system

Abstract

A system is provided.SOLUTION: A disaster prevention system for supporting prompt and appropriate evacuation action for a user in a disaster emergency, the system including means for monitoring and acquiring disaster information, GPS means for specifying a current position of the user, means for acquiring hazard information based on the acquired disaster information and the current position, means for calculating an optimal evacuation route based on the acquired hazard information and the current position, and chatbot means for voice-guiding the calculated evacuation route to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the 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] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] In disaster emergencies, even though appropriate initial actions can directly affect lives, many people panic and are unable to take evacuation actions based on accurate information. Furthermore, because optimal evacuation actions and evacuation locations vary depending on the type of disaster, the extent of damage, and the current location, a system that provides accurate information in real time is required. Furthermore, support is also needed to suppress panic during disasters and enable people to take evacuation actions calmly. The purpose of this invention is to solve these issues and support rapid and appropriate evacuation actions in disaster emergencies. [Means for solving the problem]

[0005] The present invention is a disaster prevention system for supporting users in taking prompt and appropriate evacuation actions in the event of a disaster emergency. Specifically, the system includes a means for monitoring and acquiring disaster information, a GPS means for identifying the user's current location, a means for acquiring hazard information based on the acquired disaster information and the user's current location, a means for calculating an optimal evacuation route based on the acquired hazard information and the current location, and a chatbot means for providing voice guidance to the user along the calculated evacuation route. The system also includes a means for acquiring and analyzing damage status in real time from disaster prevention cameras and drone footage, thereby grasping the disaster situation in real time and issuing appropriate evacuation instructions. The voice guidance provides the user with specific instructions to evacuation sites, supporting calm and effective evacuation actions.

[0006] "Disaster information" refers to any data or alerts related to the occurrence or progression of a disaster.

[0007] "Current location" refers to the user's actual geographic location as determined using location technology such as GPS.

[0008] "GPS Means" means a means for determining and identifying current location using the Global Positioning System (GPS).

[0009] "Hazard information" refers to data including map data and sensor information regarding the extent of the impact and risk of a disaster.

[0010] An "evacuation route" refers to a specific route or direction that a user can use to evacuate to a safe place in the event of a disaster.

[0011] "Chatbot means" refers to a means for providing voice or text guidance or instructions to a user using a dialogue system that includes artificial intelligence.

[0012] "Disaster prevention cameras" refer to cameras installed to monitor and record disaster situations in real time.

[0013] "Drone footage" refers to real-time footage of disaster damage and the scene taken from an unmanned aerial vehicle (drone).

[0014] "AI analysis" refers to a method of analyzing and calculating evacuation routes and damage situations using data analysis technology that uses artificial intelligence. [Brief explanation of the drawings]

[0015] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0016] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0017] First, the terms used in the following description will be explained.

[0018] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0019] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0020] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0021] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0022] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0023] [First embodiment]

[0024] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0025] 1, a 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.

[0026] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0027] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0028] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the 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.

[0029] 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 of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0030] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0031] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process 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.

[0033] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0034] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0035] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0036] This invention relates to a disaster prevention system that supports users in taking prompt and appropriate evacuation actions in the event of a disaster. This system, through the cooperative operation of a server and terminals, monitors and acquires disaster information, identifies the user's current location, acquires hazard information, calculates evacuation routes, and provides guidance via a voice chatbot.

[0037] The server constantly monitors disaster information notified during emergencies, and if a new disaster occurs, it immediately acquires and processes that information. It also uses the GPS function of the device to identify the user's current location, allowing the system to accurately track the user's location.

[0038] The server obtains optimal hazard information based on the user's current location and disaster information. Hazard information includes information on the extent of the disaster's impact, the level of danger, and evacuation routes. Based on this information, the server performs AI analysis and calculates the safest evacuation route for the user. At this time, real-time footage of the damage situation obtained from disaster prevention cameras and drones can also be included in the analysis, enabling immediate response.

[0039] The calculated evacuation route is then provided to the user via voice guidance via the device. By using a voice chatbot, users can receive calm instructions even in an emergency. For example, specific instructions such as, "Go to the nearest emergency exit from your current location and use the stairs to go down to the first floor. From there, turn right and evacuate to a safe evacuation site" are provided.

[0040] Specific examples

[0041] For example, consider a case where an earthquake occurs and the user is on the third floor of a high-rise building.

[0042] 1. Obtaining disaster information:

[0043] The server instantly receives emergency earthquake alerts and processes the information.

[0044] 2. Current location:

[0045] The device's GPS function determines that the user's current location is on the third floor of a high-rise building.

[0046] 3. Obtaining hazard information and calculating evacuation routes:

[0047] The server retrieves the latest information from the hazard map based on the user's current location, and analyzes real-time disaster prevention camera and drone footage to determine the extent of damage to the building. This information is used to calculate the safest evacuation route. Specifically, the system calculates a route that will guide the user down to the first floor using the emergency stairs, then out of the building and to a safe evacuation site.

[0048] 4. Voice guidance:

[0049] The voice chatbot on the device guides the user based on the calculated evacuation route, providing specific instructions such as "Use the emergency stairs to go down to the first floor, then exit through the right-hand exit and head to the designated evacuation location," allowing the user to follow the instructions and evacuate safely.

[0050] In this way, by linking each means, the disaster prevention system of the present invention can support quick and appropriate evacuation actions even in the event of a disaster emergency, thereby ensuring the safety of users.

[0051] The processing flow will be explained below.

[0052] Program processing flow

[0053] Step 1: Loading the initial settings

[0054] The server loads the initial settings of the user's disaster prevention AI system, including settings to enable GPS, hazard maps, AI analysis, and voice chatbot functions.

[0055] When a user launches an app, the device enables each function based on the initial settings.

[0056] Step 2: Disaster detection

[0057] The server monitors the emergency alert system and obtains information when a new disaster alert occurs.

[0058] The terminal notifies the user of the acquired alert information.

[0059] Step 3: Get current location

[0060] The device uses GPS to determine the user's current location, which is then immediately sent to the server.

[0061] The server prepares to calculate hazard information and evacuation routes based on the current location obtained.

[0062] Step 4: Obtaining damage and hazard information

[0063] The server obtains the latest hazard information related to the user's current location from the hazard map system.

[0064] In addition, disaster prevention camera and drone footage will be analyzed to grasp the damage situation in real time.

[0065] Step 5: Calculate the optimal evacuation route

[0066] The server performs AI analysis based on the acquired current location, hazard information, and real-time damage status to calculate the optimal evacuation route.

[0067] For example, it calculates specific routes to evacuate to a safe location using emergency stairs in a building.

[0068] Step 6: Guidance by voice chatbot

[0069] The device activates the voice chatbot and starts guiding the user on evacuation routes.

[0070] The server generates instructions for each step of the calculated evacuation route.

[0071] The device reads out the generated instructions and guides the user in real time, helping the user to take each step from their current location to the evacuation site.

[0072] Specific examples of processing

[0073] Scenario: Evacuation in the event of an earthquake

[0074] 1. Step 1: Loading the initial settings

[0075] The server loads settings that enable GPS, hazard maps, AI analysis, and voice chatbot features.

[0076] The device launches the app and enables each function.

[0077] 2. Step 2: Disaster detection

[0078] The server detects emergency earthquake alerts and obtains the information.

[0079] The terminal notifies the user of the acquired alert information.

[0080] 3. Step 3: Get current location

[0081] The device uses GPS to determine the user's current location, and this location information is sent to the server.

[0082] 4. Step 4: Obtain damage and hazard information

[0083] The server obtains the latest hazard information about the area around the building from the hazard map.

[0084] In addition, disaster prevention camera and drone footage will be analyzed to determine the extent of damage to the building.

[0085] 5. Step 5: Calculate the optimal evacuation route

[0086] The server uses AI analysis to calculate the optimal evacuation route based on the user's current location, acquired hazard information, and damage situation.

[0087] For example, a route is calculated to use the emergency stairs to descend to the first floor and then evacuate to a safe location.

[0088] 6. Step 6: Guidance by voice chatbot

[0089] The device will launch a voice chatbot that will instruct the user, "Please evacuate immediately using the emergency stairs. First, head to the emergency door on your right and use the stairs to go down to the first floor. Then, exit through the exit on your right and head to the designated evacuation location."

[0090] The device provides specific voice instructions for each step of the evacuation route.

[0091] Example 1

[0092] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0093] In the event of a disaster emergency, it may be difficult for users to take prompt and appropriate evacuation actions. If disaster information is not obtained or the user's current location is not identified promptly, the user may take incorrect evacuation actions, which could result in an even more dangerous situation. Furthermore, if real-time acquisition of disaster and hazard information is insufficient, it is difficult to calculate an appropriate evacuation route, and the user's safety cannot be ensured. Furthermore, if the evacuation route is presented ambiguously, the user may not be able to evacuate appropriately. These issues need to be resolved.

[0094] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0095] In this invention, the server includes means for monitoring and acquiring disaster information, means for identifying the user's current location, means for acquiring hazard information based on the acquired disaster information and the current location, means for calculating an optimal evacuation route based on the acquired hazard information and the current location, and interactive guidance means for providing voice guidance to the user along the calculated evacuation route, thereby supporting quick and appropriate evacuation actions even in a disaster emergency and ensuring the user's safety.

[0096] "Disaster information" refers to information relating to emergencies such as earthquakes, floods, and fires, including the occurrence, progression, and extent of impact of the disaster.

[0097] A "location information determination means" is a device or system that uses technology such as GPS to accurately determine the user's current location.

[0098] "Hazard information" refers to detailed information about the extent of a disaster's impact, the level of danger, and evacuation routes.

[0099] An "evacuation route" is a specific route that a user should follow to evacuate safely, and usually refers to the shortest and safest route.

[0100] An "interactive guide" is an interactive system that provides instructions to a user using voice or text, and guides the user to specific actions.

[0101] An "image acquisition means" is a device or system that uses a camera, drone, etc. to capture images of the surrounding situation in real time and analyzes them.

[0102] An "AI model" is an analytical method that uses artificial intelligence, and is an algorithm that makes predictions and optimizations based on large amounts of data.

[0103] This invention relates to a disaster prevention system that supports users in taking prompt and appropriate evacuation actions in the event of a disaster. This system, through the cooperative operation of a server and terminals, monitors and acquires disaster information, identifies the user's current location, acquires hazard information, calculates evacuation routes, and provides guidance via a voice chatbot.

[0104] The server constantly monitors disaster information, and when a new disaster occurs, it immediately acquires and processes that information. Specifically, it uses APIs provided by public institutions such as the Japan Meteorological Agency to collect the latest disaster information and stores it in an internal database. This allows the server to analyze the type and urgency of the disaster and generate alerts as necessary.

[0105] The user's current location is determined using the device's GPS function. The device utilizes its built-in GPS module to obtain the user's precise location information and transmit it to the server. For example, if the user is on the third floor of a high-rise building, the coordinate data will be transmitted to the server, and the next analysis step will begin.

[0106] The server collects hazard information based on the user's current location and disaster information. Specifically, it uses a hazard map database, real-time disaster prevention camera footage, and drone footage to obtain information on the extent of the disaster's impact, the level of danger, and evacuation routes. Based on this information, the server uses an AI model to calculate the optimal evacuation route.

[0107] The AI ​​model uses a specific predictive algorithm as a generative AI model to provide users with the safest and quickest evacuation route, and the results are presented in a format that makes it easy for users to take action.

[0108] After calculating the evacuation route, the device uses a voice chatbot to provide guidance to the user. Based on the calculation results, the chatbot will provide specific instructions by voice, such as which direction the user should go and which stairs to use, allowing the user to act calmly even in an emergency.

[0109] As a concrete example, consider a situation where an earthquake occurs and the user is on the third floor of a high-rise building. In this case, the server receives an emergency earthquake alert and uses the device's GPS function to identify the user's current location. The server then analyzes real-time disaster prevention camera and drone footage and calculates a safe evacuation route. The device uses a voice chatbot to provide the user with specific instructions, such as "Use the emergency stairs to go down to the first floor, then exit through the exit on your right."

[0110] Below are some example prompts using a generative AI model:

[0111] Suppose an earthquake occurs and a user is on the third floor of a high-rise building. Calculate the safest evacuation route. The device's GPS information is used to determine the user's current location, and the server obtains information from real-time disaster prevention camera footage and hazard maps to calculate the optimal evacuation route. Then, provide the user with instructions via a voice chatbot, such as "Use the emergency stairs to go down to the first floor, then exit through the exit on the right."

[0112] In this way, by linking each means, the disaster prevention system of the present invention can support quick and appropriate evacuation actions even in the event of a disaster emergency, thereby ensuring the safety of users.

[0113] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0114] Step 1:

[0115] The server monitors and acquires disaster information.

[0116] Input: Disaster information from public institutions and disaster prevention-related APIs.

[0117] Data processing: Disaster information is analyzed and stored in an internal database. The urgency and type of disaster are evaluated.

[0118] Output: Parsed disaster information and alerts.

[0119] Specific operation: The server periodically calls the Japan Meteorological Agency's API to obtain the latest disaster information. It then stores the obtained disaster information in a database, analyzes its contents, and evaluates its urgency. If necessary, it generates an alert and notifies the entire system.

[0120] Step 2:

[0121] The device uses GPS functionality to identify the user's current location.

[0122] Input: GPS data from the device.

[0123] Data processing: Analyze GPS data to obtain latitude and longitude location information.

[0124] Output: The user's current location.

[0125] Specific operation: The device uses the built-in GPS module to obtain the user's location information. After the location information is determined, it is sent to the server in the form of latitude and longitude.

[0126] Step 3:

[0127] The server acquires hazard information based on the disaster information and current location information acquired.

[0128] Input: Disaster information, user's current location information.

[0129] Data processing: Analyze hazard maps and real-time disaster prevention camera and drone footage to assess the level of risk and the extent of impact.

[0130] Output: Latest hazard information.

[0131] How it works: The server collects hazard information from an internal database and external sources (disaster prevention cameras, drone footage), then uses AI analysis to obtain the latest hazard information related to the user's current location.

[0132] Step 4:

[0133] The server calculates the optimal evacuation route based on the latest hazard information.

[0134] Input: Hazard information, user's current location information.

[0135] Data processing: Calculate safe evacuation routes using generative AI models.

[0136] Output: Optimal evacuation route.

[0137] Specific operation: The server inputs the acquired hazard information and the user's current location information into the AI ​​model to calculate a safe and quick evacuation route. The calculation results are then output as detailed route guidance for the user to take specific actions.

[0138] Step 5:

[0139] The terminal uses an interactive guidance means for providing voice guidance of the calculated evacuation route.

[0140] Input: Optimal evacuation route.

[0141] Data processing: Convert evacuation route information into voice instructions.

[0142] Output: Voice guidance.

[0143] Specific operation: The device analyzes the evacuation route information received from the server and provides guidance to the user through a voice chatbot. For example, specific instructions are provided via voice, such as "Use the emergency stairs to go down to the first floor, then exit through the exit on the right."

[0144] At each step, the system collects the necessary data and uses data analysis and AI models to generate detailed instructions to assist users in optimal evacuation behavior, thereby ensuring their safety in the event of a disaster.

[0145] (Application example 1)

[0146] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0147] In order for users to take appropriate and prompt evacuation actions during disaster emergencies, they need to obtain disaster information in real time, calculate the optimal evacuation route based on their current location, and provide accurate and specific guidance. However, conventional disaster prevention systems have issues with the speed of information acquisition and the accuracy of guidance, and there are no systems that can handle transportation methods such as autonomous vehicles. This has led to the problem of not being able to fully ensure user safety during disaster emergencies.

[0148] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0149] In this invention, the server includes means for monitoring and acquiring disaster information, means for determining the user's current location, means for acquiring danger information based on the acquired disaster information and the user's current location, means for calculating an optimal evacuation route based on the acquired danger information and the user's current location, an interactive guidance means for providing voice guidance to the user along the calculated evacuation route, and means for acquiring and analyzing damage status in real time from images captured by disaster prevention monitoring devices and autonomous aircraft devices. This enables real-time information acquisition and analysis, calculation of the optimal evacuation route, and specific and accurate voice guidance in the event of a disaster. This system is also compatible with autonomous vehicles, providing support for rapid and appropriate evacuation behavior to ensure the safety of passengers.

[0150] A "disaster emergency" is a situation in which an earthquake, fire, flood, or other natural or man-made disaster occurs and immediate evacuation action is required.

[0151] A "user" is a general public or a specific person who uses the disaster prevention system to take prompt and appropriate evacuation action.

[0152] "Quick and appropriate evacuation actions" are actions that will allow people to reach evacuation shelters in the most efficient and safe manner when a disaster occurs.

[0153] A "disaster prevention system" is a set of devices and software that provides functions such as monitoring, acquiring, and analyzing disaster information, calculating evacuation routes, and providing guidance to support users in the event of a disaster emergency.

[0154] "Disaster information" refers to information about natural and man-made disasters such as earthquakes, fires, and floods.

[0155] "Monitoring and acquisition means" refers to technologies and devices for continuously observing and collecting disaster information in real time.

[0156] "Location measurement means for identifying the current location" refers to technology and devices for determining the current location of a user or object using location information technology such as GPS.

[0157] "Means for acquiring risk information" refers to the technology and devices for collecting and analyzing information about the disaster situation and the extent of damage.

[0158] The "means for calculating an optimal evacuation route" refers to a technology and device for calculating the safest and most efficient evacuation route for a user based on real-time on-site conditions.

[0159] "Interactive guidance means" refers to technology and devices that provide specific instructions and guidance to users through voice and text.

[0160] "Disaster prevention monitoring equipment" refers to devices such as cameras and sensors that monitor the occurrence of disasters in real time.

[0161] "Autonomous Aircraft Footage" means real-time footage captured by an autonomously flying device, such as a drone.

[0162] "Means for acquiring and analyzing damage status" refers to technology and equipment for collecting and analyzing damage status at the site in real time.

[0163] This invention relates to a disaster prevention system that supports users in taking prompt and appropriate evacuation actions in the event of a disaster. This system is realized through the cooperative operation of a server, terminals, and multiple dedicated devices and software.

[0164] First, the server monitors disaster information in real time and quickly obtains it when a disaster occurs. Disaster information includes data on natural and man-made disasters such as earthquakes, fires, and floods. This disaster information is obtained using APIs provided by specialized disaster monitoring organizations and local disaster prevention systems.

[0165] The device then uses GPS to determine the user's current location, allowing it to determine exactly where the user is and calculate the most appropriate evacuation route.The device is also installed in mobile devices such as the user's smartphone or an in-car system.

[0166] The server obtains danger information based on the acquired disaster information and the user's current location information. This danger information includes the extent of the disaster's impact, the level of danger, the current damage situation on site, and information on evacuation routes. Video data transmitted in real time from disaster prevention monitoring devices and autonomous aircraft devices (drones) is used to obtain the danger information. This makes it possible to calculate the optimal evacuation route based on the latest information on site.

[0167] The calculated evacuation route is then given to the user via voice guidance. An interactive guidance system is used to provide specific instructions to help the user stay calm. For example, a specific prompt sentence such as "We will guide you to the evacuation route. Turn right and proceed to a safe evacuation location" is generated.

[0168] The entire system is implemented using the Python programming language, with API access using the requests library, location data calculation using the geopy library, and voice synthesis technology used to generate voice guidance, effectively carrying out a series of complex data processing and user guidance.

[0169] As a specific example of operation, if an earthquake occurs and the user is in an autonomous vehicle, the server obtains earthquake information in real time and determines the vehicle's current location. It then calculates the optimal evacuation route and issues instructions to the autonomous driving system through the vehicle's on-board system. Specific evacuation instructions are provided to passengers through audio guidance and display. This series of operations allows the user to evacuate quickly and safely.

[0170] Examples of prompts include:

[0171] Get the latest earthquake information.

[0172] Check the current vehicle location using GPS.

[0173] "Calculate the best route based on your current location and nearby safe evacuation locations."

[0174] "Please provide voice instructions for evacuation routes. Instruct them to 'turn right and proceed to a safe evacuation area.'"

[0175] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0176] Step 1:

[0177] The server monitors and acquires disaster information in real time.

[0178] (Input) Real-time disaster information (earthquakes, fires, floods, etc.) provided by various disaster monitoring organizations and disaster prevention systems

[0179] (Data processing) Collect the latest disaster data using disaster information API

[0180] (Output) Update the internal database that holds the latest disaster information

[0181] Specifically, the server uses the requests library to periodically retrieve data from disaster information providers and store it in an internal database.

[0182] Step 2:

[0183] The terminal uses the GPS function to determine the user's current location.

[0184] (Input) GPS data from the user's device (smartphone or in-car system)

[0185] (Data processing) Use the GPS function to obtain the user's latitude and longitude

[0186] (Output) Current location of the user (latitude and longitude)

[0187] Specifically, the device uses its built-in GPS module to obtain current location data (latitude and longitude).

[0188] Step 3:

[0189] The server obtains danger information based on the acquired disaster information and the user's current location information.

[0190] (Input) Latest disaster information and user's current location information

[0191] (Data processing) Integrate disaster information and user location information, and perform search and filter processing to extract relevant danger information from the danger information database.

[0192] (Output) Risk information related to the user's current location

[0193] Specifically, the server retrieves danger information related to a specific area through a database query based on disaster information and location information.

[0194] Step 4:

[0195] The server calculates the optimal evacuation route based on the acquired danger information and the current location.

[0196] (Input) Risk information and user's current location information

[0197] (Data processing) Route calculation algorithm that calculates the shortest and safest route taking into account geographical information and disaster conditions

[0198] (Output) Optimal evacuation route

[0199] Specifically, the server uses a map database and a route calculation algorithm to calculate the optimal route from the user's current location to a safe evacuation site.

[0200] Step 5:

[0201] The terminal provides the user with voice guidance on the calculated evacuation route.

[0202] (Input) Calculated evacuation route

[0203] (Data processing) Convert evacuation route information into voice guidance prompts and generate voice data using voice synthesis technology

[0204] (Output) Voice guidance instructions

[0205] Specifically, the terminal generates instructions in the interactive guidance system based on the calculated evacuation route, converts them into voice using a speech synthesis engine, and provides guidance to the user.

[0206] Step 6:

[0207] The server acquires and analyzes damage information in real time from disaster prevention monitoring equipment and autonomous aircraft equipment footage.

[0208] (Input) Real-time video data from disaster prevention monitoring devices and autonomous aircraft devices (drones, etc.)

[0209] (Data processing) Analysis of video data, understanding of disaster situation, identification of damage extent

[0210] (Output) Latest damage situation information

[0211] Specifically, the server analyzes video data sent from disaster prevention cameras and drones, determines the extent and situation of the damage, and updates the danger information database.

[0212] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0213] This invention is a disaster prevention system that helps users take prompt and appropriate evacuation actions in the event of a disaster emergency, and in particular has the function of recognizing the user's emotional state and providing appropriate guidance and psychological support based on that. This system, through the cooperative operation of a server and terminal, monitors and acquires disaster information, identifies the user's current location, acquires hazard information, calculates evacuation routes, recognizes emotions using an emotion engine, and provides guidance using a voice chatbot.

[0214] Server and terminal roles

[0215] The server constantly monitors disaster information notified in emergencies, and when a new disaster occurs, it immediately acquires and processes that information. It also uses the GPS function of the device to pinpoint the user's current location, accurately determining where the user is.

[0216] Hazard information is obtained based on the user's current location and disaster information. This includes information on the extent of the disaster's impact, the level of risk, and evacuation routes. The obtained hazard information and real-time disaster prevention camera and drone footage are analyzed, and the safest evacuation route for the user is calculated using AI analysis.

[0217] Emotion Engine Functions

[0218] The system also features an emotion engine that recognizes the user's emotional state in real time. This emotion engine can grasp the user's stress level and emotional state by analyzing the user's facial expressions, voice tone, and even physical movements. If the emotion engine detects the user's state of panic or high stress, it dynamically adjusts the tone and content of the voice guidance to help the user stay calm.

[0219] The emotion engine also provides relaxation guidance and psychological support. For example, if a user is experiencing extreme panic, the voice chatbot can provide relaxation guidance such as "Take a deep breath, slowly calm down, and everything will be okay," thereby reducing the user's stress.

[0220] Specific examples

[0221] As a specific example, consider a case where an earthquake occurs and the user is on the third floor of a high-rise building.

[0222] 1. Obtaining disaster information:

[0223] The server instantly receives emergency earthquake alerts and processes the information.

[0224] 2. Current location:

[0225] The GPS function of the device determines that the user's current location is on the third floor of a high-rise building.

[0226] 3. Obtaining hazard information and calculating evacuation routes:

[0227] The server retrieves the latest information from the hazard map based on the user's current location and analyzes real-time disaster prevention camera and drone footage to determine the extent of damage to the building. This information is used to calculate the safest evacuation route. Specifically, the system calculates a route that will lead the user down the emergency stairs to the first floor, then out of the building and to a safe evacuation site.

[0228] 4. Emotion recognition and guidance using emotion engine:

[0229] The device's emotion engine analyzes the user's facial expressions and voice tone in real time to understand their emotional state. If the user is in a panic, the emotion engine softens the tone of the voice guidance and provides relaxation instructions. Specific and calm instructions are provided via voice, such as "Take a deep breath and stay calm. Use the emergency stairs to go down to the first floor. Exit through the exit on the right and head to the designated evacuation site."

[0230] In this way, the disaster prevention system of the present invention can ensure the safety of users by supporting prompt and appropriate evacuation behavior even in the event of a disaster and encouraging them to evacuate calmly while taking into account the user's emotional state. This system is expected to enable users to act calmly and evacuate effectively even in an emergency.

[0231] The processing flow will be explained below.

[0232] Program processing flow

[0233] Step 1: Loading the initial settings

[0234] The server loads the initial settings of the user's disaster prevention AI system, including settings to enable GPS, hazard maps, AI analysis, emotion engines, and voice chatbot functions.

[0235] When a user launches an app, the device enables each function based on the initial settings.

[0236] Step 2: Disaster detection

[0237] The server monitors the emergency alert system and immediately obtains information about any new disaster alerts that occur.

[0238] The terminal notifies the user of the acquired alert information.

[0239] Step 3: Get current location

[0240] The device uses GPS to determine the user's current location, and this information is sent to the server in real time.

[0241] The server prepares to calculate hazard information and evacuation routes based on the current location obtained.

[0242] Step 4: Obtaining damage and hazard information

[0243] The server obtains the latest hazard information related to the user's current location from the hazard map system.

[0244] In addition, disaster prevention camera and drone footage will be analyzed to grasp the damage situation in real time.

[0245] Step 5: Calculate the optimal evacuation route

[0246] The server performs AI analysis based on the acquired current location, hazard information, and real-time damage status to calculate the optimal evacuation route.

[0247] For example, it calculates specific routes to evacuate to a safe location using emergency stairs in a building.

[0248] Step 6: Start Emotion Recognition

[0249] The device activates an emotion engine to analyze the user's emotional state in real time, judging from facial expressions, voice tone, and body movements to determine whether the user is in a panic state.

[0250] Step 7: Guidance by voice chatbot

[0251] The device activates the voice chatbot and starts guiding the user on evacuation routes.

[0252] The server generates instructions for each step of the calculated evacuation route.

[0253] The device dynamically adjusts the tone and content of the voice guidance based on the analysis results of the emotion engine. For example, if the user is in a panic, it generates and provides specific instructions, including relaxation guidance such as "Take a deep breath and calm down."

[0254] Specific examples

[0255] Scenario: Evacuation in the event of an earthquake

[0256] 1. Step 1: Loading the initial settings

[0257] The server loads settings that enable GPS, hazard maps, AI analysis, emotion engines, and voice chatbots.

[0258] The device launches the app and enables each function.

[0259] 2. Step 2: Disaster detection

[0260] The server detects emergency earthquake alerts and retrieves the information immediately.

[0261] The terminal notifies the user of the acquired alert information.

[0262] 3. Step 3: Get current location

[0263] The device uses GPS to determine the user's current location, and this location information is sent to the server.

[0264] 4. Step 4: Obtain damage and hazard information

[0265] The server obtains the latest hazard information about the area around the building from the hazard map.

[0266] In addition, disaster prevention camera and drone footage will be analyzed to determine the extent of damage to the building.

[0267] 5. Step 5: Calculate the optimal evacuation route

[0268] The server uses AI analysis to calculate the optimal evacuation route based on the user's current location, acquired hazard information, and damage situation.

[0269] For example, a route is calculated to use the emergency stairs to descend to the first floor and then evacuate to a safe location.

[0270] 6. Step 6: Start Emotion Recognition

[0271] The device's emotion engine analyzes the user's emotional state in real time, determining whether the user is panicking based on facial expressions and tone of voice.

[0272] 7. Step 7: Guidance by voice chatbot

[0273] The device will launch a voice chatbot that will instruct the user, "Please evacuate immediately using the emergency stairs. First, head to the emergency door on your right and use the stairs to go down to the first floor. Then, exit through the exit on your right and head to the designated evacuation location."

[0274] Based on the analysis results of the emotion engine, the device will soften the tone of the voice guidance and provide specific instructions, including relaxation advice such as "Take a deep breath and stay calm. It's okay." In this way, the device helps the user calmly grasp the situation and take appropriate evacuation actions.

[0275] Example 2

[0276] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0277] In the event of a disaster or emergency, it is extremely important for users to take prompt and appropriate evacuation actions. However, systems that efficiently integrate multiple elements, such as obtaining disaster information, calculating evacuation routes, and identifying the user's current location, and that also provide psychological support according to the user's emotional state, are still insufficient. Providing appropriate guidance is particularly difficult for users who are in a state of panic or high stress. Therefore, a comprehensive disaster prevention system that enables users to take appropriate evacuation actions while remaining calm is needed.

[0278] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0279] In this invention, the server includes a means for monitoring and acquiring disaster information, a location information acquisition means for identifying the user's current location, and a means for acquiring hazard information based on the acquired disaster information and current location. This allows for the calculation of a quick and appropriate evacuation route based on the user's current location information and the provision of voice guidance. The system also includes an automated dialogue system for providing voice guidance to the user along the calculated evacuation route, an emotion recognition engine for recognizing the user's emotional state in real time, and a means for dynamically adjusting the guidance content and guidance tone based on the recognized emotional state. This allows the user to take calm and effective evacuation actions even during a disaster, thereby ensuring the user's overall safety.

[0280] "Disaster information" refers to information related to natural disasters, including the occurrence status and extent of impact of earthquakes, tsunamis, typhoons, floods, etc., as well as emergency warnings.

[0281] "Location information acquisition means" refers to technology or equipment for identifying the user's current location, and primarily refers to a location identification system that uses GPS.

[0282] "Hazard information" refers to detailed information about the danger areas and scope of impact caused by disasters, the need for evacuation, etc.

[0283] "Means for calculating the optimal evacuation route" refers to a method or system for calculating the safest and quickest evacuation route for a user based on the acquired information.

[0284] An "automated dialogue system" is a system that provides information through dialogue with a user, and is particularly intended to give instructions to users through voice guidance or chatbots.

[0285] An "emotion recognition engine" refers to technology or software that analyzes a user's facial expressions, tone of voice, body movements, etc. to recognize the user's emotional state (e.g., panic, calm, stress, etc.) in real time.

[0286] "Means for acquiring and analyzing damage situations in real time" refers to methods and technologies for acquiring video data sent from disaster prevention cameras, unmanned aerial vehicles, etc. in real time and analyzing the extent of damage and the scope of impact.

[0287] "Specific instructions at each step provided by voice guidance" refers to a method of providing detailed and specific voice guidance for each step, such as directions to travel by car and walking routes, in order to clearly communicate evacuation routes to the user.

[0288] This invention is a disaster prevention system that helps users take prompt and appropriate evacuation actions in the event of a disaster. This system, through the cooperative operation of a server and terminals, monitors and acquires disaster information, identifies current locations, acquires hazard information, calculates evacuation routes, recognizes emotions using an emotion engine, and provides guidance using a voice chatbot.

[0289] Hardware and software used

[0290] server

[0291] Hardware: High-performance database and analysis servers

[0292] Software: Database management system (e.g., MySQL (registered trademark)), disaster information acquisition API (e.g., Japan Meteorological Agency API), AI analysis engine (e.g., TENSORFLOW (registered trademark))

[0293] Terminal

[0294] Hardware: Smartphone or tablet with GPS sensor, camera, and microphone

[0295] Software: GPS location acquisition software, voice recognition programs (e.g., Google® Speech-to-Text), emotion recognition engines

[0296] System Operation

[0297] 1. Monitoring and obtaining disaster information

[0298] The server constantly monitors various disaster information sources (e.g., the Japan Meteorological Agency, the Fire and Disaster Management Agency, etc.), and when new disaster information occurs, it immediately acquires the information and stores it in a database. It analyzes important parameters related to the disaster information (e.g., type of disaster, location of occurrence, scale, etc.) and evaluates its importance.

[0299] 2. Identifying your current location

[0300] The device uses the built-in GPS sensor to determine the user's current location, which is then periodically sent to a server. The GPS data is stored in the form of latitude and longitude coordinates, and is also subjected to a correction algorithm to improve accuracy.

[0301] 3. Acquisition and analysis of hazard information

[0302] The server obtains hazard information about the extent of the disaster impact and evacuation routes based on the user's current location, including analysis of hazard map databases, real-time disaster prevention camera footage, and drone footage, to identify high-risk areas.

[0303] 4. Evacuation route calculation

[0304] The server calculates the optimal evacuation route based on the acquired hazard information. The evacuation route is calculated using an AI algorithm (e.g., a pathfinding algorithm), and detailed instructions are prepared for each step of the evacuation route.

[0305] 5. Real-time recognition of emotional states

[0306] The device's camera and microphone are used to analyze the user's facial expressions and voice tone in real time. The emotion engine uses this data to determine the user's emotional state (e.g., panic, calm, stress, etc.) and sends that information to the server.

[0307] 6. Providing appropriate guidance and psychological support

[0308] Based on the evacuation route information sent from the server and the user's emotional state as recognized by the emotion engine, the device dynamically provides guidance to the user. The voice chatbot provides relaxation instructions and specific evacuation instructions in a gentle tone.

[0309] Specific examples

[0310] Below is a specific example where an earthquake occurs and the user is on the third floor of a high-rise building.

[0311] 1. Obtaining disaster information

[0312] The server instantly receives emergency earthquake alerts and processes the information.

[0313] 2. Identifying your current location

[0314] The device's GPS function determines that the user's current location is on the third floor of a high-rise building.

[0315] 3. Obtaining hazard information and calculating evacuation routes

[0316] The server analyzes the latest hazard information and real-time footage from disaster prevention cameras and drones to calculate the safest evacuation route, guiding the user to the first floor via the emergency stairs, then out of the building and towards a safe evacuation site.

[0317] 4. Emotion recognition and guidance using an emotion engine

[0318] The device's emotion engine analyzes the user's facial expressions and voice tone in real time, and if the user is in a panic, it provides gentle voice guidance including instructions to relax. For example, it might say, "Take a deep breath and stay calm. Use the emergency stairs to go down to the first floor. Exit through the exit on your right and head to the designated evacuation area."

[0319] Prompt Sentence Examples

[0320] "Take a deep breath and stay calm. Use the emergency stairs to safely descend to the first floor. Exit through the exit on your right and proceed to the designated evacuation area."

[0321] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0322] Specific processing flow of the program

[0323] Step 1: Monitoring and obtaining disaster information

[0324] The server constantly monitors various disaster information sources (e.g., the Japan Meteorological Agency API, the Fire and Disaster Management Agency database, etc.) When new disaster information occurs, it immediately acquires that information and stores it in the database.

[0325] Input: New disaster data from disaster sources

[0326] Data processing: Analyze the type, location, and scale of disasters to assess their importance

[0327] Output: Disaster information stored in a database

[0328] Specific operation: Sends a request to the disaster information API, receives and analyzes new data, and registers it in the database.

[0329] Step 2: Determine your current location

[0330] The device uses the built-in GPS sensor to determine the user's current location, which is then periodically sent to a server.

[0331] Input: Location data (latitude and longitude) from the GPS sensor

[0332] Data processing: Position data is corrected using a correction algorithm to improve accuracy

[0333] Output: User's current location (latitude and longitude)

[0334] Specific operation: Enables the GPS function to obtain the current location and transmits it to the server in real time.

[0335] Step 3: Obtain and analyze hazard information

[0336] Based on the current location information sent, the server obtains and analyzes hazard information from hazard map databases, disaster prevention cameras, drone footage, etc.

[0337] Input: User's current location and data from hazard information sources

[0338] Data processing: Integrate and analyze risk information from hazard maps and real-time video data

[0339] Output: Hazard information based on current location

[0340] Specific operation: Data is obtained from the hazard map API, and the video data is analyzed using an image recognition algorithm to assess the level of danger.

[0341] Step 4: Calculate evacuation routes

[0342] The server uses an AI algorithm to calculate the optimal evacuation route based on the acquired hazard information and the transmitted current location information.

[0343] Input: Current location and hazard information

[0344] Data processing: Calculate the optimal route using a route search algorithm

[0345] Output: The optimal evacuation route for the user

[0346] Specific operation: Uses an AI algorithm (e.g., A algorithm) to calculate an evacuation route and send the results to the device.

[0347] Step 5: Real-time recognition of emotional states

[0348] The device uses a camera and microphone to analyze the user's facial expressions, voice tone, body movements, etc. in real time. The emotion engine recognizes the user's emotional state based on this data and sends it to the server.

[0349] Input: Real-time video and audio data from camera and microphone

[0350] Data processing: Analyzing emotions with facial and voice recognition algorithms

[0351] Output: User's emotional state data (e.g., panic, calm, etc.)

[0352] What it does: Facial expression analysis software and voice recognition programs analyze raw data to detect emotional states.

[0353] Step 6: Providing appropriate guidance and psychological support

[0354] Based on the evacuation route information and emotional state data sent from the server, the device dynamically provides guidance to the user through a voice chatbot, with the tone and content of the guidance adjusted according to the user's emotional state.

[0355] Input: Evacuation route information and emotional state data

[0356] Data processing: Dynamically adjust the content and tone of announcements according to emotional state

[0357] Output: Audio prompts provided to the user

[0358] Specific actions: Generate prompts such as "Take a deep breath and stay calm. Use the emergency stairs to go down to the first floor. Exit through the exit on your right and head to the designated evacuation location," and provide audio guidance to the user.

[0359] This is expected to enable users to remain calm and evacuate effectively in an emergency.

[0360] (Application example 2)

[0361] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0362] In the event of a disaster or emergency, it is extremely important for users to take prompt and appropriate evacuation actions. However, in emergencies, it is easy to fall into panic and confusion, making it difficult to make calm decisions. Furthermore, conventional disaster prevention systems generally only provide disaster information and evacuation routes, but do not provide support that takes into account the user's emotional state. Therefore, there is a need for a disaster prevention system that can recognize the user's emotional state in real time and provide appropriate guidance and psychological support.

[0363] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0364] In this invention, the server includes means for monitoring and acquiring disaster information, means for determining the user's current location, means for acquiring hazard information based on the acquired disaster information and the current location, means for calculating an optimal evacuation route based on the acquired hazard information and the current location, dialogue device means for providing voice guidance to the user along the calculated evacuation route, emotion recognition means for detecting the user's emotional state, and means for adjusting the content and tone of the guidance provided based on the user's emotional state. This allows the user to calmly take evacuation action and take appropriate measures even in an emergency.

[0365] "Disaster emergency" refers to the period of time when a natural disaster such as an earthquake, tsunami, fire, or flood, or a similar emergency occurs.

[0366] "User" refers to an individual or organization using this disaster prevention system.

[0367] "Evacuation behavior" refers to planned actions to move to a safe place.

[0368] "Disaster prevention system" refers to a comprehensive set of devices and means designed to assist users in ensuring their safety during disaster emergencies.

[0369] "Disaster information" refers to data and reports on the occurrence of a disaster, the extent of its impact, etc.

[0370] "Location measurement means" refers to technologies or devices used to determine a user's current location.

[0371] "Hazard information" refers to data on dangerous areas caused by disasters and the extent of their impact.

[0372] An "evacuation route" refers to a recommended route for a user to safely evacuate in the event of a disaster.

[0373] "Calculation means" refers to algorithms or devices for calculating optimal evacuation routes based on disaster and hazard information.

[0374] "Interactive device means" refers to equipment or software for exchanging information with a user through voice or text.

[0375] "Emotion recognition means" refers to technology or devices that analyze and evaluate a user's emotional state from facial expressions, vocal tone, body movements, etc.

[0376] "Emotional state" refers to a user's mental and emotional response or state.

[0377] "Content of guidance" refers to specific items of information or instructions provided to the user.

[0378] "Tone" refers to the tone and atmosphere of the voice used when providing audio guidance.

[0379] This invention is a disaster prevention system that supports users in taking prompt and appropriate evacuation actions in the event of a disaster. This system is mainly composed of a server and terminals working together, and has the following functions:

[0380] Server Roles

[0381] The server constantly monitors disaster information and immediately acquires and processes any new disaster information when it occurs. Disaster information is acquired using APIs publicly available via the Internet (e.g., government disaster information APIs).

[0382] Next, the device's GPS function is used to determine the user's current location. Once the current location is determined, the server obtains hazard information based on that location. This includes referencing hazard maps and analyzing real-time disaster prevention camera and drone footage. From the obtained hazard information, the server uses AI algorithms to calculate the optimal evacuation route.

[0383] Device Role

[0384] The user's device uses GPS to send its current location to a server. It also has an emotion engine that recognizes the user's emotional state in real time. The emotion engine analyzes the user's facial expressions, vocal tone, and body movements to detect panic or high stress.

[0385] The terminal further includes a dialogue device means for providing voice guidance along the calculated evacuation route to the user. The dialogue device means adjusts the content and tone of the guidance based on the emotional state of the user and provides appropriate instructions. For example, if the user is in a panic, the dialogue device means softens the tone of the voice guidance and provides instructions such as "Take a deep breath and stay calm. Use the emergency stairs to go down to the first floor."

[0386] Hardware and Software Configuration

[0387] On the server side, a database server and an AI analysis server are used. The database server stores disaster and hazard information, while the AI ​​analysis server calculates evacuation routes and performs emotion recognition. Specific software includes the Python library requests to obtain disaster information and the geopy library to identify the current location. Furthermore, face_recognition and cv2 (OpenCV) are used for emotion recognition.

[0388] Specific examples

[0389] Let's imagine that an earthquake occurs and the user is on the third floor of a high-rise building. In this case, the server immediately obtains earthquake information, uses the device's GPS function to identify the user's location (third floor of a high-rise building), obtains hazard information based on the user's current location, and calculates the optimal evacuation route. In addition, the device's emotion engine analyzes the user's emotional state, and if it detects a state of panic, it changes the tone of the voice guidance and provides specific instructions. For example, it may say, "Take a deep breath and stay calm. Use the emergency stairs to go down to the first floor."

[0390] Example prompts for generative AI models

[0391] "Receive an emergency earthquake alert, identify the user's current location, calculate the optimal evacuation route, and guide the user to evacuate calmly. Also, if the user's emotional state is panicked, notify them and provide psychological support."

[0392] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0393] Step 1:

[0394] Obtaining disaster information

[0395] The server sends a request to an API that provides disaster information via the Internet (e.g., a government disaster information API) to obtain the latest disaster information. The input is the API request, and the output is JSON data of the disaster information. The server analyzes this data and identifies currently ongoing disasters.

[0396] Step 2:

[0397] Identifying your current location

[0398] The device uses its built-in GPS to determine the user's current location and sends that information to the server. The input is a GPS signal, and the output is the user's latitude and longitude coordinate data. The server receives this data and combines it with disaster information for further processing.

[0399] Step 3:

[0400] Obtaining hazard information

[0401] The server retrieves relevant hazard information from a hazard map based on the user's current location. If necessary, it analyzes real-time video data from disaster prevention cameras and aerial devices (drones). The input is the user's coordinate data and disaster information, and the output is hazard information. The server uses this to collect data on specific dangerous areas and safe evacuation routes.

[0402] Step 4:

[0403] Evacuation route calculation

[0404] The server calculates the optimal evacuation route based on the acquired hazard information and the user's current location. The input is the hazard information and the user's coordinate data, and the output is the calculated evacuation route. In this process, the server uses an artificial intelligence (AI) algorithm to derive the optimal route.

[0405] Step 5:

[0406] Recognition of emotional states

[0407] The device uses a camera and microphone to recognize the user's emotional state. The device's emotion engine analyzes the user's facial expressions, voice tone, and body movements to identify the user's emotional state in real time. The input is the user's video and audio data, and the output is the analyzed emotional state (e.g., panicked, calm, etc.).

[0408] Step 6:

[0409] Providing audio guidance

[0410] The device provides voice guidance to the user based on the evacuation route information and emotional state data received from the server. If the user is in a panic state, the device softens the tone of the voice guidance and adds instructions to stay calm. The input is the evacuation route information and emotional state data, and the output is voice guidance for the user.

[0411] Step 7:

[0412] Emergency contact function

[0413] In the event of a disaster, the device automatically notifies the user's designated emergency contacts (family, friends, etc.) of the current location and situation. The input is the user's current location and disaster information, and the output is an emergency contact message. This function enables the user to receive prompt and appropriate assistance.

[0414] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0415] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0416] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0417] [Second embodiment]

[0418] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0419] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0420] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0421] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0422] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0423] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0424] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0425] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0426] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0427] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0428] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0429] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0430] This invention relates to a disaster prevention system that supports users in taking prompt and appropriate evacuation actions in the event of a disaster. This system, through the cooperative operation of a server and terminals, monitors and acquires disaster information, identifies the user's current location, acquires hazard information, calculates evacuation routes, and provides guidance via a voice chatbot.

[0431] The server constantly monitors disaster information notified during emergencies, and if a new disaster occurs, it immediately acquires and processes that information. It also uses the GPS function of the device to identify the user's current location, allowing the system to accurately track the user's location.

[0432] The server obtains optimal hazard information based on the user's current location and disaster information. Hazard information includes information on the extent of the disaster's impact, the level of danger, and evacuation routes. Based on this information, the server performs AI analysis and calculates the safest evacuation route for the user. At this time, real-time footage of the damage situation obtained from disaster prevention cameras and drones can also be included in the analysis, enabling immediate response.

[0433] The calculated evacuation route is then provided to the user via voice guidance via the device. By using a voice chatbot, users can receive calm instructions even in an emergency. For example, specific instructions such as, "Go to the nearest emergency exit from your current location and use the stairs to go down to the first floor. From there, turn right and evacuate to a safe evacuation site" are provided.

[0434] Specific examples

[0435] For example, consider a case where an earthquake occurs and the user is on the third floor of a high-rise building.

[0436] 1. Obtaining disaster information:

[0437] The server instantly receives emergency earthquake alerts and processes the information.

[0438] 2. Current location:

[0439] The device's GPS function determines that the user's current location is on the third floor of a high-rise building.

[0440] 3. Obtaining hazard information and calculating evacuation routes:

[0441] The server retrieves the latest information from the hazard map based on the user's current location, and analyzes real-time disaster prevention camera and drone footage to determine the extent of damage to the building. This information is used to calculate the safest evacuation route. Specifically, the system calculates a route that will guide the user down to the first floor using the emergency stairs, then out of the building and to a safe evacuation site.

[0442] 4. Voice guidance:

[0443] The voice chatbot on the device guides the user based on the calculated evacuation route, providing specific instructions such as "Use the emergency stairs to go down to the first floor, then exit through the right-hand exit and head to the designated evacuation location," allowing the user to follow the instructions and evacuate safely.

[0444] In this way, by linking each means, the disaster prevention system of the present invention can support quick and appropriate evacuation actions even in the event of a disaster emergency, thereby ensuring the safety of users.

[0445] The processing flow will be explained below.

[0446] Program processing flow

[0447] Step 1: Loading the initial settings

[0448] The server loads the initial settings of the user's disaster prevention AI system, including settings to enable GPS, hazard maps, AI analysis, and voice chatbot functions.

[0449] When a user launches an app, the device enables each function based on the initial settings.

[0450] Step 2: Disaster detection

[0451] The server monitors the emergency alert system and obtains information when a new disaster alert occurs.

[0452] The terminal notifies the user of the acquired alert information.

[0453] Step 3: Get current location

[0454] The device uses GPS to determine the user's current location, which is then immediately sent to the server.

[0455] The server prepares to calculate hazard information and evacuation routes based on the current location obtained.

[0456] Step 4: Obtaining damage and hazard information

[0457] The server obtains the latest hazard information related to the user's current location from the hazard map system.

[0458] In addition, disaster prevention camera and drone footage will be analyzed to grasp the damage situation in real time.

[0459] Step 5: Calculate the optimal evacuation route

[0460] The server performs AI analysis based on the acquired current location, hazard information, and real-time damage status to calculate the optimal evacuation route.

[0461] For example, it calculates specific routes to evacuate to a safe location using emergency stairs in a building.

[0462] Step 6: Guidance by voice chatbot

[0463] The device activates the voice chatbot and starts guiding the user on evacuation routes.

[0464] The server generates instructions for each step of the calculated evacuation route.

[0465] The device reads out the generated instructions and guides the user in real time, helping the user to take each step from their current location to the evacuation site.

[0466] Specific examples of processing

[0467] Scenario: Evacuation in the event of an earthquake

[0468] 1. Step 1: Loading the initial settings

[0469] The server loads settings that enable GPS, hazard maps, AI analysis, and voice chatbot features.

[0470] The device launches the app and enables each function.

[0471] 2. Step 2: Disaster detection

[0472] The server detects emergency earthquake alerts and obtains the information.

[0473] The terminal notifies the user of the acquired alert information.

[0474] 3. Step 3: Get current location

[0475] The device uses GPS to determine the user's current location, and this location information is sent to the server.

[0476] 4. Step 4: Obtain damage and hazard information

[0477] The server obtains the latest hazard information about the area around the building from the hazard map.

[0478] In addition, disaster prevention camera and drone footage will be analyzed to determine the extent of damage to the building.

[0479] 5. Step 5: Calculate the optimal evacuation route

[0480] The server uses AI analysis to calculate the optimal evacuation route based on the user's current location, acquired hazard information, and damage situation.

[0481] For example, a route is calculated to use the emergency stairs to descend to the first floor and then evacuate to a safe location.

[0482] 6. Step 6: Guidance by voice chatbot

[0483] The device will launch a voice chatbot that will instruct the user, "Please evacuate immediately using the emergency stairs. First, head to the emergency door on your right and use the stairs to go down to the first floor. Then, exit through the exit on your right and head to the designated evacuation location."

[0484] The device provides specific voice instructions for each step of the evacuation route.

[0485] Example 1

[0486] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0487] In the event of a disaster emergency, it may be difficult for users to take prompt and appropriate evacuation actions. If disaster information is not obtained or the user's current location is not identified promptly, the user may take incorrect evacuation actions, which could result in an even more dangerous situation. Furthermore, if real-time acquisition of disaster and hazard information is insufficient, it is difficult to calculate an appropriate evacuation route, and the user's safety cannot be ensured. Furthermore, if the evacuation route is presented ambiguously, the user may not be able to evacuate appropriately. These issues need to be resolved.

[0488] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0489] In this invention, the server includes means for monitoring and acquiring disaster information, means for identifying the user's current location, means for acquiring hazard information based on the acquired disaster information and the current location, means for calculating an optimal evacuation route based on the acquired hazard information and the current location, and interactive guidance means for providing voice guidance to the user along the calculated evacuation route, thereby supporting quick and appropriate evacuation actions even in a disaster emergency and ensuring the user's safety.

[0490] "Disaster information" refers to information relating to emergencies such as earthquakes, floods, and fires, including the occurrence, progression, and extent of impact of the disaster.

[0491] A "location information determination means" is a device or system that uses technology such as GPS to accurately determine the user's current location.

[0492] "Hazard information" refers to detailed information about the extent of a disaster's impact, the level of danger, and evacuation routes.

[0493] An "evacuation route" is a specific route that a user should follow to evacuate safely, and usually refers to the shortest and safest route.

[0494] An "interactive guide" is an interactive system that provides instructions to a user using voice or text, and guides the user to specific actions.

[0495] An "image acquisition means" is a device or system that uses a camera, drone, etc. to capture images of the surrounding situation in real time and analyzes them.

[0496] An "AI model" is an analytical method that uses artificial intelligence, and is an algorithm that makes predictions and optimizations based on large amounts of data.

[0497] This invention relates to a disaster prevention system that supports users in taking prompt and appropriate evacuation actions in the event of a disaster. This system, through the cooperative operation of a server and terminals, monitors and acquires disaster information, identifies the user's current location, acquires hazard information, calculates evacuation routes, and provides guidance via a voice chatbot.

[0498] The server constantly monitors disaster information, and when a new disaster occurs, it immediately acquires and processes that information. Specifically, it uses APIs provided by public institutions such as the Japan Meteorological Agency to collect the latest disaster information and stores it in an internal database. This allows the server to analyze the type and urgency of the disaster and generate alerts as necessary.

[0499] The user's current location is determined using the device's GPS function. The device utilizes its built-in GPS module to obtain the user's precise location information and transmit it to the server. For example, if the user is on the third floor of a high-rise building, the coordinate data will be transmitted to the server, and the next analysis step will begin.

[0500] The server collects hazard information based on the user's current location and disaster information. Specifically, it uses a hazard map database, real-time disaster prevention camera footage, and drone footage to obtain information on the extent of the disaster's impact, the level of danger, and evacuation routes. Based on this information, the server uses an AI model to calculate the optimal evacuation route.

[0501] The AI ​​model uses a specific predictive algorithm as a generative AI model to provide users with the safest and quickest evacuation route, and the results are presented in a format that makes it easy for users to take action.

[0502] After calculating the evacuation route, the device uses a voice chatbot to provide guidance to the user. Based on the calculation results, the chatbot will provide specific instructions by voice, such as which direction the user should go and which stairs to use, allowing the user to act calmly even in an emergency.

[0503] As a concrete example, consider a situation where an earthquake occurs and the user is on the third floor of a high-rise building. In this case, the server receives an emergency earthquake alert and uses the device's GPS function to identify the user's current location. The server then analyzes real-time disaster prevention camera and drone footage and calculates a safe evacuation route. The device uses a voice chatbot to provide the user with specific instructions, such as "Use the emergency stairs to go down to the first floor, then exit through the exit on your right."

[0504] Below are some example prompts using a generative AI model:

[0505] Suppose an earthquake occurs and a user is on the third floor of a high-rise building. Calculate the safest evacuation route. The device's GPS information is used to determine the user's current location, and the server obtains information from real-time disaster prevention camera footage and hazard maps to calculate the optimal evacuation route. Then, provide the user with instructions via a voice chatbot, such as "Use the emergency stairs to go down to the first floor, then exit through the exit on the right."

[0506] In this way, by linking each means, the disaster prevention system of the present invention can support quick and appropriate evacuation actions even in the event of a disaster emergency, thereby ensuring the safety of users.

[0507] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0508] Step 1:

[0509] The server monitors and acquires disaster information.

[0510] Input: Disaster information from public institutions and disaster prevention-related APIs.

[0511] Data processing: Disaster information is analyzed and stored in an internal database. The urgency and type of disaster are evaluated.

[0512] Output: Parsed disaster information and alerts.

[0513] Specific operation: The server periodically calls the Japan Meteorological Agency's API to obtain the latest disaster information. It then stores the obtained disaster information in a database, analyzes its contents, and evaluates its urgency. If necessary, it generates an alert and notifies the entire system.

[0514] Step 2:

[0515] The device uses GPS functionality to identify the user's current location.

[0516] Input: GPS data from the device.

[0517] Data processing: Analyze GPS data to obtain latitude and longitude location information.

[0518] Output: The user's current location.

[0519] Specific operation: The device uses the built-in GPS module to obtain the user's location information. After the location information is determined, it is sent to the server in the form of latitude and longitude.

[0520] Step 3:

[0521] The server acquires hazard information based on the disaster information and current location information acquired.

[0522] Input: Disaster information, user's current location information.

[0523] Data processing: Analyze hazard maps and real-time disaster prevention camera and drone footage to assess the level of risk and the extent of impact.

[0524] Output: Latest hazard information.

[0525] How it works: The server collects hazard information from an internal database and external sources (disaster prevention cameras, drone footage), then uses AI analysis to obtain the latest hazard information related to the user's current location.

[0526] Step 4:

[0527] The server calculates the optimal evacuation route based on the latest hazard information.

[0528] Input: Hazard information, user's current location information.

[0529] Data processing: Calculate safe evacuation routes using generative AI models.

[0530] Output: Optimal evacuation route.

[0531] Specific operation: The server inputs the acquired hazard information and the user's current location information into the AI ​​model to calculate a safe and quick evacuation route. The calculation results are then output as detailed route guidance for the user to take specific actions.

[0532] Step 5:

[0533] The terminal uses an interactive guidance means for providing voice guidance of the calculated evacuation route.

[0534] Input: Optimal evacuation route.

[0535] Data processing: Convert evacuation route information into voice instructions.

[0536] Output: Voice guidance.

[0537] Specific operation: The device analyzes the evacuation route information received from the server and provides guidance to the user through a voice chatbot. For example, specific instructions are provided via voice, such as "Use the emergency stairs to go down to the first floor, then exit through the exit on the right."

[0538] At each step, the system collects the necessary data and uses data analysis and AI models to generate detailed instructions to assist users in optimal evacuation behavior, thereby ensuring their safety in the event of a disaster.

[0539] (Application example 1)

[0540] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0541] In order for users to take appropriate and prompt evacuation actions during disaster emergencies, they need to obtain disaster information in real time, calculate the optimal evacuation route based on their current location, and provide accurate and specific guidance. However, conventional disaster prevention systems have issues with the speed of information acquisition and the accuracy of guidance, and there are no systems that can handle transportation methods such as autonomous vehicles. This has led to the problem of not being able to fully ensure user safety during disaster emergencies.

[0542] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0543] In this invention, the server includes means for monitoring and acquiring disaster information, means for determining the user's current location, means for acquiring danger information based on the acquired disaster information and the user's current location, means for calculating an optimal evacuation route based on the acquired danger information and the user's current location, an interactive guidance means for providing voice guidance to the user along the calculated evacuation route, and means for acquiring and analyzing damage status in real time from images captured by disaster prevention monitoring devices and autonomous aircraft devices. This enables real-time information acquisition and analysis, calculation of the optimal evacuation route, and specific and accurate voice guidance in the event of a disaster. This system is also compatible with autonomous vehicles, providing support for rapid and appropriate evacuation behavior to ensure the safety of passengers.

[0544] A "disaster emergency" is a situation in which an earthquake, fire, flood, or other natural or man-made disaster occurs and immediate evacuation action is required.

[0545] A "user" is a general public or a specific person who uses the disaster prevention system to take prompt and appropriate evacuation action.

[0546] "Quick and appropriate evacuation actions" are actions that will allow people to reach evacuation shelters in the most efficient and safe manner when a disaster occurs.

[0547] A "disaster prevention system" is a set of devices and software that provides functions such as monitoring, acquiring, and analyzing disaster information, calculating evacuation routes, and providing guidance to support users in the event of a disaster emergency.

[0548] "Disaster information" refers to information about natural and man-made disasters such as earthquakes, fires, and floods.

[0549] "Monitoring and acquisition means" refers to technologies and devices for continuously observing and collecting disaster information in real time.

[0550] "Location measurement means for identifying the current location" refers to technology and devices for determining the current location of a user or object using location information technology such as GPS.

[0551] "Means for acquiring risk information" refers to the technology and devices for collecting and analyzing information about the disaster situation and the extent of damage.

[0552] The "means for calculating an optimal evacuation route" refers to a technology and device for calculating the safest and most efficient evacuation route for a user based on real-time on-site conditions.

[0553] "Interactive guidance means" refers to technology and devices that provide specific instructions and guidance to users through voice and text.

[0554] "Disaster prevention monitoring equipment" refers to devices such as cameras and sensors that monitor the occurrence of disasters in real time.

[0555] "Autonomous Aircraft Footage" means real-time footage captured by an autonomously flying device, such as a drone.

[0556] "Means for acquiring and analyzing damage status" refers to technology and equipment for collecting and analyzing damage status at the site in real time.

[0557] This invention relates to a disaster prevention system that supports users in taking prompt and appropriate evacuation actions in the event of a disaster. This system is realized through the cooperative operation of a server, terminals, and multiple dedicated devices and software.

[0558] First, the server monitors disaster information in real time and quickly obtains it when a disaster occurs. Disaster information includes data on natural and man-made disasters such as earthquakes, fires, and floods. This disaster information is obtained using APIs provided by specialized disaster monitoring organizations and local disaster prevention systems.

[0559] The device then uses GPS to determine the user's current location, allowing it to determine exactly where the user is and calculate the most appropriate evacuation route.The device is also installed in mobile devices such as the user's smartphone or an in-car system.

[0560] The server obtains danger information based on the acquired disaster information and the user's current location information. This danger information includes the extent of the disaster's impact, the level of danger, the current damage situation on site, and information on evacuation routes. Video data transmitted in real time from disaster prevention monitoring devices and autonomous aircraft devices (drones) is used to obtain the danger information. This makes it possible to calculate the optimal evacuation route based on the latest information on site.

[0561] The calculated evacuation route is then given to the user via voice guidance. An interactive guidance system is used to provide specific instructions to help the user stay calm. For example, a specific prompt sentence such as "We will guide you to the evacuation route. Turn right and proceed to a safe evacuation location" is generated.

[0562] The entire system is implemented using the Python programming language, with API access using the requests library, location data calculation using the geopy library, and voice synthesis technology used to generate voice guidance, effectively carrying out a series of complex data processing and user guidance.

[0563] As a specific example of operation, if an earthquake occurs and the user is in an autonomous vehicle, the server obtains earthquake information in real time and determines the vehicle's current location. It then calculates the optimal evacuation route and issues instructions to the autonomous driving system through the vehicle's on-board system. Specific evacuation instructions are provided to passengers through audio guidance and display. This series of operations allows the user to evacuate quickly and safely.

[0564] Examples of prompts include:

[0565] Get the latest earthquake information.

[0566] Check the current vehicle location using GPS.

[0567] "Calculate the best route based on your current location and nearby safe evacuation locations."

[0568] "Please provide voice instructions for evacuation routes. Instruct them to 'turn right and proceed to a safe evacuation area.'"

[0569] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0570] Step 1:

[0571] The server monitors and acquires disaster information in real time.

[0572] (Input) Real-time disaster information (earthquakes, fires, floods, etc.) provided by various disaster monitoring organizations and disaster prevention systems

[0573] (Data processing) Collect the latest disaster data using disaster information API

[0574] (Output) Update the internal database that holds the latest disaster information

[0575] Specifically, the server uses the requests library to periodically retrieve data from disaster information providers and store it in an internal database.

[0576] Step 2:

[0577] The terminal uses the GPS function to determine the user's current location.

[0578] (Input) GPS data from the user's device (smartphone or in-car system)

[0579] (Data processing) Use the GPS function to obtain the user's latitude and longitude

[0580] (Output) Current location of the user (latitude and longitude)

[0581] Specifically, the device uses its built-in GPS module to obtain current location data (latitude and longitude).

[0582] Step 3:

[0583] The server obtains danger information based on the acquired disaster information and the user's current location information.

[0584] (Input) Latest disaster information and user's current location information

[0585] (Data processing) Integrate disaster information and user location information, and perform search and filter processing to extract relevant danger information from the danger information database.

[0586] (Output) Risk information related to the user's current location

[0587] Specifically, the server retrieves danger information related to a specific area through a database query based on disaster information and location information.

[0588] Step 4:

[0589] The server calculates the optimal evacuation route based on the acquired danger information and the current location.

[0590] (Input) Risk information and user's current location information

[0591] (Data processing) Route calculation algorithm that calculates the shortest and safest route taking into account geographical information and disaster conditions

[0592] (Output) Optimal evacuation route

[0593] Specifically, the server uses a map database and a route calculation algorithm to calculate the optimal route from the user's current location to a safe evacuation site.

[0594] Step 5:

[0595] The terminal provides the user with voice guidance on the calculated evacuation route.

[0596] (Input) Calculated evacuation route

[0597] (Data processing) Convert evacuation route information into voice guidance prompts and generate voice data using voice synthesis technology

[0598] (Output) Voice guidance instructions

[0599] Specifically, the terminal generates instructions in the interactive guidance system based on the calculated evacuation route, converts them into voice using a speech synthesis engine, and provides guidance to the user.

[0600] Step 6:

[0601] The server acquires and analyzes damage information in real time from disaster prevention monitoring equipment and autonomous aircraft equipment footage.

[0602] (Input) Real-time video data from disaster prevention monitoring devices and autonomous aircraft devices (drones, etc.)

[0603] (Data processing) Analysis of video data, understanding of disaster situation, identification of damage extent

[0604] (Output) Latest damage situation information

[0605] Specifically, the server analyzes video data sent from disaster prevention cameras and drones, determines the extent and situation of the damage, and updates the danger information database.

[0606] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0607] This invention is a disaster prevention system that helps users take prompt and appropriate evacuation actions in the event of a disaster emergency, and in particular has the function of recognizing the user's emotional state and providing appropriate guidance and psychological support based on that. This system, through the cooperative operation of a server and terminal, monitors and acquires disaster information, identifies the user's current location, acquires hazard information, calculates evacuation routes, recognizes emotions using an emotion engine, and provides guidance using a voice chatbot.

[0608] Server and terminal roles

[0609] The server constantly monitors disaster information notified in emergencies, and when a new disaster occurs, it immediately acquires and processes that information. It also uses the GPS function of the device to pinpoint the user's current location, accurately determining where the user is.

[0610] Hazard information is obtained based on the user's current location and disaster information. This includes information on the extent of the disaster's impact, the level of risk, and evacuation routes. The obtained hazard information and real-time disaster prevention camera and drone footage are analyzed, and the safest evacuation route for the user is calculated using AI analysis.

[0611] Emotion Engine Functions

[0612] The system also features an emotion engine that recognizes the user's emotional state in real time. This emotion engine can grasp the user's stress level and emotional state by analyzing the user's facial expressions, voice tone, and even physical movements. If the emotion engine detects the user's state of panic or high stress, it dynamically adjusts the tone and content of the voice guidance to help the user stay calm.

[0613] The emotion engine also provides relaxation guidance and psychological support. For example, if a user is experiencing extreme panic, the voice chatbot can provide relaxation guidance such as "Take a deep breath, slowly calm down, and everything will be okay," thereby reducing the user's stress.

[0614] Specific examples

[0615] As a specific example, consider a case where an earthquake occurs and the user is on the third floor of a high-rise building.

[0616] 1. Obtaining disaster information:

[0617] The server instantly receives emergency earthquake alerts and processes the information.

[0618] 2. Current location:

[0619] The GPS function of the device determines that the user's current location is on the third floor of a high-rise building.

[0620] 3. Obtaining hazard information and calculating evacuation routes:

[0621] The server retrieves the latest information from the hazard map based on the user's current location and analyzes real-time disaster prevention camera and drone footage to determine the extent of damage to the building. This information is used to calculate the safest evacuation route. Specifically, the system calculates a route that will lead the user down the emergency stairs to the first floor, then out of the building and to a safe evacuation site.

[0622] 4. Emotion recognition and guidance using emotion engine:

[0623] The device's emotion engine analyzes the user's facial expressions and voice tone in real time to understand their emotional state. If the user is in a panic, the emotion engine softens the tone of the voice guidance and provides relaxation instructions. Specific and calm instructions are provided via voice, such as "Take a deep breath and stay calm. Use the emergency stairs to go down to the first floor. Exit through the exit on the right and head to the designated evacuation site."

[0624] In this way, the disaster prevention system of the present invention can ensure the safety of users by supporting prompt and appropriate evacuation behavior even in the event of a disaster and encouraging them to evacuate calmly while taking into account the user's emotional state. This system is expected to enable users to act calmly and evacuate effectively even in an emergency.

[0625] The processing flow will be explained below.

[0626] Program processing flow

[0627] Step 1: Loading the initial settings

[0628] The server loads the initial settings of the user's disaster prevention AI system, including settings to enable GPS, hazard maps, AI analysis, emotion engines, and voice chatbot functions.

[0629] When a user launches an app, the device enables each function based on the initial settings.

[0630] Step 2: Disaster detection

[0631] The server monitors the emergency alert system and immediately obtains information about any new disaster alerts that occur.

[0632] The terminal notifies the user of the acquired alert information.

[0633] Step 3: Get current location

[0634] The device uses GPS to determine the user's current location, and this information is sent to the server in real time.

[0635] The server prepares to calculate hazard information and evacuation routes based on the current location obtained.

[0636] Step 4: Obtaining damage and hazard information

[0637] The server obtains the latest hazard information related to the user's current location from the hazard map system.

[0638] In addition, disaster prevention camera and drone footage will be analyzed to grasp the damage situation in real time.

[0639] Step 5: Calculate the optimal evacuation route

[0640] The server performs AI analysis based on the acquired current location, hazard information, and real-time damage status to calculate the optimal evacuation route.

[0641] For example, it calculates specific routes to evacuate to a safe location using emergency stairs in a building.

[0642] Step 6: Start Emotion Recognition

[0643] The device activates an emotion engine to analyze the user's emotional state in real time, judging from facial expressions, voice tone, and body movements to determine whether the user is in a panic state.

[0644] Step 7: Guidance by voice chatbot

[0645] The device activates the voice chatbot and starts guiding the user on evacuation routes.

[0646] The server generates instructions for each step of the calculated evacuation route.

[0647] The device dynamically adjusts the tone and content of the voice guidance based on the analysis results of the emotion engine. For example, if the user is in a panic, it generates and provides specific instructions, including relaxation guidance such as "Take a deep breath and calm down."

[0648] Specific examples

[0649] Scenario: Evacuation in the event of an earthquake

[0650] 1. Step 1: Loading the initial settings

[0651] The server loads settings that enable GPS, hazard maps, AI analysis, emotion engines, and voice chatbots.

[0652] The device launches the app and enables each function.

[0653] 2. Step 2: Disaster detection

[0654] The server detects emergency earthquake alerts and retrieves the information immediately.

[0655] The terminal notifies the user of the acquired alert information.

[0656] 3. Step 3: Get current location

[0657] The device uses GPS to determine the user's current location, and this location information is sent to the server.

[0658] 4. Step 4: Obtain damage and hazard information

[0659] The server obtains the latest hazard information about the area around the building from the hazard map.

[0660] In addition, disaster prevention camera and drone footage will be analyzed to determine the extent of damage to the building.

[0661] 5. Step 5: Calculate the optimal evacuation route

[0662] The server uses AI analysis to calculate the optimal evacuation route based on the user's current location, acquired hazard information, and damage situation.

[0663] For example, a route is calculated to use the emergency stairs to descend to the first floor and then evacuate to a safe location.

[0664] 6. Step 6: Start Emotion Recognition

[0665] The device's emotion engine analyzes the user's emotional state in real time, determining whether the user is panicking based on facial expressions and tone of voice.

[0666] 7. Step 7: Guidance by voice chatbot

[0667] The device will launch a voice chatbot that will instruct the user, "Please evacuate immediately using the emergency stairs. First, head to the emergency door on your right and use the stairs to go down to the first floor. Then, exit through the exit on your right and head to the designated evacuation location."

[0668] Based on the analysis results of the emotion engine, the device will soften the tone of the voice guidance and provide specific instructions, including relaxation advice such as "Take a deep breath and stay calm. It's okay." In this way, the device helps the user calmly grasp the situation and take appropriate evacuation actions.

[0669] Example 2

[0670] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0671] In the event of a disaster or emergency, it is extremely important for users to take prompt and appropriate evacuation actions. However, systems that efficiently integrate multiple elements, such as obtaining disaster information, calculating evacuation routes, and identifying the user's current location, and that also provide psychological support according to the user's emotional state, are still insufficient. Providing appropriate guidance is particularly difficult for users who are in a state of panic or high stress. Therefore, a comprehensive disaster prevention system that enables users to take appropriate evacuation actions while remaining calm is needed.

[0672] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0673] In this invention, the server includes a means for monitoring and acquiring disaster information, a location information acquisition means for identifying the user's current location, and a means for acquiring hazard information based on the acquired disaster information and current location. This allows for the calculation of a quick and appropriate evacuation route based on the user's current location information and the provision of voice guidance. The system also includes an automated dialogue system for providing voice guidance to the user along the calculated evacuation route, an emotion recognition engine for recognizing the user's emotional state in real time, and a means for dynamically adjusting the guidance content and guidance tone based on the recognized emotional state. This allows the user to take calm and effective evacuation actions even during a disaster, thereby ensuring the user's overall safety.

[0674] "Disaster information" refers to information related to natural disasters, including the occurrence status and extent of impact of earthquakes, tsunamis, typhoons, floods, etc., as well as emergency warnings.

[0675] "Location information acquisition means" refers to technology or equipment for identifying the user's current location, and primarily refers to a location identification system that uses GPS.

[0676] "Hazard information" refers to detailed information about the danger areas and scope of impact caused by disasters, the need for evacuation, etc.

[0677] "Means for calculating the optimal evacuation route" refers to a method or system for calculating the safest and quickest evacuation route for a user based on the acquired information.

[0678] An "automated dialogue system" is a system that provides information through dialogue with a user, and is particularly intended to give instructions to users through voice guidance or chatbots.

[0679] An "emotion recognition engine" refers to technology or software that analyzes a user's facial expressions, tone of voice, body movements, etc. to recognize the user's emotional state (e.g., panic, calm, stress, etc.) in real time.

[0680] "Means for acquiring and analyzing damage situations in real time" refers to methods and technologies for acquiring video data sent from disaster prevention cameras, unmanned aerial vehicles, etc. in real time and analyzing the extent of damage and the scope of impact.

[0681] "Specific instructions at each step provided by voice guidance" refers to a method of providing detailed and specific voice guidance for each step, such as directions to travel by car and walking routes, in order to clearly communicate evacuation routes to the user.

[0682] This invention is a disaster prevention system that helps users take prompt and appropriate evacuation actions in the event of a disaster. This system, through the cooperative operation of a server and terminals, monitors and acquires disaster information, identifies current locations, acquires hazard information, calculates evacuation routes, recognizes emotions using an emotion engine, and provides guidance using a voice chatbot.

[0683] Hardware and software used

[0684] server

[0685] Hardware: High-performance database and analysis servers

[0686] Software: Database management system (e.g., MySQL), disaster information acquisition API (e.g., Japan Meteorological Agency API), AI analysis engine (e.g., TensorFlow)

[0687] Terminal

[0688] Hardware: Smartphone or tablet with GPS sensor, camera, and microphone

[0689] Software: GPS location acquisition software, voice recognition programs (e.g., Google Speech-to-Text), emotion recognition engines

[0690] System Operation

[0691] 1. Monitoring and obtaining disaster information

[0692] The server constantly monitors various disaster information sources (e.g., the Japan Meteorological Agency, the Fire and Disaster Management Agency, etc.), and when new disaster information occurs, it immediately acquires the information and stores it in a database. It analyzes important parameters related to the disaster information (e.g., type of disaster, location of occurrence, scale, etc.) and evaluates its importance.

[0693] 2. Identifying your current location

[0694] The device uses the built-in GPS sensor to determine the user's current location, which is then periodically sent to a server. The GPS data is stored in the form of latitude and longitude coordinates, and is also subjected to a correction algorithm to improve accuracy.

[0695] 3. Acquisition and analysis of hazard information

[0696] The server obtains hazard information about the extent of the disaster impact and evacuation routes based on the user's current location, including analysis of hazard map databases, real-time disaster prevention camera footage, and drone footage, to identify high-risk areas.

[0697] 4. Evacuation route calculation

[0698] The server calculates the optimal evacuation route based on the acquired hazard information. The evacuation route is calculated using an AI algorithm (e.g., a pathfinding algorithm), and detailed instructions are prepared for each step of the evacuation route.

[0699] 5. Real-time recognition of emotional states

[0700] The device's camera and microphone are used to analyze the user's facial expressions and voice tone in real time. The emotion engine uses this data to determine the user's emotional state (e.g., panic, calm, stress, etc.) and sends that information to the server.

[0701] 6. Providing appropriate guidance and psychological support

[0702] Based on the evacuation route information sent from the server and the user's emotional state as recognized by the emotion engine, the device dynamically provides guidance to the user. The voice chatbot provides relaxation instructions and specific evacuation instructions in a gentle tone.

[0703] Specific examples

[0704] Below is a specific example where an earthquake occurs and the user is on the third floor of a high-rise building.

[0705] 1. Obtaining disaster information

[0706] The server instantly receives emergency earthquake alerts and processes the information.

[0707] 2. Identifying your current location

[0708] The device's GPS function determines that the user's current location is on the third floor of a high-rise building.

[0709] 3. Obtaining hazard information and calculating evacuation routes

[0710] The server analyzes the latest hazard information and real-time footage from disaster prevention cameras and drones to calculate the safest evacuation route, guiding the user to the first floor via the emergency stairs, then out of the building and towards a safe evacuation site.

[0711] 4. Emotion recognition and guidance using an emotion engine

[0712] The device's emotion engine analyzes the user's facial expressions and voice tone in real time, and if the user is in a panic, it provides gentle voice guidance including instructions to relax. For example, it might say, "Take a deep breath and stay calm. Use the emergency stairs to go down to the first floor. Exit through the exit on your right and head to the designated evacuation area."

[0713] Prompt Sentence Examples

[0714] "Take a deep breath and stay calm. Use the emergency stairs to safely descend to the first floor. Exit through the exit on your right and proceed to the designated evacuation area."

[0715] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0716] Specific processing flow of the program

[0717] Step 1: Monitoring and obtaining disaster information

[0718] The server constantly monitors various disaster information sources (e.g., the Japan Meteorological Agency API, the Fire and Disaster Management Agency database, etc.) When new disaster information occurs, it immediately acquires that information and stores it in the database.

[0719] Input: New disaster data from disaster sources

[0720] Data processing: Analyze the type, location, and scale of disasters to assess their importance

[0721] Output: Disaster information stored in a database

[0722] Specific operation: Sends a request to the disaster information API, receives and analyzes new data, and registers it in the database.

[0723] Step 2: Determine your current location

[0724] The device uses the built-in GPS sensor to determine the user's current location, which is then periodically sent to a server.

[0725] Input: Location data (latitude and longitude) from the GPS sensor

[0726] Data processing: Position data is corrected using a correction algorithm to improve accuracy

[0727] Output: User's current location (latitude and longitude)

[0728] Specific operation: Enables the GPS function to obtain the current location and transmits it to the server in real time.

[0729] Step 3: Obtain and analyze hazard information

[0730] Based on the current location information sent, the server obtains and analyzes hazard information from hazard map databases, disaster prevention cameras, drone footage, etc.

[0731] Input: User's current location and data from hazard information sources

[0732] Data processing: Integrate and analyze risk information from hazard maps and real-time video data

[0733] Output: Hazard information based on current location

[0734] Specific operation: Data is obtained from the hazard map API, and the video data is analyzed using an image recognition algorithm to assess the level of danger.

[0735] Step 4: Calculate evacuation routes

[0736] The server uses an AI algorithm to calculate the optimal evacuation route based on the acquired hazard information and the transmitted current location information.

[0737] Input: Current location and hazard information

[0738] Data processing: Calculate the optimal route using a route search algorithm

[0739] Output: The optimal evacuation route for the user

[0740] Specific operation: Uses an AI algorithm (e.g., A algorithm) to calculate an evacuation route and send the results to the device.

[0741] Step 5: Real-time recognition of emotional states

[0742] The device uses a camera and microphone to analyze the user's facial expressions, voice tone, body movements, etc. in real time. The emotion engine recognizes the user's emotional state based on this data and sends it to the server.

[0743] Input: Real-time video and audio data from camera and microphone

[0744] Data processing: Analyzing emotions with facial and voice recognition algorithms

[0745] Output: User's emotional state data (e.g., panic, calm, etc.)

[0746] What it does: Facial expression analysis software and voice recognition programs analyze raw data to detect emotional states.

[0747] Step 6: Providing appropriate guidance and psychological support

[0748] Based on the evacuation route information and emotional state data sent from the server, the device dynamically provides guidance to the user through a voice chatbot, with the tone and content of the guidance adjusted according to the user's emotional state.

[0749] Input: Evacuation route information and emotional state data

[0750] Data processing: Dynamically adjust the content and tone of announcements according to emotional state

[0751] Output: Audio prompts provided to the user

[0752] Specific actions: Generate prompts such as "Take a deep breath and stay calm. Use the emergency stairs to go down to the first floor. Exit through the exit on your right and head to the designated evacuation location," and provide audio guidance to the user.

[0753] This is expected to enable users to remain calm and evacuate effectively in an emergency.

[0754] (Application example 2)

[0755] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0756] In the event of a disaster or emergency, it is extremely important for users to take prompt and appropriate evacuation actions. However, in emergencies, it is easy to fall into panic and confusion, making it difficult to make calm decisions. Furthermore, conventional disaster prevention systems generally only provide disaster information and evacuation routes, but do not provide support that takes into account the user's emotional state. Therefore, there is a need for a disaster prevention system that can recognize the user's emotional state in real time and provide appropriate guidance and psychological support.

[0757] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0758] In this invention, the server includes means for monitoring and acquiring disaster information, means for determining the user's current location, means for acquiring hazard information based on the acquired disaster information and the current location, means for calculating an optimal evacuation route based on the acquired hazard information and the current location, dialogue device means for providing voice guidance to the user along the calculated evacuation route, emotion recognition means for detecting the user's emotional state, and means for adjusting the content and tone of the guidance provided based on the user's emotional state. This allows the user to calmly take evacuation action and take appropriate measures even in an emergency.

[0759] "Disaster emergency" refers to the period of time when a natural disaster such as an earthquake, tsunami, fire, or flood, or a similar emergency occurs.

[0760] "User" refers to an individual or organization using this disaster prevention system.

[0761] "Evacuation behavior" refers to planned actions to move to a safe place.

[0762] "Disaster prevention system" refers to a comprehensive set of devices and means designed to assist users in ensuring their safety during disaster emergencies.

[0763] "Disaster information" refers to data and reports on the occurrence of a disaster, the extent of its impact, etc.

[0764] "Location measurement means" refers to technologies or devices used to determine a user's current location.

[0765] "Hazard information" refers to data on dangerous areas caused by disasters and the extent of their impact.

[0766] An "evacuation route" refers to a recommended route for a user to safely evacuate in the event of a disaster.

[0767] "Calculation means" refers to algorithms or devices for calculating optimal evacuation routes based on disaster and hazard information.

[0768] "Interactive device means" refers to equipment or software for exchanging information with a user through voice or text.

[0769] "Emotion recognition means" refers to technology or devices that analyze and evaluate a user's emotional state from facial expressions, vocal tone, body movements, etc.

[0770] "Emotional state" refers to a user's mental and emotional response or state.

[0771] "Content of guidance" refers to specific items of information or instructions provided to the user.

[0772] "Tone" refers to the tone and atmosphere of the voice used when providing audio guidance.

[0773] This invention is a disaster prevention system that supports users in taking prompt and appropriate evacuation actions in the event of a disaster. This system is mainly composed of a server and terminals working together, and has the following functions:

[0774] Server Roles

[0775] The server constantly monitors disaster information and immediately acquires and processes any new disaster information when it occurs. Disaster information is acquired using APIs publicly available via the Internet (e.g., government disaster information APIs).

[0776] Next, the device's GPS function is used to determine the user's current location. Once the current location is determined, the server obtains hazard information based on that location. This includes referencing hazard maps and analyzing real-time disaster prevention camera and drone footage. From the obtained hazard information, the server uses AI algorithms to calculate the optimal evacuation route.

[0777] Device Role

[0778] The user's device uses GPS to send its current location to a server. It also has an emotion engine that recognizes the user's emotional state in real time. The emotion engine analyzes the user's facial expressions, vocal tone, and body movements to detect panic or high stress.

[0779] The terminal further includes a dialogue device means for providing voice guidance along the calculated evacuation route to the user. The dialogue device means adjusts the content and tone of the guidance based on the emotional state of the user and provides appropriate instructions. For example, if the user is in a panic, the dialogue device means softens the tone of the voice guidance and provides instructions such as "Take a deep breath and stay calm. Use the emergency stairs to go down to the first floor."

[0780] Hardware and Software Configuration

[0781] On the server side, a database server and an AI analysis server are used. The database server stores disaster and hazard information, while the AI ​​analysis server calculates evacuation routes and performs emotion recognition. Specific software includes the Python library requests to obtain disaster information and the geopy library to identify the current location. Furthermore, face_recognition and cv2 (OpenCV) are used for emotion recognition.

[0782] Specific examples

[0783] Let's imagine that an earthquake occurs and the user is on the third floor of a high-rise building. In this case, the server immediately obtains earthquake information, uses the device's GPS function to identify the user's location (third floor of a high-rise building), obtains hazard information based on the user's current location, and calculates the optimal evacuation route. In addition, the device's emotion engine analyzes the user's emotional state, and if it detects a state of panic, it changes the tone of the voice guidance and provides specific instructions. For example, it may say, "Take a deep breath and stay calm. Use the emergency stairs to go down to the first floor."

[0784] Example prompts for generative AI models

[0785] "Receive an emergency earthquake alert, identify the user's current location, calculate the optimal evacuation route, and guide the user to evacuate calmly. Also, if the user's emotional state is panicked, notify them and provide psychological support."

[0786] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0787] Step 1:

[0788] Obtaining disaster information

[0789] The server sends a request to an API that provides disaster information via the Internet (e.g., a government disaster information API) to obtain the latest disaster information. The input is the API request, and the output is JSON data of the disaster information. The server analyzes this data and identifies currently ongoing disasters.

[0790] Step 2:

[0791] Identifying your current location

[0792] The device uses its built-in GPS to determine the user's current location and sends that information to the server. The input is a GPS signal, and the output is the user's latitude and longitude coordinate data. The server receives this data and combines it with disaster information for further processing.

[0793] Step 3:

[0794] Obtaining hazard information

[0795] The server retrieves relevant hazard information from a hazard map based on the user's current location. If necessary, it analyzes real-time video data from disaster prevention cameras and aerial devices (drones). The input is the user's coordinate data and disaster information, and the output is hazard information. The server uses this to collect data on specific dangerous areas and safe evacuation routes.

[0796] Step 4:

[0797] Evacuation route calculation

[0798] The server calculates the optimal evacuation route based on the acquired hazard information and the user's current location. The input is the hazard information and the user's coordinate data, and the output is the calculated evacuation route. In this process, the server uses an artificial intelligence (AI) algorithm to derive the optimal route.

[0799] Step 5:

[0800] Recognition of emotional states

[0801] The device uses a camera and microphone to recognize the user's emotional state. The device's emotion engine analyzes the user's facial expressions, voice tone, and body movements to identify the user's emotional state in real time. The input is the user's video and audio data, and the output is the analyzed emotional state (e.g., panicked, calm, etc.).

[0802] Step 6:

[0803] Providing audio guidance

[0804] The device provides voice guidance to the user based on the evacuation route information and emotional state data received from the server. If the user is in a panic state, the device softens the tone of the voice guidance and adds instructions to stay calm. The input is the evacuation route information and emotional state data, and the output is voice guidance for the user.

[0805] Step 7:

[0806] Emergency contact function

[0807] In the event of a disaster, the device automatically notifies the user's designated emergency contacts (family, friends, etc.) of the current location and situation. The input is the user's current location and disaster information, and the output is an emergency contact message. This function enables the user to receive prompt and appropriate assistance.

[0808] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0809] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0810] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0811] [Third embodiment]

[0812] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0813] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0814] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0815] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0816] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0817] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0818] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0819] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0820] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0821] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0822] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0823] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0824] This invention relates to a disaster prevention system that supports users in taking prompt and appropriate evacuation actions in the event of a disaster. This system, through the cooperative operation of a server and terminals, monitors and acquires disaster information, identifies the user's current location, acquires hazard information, calculates evacuation routes, and provides guidance via a voice chatbot.

[0825] The server constantly monitors disaster information notified during emergencies, and if a new disaster occurs, it immediately acquires and processes that information. It also uses the GPS function of the device to identify the user's current location, allowing the system to accurately track the user's location.

[0826] The server obtains optimal hazard information based on the user's current location and disaster information. Hazard information includes information on the extent of the disaster's impact, the level of danger, and evacuation routes. Based on this information, the server performs AI analysis and calculates the safest evacuation route for the user. At this time, real-time footage of the damage situation obtained from disaster prevention cameras and drones can also be included in the analysis, enabling immediate response.

[0827] The calculated evacuation route is then provided to the user via voice guidance via the device. By using a voice chatbot, users can receive calm instructions even in an emergency. For example, specific instructions such as, "Go to the nearest emergency exit from your current location and use the stairs to go down to the first floor. From there, turn right and evacuate to a safe evacuation site" are provided.

[0828] Specific examples

[0829] For example, consider a case where an earthquake occurs and the user is on the third floor of a high-rise building.

[0830] 1. Obtaining disaster information:

[0831] The server instantly receives emergency earthquake alerts and processes the information.

[0832] 2. Current location:

[0833] The device's GPS function determines that the user's current location is on the third floor of a high-rise building.

[0834] 3. Obtaining hazard information and calculating evacuation routes:

[0835] The server retrieves the latest information from the hazard map based on the user's current location, and analyzes real-time disaster prevention camera and drone footage to determine the extent of damage to the building. This information is used to calculate the safest evacuation route. Specifically, the system calculates a route that will guide the user down to the first floor using the emergency stairs, then out of the building and to a safe evacuation site.

[0836] 4. Voice guidance:

[0837] The voice chatbot on the device guides the user based on the calculated evacuation route, providing specific instructions such as "Use the emergency stairs to go down to the first floor, then exit through the right-hand exit and head to the designated evacuation location," allowing the user to follow the instructions and evacuate safely.

[0838] In this way, by linking each means, the disaster prevention system of the present invention can support quick and appropriate evacuation actions even in the event of a disaster emergency, thereby ensuring the safety of users.

[0839] The processing flow will be explained below.

[0840] Program processing flow

[0841] Step 1: Loading the initial settings

[0842] The server loads the initial settings of the user's disaster prevention AI system, including settings to enable GPS, hazard maps, AI analysis, and voice chatbot functions.

[0843] When a user launches an app, the device enables each function based on the initial settings.

[0844] Step 2: Disaster detection

[0845] The server monitors the emergency alert system and obtains information when a new disaster alert occurs.

[0846] The terminal notifies the user of the acquired alert information.

[0847] Step 3: Get current location

[0848] The device uses GPS to determine the user's current location, which is then immediately sent to the server.

[0849] The server prepares to calculate hazard information and evacuation routes based on the current location obtained.

[0850] Step 4: Obtaining damage and hazard information

[0851] The server obtains the latest hazard information related to the user's current location from the hazard map system.

[0852] In addition, disaster prevention camera and drone footage will be analyzed to grasp the damage situation in real time.

[0853] Step 5: Calculate the optimal evacuation route

[0854] The server performs AI analysis based on the acquired current location, hazard information, and real-time damage status to calculate the optimal evacuation route.

[0855] For example, it calculates specific routes to evacuate to a safe location using emergency stairs in a building.

[0856] Step 6: Guidance by voice chatbot

[0857] The device activates the voice chatbot and starts guiding the user on evacuation routes.

[0858] The server generates instructions for each step of the calculated evacuation route.

[0859] The device reads out the generated instructions and guides the user in real time, helping the user to take each step from their current location to the evacuation site.

[0860] Specific examples of processing

[0861] Scenario: Evacuation in the event of an earthquake

[0862] 1. Step 1: Loading the initial settings

[0863] The server loads settings that enable GPS, hazard maps, AI analysis, and voice chatbot features.

[0864] The device launches the app and enables each function.

[0865] 2. Step 2: Disaster detection

[0866] The server detects emergency earthquake alerts and obtains the information.

[0867] The terminal notifies the user of the acquired alert information.

[0868] 3. Step 3: Get current location

[0869] The device uses GPS to determine the user's current location, and this location information is sent to the server.

[0870] 4. Step 4: Obtain damage and hazard information

[0871] The server obtains the latest hazard information about the area around the building from the hazard map.

[0872] In addition, disaster prevention camera and drone footage will be analyzed to determine the extent of damage to the building.

[0873] 5. Step 5: Calculate the optimal evacuation route

[0874] The server uses AI analysis to calculate the optimal evacuation route based on the user's current location, acquired hazard information, and damage situation.

[0875] For example, a route is calculated to use the emergency stairs to descend to the first floor and then evacuate to a safe location.

[0876] 6. Step 6: Guidance by voice chatbot

[0877] The device will launch a voice chatbot that will instruct the user, "Please evacuate immediately using the emergency stairs. First, head to the emergency door on your right and use the stairs to go down to the first floor. Then, exit through the exit on your right and head to the designated evacuation location."

[0878] The device provides specific voice instructions for each step of the evacuation route.

[0879] Example 1

[0880] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0881] In the event of a disaster emergency, it may be difficult for users to take prompt and appropriate evacuation actions. If disaster information is not obtained or the user's current location is not identified promptly, the user may take incorrect evacuation actions, which could result in an even more dangerous situation. Furthermore, if real-time acquisition of disaster and hazard information is insufficient, it is difficult to calculate an appropriate evacuation route, and the user's safety cannot be ensured. Furthermore, if the evacuation route is presented ambiguously, the user may not be able to evacuate appropriately. These issues need to be resolved.

[0882] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0883] In this invention, the server includes means for monitoring and acquiring disaster information, means for identifying the user's current location, means for acquiring hazard information based on the acquired disaster information and the current location, means for calculating an optimal evacuation route based on the acquired hazard information and the current location, and interactive guidance means for providing voice guidance to the user along the calculated evacuation route, thereby supporting quick and appropriate evacuation actions even in a disaster emergency and ensuring the user's safety.

[0884] "Disaster information" refers to information relating to emergencies such as earthquakes, floods, and fires, including the occurrence, progression, and extent of impact of the disaster.

[0885] A "location information determination means" is a device or system that uses technology such as GPS to accurately determine the user's current location.

[0886] "Hazard information" refers to detailed information about the extent of a disaster's impact, the level of danger, and evacuation routes.

[0887] An "evacuation route" is a specific route that a user should follow to evacuate safely, and usually refers to the shortest and safest route.

[0888] An "interactive guide" is an interactive system that provides instructions to a user using voice or text, and guides the user to specific actions.

[0889] An "image acquisition means" is a device or system that uses a camera, drone, etc. to capture images of the surrounding situation in real time and analyzes them.

[0890] An "AI model" is an analytical method that uses artificial intelligence, and is an algorithm that makes predictions and optimizations based on large amounts of data.

[0891] This invention relates to a disaster prevention system that supports users in taking prompt and appropriate evacuation actions in the event of a disaster. This system, through the cooperative operation of a server and terminals, monitors and acquires disaster information, identifies the user's current location, acquires hazard information, calculates evacuation routes, and provides guidance via a voice chatbot.

[0892] The server constantly monitors disaster information, and when a new disaster occurs, it immediately acquires and processes that information. Specifically, it uses APIs provided by public institutions such as the Japan Meteorological Agency to collect the latest disaster information and stores it in an internal database. This allows the server to analyze the type and urgency of the disaster and generate alerts as necessary.

[0893] The user's current location is determined using the device's GPS function. The device utilizes its built-in GPS module to obtain the user's precise location information and transmit it to the server. For example, if the user is on the third floor of a high-rise building, the coordinate data will be transmitted to the server, and the next analysis step will begin.

[0894] The server collects hazard information based on the user's current location and disaster information. Specifically, it uses a hazard map database, real-time disaster prevention camera footage, and drone footage to obtain information on the extent of the disaster's impact, the level of danger, and evacuation routes. Based on this information, the server uses an AI model to calculate the optimal evacuation route.

[0895] The AI ​​model uses a specific predictive algorithm as a generative AI model to provide users with the safest and quickest evacuation route, and the results are presented in a format that makes it easy for users to take action.

[0896] After calculating the evacuation route, the device uses a voice chatbot to provide guidance to the user. Based on the calculation results, the chatbot will provide specific instructions by voice, such as which direction the user should go and which stairs to use, allowing the user to act calmly even in an emergency.

[0897] As a concrete example, consider a situation where an earthquake occurs and the user is on the third floor of a high-rise building. In this case, the server receives an emergency earthquake alert and uses the device's GPS function to identify the user's current location. The server then analyzes real-time disaster prevention camera and drone footage and calculates a safe evacuation route. The device uses a voice chatbot to provide the user with specific instructions, such as "Use the emergency stairs to go down to the first floor, then exit through the exit on your right."

[0898] Below are some example prompts using a generative AI model:

[0899] Suppose an earthquake occurs and a user is on the third floor of a high-rise building. Calculate the safest evacuation route. The device's GPS information is used to determine the user's current location, and the server obtains information from real-time disaster prevention camera footage and hazard maps to calculate the optimal evacuation route. Then, provide the user with instructions via a voice chatbot, such as "Use the emergency stairs to go down to the first floor, then exit through the exit on the right."

[0900] In this way, by linking each means, the disaster prevention system of the present invention can support quick and appropriate evacuation actions even in the event of a disaster emergency, thereby ensuring the safety of users.

[0901] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0902] Step 1:

[0903] The server monitors and acquires disaster information.

[0904] Input: Disaster information from public institutions and disaster prevention-related APIs.

[0905] Data processing: Disaster information is analyzed and stored in an internal database. The urgency and type of disaster are evaluated.

[0906] Output: Parsed disaster information and alerts.

[0907] Specific operation: The server periodically calls the Japan Meteorological Agency's API to obtain the latest disaster information. It then stores the obtained disaster information in a database, analyzes its contents, and evaluates its urgency. If necessary, it generates an alert and notifies the entire system.

[0908] Step 2:

[0909] The device uses GPS functionality to identify the user's current location.

[0910] Input: GPS data from the device.

[0911] Data processing: Analyze GPS data to obtain latitude and longitude location information.

[0912] Output: The user's current location.

[0913] Specific operation: The device uses the built-in GPS module to obtain the user's location information. After the location information is determined, it is sent to the server in the form of latitude and longitude.

[0914] Step 3:

[0915] The server acquires hazard information based on the disaster information and current location information acquired.

[0916] Input: Disaster information, user's current location information.

[0917] Data processing: Analyze hazard maps and real-time disaster prevention camera and drone footage to assess the level of risk and the extent of impact.

[0918] Output: Latest hazard information.

[0919] How it works: The server collects hazard information from an internal database and external sources (disaster prevention cameras, drone footage), then uses AI analysis to obtain the latest hazard information related to the user's current location.

[0920] Step 4:

[0921] The server calculates the optimal evacuation route based on the latest hazard information.

[0922] Input: Hazard information, user's current location information.

[0923] Data processing: Calculate safe evacuation routes using generative AI models.

[0924] Output: Optimal evacuation route.

[0925] Specific operation: The server inputs the acquired hazard information and the user's current location information into the AI ​​model to calculate a safe and quick evacuation route. The calculation results are then output as detailed route guidance for the user to take specific actions.

[0926] Step 5:

[0927] The terminal uses an interactive guidance means for providing voice guidance of the calculated evacuation route.

[0928] Input: Optimal evacuation route.

[0929] Data processing: Convert evacuation route information into voice instructions.

[0930] Output: Voice guidance.

[0931] Specific operation: The device analyzes the evacuation route information received from the server and provides guidance to the user through a voice chatbot. For example, specific instructions are provided via voice, such as "Use the emergency stairs to go down to the first floor, then exit through the exit on the right."

[0932] At each step, the system collects the necessary data and uses data analysis and AI models to generate detailed instructions to assist users in optimal evacuation behavior, thereby ensuring their safety in the event of a disaster.

[0933] (Application example 1)

[0934] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0935] In order for users to take appropriate and prompt evacuation actions during disaster emergencies, they need to obtain disaster information in real time, calculate the optimal evacuation route based on their current location, and provide accurate and specific guidance. However, conventional disaster prevention systems have issues with the speed of information acquisition and the accuracy of guidance, and there are no systems that can handle transportation methods such as autonomous vehicles. This has led to the problem of not being able to fully ensure user safety during disaster emergencies.

[0936] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0937] In this invention, the server includes means for monitoring and acquiring disaster information, means for determining the user's current location, means for acquiring danger information based on the acquired disaster information and the user's current location, means for calculating an optimal evacuation route based on the acquired danger information and the user's current location, an interactive guidance means for providing voice guidance to the user along the calculated evacuation route, and means for acquiring and analyzing damage status in real time from images captured by disaster prevention monitoring devices and autonomous aircraft devices. This enables real-time information acquisition and analysis, calculation of the optimal evacuation route, and specific and accurate voice guidance in the event of a disaster. This system is also compatible with autonomous vehicles, providing support for rapid and appropriate evacuation behavior to ensure the safety of passengers.

[0938] A "disaster emergency" is a situation in which an earthquake, fire, flood, or other natural or man-made disaster occurs and immediate evacuation action is required.

[0939] A "user" is a general public or a specific person who uses the disaster prevention system to take prompt and appropriate evacuation action.

[0940] "Quick and appropriate evacuation actions" are actions that will allow people to reach evacuation shelters in the most efficient and safe manner when a disaster occurs.

[0941] A "disaster prevention system" is a set of devices and software that provides functions such as monitoring, acquiring, and analyzing disaster information, calculating evacuation routes, and providing guidance to support users in the event of a disaster emergency.

[0942] "Disaster information" refers to information about natural and man-made disasters such as earthquakes, fires, and floods.

[0943] "Monitoring and acquisition means" refers to technologies and devices for continuously observing and collecting disaster information in real time.

[0944] "Location measurement means for identifying the current location" refers to technology and devices for determining the current location of a user or object using location information technology such as GPS.

[0945] "Means for acquiring risk information" refers to the technology and devices for collecting and analyzing information about the disaster situation and the extent of damage.

[0946] The "means for calculating an optimal evacuation route" refers to a technology and device for calculating the safest and most efficient evacuation route for a user based on real-time on-site conditions.

[0947] "Interactive guidance means" refers to technology and devices that provide specific instructions and guidance to users through voice and text.

[0948] "Disaster prevention monitoring equipment" refers to devices such as cameras and sensors that monitor the occurrence of disasters in real time.

[0949] "Autonomous Aircraft Footage" means real-time footage captured by an autonomously flying device, such as a drone.

[0950] "Means for acquiring and analyzing damage status" refers to technology and equipment for collecting and analyzing damage status at the site in real time.

[0951] This invention relates to a disaster prevention system that supports users in taking prompt and appropriate evacuation actions in the event of a disaster. This system is realized through the cooperative operation of a server, terminals, and multiple dedicated devices and software.

[0952] First, the server monitors disaster information in real time and quickly obtains it when a disaster occurs. Disaster information includes data on natural and man-made disasters such as earthquakes, fires, and floods. This disaster information is obtained using APIs provided by specialized disaster monitoring organizations and local disaster prevention systems.

[0953] The device then uses GPS to determine the user's current location, allowing it to determine exactly where the user is and calculate the most appropriate evacuation route.The device is also installed in mobile devices such as the user's smartphone or an in-car system.

[0954] The server obtains danger information based on the acquired disaster information and the user's current location information. This danger information includes the extent of the disaster's impact, the level of danger, the current damage situation on site, and information on evacuation routes. Video data transmitted in real time from disaster prevention monitoring devices and autonomous aircraft devices (drones) is used to obtain the danger information. This makes it possible to calculate the optimal evacuation route based on the latest information on site.

[0955] The calculated evacuation route is then given to the user via voice guidance. An interactive guidance system is used to provide specific instructions to help the user stay calm. For example, a specific prompt sentence such as "We will guide you to the evacuation route. Turn right and proceed to a safe evacuation location" is generated.

[0956] The entire system is implemented using the Python programming language, with API access using the requests library, location data calculation using the geopy library, and voice synthesis technology used to generate voice guidance, effectively carrying out a series of complex data processing and user guidance.

[0957] As a specific example of operation, if an earthquake occurs and the user is in an autonomous vehicle, the server obtains earthquake information in real time and determines the vehicle's current location. It then calculates the optimal evacuation route and issues instructions to the autonomous driving system through the vehicle's on-board system. Specific evacuation instructions are provided to passengers through audio guidance and display. This series of operations allows the user to evacuate quickly and safely.

[0958] Examples of prompts include:

[0959] Get the latest earthquake information.

[0960] Check the current vehicle location using GPS.

[0961] "Calculate the best route based on your current location and nearby safe evacuation locations."

[0962] "Please provide voice instructions for evacuation routes. Instruct them to 'turn right and proceed to a safe evacuation area.'"

[0963] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0964] Step 1:

[0965] The server monitors and acquires disaster information in real time.

[0966] (Input) Real-time disaster information (earthquakes, fires, floods, etc.) provided by various disaster monitoring organizations and disaster prevention systems

[0967] (Data processing) Collect the latest disaster data using disaster information API

[0968] (Output) Update the internal database that holds the latest disaster information

[0969] Specifically, the server uses the requests library to periodically retrieve data from disaster information providers and store it in an internal database.

[0970] Step 2:

[0971] The terminal uses the GPS function to determine the user's current location.

[0972] (Input) GPS data from the user's device (smartphone or in-car system)

[0973] (Data processing) Use the GPS function to obtain the user's latitude and longitude

[0974] (Output) Current location of the user (latitude and longitude)

[0975] Specifically, the device uses its built-in GPS module to obtain current location data (latitude and longitude).

[0976] Step 3:

[0977] The server obtains danger information based on the acquired disaster information and the user's current location information.

[0978] (Input) Latest disaster information and user's current location information

[0979] (Data processing) Integrate disaster information and user location information, and perform search and filter processing to extract relevant danger information from the danger information database.

[0980] (Output) Risk information related to the user's current location

[0981] Specifically, the server retrieves danger information related to a specific area through a database query based on disaster information and location information.

[0982] Step 4:

[0983] The server calculates the optimal evacuation route based on the acquired danger information and the current location.

[0984] (Input) Risk information and user's current location information

[0985] (Data processing) Route calculation algorithm that calculates the shortest and safest route taking into account geographical information and disaster conditions

[0986] (Output) Optimal evacuation route

[0987] Specifically, the server uses a map database and a route calculation algorithm to calculate the optimal route from the user's current location to a safe evacuation site.

[0988] Step 5:

[0989] The terminal provides the user with voice guidance on the calculated evacuation route.

[0990] (Input) Calculated evacuation route

[0991] (Data processing) Convert evacuation route information into voice guidance prompts and generate voice data using voice synthesis technology

[0992] (Output) Voice guidance instructions

[0993] Specifically, the terminal generates instructions in the interactive guidance system based on the calculated evacuation route, converts them into voice using a speech synthesis engine, and provides guidance to the user.

[0994] Step 6:

[0995] The server acquires and analyzes damage information in real time from disaster prevention monitoring equipment and autonomous aircraft equipment footage.

[0996] (Input) Real-time video data from disaster prevention monitoring devices and autonomous aircraft devices (drones, etc.)

[0997] (Data processing) Analysis of video data, understanding of disaster situation, identification of damage extent

[0998] (Output) Latest damage situation information

[0999] Specifically, the server analyzes video data sent from disaster prevention cameras and drones, determines the extent and situation of the damage, and updates the danger information database.

[1000] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1001] This invention is a disaster prevention system that helps users take prompt and appropriate evacuation actions in the event of a disaster emergency, and in particular has the function of recognizing the user's emotional state and providing appropriate guidance and psychological support based on that. This system, through the cooperative operation of a server and terminal, monitors and acquires disaster information, identifies the user's current location, acquires hazard information, calculates evacuation routes, recognizes emotions using an emotion engine, and provides guidance using a voice chatbot.

[1002] Server and terminal roles

[1003] The server constantly monitors disaster information notified in emergencies, and when a new disaster occurs, it immediately acquires and processes that information. It also uses the GPS function of the device to pinpoint the user's current location, accurately determining where the user is.

[1004] Hazard information is obtained based on the user's current location and disaster information. This includes information on the extent of the disaster's impact, the level of risk, and evacuation routes. The obtained hazard information and real-time disaster prevention camera and drone footage are analyzed, and the safest evacuation route for the user is calculated using AI analysis.

[1005] Emotion Engine Functions

[1006] The system also features an emotion engine that recognizes the user's emotional state in real time. This emotion engine can grasp the user's stress level and emotional state by analyzing the user's facial expressions, voice tone, and even physical movements. If the emotion engine detects the user's state of panic or high stress, it dynamically adjusts the tone and content of the voice guidance to help the user stay calm.

[1007] The emotion engine also provides relaxation guidance and psychological support. For example, if a user is experiencing extreme panic, the voice chatbot can provide relaxation guidance such as "Take a deep breath, slowly calm down, and everything will be okay," thereby reducing the user's stress.

[1008] Specific examples

[1009] As a specific example, consider a case where an earthquake occurs and the user is on the third floor of a high-rise building.

[1010] 1. Obtaining disaster information:

[1011] The server instantly receives emergency earthquake alerts and processes the information.

[1012] 2. Current location:

[1013] The GPS function of the device determines that the user's current location is on the third floor of a high-rise building.

[1014] 3. Obtaining hazard information and calculating evacuation routes:

[1015] The server retrieves the latest information from the hazard map based on the user's current location and analyzes real-time disaster prevention camera and drone footage to determine the extent of damage to the building. This information is used to calculate the safest evacuation route. Specifically, the system calculates a route that will lead the user down the emergency stairs to the first floor, then out of the building and to a safe evacuation site.

[1016] 4. Emotion recognition and guidance using emotion engine:

[1017] The device's emotion engine analyzes the user's facial expressions and voice tone in real time to understand their emotional state. If the user is in a panic, the emotion engine softens the tone of the voice guidance and provides relaxation instructions. Specific and calm instructions are provided via voice, such as "Take a deep breath and stay calm. Use the emergency stairs to go down to the first floor. Exit through the exit on the right and head to the designated evacuation site."

[1018] In this way, the disaster prevention system of the present invention can ensure the safety of users by supporting prompt and appropriate evacuation behavior even in the event of a disaster and encouraging them to evacuate calmly while taking into account the user's emotional state. This system is expected to enable users to act calmly and evacuate effectively even in an emergency.

[1019] The processing flow will be explained below.

[1020] Program processing flow

[1021] Step 1: Loading the initial settings

[1022] The server loads the initial settings of the user's disaster prevention AI system, including settings to enable GPS, hazard maps, AI analysis, emotion engines, and voice chatbot functions.

[1023] When a user launches an app, the device enables each function based on the initial settings.

[1024] Step 2: Disaster detection

[1025] The server monitors the emergency alert system and immediately obtains information about any new disaster alerts that occur.

[1026] The terminal notifies the user of the acquired alert information.

[1027] Step 3: Get current location

[1028] The device uses GPS to determine the user's current location, and this information is sent to the server in real time.

[1029] The server prepares to calculate hazard information and evacuation routes based on the current location obtained.

[1030] Step 4: Obtaining damage and hazard information

[1031] The server obtains the latest hazard information related to the user's current location from the hazard map system.

[1032] In addition, disaster prevention camera and drone footage will be analyzed to grasp the damage situation in real time.

[1033] Step 5: Calculate the optimal evacuation route

[1034] The server performs AI analysis based on the acquired current location, hazard information, and real-time damage status to calculate the optimal evacuation route.

[1035] For example, it calculates specific routes to evacuate to a safe location using emergency stairs in a building.

[1036] Step 6: Start Emotion Recognition

[1037] The device activates an emotion engine to analyze the user's emotional state in real time, judging from facial expressions, voice tone, and body movements to determine whether the user is in a panic state.

[1038] Step 7: Guidance by voice chatbot

[1039] The device activates the voice chatbot and starts guiding the user on evacuation routes.

[1040] The server generates instructions for each step of the calculated evacuation route.

[1041] The device dynamically adjusts the tone and content of the voice guidance based on the analysis results of the emotion engine. For example, if the user is in a panic, it generates and provides specific instructions, including relaxation guidance such as "Take a deep breath and calm down."

[1042] Specific examples

[1043] Scenario: Evacuation in the event of an earthquake

[1044] 1. Step 1: Loading the initial settings

[1045] The server loads settings that enable GPS, hazard maps, AI analysis, emotion engines, and voice chatbots.

[1046] The device launches the app and enables each function.

[1047] 2. Step 2: Disaster detection

[1048] The server detects emergency earthquake alerts and retrieves the information immediately.

[1049] The terminal notifies the user of the acquired alert information.

[1050] 3. Step 3: Get current location

[1051] The device uses GPS to determine the user's current location, and this location information is sent to the server.

[1052] 4. Step 4: Obtain damage and hazard information

[1053] The server obtains the latest hazard information about the area around the building from the hazard map.

[1054] In addition, disaster prevention camera and drone footage will be analyzed to determine the extent of damage to the building.

[1055] 5. Step 5: Calculate the optimal evacuation route

[1056] The server uses AI analysis to calculate the optimal evacuation route based on the user's current location, acquired hazard information, and damage situation.

[1057] For example, a route is calculated to use the emergency stairs to descend to the first floor and then evacuate to a safe location.

[1058] 6. Step 6: Start Emotion Recognition

[1059] The device's emotion engine analyzes the user's emotional state in real time, determining whether the user is panicking based on facial expressions and tone of voice.

[1060] 7. Step 7: Guidance by voice chatbot

[1061] The device will launch a voice chatbot that will instruct the user, "Please evacuate immediately using the emergency stairs. First, head to the emergency door on your right and use the stairs to go down to the first floor. Then, exit through the exit on your right and head to the designated evacuation location."

[1062] Based on the analysis results of the emotion engine, the device will soften the tone of the voice guidance and provide specific instructions, including relaxation advice such as "Take a deep breath and stay calm. It's okay." In this way, the device helps the user calmly grasp the situation and take appropriate evacuation actions.

[1063] Example 2

[1064] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1065] In the event of a disaster or emergency, it is extremely important for users to take prompt and appropriate evacuation actions. However, systems that efficiently integrate multiple elements, such as obtaining disaster information, calculating evacuation routes, and identifying the user's current location, and that also provide psychological support according to the user's emotional state, are still insufficient. Providing appropriate guidance is particularly difficult for users who are in a state of panic or high stress. Therefore, a comprehensive disaster prevention system that enables users to take appropriate evacuation actions while remaining calm is needed.

[1066] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1067] In this invention, the server includes a means for monitoring and acquiring disaster information, a location information acquisition means for identifying the user's current location, and a means for acquiring hazard information based on the acquired disaster information and current location. This allows for the calculation of a quick and appropriate evacuation route based on the user's current location information and the provision of voice guidance. The system also includes an automated dialogue system for providing voice guidance to the user along the calculated evacuation route, an emotion recognition engine for recognizing the user's emotional state in real time, and a means for dynamically adjusting the guidance content and guidance tone based on the recognized emotional state. This allows the user to take calm and effective evacuation actions even during a disaster, thereby ensuring the user's overall safety.

[1068] "Disaster information" refers to information related to natural disasters, including the occurrence status and extent of impact of earthquakes, tsunamis, typhoons, floods, etc., as well as emergency warnings.

[1069] "Location information acquisition means" refers to technology or equipment for identifying the user's current location, and primarily refers to a location identification system that uses GPS.

[1070] "Hazard information" refers to detailed information about the danger areas and scope of impact caused by disasters, the need for evacuation, etc.

[1071] "Means for calculating the optimal evacuation route" refers to a method or system for calculating the safest and quickest evacuation route for a user based on the acquired information.

[1072] An "automated dialogue system" is a system that provides information through dialogue with a user, and is particularly intended to give instructions to users through voice guidance or chatbots.

[1073] An "emotion recognition engine" refers to technology or software that analyzes a user's facial expressions, tone of voice, body movements, etc. to recognize the user's emotional state (e.g., panic, calm, stress, etc.) in real time.

[1074] "Means for acquiring and analyzing damage situations in real time" refers to methods and technologies for acquiring video data sent from disaster prevention cameras, unmanned aerial vehicles, etc. in real time and analyzing the extent of damage and the scope of impact.

[1075] "Specific instructions at each step provided by voice guidance" refers to a method of providing detailed and specific voice guidance for each step, such as directions to travel by car and walking routes, in order to clearly communicate evacuation routes to the user.

[1076] This invention is a disaster prevention system that helps users take prompt and appropriate evacuation actions in the event of a disaster. This system, through the cooperative operation of a server and terminals, monitors and acquires disaster information, identifies current locations, acquires hazard information, calculates evacuation routes, recognizes emotions using an emotion engine, and provides guidance using a voice chatbot.

[1077] Hardware and software used

[1078] server

[1079] Hardware: High-performance database and analysis servers

[1080] Software: Database management system (e.g., MySQL), disaster information acquisition API (e.g., Japan Meteorological Agency API), AI analysis engine (e.g., TensorFlow)

[1081] Terminal

[1082] Hardware: Smartphone or tablet with GPS sensor, camera, and microphone

[1083] Software: GPS location acquisition software, voice recognition programs (e.g., Google Speech-to-Text), emotion recognition engines

[1084] System Operation

[1085] 1. Monitoring and obtaining disaster information

[1086] The server constantly monitors various disaster information sources (e.g., the Japan Meteorological Agency, the Fire and Disaster Management Agency, etc.), and when new disaster information occurs, it immediately acquires the information and stores it in a database. It analyzes important parameters related to the disaster information (e.g., type of disaster, location of occurrence, scale, etc.) and evaluates its importance.

[1087] 2. Identifying your current location

[1088] The device uses the built-in GPS sensor to determine the user's current location, which is then periodically sent to a server. The GPS data is stored in the form of latitude and longitude coordinates, and is also subjected to a correction algorithm to improve accuracy.

[1089] 3. Acquisition and analysis of hazard information

[1090] The server obtains hazard information about the extent of the disaster impact and evacuation routes based on the user's current location, including analysis of hazard map databases, real-time disaster prevention camera footage, and drone footage, to identify high-risk areas.

[1091] 4. Evacuation route calculation

[1092] The server calculates the optimal evacuation route based on the acquired hazard information. The evacuation route is calculated using an AI algorithm (e.g., a pathfinding algorithm), and detailed instructions are prepared for each step of the evacuation route.

[1093] 5. Real-time recognition of emotional states

[1094] The device's camera and microphone are used to analyze the user's facial expressions and voice tone in real time. The emotion engine uses this data to determine the user's emotional state (e.g., panic, calm, stress, etc.) and sends that information to the server.

[1095] 6. Providing appropriate guidance and psychological support

[1096] Based on the evacuation route information sent from the server and the user's emotional state as recognized by the emotion engine, the device dynamically provides guidance to the user. The voice chatbot provides relaxation instructions and specific evacuation instructions in a gentle tone.

[1097] Specific examples

[1098] Below is a specific example where an earthquake occurs and the user is on the third floor of a high-rise building.

[1099] 1. Obtaining disaster information

[1100] The server instantly receives emergency earthquake alerts and processes the information.

[1101] 2. Identifying your current location

[1102] The device's GPS function determines that the user's current location is on the third floor of a high-rise building.

[1103] 3. Obtaining hazard information and calculating evacuation routes

[1104] The server analyzes the latest hazard information and real-time footage from disaster prevention cameras and drones to calculate the safest evacuation route, guiding the user to the first floor via the emergency stairs, then out of the building and towards a safe evacuation site.

[1105] 4. Emotion recognition and guidance using an emotion engine

[1106] The device's emotion engine analyzes the user's facial expressions and voice tone in real time, and if the user is in a panic, it provides gentle voice guidance including instructions to relax. For example, it might say, "Take a deep breath and stay calm. Use the emergency stairs to go down to the first floor. Exit through the exit on your right and head to the designated evacuation area."

[1107] Prompt Sentence Examples

[1108] "Take a deep breath and stay calm. Use the emergency stairs to safely descend to the first floor. Exit through the exit on your right and proceed to the designated evacuation area."

[1109] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1110] Specific processing flow of the program

[1111] Step 1: Monitoring and obtaining disaster information

[1112] The server constantly monitors various disaster information sources (e.g., the Japan Meteorological Agency API, the Fire and Disaster Management Agency database, etc.) When new disaster information occurs, it immediately acquires that information and stores it in the database.

[1113] Input: New disaster data from disaster sources

[1114] Data processing: Analyze the type, location, and scale of disasters to assess their importance

[1115] Output: Disaster information stored in a database

[1116] Specific operation: Sends a request to the disaster information API, receives and analyzes new data, and registers it in the database.

[1117] Step 2: Determine your current location

[1118] The device uses the built-in GPS sensor to determine the user's current location, which is then periodically sent to a server.

[1119] Input: Location data (latitude and longitude) from the GPS sensor

[1120] Data processing: Position data is corrected using a correction algorithm to improve accuracy

[1121] Output: User's current location (latitude and longitude)

[1122] Specific operation: Enables the GPS function to obtain the current location and transmits it to the server in real time.

[1123] Step 3: Obtain and analyze hazard information

[1124] Based on the current location information sent, the server obtains and analyzes hazard information from hazard map databases, disaster prevention cameras, drone footage, etc.

[1125] Input: User's current location and data from hazard information sources

[1126] Data processing: Integrate and analyze risk information from hazard maps and real-time video data

[1127] Output: Hazard information based on current location

[1128] Specific operation: Data is obtained from the hazard map API, and the video data is analyzed using an image recognition algorithm to assess the level of danger.

[1129] Step 4: Calculate evacuation routes

[1130] The server uses an AI algorithm to calculate the optimal evacuation route based on the acquired hazard information and the transmitted current location information.

[1131] Input: Current location and hazard information

[1132] Data processing: Calculate the optimal route using a route search algorithm

[1133] Output: The optimal evacuation route for the user

[1134] Specific operation: Uses an AI algorithm (e.g., A algorithm) to calculate an evacuation route and send the results to the device.

[1135] Step 5: Real-time recognition of emotional states

[1136] The device uses a camera and microphone to analyze the user's facial expressions, voice tone, body movements, etc. in real time. The emotion engine recognizes the user's emotional state based on this data and sends it to the server.

[1137] Input: Real-time video and audio data from camera and microphone

[1138] Data processing: Analyzing emotions with facial and voice recognition algorithms

[1139] Output: User's emotional state data (e.g., panic, calm, etc.)

[1140] What it does: Facial expression analysis software and voice recognition programs analyze raw data to detect emotional states.

[1141] Step 6: Providing appropriate guidance and psychological support

[1142] Based on the evacuation route information and emotional state data sent from the server, the device dynamically provides guidance to the user through a voice chatbot, with the tone and content of the guidance adjusted according to the user's emotional state.

[1143] Input: Evacuation route information and emotional state data

[1144] Data processing: Dynamically adjust the content and tone of announcements according to emotional state

[1145] Output: Audio prompts provided to the user

[1146] Specific actions: Generate prompts such as "Take a deep breath and stay calm. Use the emergency stairs to go down to the first floor. Exit through the exit on your right and head to the designated evacuation location," and provide audio guidance to the user.

[1147] This is expected to enable users to remain calm and evacuate effectively in an emergency.

[1148] (Application example 2)

[1149] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1150] In the event of a disaster or emergency, it is extremely important for users to take prompt and appropriate evacuation actions. However, in emergencies, it is easy to fall into panic and confusion, making it difficult to make calm decisions. Furthermore, conventional disaster prevention systems generally only provide disaster information and evacuation routes, but do not provide support that takes into account the user's emotional state. Therefore, there is a need for a disaster prevention system that can recognize the user's emotional state in real time and provide appropriate guidance and psychological support.

[1151] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1152] In this invention, the server includes means for monitoring and acquiring disaster information, means for determining the user's current location, means for acquiring hazard information based on the acquired disaster information and the current location, means for calculating an optimal evacuation route based on the acquired hazard information and the current location, dialogue device means for providing voice guidance to the user along the calculated evacuation route, emotion recognition means for detecting the user's emotional state, and means for adjusting the content and tone of the guidance provided based on the user's emotional state. This allows the user to calmly take evacuation action and take appropriate measures even in an emergency.

[1153] "Disaster emergency" refers to the period of time when a natural disaster such as an earthquake, tsunami, fire, or flood, or a similar emergency occurs.

[1154] "User" refers to an individual or organization using this disaster prevention system.

[1155] "Evacuation behavior" refers to planned actions to move to a safe place.

[1156] "Disaster prevention system" refers to a comprehensive set of devices and means designed to assist users in ensuring their safety during disaster emergencies.

[1157] "Disaster information" refers to data and reports on the occurrence of a disaster, the extent of its impact, etc.

[1158] "Location measurement means" refers to technologies or devices used to determine a user's current location.

[1159] "Hazard information" refers to data on dangerous areas caused by disasters and the extent of their impact.

[1160] An "evacuation route" refers to a recommended route for a user to safely evacuate in the event of a disaster.

[1161] "Calculation means" refers to algorithms or devices for calculating optimal evacuation routes based on disaster and hazard information.

[1162] "Interactive device means" refers to equipment or software for exchanging information with a user through voice or text.

[1163] "Emotion recognition means" refers to technology or devices that analyze and evaluate a user's emotional state from facial expressions, vocal tone, body movements, etc.

[1164] "Emotional state" refers to a user's mental and emotional response or state.

[1165] "Content of guidance" refers to specific items of information or instructions provided to the user.

[1166] "Tone" refers to the tone and atmosphere of the voice used when providing audio guidance.

[1167] This invention is a disaster prevention system that supports users in taking prompt and appropriate evacuation actions in the event of a disaster. This system is mainly composed of a server and terminals working together, and has the following functions:

[1168] Server Roles

[1169] The server constantly monitors disaster information and immediately acquires and processes any new disaster information when it occurs. Disaster information is acquired using APIs publicly available via the Internet (e.g., government disaster information APIs).

[1170] Next, the device's GPS function is used to determine the user's current location. Once the current location is determined, the server obtains hazard information based on that location. This includes referencing hazard maps and analyzing real-time disaster prevention camera and drone footage. From the obtained hazard information, the server uses AI algorithms to calculate the optimal evacuation route.

[1171] Device Role

[1172] The user's device uses GPS to send its current location to a server. It also has an emotion engine that recognizes the user's emotional state in real time. The emotion engine analyzes the user's facial expressions, vocal tone, and body movements to detect panic or high stress.

[1173] The terminal further includes a dialogue device means for providing voice guidance along the calculated evacuation route to the user. The dialogue device means adjusts the content and tone of the guidance based on the emotional state of the user and provides appropriate instructions. For example, if the user is in a panic, the dialogue device means softens the tone of the voice guidance and provides instructions such as "Take a deep breath and stay calm. Use the emergency stairs to go down to the first floor."

[1174] Hardware and Software Configuration

[1175] On the server side, a database server and an AI analysis server are used. The database server stores disaster and hazard information, while the AI ​​analysis server calculates evacuation routes and performs emotion recognition. Specific software includes the Python library requests to obtain disaster information and the geopy library to identify the current location. Furthermore, face_recognition and cv2 (OpenCV) are used for emotion recognition.

[1176] Specific examples

[1177] Let's imagine that an earthquake occurs and the user is on the third floor of a high-rise building. In this case, the server immediately obtains earthquake information, uses the device's GPS function to identify the user's location (third floor of a high-rise building), obtains hazard information based on the user's current location, and calculates the optimal evacuation route. In addition, the device's emotion engine analyzes the user's emotional state, and if it detects a state of panic, it changes the tone of the voice guidance and provides specific instructions. For example, it may say, "Take a deep breath and stay calm. Use the emergency stairs to go down to the first floor."

[1178] Example prompts for generative AI models

[1179] "Receive an emergency earthquake alert, identify the user's current location, calculate the optimal evacuation route, and guide the user to evacuate calmly. Also, if the user's emotional state is panicked, notify them and provide psychological support."

[1180] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1181] Step 1:

[1182] Obtaining disaster information

[1183] The server sends a request to an API that provides disaster information via the Internet (e.g., a government disaster information API) to obtain the latest disaster information. The input is the API request, and the output is JSON data of the disaster information. The server analyzes this data and identifies currently ongoing disasters.

[1184] Step 2:

[1185] Identifying your current location

[1186] The device uses its built-in GPS to determine the user's current location and sends that information to the server. The input is a GPS signal, and the output is the user's latitude and longitude coordinate data. The server receives this data and combines it with disaster information for further processing.

[1187] Step 3:

[1188] Obtaining hazard information

[1189] The server retrieves relevant hazard information from a hazard map based on the user's current location. If necessary, it analyzes real-time video data from disaster prevention cameras and aerial devices (drones). The input is the user's coordinate data and disaster information, and the output is hazard information. The server uses this to collect data on specific dangerous areas and safe evacuation routes.

[1190] Step 4:

[1191] Evacuation route calculation

[1192] The server calculates the optimal evacuation route based on the acquired hazard information and the user's current location. The input is the hazard information and the user's coordinate data, and the output is the calculated evacuation route. In this process, the server uses an artificial intelligence (AI) algorithm to derive the optimal route.

[1193] Step 5:

[1194] Recognition of emotional states

[1195] The device uses a camera and microphone to recognize the user's emotional state. The device's emotion engine analyzes the user's facial expressions, voice tone, and body movements to identify the user's emotional state in real time. The input is the user's video and audio data, and the output is the analyzed emotional state (e.g., panicked, calm, etc.).

[1196] Step 6:

[1197] Providing audio guidance

[1198] The device provides voice guidance to the user based on the evacuation route information and emotional state data received from the server. If the user is in a panic state, the device softens the tone of the voice guidance and adds instructions to stay calm. The input is the evacuation route information and emotional state data, and the output is voice guidance for the user.

[1199] Step 7:

[1200] Emergency contact function

[1201] In the event of a disaster, the device automatically notifies the user's designated emergency contacts (family, friends, etc.) of the current location and situation. The input is the user's current location and disaster information, and the output is an emergency contact message. This function enables the user to receive prompt and appropriate assistance.

[1202] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1203] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1204] 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 the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1205] [Fourth embodiment]

[1206] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1207] 7, a 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.

[1208] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1209] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1210] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1211] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1212] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1213] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1214] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1215] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[1216] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1217] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1218] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1219] This invention relates to a disaster prevention system that supports users in taking prompt and appropriate evacuation actions in the event of a disaster. This system, through the cooperative operation of a server and terminals, monitors and acquires disaster information, identifies the user's current location, acquires hazard information, calculates evacuation routes, and provides guidance via a voice chatbot.

[1220] The server constantly monitors disaster information notified during emergencies, and if a new disaster occurs, it immediately acquires and processes that information. It also uses the GPS function of the device to identify the user's current location, allowing the system to accurately track the user's location.

[1221] The server obtains optimal hazard information based on the user's current location and disaster information. Hazard information includes information on the extent of the disaster's impact, the level of danger, and evacuation routes. Based on this information, the server performs AI analysis and calculates the safest evacuation route for the user. At this time, real-time footage of the damage situation obtained from disaster prevention cameras and drones can also be included in the analysis, enabling immediate response.

[1222] The calculated evacuation route is then provided to the user via voice guidance via the device. By using a voice chatbot, users can receive calm instructions even in an emergency. For example, specific instructions such as, "Go to the nearest emergency exit from your current location and use the stairs to go down to the first floor. From there, turn right and evacuate to a safe evacuation site" are provided.

[1223] Specific examples

[1224] For example, consider a case where an earthquake occurs and the user is on the third floor of a high-rise building.

[1225] 1. Obtaining disaster information:

[1226] The server instantly receives emergency earthquake alerts and processes the information.

[1227] 2. Current location:

[1228] The device's GPS function determines that the user's current location is on the third floor of a high-rise building.

[1229] 3. Obtaining hazard information and calculating evacuation routes:

[1230] The server retrieves the latest information from the hazard map based on the user's current location, and analyzes real-time disaster prevention camera and drone footage to determine the extent of damage to the building. This information is used to calculate the safest evacuation route. Specifically, the system calculates a route that will guide the user down to the first floor using the emergency stairs, then out of the building and to a safe evacuation site.

[1231] 4. Voice guidance:

[1232] The voice chatbot on the device guides the user based on the calculated evacuation route, providing specific instructions such as "Use the emergency stairs to go down to the first floor, then exit through the right-hand exit and head to the designated evacuation location," allowing the user to follow the instructions and evacuate safely.

[1233] In this way, by linking each means, the disaster prevention system of the present invention can support quick and appropriate evacuation actions even in the event of a disaster emergency, thereby ensuring the safety of users.

[1234] The processing flow will be explained below.

[1235] Program processing flow

[1236] Step 1: Loading the initial settings

[1237] The server loads the initial settings of the user's disaster prevention AI system, including settings to enable GPS, hazard maps, AI analysis, and voice chatbot functions.

[1238] When a user launches an app, the device enables each function based on the initial settings.

[1239] Step 2: Disaster detection

[1240] The server monitors the emergency alert system and obtains information when a new disaster alert occurs.

[1241] The terminal notifies the user of the acquired alert information.

[1242] Step 3: Get current location

[1243] The device uses GPS to determine the user's current location, which is then immediately sent to the server.

[1244] The server prepares to calculate hazard information and evacuation routes based on the current location obtained.

[1245] Step 4: Obtaining damage and hazard information

[1246] The server obtains the latest hazard information related to the user's current location from the hazard map system.

[1247] In addition, disaster prevention camera and drone footage will be analyzed to grasp the damage situation in real time.

[1248] Step 5: Calculate the optimal evacuation route

[1249] The server performs AI analysis based on the acquired current location, hazard information, and real-time damage status to calculate the optimal evacuation route.

[1250] For example, it calculates specific routes to evacuate to a safe location using emergency stairs in a building.

[1251] Step 6: Guidance by voice chatbot

[1252] The device activates the voice chatbot and starts guiding the user on evacuation routes.

[1253] The server generates instructions for each step of the calculated evacuation route.

[1254] The device reads out the generated instructions and guides the user in real time, helping the user to take each step from their current location to the evacuation site.

[1255] Specific examples of processing

[1256] Scenario: Evacuation in the event of an earthquake

[1257] 1. Step 1: Loading the initial settings

[1258] The server loads settings that enable GPS, hazard maps, AI analysis, and voice chatbot features.

[1259] The device launches the app and enables each function.

[1260] 2. Step 2: Disaster detection

[1261] The server detects emergency earthquake alerts and obtains the information.

[1262] The terminal notifies the user of the acquired alert information.

[1263] 3. Step 3: Get current location

[1264] The device uses GPS to determine the user's current location, and this location information is sent to the server.

[1265] 4. Step 4: Obtain damage and hazard information

[1266] The server obtains the latest hazard information about the area around the building from the hazard map.

[1267] In addition, disaster prevention camera and drone footage will be analyzed to determine the extent of damage to the building.

[1268] 5. Step 5: Calculate the optimal evacuation route

[1269] The server uses AI analysis to calculate the optimal evacuation route based on the user's current location, acquired hazard information, and damage situation.

[1270] For example, a route is calculated to use the emergency stairs to descend to the first floor and then evacuate to a safe location.

[1271] 6. Step 6: Guidance by voice chatbot

[1272] The device will launch a voice chatbot that will instruct the user, "Please evacuate immediately using the emergency stairs. First, head to the emergency door on your right and use the stairs to go down to the first floor. Then, exit through the exit on your right and head to the designated evacuation location."

[1273] The device provides specific voice instructions for each step of the evacuation route.

[1274] Example 1

[1275] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1276] In the event of a disaster emergency, it may be difficult for users to take prompt and appropriate evacuation actions. If disaster information is not obtained or the user's current location is not identified promptly, the user may take incorrect evacuation actions, which could result in an even more dangerous situation. Furthermore, if real-time acquisition of disaster and hazard information is insufficient, it is difficult to calculate an appropriate evacuation route, and the user's safety cannot be ensured. Furthermore, if the evacuation route is presented ambiguously, the user may not be able to evacuate appropriately. These issues need to be resolved.

[1277] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1278] In this invention, the server includes means for monitoring and acquiring disaster information, means for identifying the user's current location, means for acquiring hazard information based on the acquired disaster information and the current location, means for calculating an optimal evacuation route based on the acquired hazard information and the current location, and interactive guidance means for providing voice guidance to the user along the calculated evacuation route, thereby supporting quick and appropriate evacuation actions even in a disaster emergency and ensuring the user's safety.

[1279] "Disaster information" refers to information relating to emergencies such as earthquakes, floods, and fires, including the occurrence, progression, and extent of impact of the disaster.

[1280] A "location information determination means" is a device or system that uses technology such as GPS to accurately determine the user's current location.

[1281] "Hazard information" refers to detailed information about the extent of a disaster's impact, the level of danger, and evacuation routes.

[1282] An "evacuation route" is a specific route that a user should follow to evacuate safely, and usually refers to the shortest and safest route.

[1283] An "interactive guide" is an interactive system that provides instructions to a user using voice or text, and guides the user to specific actions.

[1284] An "image acquisition means" is a device or system that uses a camera, drone, etc. to capture images of the surrounding situation in real time and analyzes them.

[1285] An "AI model" is an analytical method that uses artificial intelligence, and is an algorithm that makes predictions and optimizations based on large amounts of data.

[1286] This invention relates to a disaster prevention system that supports users in taking prompt and appropriate evacuation actions in the event of a disaster. This system, through the cooperative operation of a server and terminals, monitors and acquires disaster information, identifies the user's current location, acquires hazard information, calculates evacuation routes, and provides guidance via a voice chatbot.

[1287] The server constantly monitors disaster information, and when a new disaster occurs, it immediately acquires and processes that information. Specifically, it uses APIs provided by public institutions such as the Japan Meteorological Agency to collect the latest disaster information and stores it in an internal database. This allows the server to analyze the type and urgency of the disaster and generate alerts as necessary.

[1288] The user's current location is determined using the device's GPS function. The device utilizes its built-in GPS module to obtain the user's precise location information and transmit it to the server. For example, if the user is on the third floor of a high-rise building, the coordinate data will be transmitted to the server, and the next analysis step will begin.

[1289] The server collects hazard information based on the user's current location and disaster information. Specifically, it uses a hazard map database, real-time disaster prevention camera footage, and drone footage to obtain information on the extent of the disaster's impact, the level of danger, and evacuation routes. Based on this information, the server uses an AI model to calculate the optimal evacuation route.

[1290] The AI ​​model uses a specific predictive algorithm as a generative AI model to provide users with the safest and quickest evacuation route, and the results are presented in a format that makes it easy for users to take action.

[1291] After calculating the evacuation route, the device uses a voice chatbot to provide guidance to the user. Based on the calculation results, the chatbot will provide specific instructions by voice, such as which direction the user should go and which stairs to use, allowing the user to act calmly even in an emergency.

[1292] As a concrete example, consider a situation where an earthquake occurs and the user is on the third floor of a high-rise building. In this case, the server receives an emergency earthquake alert and uses the device's GPS function to identify the user's current location. The server then analyzes real-time disaster prevention camera and drone footage and calculates a safe evacuation route. The device uses a voice chatbot to provide the user with specific instructions, such as "Use the emergency stairs to go down to the first floor, then exit through the exit on your right."

[1293] Below are some example prompts using a generative AI model:

[1294] Suppose an earthquake occurs and a user is on the third floor of a high-rise building. Calculate the safest evacuation route. The device's GPS information is used to determine the user's current location, and the server obtains information from real-time disaster prevention camera footage and hazard maps to calculate the optimal evacuation route. Then, provide the user with instructions via a voice chatbot, such as "Use the emergency stairs to go down to the first floor, then exit through the exit on the right."

[1295] In this way, by linking each means, the disaster prevention system of the present invention can support quick and appropriate evacuation actions even in the event of a disaster emergency, thereby ensuring the safety of users.

[1296] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1297] Step 1:

[1298] The server monitors and acquires disaster information.

[1299] Input: Disaster information from public institutions and disaster prevention-related APIs.

[1300] Data processing: Disaster information is analyzed and stored in an internal database. The urgency and type of disaster are evaluated.

[1301] Output: Parsed disaster information and alerts.

[1302] Specific operation: The server periodically calls the Japan Meteorological Agency's API to obtain the latest disaster information. It then stores the obtained disaster information in a database, analyzes its contents, and evaluates its urgency. If necessary, it generates an alert and notifies the entire system.

[1303] Step 2:

[1304] The device uses GPS functionality to identify the user's current location.

[1305] Input: GPS data from the device.

[1306] Data processing: Analyze GPS data to obtain latitude and longitude location information.

[1307] Output: The user's current location.

[1308] Specific operation: The device uses the built-in GPS module to obtain the user's location information. After the location information is determined, it is sent to the server in the form of latitude and longitude.

[1309] Step 3:

[1310] The server acquires hazard information based on the disaster information and current location information acquired.

[1311] Input: Disaster information, user's current location information.

[1312] Data processing: Analyze hazard maps and real-time disaster prevention camera and drone footage to assess the level of risk and the extent of impact.

[1313] Output: Latest hazard information.

[1314] How it works: The server collects hazard information from an internal database and external sources (disaster prevention cameras, drone footage), then uses AI analysis to obtain the latest hazard information related to the user's current location.

[1315] Step 4:

[1316] The server calculates the optimal evacuation route based on the latest hazard information.

[1317] Input: Hazard information, user's current location information.

[1318] Data processing: Calculate safe evacuation routes using generative AI models.

[1319] Output: Optimal evacuation route.

[1320] Specific operation: The server inputs the acquired hazard information and the user's current location information into the AI ​​model to calculate a safe and quick evacuation route. The calculation results are then output as detailed route guidance for the user to take specific actions.

[1321] Step 5:

[1322] The terminal uses an interactive guidance means for providing voice guidance of the calculated evacuation route.

[1323] Input: Optimal evacuation route.

[1324] Data processing: Convert evacuation route information into voice instructions.

[1325] Output: Voice guidance.

[1326] Specific operation: The device analyzes the evacuation route information received from the server and provides guidance to the user through a voice chatbot. For example, specific instructions are provided via voice, such as "Use the emergency stairs to go down to the first floor, then exit through the exit on the right."

[1327] At each step, the system collects the necessary data and uses data analysis and AI models to generate detailed instructions to assist users in optimal evacuation behavior, thereby ensuring their safety in the event of a disaster.

[1328] (Application example 1)

[1329] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1330] In order for users to take appropriate and prompt evacuation actions during disaster emergencies, they need to obtain disaster information in real time, calculate the optimal evacuation route based on their current location, and provide accurate and specific guidance. However, conventional disaster prevention systems have issues with the speed of information acquisition and the accuracy of guidance, and there are no systems that can handle transportation methods such as autonomous vehicles. This has led to the problem of not being able to fully ensure user safety during disaster emergencies.

[1331] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1332] In this invention, the server includes means for monitoring and acquiring disaster information, means for determining the user's current location, means for acquiring danger information based on the acquired disaster information and the user's current location, means for calculating an optimal evacuation route based on the acquired danger information and the user's current location, an interactive guidance means for providing voice guidance to the user along the calculated evacuation route, and means for acquiring and analyzing damage status in real time from images captured by disaster prevention monitoring devices and autonomous aircraft devices. This enables real-time information acquisition and analysis, calculation of the optimal evacuation route, and specific and accurate voice guidance in the event of a disaster. This system is also compatible with autonomous vehicles, providing support for rapid and appropriate evacuation behavior to ensure the safety of passengers.

[1333] A "disaster emergency" is a situation in which an earthquake, fire, flood, or other natural or man-made disaster occurs and immediate evacuation action is required.

[1334] A "user" is a general public or a specific person who uses the disaster prevention system to take prompt and appropriate evacuation action.

[1335] "Quick and appropriate evacuation actions" are actions that will allow people to reach evacuation shelters in the most efficient and safe manner when a disaster occurs.

[1336] A "disaster prevention system" is a set of devices and software that provides functions such as monitoring, acquiring, and analyzing disaster information, calculating evacuation routes, and providing guidance to support users in the event of a disaster emergency.

[1337] "Disaster information" refers to information about natural and man-made disasters such as earthquakes, fires, and floods.

[1338] "Monitoring and acquisition means" refers to technologies and devices for continuously observing and collecting disaster information in real time.

[1339] "Location measurement means for identifying the current location" refers to technology and devices for determining the current location of a user or object using location information technology such as GPS.

[1340] "Means for acquiring risk information" refers to the technology and devices for collecting and analyzing information about the disaster situation and the extent of damage.

[1341] The "means for calculating an optimal evacuation route" refers to a technology and device for calculating the safest and most efficient evacuation route for a user based on real-time on-site conditions.

[1342] "Interactive guidance means" refers to technology and devices that provide specific instructions and guidance to users through voice and text.

[1343] "Disaster prevention monitoring equipment" refers to devices such as cameras and sensors that monitor the occurrence of disasters in real time.

[1344] "Autonomous Aircraft Footage" means real-time footage captured by an autonomously flying device, such as a drone.

[1345] "Means for acquiring and analyzing damage status" refers to technology and equipment for collecting and analyzing damage status at the site in real time.

[1346] This invention relates to a disaster prevention system that supports users in taking prompt and appropriate evacuation actions in the event of a disaster. This system is realized through the cooperative operation of a server, terminals, and multiple dedicated devices and software.

[1347] First, the server monitors disaster information in real time and quickly obtains it when a disaster occurs. Disaster information includes data on natural and man-made disasters such as earthquakes, fires, and floods. This disaster information is obtained using APIs provided by specialized disaster monitoring organizations and local disaster prevention systems.

[1348] The device then uses GPS to determine the user's current location, allowing it to determine exactly where the user is and calculate the most appropriate evacuation route.The device is also installed in mobile devices such as the user's smartphone or an in-car system.

[1349] The server obtains danger information based on the acquired disaster information and the user's current location information. This danger information includes the extent of the disaster's impact, the level of danger, the current damage situation on site, and information on evacuation routes. Video data transmitted in real time from disaster prevention monitoring devices and autonomous aircraft devices (drones) is used to obtain the danger information. This makes it possible to calculate the optimal evacuation route based on the latest information on site.

[1350] The calculated evacuation route is then given to the user via voice guidance. An interactive guidance system is used to provide specific instructions to help the user stay calm. For example, a specific prompt sentence such as "We will guide you to the evacuation route. Turn right and proceed to a safe evacuation location" is generated.

[1351] The entire system is implemented using the Python programming language, with API access using the requests library, location data calculation using the geopy library, and voice synthesis technology used to generate voice guidance, effectively carrying out a series of complex data processing and user guidance.

[1352] As a specific example of operation, if an earthquake occurs and the user is in an autonomous vehicle, the server obtains earthquake information in real time and determines the vehicle's current location. It then calculates the optimal evacuation route and issues instructions to the autonomous driving system through the vehicle's on-board system. Specific evacuation instructions are provided to passengers through audio guidance and display. This series of operations allows the user to evacuate quickly and safely.

[1353] Examples of prompts include:

[1354] Get the latest earthquake information.

[1355] Check the current vehicle location using GPS.

[1356] "Calculate the best route based on your current location and nearby safe evacuation locations."

[1357] "Please provide voice instructions for evacuation routes. Instruct them to 'turn right and proceed to a safe evacuation area.'"

[1358] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1359] Step 1:

[1360] The server monitors and acquires disaster information in real time.

[1361] (Input) Real-time disaster information (earthquakes, fires, floods, etc.) provided by various disaster monitoring organizations and disaster prevention systems

[1362] (Data processing) Collect the latest disaster data using disaster information API

[1363] (Output) Update the internal database that holds the latest disaster information

[1364] Specifically, the server uses the requests library to periodically retrieve data from disaster information providers and store it in an internal database.

[1365] Step 2:

[1366] The terminal uses the GPS function to determine the user's current location.

[1367] (Input) GPS data from the user's device (smartphone or in-car system)

[1368] (Data processing) Use the GPS function to obtain the user's latitude and longitude

[1369] (Output) Current location of the user (latitude and longitude)

[1370] Specifically, the device uses its built-in GPS module to obtain current location data (latitude and longitude).

[1371] Step 3:

[1372] The server obtains danger information based on the acquired disaster information and the user's current location information.

[1373] (Input) Latest disaster information and user's current location information

[1374] (Data processing) Integrate disaster information and user location information, and perform search and filter processing to extract relevant danger information from the danger information database.

[1375] (Output) Risk information related to the user's current location

[1376] Specifically, the server retrieves danger information related to a specific area through a database query based on disaster information and location information.

[1377] Step 4:

[1378] The server calculates the optimal evacuation route based on the acquired danger information and the current location.

[1379] (Input) Risk information and user's current location information

[1380] (Data processing) Route calculation algorithm that calculates the shortest and safest route taking into account geographical information and disaster conditions

[1381] (Output) Optimal evacuation route

[1382] Specifically, the server uses a map database and a route calculation algorithm to calculate the optimal route from the user's current location to a safe evacuation site.

[1383] Step 5:

[1384] The terminal provides the user with voice guidance on the calculated evacuation route.

[1385] (Input) Calculated evacuation route

[1386] (Data processing) Convert evacuation route information into voice guidance prompts and generate voice data using voice synthesis technology

[1387] (Output) Voice guidance instructions

[1388] Specifically, the terminal generates instructions in the interactive guidance system based on the calculated evacuation route, converts them into voice using a speech synthesis engine, and provides guidance to the user.

[1389] Step 6:

[1390] The server acquires and analyzes damage information in real time from disaster prevention monitoring equipment and autonomous aircraft equipment footage.

[1391] (Input) Real-time video data from disaster prevention monitoring devices and autonomous aircraft devices (drones, etc.)

[1392] (Data processing) Analysis of video data, understanding of disaster situation, identification of damage extent

[1393] (Output) Latest damage situation information

[1394] Specifically, the server analyzes video data sent from disaster prevention cameras and drones, determines the extent and situation of the damage, and updates the danger information database.

[1395] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1396] This invention is a disaster prevention system that helps users take prompt and appropriate evacuation actions in the event of a disaster emergency, and in particular has the function of recognizing the user's emotional state and providing appropriate guidance and psychological support based on that. This system, through the cooperative operation of a server and terminal, monitors and acquires disaster information, identifies the user's current location, acquires hazard information, calculates evacuation routes, recognizes emotions using an emotion engine, and provides guidance using a voice chatbot.

[1397] Server and terminal roles

[1398] The server constantly monitors disaster information notified in emergencies, and when a new disaster occurs, it immediately acquires and processes that information. It also uses the GPS function of the device to pinpoint the user's current location, accurately determining where the user is.

[1399] Hazard information is obtained based on the user's current location and disaster information. This includes information on the extent of the disaster's impact, the level of risk, and evacuation routes. The obtained hazard information and real-time disaster prevention camera and drone footage are analyzed, and the safest evacuation route for the user is calculated using AI analysis.

[1400] Emotion Engine Functions

[1401] The system also features an emotion engine that recognizes the user's emotional state in real time. This emotion engine can grasp the user's stress level and emotional state by analyzing the user's facial expressions, voice tone, and even physical movements. If the emotion engine detects the user's state of panic or high stress, it dynamically adjusts the tone and content of the voice guidance to help the user stay calm.

[1402] The emotion engine also provides relaxation guidance and psychological support. For example, if a user is experiencing extreme panic, the voice chatbot can provide relaxation guidance such as "Take a deep breath, slowly calm down, and everything will be okay," thereby reducing the user's stress.

[1403] Specific examples

[1404] As a specific example, consider a case where an earthquake occurs and the user is on the third floor of a high-rise building.

[1405] 1. Obtaining disaster information:

[1406] The server instantly receives emergency earthquake alerts and processes the information.

[1407] 2. Current location:

[1408] The GPS function of the device determines that the user's current location is on the third floor of a high-rise building.

[1409] 3. Obtaining hazard information and calculating evacuation routes:

[1410] The server retrieves the latest information from the hazard map based on the user's current location and analyzes real-time disaster prevention camera and drone footage to determine the extent of damage to the building. This information is used to calculate the safest evacuation route. Specifically, the system calculates a route that will lead the user down the emergency stairs to the first floor, then out of the building and to a safe evacuation site.

[1411] 4. Emotion recognition and guidance using emotion engine:

[1412] The device's emotion engine analyzes the user's facial expressions and voice tone in real time to understand their emotional state. If the user is in a panic, the emotion engine softens the tone of the voice guidance and provides relaxation instructions. Specific and calm instructions are provided via voice, such as "Take a deep breath and stay calm. Use the emergency stairs to go down to the first floor. Exit through the exit on the right and head to the designated evacuation site."

[1413] In this way, the disaster prevention system of the present invention can ensure the safety of users by supporting prompt and appropriate evacuation behavior even in the event of a disaster and encouraging them to evacuate calmly while taking into account the user's emotional state. This system is expected to enable users to act calmly and evacuate effectively even in an emergency.

[1414] The processing flow will be explained below.

[1415] Program processing flow

[1416] Step 1: Loading the initial settings

[1417] The server loads the initial settings of the user's disaster prevention AI system, including settings to enable GPS, hazard maps, AI analysis, emotion engines, and voice chatbot functions.

[1418] When a user launches an app, the device enables each function based on the initial settings.

[1419] Step 2: Disaster detection

[1420] The server monitors the emergency alert system and immediately obtains information about any new disaster alerts that occur.

[1421] The terminal notifies the user of the acquired alert information.

[1422] Step 3: Get current location

[1423] The device uses GPS to determine the user's current location, and this information is sent to the server in real time.

[1424] The server prepares to calculate hazard information and evacuation routes based on the current location obtained.

[1425] Step 4: Obtaining damage and hazard information

[1426] The server obtains the latest hazard information related to the user's current location from the hazard map system.

[1427] In addition, disaster prevention camera and drone footage will be analyzed to grasp the damage situation in real time.

[1428] Step 5: Calculate the optimal evacuation route

[1429] The server performs AI analysis based on the acquired current location, hazard information, and real-time damage status to calculate the optimal evacuation route.

[1430] For example, it calculates specific routes to evacuate to a safe location using emergency stairs in a building.

[1431] Step 6: Start Emotion Recognition

[1432] The device activates an emotion engine to analyze the user's emotional state in real time, judging from facial expressions, voice tone, and body movements to determine whether the user is in a panic state.

[1433] Step 7: Guidance by voice chatbot

[1434] The device activates the voice chatbot and starts guiding the user on evacuation routes.

[1435] The server generates instructions for each step of the calculated evacuation route.

[1436] The device dynamically adjusts the tone and content of the voice guidance based on the analysis results of the emotion engine. For example, if the user is in a panic, it generates and provides specific instructions, including relaxation guidance such as "Take a deep breath and calm down."

[1437] Specific examples

[1438] Scenario: Evacuation in the event of an earthquake

[1439] 1. Step 1: Loading the initial settings

[1440] The server loads settings that enable GPS, hazard maps, AI analysis, emotion engines, and voice chatbots.

[1441] The device launches the app and enables each function.

[1442] 2. Step 2: Disaster detection

[1443] The server detects emergency earthquake alerts and retrieves the information immediately.

[1444] The terminal notifies the user of the acquired alert information.

[1445] 3. Step 3: Get current location

[1446] The device uses GPS to determine the user's current location, and this location information is sent to the server.

[1447] 4. Step 4: Obtain damage and hazard information

[1448] The server obtains the latest hazard information about the area around the building from the hazard map.

[1449] In addition, disaster prevention camera and drone footage will be analyzed to determine the extent of damage to the building.

[1450] 5. Step 5: Calculate the optimal evacuation route

[1451] The server uses AI analysis to calculate the optimal evacuation route based on the user's current location, acquired hazard information, and damage situation.

[1452] For example, a route is calculated to use the emergency stairs to descend to the first floor and then evacuate to a safe location.

[1453] 6. Step 6: Start Emotion Recognition

[1454] The device's emotion engine analyzes the user's emotional state in real time, determining whether the user is panicking based on facial expressions and tone of voice.

[1455] 7. Step 7: Guidance by voice chatbot

[1456] The device will launch a voice chatbot that will instruct the user, "Please evacuate immediately using the emergency stairs. First, head to the emergency door on your right and use the stairs to go down to the first floor. Then, exit through the exit on your right and head to the designated evacuation location."

[1457] Based on the analysis results of the emotion engine, the device will soften the tone of the voice guidance and provide specific instructions, including relaxation advice such as "Take a deep breath and stay calm. It's okay." In this way, the device helps the user calmly grasp the situation and take appropriate evacuation actions.

[1458] Example 2

[1459] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1460] In the event of a disaster or emergency, it is extremely important for users to take prompt and appropriate evacuation actions. However, systems that efficiently integrate multiple elements, such as obtaining disaster information, calculating evacuation routes, and identifying the user's current location, and that also provide psychological support according to the user's emotional state, are still insufficient. Providing appropriate guidance is particularly difficult for users who are in a state of panic or high stress. Therefore, a comprehensive disaster prevention system that enables users to take appropriate evacuation actions while remaining calm is needed.

[1461] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1462] In this invention, the server includes a means for monitoring and acquiring disaster information, a location information acquisition means for identifying the user's current location, and a means for acquiring hazard information based on the acquired disaster information and current location. This allows for the calculation of a quick and appropriate evacuation route based on the user's current location information and the provision of voice guidance. The system also includes an automated dialogue system for providing voice guidance to the user along the calculated evacuation route, an emotion recognition engine for recognizing the user's emotional state in real time, and a means for dynamically adjusting the guidance content and guidance tone based on the recognized emotional state. This allows the user to take calm and effective evacuation actions even during a disaster, thereby ensuring the user's overall safety.

[1463] "Disaster information" refers to information related to natural disasters, including the occurrence status and extent of impact of earthquakes, tsunamis, typhoons, floods, etc., as well as emergency warnings.

[1464] "Location information acquisition means" refers to technology or equipment for identifying the user's current location, and primarily refers to a location identification system that uses GPS.

[1465] "Hazard information" refers to detailed information about the danger areas and scope of impact caused by disasters, the need for evacuation, etc.

[1466] "Means for calculating the optimal evacuation route" refers to a method or system for calculating the safest and quickest evacuation route for a user based on the acquired information.

[1467] An "automated dialogue system" is a system that provides information through dialogue with a user, and is particularly intended to give instructions to users through voice guidance or chatbots.

[1468] An "emotion recognition engine" refers to technology or software that analyzes a user's facial expressions, tone of voice, body movements, etc. to recognize the user's emotional state (e.g., panic, calm, stress, etc.) in real time.

[1469] "Means for acquiring and analyzing damage situations in real time" refers to methods and technologies for acquiring video data sent from disaster prevention cameras, unmanned aerial vehicles, etc. in real time and analyzing the extent of damage and the scope of impact.

[1470] "Specific instructions at each step provided by voice guidance" refers to a method of providing detailed and specific voice guidance for each step, such as directions to travel by car and walking routes, in order to clearly communicate evacuation routes to the user.

[1471] This invention is a disaster prevention system that helps users take prompt and appropriate evacuation actions in the event of a disaster. This system, through the cooperative operation of a server and terminals, monitors and acquires disaster information, identifies current locations, acquires hazard information, calculates evacuation routes, recognizes emotions using an emotion engine, and provides guidance using a voice chatbot.

[1472] Hardware and software used

[1473] server

[1474] Hardware: High-performance database and analysis servers

[1475] Software: Database management system (e.g., MySQL), disaster information acquisition API (e.g., Japan Meteorological Agency API), AI analysis engine (e.g., TensorFlow)

[1476] Terminal

[1477] Hardware: Smartphone or tablet with GPS sensor, camera, and microphone

[1478] Software: GPS location acquisition software, voice recognition programs (e.g., Google Speech-to-Text), emotion recognition engines

[1479] System Operation

[1480] 1. Monitoring and obtaining disaster information

[1481] The server constantly monitors various disaster information sources (e.g., the Japan Meteorological Agency, the Fire and Disaster Management Agency, etc.), and when new disaster information occurs, it immediately acquires the information and stores it in a database. It analyzes important parameters related to the disaster information (e.g., type of disaster, location of occurrence, scale, etc.) and evaluates its importance.

[1482] 2. Identifying your current location

[1483] The device uses the built-in GPS sensor to determine the user's current location, which is then periodically sent to a server. The GPS data is stored in the form of latitude and longitude coordinates, and is also subjected to a correction algorithm to improve accuracy.

[1484] 3. Acquisition and analysis of hazard information

[1485] The server obtains hazard information about the extent of the disaster impact and evacuation routes based on the user's current location, including analysis of hazard map databases, real-time disaster prevention camera footage, and drone footage, to identify high-risk areas.

[1486] 4. Evacuation route calculation

[1487] The server calculates the optimal evacuation route based on the acquired hazard information. The evacuation route is calculated using an AI algorithm (e.g., a pathfinding algorithm), and detailed instructions are prepared for each step of the evacuation route.

[1488] 5. Real-time recognition of emotional states

[1489] The device's camera and microphone are used to analyze the user's facial expressions and voice tone in real time. The emotion engine uses this data to determine the user's emotional state (e.g., panic, calm, stress, etc.) and sends that information to the server.

[1490] 6. Providing appropriate guidance and psychological support

[1491] Based on the evacuation route information sent from the server and the user's emotional state as recognized by the emotion engine, the device dynamically provides guidance to the user. The voice chatbot provides relaxation instructions and specific evacuation instructions in a gentle tone.

[1492] Specific examples

[1493] Below is a specific example where an earthquake occurs and the user is on the third floor of a high-rise building.

[1494] 1. Obtaining disaster information

[1495] The server instantly receives emergency earthquake alerts and processes the information.

[1496] 2. Identifying your current location

[1497] The device's GPS function determines that the user's current location is on the third floor of a high-rise building.

[1498] 3. Obtaining hazard information and calculating evacuation routes

[1499] The server analyzes the latest hazard information and real-time footage from disaster prevention cameras and drones to calculate the safest evacuation route, guiding the user to the first floor via the emergency stairs, then out of the building and towards a safe evacuation site.

[1500] 4. Emotion recognition and guidance using an emotion engine

[1501] The device's emotion engine analyzes the user's facial expressions and voice tone in real time, and if the user is in a panic, it provides gentle voice guidance including instructions to relax. For example, it might say, "Take a deep breath and stay calm. Use the emergency stairs to go down to the first floor. Exit through the exit on your right and head to the designated evacuation area."

[1502] Prompt Sentence Examples

[1503] "Take a deep breath and stay calm. Use the emergency stairs to safely descend to the first floor. Exit through the exit on your right and proceed to the designated evacuation area."

[1504] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1505] Specific processing flow of the program

[1506] Step 1: Monitoring and obtaining disaster information

[1507] The server constantly monitors various disaster information sources (e.g., the Japan Meteorological Agency API, the Fire and Disaster Management Agency database, etc.) When new disaster information occurs, it immediately acquires that information and stores it in the database.

[1508] Input: New disaster data from disaster sources

[1509] Data processing: Analyze the type, location, and scale of disasters to assess their importance

[1510] Output: Disaster information stored in a database

[1511] Specific operation: Sends a request to the disaster information API, receives and analyzes new data, and registers it in the database.

[1512] Step 2: Determine your current location

[1513] The device uses the built-in GPS sensor to determine the user's current location, which is then periodically sent to a server.

[1514] Input: Location data (latitude and longitude) from the GPS sensor

[1515] Data processing: Position data is corrected using a correction algorithm to improve accuracy

[1516] Output: User's current location (latitude and longitude)

[1517] Specific operation: Enables the GPS function to obtain the current location and transmits it to the server in real time.

[1518] Step 3: Obtain and analyze hazard information

[1519] Based on the current location information sent, the server obtains and analyzes hazard information from hazard map databases, disaster prevention cameras, drone footage, etc.

[1520] Input: User's current location and data from hazard information sources

[1521] Data processing: Integrate and analyze risk information from hazard maps and real-time video data

[1522] Output: Hazard information based on current location

[1523] Specific operation: Data is obtained from the hazard map API, and the video data is analyzed using an image recognition algorithm to assess the level of danger.

[1524] Step 4: Calculate evacuation routes

[1525] The server uses an AI algorithm to calculate the optimal evacuation route based on the acquired hazard information and the transmitted current location information.

[1526] Input: Current location and hazard information

[1527] Data processing: Calculate the optimal route using a route search algorithm

[1528] Output: The optimal evacuation route for the user

[1529] Specific operation: Uses an AI algorithm (e.g., A algorithm) to calculate an evacuation route and send the results to the device.

[1530] Step 5: Real-time recognition of emotional states

[1531] The device uses a camera and microphone to analyze the user's facial expressions, voice tone, body movements, etc. in real time. The emotion engine recognizes the user's emotional state based on this data and sends it to the server.

[1532] Input: Real-time video and audio data from camera and microphone

[1533] Data processing: Analyzing emotions with facial and voice recognition algorithms

[1534] Output: User's emotional state data (e.g., panic, calm, etc.)

[1535] What it does: Facial expression analysis software and voice recognition programs analyze raw data to detect emotional states.

[1536] Step 6: Providing appropriate guidance and psychological support

[1537] Based on the evacuation route information and emotional state data sent from the server, the device dynamically provides guidance to the user through a voice chatbot, with the tone and content of the guidance adjusted according to the user's emotional state.

[1538] Input: Evacuation route information and emotional state data

[1539] Data processing: Dynamically adjust the content and tone of announcements according to emotional state

[1540] Output: Audio prompts provided to the user

[1541] Specific actions: Generate prompts such as "Take a deep breath and stay calm. Use the emergency stairs to go down to the first floor. Exit through the exit on your right and head to the designated evacuation location," and provide audio guidance to the user.

[1542] This is expected to enable users to remain calm and evacuate effectively in an emergency.

[1543] (Application example 2)

[1544] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1545] In the event of a disaster or emergency, it is extremely important for users to take prompt and appropriate evacuation actions. However, in emergencies, it is easy to fall into panic and confusion, making it difficult to make calm decisions. Furthermore, conventional disaster prevention systems generally only provide disaster information and evacuation routes, but do not provide support that takes into account the user's emotional state. Therefore, there is a need for a disaster prevention system that can recognize the user's emotional state in real time and provide appropriate guidance and psychological support.

[1546] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1547] In this invention, the server includes means for monitoring and acquiring disaster information, means for determining the user's current location, means for acquiring hazard information based on the acquired disaster information and the current location, means for calculating an optimal evacuation route based on the acquired hazard information and the current location, dialogue device means for providing voice guidance to the user along the calculated evacuation route, emotion recognition means for detecting the user's emotional state, and means for adjusting the content and tone of the guidance provided based on the user's emotional state. This allows the user to calmly take evacuation action and take appropriate measures even in an emergency.

[1548] "Disaster emergency" refers to the period of time when a natural disaster such as an earthquake, tsunami, fire, or flood, or a similar emergency occurs.

[1549] "User" refers to an individual or organization using this disaster prevention system.

[1550] "Evacuation behavior" refers to planned actions to move to a safe place.

[1551] "Disaster prevention system" refers to a comprehensive set of devices and means designed to assist users in ensuring their safety during disaster emergencies.

[1552] "Disaster information" refers to data and reports on the occurrence of a disaster, the extent of its impact, etc.

[1553] "Location measurement means" refers to technologies or devices used to determine a user's current location.

[1554] "Hazard information" refers to data on dangerous areas caused by disasters and the extent of their impact.

[1555] An "evacuation route" refers to a recommended route for a user to safely evacuate in the event of a disaster.

[1556] "Calculation means" refers to algorithms or devices for calculating optimal evacuation routes based on disaster and hazard information.

[1557] "Interactive device means" refers to equipment or software for exchanging information with a user through voice or text.

[1558] "Emotion recognition means" refers to technology or devices that analyze and evaluate a user's emotional state from facial expressions, vocal tone, body movements, etc.

[1559] "Emotional state" refers to a user's mental and emotional response or state.

[1560] "Content of guidance" refers to specific items of information or instructions provided to the user.

[1561] "Tone" refers to the tone and atmosphere of the voice used when providing audio guidance.

[1562] This invention is a disaster prevention system that supports users in taking prompt and appropriate evacuation actions in the event of a disaster. This system is mainly composed of a server and terminals working together, and has the following functions:

[1563] Server Roles

[1564] The server constantly monitors disaster information and immediately acquires and processes any new disaster information when it occurs. Disaster information is acquired using APIs publicly available via the Internet (e.g., government disaster information APIs).

[1565] Next, the device's GPS function is used to determine the user's current location. Once the current location is determined, the server obtains hazard information based on that location. This includes referencing hazard maps and analyzing real-time disaster prevention camera and drone footage. From the obtained hazard information, the server uses AI algorithms to calculate the optimal evacuation route.

[1566] Device Role

[1567] The user's device uses GPS to send its current location to a server. It also has an emotion engine that recognizes the user's emotional state in real time. The emotion engine analyzes the user's facial expressions, vocal tone, and body movements to detect panic or high stress.

[1568] The terminal further includes a dialogue device means for providing voice guidance along the calculated evacuation route to the user. The dialogue device means adjusts the content and tone of the guidance based on the emotional state of the user and provides appropriate instructions. For example, if the user is in a panic, the dialogue device means softens the tone of the voice guidance and provides instructions such as "Take a deep breath and stay calm. Use the emergency stairs to go down to the first floor."

[1569] Hardware and Software Configuration

[1570] On the server side, a database server and an AI analysis server are used. The database server stores disaster and hazard information, while the AI ​​analysis server calculates evacuation routes and performs emotion recognition. Specific software includes the Python library requests to obtain disaster information and the geopy library to identify the current location. Furthermore, face_recognition and cv2 (OpenCV) are used for emotion recognition.

[1571] Specific examples

[1572] Let's imagine that an earthquake occurs and the user is on the third floor of a high-rise building. In this case, the server immediately obtains earthquake information, uses the device's GPS function to identify the user's location (third floor of a high-rise building), obtains hazard information based on the user's current location, and calculates the optimal evacuation route. In addition, the device's emotion engine analyzes the user's emotional state, and if it detects a state of panic, it changes the tone of the voice guidance and provides specific instructions. For example, it may say, "Take a deep breath and stay calm. Use the emergency stairs to go down to the first floor."

[1573] Example prompts for generative AI models

[1574] "Receive an emergency earthquake alert, identify the user's current location, calculate the optimal evacuation route, and guide the user to evacuate calmly. Also, if the user's emotional state is panicked, notify them and provide psychological support."

[1575] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1576] Step 1:

[1577] Obtaining disaster information

[1578] The server sends a request to an API that provides disaster information via the Internet (e.g., a government disaster information API) to obtain the latest disaster information. The input is the API request, and the output is JSON data of the disaster information. The server analyzes this data and identifies currently ongoing disasters.

[1579] Step 2:

[1580] Identifying your current location

[1581] The device uses its built-in GPS to determine the user's current location and sends that information to the server. The input is a GPS signal, and the output is the user's latitude and longitude coordinate data. The server receives this data and combines it with disaster information for further processing.

[1582] Step 3:

[1583] Obtaining hazard information

[1584] The server retrieves relevant hazard information from a hazard map based on the user's current location. If necessary, it analyzes real-time video data from disaster prevention cameras and aerial devices (drones). The input is the user's coordinate data and disaster information, and the output is hazard information. The server uses this to collect data on specific dangerous areas and safe evacuation routes.

[1585] Step 4:

[1586] Evacuation route calculation

[1587] The server calculates the optimal evacuation route based on the acquired hazard information and the user's current location. The input is the hazard information and the user's coordinate data, and the output is the calculated evacuation route. In this process, the server uses an artificial intelligence (AI) algorithm to derive the optimal route.

[1588] Step 5:

[1589] Recognition of emotional states

[1590] The device uses a camera and microphone to recognize the user's emotional state. The device's emotion engine analyzes the user's facial expressions, voice tone, and body movements to identify the user's emotional state in real time. The input is the user's video and audio data, and the output is the analyzed emotional state (e.g., panicked, calm, etc.).

[1591] Step 6:

[1592] Providing audio guidance

[1593] The device provides voice guidance to the user based on the evacuation route information and emotional state data received from the server. If the user is in a panic state, the device softens the tone of the voice guidance and adds instructions to stay calm. The input is the evacuation route information and emotional state data, and the output is voice guidance for the user.

[1594] Step 7:

[1595] Emergency contact function

[1596] In the event of a disaster, the device automatically notifies the user's designated emergency contacts (family, friends, etc.) of the current location and situation. The input is the user's current location and disaster information, and the output is an emergency contact message. This function enables the user to receive prompt and appropriate assistance.

[1597] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1598] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1599] 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 the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1600] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1601] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1602] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1603] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1604] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1605] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1606] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1607] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1608] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1609] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1610] 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.

[1611] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1612] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1613] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific processing may be a single processor.

[1614] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1615] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1616] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1617] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1618] The following is further disclosed regarding the above embodiment.

[1619] (Claim 1)

[1620] A disaster prevention system for supporting a user in taking prompt and appropriate evacuation actions in an emergency disaster,

[1621] a means for monitoring and obtaining disaster information;

[1622] GPS means for determining the user's current location;

[1623] means for acquiring hazard information based on the acquired disaster information and the current location;

[1624] A means for calculating an optimal evacuation route based on the acquired hazard information and the current location;

[1625] a chatbot means for providing voice guidance to a user about the calculated evacuation route;

[1626] A system including:

[1627] (Claim 2)

[1628] The system according to claim 1, further comprising means for acquiring and analyzing damage conditions in real time from disaster prevention camera or drone footage.

[1629] (Claim 3)

[1630] 2. The system according to claim 1, wherein the voice guidance is a means for providing the user with specific instructions for each step from their current location to an evacuation site.

[1631] "Example 1"

[1632] (Claim 1)

[1633] A disaster prevention system for supporting a user in taking prompt and appropriate evacuation actions in an emergency disaster,

[1634] a means for monitoring and obtaining disaster information;

[1635] a location information specifying means for specifying a current location of a user;

[1636] means for acquiring hazard information based on the acquired disaster information and the current location;

[1637] A means for calculating an optimal evacuation route based on the acquired hazard information and the current location;

[1638] interactive guidance means for providing audio guidance to the user about the calculated evacuation route;

[1639] A system including:

[1640] (Claim 2)

[1641] 2. The system according to claim 1, further comprising an image acquisition means for acquiring and analyzing damage conditions in real time when monitoring disaster information.

[1642] (Claim 3)

[1643] 2. The system according to claim 1, wherein the interactive guidance means is a means for providing the user with specific instructions for each step from the current location to the evacuation site.

[1644] "Application Example 1"

[1645] (Claim 1)

[1646] A disaster prevention system for supporting a user in taking prompt and appropriate evacuation actions in an emergency disaster,

[1647] a means for monitoring and obtaining disaster information;

[1648] a location measurement means for identifying a current location of a user;

[1649] A means for acquiring danger information based on the acquired disaster information and the current location;

[1650] A means for calculating an optimal evacuation route based on the acquired danger information and the current location;

[1651] interactive guidance means for providing audio guidance to the user about the calculated evacuation route;

[1652] A system including:

[1653] (Claim 2)

[1654] 10. The system according to claim 1, further comprising means for acquiring and analyzing damage situations in real time from disaster prevention monitoring devices and autonomous aircraft device images.

[1655] (Claim 3)

[1656] 2. The system according to claim 1, wherein the voice guidance is a means for providing the user with specific instructions for each step from their current location to an evacuation site.

[1657] "Example 2: Combining Emotion Engines"

[1658] (Claim 1)

[1659] A disaster prevention system for supporting a user in taking prompt and appropriate evacuation actions in an emergency disaster,

[1660] means for monitoring and obtaining disaster information;

[1661] location information acquisition means for identifying the current location of the user;

[1662] means for acquiring hazard information based on the acquired disaster information and the current location;

[1663] means for calculating an optimal evacuation route based on the acquired hazard information and the current location;

[1664] an automated dialogue system for providing audible guidance to a user on the calculated evacuation route;

[1665] an emotion recognition engine for recognizing the user's emotional state in real time;

[1666] means for dynamically adjusting guidance content and guidance tone based on the recognized emotional state;

[1667] A system including:

[1668] (Claim 2)

[1669] 10. The system according to claim 1, further comprising means for acquiring and analyzing damage situations in real time from disaster prevention cameras and unmanned aerial vehicle footage.

[1670] (Claim 3)

[1671] 2. The system according to claim 1, wherein the voice guidance is a means for providing the user with specific instructions for each step from their current location to an evacuation site.

[1672] "Application example 2 when combining emotion engines"

[1673] (Claim 1)

[1674] A disaster prevention system for supporting a user in taking prompt and appropriate evacuation actions in an emergency disaster,

[1675] a means for monitoring and obtaining disaster information;

[1676] a location measurement means for identifying a current location of a user;

[1677] means for acquiring hazard information based on the acquired disaster information and the current location;

[1678] A means for calculating an optimal evacuation route based on the acquired hazard information and the current location;

[1679] dialogue device means for providing audible guidance to a user about the calculated evacuation route;

[1680] emotion recognition means for detecting an emotional state of a user; and

[1681] means for adjusting the content and tone of the guidance provided based on the user's emotional state;

[1682] A system including:

[1683] (Claim 2)

[1684] The system according to claim 1, further comprising means for acquiring and analyzing damage conditions in real time from disaster prevention cameras and aerial equipment.

[1685] (Claim 3)

[1686] The system of claim 1, wherein the voice guidance is a means for providing the user with specific instructions for each step from their current location to an evacuation site, and when the user is in a panic state, the emotion recognition means softens the tone of the guidance and provides instructions to act calmly. [Explanation of symbols]

[1687] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A disaster prevention system for supporting a user in taking prompt and appropriate evacuation actions in an emergency disaster, a means for monitoring and obtaining disaster information; GPS means for determining the user's current location; means for acquiring hazard information based on the acquired disaster information and the current location; A means for calculating an optimal evacuation route based on the acquired hazard information and the current location; a chatbot means for providing voice guidance to a user about the calculated evacuation route; A system including:

2. The system according to claim 1, further comprising means for acquiring and analyzing damage conditions in real time from disaster prevention camera or drone footage.

3. 2. The system according to claim 1, wherein the voice guidance is a means for providing the user with specific instructions for each step from the current location to the evacuation site.

Images