System

Abstract

An object of a system according to an embodiment is to predict the occurrence and spread of a flood in real time and to quickly cope with the flood.SOLUTION: A system according to an embodiment includes a data collection unit, an analysis unit, a warning information provision unit, an evacuation guidance unit, a risk map generation unit, and a safety measure provision unit. The data collection unit collects weather data and water level sensor information in real time. The analysis unit analyzes the weather data and the water level sensor information collected by the data collection unit, and predicts occurrence and expansion of a flood. The alert information providing unit provides a user with early alert information on the basis of the result of the occurrence or spread of the flood predicted by the analysis unit. The evacuation guidance unit guides the user to an evacuation route and an evacuation shelter on the basis of the warning information provided by the warning information providing unit. The risk map generation unit generates a flood risk map based on the data analyzed by the analysis unit. The safety measure provider provides a safety measure based on the flood risk map generated by the risk map generator.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 of making it difficult to predict the occurrence and spread of floods in real time and to respond quickly.

[0005] The system according to the embodiment aims to predict the occurrence and expansion of floods in real time and to respond quickly. [Means for solving the problem]

[0006] The system according to the embodiment includes a data collection unit, an analysis unit, an alert information provision unit, an evacuation guidance unit, a risk map generation unit, and a safety measure provision unit. The data collection unit collects meteorological data and water level sensor information in real time. The analysis unit analyzes the meteorological data and water level sensor information collected by the data collection unit and predicts the occurrence and expansion of floods. The alert information provision unit provides early warning information to a user based on the results of the flood occurrence and expansion predicted by the analysis unit. The evacuation guidance unit guides a user to evacuation routes and evacuation shelters based on the alert information provided by the alert information provision unit. The risk map generation unit generates a flood risk map based on the data analyzed by the analysis unit. The safety measure provision unit provides safety measures based on the flood risk map generated by the risk map generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can predict the occurrence and expansion of floods in real time and respond quickly. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A flood prediction system according to an embodiment of the present invention analyzes meteorological data and information from water level sensors in real time to predict the occurrence and expansion of floods. This system provides users with early warning information and automatically guides them to evacuation routes and shelters. It also uses AI to create flood risk maps and provides advice on safety measures. This allows the flood prediction system to efficiently and effectively predict and warn of floods.

[0029] A flood prediction system according to an embodiment includes a data collection unit, an analysis unit, a warning information provision unit, an evacuation guidance unit, a risk map generation unit, and a safety measure provision unit. The data collection unit collects meteorological data and water level sensor information in real time. For example, the data collection unit collects meteorological data such as temperature, precipitation, and wind speed. The data collection unit also collects water level sensor information such as river and reservoir water levels. The analysis unit analyzes the meteorological data and water level sensor information collected by the data collection unit to predict the occurrence and expansion of floods. For example, the analysis unit predicts the risk of flood occurrence using a machine learning algorithm. The analysis unit can also build a prediction model based on past flood data. The warning information provision unit provides early warning information to a user based on the flood occurrence and expansion predicted by the analysis unit. For example, the warning information provision unit sends a warning message to the user, such as, "There is a risk of flooding. Please begin evacuation." The warning information provision unit can also provide warning information via a voice assistant or smart speaker. The evacuation guidance unit guides the user to evacuation routes and evacuation shelters based on the alert information provided by the alert information providing unit. For example, the evacuation guidance unit guides the user to the optimal evacuation route based on the user's current location. The evacuation guidance unit can also monitor the congestion status of evacuation shelters in real time and prioritize vacant shelters. The risk map generation unit generates a flood risk map based on the data analyzed by the analysis unit. For example, the risk map generation unit displays areas where flooding is expected on a map using different colors. The risk map generation unit can also display the flood risk map as a 3D model. The safety measure provision unit provides safety measures based on the flood risk map generated by the risk map generation unit. For example, the safety measure provision unit provides specific advice such as, "In areas with a high risk of flooding, please move valuables to higher ground." The safety measure provision unit can also provide individually customized safety measure advice taking into account the user's home environment and lifestyle. This enables the flood prediction system according to the embodiment to efficiently and effectively predict and monitor floods.For example, if the risk of flooding increases, people can receive early warning information and take appropriate evacuation action. Also, by taking safety measures based on the flood risk map, damage can be minimized.

[0030] The data collection unit can simultaneously analyze weather data and water level sensor information as well as other natural disaster data such as earthquakes and tsunamis to predict complex disaster risks. For example, the data collection unit collects weather data and water level sensor information as well as earthquake intensity data and tsunami height data in real time, and the generation AI performs an integrated analysis of this data. For example, the generation AI predicts the risk of an earthquake occurring based on earthquake intensity data. The generation AI also predicts the risk of a tsunami occurring based on tsunami height data. This allows for the prediction of complex disaster risks, enabling more comprehensive disaster prevention measures.

[0031] The data collection unit uses a drone to collect aerial photographic data in real time, and the generation AI analyzes that data to grasp the progress of the flood. The data collection unit, for example, uses a drone to collect aerial photographic data from above a river or lake in real time, and the generation AI analyzes that data. For example, the generation AI analyzes the aerial photographic images to grasp changes in water levels and the progress of the flood. The generation AI can also monitor the progress of the flood in real time using the drone's camera. This makes it possible to grasp the progress of the flood in real time by using a drone.

[0032] The analysis unit can predict the impact on industries such as agriculture and fishing based on meteorological data and water level sensor information, and propose appropriate countermeasures. For example, the analysis unit predicts the impact on crop growth conditions and yields based on meteorological data and water level sensor information. For example, the generation AI can analyze fluctuations in rainfall and water levels and propose appropriate irrigation and drainage measures to farmers. The generation AI can also predict the impact on fishing and propose appropriate fishing countermeasures to fishermen. This makes it possible to minimize damage by predicting the impact on industry and proposing appropriate countermeasures.

[0033] The analysis unit can evaluate the safety of tourist destinations and events based on weather data and water level sensor information, and provide safety information to tourists and event participants. The analysis unit can evaluate the safety of tourist destinations, for example, based on weather data and water level sensor information. For example, the generation AI can analyze fluctuations in rainfall and water levels and guide tourists to safe tourist routes and evacuation sites. The generation AI can also evaluate the safety of events and provide safety information to event participants. This makes it possible to ensure the safety of tourists and event participants by evaluating the safety of tourist destinations and events and providing appropriate safety information.

[0034] The analysis unit incorporates a machine learning model that uses past flood data, which can improve the accuracy of flood occurrence and expansion predictions. For example, the analysis unit collects past flood data, and the generation AI uses that data to improve the accuracy of flood occurrence and expansion predictions. For example, the generation AI trains the model using past rainfall and water level fluctuation data. The generation AI can also build a prediction model based on past flood data. This improves the accuracy of flood predictions by utilizing past data.

[0035] The analysis unit can combine topographical data and urban infrastructure data to make more detailed predictions of flood occurrence and expansion. For example, the analysis unit collects topographical data, and the generation AI incorporates that data into flood predictions. For example, the generation AI predicts the extent of flood expansion by taking into account river basins and the slope of the terrain. The generation AI can also predict the impact of floods based on urban infrastructure data. In this way, the accuracy of flood predictions can be improved by utilizing topographical data and urban infrastructure data.

[0036] The analysis unit can integrate the flood prediction system with other natural disaster prediction systems to build a comprehensive disaster prediction platform. For example, the analysis unit can integrate the flood prediction system with a wildfire prediction system to build a comprehensive disaster prediction platform. For example, the generation AI can simultaneously analyze the risk of wildfires and floods. The generation AI can also be integrated with an earthquake prediction system to simultaneously analyze the risk of earthquakes and floods. This allows for more comprehensive disaster prevention measures to be taken by predicting multiple natural disasters simultaneously.

[0037] The alert information providing unit can provide individually customized alert information based on the user's location information. For example, the alert information providing unit collects location information in real time from the user's smartphone or GPS device, and the generating AI provides individually customized alert information based on that data. For example, the generating AI guides the user to the optimal evacuation route based on the user's current location. The generating AI can also provide alert information for areas with a high risk of flooding based on the user's location information. This makes it possible to provide more appropriate alert information based on the user's location information.

[0038] The warning information provision unit provides early warning information through voice assistants and smart speakers, and can also accommodate the visually impaired and elderly. The warning information provision unit builds a system in which the generation AI provides early warning information through voice assistants and smart speakers. For example, the generation AI can alert the visually impaired and elderly to the risk of flooding by voice. The generation AI can also provide evacuation routes and shelters through the voice assistant. This makes it possible to provide appropriate warning information to the visually impaired and elderly.

[0039] The warning information provision unit can spread early warning information widely through social media and messaging apps, enabling rapid information sharing. The warning information provision unit, for example, builds a system in which the generation AI spreads early warning information widely through social media and messaging apps. For example, the generation AI can notify users of the risk of flooding through Twitter and Facebook. The generation AI can also send emergency warnings to users through messaging apps. This makes it possible to share information quickly by utilizing social media and messaging apps.

[0040] The warning information provision unit can automatically notify local community centers, schools, and other public facilities, promoting evacuation behavior throughout the entire region. For example, the warning information provision unit constructs a system in which the generation AI automatically notifies local community centers, schools, and other public facilities of early warning information. For example, the generation AI sends emergency warnings to public facilities in areas where there is imminent risk of flooding. The generation AI can also work with public facilities to issue evacuation instructions in order to promote evacuation behavior throughout the entire region. In this way, by notifying public facilities, evacuation behavior throughout the entire region is promoted.

[0041] The evacuation guidance section can incorporate real-time traffic information and obstacle information to provide the optimal evacuation route. For example, the evacuation guidance section collects real-time traffic information, and the generation AI provides the optimal evacuation route based on that data. For example, the generation AI provides evacuation route guidance taking into account information on traffic congestion and traffic restrictions. The generation AI can also adjust the evacuation route based on obstacle information. This makes it possible to provide a more appropriate evacuation route by utilizing real-time information.

[0042] The evacuation guidance unit can monitor the congestion status of evacuation shelters in real time and give priority to vacant evacuation shelters. For example, the evacuation guidance unit can monitor the congestion status of evacuation shelters in real time, and the generation AI can use that data to give priority to vacant evacuation shelters. For example, the generation AI can analyze the capacity and congestion level of the evacuation shelter and provide guidance. The generation AI can also suggest the most suitable evacuation shelter to the user based on the congestion status of the evacuation shelter. This makes it possible to provide more appropriate evacuation shelter guidance by understanding the congestion status of the evacuation shelter in real time.

[0043] The evacuation guidance unit can visually display evacuation routes using AR technology, allowing users to intuitively understand them. The evacuation guidance unit, for example, uses AR technology to build a system that visually displays evacuation routes. For example, the generation AI can display evacuation routes by superimposing them on real scenery using a smartphone camera. The generation AI can also display evacuation routes using AR glasses. In this way, the use of AR technology allows users to intuitively understand evacuation routes.

[0044] The evacuation guidance unit can also take into account information about pets and family members to ensure everyone can evacuate safely. For example, the evacuation guidance unit collects information about the user's pets and family members, and the generation AI provides evacuation shelter guidance based on that data. For example, the generation AI can guide users to evacuation shelters that accept pets or that can accommodate the entire family. The generation AI can also propose evacuation plans that take into account the number of family members and the type of pets. This allows everyone to evacuate safely by taking into account information about pets and family members.

[0045] The risk map generation unit can incorporate past flood data and topographical data to create more detailed and accurate flood risk maps. For example, the risk map generation unit collects past flood data, and the generation AI creates a flood risk map based on that data. For example, the generation AI uses past rainfall and water level fluctuation data to refine the risk map. The generation AI can also create a flood risk map based on topographical data. In this way, by utilizing past data and topographical data, more detailed and accurate flood risk maps can be created.

[0046] The risk map generation unit can display the flood risk map as a 3D model, allowing users to grasp the risk in three dimensions. The risk map generation unit, for example, builds a system that displays the flood risk map as a 3D model. For example, the generation AI can display the elevation difference of the terrain and the layout of buildings in three dimensions, allowing users to intuitively grasp the risk. The generation AI can also display the progression of the flood in a 3D model. In this way, the 3D model allows users to grasp the risk in three dimensions.

[0047] The risk map generation unit can integrate the flood risk map with other natural disaster risk maps to provide a comprehensive risk map. For example, the risk map generation unit can integrate the flood risk map with an earthquake risk map to build a system that provides a comprehensive risk map. For example, the generation AI can simultaneously display the risk of earthquakes and the risk of floods. The generation AI can also integrate with a wildfire risk map to simultaneously display the risk of wildfires and the risk of floods. This makes it possible to provide a more comprehensive risk map by integrating multiple natural disaster risks.

[0048] The risk map generation unit can share the flood risk map with local governments and companies and use it in urban planning and disaster prevention measures. The risk map generation unit can, for example, share the flood risk map with local governments and use it in urban planning and disaster prevention measures. For example, the generation AI can review urban planning in areas with high flood risk and take appropriate disaster prevention measures. The generation AI can also be shared with companies and used in their risk management and disaster prevention measures. In this way, sharing the flood risk map can lead to more appropriate urban planning and disaster prevention measures.

[0049] The safety measure providing unit can provide individually customized advice on safety measures, taking into consideration the user's home environment and lifestyle. For example, the safety measure providing unit collects information on the user's home environment and lifestyle, and the generating AI provides individually customized advice on safety measures based on that data. For example, the generating AI can propose an evacuation plan based on the structure of the home and family composition. The generating AI can also propose appropriate safety measures based on the user's lifestyle habits. This makes it possible to provide advice on safety measures that is tailored to the user's home environment and lifestyle.

[0050] The safety measure provision unit can visually explain safety measure advice using videos and illustrations to make it easier to understand. The safety measure provision unit, for example, builds a system that visually explains safety measure advice using videos and illustrations. For example, the generation AI can show evacuation routes and the locations of evacuation shelters in videos, allowing users to intuitively understand. The generation AI can also explain safety measure procedures using illustrations. In this way, the use of videos and illustrations makes the safety measure advice easier to understand.

[0051] The Safety Measures Providing Department can put safety measure advice into practice in local disaster prevention drills and workshops, linking it to actual actions. The Safety Measures Providing Department, for example, builds a system to put safety measure advice into practice in local disaster prevention drills and workshops. For example, the Generating AI conducts training to actually check the locations of evacuation routes and evacuation shelters. The Generating AI can also encourage users to take actual actions through disaster prevention drills and workshops. This makes it possible to link the advice to actual actions through practice in disaster prevention drills and workshops.

[0052] The safety measure provision unit can apply the safety measure advice to other household risks and provide comprehensive household safety measures. The safety measure provision unit, for example, builds a system that applies the safety measure advice to other household risks. For example, the generation AI provides evacuation plans and measures for fires and earthquakes. The generation AI can also suggest safety measures for other household risks. This makes it possible to provide comprehensive household safety measures by addressing other household risks such as fires and earthquakes.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The flood prediction system also includes an energy management unit. The energy management unit can predict the risk to power supply due to the occurrence and expansion of floods and propose appropriate energy management measures. For example, the energy management unit can predict the risk of power supply interruptions due to floods and propose ways to secure backup power sources and save power. The energy management unit can also adjust the power supply in advance in areas where the risk to power supply is high. This makes it possible to minimize the risk to power supply due to floods.

[0055] The data collection unit can further collect ecosystem data and predict the impact of floods on ecosystems. For example, the data collection unit collects water quality data and biodiversity data from rivers and lakes, and the generation AI analyzes that data. The generation AI predicts changes in water quality and biodiversity and evaluates the impact of floods on ecosystems. The generation AI can also propose measures to minimize the impact on ecosystems. This makes it possible to predict the impact of floods on ecosystems and take appropriate measures.

[0056] The data collection unit further collects satellite data, enabling it to predict flood risks over a wide area. For example, the data collection unit collects satellite images in real time, and the generation AI analyzes the data. The generation AI analyzes fluctuations in rainfall and water levels over a wide area and predicts the risk of flooding. The generation AI can also monitor the progress of floods based on satellite data. This makes it possible to predict flood risks over a wide area by utilizing satellite data.

[0057] The analysis unit can predict the impact on transportation infrastructure based on weather data and water level sensor information and propose appropriate countermeasures. For example, the analysis unit analyzes fluctuations in rainfall and water levels to predict the risk of flooding on roads and bridges. The generation AI evaluates the impact on transportation infrastructure and proposes traffic restrictions and detour routes. The generation AI can also monitor the operation status of public transportation and propose appropriate operation plans. This makes it possible to predict the impact on transportation infrastructure and take appropriate countermeasures.

[0058] The analysis unit can predict the impact on buildings based on weather data and water level sensor information and propose appropriate countermeasures. For example, the analysis unit analyzes rainfall and water level fluctuations to predict the risk of flooding for buildings. The generation AI evaluates the impact on buildings and proposes flood prevention measures and reinforcement work. The generation AI can also evaluate the durability of buildings and propose appropriate maintenance plans. This makes it possible to predict the impact on buildings and take appropriate countermeasures.

[0059] The analysis unit can predict the impact on industries such as agriculture and fishing based on meteorological data and water level sensor information, and propose appropriate countermeasures. For example, the analysis unit predicts the impact on crop growth conditions and harvest yields based on meteorological data and water level sensor information. The generation AI analyzes fluctuations in rainfall and water levels, and proposes appropriate irrigation and drainage measures to farmers. The generation AI can also predict the impact on fishing and propose appropriate fishing countermeasures to fishermen. This makes it possible to minimize damage by predicting the impact on industry and proposing appropriate countermeasures.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The data collection unit collects meteorological data and water level sensor information in real time. For example, the data collection unit collects temperature, precipitation, wind speed, etc. as meteorological data, and collects river and reservoir water levels as water level sensor information. Step 2: The analysis unit analyzes the meteorological data and water level sensor information collected by the data collection unit to predict the occurrence and expansion of floods. For example, the analysis unit can use machine learning algorithms to predict the risk of flooding and build a prediction model based on past flood data. Step 3: The warning information provider provides early warning information to the user based on the results of the flood occurrence and expansion predicted by the analysis unit. For example, the warning information provider can send a warning message to the user such as "There is a risk of flooding. Please begin evacuation." The warning information can also be provided through a voice assistant or smart speaker. Step 4: The evacuation guidance unit guides the user to evacuation routes and evacuation shelters based on the alert information provided by the alert information providing unit. For example, the evacuation guidance unit can guide the user to the optimal evacuation route based on the user's current location, monitor the congestion status of evacuation shelters in real time, and preferentially guide the user to shelters that are less crowded. Step 5: The risk map generator generates a flood risk map based on the data analyzed by the analyzer. For example, the risk map generator may color-code areas where flooding is expected on a map, and may also display the flood risk map as a 3D model. Step 6: The safety measure provider provides safety measures based on the flood risk map generated by the risk map generator. For example, the safety measure provider may provide specific advice such as, "In areas with a high risk of flooding, move valuables to higher places," and may provide individually customized advice on safety measures taking into account the user's home environment and lifestyle.

[0062] (Example 2) A flood prediction system according to an embodiment of the present invention analyzes meteorological data and information from water level sensors in real time to predict the occurrence and expansion of floods. This system provides users with early warning information and automatically guides them to evacuation routes and shelters. It also uses AI to create flood risk maps and provides advice on safety measures. This allows the flood prediction system to efficiently and effectively predict and warn of floods.

[0063] A flood prediction system according to an embodiment includes a data collection unit, an analysis unit, a warning information provision unit, an evacuation guidance unit, a risk map generation unit, and a safety measure provision unit. The data collection unit collects meteorological data and water level sensor information in real time. For example, the data collection unit collects meteorological data such as temperature, precipitation, and wind speed. The data collection unit also collects water level sensor information such as river and reservoir water levels. The analysis unit analyzes the meteorological data and water level sensor information collected by the data collection unit to predict the occurrence and expansion of floods. For example, the analysis unit predicts the risk of flood occurrence using a machine learning algorithm. The analysis unit can also build a prediction model based on past flood data. The warning information provision unit provides early warning information to a user based on the flood occurrence and expansion predicted by the analysis unit. For example, the warning information provision unit sends a warning message to the user, such as, "There is a risk of flooding. Please begin evacuation." The warning information provision unit can also provide warning information via a voice assistant or smart speaker. The evacuation guidance unit guides the user to evacuation routes and evacuation shelters based on the alert information provided by the alert information providing unit. For example, the evacuation guidance unit guides the user to the optimal evacuation route based on the user's current location. The evacuation guidance unit can also monitor the congestion status of evacuation shelters in real time and prioritize vacant shelters. The risk map generation unit generates a flood risk map based on the data analyzed by the analysis unit. For example, the risk map generation unit displays areas where flooding is expected on a map using different colors. The risk map generation unit can also display the flood risk map as a 3D model. The safety measure provision unit provides safety measures based on the flood risk map generated by the risk map generation unit. For example, the safety measure provision unit provides specific advice such as, "In areas with a high risk of flooding, please move valuables to higher ground." The safety measure provision unit can also provide individually customized safety measure advice taking into account the user's home environment and lifestyle. This enables the flood prediction system according to the embodiment to efficiently and effectively predict and monitor floods.For example, if the risk of flooding increases, people can receive early warning information and take appropriate evacuation action. Also, by taking safety measures based on the flood risk map, damage can be minimized.

[0064] The data collection unit can simultaneously analyze weather data and water level sensor information as well as other natural disaster data such as earthquakes and tsunamis to predict complex disaster risks. For example, the data collection unit collects weather data and water level sensor information as well as earthquake intensity data and tsunami height data in real time, and the generation AI performs an integrated analysis of this data. For example, the generation AI predicts the risk of an earthquake occurring based on earthquake intensity data. The generation AI also predicts the risk of a tsunami occurring based on tsunami height data. This allows for the prediction of complex disaster risks, enabling more comprehensive disaster prevention measures.

[0065] The data collection unit uses a drone to collect aerial photographic data in real time, and the generation AI analyzes that data to grasp the progress of the flood. The data collection unit, for example, uses a drone to collect aerial photographic data from above a river or lake in real time, and the generation AI analyzes that data. For example, the generation AI analyzes the aerial photographic images to grasp changes in water levels and the progress of the flood. The generation AI can also monitor the progress of the flood in real time using the drone's camera. This makes it possible to grasp the progress of the flood in real time by using a drone.

[0066] The analysis unit can use its emotion estimation function to collect the user's emotions of anxiety and fear in real time and reflect that emotion data in the analysis. The analysis unit, for example, collects emotion data in real time from the user's smartphone or wearable device, and the generation AI analyzes that data. For example, the generation AI can detect the user's emotions of anxiety or fear using heart rate or facial expression recognition. The generation AI can also estimate the user's emotions using voice analysis technology. This allows for more appropriate flood prediction and alerts by taking the user's emotions into consideration.

[0067] The analysis unit can predict the impact on industries such as agriculture and fishing based on meteorological data and water level sensor information, and propose appropriate countermeasures. For example, the analysis unit predicts the impact on crop growth conditions and yields based on meteorological data and water level sensor information. For example, the generation AI can analyze fluctuations in rainfall and water levels and propose appropriate irrigation and drainage measures to farmers. The generation AI can also predict the impact on fishing and propose appropriate fishing countermeasures to fishermen. This makes it possible to minimize damage by predicting the impact on industry and proposing appropriate countermeasures.

[0068] The analysis unit can evaluate the safety of tourist destinations and events based on weather data and water level sensor information, and provide safety information to tourists and event participants. The analysis unit can evaluate the safety of tourist destinations, for example, based on weather data and water level sensor information. For example, the generation AI can analyze fluctuations in rainfall and water levels and guide tourists to safe tourist routes and evacuation sites. The generation AI can also evaluate the safety of events and provide safety information to event participants. This makes it possible to ensure the safety of tourists and event participants by evaluating the safety of tourist destinations and events and providing appropriate safety information.

[0069] The analysis unit uses the emotion estimation function to analyze the emotional reactions of users when they receive flood information and can optimize the method of providing information. For example, the analysis unit collects emotional reactions of users when they receive flood information in real time, and the generation AI analyzes that data. For example, the generation AI analyzes the user's facial expressions and tone of voice to estimate their emotions and adjust the method of providing information. The generation AI can also optimize the timing and content of information provision based on the user's emotional data. This makes it possible to provide more effective information by taking the user's emotional reactions into consideration.

[0070] The analysis unit incorporates a machine learning model that uses past flood data, which can improve the accuracy of flood occurrence and expansion predictions. For example, the analysis unit collects past flood data, and the generation AI uses that data to improve the accuracy of flood occurrence and expansion predictions. For example, the generation AI trains the model using past rainfall and water level fluctuation data. The generation AI can also build a prediction model based on past flood data. This improves the accuracy of flood predictions by utilizing past data.

[0071] The analysis unit can combine topographical data and urban infrastructure data to make more detailed predictions of flood occurrence and expansion. For example, the analysis unit collects topographical data, and the generation AI incorporates that data into flood predictions. For example, the generation AI predicts the extent of flood expansion by taking into account river basins and the slope of the terrain. The generation AI can also predict the impact of floods based on urban infrastructure data. In this way, the accuracy of flood predictions can be improved by utilizing topographical data and urban infrastructure data.

[0072] The analysis unit can use the emotion estimation function to analyze the emotional barriers to a user's evacuation behavior and reflect the results in the prediction model. For example, the analysis unit collects the user's emotional barriers to evacuation behavior in real time, and the generation AI analyzes the data. For example, the generation AI detects feelings of anxiety or fear regarding evacuation and reflects them in the prediction model. The generation AI can also improve the accuracy of evacuation behavior predictions based on the user's emotional data. This makes it possible to predict more realistic evacuation behavior by taking the user's emotional barriers into account.

[0073] The analysis unit can integrate the flood prediction system with other natural disaster prediction systems to build a comprehensive disaster prediction platform. For example, the analysis unit can integrate the flood prediction system with a wildfire prediction system to build a comprehensive disaster prediction platform. For example, the generation AI can simultaneously analyze the risk of wildfires and floods. The generation AI can also be integrated with an earthquake prediction system to simultaneously analyze the risk of earthquakes and floods. This allows for more comprehensive disaster prevention measures to be taken by predicting multiple natural disasters simultaneously.

[0074] The analysis unit uses the emotion estimation function to analyze the emotional reactions of users who receive flood forecast information and improve the method of providing forecast information. For example, the analysis unit collects the emotional reactions of users who receive flood forecast information in real time, and the generation AI analyzes that data. For example, the generation AI analyzes the user's facial expressions and tone of voice to estimate their emotions and adjust the method of providing information. The generation AI can also optimize the timing and content of information provision based on the user's emotional data. This makes it possible to provide more effective forecast information by taking the user's emotional reactions into consideration.

[0075] The alert information providing unit can provide individually customized alert information based on the user's location information. For example, the alert information providing unit collects location information in real time from the user's smartphone or GPS device, and the generating AI provides individually customized alert information based on that data. For example, the generating AI guides the user to the optimal evacuation route based on the user's current location. The generating AI can also provide alert information for areas with a high risk of flooding based on the user's location information. This makes it possible to provide more appropriate alert information based on the user's location information.

[0076] The warning information provision unit provides early warning information through voice assistants and smart speakers, and can also accommodate the visually impaired and elderly. The warning information provision unit builds a system in which the generation AI provides early warning information through voice assistants and smart speakers. For example, the generation AI can alert the visually impaired and elderly to the risk of flooding by voice. The generation AI can also provide evacuation routes and shelters through the voice assistant. This makes it possible to provide appropriate warning information to the visually impaired and elderly.

[0077] The alert information provision unit uses the emotion estimation function to monitor the emotional reactions of users who receive alert information in real time and can provide additional information or support as needed. For example, the alert information provision unit collects the emotional reactions of users who receive alert information in real time, and the generation AI analyzes the data. For example, the generation AI analyzes the user's facial expressions and tone of voice to estimate their emotions and provide additional information as needed. The generation AI can also provide appropriate support based on the user's emotional data. This allows more appropriate support to be provided by monitoring the user's emotional reactions.

[0078] The warning information provision unit can spread early warning information widely through social media and messaging apps, enabling rapid information sharing. The warning information provision unit, for example, builds a system in which the generation AI spreads early warning information widely through social media and messaging apps. For example, the generation AI can notify users of the risk of flooding through Twitter and Facebook. The generation AI can also send emergency warnings to users through messaging apps. This makes it possible to share information quickly by utilizing social media and messaging apps.

[0079] The warning information provision unit can automatically notify local community centers, schools, and other public facilities, promoting evacuation behavior throughout the entire region. For example, the warning information provision unit constructs a system in which the generation AI automatically notifies local community centers, schools, and other public facilities of early warning information. For example, the generation AI sends emergency warnings to public facilities in areas where there is imminent risk of flooding. The generation AI can also work with public facilities to issue evacuation instructions in order to promote evacuation behavior throughout the entire region. In this way, by notifying public facilities, evacuation behavior throughout the entire region is promoted.

[0080] The alert information provision unit can use the emotion estimation function to analyze the emotional data of users who receive alert information and optimize the timing and content of information provision. For example, the alert information provision unit collects emotional data of users who receive alert information in real time, and the generation AI analyzes that data. For example, the generation AI analyzes the user's facial expressions and tone of voice to estimate their emotions and adjust the timing of information provision. The generation AI can also optimize the content of the information provision based on the user's emotional data. This makes it possible to provide more effective information by analyzing the user's emotional data.

[0081] The evacuation guidance section can incorporate real-time traffic information and obstacle information to provide the optimal evacuation route. For example, the evacuation guidance section collects real-time traffic information, and the generation AI provides the optimal evacuation route based on that data. For example, the generation AI provides evacuation route guidance taking into account information on traffic congestion and traffic restrictions. The generation AI can also adjust the evacuation route based on obstacle information. This makes it possible to provide a more appropriate evacuation route by utilizing real-time information.

[0082] The evacuation guidance unit can monitor the congestion status of evacuation shelters in real time and give priority to vacant evacuation shelters. For example, the evacuation guidance unit can monitor the congestion status of evacuation shelters in real time, and the generation AI can use that data to give priority to vacant evacuation shelters. For example, the generation AI can analyze the capacity and congestion level of the evacuation shelter and provide guidance. The generation AI can also suggest the most suitable evacuation shelter to the user based on the congestion status of the evacuation shelter. This makes it possible to provide more appropriate evacuation shelter guidance by understanding the congestion status of the evacuation shelter in real time.

[0083] The evacuation guidance unit can use the emotion estimation function to monitor the user's stress and anxiety during evacuation and provide appropriate support information. For example, the evacuation guidance unit collects the user's stress and anxiety during evacuation in real time, and the generation AI analyzes the data. For example, the generation AI can analyze the user's facial expressions and tone of voice to estimate their emotions and provide appropriate support information. The generation AI can also strengthen the support system during evacuation based on the user's emotion data. This makes it possible to provide more appropriate support information by monitoring the user's emotions during evacuation.

[0084] The evacuation guidance unit can visually display evacuation routes using AR technology, allowing users to intuitively understand them. The evacuation guidance unit, for example, uses AR technology to build a system that visually displays evacuation routes. For example, the generation AI can display evacuation routes by superimposing them on real scenery using a smartphone camera. The generation AI can also display evacuation routes using AR glasses. In this way, the use of AR technology allows users to intuitively understand evacuation routes.

[0085] The evacuation guidance unit can also take into account information about pets and family members to ensure everyone can evacuate safely. For example, the evacuation guidance unit collects information about the user's pets and family members, and the generation AI provides evacuation shelter guidance based on that data. For example, the generation AI can guide users to evacuation shelters that accept pets or that can accommodate the entire family. The generation AI can also propose evacuation plans that take into account the number of family members and the type of pets. This allows everyone to evacuate safely by taking into account information about pets and family members.

[0086] The evacuation guidance unit can use the emotion estimation function to collect emotion data of users during evacuation and strengthen the support system at evacuation shelters. For example, the evacuation guidance unit collects emotion data of users during evacuation in real time, and the generation AI analyzes the data. For example, the generation AI can analyze the user's facial expressions and tone of voice to estimate their emotions and strengthen the support system at evacuation shelters. The generation AI can also adjust the support content at evacuation shelters based on the user's emotion data. In this way, by collecting emotion data of users during evacuation, the support system at evacuation shelters can be strengthened.

[0087] The risk map generation unit can incorporate past flood data and topographical data to create more detailed and accurate flood risk maps. For example, the risk map generation unit collects past flood data, and the generation AI creates a flood risk map based on that data. For example, the generation AI uses past rainfall and water level fluctuation data to refine the risk map. The generation AI can also create a flood risk map based on topographical data. In this way, by utilizing past data and topographical data, more detailed and accurate flood risk maps can be created.

[0088] The risk map generation unit can display the flood risk map as a 3D model, allowing users to grasp the risk in three dimensions. The risk map generation unit, for example, builds a system that displays the flood risk map as a 3D model. For example, the generation AI can display the elevation difference of the terrain and the layout of buildings in three dimensions, allowing users to intuitively grasp the risk. The generation AI can also display the progression of the flood in a 3D model. In this way, the 3D model allows users to grasp the risk in three dimensions.

[0089] The risk map generation unit can integrate the flood risk map with other natural disaster risk maps to provide a comprehensive risk map. For example, the risk map generation unit can integrate the flood risk map with an earthquake risk map to build a system that provides a comprehensive risk map. For example, the generation AI can simultaneously display the risk of earthquakes and the risk of floods. The generation AI can also integrate with a wildfire risk map to simultaneously display the risk of wildfires and the risk of floods. This makes it possible to provide a more comprehensive risk map by integrating multiple natural disaster risks.

[0090] The risk map generation unit can share the flood risk map with local governments and companies and use it in urban planning and disaster prevention measures. The risk map generation unit can, for example, share the flood risk map with local governments and use it in urban planning and disaster prevention measures. For example, the generation AI can review urban planning in areas with high flood risk and take appropriate disaster prevention measures. The generation AI can also be shared with companies and used in their risk management and disaster prevention measures. In this way, sharing the flood risk map can lead to more appropriate urban planning and disaster prevention measures.

[0091] The risk map generation unit can use the emotion estimation function to collect emotional data from users who view the risk map and optimize the method of providing risk information. For example, the risk map generation unit collects emotional data from users who view the risk map in real time, and the generation AI analyzes the data. For example, the generation AI analyzes the user's facial expressions and tone of voice to estimate their emotions and optimize the method of providing risk information. The generation AI can also adjust the display content of risk information based on the user's emotional data. In this way, collecting user emotional data makes it possible to provide more effective risk information.

[0092] The safety measure providing unit can provide individually customized advice on safety measures, taking into consideration the user's home environment and lifestyle. For example, the safety measure providing unit collects information on the user's home environment and lifestyle, and the generating AI provides individually customized advice on safety measures based on that data. For example, the generating AI can propose an evacuation plan based on the structure of the home and family composition. The generating AI can also propose appropriate safety measures based on the user's lifestyle habits. This makes it possible to provide advice on safety measures that is tailored to the user's home environment and lifestyle.

[0093] The safety measure provision unit can visually explain safety measure advice using videos and illustrations to make it easier to understand. The safety measure provision unit, for example, builds a system that visually explains safety measure advice using videos and illustrations. For example, the generation AI can show evacuation routes and the locations of evacuation shelters in videos, allowing users to intuitively understand. The generation AI can also explain safety measure procedures using illustrations. In this way, the use of videos and illustrations makes the safety measure advice easier to understand.

[0094] The safety measure providing unit can use the emotion estimation function to analyze the emotional reactions of users who receive advice and improve the content and method of the advice. For example, the safety measure providing unit collects the emotional reactions of users who receive advice in real time, and the generation AI analyzes the data. For example, the generation AI analyzes the user's facial expressions and tone of voice to estimate their emotions and adjust the content and method of the advice. The generation AI can also optimize the method of providing advice based on the user's emotional data. This allows more effective advice to be provided by analyzing the user's emotional reactions.

[0095] The Safety Measures Providing Department can put safety measure advice into practice in local disaster prevention drills and workshops, linking it to actual actions. The Safety Measures Providing Department, for example, builds a system to put safety measure advice into practice in local disaster prevention drills and workshops. For example, the Generating AI conducts training to actually check the locations of evacuation routes and evacuation shelters. The Generating AI can also encourage users to take actual actions through disaster prevention drills and workshops. This makes it possible to link the advice to actual actions through practice in disaster prevention drills and workshops.

[0096] The safety measure provision unit can apply the safety measure advice to other household risks and provide comprehensive household safety measures. The safety measure provision unit, for example, builds a system that applies the safety measure advice to other household risks. For example, the generation AI provides evacuation plans and measures for fires and earthquakes. The generation AI can also suggest safety measures for other household risks. This makes it possible to provide comprehensive household safety measures by addressing other household risks such as fires and earthquakes.

[0097] The safety measure providing unit can use the emotion estimation function to collect emotion data of users who receive advice and evaluate the effectiveness of the advice. For example, the safety measure providing unit collects emotion data of users who receive advice in real time, and the generation AI analyzes the data. For example, the generation AI analyzes the user's facial expressions and tone of voice to estimate their emotions and evaluate the effectiveness of the advice. The generation AI can also improve the content and method of advice based on the user's emotion data. In this way, the effectiveness of advice can be evaluated by collecting user's emotion data.

[0098] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0099] The flood prediction system also includes an energy management unit. The energy management unit can predict the risk to power supply due to the occurrence and expansion of floods and propose appropriate energy management measures. For example, the energy management unit can predict the risk of power supply interruptions due to floods and propose ways to secure backup power sources and save power. The energy management unit can also adjust the power supply in advance in areas where the risk to power supply is high. This makes it possible to minimize the risk to power supply due to floods.

[0100] The data collection unit can further collect ecosystem data and predict the impact of floods on ecosystems. For example, the data collection unit collects water quality data and biodiversity data from rivers and lakes, and the generation AI analyzes that data. The generation AI predicts changes in water quality and biodiversity and evaluates the impact of floods on ecosystems. The generation AI can also propose measures to minimize the impact on ecosystems. This makes it possible to predict the impact of floods on ecosystems and take appropriate measures.

[0101] The data collection unit further collects satellite data, enabling it to predict flood risks over a wide area. For example, the data collection unit collects satellite images in real time, and the generation AI analyzes the data. The generation AI analyzes fluctuations in rainfall and water levels over a wide area and predicts the risk of flooding. The generation AI can also monitor the progress of floods based on satellite data. This makes it possible to predict flood risks over a wide area by utilizing satellite data.

[0102] The analysis unit can use the emotion estimation function to estimate the user's willingness to evacuate and provide information to encourage evacuation. For example, the analysis unit collects the user's emotion data, and the generation AI analyzes that data. The generation AI detects anxiety or resistance to evacuation and provides appropriate information. The generation AI can also customize messages to encourage evacuation based on the user's emotion data. This can increase the user's willingness to evacuate and encourage rapid evacuation.

[0103] The analysis unit can predict the impact on transportation infrastructure based on weather data and water level sensor information and propose appropriate countermeasures. For example, the analysis unit analyzes fluctuations in rainfall and water levels to predict the risk of flooding on roads and bridges. The generation AI evaluates the impact on transportation infrastructure and proposes traffic restrictions and detour routes. The generation AI can also monitor the operation status of public transportation and propose appropriate operation plans. This makes it possible to predict the impact on transportation infrastructure and take appropriate countermeasures.

[0104] The analysis unit can use the emotion estimation function to analyze the emotional barriers to a user's evacuation behavior and reflect the results in the prediction model. For example, the analysis unit collects the user's emotional barriers to evacuation behavior in real time, and the generation AI analyzes the data. The generation AI detects feelings of anxiety and fear regarding evacuation and reflects them in the prediction model. The generation AI can also improve the accuracy of evacuation behavior predictions based on the user's emotional data. This makes it possible to predict more realistic evacuation behavior by taking the user's emotional barriers into account.

[0105] The analysis unit can predict the impact on buildings based on weather data and water level sensor information and propose appropriate countermeasures. For example, the analysis unit analyzes rainfall and water level fluctuations to predict the risk of flooding for buildings. The generation AI evaluates the impact on buildings and proposes flood prevention measures and reinforcement work. The generation AI can also evaluate the durability of buildings and propose appropriate maintenance plans. This makes it possible to predict the impact on buildings and take appropriate countermeasures.

[0106] The analysis unit uses the emotion estimation function to analyze the emotional reactions of users who receive flood forecast information and improve the way in which forecast information is provided. For example, the analysis unit collects the emotional reactions of users who receive flood forecast information in real time, and the generation AI analyzes that data. The generation AI analyzes the user's facial expressions and tone of voice to estimate their emotions and adjust the way in which information is provided. The generation AI can also optimize the timing and content of information provision based on the user's emotional data. This makes it possible to provide more effective forecast information by taking into account the user's emotional reactions.

[0107] The analysis unit can predict the impact on industries such as agriculture and fishing based on meteorological data and water level sensor information, and propose appropriate countermeasures. For example, the analysis unit predicts the impact on crop growth conditions and harvest yields based on meteorological data and water level sensor information. The generation AI analyzes fluctuations in rainfall and water levels, and proposes appropriate irrigation and drainage measures to farmers. The generation AI can also predict the impact on fishing and propose appropriate fishing countermeasures to fishermen. This makes it possible to minimize damage by predicting the impact on industry and proposing appropriate countermeasures.

[0108] The analysis unit uses the emotion estimation function to analyze the emotional reactions of users when they receive flood information, and can optimize the method of providing information. For example, the analysis unit collects emotional reactions of users when they receive flood information in real time, and the generation AI analyzes that data. The generation AI analyzes the user's facial expressions and tone of voice to estimate their emotions and adjust the method of providing information. The generation AI can also optimize the timing and content of information provision based on the user's emotional data. This makes it possible to provide more effective information by taking the user's emotional reactions into consideration.

[0109] The processing flow of the second embodiment will be briefly explained below.

[0110] Step 1: The data collection unit collects meteorological data and water level sensor information in real time. For example, the data collection unit collects temperature, precipitation, wind speed, etc. as meteorological data, and collects river and reservoir water levels as water level sensor information. Step 2: The analysis unit analyzes the meteorological data and water level sensor information collected by the data collection unit to predict the occurrence and expansion of floods. For example, the analysis unit can use machine learning algorithms to predict the risk of flooding and build a prediction model based on past flood data. Step 3: The warning information provider provides early warning information to the user based on the results of the flood occurrence and expansion predicted by the analysis unit. For example, the warning information provider can send a warning message to the user such as "There is a risk of flooding. Please begin evacuation." The warning information can also be provided through a voice assistant or smart speaker. Step 4: The evacuation guidance unit guides the user to evacuation routes and evacuation shelters based on the alert information provided by the alert information providing unit. For example, the evacuation guidance unit can guide the user to the optimal evacuation route based on the user's current location, monitor the congestion status of evacuation shelters in real time, and preferentially guide the user to shelters that are less crowded. Step 5: The risk map generator generates a flood risk map based on the data analyzed by the analyzer. For example, the risk map generator may color-code areas where flooding is expected on a map, and may also display the flood risk map as a 3D model. Step 6: The safety measure provider provides safety measures based on the flood risk map generated by the risk map generator. For example, the safety measure provider may provide specific advice such as, "In areas with a high risk of flooding, move valuables to higher places," and may provide individually customized advice on safety measures taking into account the user's home environment and lifestyle.

[0111] 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.

[0112] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0113] 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.

[0114] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0124] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0139] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0145] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0155] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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).

[0164] 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.

[0165] 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."

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0178] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a data collection unit that collects meteorological data and water level sensor information in real time; an analysis unit that analyzes the meteorological data and the water level sensor information collected by the data collection unit and predicts the occurrence and expansion of floods; a warning information providing unit that provides early warning information to a user based on the results of the flood occurrence and expansion predicted by the analysis unit; an evacuation guidance unit that guides the user to evacuation routes and evacuation shelters based on the alert information provided by the alert information providing unit; a risk map generation unit that generates a flood risk map based on the data analyzed by the analysis unit; a safety measure provision unit that provides safety measures based on the flood risk map generated by the risk map generation unit. A system characterized by:

2. The data collection unit In addition to the weather data and water level sensor information, data on other natural disasters such as earthquakes and tsunamis are also analyzed simultaneously to predict complex disaster risks.

2. The system of claim 1.

3. The analysis unit Based on the weather data and water level sensor information, the impact on industries such as agriculture and fishing will be predicted and appropriate measures will be proposed.

2. The system of claim 1.

4. The analysis unit Incorporating machine learning models using past flood data to improve the accuracy of forecasting the occurrence and expansion of floods.

2. The system of claim 1.

5. The warning information providing unit Providing individually customized alert information based on the user's location information 2. The system of claim 1.

6. The evacuation guidance unit Monitor the user's stress and anxiety during evacuation and provide appropriate support information 2. The system of claim 1.

7. The risk map generation unit Analyze the emotional reactions of users who view the risk map and improve the way risk information is displayed 2. The system of claim 1.

8. The safety measure providing unit Analyzing the emotional response of the user who received the advice and improving the content and method of the advice.

2. The system of claim 1.

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