System

Abstract

An object of a system according to an embodiment is to quickly provide an optimal evacuation route in the event of a disaster.SOLUTION: A system includes a data collection unit, an analysis unit, an evacuation route generation unit, and a display unit. The data collection part collects image data of a street view, predicted damage data at the time of disaster, topographic data, and past natural disaster damage data. The analysis unit analyzes the data collected by the data collection unit. The evacuation route generation unit generates an optimum evacuation route based on the damage prediction data analyzed by the analysis unit. The display unit displays the evacuation route generated by the evacuation route generation unit on a smartphone or a tablet of the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies have had the problem of making it difficult to quickly provide optimal evacuation routes in the event of a disaster.

[0005] The system according to the embodiment aims to quickly provide the optimal evacuation route in the event of a disaster. [Means for solving the problem]

[0006] The system according to the embodiment includes a data collection unit, an analysis unit, an evacuation route generation unit, and a display unit. The data collection unit collects street view image data, predicted damage data in the event of a disaster, topographical data, and data on damage caused by past natural disasters. The analysis unit analyzes the data collected by the data collection unit. The evacuation route generation unit generates an optimal evacuation route based on the predicted damage data analyzed by the analysis unit. The display unit displays the evacuation route generated by the evacuation route generation unit on a user's smartphone or tablet. [Effects of the Invention]

[0007] The system according to the embodiment can quickly provide the optimal evacuation route in the event of a disaster. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10]1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The evacuation route securing system according to the embodiment of the present invention is a system that predicts damage in the event of a natural disaster by using image data from Google Street View, predicted damage data in the event of a disaster, topographical data, and data on damage caused by past natural disasters, and provides optimal evacuation routes. As a result, the evacuation route securing system can efficiently and safely provide evacuation routes in the event of a disaster.

[0029] An evacuation route securing system according to an embodiment includes a data collection unit, an analysis unit, an evacuation route generation unit, and a display unit. The data collection unit collects image data from Google Street View, disaster damage prediction data, topographical data, and past natural disaster damage data. For example, the data collection unit acquires high-resolution image data from Google Street View. The data collection unit can also collect disaster damage prediction data provided by government agencies and research institutions. The data collection unit can also collect topographical data such as elevation data and geological data. The past natural disaster damage data collects data including the damage situation and extent of past disasters. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit analyzes the collected data using a generation AI to perform disaster damage prediction. The generation AI uses a text generation AI (e.g., LLM) or a multimodal generation AI to perform damage prediction based on instructions such as the type, scale, and location of the disaster. The evacuation route generation unit generates an optimal evacuation route based on the damage prediction data analyzed by the analysis unit. For example, in the event of a flood, the evacuation route generation unit proposes a route that avoids areas where water levels are likely to rise. Furthermore, in the event of an earthquake, the evacuation route generation unit proposes a route that avoids buildings and bridges that are at high risk of collapse. The display unit displays the evacuation route generated by the evacuation route generation unit on the user's smartphone or tablet. For example, the display unit displays the evacuation route on a map, allowing the user to check the route from their current location to their destination. Furthermore, the display unit can also use a voice guidance function to provide the user with evacuation route guidance in real time. This allows the evacuation route securing system to efficiently and safely provide evacuation routes in the event of a disaster.

[0030] The data collection unit collects real-time video data from drones in addition to Street View image data, and the generation AI analyzes the video. For example, the data collection unit uses drones to collect real-time video data when a disaster occurs, and the generation AI analyzes the video. For example, it grasps the progress of flooding and the collapse of buildings due to earthquakes in real time. The data collection unit also integrates the video data from the drone with Street View image data, and the generation AI performs more detailed damage predictions. For example, it analyzes changes in the terrain and newly formed obstacles. The data collection unit also uses drone video data to quickly grasp the situation immediately after a disaster occurs, and the generation AI uses this to generate evacuation routes. For example, it identifies obstacles and dangerous areas on evacuation routes. This allows the disaster situation to be grasped in real time and more detailed damage predictions to be performed.

[0031] The analysis unit adds a 3D model to the topographical data, and the generation AI performs a three-dimensional damage prediction. The analysis unit adds a 3D model to the topographical data, and the generation AI performs a three-dimensional damage prediction. For example, the 3D model is used to simulate rising water levels during a flood and predict the extent of damage. The analysis unit also uses the 3D model to perform a three-dimensional analysis of the risk of building collapse due to an earthquake. For example, it performs damage predictions that take into account the height and structure of the building. The analysis unit also uses the 3D model to perform a three-dimensional simulation of wind speed and direction during a typhoon and perform damage predictions. For example, it analyzes the risk of fallen trees and flying debris caused by the wind. This makes it possible to provide more accurate evacuation routes through three-dimensional damage predictions.

[0032] The data collection unit adds satellite image data, and the generation AI performs wide-area damage predictions. The data collection unit, for example, uses satellite image data to perform wide-area damage predictions. For example, satellite images are used to grasp the progress of floods and typhoons and predict the extent of damage. The data collection unit also integrates satellite image data with street view image data, and the generation AI performs detailed damage predictions. For example, it analyzes changes in terrain and newly created obstacles. The data collection unit also uses satellite image data to quickly grasp the situation immediately after a disaster occurs, and the generation AI uses this to generate evacuation routes. For example, it identifies obstacles and dangerous areas on evacuation routes. This enables wide-area damage predictions to provide more accurate evacuation routes.

[0033] The data collection unit collects real-time posted data from social media, and the generation AI analyzes the progression of the disaster. The data collection unit, for example, collects real-time posted data from social media, and the generation AI analyzes the progression of the disaster. For example, it analyzes the content of posts by evacuees to grasp the damage situation. The data collection unit also uses social media data to quickly grasp the situation immediately after a disaster occurs, and the generation AI uses this to generate evacuation routes. For example, it identifies obstacles and dangerous areas on evacuation routes. The data collection unit also integrates social media data with Street View image data, and the generation AI makes detailed damage predictions. For example, it analyzes changes in the terrain and newly formed obstacles. This makes it possible to grasp the progression of a disaster in real time and use this to generate evacuation routes.

[0034] The evacuation route generation unit generates an evacuation route taking into consideration the physical strength and health condition of the evacuees. For example, when the generation AI generates an evacuation route, the evacuation route generation unit takes into consideration the physical strength and health condition of the evacuees. For example, it proposes routes that are suitable for elderly people and children. The evacuation route generation unit also generates an optimal evacuation route based on the health condition data of the evacuees. For example, it proposes a route that suits the evacuees' chronic illnesses and physical strength. The evacuation route generation unit also monitors the physical strength and health condition of the evacuees in real time using the generation AI and generates an evacuation route accordingly. For example, it proposes a route that includes rest points that suit the evacuees' fatigue level. This makes it possible to provide an optimal evacuation route that suits the evacuees' physical strength and health condition.

[0035] The evacuation route generation unit checks the capacity of evacuation shelters along the evacuation route in real time and suggests the optimal evacuation shelter. For example, the generation AI of the evacuation route generation unit checks the capacity of evacuation shelters along the evacuation route in real time and suggests the optimal evacuation shelter. For example, the evacuation route generation unit adjusts the evacuation route based on the congestion status of the evacuation shelter. The evacuation route generation unit also generates the optimal evacuation route based on evacuation shelter capacity data. For example, it prioritizes suggesting evacuation shelters with high capacity. The evacuation route generation unit also monitors the capacity of evacuation shelters in real time and generates evacuation routes accordingly. For example, it adjusts the route depending on the congestion status of the evacuation shelter. This makes it possible to provide the optimal evacuation route that takes into account the capacity of the evacuation shelter.

[0036] The evacuation route generation unit simultaneously generates evacuation routes for pets and livestock, allowing them to evacuate together with evacuees. For example, the generation AI of the evacuation route generation unit simultaneously generates evacuation routes for pets and livestock, allowing them to evacuate together with evacuees. For example, it proposes a route that pets can take. When generating evacuation routes for pets and livestock, the evacuation route generation unit integrates them with the routes of evacuees. For example, it proposes a route that takes into account whether pets can enter an evacuation shelter. Furthermore, the generation AI of the evacuation route generation unit monitors the evacuation routes for pets and livestock in real time and generates evacuation routes accordingly. For example, it proposes a route that takes into account whether pets can enter an evacuation shelter. This makes it possible to provide evacuation routes that allow pets and livestock to evacuate together.

[0037] The evacuation route generation unit takes into account traffic congestion and road congestion conditions in real time when generating an evacuation route. For example, when the generation AI generates an evacuation route, the evacuation route generation unit takes into account traffic congestion and road congestion conditions in real time. For example, it proposes a route with less congestion. The evacuation route generation unit also generates an optimal evacuation route based on road congestion data. For example, it proposes a route that takes into account less congested time periods. The evacuation route generation unit also monitors traffic congestion and road congestion conditions in real time and generates an evacuation route accordingly. For example, it proposes a route with less congestion. This makes it possible to provide an optimal evacuation route that takes into account traffic congestion and road congestion conditions.

[0038] The display unit uses AR technology to display an evacuation route superimposed on the real world through the smartphone camera. The display unit, for example, uses AR technology to display an evacuation route superimposed on the real world through the smartphone camera. For example, the evacuation route is displayed superimposed on a real landscape. Furthermore, the display unit uses AR technology to display an evacuation route superimposed on the real world through the smartphone camera, allowing a user to intuitively understand the evacuation route. For example, the evacuation route is displayed superimposed on a real landscape. Furthermore, the display unit uses AR technology to display an evacuation route superimposed on the real world, allowing a user to intuitively understand the evacuation route. For example, the evacuation route is displayed superimposed on a real landscape. This allows a user to intuitively understand the evacuation route.

[0039] The display unit displays the congestion status of the evacuation shelter and information on necessary supplies in real time in addition to displaying the evacuation route. The display unit, for example, displays the congestion status of the evacuation shelter in real time in addition to displaying the evacuation route. For example, it displays the capacity and congestion level of the evacuation shelter on a map. The display unit also displays information on necessary supplies in real time in addition to displaying the evacuation route. For example, it displays the stock status of food and water at the evacuation shelter. The display unit also displays the congestion status of the evacuation shelter and information on necessary supplies in an integrated manner in addition to displaying the evacuation route. For example, it simultaneously displays the congestion level of the evacuation shelter and the stock status of supplies. This allows the congestion status of the evacuation shelter and information on necessary supplies to be grasped in real time.

[0040] The display unit displays an evacuation route on the smartwatch or wearable device and provides guidance using vibrations or sounds. The display unit, for example, displays an evacuation route on the smartwatch or wearable device and provides guidance using vibrations or sounds. For example, the evacuation route is displayed on the device's screen and the direction is indicated by vibrations. The display unit also uses the wearable device to display the evacuation route and provide audio guidance. For example, the device provides audio guidance along the evacuation route. The display unit also uses the smartwatch or wearable device to display an evacuation route and provides guidance using vibrations or sounds. For example, the device provides direction by vibrations and provides detailed guidance by audio. This allows intuitive guidance along the evacuation route using the smartwatch or wearable device.

[0041] The display unit displays the distance and time from the evacuee's current location in real time when displaying the evacuation route. The display unit, for example, displays the distance from the evacuee's current location in real time when displaying the evacuation route. For example, it displays the distance to an evacuation shelter on a map. The display unit also displays the required time from the evacuee's current location in real time when displaying the evacuation route. For example, it displays the required time to reach the evacuation shelter on a map. The display unit also displays the distance and time from the evacuee's current location in an integrated manner when displaying the evacuation route. For example, it displays the distance and required time to the evacuation shelter simultaneously. This allows the evacuee to know the distance and time from their current location to the evacuation shelter in real time.

[0042] The evacuation route generation unit uses real-time data from drones and satellites to constantly update evacuation routes based on the latest information. The evacuation route generation unit, for example, uses real-time data from drones and satellites to constantly update evacuation routes based on the latest information. For example, it grasps the progress of floods and the collapse of buildings due to earthquakes in real time. The evacuation route generation unit also integrates drone and satellite data with Street View image data, and the generation AI makes detailed damage predictions. For example, it analyzes changes in the terrain and newly appeared obstacles. The evacuation route generation unit also uses drone and satellite data to quickly grasp the situation immediately after a disaster occurs, and the generation AI uses this to generate evacuation routes. For example, it identifies obstacles and dangerous areas on the evacuation route. This allows evacuation routes to always be updated based on the latest information.

[0043] The evacuation route generation unit tracks the location information of evacuees in real time and provides the optimal evacuation route for each individual. The evacuation route generation unit, for example, tracks the location information of evacuees in real time and provides the optimal evacuation route for each individual. For example, it proposes the optimal route based on the evacuee's current location. The evacuation route generation unit also generates the optimal evacuation route based on the location information data of the evacuees. For example, it proposes the safest route from the evacuee's current location. The evacuation route generation unit also uses a generation AI to monitor the location information of evacuees in real time and generate an evacuation route accordingly. For example, it proposes the optimal route based on the evacuee's current location. This makes it possible to provide the optimal evacuation route for each individual based on the evacuee's location information.

[0044] The display unit also shares the updated information about the evacuation route with the evacuee's family and friends in real time. The display unit, for example, shares the updated information about the evacuation route with the evacuee's family and friends in real time. For example, it notifies the family of the evacuee's current location and the evacuation route. The display unit also builds a system that shares the updated information about the evacuation route with the evacuee's family and friends in real time. For example, it notifies the family of the evacuee's location information and the evacuation route. The display unit also ensures the safety of the evacuee by sharing the updated information about the evacuation route with the evacuee's family and friends in real time. For example, it notifies the family of the evacuee's current location and the evacuation route. This allows the updated information about the evacuation route to be shared with the evacuee's family and friends in real time.

[0045] The evacuation route generation unit takes into account changes in the health condition and physical strength of evacuees when updating the evacuation route. The evacuation route generation unit, for example, takes into account changes in the health condition and physical strength of evacuees when updating the evacuation route. For example, it proposes a route that includes rest points according to the evacuees' level of fatigue. The evacuation route generation unit also generates an optimal evacuation route based on the health condition data of the evacuees. For example, it proposes a route that depends on the evacuees' chronic illnesses and physical strength. The evacuation route generation unit also uses a generation AI to monitor changes in the health condition and physical strength of evacuees in real time and generate an evacuation route accordingly. For example, it proposes a route that includes rest points according to the evacuees' level of fatigue. This makes it possible to update the evacuation route according to changes in the health condition and physical strength of the evacuees.

[0046] The analysis unit analyzes past evacuation data and develops an algorithm to evaluate the success rate and failure rate of evacuation routes. The analysis unit, for example, analyzes past evacuation data and develops an algorithm to evaluate the success rate and failure rate of evacuation routes. For example, it calculates the success rate based on data on past evacuation routes. The analysis unit also builds a system to evaluate the success rate and failure rate of evacuation routes based on past evacuation data. For example, it proposes an optimal evacuation route based on past data. The analysis unit also analyzes past evacuation data and develops an algorithm to evaluate the success rate and failure rate of evacuation routes. For example, it calculates the success rate based on data on past evacuation routes. This makes it possible to evaluate the success rate and failure rate of evacuation routes based on past evacuation data.

[0047] The analysis unit builds a system that automatically suggests improvements to evacuation routes based on past evacuation data. The analysis unit, for example, builds a system that automatically suggests improvements to evacuation routes based on past evacuation data. For example, it suggests an optimal evacuation route based on past data. The analysis unit also analyzes past evacuation data and develops an algorithm that automatically suggests improvements to evacuation routes. For example, it suggests improvements based on past evacuation route data. The analysis unit also builds a system that automatically suggests improvements to evacuation routes based on past evacuation data. For example, it suggests an optimal evacuation route based on past data. This makes it possible to automatically suggest improvements to evacuation routes based on past evacuation data.

[0048] The analysis unit compares past evacuation data with data from different regions and countries to find areas for improvement from a global perspective. For example, the analysis unit compares past evacuation data with data from different regions and countries to find areas for improvement from a global perspective. For example, it proposes a new route based on successful evacuation routes in different regions. The analysis unit also generates an optimal evacuation route based on evacuation data from different regions and countries. For example, it proposes a route based on successful evacuation routes in other countries. The analysis unit also compares past evacuation data with data from different regions and countries to find areas for improvement from a global perspective. For example, it proposes a new route based on successful evacuation routes in different regions. In this way, by comparing with data from different regions and countries, it is possible to find areas for improvement from a global perspective.

[0049] The analysis unit simulates evacuation drills based on past evacuation data and provides information that will be useful in actual evacuations. The analysis unit, for example, simulates evacuation drills based on past evacuation data and provides information that will be useful in actual evacuations. For example, it proposes an optimal evacuation route based on past data. The analysis unit also analyzes past evacuation data and develops an algorithm for simulating evacuation drills. For example, it performs a simulation based on past evacuation route data. The analysis unit also simulates evacuation drills based on past evacuation data and provides information that will be useful in actual evacuations. For example, it proposes an optimal evacuation route based on past data. This makes it possible to simulate evacuation drills based on past evacuation data and provide information that will be useful in actual evacuations.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The evacuation route generation unit generates an evacuation route taking into account the physical strength and health condition of the evacuees. For example, when the generation AI generates an evacuation route, it takes into account the physical strength and health condition of the evacuees. For example, it may propose a route that is suitable for the elderly and children. The evacuation route generation unit also generates the optimal evacuation route based on the health condition data of the evacuees. For example, it may propose a route that suits the evacuees' chronic illnesses and physical strength. The evacuation route generation unit also monitors the physical strength and health condition of the evacuees in real time and generates an evacuation route accordingly. For example, it may propose a route that includes rest points that suit the evacuees' fatigue level. This makes it possible to provide the optimal evacuation route that suits the evacuees' physical strength and health condition.

[0052] The evacuation route generation unit checks the capacity of evacuation shelters along the evacuation route in real time and suggests the optimal evacuation shelter. For example, the generation AI checks the capacity of evacuation shelters along the evacuation route in real time and suggests the optimal evacuation shelter. For example, it adjusts the evacuation route based on the congestion status of the evacuation shelter. The evacuation route generation unit also generates the optimal evacuation route based on evacuation shelter capacity data. For example, it prioritizes suggesting evacuation shelters with high capacity. The evacuation route generation unit also monitors the capacity of evacuation shelters in real time and generates evacuation routes accordingly. For example, it adjusts the route depending on the congestion status of the evacuation shelter. This makes it possible to provide the optimal evacuation route that takes into account the capacity of the evacuation shelter.

[0053] The evacuation route generation unit simultaneously generates evacuation routes for pets and livestock, allowing them to evacuate together with evacuees. For example, the generation AI simultaneously generates evacuation routes for pets and livestock, allowing them to evacuate together with evacuees. For example, it proposes a route that pets can take. Furthermore, when generating evacuation routes for pets and livestock, the evacuation route generation unit integrates them with the routes of evacuees. For example, it proposes a route that takes into account whether pets can enter an evacuation shelter. Furthermore, the evacuation route generation unit monitors the evacuation routes for pets and livestock in real time and generates evacuation routes accordingly. For example, it proposes a route that takes into account whether pets can enter an evacuation shelter. This makes it possible to provide evacuation routes that allow pets and livestock to evacuate together.

[0054] The evacuation route generation unit takes into account traffic congestion and road congestion conditions in real time when generating an evacuation route. For example, when the generation AI generates an evacuation route, it takes traffic congestion and road congestion conditions into account in real time. For example, it may propose a route with less congestion. The evacuation route generation unit also generates an optimal evacuation route based on road congestion data. For example, it may propose a route that takes into account less congested time periods. The evacuation route generation unit also monitors traffic congestion and road congestion conditions in real time and generates an evacuation route accordingly. For example, it may propose a route with less congestion. This makes it possible to provide an optimal evacuation route that takes traffic congestion and road congestion conditions into account.

[0055] The evacuation route generation unit tracks the location information of evacuees in real time and provides the optimal evacuation route for each individual. For example, it tracks the location information of evacuees in real time and provides the optimal evacuation route for each individual. For example, it proposes the optimal route based on the evacuee's current location. The evacuation route generation unit also generates the optimal evacuation route based on the location information data of evacuees. For example, it proposes the safest route from the evacuee's current location. The evacuation route generation unit also uses a generation AI to monitor the location information of evacuees in real time and generate an evacuation route accordingly. For example, it proposes the optimal route based on the evacuee's current location. This makes it possible to provide the optimal evacuation route for each individual based on the evacuee's location information.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The data collection unit collects image data from Google Street View, predicted damage data in the event of a disaster, topographical data, and data on damage caused by past natural disasters. For example, the data collection unit obtains high-resolution image data from Google Street View. The data collection unit can also collect predicted damage data in the event of a disaster provided by government agencies and research institutions. Furthermore, the data collection unit can collect topographical data as elevation data and geological data. Data on damage caused by past natural disasters is collected, including the damage situation and extent of damage caused by past disasters. Step 2: The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit uses a generation AI to analyze the collected data and predict damage in the event of a disaster. The generation AI uses a text generation AI (e.g., LLM) or a multimodal generation AI to predict damage based on instructions such as the type, scale, and location of the disaster. Step 3: The evacuation route generation unit generates the optimal evacuation route based on the damage prediction data analyzed by the analysis unit. For example, in the event of a flood, the evacuation route generation unit proposes a route that avoids areas where water levels are likely to rise. In the event of an earthquake, the evacuation route generation unit proposes a route that avoids buildings and bridges that are at high risk of collapse. Step 4: The display unit displays the evacuation route generated by the evacuation route generation unit on the user's smartphone or tablet. For example, the display unit displays the evacuation route on a map so that the user can check the route from their current location to their destination. The display unit can also use a voice guidance function to provide the user with real-time evacuation route guidance.

[0058] (Example 2) The evacuation route securing system according to the embodiment of the present invention is a system that predicts damage in the event of a natural disaster by using image data from Google Street View, predicted damage data in the event of a disaster, topographical data, and data on damage caused by past natural disasters, and provides optimal evacuation routes. As a result, the evacuation route securing system can efficiently and safely provide evacuation routes in the event of a disaster.

[0059] An evacuation route securing system according to an embodiment includes a data collection unit, an analysis unit, an evacuation route generation unit, and a display unit. The data collection unit collects image data from Google Street View, disaster damage prediction data, topographical data, and past natural disaster damage data. For example, the data collection unit acquires high-resolution image data from Google Street View. The data collection unit can also collect disaster damage prediction data provided by government agencies and research institutions. The data collection unit can also collect topographical data such as elevation data and geological data. The past natural disaster damage data collects data including the damage situation and extent of past disasters. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit analyzes the collected data using a generation AI to perform disaster damage prediction. The generation AI uses a text generation AI (e.g., LLM) or a multimodal generation AI to perform damage prediction based on instructions such as the type, scale, and location of the disaster. The evacuation route generation unit generates an optimal evacuation route based on the damage prediction data analyzed by the analysis unit. For example, in the event of a flood, the evacuation route generation unit proposes a route that avoids areas where water levels are likely to rise. Furthermore, in the event of an earthquake, the evacuation route generation unit proposes a route that avoids buildings and bridges that are at high risk of collapse. The display unit displays the evacuation route generated by the evacuation route generation unit on the user's smartphone or tablet. For example, the display unit displays the evacuation route on a map, allowing the user to check the route from their current location to their destination. Furthermore, the display unit can also use a voice guidance function to provide the user with evacuation route guidance in real time. This allows the evacuation route securing system to efficiently and safely provide evacuation routes in the event of a disaster.

[0060] The data collection unit collects real-time video data from drones in addition to Street View image data, and the generation AI analyzes the video. For example, the data collection unit uses drones to collect real-time video data when a disaster occurs, and the generation AI analyzes the video. For example, it grasps the progress of flooding and the collapse of buildings due to earthquakes in real time. The data collection unit also integrates the video data from the drone with Street View image data, and the generation AI performs more detailed damage predictions. For example, it analyzes changes in the terrain and newly formed obstacles. The data collection unit also uses drone video data to quickly grasp the situation immediately after a disaster occurs, and the generation AI uses this to generate evacuation routes. For example, it identifies obstacles and dangerous areas on evacuation routes. This allows the disaster situation to be grasped in real time and more detailed damage predictions to be performed.

[0061] The analysis unit adds a 3D model to the topographical data, and the generation AI performs a three-dimensional damage prediction. The analysis unit adds a 3D model to the topographical data, and the generation AI performs a three-dimensional damage prediction. For example, the 3D model is used to simulate rising water levels during a flood and predict the extent of damage. The analysis unit also uses the 3D model to perform a three-dimensional analysis of the risk of building collapse due to an earthquake. For example, it performs damage predictions that take into account the height and structure of the building. The analysis unit also uses the 3D model to perform a three-dimensional simulation of wind speed and direction during a typhoon and perform damage predictions. For example, it analyzes the risk of fallen trees and flying debris caused by the wind. This makes it possible to provide more accurate evacuation routes through three-dimensional damage predictions.

[0062] The analysis unit uses the emotion estimation function to collect emotional data of evacuees and predict the stress level of the evacuation route. The analysis unit, for example, uses the emotion estimation function to collect emotional data of evacuees and predict the stress level of the evacuation route. For example, it analyzes the facial expressions and voices of evacuees to identify routes that are high in stress. The analysis unit also proposes evacuation routes that are less stressful based on the emotional data of evacuees. For example, it generates routes that include points where evacuees can relax. The analysis unit also uses the emotion estimation data to make proposals to reduce the psychological burden of the evacuation route. For example, it provides encouraging messages that correspond to the emotional state of the evacuees. This makes it possible to provide evacuation routes that reduce the psychological burden of evacuees.

[0063] The data collection unit adds satellite image data, and the generation AI performs wide-area damage predictions. The data collection unit, for example, uses satellite image data to perform wide-area damage predictions. For example, satellite images are used to grasp the progress of floods and typhoons and predict the extent of damage. The data collection unit also integrates satellite image data with street view image data, and the generation AI performs detailed damage predictions. For example, it analyzes changes in terrain and newly created obstacles. The data collection unit also uses satellite image data to quickly grasp the situation immediately after a disaster occurs, and the generation AI uses this to generate evacuation routes. For example, it identifies obstacles and dangerous areas on evacuation routes. This enables wide-area damage predictions to provide more accurate evacuation routes.

[0064] The data collection unit collects real-time posted data from social media, and the generation AI analyzes the progression of the disaster. The data collection unit, for example, collects real-time posted data from social media, and the generation AI analyzes the progression of the disaster. For example, it analyzes the content of posts by evacuees to grasp the damage situation. The data collection unit also uses social media data to quickly grasp the situation immediately after a disaster occurs, and the generation AI uses this to generate evacuation routes. For example, it identifies obstacles and dangerous areas on evacuation routes. The data collection unit also integrates social media data with Street View image data, and the generation AI makes detailed damage predictions. For example, it analyzes changes in the terrain and newly formed obstacles. This makes it possible to grasp the progression of a disaster in real time and use this to generate evacuation routes.

[0065] The analysis unit uses the emotion estimation function to make suggestions to reduce the psychological burden of evacuation routes based on the emotion data of evacuees. The analysis unit, for example, uses the emotion estimation function to make suggestions to reduce the psychological burden of evacuation routes based on the emotion data of evacuees. For example, it generates a route that includes points where evacuees can relax. The analysis unit also suggests a low-stress evacuation route based on the emotion data of evacuees. For example, it provides an encouraging message according to the emotional state of the evacuees. The analysis unit also uses the emotion estimation data to make suggestions to reduce the psychological burden of evacuation routes. For example, it generates a route that includes points where evacuees can relax based on the emotional state of the evacuees. This makes it possible to make suggestions to reduce the psychological burden of evacuees.

[0066] The evacuation route generation unit generates an evacuation route taking into consideration the physical strength and health condition of the evacuees. For example, when the generation AI generates an evacuation route, the evacuation route generation unit takes into consideration the physical strength and health condition of the evacuees. For example, it proposes routes that are suitable for elderly people and children. The evacuation route generation unit also generates an optimal evacuation route based on the health condition data of the evacuees. For example, it proposes a route that suits the evacuees' chronic illnesses and physical strength. The evacuation route generation unit also monitors the physical strength and health condition of the evacuees in real time using the generation AI and generates an evacuation route accordingly. For example, it proposes a route that includes rest points that suit the evacuees' fatigue level. This makes it possible to provide an optimal evacuation route that suits the evacuees' physical strength and health condition.

[0067] The evacuation route generation unit checks the capacity of evacuation shelters along the evacuation route in real time and suggests the optimal evacuation shelter. For example, the generation AI of the evacuation route generation unit checks the capacity of evacuation shelters along the evacuation route in real time and suggests the optimal evacuation shelter. For example, the evacuation route generation unit adjusts the evacuation route based on the congestion status of the evacuation shelter. The evacuation route generation unit also generates the optimal evacuation route based on evacuation shelter capacity data. For example, it prioritizes suggesting evacuation shelters with high capacity. The evacuation route generation unit also monitors the capacity of evacuation shelters in real time and generates evacuation routes accordingly. For example, it adjusts the route depending on the congestion status of the evacuation shelter. This makes it possible to provide the optimal evacuation route that takes into account the capacity of the evacuation shelter.

[0068] The evacuation route generation unit uses the emotion estimation function to generate a route that provides psychological security by taking into account the emotional state of the evacuees. The evacuation route generation unit, for example, uses the emotion estimation function to generate a route that provides psychological security by taking into account the emotional state of the evacuees. For example, it proposes a route that includes points where the evacuees can relax. The evacuation route generation unit also proposes a low-stress evacuation route based on the emotion data of the evacuees. For example, it provides an encouraging message according to the emotional state of the evacuees. The evacuation route generation unit also uses the emotion estimation data to make proposals to reduce the psychological burden of the evacuation route. For example, it generates a route that includes points where the evacuees can relax according to the emotional state of the evacuees. This makes it possible to provide an evacuation route that reduces the psychological burden of the evacuees.

[0069] The evacuation route generation unit simultaneously generates evacuation routes for pets and livestock, allowing them to evacuate together with evacuees. For example, the generation AI of the evacuation route generation unit simultaneously generates evacuation routes for pets and livestock, allowing them to evacuate together with evacuees. For example, it proposes a route that pets can take. When generating evacuation routes for pets and livestock, the evacuation route generation unit integrates them with the routes of evacuees. For example, it proposes a route that takes into account whether pets can enter an evacuation shelter. Furthermore, the generation AI of the evacuation route generation unit monitors the evacuation routes for pets and livestock in real time and generates evacuation routes accordingly. For example, it proposes a route that takes into account whether pets can enter an evacuation shelter. This makes it possible to provide evacuation routes that allow pets and livestock to evacuate together.

[0070] The evacuation route generation unit takes into account traffic congestion and road congestion conditions in real time when generating an evacuation route. For example, when the generation AI generates an evacuation route, the evacuation route generation unit takes into account traffic congestion and road congestion conditions in real time. For example, it proposes a route with less congestion. The evacuation route generation unit also generates an optimal evacuation route based on road congestion data. For example, it proposes a route that takes into account less congested time periods. The evacuation route generation unit also monitors traffic congestion and road congestion conditions in real time and generates an evacuation route accordingly. For example, it proposes a route with less congestion. This makes it possible to provide an optimal evacuation route that takes into account traffic congestion and road congestion conditions.

[0071] The evacuation route generation unit uses the emotion estimation function to suggest relaxation points along the evacuation route based on the emotion data of the evacuees. The evacuation route generation unit, for example, uses the emotion estimation function to suggest relaxation points along the evacuation route based on the emotion data of the evacuees. For example, it generates a route that includes places where the evacuees can relax. The evacuation route generation unit also suggests a low-stress evacuation route based on the emotion data of the evacuees. For example, it generates a route that includes relaxation points that correspond to the emotional state of the evacuees. The evacuation route generation unit also uses the emotion estimation data to make suggestions to reduce the psychological burden of the evacuation route. For example, it generates a route that includes relaxation points that correspond to the emotional state of the evacuees. This makes it possible to provide relaxation points that reduce the psychological burden of the evacuees.

[0072] The display unit uses AR technology to display an evacuation route superimposed on the real world through the smartphone camera. The display unit, for example, uses AR technology to display an evacuation route superimposed on the real world through the smartphone camera. For example, the evacuation route is displayed superimposed on a real landscape. Furthermore, the display unit uses AR technology to display an evacuation route superimposed on the real world through the smartphone camera, allowing a user to intuitively understand the evacuation route. For example, the evacuation route is displayed superimposed on a real landscape. Furthermore, the display unit uses AR technology to display an evacuation route superimposed on the real world, allowing a user to intuitively understand the evacuation route. For example, the evacuation route is displayed superimposed on a real landscape. This allows a user to intuitively understand the evacuation route.

[0073] The display unit displays the congestion status of the evacuation shelter and information on necessary supplies in real time in addition to displaying the evacuation route. The display unit, for example, displays the congestion status of the evacuation shelter in real time in addition to displaying the evacuation route. For example, it displays the capacity and congestion level of the evacuation shelter on a map. The display unit also displays information on necessary supplies in real time in addition to displaying the evacuation route. For example, it displays the stock status of food and water at the evacuation shelter. The display unit also displays the congestion status of the evacuation shelter and information on necessary supplies in an integrated manner in addition to displaying the evacuation route. For example, it simultaneously displays the congestion level of the evacuation shelter and the stock status of supplies. This allows the congestion status of the evacuation shelter and information on necessary supplies to be grasped in real time.

[0074] The display unit uses the emotion estimation function to provide voice guidance that gives encouragement and a sense of security according to the emotional state of the evacuee. The display unit, for example, uses the emotion estimation function to provide voice guidance that gives encouragement and a sense of security according to the emotional state of the evacuee. For example, an encouraging message is provided when the evacuee is feeling anxious. The display unit also provides voice guidance that reduces stress based on the emotion data of the evacuee. For example, voice guidance that helps the evacuee relax is provided. The display unit also uses the emotion estimation data to provide voice guidance according to the emotional state of the evacuee. For example, a message that gives the evacuee a sense of security is provided. In this way, psychological support can be provided by providing voice guidance according to the emotional state of the evacuee.

[0075] The display unit displays an evacuation route on the smartwatch or wearable device and provides guidance using vibrations or sounds. The display unit, for example, displays an evacuation route on the smartwatch or wearable device and provides guidance using vibrations or sounds. For example, the evacuation route is displayed on the device's screen and the direction is indicated by vibrations. The display unit also uses the wearable device to display the evacuation route and provide audio guidance. For example, the device provides audio guidance along the evacuation route. The display unit also uses the smartwatch or wearable device to display an evacuation route and provides guidance using vibrations or sounds. For example, the device provides direction by vibrations and provides detailed guidance by audio. This allows intuitive guidance along the evacuation route using the smartwatch or wearable device.

[0076] The display unit displays the distance and time from the evacuee's current location in real time when displaying the evacuation route. The display unit, for example, displays the distance from the evacuee's current location in real time when displaying the evacuation route. For example, it displays the distance to an evacuation shelter on a map. The display unit also displays the required time from the evacuee's current location in real time when displaying the evacuation route. For example, it displays the required time to reach the evacuation shelter on a map. The display unit also displays the distance and time from the evacuee's current location in an integrated manner when displaying the evacuation route. For example, it displays the distance and required time to the evacuation shelter simultaneously. This allows the evacuee to know the distance and time from their current location to the evacuation shelter in real time.

[0077] The display unit uses the emotion estimation function to suggest rest points along the evacuation route based on the emotion data of the evacuees. The display unit, for example, uses the emotion estimation function to suggest rest points along the evacuation route based on the emotion data of the evacuees. For example, it generates a route that includes places where the evacuees can relax. The display unit also suggests low-stress rest points based on the emotion data of the evacuees. For example, it generates a route that includes relaxation points according to the emotional state of the evacuees. The display unit also uses the emotion estimation data to make suggestions to reduce the psychological burden of the evacuation route. For example, it generates a route that includes relaxation points according to the emotional state of the evacuees. This makes it possible to provide rest points to reduce the psychological burden of the evacuees.

[0078] The evacuation route generation unit uses real-time data from drones and satellites to constantly update evacuation routes based on the latest information. The evacuation route generation unit, for example, uses real-time data from drones and satellites to constantly update evacuation routes based on the latest information. For example, it grasps the progress of floods and the collapse of buildings due to earthquakes in real time. The evacuation route generation unit also integrates drone and satellite data with Street View image data, and the generation AI makes detailed damage predictions. For example, it analyzes changes in the terrain and newly appeared obstacles. The evacuation route generation unit also uses drone and satellite data to quickly grasp the situation immediately after a disaster occurs, and the generation AI uses this to generate evacuation routes. For example, it identifies obstacles and dangerous areas on the evacuation route. This allows evacuation routes to always be updated based on the latest information.

[0079] The evacuation route generation unit tracks the location information of evacuees in real time and provides the optimal evacuation route for each individual. The evacuation route generation unit, for example, tracks the location information of evacuees in real time and provides the optimal evacuation route for each individual. For example, it proposes the optimal route based on the evacuee's current location. The evacuation route generation unit also generates the optimal evacuation route based on the location information data of the evacuees. For example, it proposes the safest route from the evacuee's current location. The evacuation route generation unit also uses a generation AI to monitor the location information of evacuees in real time and generate an evacuation route accordingly. For example, it proposes the optimal route based on the evacuee's current location. This makes it possible to provide the optimal evacuation route for each individual based on the evacuee's location information.

[0080] The display unit also shares the updated information about the evacuation route with the evacuee's family and friends in real time. The display unit, for example, shares the updated information about the evacuation route with the evacuee's family and friends in real time. For example, it notifies the family of the evacuee's current location and the evacuation route. The display unit also builds a system that shares the updated information about the evacuation route with the evacuee's family and friends in real time. For example, it notifies the family of the evacuee's location information and the evacuation route. The display unit also ensures the safety of the evacuee by sharing the updated information about the evacuation route with the evacuee's family and friends in real time. For example, it notifies the family of the evacuee's current location and the evacuation route. This allows the updated information about the evacuation route to be shared with the evacuee's family and friends in real time.

[0081] The evacuation route generation unit takes into account changes in the health condition and physical strength of evacuees when updating the evacuation route. The evacuation route generation unit, for example, takes into account changes in the health condition and physical strength of evacuees when updating the evacuation route. For example, it proposes a route that includes rest points according to the evacuees' level of fatigue. The evacuation route generation unit also generates an optimal evacuation route based on the health condition data of the evacuees. For example, it proposes a route that depends on the evacuees' chronic illnesses and physical strength. The evacuation route generation unit also uses a generation AI to monitor changes in the health condition and physical strength of evacuees in real time and generate an evacuation route accordingly. For example, it proposes a route that includes rest points according to the evacuees' level of fatigue. This makes it possible to update the evacuation route according to changes in the health condition and physical strength of the evacuees.

[0082] The display unit uses the emotion estimation function to send a message that gives a sense of security to the evacuees when updating the evacuation route, based on the emotion data of the evacuees. The display unit, for example, uses the emotion estimation function to send a message that gives a sense of security to the evacuees when updating the evacuation route, based on the emotion data of the evacuees. For example, it sends an encouraging message if the evacuees are feeling anxious. The display unit also sends a message to reduce stress based on the emotion data of the evacuees. For example, it sends a message that helps the evacuees relax. The display unit also uses the emotion estimation data to send a message that gives a sense of security to the evacuees when updating the evacuation route. For example, it sends a message that makes the evacuees feel secure. This makes it possible to send a message that gives a sense of security to the evacuees when updating the evacuation route.

[0083] The analysis unit analyzes past evacuation data and develops an algorithm to evaluate the success rate and failure rate of evacuation routes. The analysis unit, for example, analyzes past evacuation data and develops an algorithm to evaluate the success rate and failure rate of evacuation routes. For example, it calculates the success rate based on data on past evacuation routes. The analysis unit also builds a system to evaluate the success rate and failure rate of evacuation routes based on past evacuation data. For example, it proposes an optimal evacuation route based on past data. The analysis unit also analyzes past evacuation data and develops an algorithm to evaluate the success rate and failure rate of evacuation routes. For example, it calculates the success rate based on data on past evacuation routes. This makes it possible to evaluate the success rate and failure rate of evacuation routes based on past evacuation data.

[0084] The analysis unit builds a system that automatically suggests improvements to evacuation routes based on past evacuation data. The analysis unit, for example, builds a system that automatically suggests improvements to evacuation routes based on past evacuation data. For example, it suggests an optimal evacuation route based on past data. The analysis unit also analyzes past evacuation data and develops an algorithm that automatically suggests improvements to evacuation routes. For example, it suggests improvements based on past evacuation route data. The analysis unit also builds a system that automatically suggests improvements to evacuation routes based on past evacuation data. For example, it suggests an optimal evacuation route based on past data. This makes it possible to automatically suggest improvements to evacuation routes based on past evacuation data.

[0085] The analysis unit uses the emotion estimation function to analyze emotion data of past evacuees and propose an evacuation route that provides emotional peace of mind. The analysis unit, for example, uses the emotion estimation function to analyze emotion data of past evacuees and propose an evacuation route that provides emotional peace of mind. For example, a new route is generated based on routes that past evacuees found relaxing. The analysis unit also proposes a less stressful evacuation route based on emotion data of past evacuees. For example, a new route is generated based on routes that past evacuees found relaxing. The analysis unit also uses the emotion estimation data to analyze emotion data of past evacuees and propose an evacuation route that provides emotional peace of mind. For example, a new route is generated based on routes that past evacuees found relaxing. In this way, it is possible to propose an evacuation route that provides emotional peace of mind based on emotion data of past evacuees.

[0086] The analysis unit compares past evacuation data with data from different regions and countries to find areas for improvement from a global perspective. For example, the analysis unit compares past evacuation data with data from different regions and countries to find areas for improvement from a global perspective. For example, it proposes a new route based on successful evacuation routes in different regions. The analysis unit also generates an optimal evacuation route based on evacuation data from different regions and countries. For example, it proposes a route based on successful evacuation routes in other countries. The analysis unit also compares past evacuation data with data from different regions and countries to find areas for improvement from a global perspective. For example, it proposes a new route based on successful evacuation routes in different regions. In this way, by comparing with data from different regions and countries, it is possible to find areas for improvement from a global perspective.

[0087] The analysis unit simulates evacuation drills based on past evacuation data and provides information that will be useful in actual evacuations. The analysis unit, for example, simulates evacuation drills based on past evacuation data and provides information that will be useful in actual evacuations. For example, it proposes an optimal evacuation route based on past data. The analysis unit also analyzes past evacuation data and develops an algorithm for simulating evacuation drills. For example, it performs a simulation based on past evacuation route data. The analysis unit also simulates evacuation drills based on past evacuation data and provides information that will be useful in actual evacuations. For example, it proposes an optimal evacuation route based on past data. This makes it possible to simulate evacuation drills based on past evacuation data and provide information that will be useful in actual evacuations.

[0088] The analysis unit uses the emotion estimation function to provide psychological support during evacuation drills based on past emotional data of evacuees. The analysis unit, for example, uses the emotion estimation function to provide psychological support during evacuation drills based on past emotional data of evacuees. For example, it proposes new training based on methods that helped past evacuees to relax. The analysis unit also proposes evacuation drills that are less stressful based on past emotional data of evacuees. For example, it proposes new training based on methods that helped past evacuees to feel safe. The analysis unit also uses the emotion estimation data to provide psychological support during evacuation drills based on past emotional data of evacuees. For example, it proposes new training based on methods that helped past evacuees to feel relaxed. In this way, psychological support can be provided during evacuation drills based on past emotional data of evacuees.

[0089] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0090] The evacuation route generation unit generates an evacuation route taking into account the physical strength and health condition of the evacuees. For example, when the generation AI generates an evacuation route, it takes into account the physical strength and health condition of the evacuees. For example, it may propose a route that is suitable for the elderly and children. The evacuation route generation unit also generates the optimal evacuation route based on the health condition data of the evacuees. For example, it may propose a route that suits the evacuees' chronic illnesses and physical strength. The evacuation route generation unit also monitors the physical strength and health condition of the evacuees in real time and generates an evacuation route accordingly. For example, it may propose a route that includes rest points that suit the evacuees' fatigue level. This makes it possible to provide the optimal evacuation route that suits the evacuees' physical strength and health condition.

[0091] The evacuation route generation unit checks the capacity of evacuation shelters along the evacuation route in real time and suggests the optimal evacuation shelter. For example, the generation AI checks the capacity of evacuation shelters along the evacuation route in real time and suggests the optimal evacuation shelter. For example, it adjusts the evacuation route based on the congestion status of the evacuation shelter. The evacuation route generation unit also generates the optimal evacuation route based on evacuation shelter capacity data. For example, it prioritizes suggesting evacuation shelters with high capacity. The evacuation route generation unit also monitors the capacity of evacuation shelters in real time and generates evacuation routes accordingly. For example, it adjusts the route depending on the congestion status of the evacuation shelter. This makes it possible to provide the optimal evacuation route that takes into account the capacity of the evacuation shelter.

[0092] The evacuation route generation unit simultaneously generates evacuation routes for pets and livestock, allowing them to evacuate together with evacuees. For example, the generation AI simultaneously generates evacuation routes for pets and livestock, allowing them to evacuate together with evacuees. For example, it proposes a route that pets can take. Furthermore, when generating evacuation routes for pets and livestock, the evacuation route generation unit integrates them with the routes of evacuees. For example, it proposes a route that takes into account whether pets can enter an evacuation shelter. Furthermore, the evacuation route generation unit monitors the evacuation routes for pets and livestock in real time and generates evacuation routes accordingly. For example, it proposes a route that takes into account whether pets can enter an evacuation shelter. This makes it possible to provide evacuation routes that allow pets and livestock to evacuate together.

[0093] The evacuation route generation unit takes into account traffic congestion and road congestion conditions in real time when generating an evacuation route. For example, when the generation AI generates an evacuation route, it takes traffic congestion and road congestion conditions into account in real time. For example, it may propose a route with less congestion. The evacuation route generation unit also generates an optimal evacuation route based on road congestion data. For example, it may propose a route that takes into account less congested time periods. The evacuation route generation unit also monitors traffic congestion and road congestion conditions in real time and generates an evacuation route accordingly. For example, it may propose a route with less congestion. This makes it possible to provide an optimal evacuation route that takes traffic congestion and road congestion conditions into account.

[0094] The evacuation route generation unit tracks the location information of evacuees in real time and provides the optimal evacuation route for each individual. For example, it tracks the location information of evacuees in real time and provides the optimal evacuation route for each individual. For example, it proposes the optimal route based on the evacuee's current location. The evacuation route generation unit also generates the optimal evacuation route based on the location information data of evacuees. For example, it proposes the safest route from the evacuee's current location. The evacuation route generation unit also uses a generation AI to monitor the location information of evacuees in real time and generate an evacuation route accordingly. For example, it proposes the optimal route based on the evacuee's current location. This makes it possible to provide the optimal evacuation route for each individual based on the evacuee's location information.

[0095] The analysis unit uses the emotion estimation function to collect emotional data of evacuees and predict the stress level of the evacuation route. For example, the emotion estimation function is used to collect emotional data of evacuees and predict the stress level of the evacuation route. For example, the emotion estimation function is used to analyze the facial expressions and voices of evacuees and identify routes that are high in stress. The analysis unit also proposes evacuation routes that are less stressful based on the emotional data of evacuees. For example, it generates routes that include points where evacuees can relax. The analysis unit also uses the emotion estimation data to make proposals to reduce the psychological burden of the evacuation route. For example, it provides encouraging messages that correspond to the emotional state of the evacuees. This makes it possible to provide evacuation routes that reduce the psychological burden of evacuees.

[0096] The analysis unit uses the emotion estimation function to make suggestions to reduce the psychological burden of evacuation routes based on the emotion data of evacuees. For example, the analysis unit uses the emotion estimation function to make suggestions to reduce the psychological burden of evacuation routes based on the emotion data of evacuees. For example, the analysis unit generates a route that includes points where evacuees can relax. The analysis unit also makes suggestions to reduce stress on evacuation routes based on the emotion data of evacuees. For example, the analysis unit provides encouraging messages according to the emotional state of the evacuees. The analysis unit also uses the emotion estimation data to make suggestions to reduce the psychological burden of evacuation routes. For example, the analysis unit generates a route that includes points where evacuees can relax based on the emotional state of the evacuees. This makes it possible to make suggestions to reduce the psychological burden on evacuees.

[0097] The evacuation route generation unit uses the emotion estimation function to generate a psychologically reassuring route by taking into account the emotional state of the evacuees. For example, the emotion estimation function is used to generate a psychologically reassuring route by taking into account the emotional state of the evacuees. For example, a route including points where the evacuees can relax is proposed. The evacuation route generation unit also proposes a low-stress evacuation route based on the emotional data of the evacuees. For example, an encouraging message is provided according to the emotional state of the evacuees. The evacuation route generation unit also uses the emotion estimation data to make proposals to reduce the psychological burden of the evacuation route. For example, a route including points where the evacuees can relax according to the emotional state of the evacuees is generated. This makes it possible to provide an evacuation route that reduces the psychological burden of the evacuees.

[0098] The display unit uses the emotion estimation function to provide voice guidance that gives encouragement and a sense of security according to the emotional state of the evacuee. For example, the emotion estimation function is used to provide voice guidance that gives encouragement and a sense of security according to the emotional state of the evacuee. For example, an encouraging message is provided when the evacuee is feeling anxious. The display unit also provides voice guidance that reduces stress based on the emotional data of the evacuee. For example, voice guidance that helps the evacuee relax is provided. The display unit also uses the emotion estimation data to provide voice guidance according to the emotional state of the evacuee. For example, a message that gives the evacuee a sense of security is provided. In this way, psychological support can be provided by providing voice guidance according to the emotional state of the evacuee.

[0099] The display unit uses the emotion estimation function to send a message that gives a sense of security to evacuees based on emotion data of evacuees when updating evacuation routes. For example, the emotion estimation function is used to send a message that gives a sense of security to evacuees based on emotion data of evacuees when updating evacuation routes. For example, an encouraging message is sent if the evacuee is feeling anxious. The display unit also sends a message to reduce stress based on emotion data of evacuees. For example, a message that helps the evacuee relax. The display unit also uses emotion estimation data to send a message that gives a sense of security to evacuees when updating evacuation routes. For example, a message that makes the evacuee feel secure is sent. This makes it possible to send a message that gives a sense of security to evacuees when updating evacuation routes.

[0100] The processing flow of the second embodiment will be briefly explained below.

[0101] Step 1: The data collection unit collects image data from Google Street View, predicted damage data in the event of a disaster, topographical data, and data on damage caused by past natural disasters. For example, the data collection unit obtains high-resolution image data from Google Street View. The data collection unit can also collect predicted damage data in the event of a disaster provided by government agencies and research institutions. Furthermore, the data collection unit can collect topographical data as elevation data and geological data. Data on damage caused by past natural disasters is collected, including the damage situation and extent of damage caused by past disasters. Step 2: The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit uses a generation AI to analyze the collected data and predict damage in the event of a disaster. The generation AI uses a text generation AI (e.g., LLM) or a multimodal generation AI to predict damage based on instructions such as the type, scale, and location of the disaster. Step 3: The evacuation route generation unit generates the optimal evacuation route based on the damage prediction data analyzed by the analysis unit. For example, in the event of a flood, the evacuation route generation unit proposes a route that avoids areas where water levels are likely to rise. In the event of an earthquake, the evacuation route generation unit proposes a route that avoids buildings and bridges that are at high risk of collapse. Step 4: The display unit displays the evacuation route generated by the evacuation route generation unit on the user's smartphone or tablet. For example, the display unit displays the evacuation route on a map so that the user can check the route from their current location to their destination. The display unit can also use a voice guidance function to provide the user with real-time evacuation route guidance.

[0102] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0103] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0104] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0105] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0106] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0107] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0108] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0109] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0110] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0111] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0112] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0113] 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.

[0114] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0115] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0116] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0117] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0118] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0119] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0120] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0121] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0122] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0123] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0124] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0125] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0126] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0127] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0128] 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.

[0129] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0130] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0131] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0132] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0133] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0134] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0135] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0136] 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.

[0137] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0138] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0139] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0141] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0142] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0143] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0144] 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.

[0145] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0146] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0147] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0148] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0149] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0150] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0151] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0152] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0153] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0154] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0155] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0156] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0157] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0158] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0159] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0160] 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.

[0161] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0162] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0163] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0164] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0165] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0166] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0167] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0168] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0169] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a data collection unit that collects Street View image data, disaster damage forecast data, topographical data, and data on damage caused by past natural disasters; an analysis unit that analyzes the data collected by the data collection unit; an evacuation route generation unit that generates an optimal evacuation route based on the damage prediction data analyzed by the analysis unit; a display unit that displays the evacuation route generated by the evacuation route generation unit on a smartphone or tablet of a user. A system characterized by:

2. The data collection unit In addition to Street View image data, real-time video data from drones is collected and analyzed by the generative AI.

2. The system of claim 1.

3. The evacuation route generation unit Generate the evacuation route taking into account the physical strength and health condition of the evacuees 2. The system of claim 1.

4. The display unit Using AR technology, the evacuation route is superimposed on the real world and displayed through the smartphone camera.

2. The system of claim 1.

5. The evacuation route generation unit Monitor the emotional state of evacuees in real time and update the evacuation route if stress levels rise.

2. The system of claim 1.

6. The analysis unit Collecting emotional data of evacuees and predicting stress levels along the evacuation route 2. The system of claim 1.

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