system

Abstract

We provide the system. [Solution] A means of receiving and analyzing disaster information in real time, A means of automatically controlling a movable breakwater, A means of notifying the user terminal of evacuation routes and evacuation shelter information, A means of analyzing and presenting the optimal actions to take during a disaster using generative AI, A means of updating the system based on disaster information and evacuation feedback. A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance 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] Japan is exposed to risks to human lives and property due to frequent natural disasters such as earthquakes and heavy rains. In such disasters, delays in evacuation and unclear evacuation routes can cause significant damage. The purpose of the present invention is to support a prompt and effective initial response to address these issues and to protect lives and facilitate smooth evacuation during disasters.

Means for Solving the Problems

[0005] This invention incorporates a means for receiving and analyzing disaster information in real time and utilizes generating AI to automatically control breakwaters. It also supports rapid and safe evacuation by notifying user terminals of evacuation routes and shelter information. Furthermore, it updates the system based on disaster information and evacuation feedback to improve the accuracy of future responses. It provides a means for administrators to manually activate breakwater control and evacuation information, enabling flexible responses during disasters.

[0006] "Disaster information" refers to data related to natural disasters such as earthquakes and weather warnings, and is important information collected to minimize damage.

[0007] "Generative AI" is an artificial intelligence technology that analyzes large amounts of data to derive optimal actions and predictions during disasters.

[0008] A "movable breakwater" is a protective structure designed to automatically rise as needed to prevent the intrusion of seawater, such as tsunamis.

[0009] An "evacuation route" is a designated route for safely moving to an evacuation shelter during a disaster, and is set with consideration for both time efficiency and safety.

[0010] A "user terminal" is an electronic device used to receive evacuation information and warnings and to notify users in real time.

[0011] "Evacuation shelter information" refers to data on the location and occupancy status of facilities where evacuees can take temporary shelter, and is provided to help them move safely.

[0012] "Feedback" refers to information collected from users regarding the results of a system's functioning and other relevant data, which is then used to improve future disaster response efforts.

[0013] "Manual control" is a function that allows administrators to directly operate the system as needed, enabling them to take actions separate from normal automatic control. [Brief explanation of the drawing]

[0014] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0015] 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.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, a labeled 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.

[0018] In the following embodiments, a labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, a labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0020] 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).

[0021] 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."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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".

[0035] This invention provides an integrated system that enables rapid and safe evacuation during natural disasters. The embodiments thereof are described below.

[0036] The server receives disaster information in real time from earthquake and weather monitoring systems. This information includes, for example, the earthquake's epicenter, time of occurrence, predicted tsunami height, type of weather warning, and affected area. Based on this data, a generating AI analyzes the scale and extent of the disaster.

[0037] If the analysis predicts a tsunami, the server will instruct the movable breakwater system to rise. This allows for the prevention of tsunami intrusion in advance. In addition, depending on the disaster, the server collects up-to-date data such as the location of evacuation centers, the capacity of each evacuation center, and road traffic conditions.

[0038] The terminal receives information transmitted from the server and displays evacuation routes and destinations to the user in real time. This includes using map data and generating AI to suggest the best route. For example, if the usual route is congested or the road is damaged, it can suggest an alternative route.

[0039] Users can take swift and safe evacuation actions by following instructions received through their devices. Furthermore, administrators can manually control the raising of breakwaters and the distribution of evacuation information. This operation is used to deal with unexpected situations or when additional confirmation of the AI's decisions is required.

[0040] After the evacuation, feedback from users and administrators is collected and stored in the server's database. This information will be used to enable the generated AI to make more accurate decisions during future disasters, contributing to the overall improvement of the system.

[0041] With this configuration, the present invention realizes an advanced disaster prevention system aimed at rapid evacuation and protection of human lives.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] The server receives disaster information in real time from earthquake and weather monitoring systems. This information includes the epicenter, magnitude, and forecast weather conditions.

[0045] Step 2:

[0046] The server passes disaster information to the generating AI, which then analyzes whether a tsunami will occur and the risk of flooding based on this information. The generating AI predicts the scale and extent of the disaster based on past data.

[0047] Step 3:

[0048] The server automatically sends an instruction to raise the movable breakwater based on the prediction results. This is done especially when the predicted tsunami height exceeds a certain threshold.

[0049] Step 4:

[0050] The server aggregates the latest data, including shelter capacity, current congestion levels, and traffic information, and the generating AI analyzes the optimal evacuation route and destination.

[0051] Step 5:

[0052] The terminal receives evacuation information from the server and displays evacuation routes and destinations to the user in real time. Travel time and route instructions are presented along with map data.

[0053] Step 6:

[0054] Users act according to evacuation instructions received via their devices. If necessary, administrators can manually raise the breakwater or adjust evacuation instructions.

[0055] Step 7:

[0056] After the evacuation is complete, the server collects user feedback and stores it in a database. This feedback will be used to inform future decisions by the AI ​​and help improve the system.

[0057] (Example 1)

[0058] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0059] In recent years, natural disasters have become more frequent due to climate change and increased seismic activity. Consequently, there is a need for systems that enable rapid and effective evacuation. Conventional systems often lack integration, with disaster information collection and analysis, evacuation guidance provision, and disaster prevention device control being handled separately. Furthermore, errors in judgment can lead to erroneous instructions that could have fatal consequences for evacuees. Against this backdrop, the challenge lies in providing an integrated disaster prevention system that enables accurate real-time information analysis and a rapid response based on that analysis.

[0060] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0061] In this invention, the server includes an information processing device means for receiving disaster information in real time and performing data analysis, a control device means for automatically controlling a mobile disaster prevention device, and a communication device means for providing evacuation guidance and evacuation facility information to the user's information terminal. This enables immediate data analysis and accurate presentation of relief measures in the event of a disaster, as well as the implementation of automatic and manual disaster prevention measures by the system.

[0062] An "information processing device" is a device that receives disaster information in real time, analyzes that information, and determines the scale and impact of the disaster.

[0063] A "control device" is a device that automatically controls movable disaster prevention equipment based on analyzed information and takes necessary disaster prevention measures.

[0064] A "communication device" is a device that provides analysis results, evacuation instructions, and evacuation facility information to users' information terminals in real time.

[0065] An "analysis device" is a device that uses a generated artificial intelligence model to calculate the optimal response measures during a disaster and presents them to the user.

[0066] An "improvement device" is a device that updates the entire system based on disaster information and post-evacuation evaluation information, with the aim of improving performance in the event of a future disaster.

[0067] An "operating device" is a device that allows operators to manually control the control system and issue evacuation instructions.

[0068] A "suggestion device" is a device that integrates detailed information about evacuation facilities and proposes the optimal evacuation destination to users based on the generated artificial intelligence model.

[0069] This invention is a comprehensive system for supporting rapid and safe evacuation during disasters. To implement this system, the server, terminals, and users each play specific roles. Specific embodiments are described below.

[0070] First, the server uses an information processing device to receive and analyze earthquake and weather data in real time. During this process, it utilizes a database management system (e.g., MySQL®) to store the received data and, if necessary, uses a generated AI model (e.g., a model using TENSORFLOW®) to analyze the data. Based on the analysis results, it quickly determines the scale and extent of the disaster.

[0071] Next, the server uses a control device to automatically operate disaster prevention equipment, such as a movable breakwater, based on the analysis results. The server also transmits the analysis results and necessary evacuation information to the terminal via a communication device. The terminal then presents the user with the optimal evacuation route calculated by a generated AI model based on the received information.

[0072] The terminal uses a communication device to provide users with evacuation guidance and information on evacuation facilities. It has the function to display the user's location, the capacity of evacuation shelters, and traffic information in real time using a map application (e.g., Google® Maps API). This enables users to take quick and appropriate evacuation actions. For example, in areas with many hills, it will suggest a route that suits the user's physical ability. Another example of a prompt sentence to be input into the generating AI model is, "Please suggest the optimal evacuation route in the event of a flood forecast due to heavy rain."

[0073] Furthermore, after evacuation is complete, users provide feedback to the server. The server utilizes improved equipment to accumulate this feedback and use it to update the entire system. This will enable more accurate decision-making in the event of the next disaster.

[0074] In this way, the entire system functions as an integrated whole, enabling rapid and safe protection of human lives during disasters.

[0075] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0076] Step 1:

[0077] The server receives earthquake and weather data in real time from the monitoring system. The input data includes earthquake epicenters and times, predicted tsunami heights, weather warning types, and affected areas. After receiving this input data, it is stored in a database and prepared for analysis using a generated AI model. The output is the raw data to be analyzed.

[0078] Step 2:

[0079] The server uses a generated AI model based on the received data to analyze the scale and extent of the disaster. The input is the raw data obtained in step 1, and data processing involves comparative analysis with historical data. This outputs specific analysis results such as whether a tsunami is predicted and which areas are at risk.

[0080] Step 3:

[0081] Based on the analysis results, the server uses a control device to automatically operate a movable disaster prevention device, such as a breakwater. The input is the analysis results from step 2, which are output as a control signal, automatically issuing an instruction to raise the breakwater. In addition, new information such as the location and capacity of evacuation shelters and traffic conditions is collected.

[0082] Step 4:

[0083] The server uses a communication device to send the analysis results and collected evacuation information to the terminal. The input is the evacuation route and shelter information generated in step 3, and the output is notification data containing this information. This notification data is transmitted in real time.

[0084] Step 5:

[0085] The terminal receives notification data sent from the server and displays the optimal evacuation route and shelter information to the user. The input is the data sent in step 4, and route calculation is performed by a generating AI model. This outputs a specific evacuation route to the user and visualizes it on a map.

[0086] Step 6:

[0087] The user takes swift and safe evacuation actions based on the evacuation information displayed on the terminal. Input is information from the terminal, and output is the user's evacuation actions. The user also provides feedback to the system after completing the evacuation.

[0088] Step 7:

[0089] The server receives feedback from users and administrators and stores it in a database. The input is feedback data, which is then analyzed and incorporated into the system. The output is adjustment information that contributes to improving the accuracy of future analyses.

[0090] (Application Example 1)

[0091] 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."

[0092] In the event of a natural disaster, there is a need to provide real-time information to enable people to evacuate quickly and safely, suggest optimal evacuation routes, and effectively control movable breakwaters. However, current systems do not adequately provide personalized evacuation routes to individual users or improve the system based on feedback after evacuation. It is necessary to solve these problems and realize safer and more efficient evacuations.

[0093] 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.

[0094] In this invention, the server includes means for receiving and analyzing disaster information in real time, means for automatically controlling a movable breakwater, means for notifying user terminals of evacuation routes and shelter information, means for analyzing and presenting optimal actions during a disaster using generated AI, means for updating the system based on disaster information and evacuation feedback, means for immediately providing the optimal evacuation route based on the user's location data, and means for collecting user feedback after evacuation and using it to improve the accuracy of the system. This makes it possible to propose evacuation routes optimized for each user and to continuously improve the system using feedback.

[0095] "Means for receiving and analyzing disaster information in real time" refers to a system that continuously acquires data related to the occurrence of disasters and immediately analyzes that information to grasp the situation and scale of the disaster.

[0096] "Means for automatically controlling movable breakwaters" refers to a system that mechanically operates breakwaters in response to predicted disasters, thereby minimizing damage in advance.

[0097] "Means for notifying user terminals of evacuation routes and evacuation shelter information" refers to communication technology that informs users' terminals of information regarding the optimal evacuation route and evacuation destination.

[0098] "Means for analyzing and presenting optimal actions during disasters using generative AI" refers to a technology that utilizes artificial intelligence to analyze and advise on the most appropriate evacuation methods and actions during a disaster.

[0099] "Methods for updating the system based on disaster information and evacuation feedback" refers to the process of improving the system by utilizing collected disaster data and feedback from evacuees, thereby enhancing the ability to respond to future disasters.

[0100] "A means of providing the optimal evacuation route immediately based on the user's location data" refers to a technology that uses the user's location information to quickly suggest the most suitable evacuation route.

[0101] "Means for collecting user feedback after evacuation and using it to improve system accuracy" refers to technologies that analyze feedback received from users after evacuation is completed to improve the accuracy of future disaster response.

[0102] To implement this invention, it is necessary to provide a system in which a server, a user terminal, and a generative AI model work in cooperation. The server utilizes data feeds provided by the Japan Meteorological Agency and earthquake monitoring organizations to receive disaster information in real time. This allows for immediate analysis of earthquake and tsunami information and the generation of control instructions for movable breakwaters based on the generative AI model.

[0103] The user's device will receive notifications about evacuation routes and shelters. These notifications are based on direct feedback from the server and are updated in real time. When a user receives evacuation information through their device, the optimal route is generated and displayed, taking into account obstacles and traffic conditions.

[0104] The generative AI model is built using machine learning libraries such as TensorFlow and is responsible for analyzing and suggesting optimal actions during disasters. This model makes predictions and optimizations in real time based on the user's location data and suggests immediately useful evacuation routes. Furthermore, the AI ​​uses feedback collected from users after evacuation is completed as training data to improve the system's accuracy.

[0105] A concrete example is the process by which a user checks their location on their device during an earthquake and evacuates safely to a designated shelter. In this case, the generating AI might receive the following prompt: "Generate a safe evacuation route based on the location of the earthquake and the predicted tsunami height." Such specific instructions can enhance safety and efficiency during disasters.

[0106] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0107] Step 1:

[0108] The server acquires disaster information. The server receives earthquake and weather information in real time from data feeds such as the Japan Meteorological Agency, and acquires information such as the epicenter and tsunami height. Based on this information, it performs data analysis and generates necessary control instructions.

[0109] Step 2:

[0110] The server performs analysis using a generated AI model. The acquired disaster information is used as input, and the generated AI model analyzes the affected area and the scale of the disaster. This results in the output of specific control instructions regarding the operation of the movable breakwater.

[0111] Step 3:

[0112] The server generates evacuation route information and sends it to the user's terminal. Based on the analysis results by the generating AI, the optimal evacuation route is created, taking into account current traffic conditions and the capacity of evacuation shelters. The generated route information is sent to the terminal and notified to the user.

[0113] Step 4:

[0114] The user uses a device to check the evacuation route and take action. The user refers to the map and evacuation route displayed on the device and takes action to safely head to the designated evacuation shelter. The device receives the user's location information and updates the route in real time.

[0115] Step 5:

[0116] After the evacuation is complete, the user sends feedback to the server. The user inputs their opinions and experiences regarding the quality and route of the evacuation from their device and sends them to the server. This feedback is used as training data for the AI ​​model.

[0117] Step 6:

[0118] The server uses feedback to improve the system. It analyzes the collected feedback data and retrains the AI ​​model. This improves the system's accuracy, enabling it to provide more appropriate evacuation routes and information in the event of a future disaster.

[0119] 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.

[0120] This invention is an advanced system for supporting the safe and rapid evacuation of users during natural disasters. In particular, it is characterized by its use of an emotion engine to provide appropriate evacuation support that takes into account the user's psychological state.

[0121] The server first receives earthquake and weather data in real time and uses a generating AI to analyze the risk of tsunamis and floods. Based on the analysis, it issues instructions to raise movable breakwaters as needed. At this stage, the server collects real-time information on the current occupancy status of evacuation centers and traffic conditions, and uses the generating AI to identify the optimal evacuation routes and evacuation centers.

[0122] Users receive this information through their devices and obtain instructions on evacuation routes and shelters. Furthermore, an emotion engine built into the device evaluates the user's emotional state in real time through voice input and facial recognition. In particular, if stress levels or anxiety levels are high, the generated AI provides psychological support and advice tailored to the situation.

[0123] For example, if the emotion engine detects a user's anxiety, the device will display a message to the user such as, "Please stay calm and evacuate; your safety is assured." Furthermore, the user's experience and emotional feedback are stored on the server and used for future disaster response.

[0124] Administrators can manually operate breakwaters or modify evacuation instructions in emergencies. This flexibility allows the system to respond quickly to on-site needs.

[0125] This system configuration makes it possible to support safe and effective evacuation while taking into account the individual psychological state of each user. This invention utilizes an emotion engine and generative AI to maximize evacuation efficiency during disasters, providing life protection and peace of mind.

[0126] The following describes the processing flow.

[0127] Step 1:

[0128] The server receives disaster information in real time from earthquake and weather monitoring systems. This information includes the earthquake's epicenter and the predicted height of the tsunami.

[0129] Step 2:

[0130] The server inputs the collected disaster data into a generating AI for prediction and analysis. The generating AI determines the likelihood of tsunamis and flood risks, and assesses the extent of their impact.

[0131] Step 3:

[0132] Based on the analysis results, the server automatically decides whether or not to raise the movable breakwater and sends an instruction to the breakwater management system. This instruction is activated when signs of a tsunami are detected.

[0133] Step 4:

[0134] The server collects real-time information on the occupancy status of evacuation shelters and road traffic, and passes it to the generating AI. Based on this data, the generating AI selects the optimal evacuation route and shelter.

[0135] Step 5:

[0136] The terminal receives evacuation information sent from the server and notifies the user. Evacuation routes are displayed along with a map, and safe routes are shown along with estimated arrival times.

[0137] Step 6:

[0138] The emotion engine built into the device senses the user's emotional state. It uses voice recognition and facial recognition technology to assess the user's stress and anxiety.

[0139] Step 7:

[0140] The device provides psychological support to the user based on the evaluation of its emotion engine. For example, it offers messages that promote relaxation and voice guidance that provides a sense of security.

[0141] Step 8:

[0142] Users follow the instructions on the device and take safe evacuation actions. After evacuation, they can input their thoughts and problems as feedback on the device.

[0143] Step 9:

[0144] The server collects and stores user feedback and actual evacuation data, and uses this to improve the algorithms of the generating AI, thereby enhancing the accuracy of future disaster response.

[0145] (Example 2)

[0146] 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".

[0147] During natural disasters, ensuring the speed and safety of evacuation is crucial. However, conventional systems have suffered from insufficient real-time collection and analysis of disaster information, as well as a lack of optimization of evacuation routes and shelter information. Furthermore, they lacked sufficient mechanisms to alleviate the psychological burden on users, making it difficult to encourage appropriate evacuation actions. Therefore, this invention aims to address these issues, realize efficient evacuation support during disasters, and ensure the safety and mental well-being of users.

[0148] 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.

[0149] In this invention, the server includes means for receiving and analyzing disaster information in real time, means for automatically controlling movable barriers, and means for evaluating the emotional state of users and providing psychological support using an emotion engine. This makes it possible to promote rapid and safe evacuation actions during disasters and reduce the psychological stress on users.

[0150] "Disaster information" refers to data about events related to natural disasters, such as earthquakes, weather, tsunamis, and floods, which are received and analyzed in real time.

[0151] "Methods of analysis" refers to the process of using AI technology to generate data based on received disaster information, in order to derive disaster risks and optimal evacuation actions.

[0152] A "movable barrier" is a physical structure installed to mitigate damage from tsunamis, floods, and other disasters, and is controlled automatically or manually during a disaster.

[0153] "User equipment" refers to communication devices that users carry or use, including smartphones and tablets, that receive evacuation route and shelter information.

[0154] "Generative AI technology" refers to techniques that use artificial intelligence to analyze data and generate optimal evacuation actions and psychological support, such as natural language processing models.

[0155] The "emotion engine" is a system that analyzes the user's voice input and facial expression data to evaluate their psychological state in real time, and is used to measure stress levels and anxiety.

[0156] "Psychological support" refers to information and advice provided to reduce anxiety among users during disasters and to promote calm evacuation behavior.

[0157] This invention is a system that supports the safe and rapid evacuation of users during natural disasters, and includes real-time information analysis and psychological support. The system mainly includes the following elements:

[0158] The server receives earthquake and weather information in real time using the Japan Meteorological Agency's API. The received information is stored in a database and input into a generative AI model for analysis. The generative AI model used here is equipped with natural language processing technology and has the ability to assess disaster risk and propose optimal evacuation actions. If a tsunami or flood risk is determined, the server activates movable barriers via a control system.

[0159] The terminal functions as a user device such as a smartphone or tablet. While receiving evacuation route and shelter information from the server, the terminal also uses its built-in microphone and camera to acquire user voice and facial expression data. This data is analyzed by an emotion engine within the terminal and used to evaluate the user's psychological state.

[0160] Users can receive optimized evacuation routes and messages for psychological support displayed on their device screen. If the emotion engine determines that the user's stress level is high, the generating AI will provide situation-appropriate advice. Specific messages might include phrases like, "Please stay calm. The current evacuation route is safe."

[0161] Through this system, users are expected to be able to take appropriate actions with peace of mind even in the event of a disaster. Furthermore, user feedback will be used to improve the system in the future, enabling the provision of more accurate information.

[0162] As a concrete example, prompts such as, "Generate the optimal evacuation instructions in the event of an earthquake. A tsunami warning has been issued, and selecting an evacuation route is urgent. Please also include a message that will reassure the user," are input to the generation AI model, and the system then proposes the most appropriate evacuation action for the user.

[0163] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0164] Step 1:

[0165] The server receives disaster information in real time. The input consists of earthquake and weather data, which are obtained using APIs from meteorological agencies. The received data is stored in a database on the server. The server then inputs this data into a generating AI model to perform tsunami and flood risk analysis. The analysis results in a risk level assessment. Based on this output, the server determines whether barriers need to be activated.

[0166] Step 2:

[0167] The server acquires current occupancy rates and traffic information for evacuation shelters. It uses data from each evacuation shelter management system and real-time data obtained through traffic information provider APIs as input. Using this input data, the server calculates the optimal evacuation routes and shelters using a generated AI model. The optimized evacuation routes and shelter list are output, and this information is sent to the user's terminal.

[0168] Step 3:

[0169] The user's device receives evacuation information transmitted from the server. The device displays this information on its screen and notifies the user. The device also uses its built-in microphone and camera to capture the user's voice and facial expressions. This data is input into an emotion engine to evaluate the user's psychological state. Based on this evaluation, if necessary, a generative AI generates psychological support messages and displays them on the device.

[0170] Step 4:

[0171] Users begin their actions by following the evacuation route guidance and support messages displayed on their device. During the evacuation, users can input feedback on their device based on their emotions and experiences. The input feedback is sent to the server and stored as data for system improvement. The server uses this feedback as training data for the generated AI model to determine future improvements.

[0172] Step 5:

[0173] Administrators manually adjust the system as needed. Considering the situation during a disaster, administrators manually control barriers and modify evacuation information from a dedicated management screen. Changes made by administrators are immediately reflected on the server, allowing information to be quickly transmitted to user terminals.

[0174] (Application Example 2)

[0175] 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".

[0176] During a disaster, it is crucial to quickly and accurately ensure the safety of customers and staff within a store. In particular, evacuating in a crowded store requires accurate information provision and psychological support. Conventional systems have struggled to adequately consider real-time changing situations and individual psychological states during evacuation guidance. Therefore, there is a need to provide a system that enables swift and safe evacuation by providing a sense of security tailored to the customer's psychological state.

[0177] 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.

[0178] In this invention, the server includes means for receiving and analyzing disaster information in real time, means for automatically controlling a mobile disaster response device, means for notifying user information terminals of evacuation directions and evacuation facility information, means for analyzing and presenting optimal actions during a disaster using generating AI, means for updating the system based on disaster information and evacuation feedback, means for evaluating the customer's psychological state and providing reassuring messages to promote calm behavior, and means for presenting the current congestion status in the store and routes to exits. As a result, even in crowded store environments, detailed evacuation guidance tailored to the customer's psychological state becomes possible, enabling safe and effective disaster response.

[0179] "Means for receiving and analyzing disaster information in real time" refers to technologies that immediately receive information when a disaster occurs and analyze its contents.

[0180] "Means for automatically controlling mobile disaster response devices" refers to technology that operates devices that are automatically driven and used to mitigate damage in the event of a disaster.

[0181] "Means for notifying user information terminals of evacuation direction and evacuation facility information" refers to technology that transmits information about the user's direction of movement and available evacuation facilities to the terminal when evacuation is necessary.

[0182] "Means for analyzing and presenting optimal actions during disasters using generative AI" refers to a technology that utilizes AI technology to predict the most appropriate actions during a disaster and inform users of them.

[0183] "Methods for updating systems based on disaster information and evacuation feedback" refer to technologies that improve systems in preparation for future disasters based on collected disaster-related data and reviews of evacuations.

[0184] "A means of evaluating the customer's psychological state and providing reassuring messages to promote calm behavior" refers to a technology that analyzes the customer's emotions, generates and provides messages that enhance their sense of security, and thereby encourages calm behavior.

[0185] "Means of displaying the current congestion status inside the store and routes to the exit" refers to technology that analyzes the flow of people and the degree of congestion inside the store and displays directions to the exit.

[0186] In this invention, the server first receives disaster information in real time and performs analysis. In this process, external services such as weather sensor APIs and earthquake data APIs are used to acquire disaster-related data. Upon receiving this data, the server uses a generative AI model to perform predictions and analysis and evaluate the progression of the situation.

[0187] Based on the analysis results, the server automatically controls mobile disaster response equipment. This control utilizes network technology capable of operating IoT devices, enabling the rapid activation of breakwaters, shutters, and other equipment. Furthermore, the server notifies users of evacuation directions and evacuation facility information on their information terminals. This notification is delivered via the user's smartphone or smart glasses, and because the terminal has the Google Maps API built in, users can visually confirm safe evacuation routes from their current location.

[0188] The user's device is equipped with an emotion engine that uses voice input and facial recognition to assess their psychological state. If a high-stress state is detected, a generative AI creates a message that provides appropriate reassurance and displays it on the device. AI chatbot technology is used to generate this message, enabling real-time, personalized responses. For example, in a shopping mall, the user might be instructed to "calm down and head towards the exit."

[0189] Furthermore, the server constantly aggregates disaster information and evacuation feedback from users, updating the system for future disaster response. This feedback prompts the generating AI model with improvement suggestions, prompting it to ask "what should be improved for the next disaster?", thereby providing more accurate disaster countermeasures.

[0190] As a concrete example, smart glasses operate in a crowded store and issue warnings based on prompts such as "Show a safe route" and "Send a reassuring message." This system can support safe evacuation even in crowded environments.

[0191] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0192] Step 1:

[0193] The server receives disaster information in real time from external weather sensor APIs and earthquake data APIs. Based on this input data, it extracts weather conditions, earthquake magnitude, and location, and stores them in a database.

[0194] Step 2:

[0195] The server activates a generative AI model to analyze the received disaster information. Stored weather and earthquake data are used as input. The generative AI model analyzes this data, calculates the probability of tsunamis occurring and the risk of floods, and outputs the results.

[0196] Step 3:

[0197] The server sends control signals to IoT devices to automatically control the mobile disaster response equipment. Based on the risk assessment output by the generated AI model, it automatically decides whether to operate the equipment. This then executes specific actions, such as raising the breakwater.

[0198] Step 4:

[0199] The server generates evacuation directions and evacuation facility information and notifies the user's information terminal. It uses the Google Maps API as input to obtain the user's current location and calculates the shortest route to the exit. The result is output to the user's terminal to guide them to safety during evacuation.

[0200] Step 5:

[0201] The user's device activates an emotion engine and collects data from the user's voice input and facial recognition via the camera. This allows the system to evaluate the user's psychological state and process the input data to output a stress level.

[0202] Step 6:

[0203] The server receives information about the user's psychological state and uses a generated AI chatbot to create reassuring messages. The output includes messages such as "Please remain calm," which are sent to the user's device in real time.

[0204] Step 7:

[0205] The server builds a database to update the entire system based on feedback information obtained during the evacuation. Using prompt messages, it takes user feedback such as "What should be improved next time?" and generates updated data to prepare for future disasters.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] [Second Embodiment]

[0210] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0211] 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.

[0212] 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).

[0213] 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.

[0214] 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.

[0215] 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).

[0216] 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.

[0217] 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.

[0218] 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.

[0219] 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.

[0220] 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.

[0221] 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".

[0222] This invention provides an integrated system that enables rapid and safe evacuation during natural disasters. The embodiments thereof are described below.

[0223] The server receives disaster information in real time from earthquake and weather monitoring systems. This information includes, for example, the earthquake's epicenter, time of occurrence, predicted tsunami height, type of weather warning, and affected area. Based on this data, a generating AI analyzes the scale and extent of the disaster.

[0224] If the analysis predicts a tsunami, the server will instruct the movable breakwater system to rise. This allows for the prevention of tsunami intrusion in advance. In addition, depending on the disaster, the server collects up-to-date data such as the location of evacuation centers, the capacity of each evacuation center, and road traffic conditions.

[0225] The terminal receives information transmitted from the server and displays evacuation routes and destinations to the user in real time. This includes using map data and generating AI to suggest the best route. For example, if the usual route is congested or the road is damaged, it can suggest an alternative route.

[0226] Users can take swift and safe evacuation actions by following instructions received through their devices. Furthermore, administrators can manually control the raising of breakwaters and the distribution of evacuation information. This operation is used to deal with unexpected situations or when additional confirmation of the AI's decisions is required.

[0227] After the evacuation, feedback from users and administrators is collected and stored in the server's database. This information will be used to enable the generated AI to make more accurate decisions during future disasters, contributing to the overall improvement of the system.

[0228] With this configuration, the present invention realizes an advanced disaster prevention system aimed at rapid evacuation and protection of human lives.

[0229] The following describes the processing flow.

[0230] Step 1:

[0231] The server receives disaster information in real time from earthquake and weather monitoring systems. This information includes the epicenter, magnitude, and forecast weather conditions.

[0232] Step 2:

[0233] The server passes disaster information to the generating AI, which then analyzes whether a tsunami will occur and the risk of flooding based on this information. The generating AI predicts the scale and extent of the disaster based on past data.

[0234] Step 3:

[0235] The server automatically sends an instruction to raise the movable breakwater based on the prediction results. This is done especially when the predicted tsunami height exceeds a certain threshold.

[0236] Step 4:

[0237] The server aggregates the latest data, including shelter capacity, current congestion levels, and traffic information, and the generating AI analyzes the optimal evacuation route and destination.

[0238] Step 5:

[0239] The terminal receives evacuation information from the server and displays evacuation routes and destinations to the user in real time. Travel time and route instructions are presented along with map data.

[0240] Step 6:

[0241] Users act according to evacuation instructions received via their devices. If necessary, administrators can manually raise the breakwater or adjust evacuation instructions.

[0242] Step 7:

[0243] After the evacuation is complete, the server collects user feedback and stores it in a database. This feedback will be used to inform future decisions by the AI ​​and help improve the system.

[0244] (Example 1)

[0245] 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."

[0246] In recent years, natural disasters have become more frequent due to climate change and increased seismic activity. Consequently, there is a need for systems that enable rapid and effective evacuation. Conventional systems often lack integration, with disaster information collection and analysis, evacuation guidance provision, and disaster prevention device control being handled separately. Furthermore, errors in judgment can lead to erroneous instructions that could have fatal consequences for evacuees. Against this backdrop, the challenge lies in providing an integrated disaster prevention system that enables accurate real-time information analysis and a rapid response based on that analysis.

[0247] 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.

[0248] In this invention, the server includes an information processing device means for receiving disaster information in real time and performing data analysis, a control device means for automatically controlling a mobile disaster prevention device, and a communication device means for providing evacuation guidance and evacuation facility information to the user's information terminal. This enables immediate data analysis and accurate presentation of relief measures in the event of a disaster, as well as the implementation of automatic and manual disaster prevention measures by the system.

[0249] An "information processing device" is a device that receives disaster information in real time, analyzes that information, and determines the scale and impact of the disaster.

[0250] A "control device" is a device that automatically controls movable disaster prevention equipment based on analyzed information and takes necessary disaster prevention measures.

[0251] A "communication device" is a device that provides analysis results, evacuation instructions, and evacuation facility information to users' information terminals in real time.

[0252] An "analysis device" is a device that uses a generated artificial intelligence model to calculate the optimal response measures during a disaster and presents them to the user.

[0253] An "improvement device" is a device that updates the entire system based on disaster information and post-evacuation evaluation information, with the aim of improving performance in the event of a future disaster.

[0254] An "operating device" is a device that allows operators to manually control the control system and issue evacuation instructions.

[0255] A "suggestion device" is a device that integrates detailed information about evacuation facilities and proposes the optimal evacuation destination to users based on the generated artificial intelligence model.

[0256] This invention is a comprehensive system for supporting rapid and safe evacuation during disasters. To implement this system, the server, terminals, and users each play specific roles. Specific embodiments are described below.

[0257] First, the server uses an information processing device to receive and analyze earthquake and weather data in real time. During this process, it utilizes a database management system (e.g., MySQL) to store the received data and, if necessary, uses a generated AI model (e.g., a model using TensorFlow) to analyze the data. Based on the analysis results, it quickly determines the scale and extent of the disaster.

[0258] Next, the server uses a control device to automatically operate disaster prevention equipment, such as a movable breakwater, based on the analysis results. The server also transmits the analysis results and necessary evacuation information to the terminal via a communication device. The terminal then presents the user with the optimal evacuation route calculated by a generated AI model based on the received information.

[0259] The terminal uses a communication device to provide users with evacuation guidance and information on evacuation facilities. It has the function to display the user's location, the capacity of evacuation shelters, and traffic information in real time using a map application (e.g., Google Maps API). This enables users to take quick and appropriate evacuation actions. For example, in areas with many hills, it will suggest a route that suits the user's physical ability. Another example of a prompt sentence to be input into the generating AI model is, "Please suggest the optimal evacuation route in the event of a flood forecast due to heavy rain."

[0260] Furthermore, after evacuation is complete, users provide feedback to the server. The server utilizes improved equipment to accumulate this feedback and use it to update the entire system. This will enable more accurate decision-making in the event of the next disaster.

[0261] In this way, the entire system functions as an integrated whole, enabling rapid and safe protection of human lives during disasters.

[0262] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0263] Step 1:

[0264] The server receives earthquake and weather data in real time from the monitoring system. The input data includes earthquake epicenters and times, predicted tsunami heights, weather warning types, and affected areas. After receiving this input data, it is stored in a database and prepared for analysis using a generated AI model. The output is the raw data to be analyzed.

[0265] Step 2:

[0266] The server uses a generated AI model based on the received data to analyze the scale and extent of the disaster. The input is the raw data obtained in step 1, and data processing involves comparative analysis with historical data. This outputs specific analysis results such as whether a tsunami is predicted and which areas are at risk.

[0267] Step 3:

[0268] Based on the analysis results, the server uses a control device to automatically operate a movable disaster prevention device, such as a breakwater. The input is the analysis results from step 2, which are output as a control signal, automatically issuing an instruction to raise the breakwater. In addition, new information such as the location and capacity of evacuation shelters and traffic conditions is collected.

[0269] Step 4:

[0270] The server uses a communication device to send the analysis results and collected evacuation information to the terminal. The input is the evacuation route and shelter information generated in step 3, and the output is notification data containing this information. This notification data is transmitted in real time.

[0271] Step 5:

[0272] The terminal receives notification data sent from the server and displays the optimal evacuation route and shelter information to the user. The input is the data sent in step 4, and route calculation is performed by a generating AI model. This outputs a specific evacuation route to the user and visualizes it on a map.

[0273] Step 6:

[0274] The user takes swift and safe evacuation actions based on the evacuation information displayed on the terminal. Input is information from the terminal, and output is the user's evacuation actions. The user also provides feedback to the system after completing the evacuation.

[0275] Step 7:

[0276] The server receives feedback from users and administrators and stores it in a database. The input is feedback data, which is then analyzed and incorporated into the system. The output is adjustment information that contributes to improving the accuracy of future analyses.

[0277] (Application Example 1)

[0278] 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."

[0279] In the event of a natural disaster, there is a need to provide real-time information to enable people to evacuate quickly and safely, suggest optimal evacuation routes, and effectively control movable breakwaters. However, current systems do not adequately provide personalized evacuation routes to individual users or improve the system based on feedback after evacuation. It is necessary to solve these problems and realize safer and more efficient evacuations.

[0280] 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.

[0281] In this invention, the server includes means for receiving and analyzing disaster information in real time, means for automatically controlling a movable breakwater, means for notifying user terminals of evacuation routes and shelter information, means for analyzing and presenting optimal actions during a disaster using generated AI, means for updating the system based on disaster information and evacuation feedback, means for immediately providing the optimal evacuation route based on the user's location data, and means for collecting user feedback after evacuation and using it to improve the accuracy of the system. This makes it possible to propose evacuation routes optimized for each user and to continuously improve the system using feedback.

[0282] "Means for receiving and analyzing disaster information in real time" refers to a system that continuously acquires data related to the occurrence of disasters and immediately analyzes that information to grasp the situation and scale of the disaster.

[0283] "Means for automatically controlling movable breakwaters" refers to a system that mechanically operates breakwaters in response to predicted disasters, thereby minimizing damage in advance.

[0284] The means for "notifying the user terminal of evacuation route and evacuation shelter information" is a communication technology that informs the user's terminal of information regarding the optimal evacuation route and evacuation destination.

[0285] The means for "analyzing and presenting optimal actions during disasters using generative AI" is a technology that utilizes artificial intelligence to analyze and provide advice on the most appropriate evacuation methods and actions in disasters.

[0286] The means for "updating the system based on disaster information and evacuation feedback" is a process that utilizes the collected disaster data and opinions from evacuees to improve the system and enhance the response ability to the next disaster.

[0287] The means for "promptly providing an optimal evacuation route based on the user's location data" is a technology that uses the user's location information to quickly propose an optimal evacuation route.

[0288] The means for "collecting opinions from users after evacuation and using them to improve the accuracy of the system" is a technology that analyzes the feedback obtained from users after evacuation is completed and improves the accuracy of future disaster responses.

[0289] To implement this invention, it is necessary to provide a system in which a server, user terminals, and a generative AI model operate in cooperation. The server utilizes data feeds provided by the Meteorological Agency, earthquake monitoring institutions, etc. to receive disaster information in real time. Thereby, information on earthquakes and tsunamis can be immediately analyzed, and control instructions for movable breakwaters can be issued based on the generative AI model.

[0290] Evacuation route and evacuation shelter information is notified to the user terminal. This notification is based on direct feedback from the server and is updated in real time. When the user receives evacuation information through the terminal, an optimal route considering obstacles and traffic conditions is generated and displayed.

[0291] The generative AI model is built using machine learning libraries such as TensorFlow and is responsible for analyzing and suggesting optimal actions during disasters. This model makes predictions and optimizations in real time based on the user's location data and suggests immediately useful evacuation routes. Furthermore, the AI ​​uses feedback collected from users after evacuation is completed as training data to improve the system's accuracy.

[0292] A concrete example is the process by which a user checks their location on their device during an earthquake and evacuates safely to a designated shelter. In this case, the generating AI might receive the following prompt: "Generate a safe evacuation route based on the location of the earthquake and the predicted tsunami height." Such specific instructions can enhance safety and efficiency during disasters.

[0293] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0294] Step 1:

[0295] The server acquires disaster information. The server receives earthquake and weather information in real time from data feeds such as the Japan Meteorological Agency, and acquires information such as the epicenter and tsunami height. Based on this information, it performs data analysis and generates necessary control instructions.

[0296] Step 2:

[0297] The server performs analysis using a generated AI model. The acquired disaster information is used as input, and the generated AI model analyzes the affected area and the scale of the disaster. This results in the output of specific control instructions regarding the operation of the movable breakwater.

[0298] Step 3:

[0299] The server generates evacuation route information and sends it to the user terminal. Based on the analysis results by the generation AI, an optimal evacuation route is created considering the current traffic situation and the capacity of evacuation shelters. The generated route information is sent to the terminal and notified to the user.

[0300] Step 4:

[0301] The user checks the evacuation route using the terminal and acts. The user refers to the map and evacuation route displayed on the terminal and heads towards the designated evacuation shelter safely. The terminal receives the user's location information and updates the route in real time.

[0302] Step 5:

[0303] After the evacuation is completed, the user sends feedback to the server. The user inputs opinions and experiences about the quality of the evacuation and the route from the terminal and sends them to the server. This feedback is used as learning data for the AI model.

[0304] Step 6:

[0305] The server improves the system using the feedback. The collected feedback data is analyzed and the AI model is re-learned. As a result, the accuracy of the system is improved, and more appropriate evacuation routes and information can be provided in the event of the next disaster.

[0306] Furthermore, an emotion engine for estimating the user's emotion may be combined. That is, the specific processing unit 290 may estimate the user's emotion using the emotion specific model 59 and perform specific processing using the user's emotion.

[0307] The present invention is an advanced system for assisting the safe and rapid evacuation of users in the event of natural disasters. In particular, by using an emotion engine, it is characterized in that appropriate evacuation support considering the user's psychological state is provided.

[0308] The server first receives earthquake and weather data in real time and uses a generating AI to analyze the risk of tsunamis and floods. Based on the analysis, it issues instructions to raise movable breakwaters as needed. At this stage, the server collects real-time information on the current occupancy status of evacuation centers and traffic conditions, and uses the generating AI to identify the optimal evacuation routes and evacuation centers.

[0309] Users receive this information through their devices and obtain instructions on evacuation routes and shelters. Furthermore, an emotion engine built into the device evaluates the user's emotional state in real time through voice input and facial recognition. In particular, if stress levels or anxiety levels are high, the generated AI provides psychological support and advice tailored to the situation.

[0310] For example, if the emotion engine detects a user's anxiety, the device will display a message to the user such as, "Please stay calm and evacuate; your safety is assured." Furthermore, the user's experience and emotional feedback are stored on the server and used for future disaster response.

[0311] Administrators can manually operate breakwaters or modify evacuation instructions in emergencies. This flexibility allows the system to respond quickly to on-site needs.

[0312] This system configuration makes it possible to support safe and effective evacuation while taking into account the individual psychological state of each user. This invention utilizes an emotion engine and generative AI to maximize evacuation efficiency during disasters, providing life protection and peace of mind.

[0313] The following describes the processing flow.

[0314] Step 1:

[0315] The server receives disaster information in real time from earthquake and weather monitoring systems. This information includes the earthquake's epicenter and the predicted height of the tsunami.

[0316] Step 2:

[0317] The server inputs the collected disaster data into a generating AI for prediction and analysis. The generating AI determines the likelihood of tsunamis and flood risks, and assesses the extent of their impact.

[0318] Step 3:

[0319] Based on the analysis results, the server automatically decides whether or not to raise the movable breakwater and sends an instruction to the breakwater management system. This instruction is activated when signs of a tsunami are detected.

[0320] Step 4:

[0321] The server collects real-time information on the occupancy status of evacuation shelters and road traffic, and passes it to the generating AI. Based on this data, the generating AI selects the optimal evacuation route and shelter.

[0322] Step 5:

[0323] The terminal receives evacuation information sent from the server and notifies the user. Evacuation routes are displayed along with a map, and safe routes are shown along with estimated arrival times.

[0324] Step 6:

[0325] The emotion engine built into the device senses the user's emotional state. It uses voice recognition and facial recognition technology to assess the user's stress and anxiety.

[0326] Step 7:

[0327] The device provides psychological support to the user based on the evaluation of its emotion engine. For example, it offers messages that promote relaxation and voice guidance that provides a sense of security.

[0328] Step 8:

[0329] Users follow the instructions on the device and take safe evacuation actions. After evacuation, they can input their thoughts and problems as feedback on the device.

[0330] Step 9:

[0331] The server collects and stores user feedback and actual evacuation data, and uses this to improve the algorithms of the generating AI, thereby enhancing the accuracy of future disaster response.

[0332] (Example 2)

[0333] 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".

[0334] During natural disasters, ensuring the speed and safety of evacuation is crucial. However, conventional systems have suffered from insufficient real-time collection and analysis of disaster information, as well as a lack of optimization of evacuation routes and shelter information. Furthermore, they lacked sufficient mechanisms to alleviate the psychological burden on users, making it difficult to encourage appropriate evacuation actions. Therefore, this invention aims to address these issues, realize efficient evacuation support during disasters, and ensure the safety and mental well-being of users.

[0335] 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.

[0336] In this invention, the server includes means for receiving and analyzing disaster information in real time, means for automatically controlling movable barriers, and means for evaluating the emotional state of users and providing psychological support using an emotion engine. This makes it possible to promote rapid and safe evacuation actions during disasters and reduce the psychological stress on users.

[0337] "Disaster information" refers to data about events related to natural disasters, such as earthquakes, weather, tsunamis, and floods, which are received and analyzed in real time.

[0338] "Methods of analysis" refers to the process of using AI technology to generate data based on received disaster information, in order to derive disaster risks and optimal evacuation actions.

[0339] A "movable barrier" is a physical structure installed to mitigate damage from tsunamis, floods, and other disasters, and is controlled automatically or manually during a disaster.

[0340] "User equipment" refers to communication devices that users carry or use, including smartphones and tablets, that receive evacuation route and shelter information.

[0341] "Generative AI technology" refers to techniques that use artificial intelligence to analyze data and generate optimal evacuation actions and psychological support, such as natural language processing models.

[0342] The "emotion engine" is a system that analyzes the user's voice input and facial expression data to evaluate their psychological state in real time, and is used to measure stress levels and anxiety.

[0343] "Psychological support" refers to information and advice provided to reduce anxiety among users during disasters and to promote calm evacuation behavior.

[0344] This invention is a system that supports the safe and rapid evacuation of users during natural disasters, and includes real-time information analysis and psychological support. The system mainly includes the following elements:

[0345] The server receives earthquake and weather information in real time using the Japan Meteorological Agency's API. The received information is stored in a database and input into a generative AI model for analysis. The generative AI model used here is equipped with natural language processing technology and has the ability to assess disaster risk and propose optimal evacuation actions. If a tsunami or flood risk is determined, the server activates movable barriers via a control system.

[0346] The terminal functions as a user device such as a smartphone or tablet. While receiving evacuation route and shelter information from the server, the terminal also uses its built-in microphone and camera to acquire user voice and facial expression data. This data is analyzed by an emotion engine within the terminal and used to evaluate the user's psychological state.

[0347] Users can receive optimized evacuation routes and messages for psychological support displayed on their device screen. If the emotion engine determines that the user's stress level is high, the generating AI will provide situation-appropriate advice. Specific messages might include phrases like, "Please stay calm. The current evacuation route is safe."

[0348] Through this system, users are expected to be able to take appropriate actions with peace of mind even in the event of a disaster. Furthermore, user feedback will be used to improve the system in the future, enabling the provision of more accurate information.

[0349] As a concrete example, prompts such as, "Generate the optimal evacuation instructions in the event of an earthquake. A tsunami warning has been issued, and selecting an evacuation route is urgent. Please also include a message that will reassure the user," are input to the generation AI model, and the system then proposes the most appropriate evacuation action for the user.

[0350] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0351] Step 1:

[0352] The server receives disaster information in real time. The input consists of earthquake and weather data, which are obtained using APIs from meteorological agencies. The received data is stored in a database on the server. The server then inputs this data into a generating AI model to perform tsunami and flood risk analysis. The analysis results in a risk level assessment. Based on this output, the server determines whether barriers need to be activated.

[0353] Step 2:

[0354] The server acquires current occupancy rates and traffic information for evacuation shelters. It uses data from each evacuation shelter management system and real-time data obtained through traffic information provider APIs as input. Using this input data, the server calculates the optimal evacuation routes and shelters using a generated AI model. The optimized evacuation routes and shelter list are output, and this information is sent to the user's terminal.

[0355] Step 3:

[0356] The user's device receives evacuation information transmitted from the server. The device displays this information on its screen and notifies the user. The device also uses its built-in microphone and camera to capture the user's voice and facial expressions. This data is input into an emotion engine to evaluate the user's psychological state. Based on this evaluation, if necessary, a generative AI generates psychological support messages and displays them on the device.

[0357] Step 4:

[0358] Users begin their actions by following the evacuation route guidance and support messages displayed on their device. During the evacuation, users can input feedback on their device based on their emotions and experiences. The input feedback is sent to the server and stored as data for system improvement. The server uses this feedback as training data for the generated AI model to determine future improvements.

[0359] Step 5:

[0360] Administrators manually adjust the system as needed. Considering the situation during a disaster, administrators manually control barriers and modify evacuation information from a dedicated management screen. Changes made by administrators are immediately reflected on the server, allowing information to be quickly transmitted to user terminals.

[0361] (Application Example 2)

[0362] 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."

[0363] During a disaster, it is crucial to quickly and accurately ensure the safety of customers and staff within a store. In particular, evacuating in a crowded store requires accurate information provision and psychological support. Conventional systems have struggled to adequately consider real-time changing situations and individual psychological states during evacuation guidance. Therefore, there is a need to provide a system that enables swift and safe evacuation by providing a sense of security tailored to the customer's psychological state.

[0364] 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.

[0365] In this invention, the server includes means for receiving and analyzing disaster information in real time, means for automatically controlling a mobile disaster response device, means for notifying user information terminals of evacuation directions and evacuation facility information, means for analyzing and presenting optimal actions during a disaster using generating AI, means for updating the system based on disaster information and evacuation feedback, means for evaluating the customer's psychological state and providing reassuring messages to promote calm behavior, and means for presenting the current congestion status in the store and routes to exits. As a result, even in crowded store environments, detailed evacuation guidance tailored to the customer's psychological state becomes possible, enabling safe and effective disaster response.

[0366] "Means for receiving and analyzing disaster information in real time" refers to technologies that immediately receive information when a disaster occurs and analyze its contents.

[0367] "Means for automatically controlling mobile disaster response devices" refers to technology that operates devices that are automatically driven and used to mitigate damage in the event of a disaster.

[0368] "Means for notifying user information terminals of evacuation direction and evacuation facility information" refers to technology that transmits information about the user's direction of movement and available evacuation facilities to the terminal when evacuation is necessary.

[0369] "Means for analyzing and presenting optimal actions during disasters using generative AI" refers to a technology that utilizes AI technology to predict the most appropriate actions during a disaster and inform users of them.

[0370] "Methods for updating systems based on disaster information and evacuation feedback" refer to technologies that improve systems in preparation for future disasters based on collected disaster-related data and reviews of evacuations.

[0371] "A means of evaluating the customer's psychological state and providing reassuring messages to promote calm behavior" refers to a technology that analyzes the customer's emotions, generates and provides messages that enhance their sense of security, and thereby encourages calm behavior.

[0372] "Means of displaying the current congestion status inside the store and routes to the exit" refers to technology that analyzes the flow of people and the degree of congestion inside the store and displays directions to the exit.

[0373] In this invention, the server first receives disaster information in real time and performs analysis. In this process, external services such as weather sensor APIs and earthquake data APIs are used to acquire disaster-related data. Upon receiving this data, the server uses a generative AI model to perform predictions and analysis and evaluate the progression of the situation.

[0374] Based on the analysis results, the server automatically controls mobile disaster response equipment. This control utilizes network technology capable of operating IoT devices, enabling the rapid activation of breakwaters, shutters, and other equipment. Furthermore, the server notifies users of evacuation directions and evacuation facility information on their information terminals. This notification is delivered via the user's smartphone or smart glasses, and because the terminal has the Google Maps API built in, users can visually confirm safe evacuation routes from their current location.

[0375] The user's device is equipped with an emotion engine that uses voice input and facial recognition to assess their psychological state. If a high-stress state is detected, a generative AI creates a message that provides appropriate reassurance and displays it on the device. AI chatbot technology is used to generate this message, enabling real-time, personalized responses. For example, in a shopping mall, the user might be instructed to "calm down and head towards the exit."

[0376] Furthermore, the server constantly aggregates disaster information and evacuation feedback from users, updating the system for future disaster response. This feedback prompts the generating AI model with improvement suggestions, prompting it to ask "what should be improved for the next disaster?", thereby providing more accurate disaster countermeasures.

[0377] As a concrete example, smart glasses operate in a crowded store and issue warnings based on prompts such as "Show a safe route" and "Send a reassuring message." This system can support safe evacuation even in crowded environments.

[0378] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0379] Step 1:

[0380] The server receives disaster information in real time from external weather sensor APIs and earthquake data APIs. Based on this input data, it extracts weather conditions, earthquake magnitude, and location, and stores them in a database.

[0381] Step 2:

[0382] The server activates a generative AI model to analyze the received disaster information. Stored weather and earthquake data are used as input. The generative AI model analyzes this data, calculates the probability of tsunamis occurring and the risk of floods, and outputs the results.

[0383] Step 3:

[0384] The server sends control signals to IoT devices to automatically control the mobile disaster response equipment. Based on the risk assessment output by the generated AI model, it automatically decides whether to operate the equipment. This then executes specific actions, such as raising the breakwater.

[0385] Step 4:

[0386] The server generates evacuation directions and evacuation facility information and notifies the user's information terminal. It uses the Google Maps API as input to obtain the user's current location and calculates the shortest route to the exit. The result is output to the user's terminal to guide them to safety during evacuation.

[0387] Step 5:

[0388] The user's device activates an emotion engine and collects data from the user's voice input and facial recognition via the camera. This allows the system to evaluate the user's psychological state and process the input data to output a stress level.

[0389] Step 6:

[0390] The server receives information about the user's psychological state and uses a generated AI chatbot to create reassuring messages. The output includes messages such as "Please remain calm," which are sent to the user's device in real time.

[0391] Step 7:

[0392] The server builds a database to update the entire system based on feedback information obtained during the evacuation. Using prompt messages, it takes user feedback such as "What should be improved next time?" and generates updated data to prepare for future disasters.

[0393] 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.

[0394] 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.

[0395] 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.

[0396] [Third Embodiment]

[0397] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0398] 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.

[0399] 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).

[0400] 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.

[0401] 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.

[0402] 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).

[0403] 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.

[0404] 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.

[0405] 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.

[0406] 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.

[0407] 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.

[0408] 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".

[0409] This invention provides an integrated system that enables rapid and safe evacuation during natural disasters. The embodiments thereof are described below.

[0410] The server receives disaster information in real time from earthquake and weather monitoring systems. This information includes, for example, the earthquake's epicenter, time of occurrence, predicted tsunami height, type of weather warning, and affected area. Based on this data, a generating AI analyzes the scale and extent of the disaster.

[0411] If the analysis predicts a tsunami, the server will instruct the movable breakwater system to rise. This allows for the prevention of tsunami intrusion in advance. In addition, depending on the disaster, the server collects up-to-date data such as the location of evacuation centers, the capacity of each evacuation center, and road traffic conditions.

[0412] The terminal receives information transmitted from the server and displays evacuation routes and destinations to the user in real time. This includes using map data and generating AI to suggest the best route. For example, if the usual route is congested or the road is damaged, it can suggest an alternative route.

[0413] Users can take swift and safe evacuation actions by following instructions received through their devices. Furthermore, administrators can manually control the raising of breakwaters and the distribution of evacuation information. This operation is used to deal with unexpected situations or when additional confirmation of the AI's decisions is required.

[0414] After the evacuation, feedback from users and administrators is collected and stored in the server's database. This information will be used to enable the generated AI to make more accurate decisions during future disasters, contributing to the overall improvement of the system.

[0415] With this configuration, the present invention realizes an advanced disaster prevention system aimed at rapid evacuation and protection of human lives.

[0416] The following describes the processing flow.

[0417] Step 1:

[0418] The server receives disaster information in real time from earthquake and weather monitoring systems. This information includes the epicenter, magnitude, and forecast weather conditions.

[0419] Step 2:

[0420] The server passes disaster information to the generating AI, which then analyzes whether a tsunami will occur and the risk of flooding based on this information. The generating AI predicts the scale and extent of the disaster based on past data.

[0421] Step 3:

[0422] The server automatically sends an instruction to raise the movable breakwater based on the prediction results. This is done especially when the predicted tsunami height exceeds a certain threshold.

[0423] Step 4:

[0424] The server aggregates the latest data, including shelter capacity, current congestion levels, and traffic information, and the generating AI analyzes the optimal evacuation route and destination.

[0425] Step 5:

[0426] The terminal receives evacuation information from the server and displays evacuation routes and destinations to the user in real time. Travel time and route instructions are presented along with map data.

[0427] Step 6:

[0428] Users act according to evacuation instructions received via their devices. If necessary, administrators can manually raise the breakwater or adjust evacuation instructions.

[0429] Step 7:

[0430] After the evacuation is complete, the server collects user feedback and stores it in a database. This feedback will be used to inform future decisions by the AI ​​and help improve the system.

[0431] (Example 1)

[0432] 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."

[0433] In recent years, natural disasters have become more frequent due to climate change and increased seismic activity. Consequently, there is a need for systems that enable rapid and effective evacuation. Conventional systems often lack integration, with disaster information collection and analysis, evacuation guidance provision, and disaster prevention device control being handled separately. Furthermore, errors in judgment can lead to erroneous instructions that could have fatal consequences for evacuees. Against this backdrop, the challenge lies in providing an integrated disaster prevention system that enables accurate real-time information analysis and a rapid response based on that analysis.

[0434] 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.

[0435] In this invention, the server includes an information processing device means for receiving disaster information in real time and performing data analysis, a control device means for automatically controlling a mobile disaster prevention device, and a communication device means for providing evacuation guidance and evacuation facility information to the user's information terminal. This enables immediate data analysis and accurate presentation of relief measures in the event of a disaster, as well as the implementation of automatic and manual disaster prevention measures by the system.

[0436] An "information processing device" is a device that receives disaster information in real time, analyzes that information, and determines the scale and impact of the disaster.

[0437] A "control device" is a device that automatically controls movable disaster prevention equipment based on analyzed information and takes necessary disaster prevention measures.

[0438] A "communication device" is a device that provides analysis results, evacuation instructions, and evacuation facility information to users' information terminals in real time.

[0439] An "analysis device" is a device that uses a generated artificial intelligence model to calculate the optimal response measures during a disaster and presents them to the user.

[0440] An "improvement device" is a device that updates the entire system based on disaster information and post-evacuation evaluation information, with the aim of improving performance in the event of a future disaster.

[0441] An "operating device" is a device that allows operators to manually control the control system and issue evacuation instructions.

[0442] A "suggestion device" is a device that integrates detailed information about evacuation facilities and proposes the optimal evacuation destination to users based on the generated artificial intelligence model.

[0443] This invention is a comprehensive system for supporting rapid and safe evacuation during disasters. To implement this system, the server, terminals, and users each play specific roles. Specific embodiments are described below.

[0444] First, the server uses an information processing device to receive and analyze earthquake and weather data in real time. During this process, it utilizes a database management system (e.g., MySQL) to store the received data and, if necessary, uses a generated AI model (e.g., a model using TensorFlow) to analyze the data. Based on the analysis results, it quickly determines the scale and extent of the disaster.

[0445] Next, the server uses a control device to automatically operate disaster prevention equipment, such as a movable breakwater, based on the analysis results. The server also transmits the analysis results and necessary evacuation information to the terminal via a communication device. The terminal then presents the user with the optimal evacuation route calculated by a generated AI model based on the received information.

[0446] The terminal uses a communication device to provide users with evacuation guidance and information on evacuation facilities. It has the function to display the user's location, the capacity of evacuation shelters, and traffic information in real time using a map application (e.g., Google Maps API). This enables users to take quick and appropriate evacuation actions. For example, in areas with many hills, it will suggest a route that suits the user's physical ability. Another example of a prompt sentence to be input into the generating AI model is, "Please suggest the optimal evacuation route in the event of a flood forecast due to heavy rain."

[0447] Furthermore, after evacuation is complete, users provide feedback to the server. The server utilizes improved equipment to accumulate this feedback and use it to update the entire system. This will enable more accurate decision-making in the event of the next disaster.

[0448] In this way, the entire system functions as an integrated whole, enabling rapid and safe protection of human lives during disasters.

[0449] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0450] Step 1:

[0451] The server receives earthquake and weather data in real time from the monitoring system. The input data includes earthquake epicenters and times, predicted tsunami heights, weather warning types, and affected areas. After receiving this input data, it is stored in a database and prepared for analysis using a generated AI model. The output is the raw data to be analyzed.

[0452] Step 2:

[0453] The server uses a generated AI model based on the received data to analyze the scale and extent of the disaster. The input is the raw data obtained in step 1, and data processing involves comparative analysis with historical data. This outputs specific analysis results such as whether a tsunami is predicted and which areas are at risk.

[0454] Step 3:

[0455] Based on the analysis results, the server uses a control device to automatically operate a movable disaster prevention device, such as a breakwater. The input is the analysis results from step 2, which are output as a control signal, automatically issuing an instruction to raise the breakwater. In addition, new information such as the location and capacity of evacuation shelters and traffic conditions is collected.

[0456] Step 4:

[0457] The server uses a communication device to send the analysis results and collected evacuation information to the terminal. The input is the evacuation route and shelter information generated in step 3, and the output is notification data containing this information. This notification data is transmitted in real time.

[0458] Step 5:

[0459] The terminal receives notification data sent from the server and displays the optimal evacuation route and shelter information to the user. The input is the data sent in step 4, and route calculation is performed by a generating AI model. This outputs a specific evacuation route to the user and visualizes it on a map.

[0460] Step 6:

[0461] The user takes swift and safe evacuation actions based on the evacuation information displayed on the terminal. Input is information from the terminal, and output is the user's evacuation actions. The user also provides feedback to the system after completing the evacuation.

[0462] Step 7:

[0463] The server receives feedback from users and administrators and stores it in a database. The input is feedback data, which is then analyzed and incorporated into the system. The output is adjustment information that contributes to improving the accuracy of future analyses.

[0464] (Application Example 1)

[0465] 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."

[0466] In the event of a natural disaster, there is a need to provide real-time information to enable people to evacuate quickly and safely, suggest optimal evacuation routes, and effectively control movable breakwaters. However, current systems do not adequately provide personalized evacuation routes to individual users or improve the system based on feedback after evacuation. It is necessary to solve these problems and realize safer and more efficient evacuations.

[0467] 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.

[0468] In this invention, the server includes means for receiving and analyzing disaster information in real time, means for automatically controlling a movable breakwater, means for notifying user terminals of evacuation routes and shelter information, means for analyzing and presenting optimal actions during a disaster using generated AI, means for updating the system based on disaster information and evacuation feedback, means for immediately providing the optimal evacuation route based on the user's location data, and means for collecting user feedback after evacuation and using it to improve the accuracy of the system. This makes it possible to propose evacuation routes optimized for each user and to continuously improve the system using feedback.

[0469] "Means for receiving and analyzing disaster information in real time" refers to a system that continuously acquires data related to the occurrence of disasters and immediately analyzes that information to grasp the situation and scale of the disaster.

[0470] "Means for automatically controlling movable breakwaters" refers to a system that mechanically operates breakwaters in response to predicted disasters, thereby minimizing damage in advance.

[0471] "Means for notifying user terminals of evacuation routes and evacuation shelter information" refers to communication technology that informs users' terminals of information regarding the optimal evacuation route and evacuation destination.

[0472] "Means for analyzing and presenting optimal actions during disasters using generative AI" refers to a technology that utilizes artificial intelligence to analyze and advise on the most appropriate evacuation methods and actions during a disaster.

[0473] "Methods for updating the system based on disaster information and evacuation feedback" refers to the process of improving the system by utilizing collected disaster data and feedback from evacuees, thereby enhancing the ability to respond to future disasters.

[0474] "A means of providing the optimal evacuation route immediately based on the user's location data" refers to a technology that uses the user's location information to quickly suggest the most suitable evacuation route.

[0475] "Means for collecting user feedback after evacuation and using it to improve system accuracy" refers to technologies that analyze feedback received from users after evacuation is completed to improve the accuracy of future disaster response.

[0476] To implement this invention, it is necessary to provide a system in which a server, a user terminal, and a generative AI model work in cooperation. The server utilizes data feeds provided by the Japan Meteorological Agency and earthquake monitoring organizations to receive disaster information in real time. This allows for immediate analysis of earthquake and tsunami information and the generation of control instructions for movable breakwaters based on the generative AI model.

[0477] The user's device will receive notifications about evacuation routes and shelters. These notifications are based on direct feedback from the server and are updated in real time. When a user receives evacuation information through their device, the optimal route is generated and displayed, taking into account obstacles and traffic conditions.

[0478] The generative AI model is built using machine learning libraries such as TensorFlow and is responsible for analyzing and suggesting optimal actions during disasters. This model makes predictions and optimizations in real time based on the user's location data and suggests immediately useful evacuation routes. Furthermore, the AI ​​uses feedback collected from users after evacuation is completed as training data to improve the system's accuracy.

[0479] A concrete example is the process by which a user checks their location on their device during an earthquake and evacuates safely to a designated shelter. In this case, the generating AI might receive the following prompt: "Generate a safe evacuation route based on the location of the earthquake and the predicted tsunami height." Such specific instructions can enhance safety and efficiency during disasters.

[0480] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0481] Step 1:

[0482] The server acquires disaster information. The server receives earthquake and weather information in real time from data feeds such as the Japan Meteorological Agency, and acquires information such as the epicenter and tsunami height. Based on this information, it performs data analysis and generates necessary control instructions.

[0483] Step 2:

[0484] The server performs analysis using a generated AI model. The acquired disaster information is used as input, and the generated AI model analyzes the affected area and the scale of the disaster. This results in the output of specific control instructions regarding the operation of the movable breakwater.

[0485] Step 3:

[0486] The server generates evacuation route information and sends it to the user's terminal. Based on the analysis results by the generating AI, the optimal evacuation route is created, taking into account current traffic conditions and the capacity of evacuation shelters. The generated route information is sent to the terminal and notified to the user.

[0487] Step 4:

[0488] The user uses a device to check the evacuation route and take action. The user refers to the map and evacuation route displayed on the device and takes action to safely head to the designated evacuation shelter. The device receives the user's location information and updates the route in real time.

[0489] Step 5:

[0490] After the evacuation is complete, the user sends feedback to the server. The user inputs their opinions and experiences regarding the quality and route of the evacuation from their device and sends them to the server. This feedback is used as training data for the AI ​​model.

[0491] Step 6:

[0492] The server uses feedback to improve the system. It analyzes the collected feedback data and retrains the AI ​​model. This improves the system's accuracy, enabling it to provide more appropriate evacuation routes and information in the event of a future disaster.

[0493] 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.

[0494] This invention is an advanced system for supporting the safe and rapid evacuation of users during natural disasters. In particular, it is characterized by its use of an emotion engine to provide appropriate evacuation support that takes into account the user's psychological state.

[0495] The server first receives earthquake and weather data in real time and uses a generating AI to analyze the risk of tsunamis and floods. Based on the analysis, it issues instructions to raise movable breakwaters as needed. At this stage, the server collects real-time information on the current occupancy status of evacuation centers and traffic conditions, and uses the generating AI to identify the optimal evacuation routes and evacuation centers.

[0496] Users receive this information through their devices and obtain instructions on evacuation routes and shelters. Furthermore, an emotion engine built into the device evaluates the user's emotional state in real time through voice input and facial recognition. In particular, if stress levels or anxiety levels are high, the generated AI provides psychological support and advice tailored to the situation.

[0497] For example, if the emotion engine detects a user's anxiety, the device will display a message to the user such as, "Please stay calm and evacuate; your safety is assured." Furthermore, the user's experience and emotional feedback are stored on the server and used for future disaster response.

[0498] Administrators can manually operate breakwaters or modify evacuation instructions in emergencies. This flexibility allows the system to respond quickly to on-site needs.

[0499] This system configuration makes it possible to support safe and effective evacuation while taking into account the individual psychological state of each user. This invention utilizes an emotion engine and generative AI to maximize evacuation efficiency during disasters, providing life protection and peace of mind.

[0500] The following describes the processing flow.

[0501] Step 1:

[0502] The server receives disaster information in real time from earthquake and weather monitoring systems. This information includes the earthquake's epicenter and the predicted height of the tsunami.

[0503] Step 2:

[0504] The server inputs the collected disaster data into a generating AI for prediction and analysis. The generating AI determines the likelihood of tsunamis and flood risks, and assesses the extent of their impact.

[0505] Step 3:

[0506] Based on the analysis results, the server automatically decides whether or not to raise the movable breakwater and sends an instruction to the breakwater management system. This instruction is activated when signs of a tsunami are detected.

[0507] Step 4:

[0508] The server collects real-time information on the occupancy status of evacuation shelters and road traffic, and passes it to the generating AI. Based on this data, the generating AI selects the optimal evacuation route and shelter.

[0509] Step 5:

[0510] The terminal receives evacuation information sent from the server and notifies the user. Evacuation routes are displayed along with a map, and safe routes are shown along with estimated arrival times.

[0511] Step 6:

[0512] The emotion engine built into the device senses the user's emotional state. It uses voice recognition and facial recognition technology to assess the user's stress and anxiety.

[0513] Step 7:

[0514] The device provides psychological support to the user based on the evaluation of its emotion engine. For example, it offers messages that promote relaxation and voice guidance that provides a sense of security.

[0515] Step 8:

[0516] Users follow the instructions on the device and take safe evacuation actions. After evacuation, they can input their thoughts and problems as feedback on the device.

[0517] Step 9:

[0518] The server collects and stores user feedback and actual evacuation data, and uses this to improve the algorithms of the generating AI, thereby enhancing the accuracy of future disaster response.

[0519] (Example 2)

[0520] 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."

[0521] During natural disasters, ensuring the speed and safety of evacuation is crucial. However, conventional systems have suffered from insufficient real-time collection and analysis of disaster information, as well as a lack of optimization of evacuation routes and shelter information. Furthermore, they lacked sufficient mechanisms to alleviate the psychological burden on users, making it difficult to encourage appropriate evacuation actions. Therefore, this invention aims to address these issues, realize efficient evacuation support during disasters, and ensure the safety and mental well-being of users.

[0522] 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.

[0523] In this invention, the server includes means for receiving and analyzing disaster information in real time, means for automatically controlling movable barriers, and means for evaluating the emotional state of users and providing psychological support using an emotion engine. This makes it possible to promote rapid and safe evacuation actions during disasters and reduce the psychological stress on users.

[0524] "Disaster information" refers to data about events related to natural disasters, such as earthquakes, weather, tsunamis, and floods, which are received and analyzed in real time.

[0525] "Methods of analysis" refers to the process of using AI technology to generate data based on received disaster information, in order to derive disaster risks and optimal evacuation actions.

[0526] A "movable barrier" is a physical structure installed to mitigate damage from tsunamis, floods, and other disasters, and is controlled automatically or manually during a disaster.

[0527] "User equipment" refers to communication devices that users carry or use, including smartphones and tablets, that receive evacuation route and shelter information.

[0528] "Generative AI technology" refers to techniques that use artificial intelligence to analyze data and generate optimal evacuation actions and psychological support, such as natural language processing models.

[0529] The "emotion engine" is a system that analyzes the user's voice input and facial expression data to evaluate their psychological state in real time, and is used to measure stress levels and anxiety.

[0530] "Psychological support" refers to information and advice provided to reduce anxiety among users during disasters and to promote calm evacuation behavior.

[0531] This invention is a system that supports the safe and rapid evacuation of users during natural disasters, and includes real-time information analysis and psychological support. The system mainly includes the following elements:

[0532] The server receives earthquake and weather information in real time using the Japan Meteorological Agency's API. The received information is stored in a database and input into a generative AI model for analysis. The generative AI model used here is equipped with natural language processing technology and has the ability to assess disaster risk and propose optimal evacuation actions. If a tsunami or flood risk is determined, the server activates movable barriers via a control system.

[0533] The terminal functions as a user device such as a smartphone or tablet. While receiving evacuation route and shelter information from the server, the terminal also uses its built-in microphone and camera to acquire user voice and facial expression data. This data is analyzed by an emotion engine within the terminal and used to evaluate the user's psychological state.

[0534] Users can receive optimized evacuation routes and messages for psychological support displayed on their device screen. If the emotion engine determines that the user's stress level is high, the generating AI will provide situation-appropriate advice. Specific messages might include phrases like, "Please stay calm. The current evacuation route is safe."

[0535] Through this system, users are expected to be able to take appropriate actions with peace of mind even in the event of a disaster. Furthermore, user feedback will be used to improve the system in the future, enabling the provision of more accurate information.

[0536] As a concrete example, prompts such as, "Generate the optimal evacuation instructions in the event of an earthquake. A tsunami warning has been issued, and selecting an evacuation route is urgent. Please also include a message that will reassure the user," are input to the generation AI model, and the system then proposes the most appropriate evacuation action for the user.

[0537] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0538] Step 1:

[0539] The server receives disaster information in real time. The input consists of earthquake and weather data, which are obtained using APIs from meteorological agencies. The received data is stored in a database on the server. The server then inputs this data into a generating AI model to perform tsunami and flood risk analysis. The analysis results in a risk level assessment. Based on this output, the server determines whether barriers need to be activated.

[0540] Step 2:

[0541] The server acquires current occupancy rates and traffic information for evacuation shelters. It uses data from each evacuation shelter management system and real-time data obtained through traffic information provider APIs as input. Using this input data, the server calculates the optimal evacuation routes and shelters using a generated AI model. The optimized evacuation routes and shelter list are output, and this information is sent to the user's terminal.

[0542] Step 3:

[0543] The user's device receives evacuation information transmitted from the server. The device displays this information on its screen and notifies the user. The device also uses its built-in microphone and camera to capture the user's voice and facial expressions. This data is input into an emotion engine to evaluate the user's psychological state. Based on this evaluation, if necessary, a generative AI generates psychological support messages and displays them on the device.

[0544] Step 4:

[0545] Users begin their actions by following the evacuation route guidance and support messages displayed on their device. During the evacuation, users can input feedback on their device based on their emotions and experiences. The input feedback is sent to the server and stored as data for system improvement. The server uses this feedback as training data for the generated AI model to determine future improvements.

[0546] Step 5:

[0547] Administrators manually adjust the system as needed. Considering the situation during a disaster, administrators manually control barriers and modify evacuation information from a dedicated management screen. Changes made by administrators are immediately reflected on the server, allowing information to be quickly transmitted to user terminals.

[0548] (Application Example 2)

[0549] 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."

[0550] During a disaster, it is crucial to quickly and accurately ensure the safety of customers and staff within a store. In particular, evacuating in a crowded store requires accurate information provision and psychological support. Conventional systems have struggled to adequately consider real-time changing situations and individual psychological states during evacuation guidance. Therefore, there is a need to provide a system that enables swift and safe evacuation by providing a sense of security tailored to the customer's psychological state.

[0551] 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.

[0552] In this invention, the server includes means for receiving and analyzing disaster information in real time, means for automatically controlling a mobile disaster response device, means for notifying user information terminals of evacuation directions and evacuation facility information, means for analyzing and presenting optimal actions during a disaster using generating AI, means for updating the system based on disaster information and evacuation feedback, means for evaluating the customer's psychological state and providing reassuring messages to promote calm behavior, and means for presenting the current congestion status in the store and routes to exits. As a result, even in crowded store environments, detailed evacuation guidance tailored to the customer's psychological state becomes possible, enabling safe and effective disaster response.

[0553] "Means for receiving and analyzing disaster information in real time" refers to technologies that immediately receive information when a disaster occurs and analyze its contents.

[0554] "Means for automatically controlling mobile disaster response devices" refers to technology that operates devices that are automatically driven and used to mitigate damage in the event of a disaster.

[0555] "Means for notifying user information terminals of evacuation direction and evacuation facility information" refers to technology that transmits information about the user's direction of movement and available evacuation facilities to the terminal when evacuation is necessary.

[0556] "Means for analyzing and presenting optimal actions during disasters using generative AI" refers to a technology that utilizes AI technology to predict the most appropriate actions during a disaster and inform users of them.

[0557] "Methods for updating systems based on disaster information and evacuation feedback" refer to technologies that improve systems in preparation for future disasters based on collected disaster-related data and reviews of evacuations.

[0558] "A means of evaluating the customer's psychological state and providing reassuring messages to promote calm behavior" refers to a technology that analyzes the customer's emotions, generates and provides messages that enhance their sense of security, and thereby encourages calm behavior.

[0559] "Means of displaying the current congestion status inside the store and routes to the exit" refers to technology that analyzes the flow of people and the degree of congestion inside the store and displays directions to the exit.

[0560] In this invention, the server first receives disaster information in real time and performs analysis. In this process, external services such as weather sensor APIs and earthquake data APIs are used to acquire disaster-related data. Upon receiving this data, the server uses a generative AI model to perform predictions and analysis and evaluate the progression of the situation.

[0561] Based on the analysis results, the server automatically controls mobile disaster response equipment. This control utilizes network technology capable of operating IoT devices, enabling the rapid activation of breakwaters, shutters, and other equipment. Furthermore, the server notifies users of evacuation directions and evacuation facility information on their information terminals. This notification is delivered via the user's smartphone or smart glasses, and because the terminal has the Google Maps API built in, users can visually confirm safe evacuation routes from their current location.

[0562] The user's device is equipped with an emotion engine that uses voice input and facial recognition to assess their psychological state. If a high-stress state is detected, a generative AI creates a message that provides appropriate reassurance and displays it on the device. AI chatbot technology is used to generate this message, enabling real-time, personalized responses. For example, in a shopping mall, the user might be instructed to "calm down and head towards the exit."

[0563] Furthermore, the server constantly aggregates disaster information and evacuation feedback from users, updating the system for future disaster response. This feedback prompts the generating AI model with improvement suggestions, prompting it to ask "what should be improved for the next disaster?", thereby providing more accurate disaster countermeasures.

[0564] As a concrete example, smart glasses operate in a crowded store and issue warnings based on prompts such as "Show a safe route" and "Send a reassuring message." This system can support safe evacuation even in crowded environments.

[0565] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0566] Step 1:

[0567] The server receives disaster information in real time from external weather sensor APIs and earthquake data APIs. Based on this input data, it extracts weather conditions, earthquake magnitude, and location, and stores them in a database.

[0568] Step 2:

[0569] The server activates a generative AI model to analyze the received disaster information. Stored weather and earthquake data are used as input. The generative AI model analyzes this data, calculates the probability of tsunamis occurring and the risk of floods, and outputs the results.

[0570] Step 3:

[0571] The server sends control signals to IoT devices to automatically control the mobile disaster response equipment. Based on the risk assessment output by the generated AI model, it automatically decides whether to operate the equipment. This then executes specific actions, such as raising the breakwater.

[0572] Step 4:

[0573] The server generates evacuation directions and evacuation facility information and notifies the user's information terminal. It uses the Google Maps API as input to obtain the user's current location and calculates the shortest route to the exit. The result is output to the user's terminal to guide them to safety during evacuation.

[0574] Step 5:

[0575] The user's device activates an emotion engine and collects data from the user's voice input and facial recognition via the camera. This allows the system to evaluate the user's psychological state and process the input data to output a stress level.

[0576] Step 6:

[0577] The server receives information about the user's psychological state and uses a generated AI chatbot to create reassuring messages. The output includes messages such as "Please remain calm," which are sent to the user's device in real time.

[0578] Step 7:

[0579] The server builds a database to update the entire system based on feedback information obtained during the evacuation. Using prompt messages, it takes user feedback such as "What should be improved next time?" and generates updated data to prepare for future disasters.

[0580] 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.

[0581] 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.

[0582] 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.

[0583] [Fourth Embodiment]

[0584] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0585] 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.

[0586] 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).

[0587] 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.

[0588] 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.

[0589] 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).

[0590] 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.

[0591] 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.

[0592] 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.

[0593] 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.

[0594] 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.

[0595] 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.

[0596] 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".

[0597] This invention provides an integrated system that enables rapid and safe evacuation during natural disasters. The embodiments thereof are described below.

[0598] The server receives disaster information in real time from earthquake and weather monitoring systems. This information includes, for example, the earthquake's epicenter, time of occurrence, predicted tsunami height, type of weather warning, and affected area. Based on this data, a generating AI analyzes the scale and extent of the disaster.

[0599] If the analysis predicts a tsunami, the server will instruct the movable breakwater system to rise. This allows for the prevention of tsunami intrusion in advance. In addition, depending on the disaster, the server collects up-to-date data such as the location of evacuation centers, the capacity of each evacuation center, and road traffic conditions.

[0600] The terminal receives information transmitted from the server and displays evacuation routes and destinations to the user in real time. This includes using map data and generating AI to suggest the best route. For example, if the usual route is congested or the road is damaged, it can suggest an alternative route.

[0601] Users can take swift and safe evacuation actions by following instructions received through their devices. Furthermore, administrators can manually control the raising of breakwaters and the distribution of evacuation information. This operation is used to deal with unexpected situations or when additional confirmation of the AI's decisions is required.

[0602] After the evacuation, feedback from users and administrators is collected and stored in the server's database. This information will be used to enable the generated AI to make more accurate decisions during future disasters, contributing to the overall improvement of the system.

[0603] With this configuration, the present invention realizes an advanced disaster prevention system aimed at rapid evacuation and protection of human lives.

[0604] The following describes the processing flow.

[0605] Step 1:

[0606] The server receives disaster information in real time from earthquake and weather monitoring systems. This information includes the epicenter, magnitude, and forecast weather conditions.

[0607] Step 2:

[0608] The server passes disaster information to the generating AI, which then analyzes whether a tsunami will occur and the risk of flooding based on this information. The generating AI predicts the scale and extent of the disaster based on past data.

[0609] Step 3:

[0610] The server automatically sends an instruction to raise the movable breakwater based on the prediction results. This is done especially when the predicted tsunami height exceeds a certain threshold.

[0611] Step 4:

[0612] The server aggregates the latest data, including shelter capacity, current congestion levels, and traffic information, and the generating AI analyzes the optimal evacuation route and destination.

[0613] Step 5:

[0614] The terminal receives evacuation information from the server and displays evacuation routes and destinations to the user in real time. Travel time and route instructions are presented along with map data.

[0615] Step 6:

[0616] Users act according to evacuation instructions received via their devices. If necessary, administrators can manually raise the breakwater or adjust evacuation instructions.

[0617] Step 7:

[0618] After the evacuation is complete, the server collects user feedback and stores it in a database. This feedback will be used to inform future decisions by the AI ​​and help improve the system.

[0619] (Example 1)

[0620] 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".

[0621] In recent years, natural disasters have become more frequent due to climate change and increased seismic activity. Consequently, there is a need for systems that enable rapid and effective evacuation. Conventional systems often lack integration, with disaster information collection and analysis, evacuation guidance provision, and disaster prevention device control being handled separately. Furthermore, errors in judgment can lead to erroneous instructions that could have fatal consequences for evacuees. Against this backdrop, the challenge lies in providing an integrated disaster prevention system that enables accurate real-time information analysis and a rapid response based on that analysis.

[0622] 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.

[0623] In this invention, the server includes an information processing device means for receiving disaster information in real time and performing data analysis, a control device means for automatically controlling a mobile disaster prevention device, and a communication device means for providing evacuation guidance and evacuation facility information to the user's information terminal. This enables immediate data analysis and accurate presentation of relief measures in the event of a disaster, as well as the implementation of automatic and manual disaster prevention measures by the system.

[0624] An "information processing device" is a device that receives disaster information in real time, analyzes that information, and determines the scale and impact of the disaster.

[0625] A "control device" is a device that automatically controls movable disaster prevention equipment based on analyzed information and takes necessary disaster prevention measures.

[0626] A "communication device" is a device that provides analysis results, evacuation instructions, and evacuation facility information to users' information terminals in real time.

[0627] An "analysis device" is a device that uses a generated artificial intelligence model to calculate the optimal response measures during a disaster and presents them to the user.

[0628] An "improvement device" is a device that updates the entire system based on disaster information and post-evacuation evaluation information, with the aim of improving performance in the event of a future disaster.

[0629] An "operating device" is a device that allows operators to manually control the control system and issue evacuation instructions.

[0630] A "suggestion device" is a device that integrates detailed information about evacuation facilities and proposes the optimal evacuation destination to users based on the generated artificial intelligence model.

[0631] This invention is a comprehensive system for supporting rapid and safe evacuation during disasters. To implement this system, the server, terminals, and users each play specific roles. Specific embodiments are described below.

[0632] First, the server uses an information processing device to receive and analyze earthquake and weather data in real time. During this process, it utilizes a database management system (e.g., MySQL) to store the received data and, if necessary, uses a generated AI model (e.g., a model using TensorFlow) to analyze the data. Based on the analysis results, it quickly determines the scale and extent of the disaster.

[0633] Next, the server uses a control device to automatically operate disaster prevention equipment, such as a movable breakwater, based on the analysis results. The server also transmits the analysis results and necessary evacuation information to the terminal via a communication device. The terminal then presents the user with the optimal evacuation route calculated by a generated AI model based on the received information.

[0634] The terminal uses a communication device to provide users with evacuation guidance and information on evacuation facilities. It has the function to display the user's location, the capacity of evacuation shelters, and traffic information in real time using a map application (e.g., Google Maps API). This enables users to take quick and appropriate evacuation actions. For example, in areas with many hills, it will suggest a route that suits the user's physical ability. Another example of a prompt sentence to be input into the generating AI model is, "Please suggest the optimal evacuation route in the event of a flood forecast due to heavy rain."

[0635] Furthermore, after evacuation is complete, users provide feedback to the server. The server utilizes improved equipment to accumulate this feedback and use it to update the entire system. This will enable more accurate decision-making in the event of the next disaster.

[0636] In this way, the entire system functions as an integrated whole, enabling rapid and safe protection of human lives during disasters.

[0637] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0638] Step 1:

[0639] The server receives earthquake and weather data in real time from the monitoring system. The input data includes earthquake epicenters and times, predicted tsunami heights, weather warning types, and affected areas. After receiving this input data, it is stored in a database and prepared for analysis using a generated AI model. The output is the raw data to be analyzed.

[0640] Step 2:

[0641] The server uses a generated AI model based on the received data to analyze the scale and extent of the disaster. The input is the raw data obtained in step 1, and data processing involves comparative analysis with historical data. This outputs specific analysis results such as whether a tsunami is predicted and which areas are at risk.

[0642] Step 3:

[0643] Based on the analysis results, the server uses a control device to automatically operate a movable disaster prevention device, such as a breakwater. The input is the analysis results from step 2, which are output as a control signal, automatically issuing an instruction to raise the breakwater. In addition, new information such as the location and capacity of evacuation shelters and traffic conditions is collected.

[0644] Step 4:

[0645] The server uses a communication device to send the analysis results and collected evacuation information to the terminal. The input is the evacuation route and shelter information generated in step 3, and the output is notification data containing this information. This notification data is transmitted in real time.

[0646] Step 5:

[0647] The terminal receives notification data sent from the server and displays the optimal evacuation route and shelter information to the user. The input is the data sent in step 4, and route calculation is performed by a generating AI model. This outputs a specific evacuation route to the user and visualizes it on a map.

[0648] Step 6:

[0649] The user takes swift and safe evacuation actions based on the evacuation information displayed on the terminal. Input is information from the terminal, and output is the user's evacuation actions. The user also provides feedback to the system after completing the evacuation.

[0650] Step 7:

[0651] The server receives feedback from users and administrators and stores it in a database. The input is feedback data, which is then analyzed and incorporated into the system. The output is adjustment information that contributes to improving the accuracy of future analyses.

[0652] (Application Example 1)

[0653] 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".

[0654] In the event of a natural disaster, there is a need to provide real-time information to enable people to evacuate quickly and safely, suggest optimal evacuation routes, and effectively control movable breakwaters. However, current systems do not adequately provide personalized evacuation routes to individual users or improve the system based on feedback after evacuation. It is necessary to solve these problems and realize safer and more efficient evacuations.

[0655] 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.

[0656] In this invention, the server includes means for receiving and analyzing disaster information in real time, means for automatically controlling a movable breakwater, means for notifying user terminals of evacuation routes and shelter information, means for analyzing and presenting optimal actions during a disaster using generated AI, means for updating the system based on disaster information and evacuation feedback, means for immediately providing the optimal evacuation route based on the user's location data, and means for collecting user feedback after evacuation and using it to improve the accuracy of the system. This makes it possible to propose evacuation routes optimized for each user and to continuously improve the system using feedback.

[0657] "Means for receiving and analyzing disaster information in real time" refers to a system that continuously acquires data related to the occurrence of disasters and immediately analyzes that information to grasp the situation and scale of the disaster.

[0658] "Means for automatically controlling movable breakwaters" refers to a system that mechanically operates breakwaters in response to predicted disasters, thereby minimizing damage in advance.

[0659] "Means for notifying user terminals of evacuation routes and evacuation shelter information" refers to communication technology that informs users' terminals of information regarding the optimal evacuation route and evacuation destination.

[0660] "Means for analyzing and presenting optimal actions during disasters using generative AI" refers to a technology that utilizes artificial intelligence to analyze and advise on the most appropriate evacuation methods and actions during a disaster.

[0661] "Methods for updating the system based on disaster information and evacuation feedback" refers to the process of improving the system by utilizing collected disaster data and feedback from evacuees, thereby enhancing the ability to respond to future disasters.

[0662] "A means of providing the optimal evacuation route immediately based on the user's location data" refers to a technology that uses the user's location information to quickly suggest the most suitable evacuation route.

[0663] "Means for collecting user feedback after evacuation and using it to improve system accuracy" refers to technologies that analyze feedback received from users after evacuation is completed to improve the accuracy of future disaster response.

[0664] To implement this invention, it is necessary to provide a system in which a server, a user terminal, and a generative AI model work in cooperation. The server utilizes data feeds provided by the Japan Meteorological Agency and earthquake monitoring organizations to receive disaster information in real time. This allows for immediate analysis of earthquake and tsunami information and the generation of control instructions for movable breakwaters based on the generative AI model.

[0665] The user's device will receive notifications about evacuation routes and shelters. These notifications are based on direct feedback from the server and are updated in real time. When a user receives evacuation information through their device, the optimal route is generated and displayed, taking into account obstacles and traffic conditions.

[0666] The generative AI model is built using machine learning libraries such as TensorFlow and is responsible for analyzing and suggesting optimal actions during disasters. This model makes predictions and optimizations in real time based on the user's location data and suggests immediately useful evacuation routes. Furthermore, the AI ​​uses feedback collected from users after evacuation is completed as training data to improve the system's accuracy.

[0667] A concrete example is the process by which a user checks their location on their device during an earthquake and evacuates safely to a designated shelter. In this case, the generating AI might receive the following prompt: "Generate a safe evacuation route based on the location of the earthquake and the predicted tsunami height." Such specific instructions can enhance safety and efficiency during disasters.

[0668] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0669] Step 1:

[0670] The server acquires disaster information. The server receives earthquake and weather information in real time from data feeds such as the Japan Meteorological Agency, and acquires information such as the epicenter and tsunami height. Based on this information, it performs data analysis and generates necessary control instructions.

[0671] Step 2:

[0672] The server performs analysis using a generated AI model. The acquired disaster information is used as input, and the generated AI model analyzes the affected area and the scale of the disaster. This results in the output of specific control instructions regarding the operation of the movable breakwater.

[0673] Step 3:

[0674] The server generates evacuation route information and sends it to the user's terminal. Based on the analysis results by the generating AI, the optimal evacuation route is created, taking into account current traffic conditions and the capacity of evacuation shelters. The generated route information is sent to the terminal and notified to the user.

[0675] Step 4:

[0676] The user uses a device to check the evacuation route and take action. The user refers to the map and evacuation route displayed on the device and takes action to safely head to the designated evacuation shelter. The device receives the user's location information and updates the route in real time.

[0677] Step 5:

[0678] After the evacuation is complete, the user sends feedback to the server. The user inputs their opinions and experiences regarding the quality and route of the evacuation from their device and sends them to the server. This feedback is used as training data for the AI ​​model.

[0679] Step 6:

[0680] The server uses feedback to improve the system. It analyzes the collected feedback data and retrains the AI ​​model. This improves the system's accuracy, enabling it to provide more appropriate evacuation routes and information in the event of a future disaster.

[0681] 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.

[0682] This invention is an advanced system for supporting the safe and rapid evacuation of users during natural disasters. In particular, it is characterized by its use of an emotion engine to provide appropriate evacuation support that takes into account the user's psychological state.

[0683] The server first receives earthquake and weather data in real time and uses a generating AI to analyze the risk of tsunamis and floods. Based on the analysis, it issues instructions to raise movable breakwaters as needed. At this stage, the server collects real-time information on the current occupancy status of evacuation centers and traffic conditions, and uses the generating AI to identify the optimal evacuation routes and evacuation centers.

[0684] Users receive this information through their devices and obtain instructions on evacuation routes and shelters. Furthermore, an emotion engine built into the device evaluates the user's emotional state in real time through voice input and facial recognition. In particular, if stress levels or anxiety levels are high, the generated AI provides psychological support and advice tailored to the situation.

[0685] For example, if the emotion engine detects a user's anxiety, the device will display a message to the user such as, "Please stay calm and evacuate; your safety is assured." Furthermore, the user's experience and emotional feedback are stored on the server and used for future disaster response.

[0686] Administrators can manually operate breakwaters or modify evacuation instructions in emergencies. This flexibility allows the system to respond quickly to on-site needs.

[0687] This system configuration makes it possible to support safe and effective evacuation while taking into account the individual psychological state of each user. This invention utilizes an emotion engine and generative AI to maximize evacuation efficiency during disasters, providing life protection and peace of mind.

[0688] The following describes the processing flow.

[0689] Step 1:

[0690] The server receives disaster information in real time from earthquake and weather monitoring systems. This information includes the earthquake's epicenter and the predicted height of the tsunami.

[0691] Step 2:

[0692] The server inputs the collected disaster data into a generating AI for prediction and analysis. The generating AI determines the likelihood of tsunamis and flood risks, and assesses the extent of their impact.

[0693] Step 3:

[0694] Based on the analysis results, the server automatically decides whether or not to raise the movable breakwater and sends an instruction to the breakwater management system. This instruction is activated when signs of a tsunami are detected.

[0695] Step 4:

[0696] The server collects real-time information on the occupancy status of evacuation shelters and road traffic, and passes it to the generating AI. Based on this data, the generating AI selects the optimal evacuation route and shelter.

[0697] Step 5:

[0698] The terminal receives evacuation information sent from the server and notifies the user. Evacuation routes are displayed along with a map, and safe routes are shown along with estimated arrival times.

[0699] Step 6:

[0700] The emotion engine built into the device senses the user's emotional state. It uses voice recognition and facial recognition technology to assess the user's stress and anxiety.

[0701] Step 7:

[0702] The device provides psychological support to the user based on the evaluation of its emotion engine. For example, it offers messages that promote relaxation and voice guidance that provides a sense of security.

[0703] Step 8:

[0704] Users follow the instructions on the device and take safe evacuation actions. After evacuation, they can input their thoughts and problems as feedback on the device.

[0705] Step 9:

[0706] The server collects and stores user feedback and actual evacuation data, and uses this to improve the algorithms of the generating AI, thereby enhancing the accuracy of future disaster response.

[0707] (Example 2)

[0708] 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".

[0709] During natural disasters, ensuring the speed and safety of evacuation is crucial. However, conventional systems have suffered from insufficient real-time collection and analysis of disaster information, as well as a lack of optimization of evacuation routes and shelter information. Furthermore, they lacked sufficient mechanisms to alleviate the psychological burden on users, making it difficult to encourage appropriate evacuation actions. Therefore, this invention aims to address these issues, realize efficient evacuation support during disasters, and ensure the safety and mental well-being of users.

[0710] 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.

[0711] In this invention, the server includes means for receiving and analyzing disaster information in real time, means for automatically controlling movable barriers, and means for evaluating the emotional state of users and providing psychological support using an emotion engine. This makes it possible to promote rapid and safe evacuation actions during disasters and reduce the psychological stress on users.

[0712] "Disaster information" refers to data about events related to natural disasters, such as earthquakes, weather, tsunamis, and floods, which are received and analyzed in real time.

[0713] "Methods of analysis" refers to the process of using AI technology to generate data based on received disaster information, in order to derive disaster risks and optimal evacuation actions.

[0714] A "movable barrier" is a physical structure installed to mitigate damage from tsunamis, floods, and other disasters, and is controlled automatically or manually during a disaster.

[0715] "User equipment" refers to communication devices that users carry or use, including smartphones and tablets, that receive evacuation route and shelter information.

[0716] "Generative AI technology" refers to techniques that use artificial intelligence to analyze data and generate optimal evacuation actions and psychological support, such as natural language processing models.

[0717] The "emotion engine" is a system that analyzes the user's voice input and facial expression data to evaluate their psychological state in real time, and is used to measure stress levels and anxiety.

[0718] "Psychological support" refers to information and advice provided to reduce anxiety among users during disasters and to promote calm evacuation behavior.

[0719] This invention is a system that supports the safe and rapid evacuation of users during natural disasters, and includes real-time information analysis and psychological support. The system mainly includes the following elements:

[0720] The server receives earthquake and weather information in real time using the Japan Meteorological Agency's API. The received information is stored in a database and input into a generative AI model for analysis. The generative AI model used here is equipped with natural language processing technology and has the ability to assess disaster risk and propose optimal evacuation actions. If a tsunami or flood risk is determined, the server activates movable barriers via a control system.

[0721] The terminal functions as a user device such as a smartphone or tablet. While receiving evacuation route and shelter information from the server, the terminal also uses its built-in microphone and camera to acquire user voice and facial expression data. This data is analyzed by an emotion engine within the terminal and used to evaluate the user's psychological state.

[0722] Users can receive optimized evacuation routes and messages for psychological support displayed on their device screen. If the emotion engine determines that the user's stress level is high, the generating AI will provide situation-appropriate advice. Specific messages might include phrases like, "Please stay calm. The current evacuation route is safe."

[0723] Through this system, users are expected to be able to take appropriate actions with peace of mind even in the event of a disaster. Furthermore, user feedback will be used to improve the system in the future, enabling the provision of more accurate information.

[0724] As a concrete example, prompts such as, "Generate the optimal evacuation instructions in the event of an earthquake. A tsunami warning has been issued, and selecting an evacuation route is urgent. Please also include a message that will reassure the user," are input to the generation AI model, and the system then proposes the most appropriate evacuation action for the user.

[0725] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0726] Step 1:

[0727] The server receives disaster information in real time. The input consists of earthquake and weather data, which are obtained using APIs from meteorological agencies. The received data is stored in a database on the server. The server then inputs this data into a generating AI model to perform tsunami and flood risk analysis. The analysis results in a risk level assessment. Based on this output, the server determines whether barriers need to be activated.

[0728] Step 2:

[0729] The server acquires current occupancy rates and traffic information for evacuation shelters. It uses data from each evacuation shelter management system and real-time data obtained through traffic information provider APIs as input. Using this input data, the server calculates the optimal evacuation routes and shelters using a generated AI model. The optimized evacuation routes and shelter list are output, and this information is sent to the user's terminal.

[0730] Step 3:

[0731] The user's device receives evacuation information transmitted from the server. The device displays this information on its screen and notifies the user. The device also uses its built-in microphone and camera to capture the user's voice and facial expressions. This data is input into an emotion engine to evaluate the user's psychological state. Based on this evaluation, if necessary, a generative AI generates psychological support messages and displays them on the device.

[0732] Step 4:

[0733] Users begin their actions by following the evacuation route guidance and support messages displayed on their device. During the evacuation, users can input feedback on their device based on their emotions and experiences. The input feedback is sent to the server and stored as data for system improvement. The server uses this feedback as training data for the generated AI model to determine future improvements.

[0734] Step 5:

[0735] Administrators manually adjust the system as needed. Considering the situation during a disaster, administrators manually control barriers and modify evacuation information from a dedicated management screen. Changes made by administrators are immediately reflected on the server, allowing information to be quickly transmitted to user terminals.

[0736] (Application Example 2)

[0737] 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".

[0738] During a disaster, it is crucial to quickly and accurately ensure the safety of customers and staff within a store. In particular, evacuating in a crowded store requires accurate information provision and psychological support. Conventional systems have struggled to adequately consider real-time changing situations and individual psychological states during evacuation guidance. Therefore, there is a need to provide a system that enables swift and safe evacuation by providing a sense of security tailored to the customer's psychological state.

[0739] 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.

[0740] In this invention, the server includes means for receiving and analyzing disaster information in real time, means for automatically controlling a mobile disaster response device, means for notifying user information terminals of evacuation directions and evacuation facility information, means for analyzing and presenting optimal actions during a disaster using generating AI, means for updating the system based on disaster information and evacuation feedback, means for evaluating the customer's psychological state and providing reassuring messages to promote calm behavior, and means for presenting the current congestion status in the store and routes to exits. As a result, even in crowded store environments, detailed evacuation guidance tailored to the customer's psychological state becomes possible, enabling safe and effective disaster response.

[0741] "Means for receiving and analyzing disaster information in real time" refers to technologies that immediately receive information when a disaster occurs and analyze its contents.

[0742] "Means for automatically controlling mobile disaster response devices" refers to technology that operates devices that are automatically driven and used to mitigate damage in the event of a disaster.

[0743] "Means for notifying user information terminals of evacuation direction and evacuation facility information" refers to technology that transmits information about the user's direction of movement and available evacuation facilities to the terminal when evacuation is necessary.

[0744] "Means for analyzing and presenting optimal actions during disasters using generative AI" refers to a technology that utilizes AI technology to predict the most appropriate actions during a disaster and inform users of them.

[0745] "Methods for updating systems based on disaster information and evacuation feedback" refer to technologies that improve systems in preparation for future disasters based on collected disaster-related data and reviews of evacuations.

[0746] "A means of evaluating the customer's psychological state and providing reassuring messages to promote calm behavior" refers to a technology that analyzes the customer's emotions, generates and provides messages that enhance their sense of security, and thereby encourages calm behavior.

[0747] "Means of displaying the current congestion status inside the store and routes to the exit" refers to technology that analyzes the flow of people and the degree of congestion inside the store and displays directions to the exit.

[0748] In this invention, the server first receives disaster information in real time and performs analysis. In this process, external services such as weather sensor APIs and earthquake data APIs are used to acquire disaster-related data. Upon receiving this data, the server uses a generative AI model to perform predictions and analysis and evaluate the progression of the situation.

[0749] Based on the analysis results, the server automatically controls mobile disaster response equipment. This control utilizes network technology capable of operating IoT devices, enabling the rapid activation of breakwaters, shutters, and other equipment. Furthermore, the server notifies users of evacuation directions and evacuation facility information on their information terminals. This notification is delivered via the user's smartphone or smart glasses, and because the terminal has the Google Maps API built in, users can visually confirm safe evacuation routes from their current location.

[0750] The user's device is equipped with an emotion engine that uses voice input and facial recognition to assess their psychological state. If a high-stress state is detected, a generative AI creates a message that provides appropriate reassurance and displays it on the device. AI chatbot technology is used to generate this message, enabling real-time, personalized responses. For example, in a shopping mall, the user might be instructed to "calm down and head towards the exit."

[0751] Furthermore, the server constantly aggregates disaster information and evacuation feedback from users, updating the system for future disaster response. This feedback prompts the generating AI model with improvement suggestions, prompting it to ask "what should be improved for the next disaster?", thereby providing more accurate disaster countermeasures.

[0752] As a concrete example, smart glasses operate in a crowded store and issue warnings based on prompts such as "Show a safe route" and "Send a reassuring message." This system can support safe evacuation even in crowded environments.

[0753] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0754] Step 1:

[0755] The server receives disaster information in real time from external weather sensor APIs and earthquake data APIs. Based on this input data, it extracts weather conditions, earthquake magnitude, and location, and stores them in a database.

[0756] Step 2:

[0757] The server activates a generative AI model to analyze the received disaster information. Stored weather and earthquake data are used as input. The generative AI model analyzes this data, calculates the probability of tsunamis occurring and the risk of floods, and outputs the results.

[0758] Step 3:

[0759] The server sends control signals to IoT devices to automatically control the mobile disaster response equipment. Based on the risk assessment output by the generated AI model, it automatically decides whether to operate the equipment. This then executes specific actions, such as raising the breakwater.

[0760] Step 4:

[0761] The server generates evacuation directions and evacuation facility information and notifies the user's information terminal. It uses the Google Maps API as input to obtain the user's current location and calculates the shortest route to the exit. The result is output to the user's terminal to guide them to safety during evacuation.

[0762] Step 5:

[0763] The user's device activates an emotion engine and collects data from the user's voice input and facial recognition via the camera. This allows the system to evaluate the user's psychological state and process the input data to output a stress level.

[0764] Step 6:

[0765] The server receives information about the user's psychological state and uses a generated AI chatbot to create reassuring messages. The output includes messages such as "Please remain calm," which are sent to the user's device in real time.

[0766] Step 7:

[0767] The server builds a database to update the entire system based on feedback information obtained during the evacuation. Using prompt messages, it takes user feedback such as "What should be improved next time?" and generates updated data to prepare for future disasters.

[0768] 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.

[0769] 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.

[0770] 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.

[0771] 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.

[0772] 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.

[0773] 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.

[0774] 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.

[0775] 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.

[0776] 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."

[0777] 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.

[0778] 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.

[0779] 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.

[0780] 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.

[0781] 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.

[0782] 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.

[0783] 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.

[0784] 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.

[0785] 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.

[0786] 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.

[0787] 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.

[0788] 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.

[0789] The following is further disclosed regarding the embodiments described above.

[0790] (Claim 1)

[0791] A means of receiving and analyzing disaster information in real time,

[0792] A means of automatically controlling a movable breakwater,

[0793] A means of notifying the user terminal of evacuation routes and evacuation shelter information,

[0794] A means of analyzing and presenting the optimal actions to take during a disaster using generative AI,

[0795] A means of updating the system based on disaster information and evacuation feedback.

[0796] A system that includes this.

[0797] (Claim 2)

[0798] The system according to claim 1, which allows an administrator to manually activate breakwater control and evacuation information.

[0799] (Claim 3)

[0800] The system according to claim 1, which aggregates real-time information on evacuation shelters and proposes the optimal evacuation destination based on generated AI.

[0801] "Example 1"

[0802] (Claim 1)

[0803] An information processing device that receives disaster information in real time and performs data analysis,

[0804] A control device for automatically controlling a movable disaster prevention device,

[0805] A communication device for providing evacuation guidance and evacuation facility information to users' information terminals,

[0806] An analytical device means for calculating and presenting the optimal response measures during a disaster using a generated artificial intelligence model,

[0807] An improved device for updating the system based on received disaster information and post-evacuation assessment information.

[0808] A system that includes this.

[0809] (Claim 2)

[0810] The system according to claim 1, which has an operating device that allows the operator to activate the control of disaster prevention equipment and provide evacuation guidance through manual operation.

[0811] (Claim 3)

[0812] A suggestion device for integrating detailed information on evacuation facilities and proposing the optimal evacuation destination based on generated artificial intelligence, according to claim 1.

[0813] "Application Example 1"

[0814] (Claim 1)

[0815] A means of receiving and analyzing disaster information in real time,

[0816] A means of automatically controlling a movable breakwater,

[0817] A means of notifying the user terminal of evacuation routes and evacuation shelter information,

[0818] A means of analyzing and presenting the optimal actions to take during a disaster using generative AI,

[0819] A means of updating the system based on disaster information and evacuation feedback,

[0820] A means of instantly providing the optimal evacuation route based on the user's location data,

[0821] A means of collecting user feedback after evacuation and using it to improve the accuracy of the system.

[0822] A system that includes this.

[0823] (Claim 2)

[0824] The system according to claim 1, which allows an administrator to manually activate breakwater control and evacuation information.

[0825] (Claim 3)

[0826] The system according to claim 1, which aggregates real-time information on evacuation shelters and proposes the optimal evacuation destination based on generated AI.

[0827] "Example 2 of combining an emotion engine"

[0828] (Claim 1)

[0829] A means of receiving and analyzing disaster information in real time,

[0830] A means of automatically controlling a movable barrier,

[0831] A means for notifying the user device of evacuation routes and evacuation shelter information,

[0832] A means of analyzing and presenting the optimal actions to take during a disaster using generative AI technology,

[0833] A means of evaluating the emotional state of users and providing psychological support using an emotion engine,

[0834] A means of updating the system based on evacuation experiences and emotional feedback,

[0835] A system that includes this.

[0836] (Claim 2)

[0837] The system according to claim 1, which allows an administrator to manually control barriers and evacuation information.

[0838] (Claim 3)

[0839] The system according to claim 1, which aggregates real-time information on evacuation shelters and proposes the optimal evacuation destination based on generation AI technology.

[0840] "Application example 2 when combining with an emotional engine"

[0841] (Claim 1)

[0842] A means of receiving and analyzing disaster information in real time,

[0843] A means for automatically controlling a mobile disaster response device,

[0844] A means for notifying the user's information terminal of evacuation direction and evacuation facility information,

[0845] A means of analyzing and presenting the optimal actions to take during a disaster using generative AI,

[0846] A means of updating the system based on disaster information and evacuation feedback,

[0847] A means of assessing the customer's psychological state and providing reassuring messages to encourage calm behavior,

[0848] A means of displaying the current congestion level inside the store and directions to the exit.

[0849] A system that includes this.

[0850] (Claim 2)

[0851] The system according to claim 1, which allows an administrator to manually activate disaster response device control and evacuation information.

[0852] (Claim 3)

[0853] The system according to claim 1, which aggregates real-time information on evacuation facilities and proposes the optimal evacuation destination based on generated AI. [Explanation of Symbols]

[0854] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of receiving and analyzing disaster information in real time, A means of automatically controlling a movable breakwater, A means of notifying the user terminal of evacuation routes and evacuation shelter information, A means of analyzing and presenting the optimal actions to take during a disaster using generative AI, A means of updating the system based on disaster information and evacuation feedback. A system that includes this.

2. The system according to claim 1, which allows an administrator to manually activate breakwater control and evacuation information.

3. The system according to claim 1, which aggregates real-time information on evacuation shelters and proposes the optimal evacuation destination based on generated AI.

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