System

Abstract

An object of a system according to an embodiment is to predict the risk of a natural disaster and provide early warning.SOLUTION: A system according to an embodiment includes a disaster risk prediction unit and an early warning provision unit. The disaster risk prediction unit predicts a disaster risk of an earthquake, a flood, or a forest fire. The early warning providing unit provides an early warning based on the disaster risk predicted by the disaster risk predicting unit.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 are not sufficient to predict the risk of natural disasters and provide early warning, and there is room for improvement.

[0005] The system according to the embodiment aims to predict the risk of natural disasters and provide early warning. [Means for solving the problem]

[0006] The system according to the embodiment includes a disaster risk prediction unit and an early warning providing unit. The disaster risk prediction unit predicts disaster risks such as earthquakes, floods, and wildfires. The early warning providing unit provides early warnings based on the disaster risks predicted by the disaster risk prediction unit. [Effects of the Invention]

[0007] The system according to the embodiment can predict the risk of natural disasters and provide early warning. [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) A disaster response system according to an embodiment of the present invention is a system that predicts natural disasters and disaster-related risks and proposes appropriate countermeasures. This system utilizes AI to predict the occurrence of disasters such as earthquakes, floods, and wildfires, enabling early warning. It also supports ensuring the safety of disaster victims by creating evacuation plans, providing information on appropriate evacuation locations, and optimizing evacuation routes. This enables the disaster response system to predict natural disasters and disaster-related risks and propose appropriate countermeasures.

[0029] A disaster response system according to an embodiment includes a disaster risk prediction unit and an early warning providing unit. The disaster risk prediction unit predicts disaster risks of earthquakes, floods, and wildfires. For example, the disaster risk prediction unit predicts the risk of earthquakes using seismometer data. The disaster risk prediction unit can also predict the risk of floods using rainfall data. The disaster risk prediction unit can also predict the risk of wildfires using temperature, humidity, and wind speed data. For example, the disaster risk prediction unit analyzes seismometer data to calculate the probability of an earthquake. The disaster risk prediction unit analyzes rainfall data to assess the risk of floods. The disaster risk prediction unit analyzes temperature, humidity, and wind speed data to predict the risk of wildfires. The early warning providing unit provides an early warning based on the disaster risk predicted by the disaster risk prediction unit. For example, the early warning providing unit issues an alert based on the earthquake prediction results. The early warning providing unit can also issue an alert based on the flood prediction results. The early warning providing unit can also issue an alert based on the wildfire prediction results. For example, the early warning providing unit issues a warning to areas where an earthquake is likely to occur based on the earthquake prediction results. The early warning providing unit issues a warning to areas where a flood is likely to occur based on the flood prediction results. The early warning providing unit issues a warning to areas where a forest fire is likely to occur based on the forest fire prediction results. In this way, the disaster response system according to the embodiment can ensure the safety of residents by predicting disaster risks and providing early warnings.

[0030] The disaster risk prediction unit can predict disaster risk using seismometer data, rainfall data, river water level data, temperature, humidity, and wind speed data. The disaster risk prediction unit, for example, predicts earthquake risk using seismometer data. For example, it analyzes the seismometer data and calculates the probability of an earthquake occurring. The disaster risk prediction unit also predicts flood risk using rainfall data. For example, it analyzes rainfall data and evaluates the risk of flood occurrence. The disaster risk prediction unit also predicts flood risk using river water level data. For example, it analyzes river water level data and evaluates the risk of flood occurrence. The disaster risk prediction unit also predicts wildfire risk using temperature, humidity, and wind speed data. For example, it analyzes temperature, humidity, and wind speed data and predicts the risk of wildfire occurrence. In this way, by using a variety of data, the accuracy of disaster risk prediction is improved.

[0031] The disaster risk prediction unit can analyze social media posts or news articles to detect signs of disasters at an early stage. The disaster risk prediction unit, for example, analyzes social media posts to detect signs of disasters at an early stage. For example, the disaster risk prediction unit analyzes the content of social media posts to detect abnormal animal behavior that is reported as a precursor to an earthquake. The disaster risk prediction unit also analyzes news articles to detect signs of disasters at an early stage. For example, the disaster risk prediction unit analyzes the content of news articles to detect abnormal rainfall patterns that are precursors to floods. The disaster risk prediction unit also analyzes a combination of social media posts and news articles to detect signs of disasters at an early stage. For example, the disaster risk prediction unit analyzes the content of social media posts and news articles to detect abnormal weather patterns that are precursors to wildfires. In this way, signs of disasters can be detected at an early stage by analyzing social media and news articles.

[0032] The disaster risk prediction unit can compare with past disaster data to evaluate the reliability of the prediction. The disaster risk prediction unit, for example, compares past earthquake data with current earthquake predictions to evaluate the reliability of the prediction. For example, it analyzes past earthquake data to evaluate the accuracy of current earthquake predictions. The disaster risk prediction unit also compares past flood data with current flood predictions to evaluate the reliability of the prediction. For example, it analyzes past flood data to evaluate the accuracy of current flood predictions. The disaster risk prediction unit also compares past wildfire data with current wildfire predictions to evaluate the reliability of the prediction. For example, it analyzes past wildfire data to evaluate the accuracy of current wildfire predictions. In this way, the reliability of the prediction can be evaluated by comparing with past disaster data.

[0033] The disaster risk prediction unit can predict the impact on agricultural or fishing industrial activities and propose appropriate countermeasures. The disaster risk prediction unit, for example, predicts the impact on agricultural crops in the event of a flood and proposes appropriate countermeasures. For example, it evaluates the risk of flooding and proposes measures to minimize damage to agricultural crops. The disaster risk prediction unit also predicts the impact on fishing in the event of an earthquake and proposes appropriate countermeasures. For example, it evaluates the risk of an earthquake and proposes measures to minimize damage to fishing. The disaster risk prediction unit also predicts the impact on agriculture or fishing in the event of a wildfire and proposes appropriate countermeasures. For example, it evaluates the risk of wildfire occurrence and proposes measures to minimize damage to agriculture or fishing. In this way, it is possible to minimize damage by predicting the impact on industrial activities and proposing appropriate countermeasures.

[0034] The disaster risk prediction unit can evaluate risks to specific locations in a tourist destination or event venue and issue warnings to tourists or event participants. The disaster risk prediction unit, for example, predicts the impact on a tourist destination in the event of an earthquake and issues a warning to tourists. For example, it evaluates the risk of an earthquake and issues a warning to ensure the safety of the tourist destination. The disaster risk prediction unit also predicts the impact on an event venue in the event of a flood and issues a warning to event participants. For example, it evaluates the risk of a flood and issues a warning to ensure the safety of the event venue. The disaster risk prediction unit also predicts the impact on a tourist destination or event venue in the event of a wildfire and issues a warning to tourists or event participants. For example, it evaluates the risk of a wildfire and issues a warning to ensure the safety of the tourist destination or event venue. In this way, the safety of tourists and event participants can be ensured by evaluating the risk to a specific location and issuing a warning.

[0035] The early warning providing unit can customize the content of the warning based on the disaster risk prediction results and provide specific evacuation instructions according to the characteristics of each region. The early warning providing unit provides evacuation instructions for each region based on, for example, earthquake prediction results. For example, specific evacuation routes and evacuation locations are provided for regions with a high risk of earthquakes. The early warning providing unit also provides evacuation instructions for each region based on flood prediction results. For example, specific evacuation routes and evacuation locations are provided for regions with a high risk of floods. The early warning providing unit also provides evacuation instructions for each region based on wildfire prediction results. For example, specific evacuation routes and evacuation locations are provided for regions with a high risk of wildfires. In this way, the safety of residents can be ensured by providing specific evacuation instructions according to the characteristics of each region.

[0036] The early warning providing unit can specify destinations for the warning based on the disaster risk prediction results, and issue warnings with a focus on areas with a particularly high risk. The early warning providing unit, for example, issues warnings to high-risk areas based on earthquake prediction results. For example, it issues warnings with a focus on areas with a high risk of earthquakes. The early warning providing unit also issues warnings to high-risk areas based on flood prediction results. For example, it issues warnings with a focus on areas with a high risk of floods. The early warning providing unit also issues warnings to high-risk areas based on wildfire prediction results. For example, it issues warnings with a focus on areas with a high risk of wildfires. In this way, the safety of residents can be ensured by issuing warnings with a focus on high-risk areas.

[0037] The early warning unit can diversify its warning delivery methods based on disaster risk prediction results and utilize multiple channels, such as SMS, email, and app notifications. For example, the early warning unit issues warnings through multiple channels based on earthquake prediction results. For example, it issues warnings through SMS, email, and app notifications to areas with a high risk of earthquakes. The early warning unit also issues warnings through multiple channels based on flood prediction results. For example, it issues warnings through SMS, email, and app notifications to areas with a high risk of floods. The early warning unit also issues warnings through multiple channels based on wildfire prediction results. For example, it issues warnings through SMS, email, and app notifications to areas with a high risk of wildfires. By utilizing multiple channels, the reach of warnings can be improved.

[0038] The disaster risk prediction unit can analyze past evacuation data and identify the most effective evacuation route or evacuation location. The disaster risk prediction unit, for example, analyzes past earthquake evacuation data and proposes an optimal evacuation route. For example, it analyzes past earthquake evacuation data and evaluates evacuation time and the safety of the evacuation route. The disaster risk prediction unit also analyzes past flood evacuation data and proposes an optimal evacuation route. For example, it analyzes past flood evacuation data and evaluates evacuation time and the safety of the evacuation route. The disaster risk prediction unit also analyzes past wildfire evacuation data and proposes an optimal evacuation route. For example, it analyzes past wildfire evacuation data and evaluates evacuation time and the safety of the evacuation route. In this way, by analyzing past evacuation data, the most effective evacuation route and evacuation location can be identified.

[0039] The disaster risk prediction unit can propose an optimal evacuation route by taking into account the infrastructure condition of the area or the earthquake resistance of the building. The disaster risk prediction unit, for example, proposes an optimal evacuation route by taking into account the infrastructure condition of the area. For example, it evaluates road conditions, power supply, water facilities, etc., and proposes an optimal evacuation route. The disaster risk prediction unit also proposes an optimal evacuation route by taking into account the earthquake resistance of the building. For example, it evaluates the structure, age of the building, and whether or not it has been reinforced against earthquakes, and proposes an optimal evacuation route. The disaster risk prediction unit also proposes an optimal evacuation route by evaluating a combination of the infrastructure condition of the area and the earthquake resistance of the building. For example, it comprehensively evaluates the road condition and the earthquake resistance of the building and proposes an optimal evacuation route. In this way, it is possible to propose an optimal evacuation route by taking into account the infrastructure condition of the area and the earthquake resistance of the building.

[0040] The disaster risk prediction unit can individually create evacuation plans for people who require special consideration, such as pets, elderly people, and people with disabilities. The disaster risk prediction unit, for example, individually creates evacuation plans for pets. For example, an appropriate evacuation plan is created taking into account the type of pet and evacuation location. The disaster risk prediction unit also individually creates evacuation plans for elderly people. For example, an appropriate evacuation plan is created taking into account the health condition of the elderly and whether or not they require care. The disaster risk prediction unit also individually creates evacuation plans for people with disabilities. For example, an appropriate evacuation plan is created taking into account the type of disability and the need for support. The disaster risk prediction unit also comprehensively creates evacuation plans for people who require special consideration, such as pets, elderly people, and people with disabilities. For example, a comprehensive evacuation plan is created taking into account the evacuation location for pets, the evacuation route for elderly people, and the support system for people with disabilities. In this way, the safety of all residents can be ensured by individually creating evacuation plans for people who require special consideration.

[0041] The disaster risk prediction unit can analyze the congestion status of evacuation shelters in real time and suggest the optimal evacuation site. The disaster risk prediction unit, for example, uses a sensor to obtain the real-time congestion status of evacuation shelters and suggests the optimal evacuation site. For example, the congestion status of evacuation shelters is monitored by a sensor and the optimal evacuation site is suggested based on the level of congestion. The disaster risk prediction unit also uses a camera to obtain the real-time congestion status of evacuation shelters and suggests the optimal evacuation site. For example, the congestion status of evacuation shelters is photographed by a camera, the level of congestion is evaluated using image analysis technology, and the optimal evacuation site is suggested. The disaster risk prediction unit also uses social media analysis to obtain the real-time congestion status of evacuation shelters and suggests the optimal evacuation site. For example, the content of social media posts is analyzed, the congestion status of evacuation shelters is evaluated, and the optimal evacuation site is suggested. In this way, the optimal evacuation site can be suggested by analyzing the congestion status of evacuation shelters in real time.

[0042] The disaster risk prediction unit can consider the facilities or service content of the evacuation shelter and propose an evacuation shelter that meets specific needs. The disaster risk prediction unit, for example, evaluates the facilities of the evacuation shelter and proposes an evacuation shelter that meets specific needs. For example, it evaluates the number of toilets at the evacuation shelter and whether or not medical facilities are provided, and proposes an evacuation shelter that meets specific needs. The disaster risk prediction unit also evaluates the service content of the evacuation shelter and proposes an evacuation shelter that meets specific needs. For example, it evaluates whether or not medical services and meals are provided at the evacuation shelter and proposes an evacuation shelter that meets specific needs. The disaster risk prediction unit also comprehensively evaluates the facilities and service content of the evacuation shelter and proposes an evacuation shelter that meets specific needs. For example, it comprehensively evaluates the number of toilets at the evacuation shelter, whether or not medical facilities are provided, and whether or not medical services and meals are provided, and proposes an evacuation shelter that meets specific needs. In this way, it is possible to propose an evacuation shelter that meets specific needs by considering the facilities and service content of the evacuation shelter.

[0043] The disaster risk prediction unit can propose an optimal evacuation route by taking into account the access method or transportation means to the evacuation shelter. The disaster risk prediction unit, for example, evaluates the access method to the evacuation shelter and proposes an optimal evacuation route. For example, it evaluates the access route and transportation means to the evacuation shelter and proposes an optimal evacuation route. The disaster risk prediction unit also evaluates transportation means and proposes an optimal evacuation route. For example, it evaluates transportation means such as buses, trains, and bicycles and proposes an optimal evacuation route. The disaster risk prediction unit also comprehensively evaluates the access method and transportation means to the evacuation shelter and proposes an optimal evacuation route. For example, it comprehensively evaluates the access route to the evacuation shelter and transportation means such as buses, trains, and bicycles and proposes an optimal evacuation route. In this way, it is possible to propose an optimal evacuation route by taking into account the access method and transportation means to the evacuation shelter.

[0044] The disaster risk prediction unit can update the capacity or equipment status of the evacuation shelter in real time and provide the latest information. The disaster risk prediction unit, for example, updates the capacity of the evacuation shelter in real time and provides the latest information. For example, it monitors the number of people that the evacuation shelter can accommodate in real time and provides the latest information. The disaster risk prediction unit also updates the equipment status of the evacuation shelter in real time and provides the latest information. For example, it monitors the number of toilets and the presence or absence of medical equipment in the evacuation shelter in real time and provides the latest information. The disaster risk prediction unit also updates the capacity and equipment status of the evacuation shelter in real time and provides the latest information. For example, it monitors the number of people that the evacuation shelter can accommodate, the number of toilets, and the presence or absence of medical equipment in real time and provides the latest information. In this way, the capacity and equipment status of the evacuation shelter can be updated in real time to provide the latest information.

[0045] The disaster risk prediction unit can analyze real-time traffic information or disaster information and propose the safest and fastest evacuation route. The disaster risk prediction unit, for example, uses a sensor to acquire real-time traffic information and proposes the safest and fastest evacuation route. For example, the sensor monitors road traffic conditions and proposes the optimal evacuation route. The disaster risk prediction unit also uses a camera to acquire real-time traffic information and proposes the safest and fastest evacuation route. For example, the camera captures road traffic conditions and uses image analysis technology to propose the optimal evacuation route. The disaster risk prediction unit also uses weather data to acquire real-time disaster information and proposes the safest and fastest evacuation route. For example, the weather data is analyzed to evaluate the risk of floods and wildfires and propose the optimal evacuation route. In this way, the safest and fastest evacuation route can be proposed by analyzing real-time traffic information and disaster information.

[0046] The disaster risk prediction unit can identify obstacles or dangerous areas on evacuation routes and propose avoidance routes. The disaster risk prediction unit, for example, uses a sensor to identify obstacles on evacuation routes and propose avoidance routes. For example, the sensor detects fallen trees or collapsed buildings and proposes avoidance routes. The disaster risk prediction unit also uses a camera to identify obstacles on evacuation routes and proposes avoidance routes. For example, the camera photographs fallen trees or collapsed buildings and proposes avoidance routes using image analysis technology. The disaster risk prediction unit also uses weather data to identify dangerous areas on evacuation routes and proposes avoidance routes. For example, the weather data identifies landslide areas and flooded areas and proposes avoidance routes. In this way, it is possible to identify obstacles and dangerous areas on evacuation routes and propose avoidance routes.

[0047] The disaster risk prediction unit can consider transportation modes (bus, train, bicycle) on an evacuation route and propose the optimal transportation mode. The disaster risk prediction unit, for example, proposes an evacuation route using a bus. For example, it evaluates bus operation status and routes and proposes the optimal evacuation route. The disaster risk prediction unit also proposes an evacuation route using a train. For example, it evaluates train operation status and routes and proposes the optimal evacuation route. The disaster risk prediction unit also proposes an evacuation route using a bicycle. For example, it evaluates available bicycle routes and safety and proposes the optimal evacuation route. The disaster risk prediction unit also comprehensively evaluates bus, train, and bicycle transportation modes and proposes the optimal transportation mode. For example, it comprehensively evaluates bus, train, and bicycle operation status and routes and proposes the optimal evacuation route. In this way, the optimal transportation mode can be proposed by considering transportation modes on an evacuation route.

[0048] The disaster risk prediction unit can consider the location of evacuation shelters or rest areas on the evacuation route and propose optimal rest points. The disaster risk prediction unit, for example, evaluates the location of evacuation shelters on the evacuation route and proposes optimal rest points. For example, it evaluates the capacity and equipment status of evacuation shelters and proposes optimal rest points. The disaster risk prediction unit also evaluates the location of rest areas on the evacuation route and proposes optimal rest points. For example, it evaluates the capacity and equipment status of rest areas and proposes optimal rest points. The disaster risk prediction unit also comprehensively evaluates the locations of evacuation shelters and rest areas on the evacuation route and proposes optimal rest points. For example, it comprehensively evaluates the capacity and equipment status of evacuation shelters and the capacity and equipment status of rest areas and proposes optimal rest points. In this way, optimal rest points can be proposed by considering the locations of evacuation shelters and rest areas on the evacuation route.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] The disaster risk prediction unit can not only predict the risks of earthquakes, floods, and wildfires, but also predict traffic congestion and infrastructure damage in urban areas. For example, it can predict road damage in the event of an earthquake and suggest changes to evacuation routes. It can also predict the suspension of subway and bus lines in the event of a flood and suggest alternative means of transportation. It can also predict power outages in the event of a wildfire and suggest ways for residents to conserve electricity. This allows the disaster risk prediction unit to predict the impact on urban infrastructure and propose appropriate countermeasures.

[0051] The disaster risk prediction unit can predict the impact on agricultural or fishing industrial activities and propose appropriate countermeasures. For example, the unit predicts the impact on agricultural crops in the event of a flood and proposes appropriate countermeasures. For example, the unit evaluates the risk of flooding and proposes measures to minimize damage to agricultural crops. The disaster risk prediction unit can also predict the impact on fishing in the event of an earthquake and propose appropriate countermeasures. For example, the unit evaluates the risk of an earthquake and proposes measures to minimize damage to fishing. The disaster risk prediction unit can also predict the impact on agriculture or fishing in the event of a wildfire and propose appropriate countermeasures. For example, the unit evaluates the risk of wildfire occurrence and proposes measures to minimize damage to agriculture or fishing. In this way, the impact on industrial activities can be predicted and appropriate countermeasures can be proposed, thereby minimizing damage.

[0052] The disaster risk prediction unit can evaluate risks to specific locations in tourist destinations or event venues and issue warnings to tourists or event participants. For example, the disaster risk prediction unit predicts the impact on tourist destinations in the event of an earthquake and issues a warning to tourists. For example, the disaster risk prediction unit evaluates the risk of an earthquake and issues a warning to ensure the safety of the tourist destination. The disaster risk prediction unit also predicts the impact on event venues in the event of a flood and issues a warning to event participants. For example, the disaster risk prediction unit evaluates the risk of flooding and issues a warning to ensure the safety of the event venue. The disaster risk prediction unit also predicts the impact on tourist destinations or event venues in the event of a wildfire and issues a warning to tourists or event participants. For example, the disaster risk prediction unit evaluates the risk of wildfire and issues a warning to ensure the safety of the tourist destination or event venue. In this way, the safety of tourists and event participants can be ensured by evaluating risks to specific locations and issuing warnings.

[0053] The disaster risk prediction unit can analyze past evacuation data and identify the most effective evacuation route or evacuation location. For example, it can analyze past earthquake evacuation data and propose an optimal evacuation route. For example, it can analyze past earthquake evacuation data and evaluate evacuation time and the safety of the evacuation route. The disaster risk prediction unit can also analyze past flood evacuation data and propose an optimal evacuation route. For example, it can analyze past flood evacuation data and evaluate evacuation time and the safety of the evacuation route. The disaster risk prediction unit can also analyze past wildfire evacuation data and propose an optimal evacuation route. For example, it can analyze past wildfire evacuation data and evaluate evacuation time and the safety of the evacuation route. In this way, by analyzing past evacuation data, it is possible to identify the most effective evacuation route or evacuation location.

[0054] The disaster risk prediction unit can propose an optimal evacuation route by taking into account the infrastructure condition of the area or the earthquake resistance of the building. The disaster risk prediction unit, for example, proposes an optimal evacuation route by taking into account the infrastructure condition of the area. For example, it evaluates road conditions, power supply, water facilities, etc., and proposes an optimal evacuation route. The disaster risk prediction unit also proposes an optimal evacuation route by taking into account the earthquake resistance of the building. For example, it evaluates the structure, age of the building, and whether or not it has been reinforced against earthquakes, and proposes an optimal evacuation route. The disaster risk prediction unit also proposes an optimal evacuation route by evaluating a combination of the infrastructure condition of the area and the earthquake resistance of the building. For example, it comprehensively evaluates the road condition and the earthquake resistance of the building and proposes an optimal evacuation route. In this way, it is possible to propose an optimal evacuation route by taking into account the infrastructure condition of the area and the earthquake resistance of the building.

[0055] The disaster risk prediction unit can individually create evacuation plans for people who require special consideration, such as pets, elderly people, and people with disabilities. The disaster risk prediction unit, for example, individually creates evacuation plans for pets. For example, an appropriate evacuation plan is created taking into account the type of pet and evacuation location. The disaster risk prediction unit also individually creates evacuation plans for elderly people. For example, an appropriate evacuation plan is created taking into account the health condition of the elderly and whether or not they require care. The disaster risk prediction unit also individually creates evacuation plans for people with disabilities. For example, an appropriate evacuation plan is created taking into account the type of disability and the need for support. The disaster risk prediction unit also comprehensively creates evacuation plans for people who require special consideration, such as pets, elderly people, and people with disabilities. For example, a comprehensive evacuation plan is created taking into account the evacuation location for pets, the evacuation route for elderly people, and the support system for people with disabilities. In this way, the safety of all residents can be ensured by individually creating evacuation plans for people who require special consideration.

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

[0057] Step 1: The disaster risk prediction unit predicts disaster risks such as earthquakes, floods, and wildfires. Specifically, it predicts the risk of earthquakes using seismometer data, the risk of floods using rainfall data, and the risk of wildfires using temperature, humidity, and wind speed data. This allows the probability of an earthquake occurring, the risk of flooding, and the risk of wildfires to be calculated. Step 2: The early warning provider provides early warnings based on the disaster risks predicted by the disaster risk forecaster. Specifically, it issues warnings based on the earthquake prediction results, the flood prediction results, and the wildfire prediction results. This allows appropriate warnings to be issued to areas with a high probability of earthquakes, floods, and wildfires.

[0058] (Example 2) A disaster response system according to an embodiment of the present invention is a system that predicts natural disasters and disaster-related risks and proposes appropriate countermeasures. This system utilizes AI to predict the occurrence of disasters such as earthquakes, floods, and wildfires, enabling early warning. It also supports ensuring the safety of disaster victims by creating evacuation plans, providing information on appropriate evacuation locations, and optimizing evacuation routes. This enables the disaster response system to predict natural disasters and disaster-related risks and propose appropriate countermeasures.

[0059] A disaster response system according to an embodiment includes a disaster risk prediction unit and an early warning providing unit. The disaster risk prediction unit predicts disaster risks of earthquakes, floods, and wildfires. For example, the disaster risk prediction unit predicts the risk of earthquakes using seismometer data. The disaster risk prediction unit can also predict the risk of floods using rainfall data. The disaster risk prediction unit can also predict the risk of wildfires using temperature, humidity, and wind speed data. For example, the disaster risk prediction unit analyzes seismometer data to calculate the probability of an earthquake. The disaster risk prediction unit analyzes rainfall data to assess the risk of floods. The disaster risk prediction unit analyzes temperature, humidity, and wind speed data to predict the risk of wildfires. The early warning providing unit provides an early warning based on the disaster risk predicted by the disaster risk prediction unit. For example, the early warning providing unit issues an alert based on the earthquake prediction results. The early warning providing unit can also issue an alert based on the flood prediction results. The early warning providing unit can also issue an alert based on the wildfire prediction results. For example, the early warning providing unit issues a warning to areas where an earthquake is likely to occur based on the earthquake prediction results. The early warning providing unit issues a warning to areas where a flood is likely to occur based on the flood prediction results. The early warning providing unit issues a warning to areas where a forest fire is likely to occur based on the forest fire prediction results. In this way, the disaster response system according to the embodiment can ensure the safety of residents by predicting disaster risks and providing early warnings.

[0060] The disaster risk prediction unit can predict disaster risk using seismometer data, rainfall data, river water level data, temperature, humidity, and wind speed data. The disaster risk prediction unit, for example, predicts earthquake risk using seismometer data. For example, it analyzes the seismometer data and calculates the probability of an earthquake occurring. The disaster risk prediction unit also predicts flood risk using rainfall data. For example, it analyzes rainfall data and evaluates the risk of flood occurrence. The disaster risk prediction unit also predicts flood risk using river water level data. For example, it analyzes river water level data and evaluates the risk of flood occurrence. The disaster risk prediction unit also predicts wildfire risk using temperature, humidity, and wind speed data. For example, it analyzes temperature, humidity, and wind speed data and predicts the risk of wildfire occurrence. In this way, by using a variety of data, the accuracy of disaster risk prediction is improved.

[0061] The disaster risk prediction unit can analyze social media posts or news articles to detect signs of disasters at an early stage. The disaster risk prediction unit, for example, analyzes social media posts to detect signs of disasters at an early stage. For example, the disaster risk prediction unit analyzes the content of social media posts to detect abnormal animal behavior that is reported as a precursor to an earthquake. The disaster risk prediction unit also analyzes news articles to detect signs of disasters at an early stage. For example, the disaster risk prediction unit analyzes the content of news articles to detect abnormal rainfall patterns that are precursors to floods. The disaster risk prediction unit also analyzes a combination of social media posts and news articles to detect signs of disasters at an early stage. For example, the disaster risk prediction unit analyzes the content of social media posts and news articles to detect abnormal weather patterns that are precursors to wildfires. In this way, signs of disasters can be detected at an early stage by analyzing social media and news articles.

[0062] The disaster risk prediction unit can compare with past disaster data to evaluate the reliability of the prediction. The disaster risk prediction unit, for example, compares past earthquake data with current earthquake predictions to evaluate the reliability of the prediction. For example, it analyzes past earthquake data to evaluate the accuracy of current earthquake predictions. The disaster risk prediction unit also compares past flood data with current flood predictions to evaluate the reliability of the prediction. For example, it analyzes past flood data to evaluate the accuracy of current flood predictions. The disaster risk prediction unit also compares past wildfire data with current wildfire predictions to evaluate the reliability of the prediction. For example, it analyzes past wildfire data to evaluate the accuracy of current wildfire predictions. In this way, the reliability of the prediction can be evaluated by comparing with past disaster data.

[0063] The disaster risk prediction unit can use the emotion estimation function to analyze residents' emotional reactions to the disaster risk prediction and improve the accuracy of the prediction. The disaster risk prediction unit, for example, uses the emotion estimation function to analyze residents' emotional reactions to the disaster risk prediction. For example, it analyzes residents' emotions of anxiety and fear and evaluates the reliability of the disaster risk prediction. The disaster risk prediction unit also uses the emotion estimation function to analyze residents' emotional reactions to the disaster risk prediction and improve the accuracy of the prediction. For example, it adjusts the disaster risk prediction model based on the residents' emotional reactions. The disaster risk prediction unit also uses the emotion estimation function to analyze residents' emotional reactions to the disaster risk prediction and provides feedback to improve the accuracy of the prediction. For example, it improves the results of the disaster risk prediction based on the residents' emotional reactions. In this way, the accuracy of the disaster risk prediction can be improved by analyzing residents' emotional reactions.

[0064] The disaster risk prediction unit can predict the impact on agricultural or fishing industrial activities and propose appropriate countermeasures. The disaster risk prediction unit, for example, predicts the impact on agricultural crops in the event of a flood and proposes appropriate countermeasures. For example, it evaluates the risk of flooding and proposes measures to minimize damage to agricultural crops. The disaster risk prediction unit also predicts the impact on fishing in the event of an earthquake and proposes appropriate countermeasures. For example, it evaluates the risk of an earthquake and proposes measures to minimize damage to fishing. The disaster risk prediction unit also predicts the impact on agriculture or fishing in the event of a wildfire and proposes appropriate countermeasures. For example, it evaluates the risk of wildfire occurrence and proposes measures to minimize damage to agriculture or fishing. In this way, it is possible to minimize damage by predicting the impact on industrial activities and proposing appropriate countermeasures.

[0065] The disaster risk prediction unit can evaluate risks to specific locations in a tourist destination or event venue and issue warnings to tourists or event participants. The disaster risk prediction unit, for example, predicts the impact on a tourist destination in the event of an earthquake and issues a warning to tourists. For example, it evaluates the risk of an earthquake and issues a warning to ensure the safety of the tourist destination. The disaster risk prediction unit also predicts the impact on an event venue in the event of a flood and issues a warning to event participants. For example, it evaluates the risk of a flood and issues a warning to ensure the safety of the event venue. The disaster risk prediction unit also predicts the impact on a tourist destination or event venue in the event of a wildfire and issues a warning to tourists or event participants. For example, it evaluates the risk of a wildfire and issues a warning to ensure the safety of the tourist destination or event venue. In this way, the safety of tourists and event participants can be ensured by evaluating the risk to a specific location and issuing a warning.

[0066] The disaster risk prediction unit can use the emotion estimation function to provide information to reduce residents' anxiety or fear in response to the disaster risk prediction. The disaster risk prediction unit, for example, uses the emotion estimation function to analyze residents' anxiety in response to the disaster risk prediction and provide information to reduce the anxiety. For example, specific evacuation methods and safety measures are provided to reduce residents' anxiety. The disaster risk prediction unit also uses the emotion estimation function to analyze residents' fear in response to the disaster risk prediction and provide information to reduce the fear. For example, specific evacuation methods and safety measures are provided to reduce residents' fear. The disaster risk prediction unit also uses the emotion estimation function to analyze residents' anxiety and fear in response to the disaster risk prediction and provide information to reduce the anxiety and fear. For example, specific evacuation methods and safety measures are provided to reduce residents' anxiety and fear. In this way, by providing information to reduce residents' anxiety and fear, it is possible to increase residents' sense of security.

[0067] The early warning providing unit can customize the content of the warning based on the disaster risk prediction results and provide specific evacuation instructions according to the characteristics of each region. The early warning providing unit provides evacuation instructions for each region based on, for example, earthquake prediction results. For example, specific evacuation routes and evacuation locations are provided for regions with a high risk of earthquakes. The early warning providing unit also provides evacuation instructions for each region based on flood prediction results. For example, specific evacuation routes and evacuation locations are provided for regions with a high risk of floods. The early warning providing unit also provides evacuation instructions for each region based on wildfire prediction results. For example, specific evacuation routes and evacuation locations are provided for regions with a high risk of wildfires. In this way, the safety of residents can be ensured by providing specific evacuation instructions according to the characteristics of each region.

[0068] The early warning providing unit can use the emotion estimation function to analyze the emotional reactions of residents when an alert is issued and provide feedback to maximize the effectiveness of the alert. The early warning providing unit, for example, uses the emotion estimation function to analyze the emotional reactions of residents when an alert is issued. For example, it analyzes residents' emotions of anxiety and fear and adjusts the content of the alert. The early warning providing unit also uses the emotion estimation function to analyze the emotional reactions of residents when an alert is issued and provide feedback to maximize the effectiveness of the alert. For example, it improves the content and issuance method of the alert based on the residents' emotional reactions. The early warning providing unit also uses the emotion estimation function to analyze the emotional reactions of residents when an alert is issued and provide feedback to maximize the effectiveness of the alert. For example, it adjusts the content and issuance method of the alert based on the residents' emotional reactions. In this way, the effectiveness of the alert can be improved by analyzing residents' emotional reactions and providing feedback to maximize the effectiveness of the alert.

[0069] The early warning providing unit can specify destinations for the warning based on the disaster risk prediction results, and issue warnings with a focus on areas with a particularly high risk. The early warning providing unit, for example, issues warnings to high-risk areas based on earthquake prediction results. For example, it issues warnings with a focus on areas with a high risk of earthquakes. The early warning providing unit also issues warnings to high-risk areas based on flood prediction results. For example, it issues warnings with a focus on areas with a high risk of floods. The early warning providing unit also issues warnings to high-risk areas based on wildfire prediction results. For example, it issues warnings with a focus on areas with a high risk of wildfires. In this way, the safety of residents can be ensured by issuing warnings with a focus on high-risk areas.

[0070] The early warning unit can diversify its warning delivery methods based on disaster risk prediction results and utilize multiple channels, such as SMS, email, and app notifications. For example, the early warning unit issues warnings through multiple channels based on earthquake prediction results. For example, it issues warnings through SMS, email, and app notifications to areas with a high risk of earthquakes. The early warning unit also issues warnings through multiple channels based on flood prediction results. For example, it issues warnings through SMS, email, and app notifications to areas with a high risk of floods. The early warning unit also issues warnings through multiple channels based on wildfire prediction results. For example, it issues warnings through SMS, email, and app notifications to areas with a high risk of wildfires. By utilizing multiple channels, the reach of warnings can be improved.

[0071] The early warning providing unit can use the emotion estimation function to monitor residents' emotional reactions after an alert is issued, and continuously improve the content and delivery method of the alert. The early warning providing unit, for example, uses the emotion estimation function to monitor residents' emotional reactions after an alert is issued. For example, the early warning providing unit analyzes residents' emotions of anxiety and fear and adjusts the content of the alert. The early warning providing unit also uses the emotion estimation function to monitor residents' emotional reactions after an alert is issued, and continuously improves the content and delivery method of the alert. For example, the content and delivery method of the alert is improved based on the residents' emotional reactions. The early warning providing unit also uses the emotion estimation function to monitor residents' emotional reactions after an alert is issued, and continuously improves the content and delivery method of the alert. For example, the content and delivery method of the alert is adjusted based on the residents' emotional reactions. In this way, by monitoring residents' emotional reactions and continuously improving the content and delivery method of the alert, the effectiveness of the alert can be increased.

[0072] The disaster risk prediction unit can analyze past evacuation data and identify the most effective evacuation route or evacuation location. The disaster risk prediction unit, for example, analyzes past earthquake evacuation data and proposes an optimal evacuation route. For example, it analyzes past earthquake evacuation data and evaluates evacuation time and the safety of the evacuation route. The disaster risk prediction unit also analyzes past flood evacuation data and proposes an optimal evacuation route. For example, it analyzes past flood evacuation data and evaluates evacuation time and the safety of the evacuation route. The disaster risk prediction unit also analyzes past wildfire evacuation data and proposes an optimal evacuation route. For example, it analyzes past wildfire evacuation data and evaluates evacuation time and the safety of the evacuation route. In this way, by analyzing past evacuation data, the most effective evacuation route and evacuation location can be identified.

[0073] The disaster risk prediction unit can propose an optimal evacuation route by taking into account the infrastructure condition of the area or the earthquake resistance of the building. The disaster risk prediction unit, for example, proposes an optimal evacuation route by taking into account the infrastructure condition of the area. For example, it evaluates road conditions, power supply, water facilities, etc., and proposes an optimal evacuation route. The disaster risk prediction unit also proposes an optimal evacuation route by taking into account the earthquake resistance of the building. For example, it evaluates the structure, age of the building, and whether or not it has been reinforced against earthquakes, and proposes an optimal evacuation route. The disaster risk prediction unit also proposes an optimal evacuation route by evaluating a combination of the infrastructure condition of the area and the earthquake resistance of the building. For example, it comprehensively evaluates the road condition and the earthquake resistance of the building and proposes an optimal evacuation route. In this way, it is possible to propose an optimal evacuation route by taking into account the infrastructure condition of the area and the earthquake resistance of the building.

[0074] The disaster risk prediction unit uses the emotion estimation function to analyze the emotional reactions of residents when creating an evacuation plan, and can create a plan that allows residents to evacuate safely. The disaster risk prediction unit, for example, uses the emotion estimation function to analyze the emotional reactions of residents when creating an evacuation plan. For example, it analyzes residents' feelings of anxiety and fear and adjusts the evacuation plan. The disaster risk prediction unit also uses the emotion estimation function to analyze the emotional reactions of residents when creating an evacuation plan, and creates a plan that allows residents to evacuate safely. For example, it adjusts evacuation routes and evacuation locations based on the residents' emotional reactions. The disaster risk prediction unit also uses the emotion estimation function to analyze the emotional reactions of residents when creating an evacuation plan, and creates a plan that allows residents to evacuate safely. For example, it improves the evacuation plan based on the residents' emotional reactions. In this way, by analyzing the emotional reactions of residents, it is possible to create a plan that allows residents to evacuate safely.

[0075] The disaster risk prediction unit can individually create evacuation plans for people who require special consideration, such as pets, elderly people, and people with disabilities. The disaster risk prediction unit, for example, individually creates evacuation plans for pets. For example, an appropriate evacuation plan is created taking into account the type of pet and evacuation location. The disaster risk prediction unit also individually creates evacuation plans for elderly people. For example, an appropriate evacuation plan is created taking into account the health condition of the elderly and whether or not they require care. The disaster risk prediction unit also individually creates evacuation plans for people with disabilities. For example, an appropriate evacuation plan is created taking into account the type of disability and the need for support. The disaster risk prediction unit also comprehensively creates evacuation plans for people who require special consideration, such as pets, elderly people, and people with disabilities. For example, a comprehensive evacuation plan is created taking into account the evacuation location for pets, the evacuation route for elderly people, and the support system for people with disabilities. In this way, the safety of all residents can be ensured by individually creating evacuation plans for people who require special consideration.

[0076] The disaster risk prediction unit can use the emotion estimation function to provide information to reduce residents' anxiety or stress when creating an evacuation plan. The disaster risk prediction unit, for example, uses the emotion estimation function to analyze residents' anxiety when creating an evacuation plan and provide information to reduce the anxiety. For example, it provides specific evacuation methods and safety measures to reduce residents' anxiety. The disaster risk prediction unit also uses the emotion estimation function to analyze residents' stress when creating an evacuation plan and provide information to reduce stress. For example, it provides specific evacuation methods and safety measures to reduce residents' stress. The disaster risk prediction unit also uses the emotion estimation function to analyze residents' anxiety and stress when creating an evacuation plan and provide information to reduce anxiety and stress. For example, it provides specific evacuation methods and safety measures to reduce residents' anxiety and stress. In this way, by providing information to reduce residents' anxiety and stress, it is possible to increase residents' sense of security.

[0077] The disaster risk prediction unit can analyze the congestion status of evacuation shelters in real time and suggest the optimal evacuation site. The disaster risk prediction unit, for example, uses a sensor to obtain the real-time congestion status of evacuation shelters and suggests the optimal evacuation site. For example, the congestion status of evacuation shelters is monitored by a sensor and the optimal evacuation site is suggested based on the level of congestion. The disaster risk prediction unit also uses a camera to obtain the real-time congestion status of evacuation shelters and suggests the optimal evacuation site. For example, the congestion status of evacuation shelters is photographed by a camera, the level of congestion is evaluated using image analysis technology, and the optimal evacuation site is suggested. The disaster risk prediction unit also uses social media analysis to obtain the real-time congestion status of evacuation shelters and suggests the optimal evacuation site. For example, the content of social media posts is analyzed, the congestion status of evacuation shelters is evaluated, and the optimal evacuation site is suggested. In this way, the optimal evacuation site can be suggested by analyzing the congestion status of evacuation shelters in real time.

[0078] The disaster risk prediction unit can consider the facilities or service content of the evacuation shelter and propose an evacuation shelter that meets specific needs. The disaster risk prediction unit, for example, evaluates the facilities of the evacuation shelter and proposes an evacuation shelter that meets specific needs. For example, it evaluates the number of toilets at the evacuation shelter and whether or not medical facilities are provided, and proposes an evacuation shelter that meets specific needs. The disaster risk prediction unit also evaluates the service content of the evacuation shelter and proposes an evacuation shelter that meets specific needs. For example, it evaluates whether or not medical services and meals are provided at the evacuation shelter and proposes an evacuation shelter that meets specific needs. The disaster risk prediction unit also comprehensively evaluates the facilities and service content of the evacuation shelter and proposes an evacuation shelter that meets specific needs. For example, it comprehensively evaluates the number of toilets at the evacuation shelter, whether or not medical facilities are provided, and whether or not medical services and meals are provided, and proposes an evacuation shelter that meets specific needs. In this way, it is possible to propose an evacuation shelter that meets specific needs by considering the facilities and service content of the evacuation shelter.

[0079] The disaster risk prediction unit uses the emotion estimation function to analyze the emotional reactions of residents when providing evacuation locations, and can suggest locations where residents can evacuate safely. The disaster risk prediction unit, for example, uses the emotion estimation function to analyze the emotional reactions of residents when providing evacuation locations. For example, it analyzes residents' emotions of anxiety and fear and adjusts evacuation locations. The disaster risk prediction unit also uses the emotion estimation function to analyze the emotional reactions of residents when providing evacuation locations, and suggests locations where residents can evacuate safely. For example, it adjusts evacuation locations based on the residents' emotional reactions. The disaster risk prediction unit also uses the emotion estimation function to analyze the emotional reactions of residents when providing evacuation locations, and suggests locations where residents can evacuate safely. For example, it improves evacuation locations based on the residents' emotional reactions. In this way, it is possible to suggest locations where residents can evacuate safely by analyzing the emotional reactions of residents.

[0080] The disaster risk prediction unit can propose an optimal evacuation route by taking into account the access method or transportation means to the evacuation shelter. The disaster risk prediction unit, for example, evaluates the access method to the evacuation shelter and proposes an optimal evacuation route. For example, it evaluates the access route and transportation means to the evacuation shelter and proposes an optimal evacuation route. The disaster risk prediction unit also evaluates transportation means and proposes an optimal evacuation route. For example, it evaluates transportation means such as buses, trains, and bicycles and proposes an optimal evacuation route. The disaster risk prediction unit also comprehensively evaluates the access method and transportation means to the evacuation shelter and proposes an optimal evacuation route. For example, it comprehensively evaluates the access route to the evacuation shelter and transportation means such as buses, trains, and bicycles and proposes an optimal evacuation route. In this way, it is possible to propose an optimal evacuation route by taking into account the access method and transportation means to the evacuation shelter.

[0081] The disaster risk prediction unit can update the capacity or equipment status of the evacuation shelter in real time and provide the latest information. The disaster risk prediction unit, for example, updates the capacity of the evacuation shelter in real time and provides the latest information. For example, it monitors the number of people that the evacuation shelter can accommodate in real time and provides the latest information. The disaster risk prediction unit also updates the equipment status of the evacuation shelter in real time and provides the latest information. For example, it monitors the number of toilets and the presence or absence of medical equipment in the evacuation shelter in real time and provides the latest information. The disaster risk prediction unit also updates the capacity and equipment status of the evacuation shelter in real time and provides the latest information. For example, it monitors the number of people that the evacuation shelter can accommodate, the number of toilets, and the presence or absence of medical equipment in real time and provides the latest information. In this way, the capacity and equipment status of the evacuation shelter can be updated in real time to provide the latest information.

[0082] The disaster risk prediction unit can use the emotion estimation function to provide information to reduce anxiety or stress to residents when providing evacuation shelter information. The disaster risk prediction unit, for example, uses the emotion estimation function to analyze residents' anxiety when providing evacuation shelter information and provide information to reduce anxiety. For example, specific evacuation methods and safety measures are provided to reduce residents' anxiety. The disaster risk prediction unit also uses the emotion estimation function to analyze residents' stress when providing evacuation shelter information and provide information to reduce stress. For example, specific evacuation methods and safety measures are provided to reduce residents' stress. The disaster risk prediction unit also uses the emotion estimation function to analyze residents' anxiety and stress when providing evacuation shelter information and provide information to reduce anxiety and stress. For example, specific evacuation methods and safety measures are provided to reduce residents' anxiety and stress. In this way, by providing information to reduce residents' anxiety and stress, it is possible to increase residents' sense of security.

[0083] The disaster risk prediction unit can analyze real-time traffic information or disaster information and propose the safest and fastest evacuation route. The disaster risk prediction unit, for example, uses a sensor to acquire real-time traffic information and proposes the safest and fastest evacuation route. For example, the sensor monitors road traffic conditions and proposes the optimal evacuation route. The disaster risk prediction unit also uses a camera to acquire real-time traffic information and proposes the safest and fastest evacuation route. For example, the camera captures road traffic conditions and uses image analysis technology to propose the optimal evacuation route. The disaster risk prediction unit also uses weather data to acquire real-time disaster information and proposes the safest and fastest evacuation route. For example, the weather data is analyzed to evaluate the risk of floods and wildfires and propose the optimal evacuation route. In this way, the safest and fastest evacuation route can be proposed by analyzing real-time traffic information and disaster information.

[0084] The disaster risk prediction unit can identify obstacles or dangerous areas on evacuation routes and propose avoidance routes. The disaster risk prediction unit, for example, uses a sensor to identify obstacles on evacuation routes and propose avoidance routes. For example, the sensor detects fallen trees or collapsed buildings and proposes avoidance routes. The disaster risk prediction unit also uses a camera to identify obstacles on evacuation routes and proposes avoidance routes. For example, the camera photographs fallen trees or collapsed buildings and proposes avoidance routes using image analysis technology. The disaster risk prediction unit also uses weather data to identify dangerous areas on evacuation routes and proposes avoidance routes. For example, the weather data identifies landslide areas and flooded areas and proposes avoidance routes. In this way, it is possible to identify obstacles and dangerous areas on evacuation routes and propose avoidance routes.

[0085] The disaster risk prediction unit can consider transportation modes (bus, train, bicycle) on an evacuation route and propose the optimal transportation mode. The disaster risk prediction unit, for example, proposes an evacuation route using a bus. For example, it evaluates bus operation status and routes and proposes the optimal evacuation route. The disaster risk prediction unit also proposes an evacuation route using a train. For example, it evaluates train operation status and routes and proposes the optimal evacuation route. The disaster risk prediction unit also proposes an evacuation route using a bicycle. For example, it evaluates available bicycle routes and safety and proposes the optimal evacuation route. The disaster risk prediction unit also comprehensively evaluates bus, train, and bicycle transportation modes and proposes the optimal transportation mode. For example, it comprehensively evaluates bus, train, and bicycle operation status and routes and proposes the optimal evacuation route. In this way, the optimal transportation mode can be proposed by considering transportation modes on an evacuation route.

[0086] The disaster risk prediction unit can consider the location of evacuation shelters or rest areas on the evacuation route and propose optimal rest points. The disaster risk prediction unit, for example, evaluates the location of evacuation shelters on the evacuation route and proposes optimal rest points. For example, it evaluates the capacity and equipment status of evacuation shelters and proposes optimal rest points. The disaster risk prediction unit also evaluates the location of rest areas on the evacuation route and proposes optimal rest points. For example, it evaluates the capacity and equipment status of rest areas and proposes optimal rest points. The disaster risk prediction unit also comprehensively evaluates the locations of evacuation shelters and rest areas on the evacuation route and proposes optimal rest points. For example, it comprehensively evaluates the capacity and equipment status of evacuation shelters and the capacity and equipment status of rest areas and proposes optimal rest points. In this way, optimal rest points can be proposed by considering the locations of evacuation shelters and rest areas on the evacuation route.

[0087] The disaster risk prediction unit can use the emotion estimation function to provide information to reduce residents' anxiety or stress when optimizing evacuation routes. The disaster risk prediction unit, for example, uses the emotion estimation function to analyze residents' anxiety when optimizing evacuation routes and provide information to reduce anxiety. For example, specific evacuation methods and safety measures are provided to reduce residents' anxiety. The disaster risk prediction unit also uses the emotion estimation function to analyze residents' stress when optimizing evacuation routes and provide information to reduce stress. For example, specific evacuation methods and safety measures are provided to reduce residents' stress. The disaster risk prediction unit also uses the emotion estimation function to analyze residents' anxiety and stress when optimizing evacuation routes and provide information to reduce anxiety and stress. For example, specific evacuation methods and safety measures are provided to reduce residents' anxiety and stress. In this way, by providing information to reduce residents' anxiety and stress, it is possible to increase residents' sense of security.

[0088] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0089] The disaster risk prediction unit can not only predict the risks of earthquakes, floods, and wildfires, but also predict traffic congestion and infrastructure damage in urban areas. For example, it can predict road damage in the event of an earthquake and suggest changes to evacuation routes. It can also predict the suspension of subway and bus lines in the event of a flood and suggest alternative means of transportation. It can also predict power outages in the event of a wildfire and suggest ways for residents to conserve electricity. This allows the disaster risk prediction unit to predict the impact on urban infrastructure and propose appropriate countermeasures.

[0090] The disaster risk prediction unit can use the emotion estimation function to analyze residents' emotional reactions to disaster risk predictions and improve the accuracy of the predictions. For example, it analyzes residents' emotions of anxiety and fear and evaluates the reliability of the disaster risk predictions. The disaster risk prediction unit also uses the emotion estimation function to analyze residents' emotional reactions to disaster risk predictions and provides feedback to improve the accuracy of the predictions. For example, it adjusts the disaster risk prediction model based on the residents' emotional reactions. The disaster risk prediction unit also uses the emotion estimation function to analyze residents' emotional reactions to disaster risk predictions and provides feedback to improve the accuracy of the predictions. For example, it improves the results of the disaster risk predictions based on the residents' emotional reactions. In this way, the accuracy of disaster risk predictions can be improved by analyzing residents' emotional reactions.

[0091] The disaster risk prediction unit can predict the impact on agricultural or fishing industrial activities and propose appropriate countermeasures. For example, the unit predicts the impact on agricultural crops in the event of a flood and proposes appropriate countermeasures. For example, the unit evaluates the risk of flooding and proposes measures to minimize damage to agricultural crops. The disaster risk prediction unit can also predict the impact on fishing in the event of an earthquake and propose appropriate countermeasures. For example, the unit evaluates the risk of an earthquake and proposes measures to minimize damage to fishing. The disaster risk prediction unit can also predict the impact on agriculture or fishing in the event of a wildfire and propose appropriate countermeasures. For example, the unit evaluates the risk of wildfire occurrence and proposes measures to minimize damage to agriculture or fishing. In this way, the impact on industrial activities can be predicted and appropriate countermeasures can be proposed, thereby minimizing damage.

[0092] The disaster risk prediction unit can evaluate risks to specific locations in tourist destinations or event venues and issue warnings to tourists or event participants. For example, the disaster risk prediction unit predicts the impact on tourist destinations in the event of an earthquake and issues a warning to tourists. For example, the disaster risk prediction unit evaluates the risk of an earthquake and issues a warning to ensure the safety of the tourist destination. The disaster risk prediction unit also predicts the impact on event venues in the event of a flood and issues a warning to event participants. For example, the disaster risk prediction unit evaluates the risk of flooding and issues a warning to ensure the safety of the event venue. The disaster risk prediction unit also predicts the impact on tourist destinations or event venues in the event of a wildfire and issues a warning to tourists or event participants. For example, the disaster risk prediction unit evaluates the risk of wildfire and issues a warning to ensure the safety of the tourist destination or event venue. In this way, the safety of tourists and event participants can be ensured by evaluating risks to specific locations and issuing warnings.

[0093] The disaster risk prediction unit can use the emotion estimation function to provide information to reduce residents' anxiety or fear in response to the disaster risk prediction. The disaster risk prediction unit, for example, uses the emotion estimation function to analyze residents' anxiety in response to the disaster risk prediction and provide information to reduce the anxiety. For example, specific evacuation methods and safety measures are provided to reduce residents' anxiety. The disaster risk prediction unit also uses the emotion estimation function to analyze residents' fear in response to the disaster risk prediction and provide information to reduce the fear. For example, specific evacuation methods and safety measures are provided to reduce residents' fear. The disaster risk prediction unit also uses the emotion estimation function to analyze residents' anxiety and fear in response to the disaster risk prediction and provide information to reduce the anxiety and fear. For example, specific evacuation methods and safety measures are provided to reduce residents' anxiety and fear. In this way, by providing information to reduce residents' anxiety and fear, it is possible to increase residents' sense of security.

[0094] The disaster risk prediction unit can analyze past evacuation data and identify the most effective evacuation route or evacuation location. For example, it can analyze past earthquake evacuation data and propose an optimal evacuation route. For example, it can analyze past earthquake evacuation data and evaluate evacuation time and the safety of the evacuation route. The disaster risk prediction unit can also analyze past flood evacuation data and propose an optimal evacuation route. For example, it can analyze past flood evacuation data and evaluate evacuation time and the safety of the evacuation route. The disaster risk prediction unit can also analyze past wildfire evacuation data and propose an optimal evacuation route. For example, it can analyze past wildfire evacuation data and evaluate evacuation time and the safety of the evacuation route. In this way, by analyzing past evacuation data, it is possible to identify the most effective evacuation route or evacuation location.

[0095] The disaster risk prediction unit can propose an optimal evacuation route by taking into account the infrastructure condition of the area or the earthquake resistance of the building. The disaster risk prediction unit, for example, proposes an optimal evacuation route by taking into account the infrastructure condition of the area. For example, it evaluates road conditions, power supply, water facilities, etc., and proposes an optimal evacuation route. The disaster risk prediction unit also proposes an optimal evacuation route by taking into account the earthquake resistance of the building. For example, it evaluates the structure, age of the building, and whether or not it has been reinforced against earthquakes, and proposes an optimal evacuation route. The disaster risk prediction unit also proposes an optimal evacuation route by evaluating a combination of the infrastructure condition of the area and the earthquake resistance of the building. For example, it comprehensively evaluates the road condition and the earthquake resistance of the building and proposes an optimal evacuation route. In this way, it is possible to propose an optimal evacuation route by taking into account the infrastructure condition of the area and the earthquake resistance of the building.

[0096] The disaster risk prediction unit uses the emotion estimation function to analyze the emotional reactions of residents when creating an evacuation plan, and can create a plan that allows residents to evacuate safely. The disaster risk prediction unit, for example, uses the emotion estimation function to analyze the emotional reactions of residents when creating an evacuation plan. For example, it analyzes residents' feelings of anxiety and fear and adjusts the evacuation plan. The disaster risk prediction unit also uses the emotion estimation function to analyze the emotional reactions of residents when creating an evacuation plan, and creates a plan that allows residents to evacuate safely. For example, it adjusts evacuation routes and evacuation locations based on the residents' emotional reactions. The disaster risk prediction unit also uses the emotion estimation function to analyze the emotional reactions of residents when creating an evacuation plan, and creates a plan that allows residents to evacuate safely. For example, it improves the evacuation plan based on the residents' emotional reactions. In this way, by analyzing the emotional reactions of residents, it is possible to create a plan that allows residents to evacuate safely.

[0097] The disaster risk prediction unit can individually create evacuation plans for people who require special consideration, such as pets, elderly people, and people with disabilities. The disaster risk prediction unit, for example, individually creates evacuation plans for pets. For example, an appropriate evacuation plan is created taking into account the type of pet and evacuation location. The disaster risk prediction unit also individually creates evacuation plans for elderly people. For example, an appropriate evacuation plan is created taking into account the health condition of the elderly and whether or not they require care. The disaster risk prediction unit also individually creates evacuation plans for people with disabilities. For example, an appropriate evacuation plan is created taking into account the type of disability and the need for support. The disaster risk prediction unit also comprehensively creates evacuation plans for people who require special consideration, such as pets, elderly people, and people with disabilities. For example, a comprehensive evacuation plan is created taking into account the evacuation location for pets, the evacuation route for elderly people, and the support system for people with disabilities. In this way, the safety of all residents can be ensured by individually creating evacuation plans for people who require special consideration.

[0098] The disaster risk prediction unit can use the emotion estimation function to provide information to reduce residents' anxiety or stress when creating an evacuation plan. The disaster risk prediction unit, for example, uses the emotion estimation function to analyze residents' anxiety when creating an evacuation plan and provide information to reduce the anxiety. For example, it provides specific evacuation methods and safety measures to reduce residents' anxiety. The disaster risk prediction unit also uses the emotion estimation function to analyze residents' stress when creating an evacuation plan and provide information to reduce stress. For example, it provides specific evacuation methods and safety measures to reduce residents' stress. The disaster risk prediction unit also uses the emotion estimation function to analyze residents' anxiety and stress when creating an evacuation plan and provide information to reduce anxiety and stress. For example, it provides specific evacuation methods and safety measures to reduce residents' anxiety and stress. In this way, by providing information to reduce residents' anxiety and stress, it is possible to increase residents' sense of security.

[0099] The processing flow of the second embodiment will be briefly explained below.

[0100] Step 1: The disaster risk prediction unit predicts disaster risks such as earthquakes, floods, and wildfires. Specifically, it predicts the risk of earthquakes using seismometer data, the risk of floods using rainfall data, and the risk of wildfires using temperature, humidity, and wind speed data. This allows the probability of an earthquake occurring, the risk of flooding, and the risk of wildfires to be calculated. Step 2: The early warning provider provides early warnings based on the disaster risks predicted by the disaster risk forecaster. Specifically, it issues warnings based on the earthquake prediction results, the flood prediction results, and the wildfire prediction results. This allows appropriate warnings to be issued to areas with a high probability of earthquakes, floods, and wildfires.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0105] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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).

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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 AI 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.

[0118] 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.

[0119] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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 AI 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.

[0133] 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.

[0134] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0135] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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 AI 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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.

[0155] 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."

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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]

[0168] 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. Disaster Risk Prediction Division, which predicts disaster risks such as earthquakes, floods, and wildfires; an early warning providing unit that provides an early warning based on the disaster risk predicted by the disaster risk prediction unit; A system characterized by:

2. The disaster risk prediction unit Predict the risk of the disaster using data from seismometers, rainfall, river water levels, temperature, humidity, and wind speed.

2. The system of claim 1.

3. The disaster risk prediction unit Predict the impact on agricultural or fishing industry activities and propose appropriate countermeasures 2. The system of claim 1.

4. The disaster risk prediction unit Analyzing past evacuation data to identify the most effective evacuation route or location 2. The system of claim 1.

5. The early warning providing unit: Optimize the timing of issuing warnings based on disaster risk prediction results and encourage residents to evacuate.

2. The system of claim 1.

6. The disaster risk prediction unit Analyzes real-time congestion status at evacuation shelters and suggests optimal evacuation locations 2. The system of claim 1.

7. The disaster risk prediction unit Analyzing real-time traffic or disaster information and proposing the safest and quickest evacuation route 2. The system of claim 1.

8. The disaster risk prediction unit Using emotion estimation capabilities, we analyze residents' emotional responses to disaster risk predictions and improve the accuracy of said predictions.

2. The system of claim 1.

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