system

Abstract

We provide the system. [Solution] Information acquisition means for collecting data received from a sensor device, An analysis means for analyzing data acquired by the information acquisition means to identify the disaster situation, A route generation means that calculates a safe evacuation route based on the disaster situation identified by the analysis means, A communication means for transmitting evacuation route information calculated by the route generation means to a mobile information terminal, A display means that notifies the user of evacuation route information received by the mobile information terminal via an output device, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Evacuation behavior during a disaster may involve great difficulties and risks due to geographical factors of the area, the type of disaster, and the timing of its occurrence. Especially due to congested routes and rapidly changing disaster situations, evacuees may not be able to make appropriate judgments and may be unable to evacuate safely. With conventional technologies, it has been difficult to provide real-time evacuation guidance adaptable to these situations, and as a result, it has often been insufficient to ensure the safety of evacuees. In such situations, it is required to enable the provision of a quick and appropriate evacuation route so that evacuees can act safely.

Means for Solving the Problems

[0005] This invention provides a system that combines multiple means, including acquiring information from sensor devices, analyzing geographical conditions using unmanned aerial vehicles, and identifying disaster-specific conditions using generating AI, to ensure the safety of evacuees during disasters. This enables the real-time generation and notification of individual evacuation routes based on the user's current location. By incorporating route generation means that analyze data obtained in real time from sensor devices and unmanned aerial vehicles to calculate the optimal evacuation route corresponding to the identified disaster situation, rapid and effective evacuation guidance is achieved. As a result, evacuees can quickly select the safest route.

[0006] A "sensor device" is a device that detects the physical or chemical state of the environment and outputs that data electronically.

[0007] "Information acquisition means" refers to means for collecting and processing various data obtained from sensor devices and unmanned aerial vehicles.

[0008] "Analysis methods" refer to means used to analyze the type and extent of damage of a disaster based on acquired data, and to identify the extent of the damage.

[0009] A "route generation means" is a means for calculating and creating safe and efficient evacuation routes based on analyzed data.

[0010] "Communication means" refers to the means of transmitting the generated evacuation route information to the user's mobile information terminal.

[0011] "Display means" refers to means of providing received evacuation route information to users visually or audibly and giving instructions.

[0012] An "unmanned aerial vehicle" is an aircraft that flies remotely or automatically and takes photographs of the environment and collects data from above.

[0013] A "mobile information terminal" is a portable electronic device that receives evacuation information and notifies users of it. [Brief explanation of the drawing]

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

[0015] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

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

[0017] In the following embodiments, a labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

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

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

[0020] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0022] [First Embodiment]

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

[0024] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0025] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0026] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0027] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0031] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0032] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0033] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0034] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0035] This invention is an information system for realizing rapid and safe evacuation during disasters. This system includes sensor devices, unmanned aerial vehicles, information acquisition means, analysis means, route generation means, communication means, and display means. The operation of each component is described in detail below.

[0036] First, the server acquires local environmental data in real time from sensor devices and unmanned aerial vehicles (UAVs). Sensor devices monitor specific parameters such as vibrations during earthquakes, water levels during floods, and temperatures during fires, and transmit the data. UAVs collect visual data from the air, such as images and videos of the affected area, and transmit this to the server.

[0037] This collected data is processed by an analysis system running on a server. The analysis system analyzes the data to identify various disaster situations and evaluate the extent and impact of the damage. The evaluation includes a process of detecting anomalies in the data and overlaying them with geographic information to identify hazardous areas.

[0038] Next, the server uses a route generation mechanism to calculate a safe evacuation route based on the analyzed disaster situation. Here, multiple route options are considered, taking into account road closures, the location of evacuation shelters, and the topographical characteristics of the area. Each user's current location and special needs (e.g., those requiring assistance) are also taken into consideration.

[0039] The server then uses communication methods to transmit the calculated evacuation routes to each user's mobile information terminal. The information is sent in the form of text, maps, and audio, and is updated in real time.

[0040] The terminal receives this information and notifies the user through a display device. The display device visually shows evacuation routes or provides emergency alerts via voice. The user can evacuate safely by following the route displayed on the terminal. For example, after an earthquake, the user is guided to avoid the most dangerous areas and head to a nearby evacuation center.

[0041] Thus, this system is designed to enable users to evacuate efficiently and safely by integrating situation assessment, analysis, and evacuation guidance during disasters.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] The server receives environmental data from sensor devices and unmanned aerial vehicles (UAVs). Sensor devices transmit data including seismic vibrations, fire temperatures, and flood water levels, while UAVs transmit visual information capturing the current damage situation.

[0045] Step 2:

[0046] The server processes the received data using analytical tools. This analysis identifies the type of disaster and maps its impact area and dangerous locations on a map. For the analysis, modeled AI algorithms are used to detect anomalies and identify safe areas.

[0047] Step 3:

[0048] The server uses a route generation method based on the analysis results to calculate the optimal evacuation route. The calculation considers the condition of local infrastructure (roads, evacuation facilities, etc.), current congestion levels, and the progression of the disaster, and evaluates multiple routes.

[0049] Step 4:

[0050] The server distributes calculated evacuation route information to each user's mobile information terminal via communication means. In this process, customized information tailored to each user's current location and individual needs is generated and transmitted in real time.

[0051] Step 5:

[0052] The terminal interprets the evacuation route information it receives and notifies the user visually or audibly through a display device. This information is provided to the user as a route display on a map or as audio guidance, and is updated as needed.

[0053] Step 6:

[0054] The user takes evacuation action according to the instructions on the device. The user safely moves to the designated evacuation shelter while referring to the notified route. In addition, the user adjusts their actions based on real-time updates from the device in response to changes in the situation.

[0055] (Example 1)

[0056] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0057] In times of disaster, real-time, accurate collection and analysis of environmental information, as well as the provision of evacuation routes tailored to individual users, are essential for achieving rapid and safe evacuation. However, conventional systems do not adequately meet these requirements, resulting in ineffective support for disaster-prone areas and vulnerable individuals.

[0058] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0059] In this invention, the server includes an information acquisition means for collecting environmental data received from sensor components and unmanned transport devices; an analysis means for analyzing the data acquired by the information acquisition means to detect anomalies and identify dangerous areas by overlaying them with geographic information; and a route generation means for calculating safe evacuation routes considering the disaster situation, topographic characteristics, and closure information identified by the analysis means. This enables real-time situation assessment and the provision of evacuation instructions tailored to individual users.

[0060] A "sensor component" is a device used to detect specific parameters of the surrounding environment during a disaster and collect data.

[0061] An "unmanned transport device" is an autonomously flying device used to collect visual data from the air.

[0062] "Information acquisition means" refers to technologies for collecting data obtained from sensor components and unmanned transport devices.

[0063] "Analysis methods" refer to technologies that analyze collected data, detect anomalies, and overlay them with geographical information to identify dangerous areas.

[0064] "Route generation means" refers to a technology that calculates the optimal evacuation route by taking into account the identified disaster situation, topographic characteristics, and road closure information.

[0065] "Communication means" refers to a technology that transmits calculated evacuation route information to a mobile information processing device and updates the information in real time according to the situation.

[0066] "Display means" refers to technology that notifies users of received evacuation route information through visual and auditory means.

[0067] This invention is an information system for effectively supporting safe and rapid evacuation activities during disasters. This system utilizes sensor components, an unmanned transport device, information acquisition means, analysis means, route generation means, communication means, and display means.

[0068] The server acquires environmental data provided by sensor components and automated guided vehicles (AGVs) installed in the region. Sensor components detect specific environmental parameters related to disasters such as earthquakes, floods, and fires. AGVs collect wide-area visual data of the region from the air and transmit it to the server as images and videos. The hardware used includes various environmental sensors and high-resolution cameras.

[0069] This data is processed by an analysis system running on a server. The analysis system uses advanced algorithms to identify hazardous areas by detecting anomalies and overlaying them with geographic information. The analysis results are used to understand the situation of disasters, and examples include (1) designating areas with flood data showing a rapid rise from normal water levels as hazardous areas, and (2) detecting high-temperature data in fires to identify the possibility of fire outbreaks.

[0070] The server then calculates safe evacuation routes based on the analysis results. The route generation system considers road closure information, shelter locations, and topographic data to search for the optimal route. This includes special routes for vulnerable individuals, and settings such as route selection that avoids slopes are possible.

[0071] The calculated evacuation routes are transmitted to a mobile information processing device using communication means. The information is updated in real time and provided in text, map, and audio formats. In the event that an evacuation center becomes full, the server immediately recalculates and distributes the route to the next best evacuation center.

[0072] The terminal provides the user with received evacuation route information through a display device. The terminal visually displays a map and provides directional instructions via voice. For example, it provides voice guidance such as, "Turn left at the next intersection."

[0073] In operating this system, users can use prompts such as the following: "Please enter the following information into the generating AI model: a list of procedures to provide users with the fastest and safest evacuation routes after an earthquake, and the appropriate hardware and software to use for this purpose."

[0074] This system enables the provision of information to carry out evacuation activities efficiently and safely, allowing for effective support even during disasters.

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

[0076] Step 1:

[0077] The server receives initial data from sensor components and automated guided vehicles. The inputs are environmental parameters such as terrain, temperature, water level, and seismic intensity, allowing for an understanding of the initial conditions of the surrounding environment. The data is collected in real time and transmitted to the server. Specifically, the server receives digital signals transmitted from each sensor.

[0078] Step 2:

[0079] The server processes the received data using analytical tools. The input is the initial data obtained in step 1, and the data analysis includes anomaly detection techniques and the overlay of a geographic information system. Specific hazardous areas are obtained as a result of the analysis. In the specific operation, an anomaly pattern detection algorithm is executed to identify the presence and extent of disasters.

[0080] Step 3:

[0081] The server uses a route generation mechanism to calculate a safe evacuation route. The input is the hazardous area and terrain data identified in step 2, and the output is the optimal evacuation route. The server considers road closure information, shelter locations, and, occasionally, the special needs of those requiring assistance. Specifically, a shortest route search algorithm is executed to evaluate multiple route options.

[0082] Step 4:

[0083] The server sends the calculated evacuation routes to each user's terminal. The input is the output data from step 3, and the output is sent to the terminal as route information adapted to each individual user. Specifically, the system sends information in text, map, and audio formats using communication methods. This data is automatically updated according to the progress of the disaster.

[0084] Step 5:

[0085] The terminal notifies the user of evacuation route information. The input is the evacuation route information sent from the server in step 4, and the output is visual and audio route guidance. Specifically, a map is displayed on the terminal screen and audio guidance is played to help the user follow the best evacuation route.

[0086] (Application Example 1)

[0087] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0088] Rapid and safe evacuation during disasters is a critical issue for protecting the lives of many people. However, conventional evacuation systems have difficulty in real-time situation assessment and providing individualized evacuation routes, making it difficult to issue accurate evacuation instructions. This can lead to delays and confusion in evacuations, potentially threatening people's safety. Therefore, there is a need for the provision of evacuation routes based on immediate and reliable information.

[0089] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0090] In this invention, the server includes information acquisition means for collecting data received from sensor devices and monitoring devices, analysis means for analyzing the data acquired by the information acquisition means and identifying the impact, and route generation means for calculating a safe route based on the impact identified by the analysis means. This makes it possible to provide optimized evacuation routes in real time according to the situation.

[0091] A "sensor device" is a device used to monitor environmental changes in real time during a disaster and to collect data such as vibration and temperature.

[0092] A "monitoring device" is a device that uses unmanned aerial vehicles or other visual devices to acquire visual information from the air and understand the situation in the disaster area.

[0093] "Information acquisition means" refers to means that have the function of aggregating data from sensor devices and monitoring devices and transmitting it to a server.

[0094] "Analysis means" refers to the means of performing the process of analyzing acquired data to identify the impact and circumstances of a disaster.

[0095] A "route generation means" is a means that has an algorithm for calculating a safe and efficient evacuation route based on information obtained by an analysis means.

[0096] "Communication means" refers to the use of network infrastructure to transmit evacuation route information to users' information terminals.

[0097] "Display means" refers to means that provide an interface for notifying users of received evacuation route information visually or audibly.

[0098] An "information terminal" is a device carried by a user that has the function of receiving and displaying evacuation route information.

[0099] In implementing this invention, the server receives data from sensor devices and monitoring devices and analyzes that data in real time. Sensor devices collect various environmental data related to disasters on the ground, and monitoring devices (e.g., unmanned aerial vehicles) provide visual information from the air. Based on this data, the server evaluates the impact of the disaster and calculates safe evacuation routes based on the analysis results.

[0100] The calculated evacuation route is transmitted to the user's information terminal via communication means. The information terminal uses the user's current location information to dynamically adjust the evacuation route and display the optimized route. This allows the user to obtain an adaptively safe evacuation route.

[0101] As a concrete example, in the event of an earthquake, the system receives vibration data from seismometers and identifies the affected area. Unmanned aerial vehicles provide images of the affected area, and a server calculates safe evacuation routes in real time. This information is then sent to the user's smartphone, assisting evacuation by providing visual navigation.

[0102] An example of a prompt for a generative AI model is: "Design an app that provides real-time route guidance to help people reach shelters in the safest and fastest way possible when a major earthquake occurs."

[0103] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0104] Step 1:

[0105] The server receives environmental data during disasters from sensor and monitoring devices. Inputs include vibration and temperature measurements from sensor devices, and visual data obtained from monitoring devices. This data is aggregated into a specific receiving module on the server side.

[0106] Step 2:

[0107] The server processes the received data using an analysis tool. The input here is the environmental data received by the server in step 1. Based on this data, a pattern recognition algorithm is applied to evaluate the extent and intensity of the disaster's impact. The output is metadata indicating the specific circumstances of the disaster.

[0108] Step 3:

[0109] The server uses a route generation mechanism based on the analyzed data to calculate a safe evacuation route. The input requires specific disaster situation information, geographical information, and traffic information obtained in step 2. The output is evacuation route information containing multiple route options.

[0110] Step 4:

[0111] The server transmits the evacuation route, calculated using communication methods, to the user's information terminal. The input is the route data generated in step 3, and the output is the data distribution to the information terminal via the network. Data packaging and encryption are performed during this process.

[0112] Step 5:

[0113] The terminal notifies the user of evacuation route information received from the server using a display device. The input is route information transmitted from the server, and the output is navigation data presented to the user visually or audibly. The terminal uses location sensors to update and optimize the route in real time.

[0114] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0115] This invention is an information system for guiding evacuees during disasters, not only to ensure their safety but also to reduce their psychological stress. This system integrates sensor devices, unmanned aerial vehicles, information acquisition means, analysis means, route generation means, communication means, display means, and an emotion engine.

[0116] First, the server collects environmental data from sensor devices and unmanned aerial vehicles (UAVs). Sensor devices transmit physical data related to various disasters such as earthquakes, fires, and floods, while UAVs provide wide-area aerial video data. All of this data is aggregated on the server.

[0117] Next, this sensor data and video data from the unmanned aerial vehicle are processed by an analysis system on a server to identify the disaster situation in detail. The analysis system identifies dangerous areas by comparing them with map information and clarifies the extent of the disaster's impact. Based on this analysis data, the geographical information and road conditions used are updated, and safe evacuation routes are then generated.

[0118] Next, the server uses a route generation system to calculate the safest and fastest evacuation route for each type of disaster. This calculation is optimized by also considering the results of the subscribed emotion engine. The emotion engine recognizes the user's emotions and stress levels using inputs such as voice and facial images. By utilizing this information in its analysis, not only is the selection of evacuation routes customized, but the notification methods are also tailored.

[0119] The communication system transmits this customized evacuation information to the terminals. Each terminal receives information individually tailored to the user's location, stress level, and type of disaster. This ensures that users receive the most relevant information.

[0120] The device presents received information to the user in the most easily understandable way, such as through visual map displays or audio guidance. The display method is dynamically adjusted according to the user's current emotional state; for example, users in a high-stress state are provided with more concise information.

[0121] Users can follow the instructions on their device and evacuate with a sense of security. This system provides emotional support and safely guides users to their destination. For example, during an earthquake, the device might display a comforting message to users who are under high stress, while also showing them the quickest route to higher ground.

[0122] Thus, the present invention is an advanced support system for promoting safe evacuation from both a physical and psychological perspective.

[0123] The following describes the processing flow.

[0124] Step 1:

[0125] The server receives environmental data in real time from sensor devices and unmanned aerial vehicles (UAVs). The data from sensor devices includes earthquake vibration information and flood information from water level sensors, while UAVs provide video data of the affected areas.

[0126] Step 2:

[0127] The server processes the received data using analytical tools to identify the current disaster situation. AI is used to determine the extent of the damage by comparing it with maps and to identify dangerous areas. Anomaly detection algorithms and image recognition technologies are used for data analysis.

[0128] Step 3:

[0129] The server uses an emotion engine to recognize emotions and stress levels from the user's voice and facial image data. This allows the user's psychological state to be quantified and used to generate the next evacuation route.

[0130] Step 4:

[0131] The server combines the analysis results with the emotion engine's output to calculate the optimal evacuation route using a route generation system. Here, not only safety but also routes that minimize user stress are considered. For example, a route with less congestion and smoother travel is selected.

[0132] Step 5:

[0133] The server uses communication methods to send customized evacuation route information tailored to the user to their terminal. This information is individualized based on the user's location, emotional state, and disaster situation.

[0134] Step 6:

[0135] The device receives evacuation route information and notifies the user through a display device. The way the information is presented changes depending on the user's emotional state. For example, a user in a high-stress state will receive concise and intuitive voice guidance and have the evacuation route highlighted on the map.

[0136] Step 7:

[0137] Users begin evacuation actions by following instructions from their device. Through actions based on these instructions, they can move safely and smoothly to their designated shelter or safe location. Users can take the best possible action in each situation while receiving psychological support.

[0138] (Example 2)

[0139] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0140] During disasters, it is crucial to ensure the safety of evacuees while guiding them appropriately and reducing their psychological stress. However, conventional information systems have limitations in terms of real-time situation assessment and providing information tailored to the individual psychological state of each evacuee. This can lead to problems where evacuees are unable to take appropriate action, making it difficult to ensure their safety.

[0141] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0142] In this invention, the server includes an information acquisition means for collecting environmental data, an analysis means for analyzing the disaster situation based on the data collected by the information acquisition means, and a route generation means for optimizing a safe evacuation route based on the disaster situation and the user's psychological state identified by the analysis means. This makes it possible to provide evacuees with a safe and rapid evacuation route that is physically and psychologically appropriate.

[0143] "Information acquisition means" refers to mechanisms and devices for collecting environmental data, and are used to aggregate disaster-related information via sensor devices and unmanned aerial vehicles.

[0144] "Analysis means" refers to algorithms and software used to process collected environmental data and identify the current disaster situation.

[0145] The "route generation means" is a processing means for calculating and selecting the safest and fastest evacuation route, taking into account the analyzed data and the user's psychological state.

[0146] "Communication means" refers to a mechanism, including network communication, for transmitting generated evacuation route information to a mobile information terminal.

[0147] "Display means" refers to an interface for providing received evacuation route information to users, and refers to a device that notifies information visually or audibly.

[0148] An "unmanned aerial vehicle" is a device used to acquire wide-area video data from the air and is used to grasp disaster situations in real time.

[0149] "Emotional state" refers to the user's psychological and emotional condition, and is used to customize evacuation information.

[0150] The system in this invention is designed to ensure the physical safety and psychological well-being of evacuees during disasters. This system is primarily operated by a server, terminals, and users, and enables the processing of various data and the provision of adaptive evacuation information.

[0151] The server collects a wide variety of disaster-related environmental data using information acquisition methods. This data includes physical data on earthquakes, fires, and floods from sensor devices, as well as wide-area video data from unmanned aerial vehicles. Analysis methods running on the server process this data and use machine learning algorithms to identify the current state of the disaster. Based on the hazardous areas and impact areas obtained as analysis results, the server further utilizes route generation methods to calculate and select the optimal evacuation route. This calculation takes into account the user's emotional state using an emotion engine. Speech recognition and facial image analysis are used for this purpose, and the evacuation information is optimized to provide users with a sense of psychological security.

[0152] Next, the evacuation route information generated by the communication device is adapted to each user's location and emotional state and transmitted to individual terminals. The terminals present the received data to the user in an easily understandable way, both visually and audibly, through display devices. The terminal's operation is embodied in route display via a map application and voice guidance, providing users with immediate instructions.

[0153] Users can take swift and safe evacuation actions by following instructions from their terminals. Coordinated operation between the server and terminals allows users to evacuate effectively while reducing stress.

[0154] As a concrete example, in the event of an earthquake, the server identifies areas with significant damage based on data obtained from unmanned aerial vehicles and calculates safe evacuation routes through a route generation system. It also applies an emotion engine to customize notifications with reassuring language for users experiencing high levels of stress.

[0155] An example of a prompt message is: "Design a system that guides users in a highly stressed state during an earthquake to the safest evacuation route along with a comforting message." By utilizing a generative AI model, flexible responses tailored to the situation can be achieved.

[0156] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0157] Step 1:

[0158] The server collects environmental data using information acquisition methods. Inputs include physical data related to earthquakes, fires, and floods from sensor devices, and aerial video data provided by unmanned aerial vehicles. This data is transmitted to the server via secure communication, and the collected information is stored in the server's database.

[0159] Step 2:

[0160] The server analyzes environmental data collected using analytical tools. The input is the data collected in step 1, and machine learning algorithms are applied to identify the disaster situation. Specific operations include identifying hazardous areas through image analysis and mapping the extent of disaster impact by comparing with a Geographic Information System (GIS). The output is the analysis results showing the identified hazardous areas and impact areas.

[0161] Step 3:

[0162] The server uses a route generation mechanism to calculate safe evacuation routes. The input consists of disaster conditions obtained from an analysis mechanism and the user's emotional state. The emotional state is obtained as a result of analysis by an emotion engine using voice and facial image data. The evacuation route calculation uses a shortest path algorithm while also considering factors to reduce psychological burden. The output is evacuation route information optimized for each individual user.

[0163] Step 4:

[0164] The server transmits evacuation route information to each user's terminal using communication means. The input is evacuation route information generated by the route generation means, which is customized based on the user's current location and emotional state. Operationally, data is transmitted over the network, and the output is the customized evacuation route information received by each terminal.

[0165] Step 5:

[0166] The terminal presents the received evacuation route information to the user visually and audibly. The input is the evacuation route information received by the terminal in step 4. Specifically, it displays the route on a map application and provides real-time instructions to the user using voice guidance. The output is the specific evacuation instructions that the user receives through the terminal.

[0167] Step 6:

[0168] The user follows instructions from the device and acts safely along an evacuation route. The input is the information presented by the device and the user's current location. The output is the user's movement to their destination, allowing for a quick and secure evacuation.

[0169] (Application Example 2)

[0170] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0171] Conventional disaster evacuation support systems were limited to calculating evacuation routes based on physical data, and did not adequately provide information that considered the psychological state of users. As a result, evacuees were not provided with optimal evacuation information, hindering efficient and safe evacuation. This invention solves this problem and realizes efficient and psychologically supported evacuation during disasters.

[0172] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0173] In this invention, the server includes data acquisition means for collecting information received from sensor devices, data analysis means for analyzing the information acquired by the data acquisition means to identify the disaster situation, and personalization means for individually optimizing evacuation route information using emotion recognition means for analyzing the emotional state of users. This makes it possible to provide users with evacuation route information that comprehensively considers physical and psychological factors.

[0174] A "sensor device" is a device used to detect physical phenomena related to disasters and collect that data.

[0175] "Data acquisition means" refers to a function that receives information transmitted from sensor devices and manages it within the system.

[0176] "Data analysis means" refers to technical processes and devices for processing acquired information and identifying disaster situations.

[0177] "Route generation means" refers to the functions and algorithms for calculating safe evacuation routes based on analyzed disaster information.

[0178] "Emotion recognition means" refers to technology that analyzes the emotional state of a user from their voice, facial images, etc., and evaluates their psychological state.

[0179] "Personalization means" refers to a function that optimizes evacuation route information by taking into account the emotional state of the user and customizing it to suit each individual user.

[0180] "Information and communication means" refers to communication functions and technologies for transmitting generated evacuation route information to mobile terminal devices.

[0181] A "mobile terminal device" is a device that a user can carry with them to receive and display evacuation route information.

[0182] "Display means" refers to a function for presenting received evacuation route information to the user visually or audibly.

[0183] To implement this invention, the server first utilizes sensor devices and unmanned aerial vehicles (UAVs) to collect disaster information. The sensor devices collect physical data such as earthquakes and fires, and the UAVs acquire wide-area video data. This information is transmitted to the server in real time via AWS® IoT Core.

[0184] Next, the server uses Amazon SageMaker to analyze the acquired data. This analysis identifies hazardous areas and assesses the extent of disaster impact. It also uses OpenStreetMap data and the Python NetworkX library to generate the safest evacuation routes.

[0185] Furthermore, the server utilizes the Google® Cloud Vision API to analyze the user's emotional state, recognizing emotions from facial images and other data. The results of this emotion recognition are used to customize evacuation route information, which is then provided as personalized information optimized for each user.

[0186] Personalized evacuation information is transmitted to mobile terminal devices via Firebase Cloud Messaging. These terminal devices, developed using Flutter®, provide information to users through visual map displays and voice guidance. This allows users to know their customized evacuation routes in real time, enabling them to take swift and safe evacuation actions while receiving psychological support, even in highly stressful situations.

[0187] For example, in the event of an earthquake, the evacuation information generated by the server includes a prompt message for users in a highly stressed state, such as, "This path is safe. Proceed slowly and with peace of mind." This message is provided as voice guidance and is also displayed visually on the device.

[0188] An example of a prompt from a generated AI model is, "In the event of an earthquake, what kind of reassuring message can you provide to users who are under high stress?"

[0189] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0190] Step 1:

[0191] The server receives physical data related to disasters from sensor devices and wide-area video data from unmanned aerial vehicles. Inputs include sensor data and video data related to earthquakes and fires, which are aggregated to the server via AWS IoT Core. Outputs are stored as raw data in a database for analysis.

[0192] Step 2:

[0193] The server uses Amazon SageMaker as a data analysis tool to analyze the raw data. This analysis takes received sensor data and video data as input to identify the extent of disaster impact and hazardous areas. The output is mapping data of the hazardous areas, which is used in the subsequent path generation process.

[0194] Step 3:

[0195] The server generates safe evacuation routes using OpenStreetMap geographic data and the NetworkX library. The input is analyzed mapping data of hazardous areas, and the evacuation route calculations are performed. The output is optimized evacuation route data.

[0196] Step 4:

[0197] The server uses the Google Cloud Vision API as an emotion recognition tool to analyze the user's facial image sent from the device. The input consists of facial image and audio data, and emotion recognition calculations are performed. The output is information about the user's stress level.

[0198] Step 5:

[0199] The server personalizes evacuation route information based on the emotion recognition results. The input consists of the emotion recognition output and evacuation route data, and the information is customized according to the user's psychological state. The output is the personalized evacuation route information.

[0200] Step 6:

[0201] The server uses Firebase Cloud Messaging to send optimized evacuation route information to the user's mobile device. The input is personalized evacuation route information, and the output is message data to the device.

[0202] Step 7:

[0203] The terminal provides users with received evacuation route information as a visual map display and voice guidance. Input is message data from the server, and output is a user-friendly interface display and voice guidance. This allows users to confidently follow evacuation instructions.

[0204] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0205] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0206] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0207] [Second Embodiment]

[0208] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0209] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0210] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0211] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0212] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0213] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0214] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0215] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0216] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0217] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0218] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0219] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0220] This invention is an information system for realizing rapid and safe evacuation during disasters. This system includes sensor devices, unmanned aerial vehicles, information acquisition means, analysis means, route generation means, communication means, and display means. The operation of each component is described in detail below.

[0221] First, the server acquires local environmental data in real time from sensor devices and unmanned aerial vehicles (UAVs). Sensor devices monitor specific parameters such as vibrations during earthquakes, water levels during floods, and temperatures during fires, and transmit the data. UAVs collect visual data from the air, such as images and videos of the affected area, and transmit this to the server.

[0222] This collected data is processed by an analysis system running on a server. The analysis system analyzes the data to identify various disaster situations and evaluate the extent and impact of the damage. The evaluation includes a process of detecting anomalies in the data and overlaying them with geographic information to identify hazardous areas.

[0223] Next, the server uses a route generation mechanism to calculate a safe evacuation route based on the analyzed disaster situation. Here, multiple route options are considered, taking into account road closures, the location of evacuation shelters, and the topographical characteristics of the area. Each user's current location and special needs (e.g., those requiring assistance) are also taken into consideration.

[0224] The server then uses communication methods to transmit the calculated evacuation routes to each user's mobile information terminal. The information is sent in the form of text, maps, and audio, and is updated in real time.

[0225] The terminal receives this information and notifies the user through a display device. The display device visually shows evacuation routes or provides emergency alerts via voice. The user can evacuate safely by following the route displayed on the terminal. For example, after an earthquake, the user is guided to avoid the most dangerous areas and head to a nearby evacuation center.

[0226] Thus, this system is designed to enable users to evacuate efficiently and safely by integrating situation assessment, analysis, and evacuation guidance during disasters.

[0227] The following describes the processing flow.

[0228] Step 1:

[0229] The server receives environmental data from sensor devices and unmanned aerial vehicles (UAVs). Sensor devices transmit data including seismic vibrations, fire temperatures, and flood water levels, while UAVs transmit visual information capturing the current damage situation.

[0230] Step 2:

[0231] The server processes the received data using analytical tools. This analysis identifies the type of disaster and maps its impact area and dangerous locations on a map. For the analysis, modeled AI algorithms are used to detect anomalies and identify safe areas.

[0232] Step 3:

[0233] The server uses a route generation method based on the analysis results to calculate the optimal evacuation route. The calculation considers the condition of local infrastructure (roads, evacuation facilities, etc.), current congestion levels, and the progression of the disaster, and evaluates multiple routes.

[0234] Step 4:

[0235] The server distributes calculated evacuation route information to each user's mobile information terminal via communication means. In this process, customized information tailored to each user's current location and individual needs is generated and transmitted in real time.

[0236] Step 5:

[0237] The terminal interprets the evacuation route information it receives and notifies the user visually or audibly through a display device. This information is provided to the user as a route display on a map or as audio guidance, and is updated as needed.

[0238] Step 6:

[0239] The user takes evacuation action according to the instructions on the device. The user safely moves to the designated evacuation shelter while referring to the notified route. In addition, the user adjusts their actions based on real-time updates from the device in response to changes in the situation.

[0240] (Example 1)

[0241] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0242] In times of disaster, real-time, accurate collection and analysis of environmental information, as well as the provision of evacuation routes tailored to individual users, are essential for achieving rapid and safe evacuation. However, conventional systems do not adequately meet these requirements, resulting in ineffective support for disaster-prone areas and vulnerable individuals.

[0243] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0244] In this invention, the server includes an information acquisition means for collecting environmental data received from sensor components and unmanned transport devices; an analysis means for analyzing the data acquired by the information acquisition means to detect anomalies and identify dangerous areas by overlaying them with geographic information; and a route generation means for calculating safe evacuation routes considering the disaster situation, topographic characteristics, and closure information identified by the analysis means. This enables real-time situation assessment and the provision of evacuation instructions tailored to individual users.

[0245] A "sensor component" is a device used to detect specific parameters of the surrounding environment during a disaster and collect data.

[0246] An "unmanned transport device" is an autonomously flying device used to collect visual data from the air.

[0247] "Information acquisition means" refers to technologies for collecting data obtained from sensor components and unmanned transport devices.

[0248] "Analysis methods" refer to technologies that analyze collected data, detect anomalies, and overlay them with geographical information to identify dangerous areas.

[0249] "Route generation means" refers to a technology that calculates the optimal evacuation route by taking into account the identified disaster situation, topographic characteristics, and road closure information.

[0250] "Communication means" refers to a technology that transmits calculated evacuation route information to a mobile information processing device and updates the information in real time according to the situation.

[0251] "Display means" refers to technology that notifies users of received evacuation route information through visual and auditory means.

[0252] This invention is an information system for effectively supporting safe and rapid evacuation activities during disasters. This system utilizes sensor components, an unmanned transport device, information acquisition means, analysis means, route generation means, communication means, and display means.

[0253] The server acquires environmental data provided by sensor components and automated guided vehicles (AGVs) installed in the region. Sensor components detect specific environmental parameters related to disasters such as earthquakes, floods, and fires. AGVs collect wide-area visual data of the region from the air and transmit it to the server as images and videos. The hardware used includes various environmental sensors and high-resolution cameras.

[0254] This data is processed by an analysis system running on a server. The analysis system uses advanced algorithms to identify hazardous areas by detecting anomalies and overlaying them with geographic information. The analysis results are used to understand the situation of disasters, and examples include (1) designating areas with flood data showing a rapid rise from normal water levels as hazardous areas, and (2) detecting high-temperature data in fires to identify the possibility of fire outbreaks.

[0255] The server then calculates safe evacuation routes based on the analysis results. The route generation system considers road closure information, shelter locations, and topographic data to search for the optimal route. This includes special routes for vulnerable individuals, and settings such as route selection that avoids slopes are possible.

[0256] The calculated evacuation routes are transmitted to a mobile information processing device using communication means. The information is updated in real time and provided in text, map, and audio formats. In the event that an evacuation center becomes full, the server immediately recalculates and distributes the route to the next best evacuation center.

[0257] The terminal provides the user with received evacuation route information through a display device. The terminal visually displays a map and provides directional instructions via voice. For example, it provides voice guidance such as, "Turn left at the next intersection."

[0258] In operating this system, users can use prompts such as the following: "Please enter the following information into the generating AI model: a list of procedures to provide users with the fastest and safest evacuation routes after an earthquake, and the appropriate hardware and software to use for this purpose."

[0259] This system enables the provision of information to carry out evacuation activities efficiently and safely, allowing for effective support even during disasters.

[0260] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0261] Step 1:

[0262] The server receives initial data from sensor components and automated guided vehicles. The inputs are environmental parameters such as terrain, temperature, water level, and seismic intensity, allowing for an understanding of the initial conditions of the surrounding environment. The data is collected in real time and transmitted to the server. Specifically, the server receives digital signals transmitted from each sensor.

[0263] Step 2:

[0264] The server processes the received data using analytical tools. The input is the initial data obtained in step 1, and the data analysis includes anomaly detection techniques and the overlay of a geographic information system. Specific hazardous areas are obtained as a result of the analysis. In the specific operation, an anomaly pattern detection algorithm is executed to identify the presence and extent of disasters.

[0265] Step 3:

[0266] The server uses a route generation mechanism to calculate a safe evacuation route. The input is the hazardous area and terrain data identified in step 2, and the output is the optimal evacuation route. The server considers road closure information, shelter locations, and, occasionally, the special needs of those requiring assistance. Specifically, a shortest route search algorithm is executed to evaluate multiple route options.

[0267] Step 4:

[0268] The server sends the calculated evacuation routes to each user's terminal. The input is the output data from step 3, and the output is sent to the terminal as route information adapted to each individual user. Specifically, the system sends information in text, map, and audio formats using communication methods. This data is automatically updated according to the progress of the disaster.

[0269] Step 5:

[0270] The terminal notifies the user of evacuation route information. The input is the evacuation route information sent from the server in step 4, and the output is visual and audio route guidance. Specifically, a map is displayed on the terminal screen and audio guidance is played to help the user follow the best evacuation route.

[0271] (Application Example 1)

[0272] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0273] Rapid and safe evacuation during disasters is a critical issue for protecting the lives of many people. However, conventional evacuation systems have difficulty in real-time situation assessment and providing individualized evacuation routes, making it difficult to issue accurate evacuation instructions. This can lead to delays and confusion in evacuations, potentially threatening people's safety. Therefore, there is a need for the provision of evacuation routes based on immediate and reliable information.

[0274] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0275] In this invention, the server includes information acquisition means for collecting data received from sensor devices and monitoring devices, analysis means for analyzing the data acquired by the information acquisition means and identifying the impact, and route generation means for calculating a safe route based on the impact identified by the analysis means. This makes it possible to provide optimized evacuation routes in real time according to the situation.

[0276] A "sensor device" is a device used to monitor environmental changes in real time during a disaster and to collect data such as vibration and temperature.

[0277] A "monitoring device" is a device that uses unmanned aerial vehicles or other visual devices to acquire visual information from the air and understand the situation in the disaster area.

[0278] "Information acquisition means" refers to means that have the function of aggregating data from sensor devices and monitoring devices and transmitting it to a server.

[0279] "Analysis means" refers to the means of performing the process of analyzing acquired data to identify the impact and circumstances of a disaster.

[0280] A "route generation means" is a means that has an algorithm for calculating a safe and efficient evacuation route based on information obtained by an analysis means.

[0281] The "communication means" is a means of using a network infrastructure for transmitting evacuation route information to the user's information terminal.

[0282] The "display means" is a means of providing an interface for visually or audibly notifying the user of the received evacuation route information.

[0283] The "information terminal" is a device carried by the user and has a function of receiving and displaying evacuation route information.

[0284] In the implementation of this invention, the server receives data from the sensor device and the monitoring device, and analyzes the data in real time. The sensor device collects various environmental data related to disasters on the ground, and the monitoring device (for example, an unmanned aerial vehicle) provides visual information from the air. The server evaluates the impact of the disaster based on these data and calculates a safe evacuation route based on the analysis results.

[0285] The calculated evacuation route is transmitted to the user's information terminal using the communication means. The information terminal uses the user's current location information to dynamically adjust the evacuation route and display an optimized route. Thereby, the user can obtain an adaptable and safe evacuation route.

[0286] As a specific example below, when an earthquake occurs, the system receives vibration data from the seismograph and identifies the affected area. The unmanned aerial vehicle provides images of the disaster area, and the server calculates a safe evacuation route in real time. Then, the information is transmitted to the user's smartphone and visually navigates to assist in evacuation.

[0287] As an example of a prompt sentence for the generation AI model, "Please design an app that provides real-time route guidance to reach a shelter safely and quickly when a large-scale earthquake occurs." can be cited.

[0288] The flow of the specific process in Application Example 1 will be described with reference to FIG. 12.

[0289] Step 1:

[0290] The server receives environmental data during disasters from sensor and monitoring devices. Inputs include vibration and temperature measurements from sensor devices, and visual data obtained from monitoring devices. This data is aggregated into a specific receiving module on the server side.

[0291] Step 2:

[0292] The server processes the received data using an analysis tool. The input here is the environmental data received by the server in step 1. Based on this data, a pattern recognition algorithm is applied to evaluate the extent and intensity of the disaster's impact. The output is metadata indicating the specific circumstances of the disaster.

[0293] Step 3:

[0294] The server uses a route generation mechanism based on the analyzed data to calculate a safe evacuation route. The input requires specific disaster situation information, geographical information, and traffic information obtained in step 2. The output is evacuation route information containing multiple route options.

[0295] Step 4:

[0296] The server transmits the evacuation route, calculated using communication methods, to the user's information terminal. The input is the route data generated in step 3, and the output is the data distribution to the information terminal via the network. Data packaging and encryption are performed during this process.

[0297] Step 5:

[0298] The terminal notifies the user of evacuation route information received from the server using a display device. The input is route information transmitted from the server, and the output is navigation data presented to the user visually or audibly. The terminal uses location sensors to update and optimize the route in real time.

[0299] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0300] This invention is an information system for guiding evacuees during disasters, not only to ensure their safety but also to reduce their psychological stress. This system integrates sensor devices, unmanned aerial vehicles, information acquisition means, analysis means, route generation means, communication means, display means, and an emotion engine.

[0301] First, the server collects environmental data from sensor devices and unmanned aerial vehicles (UAVs). Sensor devices transmit physical data related to various disasters such as earthquakes, fires, and floods, while UAVs provide wide-area aerial video data. All of this data is aggregated on the server.

[0302] Next, this sensor data and video data from the unmanned aerial vehicle are processed by an analysis system on a server to identify the disaster situation in detail. The analysis system identifies dangerous areas by comparing them with map information and clarifies the extent of the disaster's impact. Based on this analysis data, the geographical information and road conditions used are updated, and safe evacuation routes are then generated.

[0303] Next, the server uses a route generation system to calculate the safest and fastest evacuation route for each type of disaster. This calculation is optimized by also considering the results of the subscribed emotion engine. The emotion engine recognizes the user's emotions and stress levels using inputs such as voice and facial images. By utilizing this information in its analysis, not only is the selection of evacuation routes customized, but the notification methods are also tailored.

[0304] The communication means transmits these customized evacuation information to the terminal. Each terminal individually receives information according to the user's location, stress state, and type of disaster. Thereby, the user can surely obtain the most suitable information.

[0305] The terminal presents the received information to the user in the most understandable way, such as a visual map display or voice guidance. The display means is dynamically adjusted according to the user's current emotional state. For example, more concise information is provided to users in a high-stress state.

[0306] The user can carry out evacuation actions with a sense of security according to the instructions of the terminal. With this system, the user is emotionally supported and safely guided to the destination. As a specific example, in the case of a user in a high-stress state at the time of an earthquake, the terminal displays a reassuring message while specifically showing the fastest route to a high place.

[0307] In this way, the present invention is an advanced support system for promoting safe evacuation from both physical and psychological aspects.

[0308] The following describes the processing flow.

[0309] Step 1:

[0310] The server receives environmental data in real time from the sensor device and the unmanned aircraft. The data from the sensor device includes earthquake vibration information and flood information from the water level sensor, and video data of the disaster area is provided from the unmanned aircraft.

[0311] Step 2:

[0312] The server processes the received data by the analysis means to identify the current disaster situation. Here, AI is used to grasp the damage range while comparing with the map and identify the dangerous areas. Abnormal detection algorithms and image recognition technologies are used for data analysis.

[0313] Step 3:

[0314] The server uses an emotion engine to recognize emotions and stress levels from the user's voice and facial image data. This allows the user's psychological state to be quantified and used to generate the next evacuation route.

[0315] Step 4:

[0316] The server combines the analysis results with the emotion engine's output to calculate the optimal evacuation route using a route generation system. Here, not only safety but also routes that minimize user stress are considered. For example, a route with less congestion and smoother travel is selected.

[0317] Step 5:

[0318] The server uses communication methods to send customized evacuation route information tailored to the user to their terminal. This information is individualized based on the user's location, emotional state, and disaster situation.

[0319] Step 6:

[0320] The device receives evacuation route information and notifies the user through a display device. The way the information is presented changes depending on the user's emotional state. For example, a user in a high-stress state will receive concise and intuitive voice guidance and have the evacuation route highlighted on the map.

[0321] Step 7:

[0322] Users begin evacuation actions by following instructions from their device. Through actions based on these instructions, they can move safely and smoothly to their designated shelter or safe location. Users can take the best possible action in each situation while receiving psychological support.

[0323] (Example 2)

[0324] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0325] During disasters, it is crucial to ensure the safety of evacuees while guiding them appropriately and reducing their psychological stress. However, conventional information systems have limitations in terms of real-time situation assessment and providing information tailored to the individual psychological state of each evacuee. This can lead to problems where evacuees are unable to take appropriate action, making it difficult to ensure their safety.

[0326] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0327] In this invention, the server includes an information acquisition means for collecting environmental data, an analysis means for analyzing the disaster situation based on the data collected by the information acquisition means, and a route generation means for optimizing a safe evacuation route based on the disaster situation and the user's psychological state identified by the analysis means. This makes it possible to provide evacuees with a safe and rapid evacuation route that is physically and psychologically appropriate.

[0328] "Information acquisition means" refers to mechanisms and devices for collecting environmental data, and are used to aggregate disaster-related information via sensor devices and unmanned aerial vehicles.

[0329] "Analysis means" refers to algorithms and software used to process collected environmental data and identify the current disaster situation.

[0330] The "route generation means" is a processing means for calculating and selecting the safest and fastest evacuation route, taking into account the analyzed data and the user's psychological state.

[0331] "Communication means" refers to a mechanism, including network communication, for transmitting generated evacuation route information to a mobile information terminal.

[0332] "Display means" refers to an interface for providing received evacuation route information to users, and refers to a device that notifies information visually or audibly.

[0333] An "unmanned aerial vehicle" is a device used to acquire wide-area video data from the air and is used to grasp disaster situations in real time.

[0334] "Emotional state" refers to the user's psychological and emotional condition, and is used to customize evacuation information.

[0335] The system in this invention is designed to ensure the physical safety and psychological well-being of evacuees during disasters. This system is primarily operated by a server, terminals, and users, and enables the processing of various data and the provision of adaptive evacuation information.

[0336] The server collects a wide variety of disaster-related environmental data using information acquisition methods. This data includes physical data on earthquakes, fires, and floods from sensor devices, as well as wide-area video data from unmanned aerial vehicles. Analysis methods running on the server process this data and use machine learning algorithms to identify the current state of the disaster. Based on the hazardous areas and impact areas obtained as analysis results, the server further utilizes route generation methods to calculate and select the optimal evacuation route. This calculation takes into account the user's emotional state using an emotion engine. Speech recognition and facial image analysis are used for this purpose, and the evacuation information is optimized to provide users with a sense of psychological security.

[0337] Next, the evacuation route information generated by the communication device is adapted to each user's location and emotional state and transmitted to individual terminals. The terminals present the received data to the user in an easily understandable way, both visually and audibly, through display devices. The terminal's operation is embodied in route display via a map application and voice guidance, providing users with immediate instructions.

[0338] Users can take swift and safe evacuation actions by following instructions from their terminals. Coordinated operation between the server and terminals allows users to evacuate effectively while reducing stress.

[0339] As a concrete example, in the event of an earthquake, the server identifies areas with significant damage based on data obtained from unmanned aerial vehicles and calculates safe evacuation routes through a route generation system. It also applies an emotion engine to customize notifications with reassuring language for users experiencing high levels of stress.

[0340] An example of a prompt message is: "Design a system that guides users in a highly stressed state during an earthquake to the safest evacuation route along with a comforting message." By utilizing a generative AI model, flexible responses tailored to the situation can be achieved.

[0341] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0342] Step 1:

[0343] The server collects environmental data using information acquisition methods. Inputs include physical data related to earthquakes, fires, and floods from sensor devices, and aerial video data provided by unmanned aerial vehicles. This data is transmitted to the server via secure communication, and the collected information is stored in the server's database.

[0344] Step 2:

[0345] The server analyzes environmental data collected using analytical tools. The input is the data collected in step 1, and machine learning algorithms are applied to identify the disaster situation. Specific operations include identifying hazardous areas through image analysis and mapping the extent of disaster impact by comparing with a Geographic Information System (GIS). The output is the analysis results showing the identified hazardous areas and impact areas.

[0346] Step 3:

[0347] The server uses a route generation mechanism to calculate safe evacuation routes. The input consists of disaster conditions obtained from an analysis mechanism and the user's emotional state. The emotional state is obtained as a result of analysis by an emotion engine using voice and facial image data. The evacuation route calculation uses a shortest path algorithm while also considering factors to reduce psychological burden. The output is evacuation route information optimized for each individual user.

[0348] Step 4:

[0349] The server transmits evacuation route information to each user's terminal using communication means. The input is evacuation route information generated by the route generation means, which is customized based on the user's current location and emotional state. Operationally, data is transmitted over the network, and the output is the customized evacuation route information received by each terminal.

[0350] Step 5:

[0351] The terminal presents the received evacuation route information to the user visually and audibly. The input is the evacuation route information received by the terminal in step 4. Specifically, it displays the route on a map application and provides real-time instructions to the user using voice guidance. The output is the specific evacuation instructions that the user receives through the terminal.

[0352] Step 6:

[0353] The user follows instructions from the device and acts safely along an evacuation route. The input is the information presented by the device and the user's current location. The output is the user's movement to their destination, allowing for a quick and secure evacuation.

[0354] (Application Example 2)

[0355] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0356] Conventional disaster evacuation support systems were limited to calculating evacuation routes based on physical data, and did not adequately provide information that considered the psychological state of users. As a result, evacuees were not provided with optimal evacuation information, hindering efficient and safe evacuation. This invention solves this problem and realizes efficient and psychologically supported evacuation during disasters.

[0357] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0358] In this invention, the server includes data acquisition means for collecting information received from sensor devices, data analysis means for analyzing the information acquired by the data acquisition means to identify the disaster situation, and personalization means for individually optimizing evacuation route information using emotion recognition means for analyzing the emotional state of users. This makes it possible to provide users with evacuation route information that comprehensively considers physical and psychological factors.

[0359] A "sensor device" is a device used to detect physical phenomena related to disasters and collect that data.

[0360] "Data acquisition means" refers to a function that receives information transmitted from sensor devices and manages it within the system.

[0361] "Data analysis means" refers to technical processes and devices for processing acquired information and identifying disaster situations.

[0362] "Route generation means" refers to the functions and algorithms for calculating safe evacuation routes based on analyzed disaster information.

[0363] "Emotion recognition means" refers to technology that analyzes the emotional state of a user from their voice, facial images, etc., and evaluates their psychological state.

[0364] "Personalization means" refers to a function that optimizes evacuation route information by taking into account the emotional state of the user and customizing it to suit each individual user.

[0365] "Information and communication means" refers to communication functions and technologies for transmitting generated evacuation route information to mobile terminal devices.

[0366] A "mobile terminal device" is a device that a user can carry with them to receive and display evacuation route information.

[0367] "Display means" refers to a function for presenting received evacuation route information to the user visually or audibly.

[0368] To implement this invention, the server first utilizes sensor devices and unmanned aerial vehicles (UAVs) to collect disaster information. The sensor devices collect physical data such as earthquakes and fires, while the UAVs acquire wide-area video data. This information is transmitted to the server in real time via AWS IoT Core.

[0369] Next, the server uses Amazon SageMaker to analyze the acquired data. This analysis identifies hazardous areas and assesses the extent of disaster impact. It also uses OpenStreetMap data and the Python NetworkX library to generate the safest evacuation routes.

[0370] Furthermore, the server utilizes the Google Cloud Vision API to analyze the user's emotional state, recognizing emotions from facial images and other data. The results of this emotion recognition are used to customize evacuation route information, which is then provided as personalized information optimized for each user.

[0371] Personalized evacuation information is transmitted to mobile terminal devices via Firebase Cloud Messaging. These terminal devices, developed with Flutter, provide information to users through visual map displays and voice guidance. This allows users to know their customized evacuation routes in real time, enabling them to take swift and safe evacuation actions while receiving psychological support, even in highly stressful situations.

[0372] For example, in the event of an earthquake, the evacuation information generated by the server includes a prompt message for users in a highly stressed state, such as, "This path is safe. Proceed slowly and with peace of mind." This message is provided as voice guidance and is also displayed visually on the device.

[0373] An example of a prompt from a generated AI model is, "In the event of an earthquake, what kind of reassuring message can you provide to users who are under high stress?"

[0374] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0375] Step 1:

[0376] The server receives physical data related to disasters from sensor devices and wide-area video data from unmanned aerial vehicles. Inputs include sensor data and video data related to earthquakes and fires, which are aggregated to the server via AWS IoT Core. Outputs are stored as raw data in a database for analysis.

[0377] Step 2:

[0378] The server uses Amazon SageMaker as a data analysis tool to analyze the raw data. This analysis takes received sensor data and video data as input to identify the extent of disaster impact and hazardous areas. The output is mapping data of the hazardous areas, which is used in the subsequent path generation process.

[0379] Step 3:

[0380] The server generates safe evacuation routes using OpenStreetMap geographic data and the NetworkX library. The input is analyzed mapping data of hazardous areas, and the evacuation route calculations are performed. The output is optimized evacuation route data.

[0381] Step 4:

[0382] The server uses the Google Cloud Vision API as an emotion recognition tool to analyze the user's facial image sent from the device. The input consists of facial image and audio data, and emotion recognition calculations are performed. The output is information about the user's stress level.

[0383] Step 5:

[0384] The server personalizes evacuation route information based on the emotion recognition results. The input consists of the emotion recognition output and evacuation route data, and the information is customized according to the user's psychological state. The output is the personalized evacuation route information.

[0385] Step 6:

[0386] The server uses Firebase Cloud Messaging to send optimized evacuation route information to the user's mobile device. The input is personalized evacuation route information, and the output is message data to the device.

[0387] Step 7:

[0388] The terminal provides users with received evacuation route information as a visual map display and voice guidance. Input is message data from the server, and output is a user-friendly interface display and voice guidance. This allows users to confidently follow evacuation instructions.

[0389] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0390] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0391] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0392] [Third Embodiment]

[0393] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0394] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0395] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0396] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0397] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0398] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0399] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0400] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0401] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0402] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0403] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0404] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0405] This invention is an information system for realizing rapid and safe evacuation during disasters. This system includes sensor devices, unmanned aerial vehicles, information acquisition means, analysis means, route generation means, communication means, and display means. The operation of each component is described in detail below.

[0406] First, the server acquires local environmental data in real time from sensor devices and unmanned aerial vehicles (UAVs). Sensor devices monitor specific parameters such as vibrations during earthquakes, water levels during floods, and temperatures during fires, and transmit the data. UAVs collect visual data from the air, such as images and videos of the affected area, and transmit this to the server.

[0407] This collected data is processed by an analysis system running on a server. The analysis system analyzes the data to identify various disaster situations and evaluate the extent and impact of the damage. The evaluation includes a process of detecting anomalies in the data and overlaying them with geographic information to identify hazardous areas.

[0408] Next, the server uses a route generation mechanism to calculate a safe evacuation route based on the analyzed disaster situation. Here, multiple route options are considered, taking into account road closures, the location of evacuation shelters, and the topographical characteristics of the area. Each user's current location and special needs (e.g., those requiring assistance) are also taken into consideration.

[0409] The server then uses communication methods to transmit the calculated evacuation routes to each user's mobile information terminal. The information is sent in the form of text, maps, and audio, and is updated in real time.

[0410] The terminal receives this information and notifies the user through a display device. The display device visually shows evacuation routes or provides emergency alerts via voice. The user can evacuate safely by following the route displayed on the terminal. For example, after an earthquake, the user is guided to avoid the most dangerous areas and head to a nearby evacuation center.

[0411] Thus, this system is designed to enable users to evacuate efficiently and safely by integrating situation assessment, analysis, and evacuation guidance during disasters.

[0412] The following describes the processing flow.

[0413] Step 1:

[0414] The server receives environmental data from sensor devices and unmanned aerial vehicles (UAVs). Sensor devices transmit data including seismic vibrations, fire temperatures, and flood water levels, while UAVs transmit visual information capturing the current damage situation.

[0415] Step 2:

[0416] The server processes the received data using analytical tools. This analysis identifies the type of disaster and maps its impact area and dangerous locations on a map. For the analysis, modeled AI algorithms are used to detect anomalies and identify safe areas.

[0417] Step 3:

[0418] The server uses a route generation method based on the analysis results to calculate the optimal evacuation route. The calculation considers the condition of local infrastructure (roads, evacuation facilities, etc.), current congestion levels, and the progression of the disaster, and evaluates multiple routes.

[0419] Step 4:

[0420] The server distributes calculated evacuation route information to each user's mobile information terminal via communication means. In this process, customized information tailored to each user's current location and individual needs is generated and transmitted in real time.

[0421] Step 5:

[0422] The terminal interprets the evacuation route information it receives and notifies the user visually or audibly through a display device. This information is provided to the user as a route display on a map or as audio guidance, and is updated as needed.

[0423] Step 6:

[0424] The user takes evacuation action according to the instructions on the device. The user safely moves to the designated evacuation shelter while referring to the notified route. In addition, the user adjusts their actions based on real-time updates from the device in response to changes in the situation.

[0425] (Example 1)

[0426] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0427] In times of disaster, real-time, accurate collection and analysis of environmental information, as well as the provision of evacuation routes tailored to individual users, are essential for achieving rapid and safe evacuation. However, conventional systems do not adequately meet these requirements, resulting in ineffective support for disaster-prone areas and vulnerable individuals.

[0428] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0429] In this invention, the server includes an information acquisition means for collecting environmental data received from sensor components and unmanned transport devices; an analysis means for analyzing the data acquired by the information acquisition means to detect anomalies and identify dangerous areas by overlaying them with geographic information; and a route generation means for calculating safe evacuation routes considering the disaster situation, topographic characteristics, and closure information identified by the analysis means. This enables real-time situation assessment and the provision of evacuation instructions tailored to individual users.

[0430] A "sensor component" is a device used to detect specific parameters of the surrounding environment during a disaster and collect data.

[0431] An "unmanned transport device" is an autonomously flying device used to collect visual data from the air.

[0432] "Information acquisition means" refers to technologies for collecting data obtained from sensor components and unmanned transport devices.

[0433] "Analysis methods" refer to technologies that analyze collected data, detect anomalies, and overlay them with geographical information to identify dangerous areas.

[0434] "Route generation means" refers to a technology that calculates the optimal evacuation route by taking into account the identified disaster situation, topographic characteristics, and road closure information.

[0435] "Communication means" refers to a technology that transmits calculated evacuation route information to a mobile information processing device and updates the information in real time according to the situation.

[0436] "Display means" refers to technology that notifies users of received evacuation route information through visual and auditory means.

[0437] This invention is an information system for effectively supporting safe and rapid evacuation activities during disasters. This system utilizes sensor components, an unmanned transport device, information acquisition means, analysis means, route generation means, communication means, and display means.

[0438] The server acquires environmental data provided by sensor components and automated guided vehicles (AGVs) installed in the region. Sensor components detect specific environmental parameters related to disasters such as earthquakes, floods, and fires. AGVs collect wide-area visual data of the region from the air and transmit it to the server as images and videos. The hardware used includes various environmental sensors and high-resolution cameras.

[0439] This data is processed by an analysis system running on a server. The analysis system uses advanced algorithms to identify hazardous areas by detecting anomalies and overlaying them with geographic information. The analysis results are used to understand the situation of disasters, and examples include (1) designating areas with flood data showing a rapid rise from normal water levels as hazardous areas, and (2) detecting high-temperature data in fires to identify the possibility of fire outbreaks.

[0440] The server then calculates safe evacuation routes based on the analysis results. The route generation system considers road closure information, shelter locations, and topographic data to search for the optimal route. This includes special routes for vulnerable individuals, and settings such as route selection that avoids slopes are possible.

[0441] The calculated evacuation routes are transmitted to a mobile information processing device using communication means. The information is updated in real time and provided in text, map, and audio formats. In the event that an evacuation center becomes full, the server immediately recalculates and distributes the route to the next best evacuation center.

[0442] The terminal provides the user with received evacuation route information through a display device. The terminal visually displays a map and provides directional instructions via voice. For example, it provides voice guidance such as, "Turn left at the next intersection."

[0443] In operating this system, users can use prompts such as the following: "Please enter the following information into the generating AI model: a list of procedures to provide users with the fastest and safest evacuation routes after an earthquake, and the appropriate hardware and software to use for this purpose."

[0444] This system enables the provision of information to carry out evacuation activities efficiently and safely, allowing for effective support even during disasters.

[0445] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0446] Step 1:

[0447] The server receives initial data from sensor components and automated guided vehicles. The inputs are environmental parameters such as terrain, temperature, water level, and seismic intensity, allowing for an understanding of the initial conditions of the surrounding environment. The data is collected in real time and transmitted to the server. Specifically, the server receives digital signals transmitted from each sensor.

[0448] Step 2:

[0449] The server processes the received data using analytical tools. The input is the initial data obtained in step 1, and the data analysis includes anomaly detection techniques and the overlay of a geographic information system. Specific hazardous areas are obtained as a result of the analysis. In the specific operation, an anomaly pattern detection algorithm is executed to identify the presence and extent of disasters.

[0450] Step 3:

[0451] The server uses a route generation mechanism to calculate a safe evacuation route. The input is the hazardous area and terrain data identified in step 2, and the output is the optimal evacuation route. The server considers road closure information, shelter locations, and, occasionally, the special needs of those requiring assistance. Specifically, a shortest route search algorithm is executed to evaluate multiple route options.

[0452] Step 4:

[0453] The server sends the calculated evacuation routes to each user's terminal. The input is the output data from step 3, and the output is sent to the terminal as route information adapted to each individual user. Specifically, the system sends information in text, map, and audio formats using communication methods. This data is automatically updated according to the progress of the disaster.

[0454] Step 5:

[0455] The terminal notifies the user of evacuation route information. The input is the evacuation route information sent from the server in step 4, and the output is visual and audio route guidance. Specifically, a map is displayed on the terminal screen and audio guidance is played to help the user follow the best evacuation route.

[0456] (Application Example 1)

[0457] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0458] Rapid and safe evacuation during disasters is a critical issue for protecting the lives of many people. However, conventional evacuation systems have difficulty in real-time situation assessment and providing individualized evacuation routes, making it difficult to issue accurate evacuation instructions. This can lead to delays and confusion in evacuations, potentially threatening people's safety. Therefore, there is a need for the provision of evacuation routes based on immediate and reliable information.

[0459] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0460] In this invention, the server includes information acquisition means for collecting data received from sensor devices and monitoring devices, analysis means for analyzing the data acquired by the information acquisition means and identifying the impact, and route generation means for calculating a safe route based on the impact identified by the analysis means. This makes it possible to provide optimized evacuation routes in real time according to the situation.

[0461] A "sensor device" is a device used to monitor environmental changes in real time during a disaster and to collect data such as vibration and temperature.

[0462] A "monitoring device" is a device that uses unmanned aerial vehicles or other visual devices to acquire visual information from the air and understand the situation in the disaster area.

[0463] "Information acquisition means" refers to means that have the function of aggregating data from sensor devices and monitoring devices and transmitting it to a server.

[0464] "Analysis means" refers to the means of performing the process of analyzing acquired data to identify the impact and circumstances of a disaster.

[0465] A "route generation means" is a means that has an algorithm for calculating a safe and efficient evacuation route based on information obtained by an analysis means.

[0466] "Communication means" refers to the use of network infrastructure to transmit evacuation route information to users' information terminals.

[0467] "Display means" refers to means that provide an interface for notifying users of received evacuation route information visually or audibly.

[0468] An "information terminal" is a device carried by a user that has the function of receiving and displaying evacuation route information.

[0469] In implementing this invention, the server receives data from sensor devices and monitoring devices and analyzes that data in real time. Sensor devices collect various environmental data related to disasters on the ground, and monitoring devices (e.g., unmanned aerial vehicles) provide visual information from the air. Based on this data, the server evaluates the impact of the disaster and calculates safe evacuation routes based on the analysis results.

[0470] The calculated evacuation route is transmitted to the user's information terminal via communication means. The information terminal uses the user's current location information to dynamically adjust the evacuation route and display the optimized route. This allows the user to obtain an adaptively safe evacuation route.

[0471] As a concrete example, in the event of an earthquake, the system receives vibration data from seismometers and identifies the affected area. Unmanned aerial vehicles provide images of the affected area, and a server calculates safe evacuation routes in real time. This information is then sent to the user's smartphone, assisting evacuation by providing visual navigation.

[0472] An example of a prompt for a generative AI model is: "Design an app that provides real-time route guidance to help people reach shelters in the safest and fastest way possible when a major earthquake occurs."

[0473] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0474] Step 1:

[0475] The server receives environmental data during disasters from sensor and monitoring devices. Inputs include vibration and temperature measurements from sensor devices, and visual data obtained from monitoring devices. This data is aggregated into a specific receiving module on the server side.

[0476] Step 2:

[0477] The server processes the received data using an analysis tool. The input here is the environmental data received by the server in step 1. Based on this data, a pattern recognition algorithm is applied to evaluate the extent and intensity of the disaster's impact. The output is metadata indicating the specific circumstances of the disaster.

[0478] Step 3:

[0479] The server uses a route generation mechanism based on the analyzed data to calculate a safe evacuation route. The input requires specific disaster situation information, geographical information, and traffic information obtained in step 2. The output is evacuation route information containing multiple route options.

[0480] Step 4:

[0481] The server transmits the evacuation route, calculated using communication methods, to the user's information terminal. The input is the route data generated in step 3, and the output is the data distribution to the information terminal via the network. Data packaging and encryption are performed during this process.

[0482] Step 5:

[0483] The terminal notifies the user of evacuation route information received from the server using a display device. The input is route information transmitted from the server, and the output is navigation data presented to the user visually or audibly. The terminal uses location sensors to update and optimize the route in real time.

[0484] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0485] This invention is an information system for guiding evacuees during disasters, not only to ensure their safety but also to reduce their psychological stress. This system integrates sensor devices, unmanned aerial vehicles, information acquisition means, analysis means, route generation means, communication means, display means, and an emotion engine.

[0486] First, the server collects environmental data from sensor devices and unmanned aerial vehicles (UAVs). Sensor devices transmit physical data related to various disasters such as earthquakes, fires, and floods, while UAVs provide wide-area aerial video data. All of this data is aggregated on the server.

[0487] Next, this sensor data and video data from the unmanned aerial vehicle are processed by an analysis system on a server to identify the disaster situation in detail. The analysis system identifies dangerous areas by comparing them with map information and clarifies the extent of the disaster's impact. Based on this analysis data, the geographical information and road conditions used are updated, and safe evacuation routes are then generated.

[0488] Next, the server uses a route generation system to calculate the safest and fastest evacuation route for each type of disaster. This calculation is optimized by also considering the results of the subscribed emotion engine. The emotion engine recognizes the user's emotions and stress levels using inputs such as voice and facial images. By utilizing this information in its analysis, not only is the selection of evacuation routes customized, but the notification methods are also tailored.

[0489] The communication system transmits this customized evacuation information to the terminals. Each terminal receives information individually tailored to the user's location, stress level, and type of disaster. This ensures that users receive the most relevant information.

[0490] The device presents received information to the user in the most easily understandable way, such as through visual map displays or audio guidance. The display method is dynamically adjusted according to the user's current emotional state; for example, users in a high-stress state are provided with more concise information.

[0491] Users can follow the instructions on their device and evacuate with a sense of security. This system provides emotional support and safely guides users to their destination. For example, during an earthquake, the device might display a comforting message to users who are under high stress, while also showing them the quickest route to higher ground.

[0492] Thus, the present invention is an advanced support system for promoting safe evacuation from both a physical and psychological perspective.

[0493] The following describes the processing flow.

[0494] Step 1:

[0495] The server receives environmental data in real time from sensor devices and unmanned aerial vehicles (UAVs). The data from sensor devices includes earthquake vibration information and flood information from water level sensors, while UAVs provide video data of the affected areas.

[0496] Step 2:

[0497] The server processes the received data using analytical tools to identify the current disaster situation. AI is used to determine the extent of the damage by comparing it with maps and to identify dangerous areas. Anomaly detection algorithms and image recognition technologies are used for data analysis.

[0498] Step 3:

[0499] The server uses an emotion engine to recognize emotions and stress levels from the user's voice and facial image data. This allows the user's psychological state to be quantified and used to generate the next evacuation route.

[0500] Step 4:

[0501] The server combines the analysis results with the emotion engine's output to calculate the optimal evacuation route using a route generation system. Here, not only safety but also routes that minimize user stress are considered. For example, a route with less congestion and smoother travel is selected.

[0502] Step 5:

[0503] The server uses communication methods to send customized evacuation route information tailored to the user to their terminal. This information is individualized based on the user's location, emotional state, and disaster situation.

[0504] Step 6:

[0505] The device receives evacuation route information and notifies the user through a display device. The way the information is presented changes depending on the user's emotional state. For example, a user in a high-stress state will receive concise and intuitive voice guidance and have the evacuation route highlighted on the map.

[0506] Step 7:

[0507] Users begin evacuation actions by following instructions from their device. Through actions based on these instructions, they can move safely and smoothly to their designated shelter or safe location. Users can take the best possible action in each situation while receiving psychological support.

[0508] (Example 2)

[0509] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0510] During disasters, it is crucial to ensure the safety of evacuees while guiding them appropriately and reducing their psychological stress. However, conventional information systems have limitations in terms of real-time situation assessment and providing information tailored to the individual psychological state of each evacuee. This can lead to problems where evacuees are unable to take appropriate action, making it difficult to ensure their safety.

[0511] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0512] In this invention, the server includes an information acquisition means for collecting environmental data, an analysis means for analyzing the disaster situation based on the data collected by the information acquisition means, and a route generation means for optimizing a safe evacuation route based on the disaster situation and the user's psychological state identified by the analysis means. This makes it possible to provide evacuees with a safe and rapid evacuation route that is physically and psychologically appropriate.

[0513] "Information acquisition means" refers to mechanisms and devices for collecting environmental data, and are used to aggregate disaster-related information via sensor devices and unmanned aerial vehicles.

[0514] "Analysis means" refers to algorithms and software used to process collected environmental data and identify the current disaster situation.

[0515] The "route generation means" is a processing means for calculating and selecting the safest and fastest evacuation route, taking into account the analyzed data and the user's psychological state.

[0516] "Communication means" refers to a mechanism, including network communication, for transmitting generated evacuation route information to a mobile information terminal.

[0517] "Display means" refers to an interface for providing received evacuation route information to users, and refers to a device that notifies information visually or audibly.

[0518] An "unmanned aerial vehicle" is a device used to acquire wide-area video data from the air and is used to grasp disaster situations in real time.

[0519] "Emotional state" refers to the user's psychological and emotional condition, and is used to customize evacuation information.

[0520] The system in this invention is designed to ensure the physical safety and psychological well-being of evacuees during disasters. This system is primarily operated by a server, terminals, and users, and enables the processing of various data and the provision of adaptive evacuation information.

[0521] The server collects a wide variety of disaster-related environmental data using information acquisition methods. This data includes physical data on earthquakes, fires, and floods from sensor devices, as well as wide-area video data from unmanned aerial vehicles. Analysis methods running on the server process this data and use machine learning algorithms to identify the current state of the disaster. Based on the hazardous areas and impact areas obtained as analysis results, the server further utilizes route generation methods to calculate and select the optimal evacuation route. This calculation takes into account the user's emotional state using an emotion engine. Speech recognition and facial image analysis are used for this purpose, and the evacuation information is optimized to provide users with a sense of psychological security.

[0522] Next, the evacuation route information generated by the communication device is adapted to each user's location and emotional state and transmitted to individual terminals. The terminals present the received data to the user in an easily understandable way, both visually and audibly, through display devices. The terminal's operation is embodied in route display via a map application and voice guidance, providing users with immediate instructions.

[0523] Users can take swift and safe evacuation actions by following instructions from their terminals. Coordinated operation between the server and terminals allows users to evacuate effectively while reducing stress.

[0524] As a concrete example, in the event of an earthquake, the server identifies areas with significant damage based on data obtained from unmanned aerial vehicles and calculates safe evacuation routes through a route generation system. It also applies an emotion engine to customize notifications with reassuring language for users experiencing high levels of stress.

[0525] An example of a prompt message is: "Design a system that guides users in a highly stressed state during an earthquake to the safest evacuation route along with a comforting message." By utilizing a generative AI model, flexible responses tailored to the situation can be achieved.

[0526] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0527] Step 1:

[0528] The server collects environmental data using information acquisition methods. Inputs include physical data related to earthquakes, fires, and floods from sensor devices, and aerial video data provided by unmanned aerial vehicles. This data is transmitted to the server via secure communication, and the collected information is stored in the server's database.

[0529] Step 2:

[0530] The server analyzes environmental data collected using analytical tools. The input is the data collected in step 1, and machine learning algorithms are applied to identify the disaster situation. Specific operations include identifying hazardous areas through image analysis and mapping the extent of disaster impact by comparing with a Geographic Information System (GIS). The output is the analysis results showing the identified hazardous areas and impact areas.

[0531] Step 3:

[0532] The server uses a route generation mechanism to calculate safe evacuation routes. The input consists of disaster conditions obtained from an analysis mechanism and the user's emotional state. The emotional state is obtained as a result of analysis by an emotion engine using voice and facial image data. The evacuation route calculation uses a shortest path algorithm while also considering factors to reduce psychological burden. The output is evacuation route information optimized for each individual user.

[0533] Step 4:

[0534] The server transmits evacuation route information to each user's terminal using communication means. The input is evacuation route information generated by the route generation means, which is customized based on the user's current location and emotional state. Operationally, data is transmitted over the network, and the output is the customized evacuation route information received by each terminal.

[0535] Step 5:

[0536] The terminal presents the received evacuation route information to the user visually and audibly. The input is the evacuation route information received by the terminal in step 4. Specifically, it displays the route on a map application and provides real-time instructions to the user using voice guidance. The output is the specific evacuation instructions that the user receives through the terminal.

[0537] Step 6:

[0538] The user follows instructions from the device and acts safely along an evacuation route. The input is the information presented by the device and the user's current location. The output is the user's movement to their destination, allowing for a quick and secure evacuation.

[0539] (Application Example 2)

[0540] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0541] Conventional disaster evacuation support systems were limited to calculating evacuation routes based on physical data, and did not adequately provide information that considered the psychological state of users. As a result, evacuees were not provided with optimal evacuation information, hindering efficient and safe evacuation. This invention solves this problem and realizes efficient and psychologically supported evacuation during disasters.

[0542] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0543] In this invention, the server includes data acquisition means for collecting information received from sensor devices, data analysis means for analyzing the information acquired by the data acquisition means to identify the disaster situation, and personalization means for individually optimizing evacuation route information using emotion recognition means for analyzing the emotional state of users. This makes it possible to provide users with evacuation route information that comprehensively considers physical and psychological factors.

[0544] A "sensor device" is a device used to detect physical phenomena related to disasters and collect that data.

[0545] "Data acquisition means" refers to a function that receives information transmitted from sensor devices and manages it within the system.

[0546] "Data analysis means" refers to technical processes and devices for processing acquired information and identifying disaster situations.

[0547] "Route generation means" refers to the functions and algorithms for calculating safe evacuation routes based on analyzed disaster information.

[0548] "Emotion recognition means" refers to technology that analyzes the emotional state of a user from their voice, facial images, etc., and evaluates their psychological state.

[0549] "Personalization means" refers to a function that optimizes evacuation route information by taking into account the emotional state of the user and customizing it to suit each individual user.

[0550] "Information and communication means" refers to communication functions and technologies for transmitting generated evacuation route information to mobile terminal devices.

[0551] A "mobile terminal device" is a device that a user can carry with them to receive and display evacuation route information.

[0552] "Display means" refers to a function for presenting received evacuation route information to the user visually or audibly.

[0553] To implement this invention, the server first utilizes sensor devices and unmanned aerial vehicles (UAVs) to collect disaster information. The sensor devices collect physical data such as earthquakes and fires, while the UAVs acquire wide-area video data. This information is transmitted to the server in real time via AWS IoT Core.

[0554] Next, the server uses Amazon SageMaker to analyze the acquired data. This analysis identifies hazardous areas and assesses the extent of disaster impact. It also uses OpenStreetMap data and the Python NetworkX library to generate the safest evacuation routes.

[0555] Furthermore, the server utilizes the Google Cloud Vision API to analyze the user's emotional state, recognizing emotions from facial images and other data. The results of this emotion recognition are used to customize evacuation route information, which is then provided as personalized information optimized for each user.

[0556] Personalized evacuation information is transmitted to mobile terminal devices via Firebase Cloud Messaging. These terminal devices, developed with Flutter, provide information to users through visual map displays and voice guidance. This allows users to know their customized evacuation routes in real time, enabling them to take swift and safe evacuation actions while receiving psychological support, even in highly stressful situations.

[0557] For example, in the event of an earthquake, the evacuation information generated by the server includes a prompt message for users in a highly stressed state, such as, "This path is safe. Proceed slowly and with peace of mind." This message is provided as voice guidance and is also displayed visually on the device.

[0558] An example of a prompt from a generated AI model is, "In the event of an earthquake, what kind of reassuring message can you provide to users who are under high stress?"

[0559] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0560] Step 1:

[0561] The server receives physical data related to disasters from sensor devices and wide-area video data from unmanned aerial vehicles. Inputs include sensor data and video data related to earthquakes and fires, which are aggregated to the server via AWS IoT Core. Outputs are stored as raw data in a database for analysis.

[0562] Step 2:

[0563] The server uses Amazon SageMaker as a data analysis tool to analyze the raw data. This analysis takes received sensor data and video data as input to identify the extent of disaster impact and hazardous areas. The output is mapping data of the hazardous areas, which is used in the subsequent path generation process.

[0564] Step 3:

[0565] The server generates safe evacuation routes using OpenStreetMap geographic data and the NetworkX library. The input is analyzed mapping data of hazardous areas, and the evacuation route calculations are performed. The output is optimized evacuation route data.

[0566] Step 4:

[0567] The server uses the Google Cloud Vision API as an emotion recognition tool to analyze the user's facial image sent from the device. The input consists of facial image and audio data, and emotion recognition calculations are performed. The output is information about the user's stress level.

[0568] Step 5:

[0569] The server personalizes evacuation route information based on the emotion recognition results. The input consists of the emotion recognition output and evacuation route data, and the information is customized according to the user's psychological state. The output is the personalized evacuation route information.

[0570] Step 6:

[0571] The server uses Firebase Cloud Messaging to send optimized evacuation route information to the user's mobile device. The input is personalized evacuation route information, and the output is message data to the device.

[0572] Step 7:

[0573] The terminal provides users with received evacuation route information as a visual map display and voice guidance. Input is message data from the server, and output is a user-friendly interface display and voice guidance. This allows users to confidently follow evacuation instructions.

[0574] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0575] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0576] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0577] [Fourth Embodiment]

[0578] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0579] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0580] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0581] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0582] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0583] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0584] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0585] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0586] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0587] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0588] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0589] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0590] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0591] This invention is an information system for realizing rapid and safe evacuation during disasters. This system includes sensor devices, unmanned aerial vehicles, information acquisition means, analysis means, route generation means, communication means, and display means. The operation of each component is described in detail below.

[0592] First, the server acquires local environmental data in real time from sensor devices and unmanned aerial vehicles (UAVs). Sensor devices monitor specific parameters such as vibrations during earthquakes, water levels during floods, and temperatures during fires, and transmit the data. UAVs collect visual data from the air, such as images and videos of the affected area, and transmit this to the server.

[0593] This collected data is processed by an analysis system running on a server. The analysis system analyzes the data to identify various disaster situations and evaluate the extent and impact of the damage. The evaluation includes a process of detecting anomalies in the data and overlaying them with geographic information to identify hazardous areas.

[0594] Next, the server uses a route generation mechanism to calculate a safe evacuation route based on the analyzed disaster situation. Here, multiple route options are considered, taking into account road closures, the location of evacuation shelters, and the topographical characteristics of the area. Each user's current location and special needs (e.g., those requiring assistance) are also taken into consideration.

[0595] The server then uses communication methods to transmit the calculated evacuation routes to each user's mobile information terminal. The information is sent in the form of text, maps, and audio, and is updated in real time.

[0596] The terminal receives this information and notifies the user through a display device. The display device visually shows evacuation routes or provides emergency alerts via voice. The user can evacuate safely by following the route displayed on the terminal. For example, after an earthquake, the user is guided to avoid the most dangerous areas and head to a nearby evacuation center.

[0597] Thus, this system is designed to enable users to evacuate efficiently and safely by integrating situation assessment, analysis, and evacuation guidance during disasters.

[0598] The following describes the processing flow.

[0599] Step 1:

[0600] The server receives environmental data from sensor devices and unmanned aerial vehicles (UAVs). Sensor devices transmit data including seismic vibrations, fire temperatures, and flood water levels, while UAVs transmit visual information capturing the current damage situation.

[0601] Step 2:

[0602] The server processes the received data using analytical tools. This analysis identifies the type of disaster and maps its impact area and dangerous locations on a map. For the analysis, modeled AI algorithms are used to detect anomalies and identify safe areas.

[0603] Step 3:

[0604] The server uses a route generation method based on the analysis results to calculate the optimal evacuation route. The calculation considers the condition of local infrastructure (roads, evacuation facilities, etc.), current congestion levels, and the progression of the disaster, and evaluates multiple routes.

[0605] Step 4:

[0606] The server distributes calculated evacuation route information to each user's mobile information terminal via communication means. In this process, customized information tailored to each user's current location and individual needs is generated and transmitted in real time.

[0607] Step 5:

[0608] The terminal interprets the evacuation route information it receives and notifies the user visually or audibly through a display device. This information is provided to the user as a route display on a map or as audio guidance, and is updated as needed.

[0609] Step 6:

[0610] The user takes evacuation action according to the instructions on the device. The user safely moves to the designated evacuation shelter while referring to the notified route. In addition, the user adjusts their actions based on real-time updates from the device in response to changes in the situation.

[0611] (Example 1)

[0612] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0613] In times of disaster, real-time, accurate collection and analysis of environmental information, as well as the provision of evacuation routes tailored to individual users, are essential for achieving rapid and safe evacuation. However, conventional systems do not adequately meet these requirements, resulting in ineffective support for disaster-prone areas and vulnerable individuals.

[0614] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0615] In this invention, the server includes an information acquisition means for collecting environmental data received from sensor components and unmanned transport devices; an analysis means for analyzing the data acquired by the information acquisition means to detect anomalies and identify dangerous areas by overlaying them with geographic information; and a route generation means for calculating safe evacuation routes considering the disaster situation, topographic characteristics, and closure information identified by the analysis means. This enables real-time situation assessment and the provision of evacuation instructions tailored to individual users.

[0616] A "sensor component" is a device used to detect specific parameters of the surrounding environment during a disaster and collect data.

[0617] An "unmanned transport device" is an autonomously flying device used to collect visual data from the air.

[0618] "Information acquisition means" refers to technologies for collecting data obtained from sensor components and unmanned transport devices.

[0619] "Analysis methods" refer to technologies that analyze collected data, detect anomalies, and overlay them with geographical information to identify dangerous areas.

[0620] "Route generation means" refers to a technology that calculates the optimal evacuation route by taking into account the identified disaster situation, topographic characteristics, and road closure information.

[0621] "Communication means" refers to a technology that transmits calculated evacuation route information to a mobile information processing device and updates the information in real time according to the situation.

[0622] "Display means" refers to technology that notifies users of received evacuation route information through visual and auditory means.

[0623] This invention is an information system for effectively supporting safe and rapid evacuation activities during disasters. This system utilizes sensor components, an unmanned transport device, information acquisition means, analysis means, route generation means, communication means, and display means.

[0624] The server acquires environmental data provided by sensor components and automated guided vehicles (AGVs) installed in the region. Sensor components detect specific environmental parameters related to disasters such as earthquakes, floods, and fires. AGVs collect wide-area visual data of the region from the air and transmit it to the server as images and videos. The hardware used includes various environmental sensors and high-resolution cameras.

[0625] This data is processed by an analysis system running on a server. The analysis system uses advanced algorithms to identify hazardous areas by detecting anomalies and overlaying them with geographic information. The analysis results are used to understand the situation of disasters, and examples include (1) designating areas with flood data showing a rapid rise from normal water levels as hazardous areas, and (2) detecting high-temperature data in fires to identify the possibility of fire outbreaks.

[0626] The server then calculates safe evacuation routes based on the analysis results. The route generation system considers road closure information, shelter locations, and topographic data to search for the optimal route. This includes special routes for vulnerable individuals, and settings such as route selection that avoids slopes are possible.

[0627] The calculated evacuation routes are transmitted to a mobile information processing device using communication means. The information is updated in real time and provided in text, map, and audio formats. In the event that an evacuation center becomes full, the server immediately recalculates and distributes the route to the next best evacuation center.

[0628] The terminal provides the user with received evacuation route information through a display device. The terminal visually displays a map and provides directional instructions via voice. For example, it provides voice guidance such as, "Turn left at the next intersection."

[0629] In operating this system, users can use prompts such as the following: "Please enter the following information into the generating AI model: a list of procedures to provide users with the fastest and safest evacuation routes after an earthquake, and the appropriate hardware and software to use for this purpose."

[0630] This system enables the provision of information to carry out evacuation activities efficiently and safely, allowing for effective support even during disasters.

[0631] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0632] Step 1:

[0633] The server receives initial data from sensor components and automated guided vehicles. The inputs are environmental parameters such as terrain, temperature, water level, and seismic intensity, allowing for an understanding of the initial conditions of the surrounding environment. The data is collected in real time and transmitted to the server. Specifically, the server receives digital signals transmitted from each sensor.

[0634] Step 2:

[0635] The server processes the received data using analytical tools. The input is the initial data obtained in step 1, and the data analysis includes anomaly detection techniques and the overlay of a geographic information system. Specific hazardous areas are obtained as a result of the analysis. In the specific operation, an anomaly pattern detection algorithm is executed to identify the presence and extent of disasters.

[0636] Step 3:

[0637] The server uses a route generation mechanism to calculate a safe evacuation route. The input is the hazardous area and terrain data identified in step 2, and the output is the optimal evacuation route. The server considers road closure information, shelter locations, and, occasionally, the special needs of those requiring assistance. Specifically, a shortest route search algorithm is executed to evaluate multiple route options.

[0638] Step 4:

[0639] The server sends the calculated evacuation routes to each user's terminal. The input is the output data from step 3, and the output is sent to the terminal as route information adapted to each individual user. Specifically, the system sends information in text, map, and audio formats using communication methods. This data is automatically updated according to the progress of the disaster.

[0640] Step 5:

[0641] The terminal notifies the user of evacuation route information. The input is the evacuation route information sent from the server in step 4, and the output is visual and audio route guidance. Specifically, a map is displayed on the terminal screen and audio guidance is played to help the user follow the best evacuation route.

[0642] (Application Example 1)

[0643] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0644] Rapid and safe evacuation during disasters is a critical issue for protecting the lives of many people. However, conventional evacuation systems have difficulty in real-time situation assessment and providing individualized evacuation routes, making it difficult to issue accurate evacuation instructions. This can lead to delays and confusion in evacuations, potentially threatening people's safety. Therefore, there is a need for the provision of evacuation routes based on immediate and reliable information.

[0645] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0646] In this invention, the server includes information acquisition means for collecting data received from sensor devices and monitoring devices, analysis means for analyzing the data acquired by the information acquisition means and identifying the impact, and route generation means for calculating a safe route based on the impact identified by the analysis means. This makes it possible to provide optimized evacuation routes in real time according to the situation.

[0647] A "sensor device" is a device used to monitor environmental changes in real time during a disaster and to collect data such as vibration and temperature.

[0648] A "monitoring device" is a device that uses unmanned aerial vehicles or other visual devices to acquire visual information from the air and understand the situation in the disaster area.

[0649] "Information acquisition means" refers to means that have the function of aggregating data from sensor devices and monitoring devices and transmitting it to a server.

[0650] "Analysis means" refers to the means of performing the process of analyzing acquired data to identify the impact and circumstances of a disaster.

[0651] A "route generation means" is a means that has an algorithm for calculating a safe and efficient evacuation route based on information obtained by an analysis means.

[0652] "Communication means" refers to the use of network infrastructure to transmit evacuation route information to users' information terminals.

[0653] "Display means" refers to means that provide an interface for notifying users of received evacuation route information visually or audibly.

[0654] An "information terminal" is a device carried by a user that has the function of receiving and displaying evacuation route information.

[0655] In implementing this invention, the server receives data from sensor devices and monitoring devices and analyzes that data in real time. Sensor devices collect various environmental data related to disasters on the ground, and monitoring devices (e.g., unmanned aerial vehicles) provide visual information from the air. Based on this data, the server evaluates the impact of the disaster and calculates safe evacuation routes based on the analysis results.

[0656] The calculated evacuation route is transmitted to the user's information terminal via communication means. The information terminal uses the user's current location information to dynamically adjust the evacuation route and display the optimized route. This allows the user to obtain an adaptively safe evacuation route.

[0657] As a concrete example, in the event of an earthquake, the system receives vibration data from seismometers and identifies the affected area. Unmanned aerial vehicles provide images of the affected area, and a server calculates safe evacuation routes in real time. This information is then sent to the user's smartphone, assisting evacuation by providing visual navigation.

[0658] An example of a prompt for a generative AI model is: "Design an app that provides real-time route guidance to help people reach shelters in the safest and fastest way possible when a major earthquake occurs."

[0659] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0660] Step 1:

[0661] The server receives environmental data during disasters from sensor and monitoring devices. Inputs include vibration and temperature measurements from sensor devices, and visual data obtained from monitoring devices. This data is aggregated into a specific receiving module on the server side.

[0662] Step 2:

[0663] The server processes the received data using an analysis tool. The input here is the environmental data received by the server in step 1. Based on this data, a pattern recognition algorithm is applied to evaluate the extent and intensity of the disaster's impact. The output is metadata indicating the specific circumstances of the disaster.

[0664] Step 3:

[0665] The server uses a route generation mechanism based on the analyzed data to calculate a safe evacuation route. The input requires specific disaster situation information, geographical information, and traffic information obtained in step 2. The output is evacuation route information containing multiple route options.

[0666] Step 4:

[0667] The server transmits the evacuation route, calculated using communication methods, to the user's information terminal. The input is the route data generated in step 3, and the output is the data distribution to the information terminal via the network. Data packaging and encryption are performed during this process.

[0668] Step 5:

[0669] The terminal notifies the user of evacuation route information received from the server using a display device. The input is route information transmitted from the server, and the output is navigation data presented to the user visually or audibly. The terminal uses location sensors to update and optimize the route in real time.

[0670] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0671] This invention is an information system for guiding evacuees during disasters, not only to ensure their safety but also to reduce their psychological stress. This system integrates sensor devices, unmanned aerial vehicles, information acquisition means, analysis means, route generation means, communication means, display means, and an emotion engine.

[0672] First, the server collects environmental data from sensor devices and unmanned aerial vehicles (UAVs). Sensor devices transmit physical data related to various disasters such as earthquakes, fires, and floods, while UAVs provide wide-area aerial video data. All of this data is aggregated on the server.

[0673] Next, this sensor data and video data from the unmanned aerial vehicle are processed by an analysis system on a server to identify the disaster situation in detail. The analysis system identifies dangerous areas by comparing them with map information and clarifies the extent of the disaster's impact. Based on this analysis data, the geographical information and road conditions used are updated, and safe evacuation routes are then generated.

[0674] Next, the server uses a route generation system to calculate the safest and fastest evacuation route for each type of disaster. This calculation is optimized by also considering the results of the subscribed emotion engine. The emotion engine recognizes the user's emotions and stress levels using inputs such as voice and facial images. By utilizing this information in its analysis, not only is the selection of evacuation routes customized, but the notification methods are also tailored.

[0675] The communication system transmits this customized evacuation information to the terminals. Each terminal receives information individually tailored to the user's location, stress level, and type of disaster. This ensures that users receive the most relevant information.

[0676] The device presents received information to the user in the most easily understandable way, such as through visual map displays or audio guidance. The display method is dynamically adjusted according to the user's current emotional state; for example, users in a high-stress state are provided with more concise information.

[0677] Users can follow the instructions on their device and evacuate with a sense of security. This system provides emotional support and safely guides users to their destination. For example, during an earthquake, the device might display a comforting message to users who are under high stress, while also showing them the quickest route to higher ground.

[0678] Thus, the present invention is an advanced support system for promoting safe evacuation from both a physical and psychological perspective.

[0679] The following describes the processing flow.

[0680] Step 1:

[0681] The server receives environmental data in real time from sensor devices and unmanned aerial vehicles (UAVs). The data from sensor devices includes earthquake vibration information and flood information from water level sensors, while UAVs provide video data of the affected areas.

[0682] Step 2:

[0683] The server processes the received data using analytical tools to identify the current disaster situation. AI is used to determine the extent of the damage by comparing it with maps and to identify dangerous areas. Anomaly detection algorithms and image recognition technologies are used for data analysis.

[0684] Step 3:

[0685] The server uses an emotion engine to recognize emotions and stress levels from the user's voice and facial image data. This allows the user's psychological state to be quantified and used to generate the next evacuation route.

[0686] Step 4:

[0687] The server combines the analysis results with the emotion engine's output to calculate the optimal evacuation route using a route generation system. Here, not only safety but also routes that minimize user stress are considered. For example, a route with less congestion and smoother travel is selected.

[0688] Step 5:

[0689] The server uses communication methods to send customized evacuation route information tailored to the user to their terminal. This information is individualized based on the user's location, emotional state, and disaster situation.

[0690] Step 6:

[0691] The device receives evacuation route information and notifies the user through a display device. The way the information is presented changes depending on the user's emotional state. For example, a user in a high-stress state will receive concise and intuitive voice guidance and have the evacuation route highlighted on the map.

[0692] Step 7:

[0693] Users begin evacuation actions by following instructions from their device. Through actions based on these instructions, they can move safely and smoothly to their designated shelter or safe location. Users can take the best possible action in each situation while receiving psychological support.

[0694] (Example 2)

[0695] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0696] During disasters, it is crucial to ensure the safety of evacuees while guiding them appropriately and reducing their psychological stress. However, conventional information systems have limitations in terms of real-time situation assessment and providing information tailored to the individual psychological state of each evacuee. This can lead to problems where evacuees are unable to take appropriate action, making it difficult to ensure their safety.

[0697] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0698] In this invention, the server includes an information acquisition means for collecting environmental data, an analysis means for analyzing the disaster situation based on the data collected by the information acquisition means, and a route generation means for optimizing a safe evacuation route based on the disaster situation and the user's psychological state identified by the analysis means. This makes it possible to provide evacuees with a safe and rapid evacuation route that is physically and psychologically appropriate.

[0699] "Information acquisition means" refers to mechanisms and devices for collecting environmental data, and are used to aggregate disaster-related information via sensor devices and unmanned aerial vehicles.

[0700] "Analysis means" refers to algorithms and software used to process collected environmental data and identify the current disaster situation.

[0701] The "route generation means" is a processing means for calculating and selecting the safest and fastest evacuation route, taking into account the analyzed data and the user's psychological state.

[0702] "Communication means" refers to a mechanism, including network communication, for transmitting generated evacuation route information to a mobile information terminal.

[0703] "Display means" refers to an interface for providing received evacuation route information to users, and refers to a device that notifies information visually or audibly.

[0704] An "unmanned aerial vehicle" is a device used to acquire wide-area video data from the air and is used to grasp disaster situations in real time.

[0705] "Emotional state" refers to the user's psychological and emotional condition, and is used to customize evacuation information.

[0706] The system in this invention is designed to ensure the physical safety and psychological well-being of evacuees during disasters. This system is primarily operated by a server, terminals, and users, and enables the processing of various data and the provision of adaptive evacuation information.

[0707] The server collects a wide variety of disaster-related environmental data using information acquisition methods. This data includes physical data on earthquakes, fires, and floods from sensor devices, as well as wide-area video data from unmanned aerial vehicles. Analysis methods running on the server process this data and use machine learning algorithms to identify the current state of the disaster. Based on the hazardous areas and impact areas obtained as analysis results, the server further utilizes route generation methods to calculate and select the optimal evacuation route. This calculation takes into account the user's emotional state using an emotion engine. Speech recognition and facial image analysis are used for this purpose, and the evacuation information is optimized to provide users with a sense of psychological security.

[0708] Next, the evacuation route information generated by the communication device is adapted to each user's location and emotional state and transmitted to individual terminals. The terminals present the received data to the user in an easily understandable way, both visually and audibly, through display devices. The terminal's operation is embodied in route display via a map application and voice guidance, providing users with immediate instructions.

[0709] Users can take swift and safe evacuation actions by following instructions from their terminals. Coordinated operation between the server and terminals allows users to evacuate effectively while reducing stress.

[0710] As a concrete example, in the event of an earthquake, the server identifies areas with significant damage based on data obtained from unmanned aerial vehicles and calculates safe evacuation routes through a route generation system. It also applies an emotion engine to customize notifications with reassuring language for users experiencing high levels of stress.

[0711] An example of a prompt message is: "Design a system that guides users in a highly stressed state during an earthquake to the safest evacuation route along with a comforting message." By utilizing a generative AI model, flexible responses tailored to the situation can be achieved.

[0712] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0713] Step 1:

[0714] The server collects environmental data using information acquisition methods. Inputs include physical data related to earthquakes, fires, and floods from sensor devices, and aerial video data provided by unmanned aerial vehicles. This data is transmitted to the server via secure communication, and the collected information is stored in the server's database.

[0715] Step 2:

[0716] The server analyzes environmental data collected using analytical tools. The input is the data collected in step 1, and machine learning algorithms are applied to identify the disaster situation. Specific operations include identifying hazardous areas through image analysis and mapping the extent of disaster impact by comparing with a Geographic Information System (GIS). The output is the analysis results showing the identified hazardous areas and impact areas.

[0717] Step 3:

[0718] The server uses a route generation mechanism to calculate safe evacuation routes. The input consists of disaster conditions obtained from an analysis mechanism and the user's emotional state. The emotional state is obtained as a result of analysis by an emotion engine using voice and facial image data. The evacuation route calculation uses a shortest path algorithm while also considering factors to reduce psychological burden. The output is evacuation route information optimized for each individual user.

[0719] Step 4:

[0720] The server transmits evacuation route information to each user's terminal using communication means. The input is evacuation route information generated by the route generation means, which is customized based on the user's current location and emotional state. Operationally, data is transmitted over the network, and the output is the customized evacuation route information received by each terminal.

[0721] Step 5:

[0722] The terminal presents the received evacuation route information to the user visually and audibly. The input is the evacuation route information received by the terminal in step 4. Specifically, it displays the route on a map application and provides real-time instructions to the user using voice guidance. The output is the specific evacuation instructions that the user receives through the terminal.

[0723] Step 6:

[0724] The user follows instructions from the device and acts safely along an evacuation route. The input is the information presented by the device and the user's current location. The output is the user's movement to their destination, allowing for a quick and secure evacuation.

[0725] (Application Example 2)

[0726] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0727] Conventional disaster evacuation support systems were limited to calculating evacuation routes based on physical data, and did not adequately provide information that considered the psychological state of users. As a result, evacuees were not provided with optimal evacuation information, hindering efficient and safe evacuation. This invention solves this problem and realizes efficient and psychologically supported evacuation during disasters.

[0728] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0729] In this invention, the server includes data acquisition means for collecting information received from sensor devices, data analysis means for analyzing the information acquired by the data acquisition means to identify the disaster situation, and personalization means for individually optimizing evacuation route information using emotion recognition means for analyzing the emotional state of users. This makes it possible to provide users with evacuation route information that comprehensively considers physical and psychological factors.

[0730] A "sensor device" is a device used to detect physical phenomena related to disasters and collect that data.

[0731] "Data acquisition means" refers to a function that receives information transmitted from sensor devices and manages it within the system.

[0732] "Data analysis means" refers to technical processes and devices for processing acquired information and identifying disaster situations.

[0733] "Route generation means" refers to the functions and algorithms for calculating safe evacuation routes based on analyzed disaster information.

[0734] "Emotion recognition means" refers to technology that analyzes the emotional state of a user from their voice, facial images, etc., and evaluates their psychological state.

[0735] "Personalization means" refers to a function that optimizes evacuation route information by taking into account the emotional state of the user and customizing it to suit each individual user.

[0736] "Information and communication means" refers to communication functions and technologies for transmitting generated evacuation route information to mobile terminal devices.

[0737] A "mobile terminal device" is a device that a user can carry with them to receive and display evacuation route information.

[0738] "Display means" refers to a function for presenting received evacuation route information to the user visually or audibly.

[0739] To implement this invention, the server first utilizes sensor devices and unmanned aerial vehicles (UAVs) to collect disaster information. The sensor devices collect physical data such as earthquakes and fires, while the UAVs acquire wide-area video data. This information is transmitted to the server in real time via AWS IoT Core.

[0740] Next, the server uses Amazon SageMaker to analyze the acquired data. This analysis identifies hazardous areas and assesses the extent of disaster impact. It also uses OpenStreetMap data and the Python NetworkX library to generate the safest evacuation routes.

[0741] Furthermore, the server utilizes the Google Cloud Vision API to analyze the user's emotional state, recognizing emotions from facial images and other data. The results of this emotion recognition are used to customize evacuation route information, which is then provided as personalized information optimized for each user.

[0742] Personalized evacuation information is transmitted to mobile terminal devices via Firebase Cloud Messaging. These terminal devices, developed with Flutter, provide information to users through visual map displays and voice guidance. This allows users to know their customized evacuation routes in real time, enabling them to take swift and safe evacuation actions while receiving psychological support, even in highly stressful situations.

[0743] For example, in the event of an earthquake, the evacuation information generated by the server includes a prompt message for users in a highly stressed state, such as, "This path is safe. Proceed slowly and with peace of mind." This message is provided as voice guidance and is also displayed visually on the device.

[0744] An example of a prompt from a generated AI model is, "In the event of an earthquake, what kind of reassuring message can you provide to users who are under high stress?"

[0745] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0746] Step 1:

[0747] The server receives physical data related to disasters from sensor devices and wide-area video data from unmanned aerial vehicles. Inputs include sensor data and video data related to earthquakes and fires, which are aggregated to the server via AWS IoT Core. Outputs are stored as raw data in a database for analysis.

[0748] Step 2:

[0749] The server uses Amazon SageMaker as a data analysis tool to analyze the raw data. This analysis takes received sensor data and video data as input to identify the extent of disaster impact and hazardous areas. The output is mapping data of the hazardous areas, which is used in the subsequent path generation process.

[0750] Step 3:

[0751] The server generates safe evacuation routes using OpenStreetMap geographic data and the NetworkX library. The input is analyzed mapping data of hazardous areas, and the evacuation route calculations are performed. The output is optimized evacuation route data.

[0752] Step 4:

[0753] The server uses the Google Cloud Vision API as an emotion recognition tool to analyze the user's facial image sent from the device. The input consists of facial image and audio data, and emotion recognition calculations are performed. The output is information about the user's stress level.

[0754] Step 5:

[0755] The server personalizes evacuation route information based on the emotion recognition results. The input consists of the emotion recognition output and evacuation route data, and the information is customized according to the user's psychological state. The output is the personalized evacuation route information.

[0756] Step 6:

[0757] The server uses Firebase Cloud Messaging to send optimized evacuation route information to the user's mobile device. The input is personalized evacuation route information, and the output is message data to the device.

[0758] Step 7:

[0759] The terminal provides users with received evacuation route information as a visual map display and voice guidance. Input is message data from the server, and output is a user-friendly interface display and voice guidance. This allows users to confidently follow evacuation instructions.

[0760] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0761] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0762] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0763] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0764] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0765] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0766] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0767] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0768] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0769] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0770] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0771] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0772] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0773] 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.

[0774] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0775] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0776] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0777] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0778] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0779] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0780] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0781] The following is further disclosed regarding the embodiments described above.

[0782] (Claim 1)

[0783] Information acquisition means for collecting data received from a sensor device,

[0784] An analysis means for analyzing data acquired by the information acquisition means to identify the disaster situation,

[0785] A route generation means that calculates a safe evacuation route based on the disaster situation identified by the analysis means,

[0786] A communication means for transmitting evacuation route information calculated by the route generation means to a mobile information terminal,

[0787] A display means that notifies the user of evacuation route information received by the mobile information terminal via an output device,

[0788] A system that includes this.

[0789] (Claim 2)

[0790] The system according to claim 1, wherein the analysis means analyzes the geographical conditions specific to a disaster using video data acquired from an unmanned aerial vehicle.

[0791] (Claim 3)

[0792] The system according to claim 1, wherein the communication means generates and transmits individually adapted evacuation route information based on the user's current location.

[0793] "Example 1"

[0794] (Claim 1)

[0795] Information acquisition means for collecting environmental data received from sensor components and automated guided vehicles,

[0796] An analysis means that analyzes the data acquired by the information acquisition means to detect anomalies and identifies dangerous areas by overlaying them with geographic information,

[0797] A route generation means that calculates a safe evacuation route, taking into account the disaster situation, topographic characteristics, and closure information identified by the analysis means,

[0798] A communication means that transmits evacuation route information calculated by the route generation means to a mobile information processing device and updates the information in real time according to the situation,

[0799] A display means for notifying the user of evacuation route information received by the mobile information processing device visually and audibly,

[0800] A system that includes this.

[0801] (Claim 2)

[0802] The system according to claim 1, wherein the analysis means includes means for analyzing disaster-specific geographical conditions using visual data acquired from an unmanned transport device and marking dangerous areas on a map.

[0803] (Claim 3)

[0804] The system according to claim 1, wherein the communication means includes means for generating and transmitting individually adapted evacuation route information based on the user's location and specific needs.

[0805] "Application Example 1"

[0806] (Claim 1)

[0807] Information acquisition means for collecting data received from sensor devices and monitoring devices,

[0808] An analysis means for analyzing the data acquired by the information acquisition means and identifying the impact,

[0809] A path generation means that calculates a safe path based on the influences identified by the analysis means,

[0810] A communication means for transmitting route information calculated by the route generation means to an information terminal,

[0811] A display means that notifies the user of route information received by the information terminal through an output device,

[0812] The information terminal includes a display means that visualizes an optimized route using location information,

[0813] A system that includes this.

[0814] (Claim 2)

[0815] The system according to claim 1, wherein the analysis means analyzes a specific situation using image information acquired from a monitoring device.

[0816] (Claim 3)

[0817] The system according to claim 1, wherein the communication means generates and transmits route information that is dynamically changed based on the user's current location.

[0818] "Example 2 of combining an emotion engine"

[0819] (Claim 1)

[0820] Information acquisition means for collecting environmental data,

[0821] An analysis means for analyzing the disaster situation based on the data collected by the information acquisition means,

[0822] A route generation means that optimizes safe evacuation routes based on the disaster situation and the psychological state of the user identified by the analysis means,

[0823] A communication means for transmitting customized evacuation route information calculated by the route generation means to a mobile information terminal,

[0824] A display means that dynamically adjusts and notifies the user of evacuation route information received by the mobile information terminal according to the user's emotional state,

[0825] A system that includes this.

[0826] (Claim 2)

[0827] The system according to claim 1, wherein the analysis means analyzes the detailed geographical situation of a disaster using wide-area video data acquired from an unmanned aerial vehicle.

[0828] (Claim 3)

[0829] The system according to claim 1, wherein the communication means generates and transmits individually adapted evacuation route information based on the user's location information and emotional state.

[0830] "Application example 2 when combining with an emotional engine"

[0831] (Claim 1)

[0832] A data acquisition means for collecting information received from a sensor device,

[0833] A data analysis means that analyzes the information acquired by the data acquisition means to identify the disaster situation,

[0834] A route generation means that calculates a safe evacuation route based on the disaster situation identified by the data analysis means,

[0835] An emotion recognition means for analyzing the emotional state of the user, and an individualization means for individually optimizing the evacuation route information calculated by the route generation means,

[0836] Information and communication means for transmitting evacuation route information optimized by the individualization means to a mobile terminal device,

[0837] A display means that notifies the user of evacuation route information received by the mobile terminal device via an output device,

[0838] A system that includes this.

[0839] (Claim 2)

[0840] The system according to claim 1, wherein the data analysis means analyzes the geographical conditions specific to a disaster using video data acquired from an unmanned aerial vehicle.

[0841] (Claim 3)

[0842] The system according to claim 1, wherein the information communication means generates and transmits individually adapted evacuation route information based on the user's current location and emotional state. [Explanation of Symbols]

[0843] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. Information acquisition means for collecting data received from a sensor device, An analysis means for analyzing data acquired by the information acquisition means to identify the disaster situation, A route generation means that calculates a safe evacuation route based on the disaster situation identified by the analysis means, A communication means for transmitting evacuation route information calculated by the route generation means to a mobile information terminal, A display means that notifies the user of evacuation route information received by the mobile information terminal via an output device, A system that includes this.

2. The system according to claim 1, wherein the analysis means analyzes the geographical conditions specific to a disaster using video data acquired from an unmanned aerial vehicle.

3. The system according to claim 1, wherein the communication means generates and transmits individually adapted evacuation route information based on the user's current location.

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