System

Abstract

An object of the system according to the embodiment is to provide a quick and safe evacuation route at the time of disaster.SOLUTION: A system according to an embodiment includes a collection unit, a damage prediction unit, an evacuation route selection unit, and a navigation unit. The collection unit collects data. The damage prediction unit predicts damage based on the data collected by the collection unit. The evacuation route selection unit selects an evacuation route based on the damage predicted by the damage prediction unit. The navigation unit guides the user along the evacuation route selected by the evacuation route selection 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 technology has the problem that it is difficult to select an evacuation route in the event of a disaster, and quick and safe evacuation cannot be guaranteed.

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

[0006] The system according to the embodiment includes a collection unit, a damage prediction unit, an evacuation route selection unit, and a navigation unit. The collection unit collects data. The damage prediction unit predicts damage based on the data collected by the collection unit. The evacuation route selection unit selects an evacuation route based on the damage predicted by the damage prediction unit. The navigation unit guides the user along the evacuation route selected by the evacuation route selection unit. [Effects of the Invention]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[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 evacuation route navigation system according to an embodiment of the present invention is a system that supports the selection of an evacuation route in the event of a disaster. This system utilizes road image data to analyze predicted damage during a disaster, topography, and data on past natural disaster damage. Next, damage prediction is performed based on this data. Furthermore, by predicting the damage status of exterior walls, it is possible to predict risks along evacuation routes. Based on this information, safety is ensured by navigating safe evacuation routes even during disasters. As a result, the disaster evacuation route navigation system enables calm decision-making even during disasters and supports the selection of a safe evacuation route. For example, when a disaster such as an earthquake or flood occurs, the safety of evacuees can be ensured by selecting the optimal evacuation route based on the results of damage predictions.

[0029] A disaster evacuation route navigation system according to an embodiment includes a collection unit, a damage prediction unit, an evacuation route selection unit, and a navigation unit. The collection unit collects data. Examples of the data include, but are not limited to, road image data, topographical data, and data on damage caused by past natural disasters. For example, the collection unit collects road image data using camera images. The collection unit can also collect topographical data using satellite images. The collection unit can also acquire data on damage caused by past natural disasters from a government database. For example, the collection unit monitors road conditions in real time using camera images and collects data. The collection unit detects changes in topography using satellite images and collects data. The collection unit acquires data on damage caused by past natural disasters from a government database and collects the data. The damage prediction unit predicts damage based on the data collected by the collection unit. Examples of damage include, but are not limited to, damage to buildings, endangerment of human life, and traffic disruptions. For example, the damage prediction unit analyzes the collected data and predicts the risk of damage to buildings. The damage prediction unit can also predict endangerment of human life. The damage prediction unit can also predict the risk of traffic disruption. For example, the damage prediction unit assesses the risk of building damage based on the collected data. For example, the damage prediction unit assesses the risk of human life based on the collected data. For example, the damage prediction unit assesses the risk of traffic disruption based on the collected data. The evacuation route selection unit selects an evacuation route based on the damage predicted by the damage prediction unit. Criteria for the evacuation route include, but are not limited to, distance, safety, and time. For example, the evacuation route selection unit selects the safest evacuation route based on the results of the damage prediction unit. The evacuation route selection unit can also select the most efficient evacuation route. The evacuation route selection unit can also select the quickest evacuation route. For example, the evacuation route selection unit selects the safest evacuation route based on the results of the damage prediction unit. The evacuation route selection unit selects the most efficient evacuation route based on the results of the damage prediction unit. The navigation unit guides the user along the evacuation route selected by the evacuation route selection unit. For example, the guidance can include, but is not limited to, audio guidance, visual guidance, text guidance, and the like.The navigation unit, for example, uses voice guidance to guide the user along an evacuation route. The navigation unit can also use visual guidance to guide the user along an evacuation route. The navigation unit can also use text guidance to guide the user along an evacuation route. For example, the navigation unit uses voice guidance to guide the user along an evacuation route. The navigation unit uses visual guidance to guide the user along an evacuation route. The navigation unit uses text guidance to guide the user along an evacuation route. As a result, the disaster evacuation route navigation system according to the embodiment enables calm decision-making even during a disaster, and supports the selection of a safe evacuation route.

[0030] The disaster evacuation route navigation system includes an exterior wall damage prediction unit that predicts the damage status of exterior walls. The exterior wall damage prediction unit predicts the damage status of exterior walls. Damage status of exterior walls includes, but is not limited to, cracks, collapse, flooding, etc. The exterior wall damage prediction unit, for example, predicts the risk of cracks in the exterior walls. The exterior wall damage prediction unit can also predict the risk of collapse of the exterior walls. The exterior wall damage prediction unit can also predict the risk of flooding of the exterior walls. For example, the exterior wall damage prediction unit evaluates the risk of cracks in the exterior walls. Evaluates the risk of collapse of the exterior walls. Evaluates the risk of flooding of the exterior walls. This makes it possible to predict the risk of evacuation routes by predicting the damage status of the exterior walls. Some or all of the above-mentioned processing in the exterior wall damage prediction unit may be performed, for example, using AI, or may be performed without using AI. For example, the exterior wall damage prediction unit inputs image data of the exterior walls into the generation AI and causes the generation AI to predict the damage status of the exterior walls.

[0031] The disaster evacuation route navigation system includes a risk assessment unit that assesses the risk of an evacuation route. The risk assessment unit assesses the risk of the evacuation route. Examples of risk include, but are not limited to, the degree of danger, the probability of occurrence, and the extent of impact. For example, the risk assessment unit assesses the degree of danger of the evacuation route. The risk assessment unit can also assess the probability of occurrence of the evacuation route. The risk assessment unit can also assess the extent of impact of the evacuation route. For example, the risk assessment unit assesses the degree of danger of the evacuation route. Evaluates the probability of occurrence of the evacuation route. Evaluates the extent of impact of the evacuation route. As a result, by assessing the risk of the evacuation route, it becomes possible to select a safer evacuation route. Some or all of the above-described processing in the risk assessment unit may be performed, for example, using AI, or may be performed without using AI. For example, the risk assessment unit can input evacuation route data to the generation AI and have the generation AI perform a risk assessment.

[0032] The collection unit can collect road image data, topographical data, and data on past natural disaster damage. Examples of road image data include, but are not limited to, camera images, satellite images, and drone images. For example, the collection unit collects road image data using camera images. The collection unit can also collect road image data using satellite images. The collection unit can also collect road image data using drone images. For example, the collection unit monitors road conditions in real time and collects data using camera images. The collection unit monitors road conditions over a wide area and collects data using satellite images. The collection unit monitors detailed road conditions and collects data using drone images. Examples of topographical data include, but are not limited to, map data, elevation data, and geological data. For example, the collection unit collects topographical data using map data. The collection unit can also collect topographical data using elevation data. The collection unit can also collect topographical data using geological data. For example, the collection unit collects detailed topographical information using map data. The collection unit collects elevation differences in the topography using elevation data. Geological data is used to collect topographical structure. Past natural disaster damage data includes, but is not limited to, disaster reports, news articles, and government databases. The collection unit, for example, collects past natural disaster damage data using disaster reports. The collection unit can also collect past natural disaster damage data using news articles. The collection unit can also collect past natural disaster damage data using government databases. For example, the collection unit collects detailed information on past natural disaster damage using disaster reports. The collection unit collects an overview of past natural disaster damage using news articles. The collection unit collects statistical data on past natural disaster damage using government databases. This allows for the collection of diverse data, thereby improving the accuracy of damage prediction. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input camera images into the generation AI and cause the generation AI to collect road image data.

[0033] The damage prediction unit can predict damage based on collected data. The collected data includes, for example, sensor data, user input data, environmental data, etc., but is not limited to these examples. The damage prediction unit can predict damage based on, for example, sensor data. The damage prediction unit can also predict damage based on user input data. The damage prediction unit can also predict damage based on environmental data. For example, the damage prediction unit predicts the risk of building damage based on sensor data. Predicts the risk of human life based on user input data. Predicts the risk of traffic disruption based on environmental data. This enables more accurate damage prediction by predicting damage based on collected data. Some or all of the above-mentioned processing in the damage prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the damage prediction unit can input sensor data to a generation AI and cause the generation AI to predict damage.

[0034] The evacuation route selection unit can select an evacuation route based on the results of the damage prediction unit. The optimal evacuation route includes, but is not limited to, criteria such as distance, safety, and time. For example, the evacuation route selection unit can select the safest evacuation route based on the results of the damage prediction unit. The evacuation route selection unit can also select the most efficient evacuation route. The evacuation route selection unit can also select the quickest evacuation route. For example, the evacuation route selection unit can select the safest evacuation route based on the results of the damage prediction unit. The evacuation route selection unit can select the most efficient evacuation route based on the results of the damage prediction unit. The quickest evacuation route can be selected based on the results of the damage prediction unit. This ensures the safety of evacuees by selecting an optimal evacuation route based on the results of the damage prediction. Some or all of the above-described processing in the evacuation route selection unit can be performed using, for example, AI, or without AI. For example, the evacuation route selection unit can input the results of the damage prediction unit into a generation AI and cause the generation AI to select an optimal evacuation route.

[0035] The navigation unit can guide the user along the selected evacuation route. The guidance can be, for example, voice guidance, visual guidance, text guidance, or the like, but is not limited to these examples. The navigation unit can, for example, guide the user along the evacuation route using voice guidance. The navigation unit can also guide the user along the evacuation route using visual guidance. The navigation unit can also guide the user along the evacuation route using text guidance. For example, the navigation unit can guide the user along the evacuation route using voice guidance, or visual guidance, or text guidance. By guiding the user along the selected evacuation route, evacuees can evacuate safely. Some or all of the above-described processing in the navigation unit can be performed using, for example, AI, or without AI. For example, the navigation unit can input data of the selected evacuation route into a generation AI and cause the generation AI to generate guidance.

[0036] The collection unit can analyze past disaster data and select a data collection method. The collection unit, for example, analyzes past earthquake data and selects the most effective data collection method when an earthquake occurs. The collection unit can also analyze past flood data and select the most effective data collection method when a flood occurs. The collection unit can also analyze past typhoon data and select the most effective data collection method when a typhoon occurs. For example, the collection unit selects the most effective data collection method when an earthquake occurs based on past earthquake data. The collection unit selects the most effective data collection method when a flood occurs based on past flood data. The collection unit selects the most effective data collection method when a typhoon occurs based on past typhoon data. In this way, the optimal data collection method can be selected by analyzing past disaster data. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input past disaster data into a generation AI and have the generation AI select a data collection method.

[0037] The collection unit can perform filtering based on the user's current location information and movement history when collecting data. For example, if the user's current location is in a dangerous area, the collection unit prioritizes collecting data for that area. The collection unit can also prioritize collecting data for places previously visited from the user's movement history. The collection unit can also combine the user's current location and movement history to collect the most relevant data. For example, if the user's current location is in a dangerous area, the collection unit prioritizes collecting data for that area. The collection unit prioritizes collecting data for places previously visited from the user's movement history. The collection unit combines the user's current location and movement history to collect the most relevant data. This makes it possible to collect highly relevant data by filtering based on the user's current location information and movement history. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's location information data to a generation AI and have the generation AI perform filtering.

[0038] When collecting data, the collection unit can select a collection means according to the user's input method. For example, if the user is using voice input, the collection unit prioritizes collecting voice data. Furthermore, if the user is using text input, the collection unit can also prioritize collecting text data. Furthermore, if the user is using image input, the collection unit can also prioritize collecting image data. For example, if the user is using voice input, the collection unit prioritizes collecting voice data. If the user is using text input, the collection unit prioritizes collecting text data. If the user is using image input, the collection unit prioritizes collecting image data. This enables efficient data collection by selecting the optimal collection means according to the user's input method. Some or all of the above-described processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's input data to a generation AI and have the generation AI select the collection means.

[0039] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, if the user's current location is in a dangerous area, the collection unit prioritizes collecting data for that area. The collection unit can also prioritize collecting highly relevant data based on the user's geographical location information. The collection unit can also combine the user's geographical location information with past disaster data to collect the most relevant data. For example, if the user's current location is in a dangerous area, the collection unit prioritizes collecting data for that area. The collection unit prioritizes collecting highly relevant data based on the user's geographical location information. The collection unit combines the user's geographical location information with past disaster data to collect the most relevant data. This makes it possible to prioritize collecting highly relevant data by taking the user's geographical location information into account. Some or all of the above-mentioned processing by the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's location information data to a generation AI and have the generation AI prioritize the data.

[0040] The collection unit can analyze the user's social media activities and collect related data when collecting data. For example, the collection unit collects data on locations where the user has checked in on social media. The collection unit can also analyze the content of the user's posts on social media and collect related data. The collection unit can also collect related data by referring to the activities of the user's friends on social media. For example, the collection unit collects data on locations where the user has checked in on social media. The collection unit analyzes the content of the user's posts on social media and collects related data. The collection unit collects related data by referring to the activities of the user's friends on social media. In this way, by analyzing the user's social media activities, related data can be efficiently collected. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's social media data into a generation AI and cause the generation AI to collect data.

[0041] The collection unit can customize the collection method by reflecting the user's past feedback when collecting data. The collection unit, for example, selects the optimal data collection method based on feedback provided by the user in the past. The collection unit can also customize the type of data to be collected based on the user's past feedback. The collection unit can also adjust the timing of data collection by reflecting the user's past feedback. For example, the collection unit selects the optimal data collection method based on feedback provided by the user in the past. Customizes the type of data to be collected based on the user's past feedback. Adjusts the timing of data collection by reflecting the user's past feedback. In this way, the collection method can be optimized by reflecting the user's past feedback. Some or all of the above-described processing by the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's feedback data to a generation AI and cause the generation AI to customize the collection method.

[0042] The damage prediction unit can adjust the level of detail of the prediction based on the type of disaster when predicting damage. For example, in the case of an earthquake, the damage prediction unit predicts the risk of building collapse in detail. Furthermore, in the case of a flood, the damage prediction unit can also predict the risk of inundation in detail. Furthermore, in the case of a typhoon, the damage prediction unit can also predict damage in detail based on wind speed and rainfall. For example, in the case of an earthquake, the damage prediction unit predicts the risk of building collapse in detail. In the case of a flood, the damage prediction unit predicts the risk of inundation in detail. In the case of a typhoon, the damage prediction unit predicts damage in detail based on wind speed and rainfall. In this way, by adjusting the level of detail of the prediction based on the type of disaster, more accurate damage prediction is possible. Some or all of the above-mentioned processing in the damage prediction unit may be performed using, or without, AI, for example. For example, the damage prediction unit can input disaster data to the generation AI and cause the generation AI to adjust the level of detail of the prediction.

[0043] When predicting damage, the damage prediction unit can apply different prediction algorithms depending on the characteristics of each region. For example, in urban areas, the damage prediction unit applies a prediction algorithm that takes into account the density of buildings. Furthermore, in rural areas, the damage prediction unit can also apply a prediction algorithm that takes into account damage to farmland and forests. Furthermore, in coastal areas, the damage prediction unit can also apply a prediction algorithm that takes into account the risk of tsunamis and storm surges. For example, in urban areas, the damage prediction unit applies a prediction algorithm that takes into account the density of buildings. In rural areas, the damage prediction unit applies a prediction algorithm that takes into account damage to farmland and forests. In coastal areas, the damage prediction unit applies a prediction algorithm that takes into account the risk of tsunamis and storm surges. In this way, by applying a prediction algorithm that takes into account the characteristics of each region, more accurate damage prediction is possible. Some or all of the above-mentioned processing in the damage prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the damage prediction unit can input regional data into the generation AI and cause the generation AI to apply a prediction algorithm.

[0044] When predicting damage, the damage prediction unit can improve the accuracy of the prediction by referring to past prediction results. The damage prediction unit, for example, improves the prediction algorithm based on past earthquake prediction results. The damage prediction unit can also improve the prediction algorithm based on past flood prediction results. The damage prediction unit can also improve the prediction algorithm based on past typhoon prediction results. For example, the damage prediction unit improves the prediction algorithm based on past earthquake prediction results. The damage prediction unit improves the prediction algorithm based on past flood prediction results. The damage prediction unit improves the prediction algorithm based on past typhoon prediction results. In this way, by referring to past prediction results, the accuracy of the prediction is improved. Some or all of the above-mentioned processing in the damage prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the damage prediction unit can input past prediction data into the generation AI and cause the generation AI to improve the prediction algorithm.

[0045] When predicting damage, the damage prediction unit can determine the priority of predictions based on the timing of disaster occurrence. The damage prediction unit, for example, prioritizes predicting disasters that are likely to occur in the near future. The damage prediction unit can also prioritize predicting disasters that tend to occur seasonally. The damage prediction unit can also prioritize predicting disasters that are likely to occur soon based on past data. For example, the damage prediction unit prioritizes predicting disasters that are likely to occur in the near future. The damage prediction unit prioritizes predicting disasters that tend to occur seasonally. The damage prediction unit prioritizes predicting disasters that are likely to occur soon based on past data. This enables a more rapid response by determining the priority of predictions based on the timing of disaster occurrence. Some or all of the above-mentioned processing in the damage prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the damage prediction unit can input data on the timing of disaster occurrence into the generation AI and have the generation AI determine the priority of predictions.

[0046] The damage prediction unit can adjust the order of predictions based on the relevance of disasters when predicting damage. For example, the damage prediction unit predicts a tsunami after predicting an earthquake, taking into account the relevance between earthquakes and tsunamis. The damage prediction unit can also predict a flood after predicting a typhoon, taking into account the relevance between typhoons and floods. The damage prediction unit can also predict an earthquake after predicting a volcanic eruption, taking into account the relevance between volcanic eruptions and earthquakes. For example, the damage prediction unit predicts a tsunami after predicting an earthquake, taking into account the relevance between earthquakes and tsunamis. The damage prediction unit predicts a flood after predicting a typhoon, taking into account the relevance between typhoons and floods. The damage prediction unit predicts an earthquake after predicting a volcanic eruption, taking into account the relevance between volcanic eruptions and earthquakes. This allows for more effective damage predictions by adjusting the order of predictions based on the relevance of disasters. Some or all of the above-described processing in the damage prediction unit may be performed using, or without, AI, for example. For example, the damage prediction unit can input disaster-relevant data into the generation AI and cause the generation AI to adjust the order of predictions.

[0047] When predicting damage, the damage prediction unit can adjust the use of technical terminology in the prediction according to the user's level of expertise. For example, the damage prediction unit provides a simple prediction that avoids technical terminology for general users. The damage prediction unit can also provide a prediction that includes detailed technical terminology for experts. The damage prediction unit can also estimate the user's level of expertise and provide a prediction accordingly. For example, the damage prediction unit provides a simple prediction that avoids technical terminology for general users. For experts, it can provide a prediction that includes detailed technical terminology. The damage prediction unit estimates the user's level of expertise and provides a prediction accordingly. This makes it possible to provide information that is easier to understand by adjusting the use of technical terminology in the prediction according to the user's level of expertise. Some or all of the above-mentioned processing in the damage prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the damage prediction unit can input the user's expert knowledge data into a generation AI and have the generation AI use technical terminology.

[0048] When selecting an evacuation route, the evacuation route selection unit can improve the accuracy of the selection by taking into account the results of the damage prediction. For example, the evacuation route selection unit selects a route with a low risk of collapse based on the results of the damage prediction. The evacuation route selection unit can also select a route with a low risk of flooding based on the results of the damage prediction. The evacuation route selection unit can also select a route that is less affected by wind speed and rainfall based on the results of the damage prediction. For example, the evacuation route selection unit selects a route with a low risk of collapse based on the results of the damage prediction. The evacuation route selection unit selects a route with a low risk of flooding based on the results of the damage prediction. The evacuation route selection unit selects a route that is less affected by wind speed and rainfall based on the results of the damage prediction. In this way, the accuracy of the selection is improved by taking into account the results of the damage prediction. Some or all of the above-mentioned processing in the evacuation route selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the evacuation route selection unit can input damage prediction data to the generation AI and cause the generation AI to improve the accuracy of route selection.

[0049] When selecting an evacuation route, the evacuation route selection unit can select a route taking into consideration the user's attribute information. The evacuation route selection unit, for example, selects a route with few steps for elderly people. The evacuation route selection unit can also select a route with safe sidewalks for families with children. The evacuation route selection unit can also select a barrier-free route for wheelchair users. For example, the evacuation route selection unit selects a route with few steps for elderly people. A route with safe sidewalks for families with children. A barrier-free route for wheelchair users. This allows a more appropriate evacuation route to be selected by taking the user's attribute information into consideration. Some or all of the above-described processing in the evacuation route selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the evacuation route selection unit can input the user's attribute data into a generation AI and cause the generation AI to select a route.

[0050] When selecting an evacuation route, the evacuation route selection unit can weight the route based on the user's movement history. The evacuation route selection unit, for example, selects an optimal evacuation route based on routes used by the user in the past. The evacuation route selection unit can also select a route that avoids congestion based on the user's movement history. The evacuation route selection unit can also analyze the user's movement history and select the most efficient evacuation route. For example, the evacuation route selection unit selects an optimal evacuation route based on routes used by the user in the past. A route that avoids congestion based on the user's movement history is selected. The user's movement history is analyzed and the most efficient evacuation route is selected. In this way, by weighting the route based on the user's movement history, a more appropriate evacuation route can be selected. Some or all of the above-described processing in the evacuation route selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the evacuation route selection unit can input the user's movement history data to a generation AI and cause the generation AI to weight the route.

[0051] The evacuation route selection unit can select a route taking geographical distribution into consideration when selecting an evacuation route. For example, when an earthquake occurs, the evacuation route selection unit selects a route that passes through an area that is less affected by the earthquake. Furthermore, when a flood occurs, the evacuation route selection unit can also select a route that passes through an area with a low risk of flooding. Furthermore, when a typhoon occurs, the evacuation route selection unit can also select a route that passes through an area with a low impact from wind speed and rainfall. For example, when an earthquake occurs, the evacuation route selection unit selects a route that passes through an area with a low impact from the earthquake. When a flood occurs, the evacuation route selection unit selects a route that passes through an area with a low risk of flooding. When a typhoon occurs, the evacuation route selection unit selects a route that passes through an area with a low impact from wind speed and rainfall. In this way, a safer evacuation route can be selected by taking geographical distribution into consideration. Some or all of the above-described processing in the evacuation route selection unit may be performed, for example, using AI, or may be performed without using AI. For example, the evacuation route selection unit can input geographical distribution data to a generation AI and cause the generation AI to select a route.

[0052] The evacuation route selection unit can improve the accuracy of the selection by referring to related literature when selecting an evacuation route. The evacuation route selection unit, for example, refers to literature on evacuation routes during past disasters and selects the optimal route. The evacuation route selection unit can also refer to research literature on evacuation behavior during disasters and select the optimal route. The evacuation route selection unit can also refer to literature on risk assessment during disasters and select the optimal route. For example, the evacuation route selection unit refers to literature on evacuation routes during past disasters and selects the optimal route. The evacuation route selection unit refers to research literature on evacuation behavior during disasters and selects the optimal route. The evacuation route selection unit refers to literature on risk assessment during disasters and selects the optimal route. By referring to related literature, the accuracy of the selection is improved. Some or all of the above-mentioned processing in the evacuation route selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the evacuation route selection unit can input related literature data into the generation AI and cause the generation AI to improve the accuracy of route selection.

[0053] When selecting an evacuation route, the evacuation route selection unit can select a route taking market value into consideration. The evacuation route selection unit, for example, selects a route that avoids areas with many expensive real estate. The evacuation route selection unit can also select a route that avoids areas with many commercial facilities. The evacuation route selection unit can also select a route that avoids areas with a concentration of important infrastructure. For example, the evacuation route selection unit selects a route that avoids areas with many expensive real estate. The evacuation route selection unit selects a route that avoids areas with many commercial facilities. The evacuation route selection unit selects a route that avoids areas with a concentration of important infrastructure. In this way, by taking market value into consideration, a route that avoids important infrastructure and expensive real estate can be selected. Some or all of the above-described processing in the evacuation route selection unit may be performed, for example, using AI, or may be performed without using AI. For example, the evacuation route selection unit can input market value data to the generation AI and cause the generation AI to select a route.

[0054] During navigation, the navigation unit can select a display method by referring to the user's past operation history. The navigation unit, for example, selects the optimal display method based on display methods used by the user in the past. The navigation unit can also select a display method with high visibility from the user's past operation history. The navigation unit can also analyze the user's past operation history and select the most efficient display method. For example, the navigation unit selects the optimal display method based on display methods used by the user in the past. The navigation unit selects a display method with high visibility from the user's past operation history. The navigation unit analyzes the user's past operation history and selects the most efficient display method. In this way, the optimal display method can be selected by referring to the user's past operation history. Some or all of the above-described processing in the navigation unit may be performed using, for example, AI, or may be performed without using AI. For example, the navigation unit can input the user's operation history data to a generation AI and have the generation AI select a display method.

[0055] The navigation unit can customize the display content according to the user's current task during navigation. For example, when the user is evacuating, the navigation unit prioritizes displaying information about evacuation routes. Furthermore, when the user is gathering information, the navigation unit can also display detailed damage prediction information. Furthermore, when the user is resting, the navigation unit can display information about nearby safe places. For example, when the user is evacuating, the navigation unit prioritizes displaying information about evacuation routes. When the user is gathering information, the navigation unit displays detailed damage prediction information. When the user is resting, the navigation unit displays information about nearby safe places. This allows for customizing the display content according to the user's current task, thereby enabling more appropriate information to be provided. Some or all of the above-described processing in the navigation unit may be performed, for example, using AI, or may be performed without using AI. For example, the navigation unit can input the user's task data into a generation AI and have the generation AI customize the display content.

[0056] The navigation unit can improve the display method by reflecting user feedback during navigation. The navigation unit improves the display method based on, for example, feedback provided by the user. The navigation unit can also select a display method with high visibility based on the user feedback. The navigation unit can also customize the display content by reflecting the user feedback. For example, the navigation unit improves the display method based on feedback provided by the user. Selects a display method with high visibility based on the user feedback. Customizes the display content by reflecting the user feedback. In this way, the display method can be optimized by reflecting the user feedback. Some or all of the above-mentioned processing in the navigation unit may be performed using, for example, AI, or may be performed without using AI. For example, the navigation unit can input user feedback data to a generation AI and cause the generation AI to improve the display method.

[0057] During navigation, the navigation unit can select the optimal display method by taking into account the user's device information. For example, if the user is using a smartphone, the navigation unit provides a display method that matches the screen size. Furthermore, if the user is using a tablet, the navigation unit can also provide a display method optimized for a large screen. Furthermore, if the user is using a smartwatch, the navigation unit can also provide a simple and highly visible display method. For example, if the user is using a smartphone, the navigation unit provides a display method that matches the screen size. If the user is using a tablet, the navigation unit provides a display method optimized for a large screen. If the user is using a smartwatch, the navigation unit provides a display method that matches the screen size. In this way, the optimal display method can be provided by taking into account the user's device information. Some or all of the above-described processing in the navigation unit may be performed using AI, for example, or may be performed without using AI. For example, the navigation unit can input the user's device information to the generation AI and cause the generation AI to select a display method.

[0058] During navigation, the navigation unit can make the displayed content multilingual according to the user's language setting. The navigation unit, for example, automatically sets the navigation language based on the language setting of the user's device. The navigation unit can also provide a language switching function when the user uses multiple languages. The navigation unit can also provide navigation in a specific language when the user selects that language. For example, the navigation unit automatically sets the navigation language based on the language setting of the user's device. The navigation unit provides a language switching function when the user uses multiple languages. When the user selects a specific language, the navigation unit provides navigation in that language. This makes it possible to accommodate a larger number of users by making the displayed content multilingual according to the user's language setting. Some or all of the above-described processing in the navigation unit may be performed using, for example, AI, or may be performed without using AI. For example, the navigation unit can input the user's language setting data into a generation AI and have the generation AI perform multilingual display of the displayed content.

[0059] During navigation, the navigation unit can adjust the timing of guidance according to the user's moving speed. For example, the navigation unit slows down the timing of guidance when the user is walking slowly. The navigation unit can also speed up the timing of guidance when the user is in a hurry. The navigation unit can also pause the guidance when the user stops and resume when the user starts walking again. For example, the navigation unit slows down the timing of guidance when the user is walking slowly. If the user is in a hurry, the navigation unit speeds up the timing of guidance when the user stops. If the user stops, the navigation unit pauses the guidance and resumes when the user starts walking again. In this way, by adjusting the timing of guidance according to the user's moving speed, guidance can be provided at more appropriate timing. Some or all of the above-mentioned processing in the navigation unit may be performed using, for example, AI, or may be performed without using AI. For example, the navigation unit can input the user's moving speed data to the generation AI and cause the generation AI to adjust the timing of guidance.

[0060] The exterior wall damage prediction unit can adjust the level of detail of the prediction based on the structure of the building when predicting exterior wall damage. For example, in the case of a high-rise building, the exterior wall damage prediction unit predicts the collapse risk in detail. Furthermore, in the case of a wooden building, the exterior wall damage prediction unit can also predict the fire risk in detail. Furthermore, in the case of a reinforced concrete building, the exterior wall damage prediction unit can also predict the earthquake risk in detail. For example, in the case of a high-rise building, the exterior wall damage prediction unit predicts the collapse risk in detail. In the case of a wooden building, the exterior wall damage prediction unit predicts the fire risk in detail. In the case of a reinforced concrete building, the exterior wall damage prediction unit predicts the earthquake risk in detail. This allows for more accurate exterior wall damage prediction by adjusting the level of detail of the prediction based on the building structure. Some or all of the above-mentioned processing in the exterior wall damage prediction unit may be performed using, or without, AI, for example. For example, the exterior wall damage prediction unit can input building structure data to the generation AI and cause the generation AI to adjust the level of detail of the prediction.

[0061] The exterior wall damage prediction unit can apply different prediction algorithms depending on the characteristics of each region when predicting exterior wall damage. For example, in urban areas, the exterior wall damage prediction unit applies a prediction algorithm that takes into account the density of buildings. Furthermore, in rural areas, the exterior wall damage prediction unit can also apply a prediction algorithm that takes into account damage to farmland and forests. Furthermore, in coastal areas, the exterior wall damage prediction unit can also apply a prediction algorithm that takes into account the risk of tsunamis and storm surges. For example, in urban areas, the exterior wall damage prediction unit applies a prediction algorithm that takes into account the density of buildings. In rural areas, the exterior wall damage prediction unit applies a prediction algorithm that takes into account damage to farmland and forests. In coastal areas, the exterior wall damage prediction unit applies a prediction algorithm that takes into account the risk of tsunamis and storm surges. In this way, by applying a prediction algorithm that takes into account the characteristics of each region, more accurate exterior wall damage prediction is possible. Some or all of the above-mentioned processing in the exterior wall damage prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the exterior wall damage prediction unit can input regional characteristic data into the generation AI and cause the generation AI to apply a prediction algorithm.

[0062] The exterior wall damage prediction unit can improve the accuracy of predictions by referring to past prediction results when predicting exterior wall damage. The exterior wall damage prediction unit improves the prediction algorithm, for example, based on past earthquake prediction results. The exterior wall damage prediction unit can also improve the prediction algorithm based on past flood prediction results. The exterior wall damage prediction unit can also improve the prediction algorithm based on past typhoon prediction results. For example, the exterior wall damage prediction unit improves the prediction algorithm based on past earthquake prediction results. The exterior wall damage prediction unit improves the prediction algorithm based on past flood prediction results. The exterior wall damage prediction unit improves the prediction algorithm based on past typhoon prediction results. In this way, by referring to past prediction results, the accuracy of predictions is improved. Some or all of the above-mentioned processing in the exterior wall damage prediction unit may be performed using AI, for example, or may be performed without using AI. For example, the exterior wall damage prediction unit can input past prediction data into the generation AI and have the generation AI improve the prediction algorithm.

[0063] When predicting exterior wall damage, the exterior wall damage prediction unit can determine the priority of predictions based on the time of disaster occurrence. The exterior wall damage prediction unit, for example, prioritizes predicting disasters that are likely to occur in the near future. The exterior wall damage prediction unit can also prioritize predicting disasters that are likely to occur seasonally. The exterior wall damage prediction unit can also prioritize predicting disasters that are likely to occur soon based on past data. For example, the exterior wall damage prediction unit prioritizes predicting disasters that are likely to occur in the near future. It prioritizes predicting disasters that are likely to occur seasonally. It prioritizes predicting disasters that are likely to occur soon based on past data. This enables a more rapid response by determining the priority of predictions based on the time of disaster occurrence. Some or all of the above-mentioned processing in the exterior wall damage prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the exterior wall damage prediction unit can input data on the time of disaster occurrence into the generation AI and have the generation AI determine the priority of predictions.

[0064] The exterior wall damage prediction unit can adjust the order of predictions based on the relevance of disasters when predicting exterior wall damage. For example, the exterior wall damage prediction unit predicts a tsunami after predicting an earthquake, taking into account the relevance between earthquakes and tsunamis. The exterior wall damage prediction unit can also predict a flood after predicting a typhoon, taking into account the relevance between typhoons and floods. The exterior wall damage prediction unit can also predict an earthquake after predicting a volcanic eruption, taking into account the relevance between volcanic eruptions and earthquakes. For example, the exterior wall damage prediction unit predicts a tsunami after predicting an earthquake, taking into account the relevance between earthquakes and tsunamis. The exterior wall damage prediction unit predicts a flood after predicting a typhoon, taking into account the relevance between typhoons and floods. The exterior wall damage prediction unit predicts an earthquake after predicting a volcanic eruption, taking into account the relevance between volcanic eruptions and earthquakes. In this way, adjusting the order of predictions based on the relevance of disasters enables more effective exterior wall damage prediction. Some or all of the above-mentioned processing in the exterior wall damage prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the exterior wall damage prediction unit can input disaster-related data into the generation AI and have the generation AI adjust the order of predictions.

[0065] When predicting exterior wall damage, the exterior wall damage prediction unit can adjust the use of technical terminology in the prediction according to the user's level of expertise. For example, the exterior wall damage prediction unit provides a simple prediction that avoids technical terminology for general users. The exterior wall damage prediction unit can also provide a prediction that includes detailed technical terminology for experts. The exterior wall damage prediction unit can also estimate the user's level of expertise and provide a prediction accordingly. For example, the exterior wall damage prediction unit provides a simple prediction that avoids technical terminology for general users. For experts, it can provide a prediction that includes detailed technical terminology. The exterior wall damage prediction unit can estimate the user's level of expertise and provide a prediction accordingly. This makes it possible to provide information that is easier to understand by adjusting the use of technical terminology in the prediction according to the user's level of expertise. Some or all of the above-mentioned processing in the exterior wall damage prediction unit may be performed using AI, for example, or without AI. For example, the exterior wall damage prediction unit can input the user's expert knowledge data into a generation AI and have the generation AI use technical terminology.

[0066] The risk assessment unit can improve the accuracy of the assessment by taking into account the results of the damage prediction during risk assessment. For example, the risk assessment unit evaluates buildings with a high risk of collapse based on the results of the damage prediction. The risk assessment unit can also evaluate areas with a high risk of flooding based on the results of the damage prediction. The risk assessment unit can also evaluate areas with a large impact of wind speed and rainfall based on the results of the damage prediction. For example, the risk assessment unit evaluates buildings with a high risk of collapse based on the results of the damage prediction. The risk assessment unit evaluates areas with a high risk of flooding based on the results of the damage prediction. The risk assessment unit evaluates areas with a large impact of wind speed and rainfall based on the results of the damage prediction. In this way, the accuracy of the assessment is improved by taking into account the results of the damage prediction. Some or all of the above-mentioned processing in the risk assessment unit may be performed using, for example, AI, or may be performed without using AI. For example, the risk assessment unit can input damage prediction data into a generation AI and cause the generation AI to improve the accuracy of the assessment.

[0067] The risk assessment unit can perform risk assessment by taking into account user attribute information. For example, the risk assessment unit evaluates a route with low evacuation difficulty for elderly people. The risk assessment unit can also evaluate a route with safe sidewalks for families with children. The risk assessment unit can also evaluate a barrier-free route for wheelchair users. For example, the risk assessment unit evaluates a route with low evacuation difficulty for elderly people. The risk assessment unit evaluates a route with safe sidewalks for families with children. The risk assessment unit evaluates a barrier-free route for wheelchair users. This enables more appropriate risk assessment by taking into account the user attribute information. Some or all of the above-mentioned processing in the risk assessment unit may be performed using AI, for example, or may be performed without using AI. For example, the risk assessment unit can input the user's attribute data into the generation AI and have the generation AI perform the assessment.

[0068] The risk assessment unit can weight the assessment based on the user's movement history when assessing risk. The risk assessment unit performs risk assessment based on, for example, routes used by the user in the past. The risk assessment unit can also evaluate routes that avoid congestion based on the user's movement history. The risk assessment unit can also analyze the user's movement history and evaluate the most efficient route. For example, the risk assessment unit performs risk assessment based on routes used by the user in the past. The risk assessment unit evaluates routes that avoid congestion based on the user's movement history. The risk assessment unit analyzes the user's movement history and evaluates the most efficient route. By weighting the assessment based on the user's movement history, more appropriate risk assessment is possible. Some or all of the above-described processing in the risk assessment unit may be performed using, for example, AI, or may be performed without using AI. For example, the risk assessment unit can input the user's movement history data into a generation AI and have the generation AI perform the assessment weighting.

[0069] The risk assessment unit can take geographical distribution into consideration when assessing risks. For example, when an earthquake occurs, the risk assessment unit evaluates areas that are less affected by the earthquake. Furthermore, when a flood occurs, the risk assessment unit can also evaluate areas with a low risk of flooding. Furthermore, when a typhoon occurs, the risk assessment unit can also evaluate areas with a low impact of wind speed and rainfall. For example, when an earthquake occurs, the risk assessment unit evaluates areas with a low impact of the earthquake. When a flood occurs, the risk assessment unit evaluates areas with a low risk of flooding. When a typhoon occurs, the risk assessment unit evaluates areas with a low impact of wind speed and rainfall. This enables more appropriate risk assessment by taking geographical distribution into consideration. Some or all of the above-mentioned processing in the risk assessment unit may be performed, for example, using AI, or may be performed without using AI. For example, the risk assessment unit can input geographical distribution data into a generation AI and have the generation AI perform the assessment.

[0070] The risk assessment unit can improve the accuracy of the assessment by referring to related literature during risk assessment. For example, the risk assessment unit can improve the accuracy of the assessment by referring to literature on risk assessment during past disasters. The risk assessment unit can also improve the accuracy of the assessment by referring to research literature on evacuation behavior during disasters. The risk assessment unit can also improve the accuracy of the assessment by referring to the latest research on risk assessment during disasters. For example, the risk assessment unit can improve the accuracy of the assessment by referring to literature on risk assessment during past disasters. The risk assessment unit can improve the accuracy of the assessment by referring to research literature on evacuation behavior during disasters. The risk assessment unit can improve the accuracy of the assessment by referring to the latest research on risk assessment during disasters. In this way, the accuracy of the assessment is improved by referring to related literature. Some or all of the above-mentioned processing in the risk assessment unit may be performed using, for example, AI, or may be performed without using AI. For example, the risk assessment unit can input related literature data into the generation AI and cause the generation AI to improve the accuracy of the assessment.

[0071] The risk assessment unit can take market value into consideration when assessing risk. For example, the risk assessment unit assesses risk in an area with many expensive real estate. The risk assessment unit can also assess risk in an area with many commercial facilities. The risk assessment unit can also assess risk in an area where critical infrastructure is concentrated. For example, the risk assessment unit assesses risk in an area with many expensive real estate. Evaluates risk in an area with many commercial facilities. Evaluates risk in an area where critical infrastructure is concentrated. In this way, by taking market value into consideration, the risks of critical infrastructure and expensive real estate can be appropriately assessed. Some or all of the above-mentioned processing in the risk assessment unit may be performed, for example, using AI, or may be performed without using AI. For example, the risk assessment unit can input market value data into the generation AI and have the generation AI perform the assessment.

[0072] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0073] The disaster evacuation route navigation system may further include a health monitoring unit that monitors the health status of evacuees. The health monitoring unit collects biometric data, such as the evacuees' heart rate, blood pressure, and body temperature, in real time, and if an abnormality is detected, it can recalculate the optimal evacuation route. For example, if the evacuees' heart rate suddenly rises, the health monitoring unit can select a flatter and safer route to reduce the evacuees' burden. Furthermore, if the evacuees' blood pressure is abnormally high, the health monitoring unit can also suggest a route that includes places where the evacuees can rest. Furthermore, if the evacuees' body temperature is abnormally low, the health monitoring unit can select a route that allows the evacuees to evacuate to a warmer place. This makes it possible to provide the optimal evacuation route according to the evacuees' health status.

[0074] The disaster evacuation route navigation system may further include a progress tracking unit that tracks the progress of evacuees along the evacuation route in real time based on their location information. The progress tracking unit periodically updates the current location of the evacuees and can grasp the progress of the evacuees along the evacuation route. For example, if an evacuee deviates from the planned route, the progress tracking unit can guide the evacuees to a corrected route. Furthermore, if an evacuee is behind schedule, the progress tracking unit can provide the evacuees with advice on how to evacuate quickly. Furthermore, if an evacuee reaches their destination, the progress tracking unit can guide the evacuees on their next actions. This enables real-time progress tracking based on the evacuees' location information.

[0075] The processing flow of the first embodiment will be briefly explained below.

[0076] Step 1: The collection unit collects data. The data includes, for example, road image data, topographical data, and data on damage caused by past natural disasters. The collection unit collects road image data using camera images and topographical data using satellite images. It can also obtain data on damage caused by past natural disasters from government databases. Step 2: The damage prediction unit predicts damage based on the data collected by the collection unit. Damage includes damage to buildings, endangerment of human life, and traffic disruptions. The damage prediction unit analyzes the collected data and predicts the risk of damage to buildings, endangerment of human life, and traffic disruptions. Step 3: The evacuation route selection unit selects an evacuation route based on the damage predicted by the damage prediction unit. The evacuation route includes criteria such as distance, safety, and time. The evacuation route selection unit selects the safest, most efficient, and fastest evacuation route based on the results of the damage prediction unit. Step 4: The navigation unit guides the user along the evacuation route selected by the evacuation route selection unit. The guidance may be provided by voice guidance, visual guidance, text guidance, or the like. The navigation unit guides the user along the evacuation route using voice guidance, visual guidance, or text guidance.

[0077] (Example 2) A disaster evacuation route navigation system according to an embodiment of the present invention is a system that supports the selection of an evacuation route in the event of a disaster. This system utilizes road image data to analyze predicted damage during a disaster, topography, and data on past natural disaster damage. Next, damage prediction is performed based on this data. Furthermore, by predicting the damage status of exterior walls, it is possible to predict risks along evacuation routes. Based on this information, safety is ensured by navigating safe evacuation routes even during disasters. As a result, the disaster evacuation route navigation system enables calm decision-making even during disasters and supports the selection of a safe evacuation route. For example, when a disaster such as an earthquake or flood occurs, the safety of evacuees can be ensured by selecting the optimal evacuation route based on the results of damage predictions.

[0078] A disaster evacuation route navigation system according to an embodiment includes a collection unit, a damage prediction unit, an evacuation route selection unit, and a navigation unit. The collection unit collects data. Examples of the data include, but are not limited to, road image data, topographical data, and data on damage caused by past natural disasters. For example, the collection unit collects road image data using camera images. The collection unit can also collect topographical data using satellite images. The collection unit can also acquire data on damage caused by past natural disasters from a government database. For example, the collection unit monitors road conditions in real time using camera images and collects data. The collection unit detects changes in topography using satellite images and collects data. The collection unit acquires data on damage caused by past natural disasters from a government database and collects the data. The damage prediction unit predicts damage based on the data collected by the collection unit. Examples of damage include, but are not limited to, damage to buildings, endangerment of human life, and traffic disruptions. For example, the damage prediction unit analyzes the collected data and predicts the risk of damage to buildings. The damage prediction unit can also predict endangerment of human life. The damage prediction unit can also predict the risk of traffic disruption. For example, the damage prediction unit assesses the risk of building damage based on the collected data. For example, the damage prediction unit assesses the risk of human life based on the collected data. For example, the damage prediction unit assesses the risk of traffic disruption based on the collected data. The evacuation route selection unit selects an evacuation route based on the damage predicted by the damage prediction unit. Criteria for the evacuation route include, but are not limited to, distance, safety, and time. For example, the evacuation route selection unit selects the safest evacuation route based on the results of the damage prediction unit. The evacuation route selection unit can also select the most efficient evacuation route. The evacuation route selection unit can also select the quickest evacuation route. For example, the evacuation route selection unit selects the safest evacuation route based on the results of the damage prediction unit. The evacuation route selection unit selects the most efficient evacuation route based on the results of the damage prediction unit. The navigation unit guides the user along the evacuation route selected by the evacuation route selection unit. For example, the guidance can include, but is not limited to, audio guidance, visual guidance, text guidance, and the like.The navigation unit, for example, uses voice guidance to guide the user along an evacuation route. The navigation unit can also use visual guidance to guide the user along an evacuation route. The navigation unit can also use text guidance to guide the user along an evacuation route. For example, the navigation unit uses voice guidance to guide the user along an evacuation route. The navigation unit uses visual guidance to guide the user along an evacuation route. The navigation unit uses text guidance to guide the user along an evacuation route. As a result, the disaster evacuation route navigation system according to the embodiment enables calm decision-making even during a disaster, and supports the selection of a safe evacuation route.

[0079] The disaster evacuation route navigation system includes an exterior wall damage prediction unit that predicts the damage status of exterior walls. The exterior wall damage prediction unit predicts the damage status of exterior walls. Damage status of exterior walls includes, but is not limited to, cracks, collapse, flooding, etc. The exterior wall damage prediction unit, for example, predicts the risk of cracks in the exterior walls. The exterior wall damage prediction unit can also predict the risk of collapse of the exterior walls. The exterior wall damage prediction unit can also predict the risk of flooding of the exterior walls. For example, the exterior wall damage prediction unit evaluates the risk of cracks in the exterior walls. Evaluates the risk of collapse of the exterior walls. Evaluates the risk of flooding of the exterior walls. This makes it possible to predict the risk of evacuation routes by predicting the damage status of the exterior walls. Some or all of the above-mentioned processing in the exterior wall damage prediction unit may be performed, for example, using AI, or may be performed without using AI. For example, the exterior wall damage prediction unit inputs image data of the exterior walls into the generation AI and causes the generation AI to predict the damage status of the exterior walls.

[0080] The disaster evacuation route navigation system includes a risk assessment unit that assesses the risk of an evacuation route. The risk assessment unit assesses the risk of the evacuation route. Examples of risk include, but are not limited to, the degree of danger, the probability of occurrence, and the extent of impact. For example, the risk assessment unit assesses the degree of danger of the evacuation route. The risk assessment unit can also assess the probability of occurrence of the evacuation route. The risk assessment unit can also assess the extent of impact of the evacuation route. For example, the risk assessment unit assesses the degree of danger of the evacuation route. Evaluates the probability of occurrence of the evacuation route. Evaluates the extent of impact of the evacuation route. As a result, by assessing the risk of the evacuation route, it becomes possible to select a safer evacuation route. Some or all of the above-described processing in the risk assessment unit may be performed, for example, using AI, or may be performed without using AI. For example, the risk assessment unit can input evacuation route data to the generation AI and have the generation AI perform a risk assessment.

[0081] The collection unit can collect road image data, topographical data, and data on past natural disaster damage. Examples of road image data include, but are not limited to, camera images, satellite images, and drone images. For example, the collection unit collects road image data using camera images. The collection unit can also collect road image data using satellite images. The collection unit can also collect road image data using drone images. For example, the collection unit monitors road conditions in real time and collects data using camera images. The collection unit monitors road conditions over a wide area and collects data using satellite images. The collection unit monitors detailed road conditions and collects data using drone images. Examples of topographical data include, but are not limited to, map data, elevation data, and geological data. For example, the collection unit collects topographical data using map data. The collection unit can also collect topographical data using elevation data. The collection unit can also collect topographical data using geological data. For example, the collection unit collects detailed topographical information using map data. The collection unit collects elevation differences in the topography using elevation data. Geological data is used to collect topographical structure. Past natural disaster damage data includes, but is not limited to, disaster reports, news articles, and government databases. The collection unit, for example, collects past natural disaster damage data using disaster reports. The collection unit can also collect past natural disaster damage data using news articles. The collection unit can also collect past natural disaster damage data using government databases. For example, the collection unit collects detailed information on past natural disaster damage using disaster reports. The collection unit collects an overview of past natural disaster damage using news articles. The collection unit collects statistical data on past natural disaster damage using government databases. This allows for the collection of diverse data, thereby improving the accuracy of damage prediction. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input camera images into the generation AI and cause the generation AI to collect road image data.

[0082] The damage prediction unit can predict damage based on collected data. The collected data includes, for example, sensor data, user input data, environmental data, etc., but is not limited to these examples. The damage prediction unit can predict damage based on, for example, sensor data. The damage prediction unit can also predict damage based on user input data. The damage prediction unit can also predict damage based on environmental data. For example, the damage prediction unit predicts the risk of building damage based on sensor data. Predicts the risk of human life based on user input data. Predicts the risk of traffic disruption based on environmental data. This enables more accurate damage prediction by predicting damage based on collected data. Some or all of the above-mentioned processing in the damage prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the damage prediction unit can input sensor data to a generation AI and cause the generation AI to predict damage.

[0083] The evacuation route selection unit can select an evacuation route based on the results of the damage prediction unit. The optimal evacuation route includes, but is not limited to, criteria such as distance, safety, and time. For example, the evacuation route selection unit can select the safest evacuation route based on the results of the damage prediction unit. The evacuation route selection unit can also select the most efficient evacuation route. The evacuation route selection unit can also select the quickest evacuation route. For example, the evacuation route selection unit can select the safest evacuation route based on the results of the damage prediction unit. The evacuation route selection unit can select the most efficient evacuation route based on the results of the damage prediction unit. The quickest evacuation route can be selected based on the results of the damage prediction unit. This ensures the safety of evacuees by selecting an optimal evacuation route based on the results of the damage prediction. Some or all of the above-described processing in the evacuation route selection unit can be performed using, for example, AI, or without AI. For example, the evacuation route selection unit can input the results of the damage prediction unit into a generation AI and cause the generation AI to select an optimal evacuation route.

[0084] The navigation unit can guide the user along the selected evacuation route. The guidance can be, for example, voice guidance, visual guidance, text guidance, or the like, but is not limited to these examples. The navigation unit can, for example, guide the user along the evacuation route using voice guidance. The navigation unit can also guide the user along the evacuation route using visual guidance. The navigation unit can also guide the user along the evacuation route using text guidance. For example, the navigation unit can guide the user along the evacuation route using voice guidance, or visual guidance, or text guidance. By guiding the user along the selected evacuation route, evacuees can evacuate safely. Some or all of the above-described processing in the navigation unit can be performed using, for example, AI, or without AI. For example, the navigation unit can input data of the selected evacuation route into a generation AI and cause the generation AI to generate guidance.

[0085] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, if the user is nervous, the collection unit can increase the frequency of data collection and provide the latest information in real time. Furthermore, if the user is relaxed, the collection unit can reduce the frequency of data collection and provide the minimum necessary information. Furthermore, if the user is in a hurry, the collection unit can speed up the timing of data collection and provide the necessary information immediately. For example, the collection unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. Alternatively, the collection unit can record the user's voice and estimate the emotion using voice analysis technology. Alternatively, the collection unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. This allows for more appropriate data collection by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0086] The collection unit can analyze past disaster data and select a data collection method. The collection unit, for example, analyzes past earthquake data and selects the most effective data collection method when an earthquake occurs. The collection unit can also analyze past flood data and select the most effective data collection method when a flood occurs. The collection unit can also analyze past typhoon data and select the most effective data collection method when a typhoon occurs. For example, the collection unit selects the most effective data collection method when an earthquake occurs based on past earthquake data. The collection unit selects the most effective data collection method when a flood occurs based on past flood data. The collection unit selects the most effective data collection method when a typhoon occurs based on past typhoon data. In this way, the optimal data collection method can be selected by analyzing past disaster data. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input past disaster data into a generation AI and have the generation AI select a data collection method.

[0087] The collection unit can perform filtering based on the user's current location information and movement history when collecting data. For example, if the user's current location is in a dangerous area, the collection unit prioritizes collecting data for that area. The collection unit can also prioritize collecting data for places previously visited from the user's movement history. The collection unit can also combine the user's current location and movement history to collect the most relevant data. For example, if the user's current location is in a dangerous area, the collection unit prioritizes collecting data for that area. The collection unit prioritizes collecting data for places previously visited from the user's movement history. The collection unit combines the user's current location and movement history to collect the most relevant data. This makes it possible to collect highly relevant data by filtering based on the user's current location information and movement history. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's location information data to a generation AI and have the generation AI perform filtering.

[0088] When collecting data, the collection unit can select a collection means according to the user's input method. For example, if the user is using voice input, the collection unit prioritizes collecting voice data. Furthermore, if the user is using text input, the collection unit can also prioritize collecting text data. Furthermore, if the user is using image input, the collection unit can also prioritize collecting image data. For example, if the user is using voice input, the collection unit prioritizes collecting voice data. If the user is using text input, the collection unit prioritizes collecting text data. If the user is using image input, the collection unit prioritizes collecting image data. This enables efficient data collection by selecting the optimal collection means according to the user's input method. Some or all of the above-described processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's input data to a generation AI and have the generation AI select the collection means.

[0089] The collection unit can estimate the user's emotions and prioritize the data to be collected based on the estimated user emotions. For example, if the user is nervous, the collection unit can prioritize collecting urgent data. Furthermore, if the user is relaxed, the collection unit can prioritize collecting detailed data. Furthermore, if the user is in a hurry, the collection unit can prioritize collecting data that is immediately needed. For example, the collection unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. Alternatively, the collection unit can record the user's voice and estimate the emotion using voice analysis technology. Alternatively, the collection unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. This allows the priority of data to be collected based on the user's emotions, thereby prioritizing the collection of more important data. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0090] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, if the user's current location is in a dangerous area, the collection unit prioritizes collecting data for that area. The collection unit can also prioritize collecting highly relevant data based on the user's geographical location information. The collection unit can also combine the user's geographical location information with past disaster data to collect the most relevant data. For example, if the user's current location is in a dangerous area, the collection unit prioritizes collecting data for that area. The collection unit prioritizes collecting highly relevant data based on the user's geographical location information. The collection unit combines the user's geographical location information with past disaster data to collect the most relevant data. This makes it possible to prioritize collecting highly relevant data by taking the user's geographical location information into account. Some or all of the above-mentioned processing by the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's location information data to a generation AI and have the generation AI prioritize the data.

[0091] The collection unit can analyze the user's social media activities and collect related data when collecting data. For example, the collection unit collects data on locations where the user has checked in on social media. The collection unit can also analyze the content of the user's posts on social media and collect related data. The collection unit can also collect related data by referring to the activities of the user's friends on social media. For example, the collection unit collects data on locations where the user has checked in on social media. The collection unit analyzes the content of the user's posts on social media and collects related data. The collection unit collects related data by referring to the activities of the user's friends on social media. In this way, by analyzing the user's social media activities, related data can be efficiently collected. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's social media data into a generation AI and cause the generation AI to collect data.

[0092] The collection unit can customize the collection method by reflecting the user's past feedback when collecting data. The collection unit, for example, selects the optimal data collection method based on feedback provided by the user in the past. The collection unit can also customize the type of data to be collected based on the user's past feedback. The collection unit can also adjust the timing of data collection by reflecting the user's past feedback. For example, the collection unit selects the optimal data collection method based on feedback provided by the user in the past. Customizes the type of data to be collected based on the user's past feedback. Adjusts the timing of data collection by reflecting the user's past feedback. In this way, the collection method can be optimized by reflecting the user's past feedback. Some or all of the above-described processing by the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's feedback data to a generation AI and cause the generation AI to customize the collection method.

[0093] The damage prediction unit can estimate the user's emotions and adjust the way the damage prediction is expressed based on the estimated user emotions. For example, if the user is nervous, the damage prediction unit provides a simple, highly visible expression. Furthermore, if the user is relaxed, the damage prediction unit can provide an expression that includes detailed information. Furthermore, if the user is in a hurry, the damage prediction unit can provide an expression that focuses on the main points. For example, the damage prediction unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. Alternatively, the damage prediction unit records the user's voice and estimates the emotion using voice analysis technology. Alternatively, the damage prediction unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor and estimates the emotion using an emotion estimation algorithm. This allows for adjusting the way the damage prediction is expressed based on the user's emotions, thereby providing more appropriate information. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the damage prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the damage prediction unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0094] The damage prediction unit can adjust the level of detail of the prediction based on the type of disaster when predicting damage. For example, in the case of an earthquake, the damage prediction unit predicts the risk of building collapse in detail. Furthermore, in the case of a flood, the damage prediction unit can also predict the risk of inundation in detail. Furthermore, in the case of a typhoon, the damage prediction unit can also predict damage in detail based on wind speed and rainfall. For example, in the case of an earthquake, the damage prediction unit predicts the risk of building collapse in detail. In the case of a flood, the damage prediction unit predicts the risk of inundation in detail. In the case of a typhoon, the damage prediction unit predicts damage in detail based on wind speed and rainfall. In this way, by adjusting the level of detail of the prediction based on the type of disaster, more accurate damage prediction is possible. Some or all of the above-mentioned processing in the damage prediction unit may be performed using, or without, AI, for example. For example, the damage prediction unit can input disaster data to the generation AI and cause the generation AI to adjust the level of detail of the prediction.

[0095] When predicting damage, the damage prediction unit can apply different prediction algorithms depending on the characteristics of each region. For example, in urban areas, the damage prediction unit applies a prediction algorithm that takes into account the density of buildings. Furthermore, in rural areas, the damage prediction unit can also apply a prediction algorithm that takes into account damage to farmland and forests. Furthermore, in coastal areas, the damage prediction unit can also apply a prediction algorithm that takes into account the risk of tsunamis and storm surges. For example, in urban areas, the damage prediction unit applies a prediction algorithm that takes into account the density of buildings. In rural areas, the damage prediction unit applies a prediction algorithm that takes into account damage to farmland and forests. In coastal areas, the damage prediction unit applies a prediction algorithm that takes into account the risk of tsunamis and storm surges. In this way, by applying a prediction algorithm that takes into account the characteristics of each region, more accurate damage prediction is possible. Some or all of the above-mentioned processing in the damage prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the damage prediction unit can input regional data into the generation AI and cause the generation AI to apply a prediction algorithm.

[0096] When predicting damage, the damage prediction unit can improve the accuracy of the prediction by referring to past prediction results. The damage prediction unit, for example, improves the prediction algorithm based on past earthquake prediction results. The damage prediction unit can also improve the prediction algorithm based on past flood prediction results. The damage prediction unit can also improve the prediction algorithm based on past typhoon prediction results. For example, the damage prediction unit improves the prediction algorithm based on past earthquake prediction results. The damage prediction unit improves the prediction algorithm based on past flood prediction results. The damage prediction unit improves the prediction algorithm based on past typhoon prediction results. In this way, by referring to past prediction results, the accuracy of the prediction is improved. Some or all of the above-mentioned processing in the damage prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the damage prediction unit can input past prediction data into the generation AI and cause the generation AI to improve the prediction algorithm.

[0097] The damage prediction unit can estimate the user's emotions and adjust the length of the damage prediction based on the estimated user emotions. For example, if the user is nervous, the damage prediction unit can provide a short, to-the-point prediction. If the user is relaxed, the damage prediction unit can also provide a longer prediction with detailed explanations. If the user is in a hurry, the damage prediction unit can also provide a quick, concise prediction. For example, the damage prediction unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. It can record the user's voice and estimate the emotion using voice analysis technology. It can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. This allows the length of the damage prediction to be adjusted according to the user's emotions, thereby providing more appropriate information. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the damage prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the damage prediction unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0098] When predicting damage, the damage prediction unit can determine the priority of predictions based on the timing of disaster occurrence. The damage prediction unit, for example, prioritizes predicting disasters that are likely to occur in the near future. The damage prediction unit can also prioritize predicting disasters that tend to occur seasonally. The damage prediction unit can also prioritize predicting disasters that are likely to occur soon based on past data. For example, the damage prediction unit prioritizes predicting disasters that are likely to occur in the near future. The damage prediction unit prioritizes predicting disasters that tend to occur seasonally. The damage prediction unit prioritizes predicting disasters that are likely to occur soon based on past data. This enables a more rapid response by determining the priority of predictions based on the timing of disaster occurrence. Some or all of the above-mentioned processing in the damage prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the damage prediction unit can input data on the timing of disaster occurrence into the generation AI and have the generation AI determine the priority of predictions.

[0099] The damage prediction unit can adjust the order of predictions based on the relevance of disasters when predicting damage. For example, the damage prediction unit predicts a tsunami after predicting an earthquake, taking into account the relevance between earthquakes and tsunamis. The damage prediction unit can also predict a flood after predicting a typhoon, taking into account the relevance between typhoons and floods. The damage prediction unit can also predict an earthquake after predicting a volcanic eruption, taking into account the relevance between volcanic eruptions and earthquakes. For example, the damage prediction unit predicts a tsunami after predicting an earthquake, taking into account the relevance between earthquakes and tsunamis. The damage prediction unit predicts a flood after predicting a typhoon, taking into account the relevance between typhoons and floods. The damage prediction unit predicts an earthquake after predicting a volcanic eruption, taking into account the relevance between volcanic eruptions and earthquakes. This allows for more effective damage predictions by adjusting the order of predictions based on the relevance of disasters. Some or all of the above-described processing in the damage prediction unit may be performed using, or without, AI, for example. For example, the damage prediction unit can input disaster-relevant data into the generation AI and cause the generation AI to adjust the order of predictions.

[0100] When predicting damage, the damage prediction unit can adjust the use of technical terminology in the prediction according to the user's level of expertise. For example, the damage prediction unit provides a simple prediction that avoids technical terminology for general users. The damage prediction unit can also provide a prediction that includes detailed technical terminology for experts. The damage prediction unit can also estimate the user's level of expertise and provide a prediction accordingly. For example, the damage prediction unit provides a simple prediction that avoids technical terminology for general users. For experts, it can provide a prediction that includes detailed technical terminology. The damage prediction unit estimates the user's level of expertise and provides a prediction accordingly. This makes it possible to provide information that is easier to understand by adjusting the use of technical terminology in the prediction according to the user's level of expertise. Some or all of the above-mentioned processing in the damage prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the damage prediction unit can input the user's expert knowledge data into a generation AI and have the generation AI use technical terminology.

[0101] The evacuation route selection unit can estimate the user's emotions and adjust the evacuation route selection criteria based on the estimated user emotions. For example, if the user is nervous, the evacuation route selection unit can prioritize selecting the safest route. Furthermore, if the user is relaxed, the evacuation route selection unit can prioritize selecting the most efficient route. Furthermore, if the user is in a hurry, the evacuation route selection unit can prioritize selecting the route that will take the shortest time. For example, the evacuation route selection unit can capture the user's facial expression with a camera and estimate the user's emotions using an emotion estimation algorithm. Alternatively, the evacuation route selection unit can record the user's voice and estimate the user's emotions using voice analysis technology. Alternatively, the evacuation route selection unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the user's emotions using an emotion estimation algorithm. This allows the user to adjust the evacuation route selection criteria according to the user's emotions, thereby selecting a more appropriate evacuation route. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the evacuation route selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the evacuation route selection unit may input the user's facial expression data into the generation AI and have the generation AI estimate the user's emotions.

[0102] When selecting an evacuation route, the evacuation route selection unit can improve the accuracy of the selection by taking into account the results of the damage prediction. For example, the evacuation route selection unit selects a route with a low risk of collapse based on the results of the damage prediction. The evacuation route selection unit can also select a route with a low risk of flooding based on the results of the damage prediction. The evacuation route selection unit can also select a route that is less affected by wind speed and rainfall based on the results of the damage prediction. For example, the evacuation route selection unit selects a route with a low risk of collapse based on the results of the damage prediction. The evacuation route selection unit selects a route with a low risk of flooding based on the results of the damage prediction. The evacuation route selection unit selects a route that is less affected by wind speed and rainfall based on the results of the damage prediction. In this way, the accuracy of the selection is improved by taking into account the results of the damage prediction. Some or all of the above-mentioned processing in the evacuation route selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the evacuation route selection unit can input damage prediction data to the generation AI and cause the generation AI to improve the accuracy of route selection.

[0103] When selecting an evacuation route, the evacuation route selection unit can select a route taking into consideration the user's attribute information. The evacuation route selection unit, for example, selects a route with few steps for elderly people. The evacuation route selection unit can also select a route with safe sidewalks for families with children. The evacuation route selection unit can also select a barrier-free route for wheelchair users. For example, the evacuation route selection unit selects a route with few steps for elderly people. A route with safe sidewalks for families with children. A barrier-free route for wheelchair users. This allows a more appropriate evacuation route to be selected by taking the user's attribute information into consideration. Some or all of the above-described processing in the evacuation route selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the evacuation route selection unit can input the user's attribute data into a generation AI and cause the generation AI to select a route.

[0104] When selecting an evacuation route, the evacuation route selection unit can weight the route based on the user's movement history. The evacuation route selection unit, for example, selects an optimal evacuation route based on routes used by the user in the past. The evacuation route selection unit can also select a route that avoids congestion based on the user's movement history. The evacuation route selection unit can also analyze the user's movement history and select the most efficient evacuation route. For example, the evacuation route selection unit selects an optimal evacuation route based on routes used by the user in the past. A route that avoids congestion based on the user's movement history is selected. The user's movement history is analyzed and the most efficient evacuation route is selected. In this way, by weighting the route based on the user's movement history, a more appropriate evacuation route can be selected. Some or all of the above-described processing in the evacuation route selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the evacuation route selection unit can input the user's movement history data to a generation AI and cause the generation AI to weight the route.

[0105] The evacuation route selection unit can estimate the user's emotions and adjust the display method of the evacuation route based on the estimated user emotions. For example, if the user is nervous, the evacuation route selection unit provides a simple, highly visible display method. Furthermore, if the user is relaxed, the evacuation route selection unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the evacuation route selection unit can provide a display method that focuses on the main points. For example, the evacuation route selection unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. Alternatively, the evacuation route selection unit records the user's voice and estimates the emotion using voice analysis technology. Alternatively, the evacuation route selection unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor and estimates the emotion using an emotion estimation algorithm. This allows the display method of the evacuation route to be adjusted according to the user's emotions, thereby providing more visible information. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the evacuation route selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the evacuation route selection unit may input the user's facial expression data into the generation AI and have the generation AI estimate the user's emotions.

[0106] The evacuation route selection unit can select a route taking geographical distribution into consideration when selecting an evacuation route. For example, when an earthquake occurs, the evacuation route selection unit selects a route that passes through an area that is less affected by the earthquake. Furthermore, when a flood occurs, the evacuation route selection unit can also select a route that passes through an area with a low risk of flooding. Furthermore, when a typhoon occurs, the evacuation route selection unit can also select a route that passes through an area with a low impact from wind speed and rainfall. For example, when an earthquake occurs, the evacuation route selection unit selects a route that passes through an area with a low impact from the earthquake. When a flood occurs, the evacuation route selection unit selects a route that passes through an area with a low risk of flooding. When a typhoon occurs, the evacuation route selection unit selects a route that passes through an area with a low impact from wind speed and rainfall. In this way, a safer evacuation route can be selected by taking geographical distribution into consideration. Some or all of the above-described processing in the evacuation route selection unit may be performed, for example, using AI, or may be performed without using AI. For example, the evacuation route selection unit can input geographical distribution data to a generation AI and cause the generation AI to select a route.

[0107] The evacuation route selection unit can improve the accuracy of the selection by referring to related literature when selecting an evacuation route. The evacuation route selection unit, for example, refers to literature on evacuation routes during past disasters and selects the optimal route. The evacuation route selection unit can also refer to research literature on evacuation behavior during disasters and select the optimal route. The evacuation route selection unit can also refer to literature on risk assessment during disasters and select the optimal route. For example, the evacuation route selection unit refers to literature on evacuation routes during past disasters and selects the optimal route. The evacuation route selection unit refers to research literature on evacuation behavior during disasters and selects the optimal route. The evacuation route selection unit refers to literature on risk assessment during disasters and selects the optimal route. By referring to related literature, the accuracy of the selection is improved. Some or all of the above-mentioned processing in the evacuation route selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the evacuation route selection unit can input related literature data into the generation AI and cause the generation AI to improve the accuracy of route selection.

[0108] When selecting an evacuation route, the evacuation route selection unit can select a route taking market value into consideration. The evacuation route selection unit, for example, selects a route that avoids areas with many expensive real estate. The evacuation route selection unit can also select a route that avoids areas with many commercial facilities. The evacuation route selection unit can also select a route that avoids areas with a concentration of important infrastructure. For example, the evacuation route selection unit selects a route that avoids areas with many expensive real estate. The evacuation route selection unit selects a route that avoids areas with many commercial facilities. The evacuation route selection unit selects a route that avoids areas with a concentration of important infrastructure. In this way, by taking market value into consideration, a route that avoids important infrastructure and expensive real estate can be selected. Some or all of the above-described processing in the evacuation route selection unit may be performed, for example, using AI, or may be performed without using AI. For example, the evacuation route selection unit can input market value data to the generation AI and cause the generation AI to select a route.

[0109] The navigation unit can estimate the user's emotions and adjust the navigation display method based on the estimated user emotions. For example, if the user is nervous, the navigation unit provides a simple, highly visible display method. Furthermore, if the user is relaxed, the navigation unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the navigation unit can provide a display method that focuses on the main points. For example, the navigation unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. Alternatively, the navigation unit can record the user's voice and estimate the emotion using voice analysis technology. Alternatively, the navigation unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. This allows the navigation display method to be adjusted according to the user's emotions, thereby providing more visible information. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the navigation unit may be performed using, for example, AI, or may be performed without using AI. For example, the navigation unit may input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0110] During navigation, the navigation unit can select a display method by referring to the user's past operation history. The navigation unit, for example, selects the optimal display method based on display methods used by the user in the past. The navigation unit can also select a display method with high visibility from the user's past operation history. The navigation unit can also analyze the user's past operation history and select the most efficient display method. For example, the navigation unit selects the optimal display method based on display methods used by the user in the past. The navigation unit selects a display method with high visibility from the user's past operation history. The navigation unit analyzes the user's past operation history and selects the most efficient display method. In this way, the optimal display method can be selected by referring to the user's past operation history. Some or all of the above-described processing in the navigation unit may be performed using, for example, AI, or may be performed without using AI. For example, the navigation unit can input the user's operation history data to a generation AI and have the generation AI select a display method.

[0111] The navigation unit can customize the display content according to the user's current task during navigation. For example, when the user is evacuating, the navigation unit prioritizes displaying information about evacuation routes. Furthermore, when the user is gathering information, the navigation unit can also display detailed damage prediction information. Furthermore, when the user is resting, the navigation unit can display information about nearby safe places. For example, when the user is evacuating, the navigation unit prioritizes displaying information about evacuation routes. When the user is gathering information, the navigation unit displays detailed damage prediction information. When the user is resting, the navigation unit displays information about nearby safe places. This allows for customizing the display content according to the user's current task, thereby enabling more appropriate information to be provided. Some or all of the above-described processing in the navigation unit may be performed, for example, using AI, or may be performed without using AI. For example, the navigation unit can input the user's task data into a generation AI and have the generation AI customize the display content.

[0112] The navigation unit can improve the display method by reflecting user feedback during navigation. The navigation unit improves the display method based on, for example, feedback provided by the user. The navigation unit can also select a display method with high visibility based on the user feedback. The navigation unit can also customize the display content by reflecting the user feedback. For example, the navigation unit improves the display method based on feedback provided by the user. Selects a display method with high visibility based on the user feedback. Customizes the display content by reflecting the user feedback. In this way, the display method can be optimized by reflecting the user feedback. Some or all of the above-mentioned processing in the navigation unit may be performed using, for example, AI, or may be performed without using AI. For example, the navigation unit can input user feedback data to a generation AI and cause the generation AI to improve the display method.

[0113] The navigation unit can estimate the user's emotions and adjust the navigation operation procedure based on the estimated user's emotions. For example, the navigation unit provides simple and intuitive operation procedures when the user is nervous. The navigation unit can also provide detailed operation procedures when the user is relaxed. The navigation unit can also provide quick and concise operation procedures when the user is in a hurry. For example, the navigation unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The navigation unit can record the user's voice and estimate the emotion using voice analysis technology. The navigation unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. This allows the navigation operation procedure to be adjusted according to the user's emotions, enabling more intuitive operation. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the navigation unit can be performed using, for example, AI, or without AI. For example, the navigation unit can input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0114] During navigation, the navigation unit can select the optimal display method by taking into account the user's device information. For example, if the user is using a smartphone, the navigation unit provides a display method that matches the screen size. Furthermore, if the user is using a tablet, the navigation unit can also provide a display method optimized for a large screen. Furthermore, if the user is using a smartwatch, the navigation unit can also provide a simple and highly visible display method. For example, if the user is using a smartphone, the navigation unit provides a display method that matches the screen size. If the user is using a tablet, the navigation unit provides a display method optimized for a large screen. If the user is using a smartwatch, the navigation unit provides a display method that matches the screen size. In this way, the optimal display method can be provided by taking into account the user's device information. Some or all of the above-described processing in the navigation unit may be performed using AI, for example, or may be performed without using AI. For example, the navigation unit can input the user's device information to the generation AI and cause the generation AI to select a display method.

[0115] During navigation, the navigation unit can make the displayed content multilingual according to the user's language setting. The navigation unit, for example, automatically sets the navigation language based on the language setting of the user's device. The navigation unit can also provide a language switching function when the user uses multiple languages. The navigation unit can also provide navigation in a specific language when the user selects that language. For example, the navigation unit automatically sets the navigation language based on the language setting of the user's device. The navigation unit provides a language switching function when the user uses multiple languages. When the user selects a specific language, the navigation unit provides navigation in that language. This makes it possible to accommodate a larger number of users by making the displayed content multilingual according to the user's language setting. Some or all of the above-described processing in the navigation unit may be performed using, for example, AI, or may be performed without using AI. For example, the navigation unit can input the user's language setting data into a generation AI and have the generation AI perform multilingual display of the displayed content.

[0116] During navigation, the navigation unit can adjust the timing of guidance according to the user's moving speed. For example, the navigation unit slows down the timing of guidance when the user is walking slowly. The navigation unit can also speed up the timing of guidance when the user is in a hurry. The navigation unit can also pause the guidance when the user stops and resume when the user starts walking again. For example, the navigation unit slows down the timing of guidance when the user is walking slowly. If the user is in a hurry, the navigation unit speeds up the timing of guidance when the user stops. If the user stops, the navigation unit pauses the guidance and resumes when the user starts walking again. In this way, by adjusting the timing of guidance according to the user's moving speed, guidance can be provided at more appropriate timing. Some or all of the above-mentioned processing in the navigation unit may be performed using, for example, AI, or may be performed without using AI. For example, the navigation unit can input the user's moving speed data to the generation AI and cause the generation AI to adjust the timing of guidance.

[0117] The exterior wall damage prediction unit can estimate the user's emotions and adjust the presentation method of the exterior wall damage prediction based on the estimated user emotions. For example, if the user is nervous, the exterior wall damage prediction unit provides a simple, highly visible presentation method. Furthermore, if the user is relaxed, the exterior wall damage prediction unit can provide a presentation method that includes detailed information. Furthermore, if the user is in a hurry, the exterior wall damage prediction unit can provide a presentation method that focuses on the main points. For example, the exterior wall damage prediction unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. It records the user's voice and estimates the emotion using voice analysis technology. It collects the user's biometric data (heart rate and electrodermal activity) with a sensor and estimates the emotion using an emotion estimation algorithm. This allows the presentation method of the exterior wall damage prediction to be adjusted according to the user's emotions, thereby enabling the provision of more visible information. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the exterior wall damage prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the exterior wall damage prediction unit may input user facial expression data into the generation AI and have the generation AI estimate the user's emotions.

[0118] The exterior wall damage prediction unit can adjust the level of detail of the prediction based on the structure of the building when predicting exterior wall damage. For example, in the case of a high-rise building, the exterior wall damage prediction unit predicts the collapse risk in detail. Furthermore, in the case of a wooden building, the exterior wall damage prediction unit can also predict the fire risk in detail. Furthermore, in the case of a reinforced concrete building, the exterior wall damage prediction unit can also predict the earthquake risk in detail. For example, in the case of a high-rise building, the exterior wall damage prediction unit predicts the collapse risk in detail. In the case of a wooden building, the exterior wall damage prediction unit predicts the fire risk in detail. In the case of a reinforced concrete building, the exterior wall damage prediction unit predicts the earthquake risk in detail. This allows for more accurate exterior wall damage prediction by adjusting the level of detail of the prediction based on the building structure. Some or all of the above-mentioned processing in the exterior wall damage prediction unit may be performed using, or without, AI, for example. For example, the exterior wall damage prediction unit can input building structure data to the generation AI and cause the generation AI to adjust the level of detail of the prediction.

[0119] The exterior wall damage prediction unit can apply different prediction algorithms depending on the characteristics of each region when predicting exterior wall damage. For example, in urban areas, the exterior wall damage prediction unit applies a prediction algorithm that takes into account the density of buildings. Furthermore, in rural areas, the exterior wall damage prediction unit can also apply a prediction algorithm that takes into account damage to farmland and forests. Furthermore, in coastal areas, the exterior wall damage prediction unit can also apply a prediction algorithm that takes into account the risk of tsunamis and storm surges. For example, in urban areas, the exterior wall damage prediction unit applies a prediction algorithm that takes into account the density of buildings. In rural areas, the exterior wall damage prediction unit applies a prediction algorithm that takes into account damage to farmland and forests. In coastal areas, the exterior wall damage prediction unit applies a prediction algorithm that takes into account the risk of tsunamis and storm surges. In this way, by applying a prediction algorithm that takes into account the characteristics of each region, more accurate exterior wall damage prediction is possible. Some or all of the above-mentioned processing in the exterior wall damage prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the exterior wall damage prediction unit can input regional characteristic data into the generation AI and cause the generation AI to apply a prediction algorithm.

[0120] The exterior wall damage prediction unit can improve the accuracy of predictions by referring to past prediction results when predicting exterior wall damage. The exterior wall damage prediction unit improves the prediction algorithm, for example, based on past earthquake prediction results. The exterior wall damage prediction unit can also improve the prediction algorithm based on past flood prediction results. The exterior wall damage prediction unit can also improve the prediction algorithm based on past typhoon prediction results. For example, the exterior wall damage prediction unit improves the prediction algorithm based on past earthquake prediction results. The exterior wall damage prediction unit improves the prediction algorithm based on past flood prediction results. The exterior wall damage prediction unit improves the prediction algorithm based on past typhoon prediction results. In this way, by referring to past prediction results, the accuracy of predictions is improved. Some or all of the above-mentioned processing in the exterior wall damage prediction unit may be performed using AI, for example, or may be performed without using AI. For example, the exterior wall damage prediction unit can input past prediction data into the generation AI and have the generation AI improve the prediction algorithm.

[0121] The exterior wall damage prediction unit can estimate the user's emotions and adjust the length of the exterior wall damage prediction based on the estimated user emotions. For example, if the user is nervous, the exterior wall damage prediction unit can provide a short, concise prediction. If the user is relaxed, the exterior wall damage prediction unit can also provide a longer prediction with detailed explanations. If the user is in a hurry, the exterior wall damage prediction unit can also provide a quick, concise prediction. For example, the exterior wall damage prediction unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. It can record the user's voice and estimate the emotion using voice analysis technology. It can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. This allows the length of the exterior wall damage prediction to be adjusted according to the user's emotions, thereby providing more appropriate information. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the exterior wall damage prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the exterior wall damage prediction unit may input user facial expression data into the generation AI and have the generation AI estimate the user's emotions.

[0122] When predicting exterior wall damage, the exterior wall damage prediction unit can determine the priority of predictions based on the time of disaster occurrence. The exterior wall damage prediction unit, for example, prioritizes predicting disasters that are likely to occur in the near future. The exterior wall damage prediction unit can also prioritize predicting disasters that are likely to occur seasonally. The exterior wall damage prediction unit can also prioritize predicting disasters that are likely to occur soon based on past data. For example, the exterior wall damage prediction unit prioritizes predicting disasters that are likely to occur in the near future. It prioritizes predicting disasters that are likely to occur seasonally. It prioritizes predicting disasters that are likely to occur soon based on past data. This enables a more rapid response by determining the priority of predictions based on the time of disaster occurrence. Some or all of the above-mentioned processing in the exterior wall damage prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the exterior wall damage prediction unit can input data on the time of disaster occurrence into the generation AI and have the generation AI determine the priority of predictions.

[0123] The exterior wall damage prediction unit can adjust the order of predictions based on the relevance of disasters when predicting exterior wall damage. For example, the exterior wall damage prediction unit predicts a tsunami after predicting an earthquake, taking into account the relevance between earthquakes and tsunamis. The exterior wall damage prediction unit can also predict a flood after predicting a typhoon, taking into account the relevance between typhoons and floods. The exterior wall damage prediction unit can also predict an earthquake after predicting a volcanic eruption, taking into account the relevance between volcanic eruptions and earthquakes. For example, the exterior wall damage prediction unit predicts a tsunami after predicting an earthquake, taking into account the relevance between earthquakes and tsunamis. The exterior wall damage prediction unit predicts a flood after predicting a typhoon, taking into account the relevance between typhoons and floods. The exterior wall damage prediction unit predicts an earthquake after predicting a volcanic eruption, taking into account the relevance between volcanic eruptions and earthquakes. In this way, adjusting the order of predictions based on the relevance of disasters enables more effective exterior wall damage prediction. Some or all of the above-mentioned processing in the exterior wall damage prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the exterior wall damage prediction unit can input disaster-related data into the generation AI and have the generation AI adjust the order of predictions.

[0124] When predicting exterior wall damage, the exterior wall damage prediction unit can adjust the use of technical terminology in the prediction according to the user's level of expertise. For example, the exterior wall damage prediction unit provides a simple prediction that avoids technical terminology for general users. The exterior wall damage prediction unit can also provide a prediction that includes detailed technical terminology for experts. The exterior wall damage prediction unit can also estimate the user's level of expertise and provide a prediction accordingly. For example, the exterior wall damage prediction unit provides a simple prediction that avoids technical terminology for general users. For experts, it can provide a prediction that includes detailed technical terminology. The exterior wall damage prediction unit can estimate the user's level of expertise and provide a prediction accordingly. This makes it possible to provide information that is easier to understand by adjusting the use of technical terminology in the prediction according to the user's level of expertise. Some or all of the above-mentioned processing in the exterior wall damage prediction unit may be performed using AI, for example, or without AI. For example, the exterior wall damage prediction unit can input the user's expert knowledge data into a generation AI and have the generation AI use technical terminology.

[0125] The risk assessment unit can estimate the user's emotions and adjust the risk assessment criteria based on the estimated user emotions. For example, if the user is nervous, the risk assessment unit can tighten the risk assessment criteria and prioritize safety. Furthermore, if the user is relaxed, the risk assessment unit can loosen the risk assessment criteria and prioritize efficiency. Furthermore, if the user is in a hurry, the risk assessment unit can speed up the risk assessment criteria to enable immediate judgment. For example, the risk assessment unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. Alternatively, the risk assessment unit can record the user's voice and estimate the emotion using voice analysis technology. Alternatively, the risk assessment unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. This allows for more appropriate risk assessment by adjusting the risk assessment criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the risk assessment unit may be performed using, for example, AI, or may be performed without using AI. For example, the risk assessment unit may input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0126] The risk assessment unit can improve the accuracy of the assessment by taking into account the results of the damage prediction during risk assessment. For example, the risk assessment unit evaluates buildings with a high risk of collapse based on the results of the damage prediction. The risk assessment unit can also evaluate areas with a high risk of flooding based on the results of the damage prediction. The risk assessment unit can also evaluate areas with a large impact of wind speed and rainfall based on the results of the damage prediction. For example, the risk assessment unit evaluates buildings with a high risk of collapse based on the results of the damage prediction. The risk assessment unit evaluates areas with a high risk of flooding based on the results of the damage prediction. The risk assessment unit evaluates areas with a large impact of wind speed and rainfall based on the results of the damage prediction. In this way, the accuracy of the assessment is improved by taking into account the results of the damage prediction. Some or all of the above-mentioned processing in the risk assessment unit may be performed using, for example, AI, or may be performed without using AI. For example, the risk assessment unit can input damage prediction data into a generation AI and cause the generation AI to improve the accuracy of the assessment.

[0127] The risk assessment unit can perform risk assessment by taking into account user attribute information. For example, the risk assessment unit evaluates a route with low evacuation difficulty for elderly people. The risk assessment unit can also evaluate a route with safe sidewalks for families with children. The risk assessment unit can also evaluate a barrier-free route for wheelchair users. For example, the risk assessment unit evaluates a route with low evacuation difficulty for elderly people. The risk assessment unit evaluates a route with safe sidewalks for families with children. The risk assessment unit evaluates a barrier-free route for wheelchair users. This enables more appropriate risk assessment by taking into account the user attribute information. Some or all of the above-mentioned processing in the risk assessment unit may be performed using AI, for example, or may be performed without using AI. For example, the risk assessment unit can input the user's attribute data into the generation AI and have the generation AI perform the assessment.

[0128] The risk assessment unit can weight the assessment based on the user's movement history when assessing risk. The risk assessment unit performs risk assessment based on, for example, routes used by the user in the past. The risk assessment unit can also evaluate routes that avoid congestion based on the user's movement history. The risk assessment unit can also analyze the user's movement history and evaluate the most efficient route. For example, the risk assessment unit performs risk assessment based on routes used by the user in the past. The risk assessment unit evaluates routes that avoid congestion based on the user's movement history. The risk assessment unit analyzes the user's movement history and evaluates the most efficient route. By weighting the assessment based on the user's movement history, more appropriate risk assessment is possible. Some or all of the above-described processing in the risk assessment unit may be performed using, for example, AI, or may be performed without using AI. For example, the risk assessment unit can input the user's movement history data into a generation AI and have the generation AI perform the assessment weighting.

[0129] The risk assessment unit can estimate the user's emotions and adjust the display method of the risk assessment based on the estimated user emotions. For example, if the user is nervous, the risk assessment unit provides a simple, highly visible display method. Furthermore, if the user is relaxed, the risk assessment unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the risk assessment unit can provide a display method that focuses on the main points. For example, the risk assessment unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. Alternatively, the risk assessment unit can record the user's voice and estimate the emotion using voice analysis technology. Alternatively, the risk assessment unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. This allows the display method of the risk assessment to be adjusted according to the user's emotions, thereby providing more visible information. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the risk assessment unit may be performed using, for example, AI, or may be performed without using AI. For example, the risk assessment unit may input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0130] The risk assessment unit can take geographical distribution into consideration when assessing risks. For example, when an earthquake occurs, the risk assessment unit evaluates areas that are less affected by the earthquake. Furthermore, when a flood occurs, the risk assessment unit can also evaluate areas with a low risk of flooding. Furthermore, when a typhoon occurs, the risk assessment unit can also evaluate areas with a low impact of wind speed and rainfall. For example, when an earthquake occurs, the risk assessment unit evaluates areas with a low impact of the earthquake. When a flood occurs, the risk assessment unit evaluates areas with a low risk of flooding. When a typhoon occurs, the risk assessment unit evaluates areas with a low impact of wind speed and rainfall. This enables more appropriate risk assessment by taking geographical distribution into consideration. Some or all of the above-mentioned processing in the risk assessment unit may be performed, for example, using AI, or may be performed without using AI. For example, the risk assessment unit can input geographical distribution data into a generation AI and have the generation AI perform the assessment.

[0131] The risk assessment unit can improve the accuracy of the assessment by referring to related literature during risk assessment. For example, the risk assessment unit can improve the accuracy of the assessment by referring to literature on risk assessment during past disasters. The risk assessment unit can also improve the accuracy of the assessment by referring to research literature on evacuation behavior during disasters. The risk assessment unit can also improve the accuracy of the assessment by referring to the latest research on risk assessment during disasters. For example, the risk assessment unit can improve the accuracy of the assessment by referring to literature on risk assessment during past disasters. The risk assessment unit can improve the accuracy of the assessment by referring to research literature on evacuation behavior during disasters. The risk assessment unit can improve the accuracy of the assessment by referring to the latest research on risk assessment during disasters. In this way, the accuracy of the assessment is improved by referring to related literature. Some or all of the above-mentioned processing in the risk assessment unit may be performed using, for example, AI, or may be performed without using AI. For example, the risk assessment unit can input related literature data into the generation AI and cause the generation AI to improve the accuracy of the assessment.

[0132] The risk assessment unit can take market value into consideration when assessing risk. For example, the risk assessment unit assesses risk in an area with many expensive real estate. The risk assessment unit can also assess risk in an area with many commercial facilities. The risk assessment unit can also assess risk in an area where critical infrastructure is concentrated. For example, the risk assessment unit assesses risk in an area with many expensive real estate. Evaluates risk in an area with many commercial facilities. Evaluates risk in an area where critical infrastructure is concentrated. In this way, by taking market value into consideration, the risks of critical infrastructure and expensive real estate can be appropriately assessed. Some or all of the above-mentioned processing in the risk assessment unit may be performed, for example, using AI, or may be performed without using AI. For example, the risk assessment unit can input market value data into the generation AI and have the generation AI perform the assessment. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, damage prediction unit, evacuation route selection unit, navigation unit, exterior wall damage prediction unit, and risk assessment unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects data using the camera 42 and sensors of the smart device 14, and the data is analyzed by the identification processing unit 290 of the data processing device 12. The damage prediction unit is realized by the identification processing unit 290 of the data processing device 12 and predicts damage based on the collected data. The evacuation route selection unit is realized by the identification processing unit 290 of the data processing device 12 and selects an optimal evacuation route based on the results of the damage prediction unit. The navigation unit is realized by the control unit 46A of the smart device 14 and guides the user along the selected evacuation route. The exterior wall damage prediction unit is realized by the identification processing unit 290 of the data processing device 12 and predicts the damage status of the exterior wall. The risk assessment unit is realized by the identification processing unit 290 of the data processing device 12 and evaluates the risk of the evacuation route. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, damage prediction unit, evacuation route selection unit, navigation unit, exterior wall damage prediction unit, and risk assessment unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects data using the camera 42 and sensors of the smart glasses 214, and the data is analyzed by the identification processing unit 290 of the data processing device 12. The damage prediction unit is realized by the identification processing unit 290 of the data processing device 12 and predicts damage based on the collected data. The evacuation route selection unit is realized by the identification processing unit 290 of the data processing device 12 and selects an optimal evacuation route based on the results of the damage prediction unit. The navigation unit is realized by the control unit 46A of the smart glasses 214 and guides the user along the selected evacuation route. The exterior wall damage prediction unit is realized by the identification processing unit 290 of the data processing device 12 and predicts the damage status of the exterior wall. The risk assessment unit is realized by the identification processing unit 290 of the data processing device 12 and evaluates the risk of the evacuation route. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, damage prediction unit, evacuation route selection unit, navigation unit, exterior wall damage prediction unit, and risk assessment unit, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit collects data using the camera 42 or sensors of the headset terminal 314, and the data is analyzed by the identification processing unit 290 of the data processing device 12. The damage prediction unit is realized by the identification processing unit 290 of the data processing device 12 and predicts damage based on the collected data. The evacuation route selection unit is realized by the identification processing unit 290 of the data processing device 12 and selects an optimal evacuation route based on the results of the damage prediction unit. The navigation unit is realized by the control unit 46A of the headset terminal 314 and guides the user along the selected evacuation route. The exterior wall damage prediction unit is realized by the identification processing unit 290 of the data processing device 12 and predicts the damage status of the exterior wall. The risk assessment unit is realized by the identification processing unit 290 of the data processing device 12 and evaluates the risk of the evacuation route. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, damage prediction unit, evacuation route selection unit, navigation unit, exterior wall damage prediction unit, and risk assessment unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects data using the camera 42 and sensors of the robot 414, and the data is analyzed by the identification processing unit 290 of the data processing device 12. The damage prediction unit is realized by the identification processing unit 290 of the data processing device 12 and predicts damage based on the collected data. The evacuation route selection unit is realized by the identification processing unit 290 of the data processing device 12 and selects an optimal evacuation route based on the results of the damage prediction unit. The navigation unit is realized by the control unit 46A of the robot 414 and guides the user along the selected evacuation route. The exterior wall damage prediction unit is realized by the identification processing unit 290 of the data processing device 12 and predicts the damage status of the exterior wall. The risk assessment unit is realized by the identification processing unit 290 of the data processing device 12 and evaluates the risk of the evacuation route.

[0133] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0134] The disaster evacuation route navigation system may further include a health monitoring unit that monitors the health status of evacuees. The health monitoring unit collects biometric data, such as the evacuees' heart rate, blood pressure, and body temperature, in real time, and if an abnormality is detected, it can recalculate the optimal evacuation route. For example, if the evacuees' heart rate suddenly rises, the health monitoring unit can select a flatter and safer route to reduce the evacuees' burden. Furthermore, if the evacuees' blood pressure is abnormally high, the health monitoring unit can also suggest a route that includes places where the evacuees can rest. Furthermore, if the evacuees' body temperature is abnormally low, the health monitoring unit can select a route that allows the evacuees to evacuate to a warmer place. This makes it possible to provide the optimal evacuation route according to the evacuees' health status.

[0135] The disaster evacuation route navigation system can further include a psychological monitoring unit that monitors the psychological state of evacuees. The psychological monitoring unit can analyze the facial expressions and voices of evacuees to estimate their levels of stress and anxiety. For example, if an evacuee is very nervous, the psychological monitoring unit can change the evacuation route selection criteria to reduce the psychological burden on the evacuees. Furthermore, if an evacuee is relaxed, the psychological monitoring unit can also suggest a route that allows the evacuees to evacuate more efficiently. Furthermore, if an evacuee is in a hurry, the psychological monitoring unit can select a route that will allow them to evacuate in the shortest time. This makes it possible to provide the optimal evacuation route according to the evacuees' psychological state.

[0136] The disaster evacuation route navigation system may further include a progress tracking unit that tracks the progress of evacuees along the evacuation route in real time based on their location information. The progress tracking unit periodically updates the current location of the evacuees and can grasp the progress of the evacuees along the evacuation route. For example, if an evacuee deviates from the planned route, the progress tracking unit can guide the evacuees to a corrected route. Furthermore, if an evacuee is behind schedule, the progress tracking unit can provide the evacuees with advice on how to evacuate quickly. Furthermore, if an evacuee reaches their destination, the progress tracking unit can guide the evacuees on their next actions. This enables real-time progress tracking based on the evacuees' location information.

[0137] The disaster evacuation route navigation system can further estimate the emotions of evacuees and adjust the evacuation route guidance method based on the estimated emotions. For example, if an evacuee is nervous, the navigation unit can provide a simple and highly visible guidance method. If an evacuee is relaxed, the navigation unit can also provide a guidance method that includes detailed information. Furthermore, if an evacuee is in a hurry, the navigation unit can provide a quick guidance method that focuses on the main points. This makes it possible to provide the optimal guidance method according to the evacuee's emotions.

[0138] The disaster evacuation route navigation system can further estimate the emotions of evacuees and adjust the evacuation route selection criteria based on the estimated emotions. For example, if an evacuee is nervous, the evacuation route selection unit can prioritize selecting the safest route. Also, if an evacuee is relaxed, the evacuation route selection unit can prioritize selecting the most efficient route. Furthermore, if an evacuee is in a hurry, the evacuation route selection unit can prioritize selecting the route that can be reached in the shortest time. This makes it possible to provide the optimal evacuation route according to the emotions of the evacuee.

[0139] The disaster evacuation route navigation system can further estimate the emotions of evacuees and adjust the display method of evacuation routes based on the estimated emotions. For example, if an evacuee is nervous, the evacuation route selection unit can provide a simple, highly visible display method. If an evacuee is relaxed, the evacuation route selection unit can also provide a display method that includes detailed information. Furthermore, if an evacuee is in a hurry, the evacuation route selection unit can provide a display method that focuses on the main points. This makes it possible to provide an optimal display method according to the emotions of the evacuees.

[0140] The disaster evacuation route navigation system can further estimate the emotions of evacuees and adjust the evacuation route guidance method based on the estimated emotions. For example, if an evacuee is nervous, the navigation unit can provide a simple and highly visible guidance method. If an evacuee is relaxed, the navigation unit can also provide a guidance method that includes detailed information. Furthermore, if an evacuee is in a hurry, the navigation unit can provide a quick guidance method that focuses on the main points. This makes it possible to provide the optimal guidance method according to the evacuee's emotions.

[0141] The disaster evacuation route navigation system can further estimate the emotions of evacuees and adjust the evacuation route selection criteria based on the estimated emotions. For example, if an evacuee is nervous, the evacuation route selection unit can prioritize selecting the safest route. Also, if an evacuee is relaxed, the evacuation route selection unit can prioritize selecting the most efficient route. Furthermore, if an evacuee is in a hurry, the evacuation route selection unit can prioritize selecting the route that can be reached in the shortest time. This makes it possible to provide the optimal evacuation route according to the emotions of the evacuee.

[0142] The disaster evacuation route navigation system can further estimate the emotions of evacuees and adjust the display method of evacuation routes based on the estimated emotions. For example, if an evacuee is nervous, the evacuation route selection unit can provide a simple, highly visible display method. If an evacuee is relaxed, the evacuation route selection unit can also provide a display method that includes detailed information. Furthermore, if an evacuee is in a hurry, the evacuation route selection unit can provide a display method that focuses on the main points. This makes it possible to provide an optimal display method according to the emotions of the evacuees.

[0143] The disaster evacuation route navigation system can further estimate the emotions of evacuees and adjust the evacuation route guidance method based on the estimated emotions. For example, if an evacuee is nervous, the navigation unit can provide a simple and highly visible guidance method. If an evacuee is relaxed, the navigation unit can also provide a guidance method that includes detailed information. Furthermore, if an evacuee is in a hurry, the navigation unit can provide a quick guidance method that focuses on the main points. This makes it possible to provide the optimal guidance method according to the evacuee's emotions.

[0144] The processing flow of the second embodiment will be briefly explained below.

[0145] Step 1: The collection unit collects data. The data includes, for example, road image data, topographical data, and data on damage caused by past natural disasters. The collection unit collects road image data using camera images and topographical data using satellite images. It can also obtain data on damage caused by past natural disasters from government databases. Step 2: The damage prediction unit predicts damage based on the data collected by the collection unit. Damage includes damage to buildings, endangerment of human life, and traffic disruptions. The damage prediction unit analyzes the collected data and predicts the risk of damage to buildings, endangerment of human life, and traffic disruptions. Step 3: The evacuation route selection unit selects an evacuation route based on the damage predicted by the damage prediction unit. The evacuation route includes criteria such as distance, safety, and time. The evacuation route selection unit selects the safest, most efficient, and fastest evacuation route based on the results of the damage prediction unit. Step 4: The navigation unit guides the user along the evacuation route selected by the evacuation route selection unit. The guidance may be provided by voice guidance, visual guidance, text guidance, or the like. The navigation unit guides the user along the evacuation route using voice guidance, visual guidance, or text guidance.

[0146] 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.

[0147] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> 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.

[0148] 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.

[0149] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0150] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0151] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0160] 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. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0161] 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.

[0162] 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.

[0163] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0164] 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.

[0165] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0166] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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).

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0176] 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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0177] 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.

[0178] 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.

[0179] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0180] 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.

[0181] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0182] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0183] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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).

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0193] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. 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 the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0194] 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.

[0195] 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.

[0196] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0197] 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.

[0198] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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).

[0203] 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.

[0204] 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."

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] [Explanation of symbols]

[0218] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects data; a damage prediction unit that predicts damage based on the data collected by the collection unit; an evacuation route selection unit that selects an evacuation route based on the damage predicted by the damage prediction unit; a navigation unit that guides a user along the evacuation route selected by the evacuation route selection unit; Equipped with A system characterized by:

2. Equipped with an exterior wall damage prediction unit that predicts the extent of damage to the exterior walls 2. The system of claim 1.

3. Equipping a risk assessment department to evaluate the risks of evacuation routes 2. The system of claim 1.

4. The collecting unit Collect road image data, topographical data, and data on past natural disaster damage 2. The system of claim 1.

5. The damage prediction unit Predicting damage based on collected data 2. The system of claim 1.

6. The evacuation route selection unit Select an evacuation route based on the results of the damage prediction section 2. The system of claim 1.

7. The navigation unit Guide the user along the selected evacuation route 2. The system of claim 1.

8. The collecting unit Inferring user emotions and adjusting the timing of data collection based on the estimated user emotions 2. The system of claim 1.

9. The collecting unit Analyze past disaster data and select data collection methods 2. The system of claim 1.

Images