710] "Machine learning technology" is a general term for technologies that enable computers to automatically construct patterns and rules based on large amounts of data, and it plays an important role in data analysis.

[0711] "Infrastructure restoration" refers to activities that repair infrastructure damaged by a disaster and restore it to normal function.

[0712] A "transportation route" refers to a route used to efficiently deliver goods or personnel to a specific destination, and is an important element in optimizing planning.

[0713] "External devices" refer to devices or software that are connected to a system and extend its functionality by exchanging information.

[0714] "Devices that support on-site activities" refer to mobile devices used by volunteers and rescue workers working at disaster sites, which are used to provide information and issue instructions.

[0715] This invention provides a system that enables rapid and highly accurate information gathering and analysis during disasters, facilitating efficient rescue operations and the distribution of relief supplies. This system primarily consists of a server, terminals, and users.

[0716] The server receives information from the disaster monitoring system and collects observational data from the affected area using unmanned aerial vehicles (UAVs), spacecraft, and high-altitude environmental detectors. UAVs fly autonomously over the affected area, taking high-resolution video and photographs. Spacecraft monitor the situation over a wide area and provide weather and topographic data. High-altitude environmental detectors detect local environmental signals with high precision and acquire data such as earthquakes and wind speed.

[0717] The server aggregates this data and combines it with data obtained from information sharing services for comprehensive analysis. Using technologies such as image recognition, data mining, and natural language processing, it assesses the extent of disaster damage and determines priority areas for rescue and infrastructure restoration. Machine learning techniques are applied to streamline these analyses and improve their accuracy.

[0718] Users utilize server-generated plans to ensure the effective distribution of relief supplies and direct rescue routes. They are also responsible for directing necessary personnel deployments and coordinating on-site operations. The plans include transport routes and optimal resource utilization, reducing waste and enabling a rapid response.

[0719] The terminal functions as a device to support on-site activities, receiving information from the server and providing users with detailed instructions. These instructions aim to suggest safe evacuation routes and facilitate effective relief activities. As an example of prompts generated by the AI ​​model, the terminal can display specific instructions such as, "Report the current earthquake damage situation in the affected area and identify priority rescue areas."

[0720] As described above, through the cooperation of servers, users, and terminals, this system enables rapid and accurate disaster response, supporting life-saving efforts and the optimal allocation of resources in disaster-stricken areas.

[0721] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0722] Step 1:

[0723] The server receives information about earthquakes and typhoons from the disaster monitoring system. This input information includes the magnitude of the disaster, the time of occurrence, and the location. The server uses this as a trigger to initiate the relevant data collection process and outputs instructions to other data collection methods.

[0724] Step 2:

[0725] The server controls unmanned aerial vehicles (UAVs) and spacecraft to collect observational data from the disaster area. The UAVs fly over the affected region, taking video and photographic images using cameras. This data is high-resolution and records the terrain and damage in detail. Images and environmental data from the UAVs and spacecraft are obtained as input data. The server receives this data and outputs it as initial geographic information.

[0726] Step 3:

[0727] The server also integrates data from altitude environmental detectors, including information on wind speed, temperature, and humidity from weather sensors. This input data is crucial for understanding local weather conditions. The server analyzes the weather data and outputs the results to models that predict the progression and impact of disasters.

[0728] Step 4:

[0729] The server combines data from unmanned aerial vehicles, spacecraft, and altitude environmental detectors with social network data collected from information sharing services. This data includes posts and reports from disaster victims, providing raw information from the field. Input data includes text data, image data, and location information. The server processes this data with analysis software and outputs a map that identifies the extent of the disaster's impact.

[0730] Step 5:

[0731] The server uses a generative AI model to analyze data and assess the extent of the damage. This includes image recognition algorithms and natural language processing to identify the scale of the damage and priority areas for response. Based on the input observational data, the analysis results output a disaster situation map and a list of affected areas.

[0732] Step 6:

[0733] The server generates a rescue plan based on the analysis results. It determines the optimal deployment of rescue teams and supplies to high-priority areas and plans transportation routes. The server's input includes a map of the affected area and data on priority areas, and its output provides specific action guidelines and support routes.

[0734] Step 7:

[0735] The user receives plans and instructions from the server. Based on the optimized rescue plan, they prepare supplies and deploy support teams. Input data includes the rescue plan and transport routes, and the output is rapid response and supply delivery.

[0736] Step 8:

[0737] The terminal displays instructions provided by the server to volunteers and work staff conducting field activities. The terminal receives instruction information from the server and displays safe evacuation routes and specific support activities in the field to the user. The input is server instruction data provided to the terminal, and the output is to facilitate efficient activities in the field.

[0738] (Application Example 1)

[0739] 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".

[0740] In recent years, the frequency and scale of natural disasters have increased, creating a need for rapid and efficient disaster response. Conventional disaster response systems have often been slow to prioritize information gathering and rescue operations, leading to delays in providing support to affected areas. Furthermore, the lack of effective approaches to managing residents' evacuation behavior in urban environments has frequently resulted in confusion during disasters. This invention aims to solve these problems and realize a rapid and effective disaster response.

[0741] 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.

[0742] In this invention, the server includes means for collecting drone data, satellite data, sensor data, and social network data during a disaster; means for analyzing the collected data to evaluate the extent of the damage; means for providing residents with appropriate evacuation routes using mobile communication devices; and means for instructing volunteer personnel to carry out effective support activities. This enables residents and support organizations to take action based on accurate information in real time through rapid information gathering and analysis.

[0743] "Unmanned aircraft data" refers to data on terrain and damage conditions collected by unmanned aircraft in the air.

[0744] "Satellite data" refers to data that includes images and observational information taken from satellites, and is used to understand the extent of damage over a wide area.

[0745] "Sensor data" refers to data obtained from various sensors that detect environmental information such as temperature, humidity, and vibration.

[0746] "Social network data" refers to posts and information collected from social media and network platforms on the internet, and is data that reflects the voices and circumstances of residents.

[0747] A "mobile communication device" refers to a mobile device such as a cell phone or smartphone, which is used to send and receive information in real time.

[0748] "Means of providing evacuation routes" refer to methods and technologies for guiding residents to the optimal route so that they can safely evacuate from disaster areas.

[0749] "Volunteer personnel" refers to individuals or groups who work to provide support to disaster victims and distribute supplies during a disaster.

[0750] "Means of directing effective support activities" refer to methods and techniques for planning support activities and providing instructions for their implementation in an appropriate manner and timing.

[0751] This invention is an information gathering and analysis system for streamlining disaster response in smart cities. The server comprehensively collects and analyzes drone data, satellite data, sensor data, and social network data. The hardware used is a cloud server with high computing power, and a program written in Python is implemented for data processing. Apache Kafka is used for real-time data processing, and backend computing is performed on AWS Lambda.

[0752] Based on the analyzed data, the server provides optimal evacuation routes to mobile communication devices to support residents' evacuation actions. This information is delivered to residents in real time through a dedicated app built with React Native. It also directs volunteers to carry out effective support activities, ensuring that the distribution of supplies and relief efforts proceed efficiently.

[0753] For example, during a large-scale flood in a specific area, the server quickly identifies dangerous areas and displays safe evacuation routes to residents' mobile devices. At the same time, volunteers receive instructions, including prioritizing disaster relief efforts, ensuring that support activities are carried out appropriately. This speeds up and improves the accuracy of disaster response, making it possible to ensure the safety of many people.

[0754] An example of a prompt for a generated AI model is, "Please provide the information needed to develop an application that provides optimal evacuation routes in real time when a typhoon is approaching." By using this prompt, necessary technical knowledge can be quickly obtained and utilized in implementation.

[0755] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0756] Step 1:

[0757] The server collects drone data, satellite data, sensor data, and social network data on the cloud. To enable real-time acquisition of this data, it aggregates it using the data streaming platform Apache Kafka. Inputs include raw data from various sensors, drone video data, and social media feeds, which are then processed as structured data and output.

[0758] Step 2:

[0759] The server performs data analysis on the collected data to assess the extent of the damage. This analysis uses AWS Lambda, which runs a Python program, and utilizes image recognition and natural language processing algorithms. The input is the structured data obtained in step 1, and the output generates map information that visualizes the risk assessment and damage situation of the affected areas.

[0760] Step 3:

[0761] The server sets evacuation routes based on the assessment of the disaster situation and provides information to mobile communication devices. It calculates the optimal evacuation route based on the assessment results and notifies residents through an application built with React Native. The input is map information based on the assessment results, and the output is real-time evacuation route guidance for mobile devices.

[0762] Step 4:

[0763] The terminal provides evacuation information to residents through an application and updates evacuation routes in real time. It receives notifications from the server and uses residents' location data to send personalized evacuation information. Inputs include guidance information from the server and residents' current location data, while outputs include updated evacuation routes and safety confirmation notifications.

[0764] Step 5:

[0765] The server directs volunteers to carry out effective support activities and updates information as needed. It sends prioritized relief operation instructions to volunteer terminals to support on-site activities. Inputs include analysis results of the disaster situation and inventory information of relief supplies, while outputs include volunteer action plans and supply distribution plans.

[0766] 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.

[0767] This invention combines an emotion engine with a disaster response system to automatically propose support activities that take into account the psychological state of disaster victims, enabling more appropriate resource allocation. The system has a configuration that combines information gathering, data analysis, emotion recognition, and support plan development.

[0768] Feature Overview

[0769] In addition to normal data collection (from drones, satellites, sensors, social networks, etc.), the server operates an emotion engine. The emotion engine acquires emotion data through social networks and direct interaction with users. This is done through text analysis and speech analysis.

[0770] The server integrates emotional data with other information to conduct a comprehensive assessment of the disaster situation. This assessment identifies the degree of psychological stress and specific psychological states (e.g., anxiety, fear), and then plans appropriate responses.

[0771] Users (local government officials and rescue teams) conduct disaster relief activities based on detailed assessments and suggestions provided by the server. Suggestions based on sentiment data indicate priorities for specific supplies and situations requiring special care.

[0772] The device receives feedback from the emotional engine on-site, enabling more flexible and humane responses. For example, in emergency shelter care, it can propose and implement activities to reduce psychological burden.

[0773] Specific example

[0774] For example, consider a scenario involving a large-scale flood. The server collects information from the affected area in real time while analyzing the emotional state of the residents. This involves extracting text indicating anxiety and confusion from social media posts and detecting tension and fear during voice calls through emotional analysis.

[0775] Based on the analysis results, the server identifies areas where psychological support is particularly needed and proposes specific action plans to local government officials. These plans include not only the distribution of relief supplies but also the dispatch of specialists to provide psychological care. This information is then communicated to the field by users and immediately implemented.

[0776] Furthermore, if disaster victims are experiencing anxiety in evacuation shelters, the device will inform shelter staff of the situation and, if necessary, suggest counseling services or measures to enhance their sense of security. These measures will reduce the psychological burden on disaster victims and enable efficient and humane disaster response.

[0777] Thus, the present invention aims to provide a comprehensive support system that takes into account the psychological state of disaster victims, and to maximize the use of limited resources.

[0778] The following describes the processing flow.

[0779] Step 1:

[0780] Upon receiving notification of a disaster, the server begins collecting data from drones, satellites, sensors, and social network data. Furthermore, it activates an emotion engine to collect user emotion data, which is extracted from social media posts and audio data.

[0781] Step 2:

[0782] The server performs a series of processes to analyze the collected drone data, satellite data, sensor data, and social network data. The analysis extracts topographic information from the video footage and maps the damage situation.

[0783] Step 3:

[0784] The server integrates emotional data analyzed by the emotion engine to assess the psychological state of disaster victims. This makes it possible to identify areas where people are experiencing anxiety or fear.

[0785] Step 4:

[0786] The server prioritizes rescue operations and infrastructure restoration based on disaster situation and sentiment data. Detailed support plans are then created based on the high-priority areas and activities.

[0787] Step 5:

[0788] Users receive plans and instructions from the server and begin on-site support activities. This includes distributing supplies and providing psychological care.

[0789] Step 6:

[0790] The terminals receive instructions from the server on the devices of local staff and volunteers, facilitating real-time action. If psychological care is needed, they support immediate implementation of specific support measures.

[0791] Step 7:

[0792] The server continuously collects and analyzes new data, updating plans based on changing circumstances. Sentimental data is also re-evaluated, and instructions are adjusted as needed. This ensures optimal disaster response at all times.

[0793] (Example 2)

[0794] 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".

[0795] In disaster relief efforts, it is necessary not only to provide physical rescue and restore infrastructure, but also to appropriately understand and alleviate the psychological stress and anxiety of disaster victims. However, conventional systems have made it difficult to formulate support plans that take psychological conditions into account, often resulting in insufficient emotional care. Therefore, there is a need to collect and analyze emotional data of disaster victims and provide a comprehensive and dynamic support system based on this data.

[0796] 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.

[0797] In this invention, the server includes means for collecting drone data, satellite data, sensor data, and social network data; means for analyzing the collected text and voice data using natural language processing technology to evaluate emotional states; and means for integrating emotional data and environmental data to evaluate the overall situation in the disaster area. This makes it possible to automatically generate support plans that take into account the psychological state of disaster victims.

[0798] "Unmanned aircraft data" refers to observational data acquired by aircraft or ground vehicles that operate without a driver.

[0799] "Satellite data" refers to observational data acquired from artificial satellites orbiting the Earth.

[0800] "Sensor data" refers to data collected by sensor devices to measure various environmental conditions.

[0801] "Social network data" refers to information such as posts, comments, and responses generated by users via the internet.

[0802] "Natural language processing technology" refers to the technology that enables computers to understand, interpret, and generate human natural language.

[0803] "Emotional state" refers to the mental state of an individual or group at a given point in time, and includes emotions such as joy, anxiety, anger, and fear.

[0804] "Comprehensive situation assessment" is an evaluation that integrates information from multiple data sources to grasp the overall state of the disaster site.

[0805] "Dynamic updating" means modifying and adjusting information and plans in real time as circumstances change.

[0806] A "safe evacuation route" is a route recommended to allow disaster victims to evacuate while avoiding danger.

[0807] "Psychological care" refers to the support and activities provided to maintain or improve an individual's mental health.

[0808] This invention implements a system designed to optimize support activities during disasters.

[0809] The system consists of the following components:

[0810] Data collection

[0811] The server collects data from drones, satellites, ground sensors, and social networks. Drones and satellites provide real-time images and video data to visualize local conditions. Ground sensors are used to collect weather data and environmental conditions. Data from social networks, including user-generated posts and comments, is collected via APIs developed in Python and Java.

[0812] Emotion analysis

[0813] The server analyzes collected text and audio data using natural language processing techniques to assess the emotional state of residents. This process utilizes natural language processing libraries (e.g., NLTK and spaCy). The analysis results enable the quantification of residents' emotions, such as anxiety and fear, and visualize the psychological state of disaster victims.

[0814] Data Integration and Evaluation

[0815] The server integrates emotional data with other environmental data to assess the overall situation in the affected area. This process utilizes a database system (e.g., PostgreSQL) and data visualization tools (e.g., Tableau). The assessed information is visually displayed to administrators to support the development of support plans.

[0816] Generating a support plan

[0817] Based on assessment information, the server automatically generates support plans that take into account the psychological health of disaster victims. These plans include the distribution of necessary supplies to specific areas and the dispatch of specialists to provide psychological care. The generated plans are then provided to local government officials and rescue teams.

[0818] Examples of specific cases and prompt statements

[0819] For example, in areas affected by large-scale flooding, the server analyzes social media posts to extract information indicating residents' anxiety and fear. Based on this sentiment analysis, areas requiring particular psychological care are identified, and countermeasures are formulated.

[0820] Example prompt: "In areas affected by large-scale flooding, use social media to understand the emotional state of residents and extract posts indicating anxiety or fear. Then, identify areas where psychological support is particularly needed and develop specific support plans for local government officials."

[0821] This system enables comprehensive support activities in disaster response that take psychological factors into consideration.

[0822] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0823] Step 1:

[0824] The server collects data from drones, satellites, sensors, and social networks. Its inputs include video data from drones and satellites, environmental data from sensors, and text data from social media. This data is retrieved in real time using APIs developed in Python and Java. The output is observational data in raw dataset form.

[0825] Step 2:

[0826] The server analyzes the collected text and audio data using natural language processing techniques to evaluate emotional states. The raw dataset from Step 1 is used as input. For data processing, the text data is analyzed, and positive or negative emotions are identified using an emotion analysis algorithm. The output is numerical data representing emotions. Natural language processing libraries such as NLTK and spaCy are used for this analysis.

[0827] Step 3:

[0828] The server integrates emotional and environmental data to assess the overall situation in the disaster-stricken area. It uses emotional data from Step 2 and other environmental data from Step 1 as input. For data calculation, an evaluation model is used to quantify the psychological and physical stress in the disaster-stricken area and visualize it on a map. The output is a comprehensive report of the disaster-stricken area. A database system and data visualization tools are used for this assessment.

[0829] Step 4:

[0830] The server generates a support plan that takes into account the psychological health of disaster victims, based on the evaluated information. The comprehensive situation report from Step 3 is used as input. As data calculation, a support prioritization algorithm is used to identify areas with the highest urgency and determine appropriate support measures. The output is a specific support plan, which includes details such as distribution routes for supplies and the deployment of psychological support specialists.

[0831] Step 5:

[0832] Users (local government officials and rescue workers) carry out activities on-site based on support plans provided by the server. The support plan generated in step 4 is used as input. Specific actions include transporting supplies and providing psychological care to disaster victims on-site, following the plan's instructions. The output is an improved on-site situation and stabilization of the residents' psychological state.

[0833] Step 6:

[0834] The terminal transmits feedback obtained on-site to the server in real time and adjusts the support plan as needed. It uses feedback data obtained from users on-site as input. The output is an updated support plan. This enables dynamic optimization of support activities.

[0835] (Application Example 2)

[0836] 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".

[0837] In disaster relief and infrastructure restoration, optimal allocation of supplies and personnel requires planning that considers not only the physical condition of victims but also their psychological state. Current systems place too much emphasis on assessing physical damage, and lack sufficient activities to alleviate the psychological burden on victims. Therefore, developing flexible plans that reflect psychological states is a key challenge.

[0838] 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.

[0839] This invention includes a server that analyzes collected data and evaluates the psychological state of disaster victims through sentiment analysis; a server that sets priorities for rescue operations and infrastructure restoration based on the evaluation and formulates a plan that includes psychological care for disaster victims; and a server that provides the plan and instructions to an external system and links with real-time public sentiment. This enables the implementation of efficient and effective activities that take into account the psychological state of disaster victims.

[0840] "Unmanned aircraft data" refers to video and image information acquired from the air during disasters, which is used to grasp the situation in disaster-stricken areas in real time.

[0841] "Satellite data" refers to images and sensor information acquired from outer space, which is used to understand the situation in disaster-stricken areas over a wide area.

[0842] "Sensor data" refers to data collected from various sensors installed on the ground, and is used to detect physical phenomena such as weather, earthquakes, and floods.

[0843] "Social network data" refers to text and image data collected from social media and other online communication platforms, which is used to understand the emotions and psychological state of disaster victims.

[0844] "Sentiment analysis" is a process of evaluating an individual's emotions and psychological state by analyzing text and audio data.

[0845] "Psychological care" refers to support activities aimed at reducing the emotional and psychological burden on disaster victims and maintaining and restoring their mental health.

[0846] "Real-time" refers to a state where information and data are acquired instantly and processed without any time lag.

[0847] "Public sentiment" is a general term for the psychological and emotional reactions of citizens during a disaster, and it is an important factor in determining the priorities of relief activities.

[0848] This invention is a system designed to alleviate the psychological burden on disaster-stricken areas and enable effective support activities. The system consists of a server, terminals, and users.

[0849] The server collects drone data, satellite data, sensor data, and social network data. Next, it analyzes this data and performs sentiment analysis to assess the psychological state of disaster victims. This process uses Python, employing libraries such as NLTK and spaCy for analysis.

[0850] The terminals formulate plans for specific rescue operations and psychological care based on analysis results from the server, and receive the instructions provided therein at the scene. For example, a dedicated application installed on a smartphone can immediately identify areas in need of assistance and respond based on those priorities.

[0851] Users, such as local government officials and rescue teams, will carry out disaster relief activities according to the detailed assessments and suggestions provided by the server. They are required to have a system in place to respond quickly, especially when psychological support is needed.

[0852] As a concrete example, in the event of a large-scale disaster, this system uses data collected from social media posts to detect emotions such as "tension" and "anxiety" through text analysis, and proposes emergency support plans to local governments accordingly. To ensure a rapid response, the system utilizes a generative AI model, and prompts such as "Please analyze the emotional situation in the designated area and assess the need for psychological care" are used.

[0853] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0854] Step 1:

[0855] The server collects data from drones, satellites, sensors, and social networks. Its purpose is to receive real-time images, weather data, environmental sensor information, and social media posts as input, and to centrally manage all of this data. This allows for the accumulation of information from diverse data sources.

[0856] Step 2:

[0857] The server analyzes the collected data to assess the basic damage situation in the affected areas. The input is the data set collected in step 1, which is used for natural language processing and image analysis. The output provides an initial assessment of the level of damage and geographical distribution information. Specifically, it detects flood areas using satellite imagery and identifies areas with particularly high posting rates from social media text.

[0858] Step 3:

[0859] The server performs sentiment analysis and evaluates the psychological state of disaster victims from social network data. The input is SNS post data, which is processed using text analysis tools (e.g., NLTK, spaCy). The output is a quantitative evaluation of psychological states such as anxiety, fear, and tension. Specifically, it calculates sentiment scores from the content of posts and generates a map of psychological burden for each region.

[0860] Step 4:

[0861] The server integrates the above data and develops a plan that includes rescue and psychological care activities. The input is the physical and psychological assessment data obtained in steps 2 and 3. The output generates a comprehensive action plan, including priority allocation of resources. Specifically, it plans to concentrate personnel on areas where the psychological state is deteriorating.

[0862] Step 5:

[0863] The terminal transmits plans and instructions from the server to users (rescue teams and local government officials) on-site. The input is the plan formulated in Step 4, which is then reflected in the on-site response. The output records the status and progress of ongoing activities in real time. Specific actions include explaining the plan to those receiving assistance and on-site monitoring.

[0864] 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.

[0865] 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.

[0866] 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.

[0867] 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.

[0868] 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.

[0869] 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.

[0870] 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.

[0871] 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.

[0872] 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."

[0873] 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.

[0874] 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.

[0875] 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.

[0876] 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.

[0877] 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.

[0878] 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.

[0879] 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.

[0880] 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.

[0881] 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.

[0882] 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.

[0883] 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.

[0884] 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.

[0885] The following is further disclosed regarding the embodiments described above.

[0886] (Claim 1)

[0887] In the event of a disaster, means for collecting unmanned aerial vehicle data, satellite data, sensor data, and social network data,

[0888] A means of analyzing the collected data to assess the extent of the damage,

[0889] A means of setting priorities and formulating plans for rescue operations and infrastructure restoration based on assessments,

[0890] A means to determine the necessary relief supplies and personnel allocation, and to set the optimal delivery route,

[0891] A system that includes means for providing plans and instructions to an external system.

[0892] (Claim 2)

[0893] The system according to claim 1, comprising means for monitoring the situation in real time and dynamically updating the plan based on the results of data analysis.

[0894] (Claim 3)

[0895] The system according to claim 1, comprising means for instructing an external system on safe evacuation routes and effective support activities.

[0896] "Example 1"

[0897] (Claim 1)

[0898] A means of detecting disaster situations and aggregating information,

[0899] Means for collecting observational data using unmanned aerial vehicles, spacecraft, and high-altitude environmental detectors,

[0900] A means of combining and comprehensively analyzing data obtained from information sharing services,

[0901] A means of analyzing collected data and evaluating the extent of the damage using machine learning techniques,

[0902] Means for setting priority areas for rescue and infrastructure restoration based on assessments and creating plans,

[0903] A means to determine the necessary relief supplies and manpower allocation, and to set the optimal transport route,

[0904] Means for providing plan details and instruction information to an external device,

[0905] A system that utilizes terminals to support on-site activities and includes means to recommend optimal actions to users.

[0906] (Claim 2)

[0907] The system according to claim 1, comprising means for monitoring events in real time and dynamically modifying the plan based on the analysis results.

[0908] (Claim 3)

[0909] The system according to claim 1, comprising means for suggesting safe evacuation routes and effective support activities to external devices.

[0910] "Application Example 1"

[0911] (Claim 1)

[0912] In the event of a disaster, means for collecting unmanned aerial vehicle data, satellite data, sensor data, and social network data,

[0913] A means of analyzing the collected data to assess the extent of the damage,

[0914] A means of setting priorities and formulating plans for rescue operations and infrastructure restoration based on assessments,

[0915] A means to determine the necessary relief supplies and personnel allocation, and to set the optimal delivery route,

[0916] Means for providing plans and instructions to an external system,

[0917] A means of receiving disaster information in real time within an urban environment and providing residents with appropriate evacuation routes using mobile communication devices,

[0918] A means of directing volunteer personnel to carry out effective support activities via mobile communication devices,

[0919] ...

[0920] A system that includes this.

[0921] (Claim 2)

[0922] The system according to claim 1, comprising means for monitoring the situation in real time and dynamically updating the plan based on the results of data analysis.

[0923] (Claim 3)

[0924] The system according to claim 1, comprising means for instructing an external system on safe evacuation routes and effective support activities, and providing information in real time via a mobile communication device.

[0925] "Example 2 of combining an emotion engine"

[0926] (Claim 1)

[0927] Means for collecting unmanned aerial vehicle data, satellite data, sensor data, and social network data,

[0928] A means for analyzing collected text and audio data using natural language processing technology to evaluate emotional states,

[0929] A means of integrating emotional data and environmental data to assess the overall situation in disaster-stricken areas,

[0930] A means of setting priorities and formulating plans for rescue operations and infrastructure restoration based on assessments,

[0931] A means of determining the necessary support resources and their placement, and setting the optimal route,

[0932] A means of automatically generating support plans to alleviate the psychological stress of disaster victims,

[0933] A system including means for providing plans and instructions to an external information processing device.

[0934] (Claim 2)

[0935] The system according to claim 1, which monitors the situation in real time and dynamically updates the plan taking into account the results of sentiment analysis.

[0936] (Claim 3)

[0937] The system according to claim 1, which instructs an external information processing device to provide safe evacuation routes and effective support activities, including psychological care.

[0938] "Application example 2 when combining with an emotional engine"

[0939] (Claim 1)

[0940] In the event of a disaster, means for collecting unmanned aerial vehicle data, satellite data, sensor data, and social network data,

[0941] A method for analyzing collected data and evaluating the psychological state of disaster victims through emotion analysis,

[0942] A means of setting priorities for rescue operations and infrastructure restoration based on assessments, and developing plans that include psychological care for disaster victims,

[0943] A means to determine the necessary relief supplies and personnel allocation, identify locations where psychological support is particularly needed, and set the optimal delivery routes.

[0944] A system that provides plans and instructions to an external system and includes means for linking with real-time public sentiment.

[0945] (Claim 2)

[0946] The system according to claim 1, comprising means for monitoring the situation in real time and dynamically updating a plan based on the emotional state of the victims in accordance with the data analysis results.

[0947] (Claim 3)

[0948] The system according to claim 1, comprising means for directing an external system to safe evacuation routes and effective support activities, and for supporting the implementation of a psychological care plan. [Explanation of symbols]

[0949] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. In the event of a disaster, means for collecting unmanned aerial vehicle data, satellite data, sensor data, and social network data, A means of analyzing the collected data to assess the extent of the damage, A means of setting priorities and formulating plans for rescue operations and infrastructure restoration based on assessments, A means to determine the necessary relief supplies and personnel allocation, and to set the optimal delivery route, A system that includes means for providing plans and instructions to an external system.

2. The system according to claim 1, comprising means for monitoring the situation in real time and dynamically updating the plan based on the results of data analysis.

3. The system according to claim 1, comprising means for instructing an external system on safe evacuation routes and effective support activities.

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