System

Abstract

An object of a system according to an embodiment is to predict a flood risk and propose a quick and appropriate countermeasure to a local government.SOLUTION: A system includes a data collection unit, an analysis unit, a prediction unit, and a proposal unit. The data collection unit collects river data. The analysis unit analyzes the river data collected by the data collection unit. The prediction unit predicts a flood risk based on the data analyzed by the analysis unit. The proposing unit is configured to propose an operation plan of the facility and a evacuation plan to residents to the local government on the basis of the flood risk predicted by the predicting unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology does not adequately predict flood risk and take prompt measures based on that prediction, so there is room for improvement.

[0005] The system according to the embodiment aims to predict flood risks and propose prompt and appropriate countermeasures to local governments. [Means for solving the problem]

[0006] The system according to the embodiment includes a data collection unit, an analysis unit, a prediction unit, and a proposal unit. The data collection unit collects river data. The analysis unit analyzes the river data collected by the data collection unit. The prediction unit predicts flood risk based on the data analyzed by the analysis unit. The proposal unit proposes facility operation plans and evacuation plans for residents to local governments based on the flood risk predicted by the prediction unit. [Effects of the Invention]

[0007] The system according to the embodiment can predict flood risks and propose prompt and appropriate countermeasures to local governments. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example 1) The flood forecasting system according to an embodiment of the present invention analyzes river data, predicts flood risks in real time, and proposes facility operation plans and evacuation plans to local governments. This allows the flood forecasting system to efficiently predict flood risks and implement countermeasures. Furthermore, the system is provided free of charge to developing countries, contributing to the creation of a global community of safer people.

[0029] A flood forecasting system according to an embodiment includes a data collection unit, an analysis unit, a prediction unit, and a proposal unit. The data collection unit collects river data. For example, the data collection unit collects data such as river water level, flow rate, and rainfall in real time. The data collection unit can also measure river water levels using sensors and collect the data. The data collection unit can also collect meteorological data and analyze the data by integrating it with the river data. The analysis unit analyzes the river data collected by the data collection unit. For example, the analysis unit uses a generative AI (e.g., a text generation AI or a multimodal generation AI) to analyze the river data. The analysis unit can also compare the current situation with past data to evaluate the current situation and generate data for predicting flood risk. The analysis unit can also improve the accuracy of the river data analysis using a data analysis algorithm. The prediction unit predicts flood risk based on the data analyzed by the analysis unit. For example, the prediction unit calculates the probability of flood risk occurrence using a generative AI. The prediction unit can also predict flood risk based on meteorological data. The prediction unit can also notify local governments and related organizations of the flood risk prediction results. The proposal unit proposes facility operation plans and evacuation plans for residents to local governments based on the flood risk predicted by the prediction unit. For example, the proposal unit makes proposals to optimize dam discharge plans and drainage pump operation schedules. The proposal unit can also specifically propose evacuation routes, evacuation locations, and evacuation start timing. The proposal unit can also propose optimal evacuation plans using generation AI. This enables the flood prediction system according to the embodiment to efficiently predict flood risks and take countermeasures. For example, local governments can take prompt and appropriate measures based on flood risk predictions, ensuring the safety of residents. Furthermore, by providing the system free of charge to developing countries, it can contribute to building a global community of safety.

[0030] The data collection unit can collect earthquake and landslide data in addition to river data, allowing for analysis of complex disaster risks. For example, the data collection unit can collect earthquake seismic intensity and epicenter information in addition to river data, and analyze the correlation between flood risk and earthquake risk. For example, it can evaluate the impact of earthquake damage to levees on flood risk. The data collection unit can also collect geological data and rainfall data to analyze the risk of landslides, integrating it with river data for analysis. For example, it can evaluate the risk of landslides in areas with heavy rainfall and analyze it together with flood risk. The data collection unit can also collect earthquake and landslide data in real time and build a system to incorporate it into flood risk analysis. For example, it can integrate and analyze the data with river data immediately after an earthquake occurs, allowing for rapid risk assessment. This allows for analysis of complex disaster risks, enabling more accurate risk assessment.

[0031] The data collection unit uses drones to collect river data and can incorporate real-time footage from the air into its analysis. The data collection unit, for example, uses drones to collect real-time footage from above a river and incorporates that video data into its analysis. For example, it monitors the water level and flow rate of a river from above and evaluates flood risk. The data collection unit also analyzes aerial footage taken by drones to evaluate the condition of the river's levees and bank protection. For example, it identifies damaged areas in the levees and analyzes the impact on flood risk. The data collection unit also uses drones to monitor land use around the river and reflect this in flood risk analysis. For example, it evaluates the flood risk of farmland and residential areas and proposes flood prevention measures. This makes it possible to collect data over a wider area and in greater detail by using drones.

[0032] The data collection unit can add a function to accept reports from residents using a smartphone app. For example, the data collection unit develops a smartphone app and adds a function that allows residents to report river water levels and flow rates. For example, residents report river conditions in real time through the app. The data collection unit also builds a system that collects data reported by residents and reflects it in river data analysis. For example, it evaluates flood risk based on residents' reports and notifies local governments. The data collection unit also adds a function to allow residents to report river abnormalities using a smartphone app, improving the accuracy of data collection. For example, residents report levee damage and flooding conditions. This allows for more detailed data collection by accepting reports from residents.

[0033] The data collection unit can also apply the results of river data analysis to agricultural water management and water quality management. For example, the data collection unit builds a system that optimizes agricultural water management based on the results of river data analysis. For example, it adjusts irrigation schedules based on rainfall and water level data. The data collection unit also uses the results of river data analysis to develop a system for water quality management. For example, it evaluates pollution risks based on water quality data and takes measures. The data collection unit also applies the results of river data analysis to agricultural water management and water quality management to achieve sustainable water resource management. For example, it creates water resource utilization plans based on river water level and flow rate data. In this way, sustainable water resource management can be achieved by applying the results of river data analysis to other fields.

[0034] The prediction unit incorporates meteorological satellite data to make highly accurate predictions. The prediction unit, for example, incorporates meteorological satellite data into flood risk predictions and uses meteorological information such as rainfall and wind speed in its analysis. For example, it predicts rainfall patterns based on meteorological satellite data and evaluates flood risk. The prediction unit also builds a system that collects meteorological satellite data in real time and reflects it in flood risk predictions. For example, it monitors fluctuations in flood risk in real time based on meteorological satellite data. The prediction unit also uses meteorological satellite data to improve the accuracy of flood risk predictions. For example, it analyzes fluctuations in rainfall and wind speed based on meteorological satellite data and evaluates flood risk. In this way, by incorporating meteorological satellite data, more accurate flood risk predictions are possible.

[0035] The prediction unit can visualize the results of flood risk predictions in virtual reality (VR) and provide them to local governments and residents. The prediction unit, for example, builds a system that visualizes the results of flood risk predictions in virtual reality (VR) and provides them to local governments and residents. For example, areas with high flood risk can be displayed in VR and evacuation routes can be shown. The prediction unit also uses VR technology to visually display the results of flood risk predictions and provide them to local governments and residents. For example, flood simulations can be performed in VR to assess risk. The prediction unit also visualizes the results of flood risk predictions in VR and provides them to local governments and residents to deepen their understanding of risk. For example, areas with high flood risk can be displayed in VR and evacuation plans can be created. In this way, visualization using VR deepens understanding of flood risk and enables appropriate measures to be taken.

[0036] The prediction unit can also apply flood risk prediction to natural disaster risk prediction. For example, the prediction unit applies flood risk prediction technology to typhoon risk prediction, predicting the typhoon's path and strength based on meteorological data. For example, evacuation plans are created based on the typhoon's path prediction. The prediction unit also applies flood risk prediction technology to tsunami risk prediction, evaluating the risk of a tsunami occurring based on earthquake data. For example, evacuation routes are shown based on the risk of a tsunami occurring. The prediction unit also applies flood risk prediction technology to other natural disaster risk prediction, evaluating the risk of complex disasters. For example, the risks of typhoons and tsunamis are evaluated comprehensively and countermeasures are taken. In this way, comprehensive disaster countermeasures can be implemented by applying flood risk prediction technology to other natural disasters.

[0037] The prediction unit can provide the results of the flood risk prediction to insurance companies to help them develop insurance products. For example, the prediction unit can provide the results of the flood risk prediction to insurance companies to support the development of insurance products based on flood risk. For example, the prediction unit can develop insurance products specialized for areas with high flood risk. The prediction unit can also build a system where insurance companies use the results of the flood risk prediction to evaluate the risks of insurance products. For example, the prediction unit can set insurance premiums for areas with high flood risk. The prediction unit can also provide the results of the flood risk prediction to insurance companies to help them develop insurance products. For example, the prediction unit can improve insurance products in response to fluctuations in flood risk. In this way, by providing prediction results to insurance companies, it becomes possible to develop risk-based insurance products.

[0038] The proposal unit can incorporate energy consumption optimization into the facility's operation plan to reduce the environmental load. The proposal unit, for example, incorporates energy consumption optimization into the facility's operation plan to build a system that reduces the environmental load. For example, it proposes an operation schedule for highly energy-efficient equipment. The proposal unit also analyzes energy consumption data and reflects it in the facility's operation plan. For example, it proposes an operation schedule to reduce energy consumption during peak hours. The proposal unit also proposes a facility operation plan that incorporates energy consumption optimization to reduce the environmental load. For example, it proposes an operation plan that promotes the use of renewable energy. In this way, the environmental load can be reduced by incorporating energy consumption optimization.

[0039] The proposal unit can provide facility operation plans to local government employees in the form of a simulation game as training. For example, the proposal unit provides facility operation plans in the form of a simulation game and builds a system for training local government employees. For example, the facility operation plan is executed in a virtual environment and its effectiveness is confirmed. The proposal unit also uses a simulation game to develop a training program that allows local government employees to practically learn facility operation plans. For example, it simulates disaster responses. The proposal unit also provides facility operation plans in the form of a simulation game to improve the skills of local government employees. For example, it provides feedback on the success or failure of the operation plan in the game. In this way, by providing it in the form of a simulation game, it is possible to improve the practical skills of local government employees.

[0040] The proposal unit can also apply facility operation planning to infrastructure operation planning. For example, the proposal unit applies facility operation planning technology to transportation system operation planning to alleviate traffic congestion. For example, it optimizes traffic signals. The proposal unit also applies facility operation planning technology to power grid operation planning to stabilize power supply. For example, it shifts peak power demand. The proposal unit also applies facility operation planning technology to other infrastructure operation planning to improve overall efficiency. For example, it optimizes water supply and sewerage systems. In this way, by applying facility operation planning to other infrastructure, overall efficiency can be improved.

[0041] The proposal department can incorporate the facility operation plan into the company's business continuity plan (BCP) to support business continuity in the event of a disaster. For example, the proposal department may incorporate the facility operation plan into the company's business continuity plan (BCP) and build a system to support business continuity in the event of a disaster. For example, it may prioritize the operation of important equipment. The proposal department may also incorporate the facility operation plan into the company's business continuity plan (BCP) to minimize risk in the event of a disaster. For example, it may propose an operation plan for an alternative facility. The proposal department may also incorporate the facility operation plan into the company's business continuity plan (BCP) to support business continuity in the event of a disaster. For example, it may set priorities for important operations. In this way, by incorporating it into the company's business continuity plan, business continuity in the event of a disaster is supported.

[0042] The suggestion unit can incorporate suggestions that take into account the special needs of pets, the elderly, and the disabled into the evacuation plan. For example, the suggestion unit incorporates evacuation sites and evacuation routes for pets into the evacuation plan to accommodate the needs of residents who have pets. For example, it specifies a pet-friendly evacuation site. The suggestion unit also proposes an evacuation plan that takes into account the special needs of the elderly and the disabled. For example, it proposes a barrier-free evacuation route and the placement of support staff. The suggestion unit also customizes the evacuation plan for residents with special needs. For example, it specifies an evacuation site for residents who require medical equipment. In this way, the safety of residents is ensured by proposing an evacuation plan that takes into account special needs.

[0043] The proposal unit can visualize evacuation plans using augmented reality (AR) and provide them to residents. The proposal unit, for example, builds a system that visualizes evacuation plans using augmented reality (AR) and provides them to residents. For example, evacuation routes are displayed in AR using a smartphone. The proposal unit also uses AR technology to visualize evacuation plans and provide them to residents. For example, evacuation locations and evacuation routes are displayed in AR so that residents can intuitively understand them. The proposal unit also visualizes evacuation plans using AR and provides them to residents, thereby deepening their understanding of evacuation. For example, AR can be used to check the actual evacuation route during an evacuation drill. In this way, visualization using AR deepens residents' understanding of the evacuation plan and enables them to take appropriate evacuation action.

[0044] The proposal unit can also apply the evacuation plan to evacuation plans for other disasters. For example, the proposal unit applies flood evacuation planning technology to earthquake evacuation plans to propose evacuation routes and evacuation locations in the event of an earthquake. For example, it shows evacuation behavior after the earthquake shaking has subsided. The proposal unit also applies flood evacuation planning technology to fire evacuation plans to propose evacuation routes and evacuation locations in the event of a fire. For example, it shows evacuation routes to avoid smoke from a fire. The proposal unit also applies flood evacuation planning technology to evacuation plans for other disasters to address complex disaster risks. For example, it comprehensively evaluates the risks of earthquakes and fires and proposes an evacuation plan. In this way, by applying evacuation plans to other disasters, comprehensive disaster countermeasures become possible.

[0045] The proposal department can incorporate the evacuation plan into disaster prevention drills at schools and companies to provide practical training. For example, the proposal department builds a system that incorporates the evacuation plan into school disaster prevention drills and provides practical training. For example, it conducts training to actually check evacuation routes and evacuation locations. The proposal department also incorporates the evacuation plan into company disaster prevention drills and develops a training program that allows employees to learn evacuation procedures in a practical manner. For example, it provides evacuation drill scenarios. The proposal department also improves the evacuation skills of residents and employees by incorporating the evacuation plan into disaster prevention drills and providing practical training. For example, it provides feedback on the results of the evacuation drill. In this way, the provision of practical training improves the evacuation skills of residents and employees.

[0046] The system provides a customized version adapted to the local language and culture for developing countries. For example, a system is constructed that provides a customized version translated into the local language for developing countries. For example, the system displays flood risk prediction results in the local language. The system also provides a customized version adapted to the local culture and customs. For example, the system suggests evacuation plans that suit local evacuation customs. The system also provides a customized version adapted to the local language and culture for developing countries to deepen user understanding. For example, the system is introduced in collaboration with local educational institutions. By providing a customized version adapted to the local language and culture, the system becomes easier to use in developing countries.

[0047] The system provides online training programs for local government officials in developing countries. For example, the system provides online training programs for local government officials in developing countries to learn how to use the flood risk prediction system. For example, it may hold video tutorials and webinars. The system also enables local government officials to learn how to operate the system and acquire data analysis techniques through online training programs. For example, it may provide interactive learning content. The system also provides online training programs for local government officials in developing countries to support the introduction and operation of the system. For example, it may accept questions and consultations in an online forum. In this way, providing online training programs will enable local government officials in developing countries to use the system effectively.

[0048] The system will also provide other disaster prediction systems free of charge to developing countries. For example, the system will provide a drought prediction system free of charge to developing countries in addition to a flood prediction system. For example, it will assess drought risk based on rainfall data. The system will also provide an earthquake prediction system free of charge to developing countries in addition to a flood prediction system. For example, it will assess earthquake risk based on earthquake data. The system will also provide multiple disaster prediction systems free of charge to developing countries to support comprehensive disaster prevention measures. For example, it will comprehensively assess the risks of floods, droughts, and earthquakes. In this way, by providing other disaster prediction systems free of charge, comprehensive disaster prevention measures in developing countries will be supported.

[0049] The system collaborates with educational institutions in developing countries to provide the system as a teaching material for disaster prevention education. For example, the system collaborates with educational institutions in developing countries to provide the flood prediction system as a teaching material for disaster prevention education. For example, practical disaster prevention training is conducted using the system. The system also collaborates with educational institutions to develop disaster prevention education programs using the flood prediction system. For example, a curriculum is provided to teach how to operate the system and data analysis techniques. The system also collaborates with educational institutions in developing countries to provide the system as a teaching material for disaster prevention education and to cultivate the next generation of disaster prevention leaders. For example, students use the system to analyze actual data. In this way, by providing it as a teaching material for disaster prevention education, the next generation of disaster prevention leaders can be cultivated.

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

[0051] The data collection unit, for example, collects river data as well as earthquake seismic intensity and epicenter information to analyze the correlation between flood risk and earthquake risk. For example, it evaluates the impact of earthquake damage to levees on flood risk. The data collection unit also collects geological data and rainfall data, and integrates and analyzes it with river data to analyze the risk of landslides. For example, it evaluates the risk of landslides in areas with heavy rainfall and analyzes it together with flood risk. The data collection unit also collects earthquake and landslide data in real time and builds a system that incorporates it into flood risk analysis. For example, it integrates and analyzes this data with river data immediately after an earthquake occurs, allowing for rapid risk assessment. This enables more accurate risk assessment by analyzing complex disaster risks.

[0052] The data collection unit, for example, uses a drone to collect real-time video footage from above a river and incorporates that video data into its analysis. For example, it monitors the water level and flow rate of a river from above and assesses flood risk. The data collection unit also analyzes the aerial video footage taken by the drone to evaluate the condition of the river's levees and bank protection. For example, it identifies damaged areas in the levees and analyzes their impact on flood risk. The data collection unit also uses a drone to monitor land use around the river and reflects this in flood risk analysis. For example, it evaluates the flood risk of farmland and residential areas and proposes flood prevention measures. In this way, using drones makes it possible to collect data over a wider area and in greater detail.

[0053] For example, the data collection unit develops a smartphone app and adds a function that allows residents to report river water levels and flow rates. For example, residents can use the app to report river conditions in real time. The data collection unit also builds a system that collects data reported by residents and reflects it in river data analysis. For example, it evaluates flood risk based on residents' reports and notifies local governments. The data collection unit also adds a function to the smartphone app that allows residents to report abnormalities in rivers, improving the accuracy of data collection. For example, residents can report levee damage and flooding conditions. This allows for more detailed data collection by accepting reports from residents.

[0054] For example, the data collection unit will build a system that optimizes agricultural water management based on the results of river data analysis. For example, it will adjust irrigation schedules based on rainfall and water level data. The data collection unit will also develop a system for water quality management using the results of river data analysis. For example, it will evaluate pollution risks based on water quality data and take measures. The data collection unit will also apply the results of river data analysis to agricultural water management and water quality management, thereby realizing sustainable water resource management. For example, it will create a water resource utilization plan based on river water level and flow rate data. In this way, sustainable water resource management will become possible by applying the results of river data analysis to other fields.

[0055] The prediction unit, for example, incorporates meteorological satellite data into flood risk predictions and uses meteorological information such as rainfall and wind speed in its analysis. For example, it predicts rainfall patterns based on meteorological satellite data and assesses flood risk. The prediction unit also builds a system that collects meteorological satellite data in real time and reflects it in flood risk predictions. For example, it monitors fluctuations in flood risk in real time based on meteorological satellite data. The prediction unit also uses meteorological satellite data to improve the accuracy of flood risk predictions. For example, it analyzes fluctuations in rainfall and wind speed based on meteorological satellite data and assesses flood risk. In this way, incorporating meteorological satellite data enables more accurate flood risk predictions.

[0056] The prediction unit, for example, builds a system that visualizes the results of flood risk predictions using virtual reality (VR) and provides them to local governments and residents. For example, areas with high flood risk are displayed in VR and evacuation routes are shown. The prediction unit also uses VR technology to visually display the results of flood risk predictions and provide them to local governments and residents. For example, flood simulations are performed in VR to assess risk. The prediction unit also visualizes the results of flood risk predictions in VR and provides them to local governments and residents to deepen their understanding of risk. For example, areas with high flood risk are displayed in VR and evacuation plans are created. In this way, visualization using VR deepens understanding of flood risk and enables appropriate measures to be taken.

[0057] The prediction unit, for example, applies flood risk prediction technology to typhoon risk prediction, predicting the path and strength of a typhoon based on meteorological data. For example, it creates an evacuation plan based on the predicted path of the typhoon. The prediction unit also applies flood risk prediction technology to tsunami risk prediction, evaluating the risk of a tsunami based on earthquake data. For example, it suggests evacuation routes based on the risk of a tsunami. The prediction unit also applies flood risk prediction technology to predicting the risk of other natural disasters, evaluating the risk of complex disasters. For example, it comprehensively evaluates the risks of typhoons and tsunamis and takes measures. In this way, comprehensive disaster prevention measures can be implemented by applying flood risk prediction technology to other natural disasters as well.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The data collection unit collects river data. For example, the data collection unit collects data such as the river's water level, flow rate, and rainfall in real time. The data collection unit can also measure the river's water level using a sensor and collect that data. Furthermore, the data collection unit can collect meteorological data and integrate it with the river data for analysis. Step 2: The analysis unit analyzes the river data collected by the data collection unit. For example, the analysis unit uses generative AI (e.g., text generation AI or multimodal generation AI) to analyze the river data. The analysis unit can also compare the current situation with past data to generate data for predicting flood risk. Furthermore, the analysis unit can use data analysis algorithms to improve the accuracy of the river data analysis. Step 3: The prediction unit predicts flood risk based on the data analyzed by the analysis unit. For example, the prediction unit uses generation AI to calculate the probability of flood risk occurrence. The prediction unit can also predict flood risk based on meteorological data. Furthermore, the prediction unit can notify local governments and related organizations of the flood risk prediction results. Step 4: The proposal unit proposes facility operation plans and evacuation plans for residents to local governments based on the flood risk predicted by the prediction unit. For example, the proposal unit makes proposals to optimize dam discharge plans and drainage pump operation schedules. The proposal unit can also make specific proposals for evacuation routes, evacuation locations, and the timing of when to begin evacuation. Furthermore, the proposal unit can use generation AI to propose optimal evacuation plans.

[0060] (Example 2) The flood forecasting system according to an embodiment of the present invention analyzes river data, predicts flood risks in real time, and proposes facility operation plans and evacuation plans to local governments. This allows the flood forecasting system to efficiently predict flood risks and implement countermeasures. Furthermore, the system is provided free of charge to developing countries, contributing to the creation of a global community of safer people.

[0061] A flood forecasting system according to an embodiment includes a data collection unit, an analysis unit, a prediction unit, and a proposal unit. The data collection unit collects river data. For example, the data collection unit collects data such as river water level, flow rate, and rainfall in real time. The data collection unit can also measure river water levels using sensors and collect the data. The data collection unit can also collect meteorological data and analyze the data by integrating it with the river data. The analysis unit analyzes the river data collected by the data collection unit. For example, the analysis unit uses a generative AI (e.g., a text generation AI or a multimodal generation AI) to analyze the river data. The analysis unit can also compare the current situation with past data to evaluate the current situation and generate data for predicting flood risk. The analysis unit can also improve the accuracy of the river data analysis using a data analysis algorithm. The prediction unit predicts flood risk based on the data analyzed by the analysis unit. For example, the prediction unit calculates the probability of flood risk occurrence using a generative AI. The prediction unit can also predict flood risk based on meteorological data. The prediction unit can also notify local governments and related organizations of the flood risk prediction results. The proposal unit proposes facility operation plans and evacuation plans for residents to local governments based on the flood risk predicted by the prediction unit. For example, the proposal unit makes proposals to optimize dam discharge plans and drainage pump operation schedules. The proposal unit can also specifically propose evacuation routes, evacuation locations, and evacuation start timing. The proposal unit can also propose optimal evacuation plans using generation AI. This enables the flood prediction system according to the embodiment to efficiently predict flood risks and take countermeasures. For example, local governments can take prompt and appropriate measures based on flood risk predictions, ensuring the safety of residents. Furthermore, by providing the system free of charge to developing countries, it can contribute to building a global community of safety.

[0062] The data collection unit can collect earthquake and landslide data in addition to river data, allowing for analysis of complex disaster risks. For example, the data collection unit can collect earthquake seismic intensity and epicenter information in addition to river data, and analyze the correlation between flood risk and earthquake risk. For example, it can evaluate the impact of earthquake damage to levees on flood risk. The data collection unit can also collect geological data and rainfall data to analyze the risk of landslides, integrating it with river data for analysis. For example, it can evaluate the risk of landslides in areas with heavy rainfall and analyze it together with flood risk. The data collection unit can also collect earthquake and landslide data in real time and build a system to incorporate it into flood risk analysis. For example, it can integrate and analyze the data with river data immediately after an earthquake occurs, allowing for rapid risk assessment. This allows for analysis of complex disaster risks, enabling more accurate risk assessment.

[0063] The data collection unit uses drones to collect river data and can incorporate real-time footage from the air into its analysis. The data collection unit, for example, uses drones to collect real-time footage from above a river and incorporates that video data into its analysis. For example, it monitors the water level and flow rate of a river from above and evaluates flood risk. The data collection unit also analyzes aerial footage taken by drones to evaluate the condition of the river's levees and bank protection. For example, it identifies damaged areas in the levees and analyzes the impact on flood risk. The data collection unit also uses drones to monitor land use around the river and reflect this in flood risk analysis. For example, it evaluates the flood risk of farmland and residential areas and proposes flood prevention measures. This makes it possible to collect data over a wider area and in greater detail by using drones.

[0064] The data collection unit can use the emotion estimation function to analyze residents' feelings of anxiety and fear about floods from their social media posts and reflect the results in data collection. The data collection unit, for example, analyzes residents' social media posts and extracts feelings of anxiety and fear about floods. For example, it collects posts containing keywords related to flooding and performs emotion analysis. The data collection unit also uses the emotion estimation function to collect emotion data about flood risk from residents' social media posts and reflects this in the analysis. For example, it identifies areas where emotions of anxiety and fear are strong and incorporates this into flood risk assessments. The data collection unit also builds a system that analyzes residents' social media posts in real time and collects emotion data about flood risk. For example, if there is a sudden increase in posts about flooding, risk assessments can be performed quickly. In this way, collecting residents' emotion data enables more realistic risk assessments.

[0065] The data collection unit can add a function to accept reports from residents using a smartphone app. For example, the data collection unit develops a smartphone app and adds a function that allows residents to report river water levels and flow rates. For example, residents report river conditions in real time through the app. The data collection unit also builds a system that collects data reported by residents and reflects it in river data analysis. For example, it evaluates flood risk based on residents' reports and notifies local governments. The data collection unit also adds a function to allow residents to report river abnormalities using a smartphone app, improving the accuracy of data collection. For example, residents report levee damage and flooding conditions. This allows for more detailed data collection by accepting reports from residents.

[0066] The data collection unit can also apply the results of river data analysis to agricultural water management and water quality management. For example, the data collection unit builds a system that optimizes agricultural water management based on the results of river data analysis. For example, it adjusts irrigation schedules based on rainfall and water level data. The data collection unit also uses the results of river data analysis to develop a system for water quality management. For example, it evaluates pollution risks based on water quality data and takes measures. The data collection unit also applies the results of river data analysis to agricultural water management and water quality management to achieve sustainable water resource management. For example, it creates water resource utilization plans based on river water level and flow rate data. In this way, sustainable water resource management can be achieved by applying the results of river data analysis to other fields.

[0067] The data collection unit can use the emotion estimation function to collect residents' emotion data and reflect it in flood risk analysis. The data collection unit, for example, uses the emotion estimation function to collect residents' emotion data and build a system that reflects it in flood risk analysis. For example, it analyzes residents' emotions of anxiety and fear and incorporates them into risk assessment. The data collection unit also adjusts the flood risk analysis results based on residents' emotion data. For example, it prioritizes evaluation of areas with high risk indicated by the emotion data. The data collection unit also uses the emotion estimation function to collect residents' emotion data in real time and reflect it in flood risk analysis. For example, it can quickly perform risk assessment if there is a sudden increase in emotion data. In this way, collecting residents' emotion data enables more realistic risk assessment.

[0068] The prediction unit incorporates meteorological satellite data to make highly accurate predictions. The prediction unit, for example, incorporates meteorological satellite data into flood risk predictions and uses meteorological information such as rainfall and wind speed in its analysis. For example, it predicts rainfall patterns based on meteorological satellite data and evaluates flood risk. The prediction unit also builds a system that collects meteorological satellite data in real time and reflects it in flood risk predictions. For example, it monitors fluctuations in flood risk in real time based on meteorological satellite data. The prediction unit also uses meteorological satellite data to improve the accuracy of flood risk predictions. For example, it analyzes fluctuations in rainfall and wind speed based on meteorological satellite data and evaluates flood risk. In this way, by incorporating meteorological satellite data, more accurate flood risk predictions are possible.

[0069] The prediction unit can visualize the results of flood risk predictions in virtual reality (VR) and provide them to local governments and residents. The prediction unit, for example, builds a system that visualizes the results of flood risk predictions in virtual reality (VR) and provides them to local governments and residents. For example, areas with high flood risk can be displayed in VR and evacuation routes can be shown. The prediction unit also uses VR technology to visually display the results of flood risk predictions and provide them to local governments and residents. For example, flood simulations can be performed in VR to assess risk. The prediction unit also visualizes the results of flood risk predictions in VR and provides them to local governments and residents to deepen their understanding of risk. For example, areas with high flood risk can be displayed in VR and evacuation plans can be created. In this way, visualization using VR deepens understanding of flood risk and enables appropriate measures to be taken.

[0070] The prediction unit can use the emotion estimation function to analyze residents' emotion data and provide prediction results that are emotionally easy to accept. For example, the prediction unit uses the emotion estimation function to analyze residents' emotion data and build a system that provides prediction results that are emotionally easy to accept. For example, it provides prediction results that reduce residents' anxiety and fear. The prediction unit also adjusts the results of flood risk predictions based on residents' emotion data. For example, it prioritizes evaluation of high-risk areas indicated by the emotion data and provides prediction results. The prediction unit also uses the emotion estimation function to collect residents' emotion data in real time and provide prediction results that are emotionally easy to accept. For example, it quickly provides prediction results when there is a sudden increase in emotion data. In this way, by providing prediction results that take residents' emotions into consideration, it becomes easier for them to accept risks.

[0071] The prediction unit can also apply flood risk prediction to natural disaster risk prediction. For example, the prediction unit applies flood risk prediction technology to typhoon risk prediction, predicting the typhoon's path and strength based on meteorological data. For example, evacuation plans are created based on the typhoon's path prediction. The prediction unit also applies flood risk prediction technology to tsunami risk prediction, evaluating the risk of a tsunami occurring based on earthquake data. For example, evacuation routes are shown based on the risk of a tsunami occurring. The prediction unit also applies flood risk prediction technology to other natural disaster risk prediction, evaluating the risk of complex disasters. For example, the risks of typhoons and tsunamis are evaluated comprehensively and countermeasures are taken. In this way, comprehensive disaster countermeasures can be implemented by applying flood risk prediction technology to other natural disasters.

[0072] The prediction unit can provide the results of the flood risk prediction to insurance companies to help them develop insurance products. For example, the prediction unit can provide the results of the flood risk prediction to insurance companies to support the development of insurance products based on flood risk. For example, the prediction unit can develop insurance products specialized for areas with high flood risk. The prediction unit can also build a system where insurance companies use the results of the flood risk prediction to evaluate the risks of insurance products. For example, the prediction unit can set insurance premiums for areas with high flood risk. The prediction unit can also provide the results of the flood risk prediction to insurance companies to help them develop insurance products. For example, the prediction unit can improve insurance products in response to fluctuations in flood risk. In this way, by providing prediction results to insurance companies, it becomes possible to develop risk-based insurance products.

[0073] The prediction unit can use the emotion estimation function to customize the results of flood risk predictions based on residents' emotion data. For example, the prediction unit uses the emotion estimation function to build a system that customizes the results of flood risk predictions based on residents' emotion data. For example, it provides prediction results that reduce residents' anxiety and fear. The prediction unit also adjusts the results of flood risk predictions based on residents' emotion data. For example, it prioritizes evaluation of high-risk areas indicated by the emotion data and provides prediction results. The prediction unit also uses the emotion estimation function to collect residents' emotion data in real time and customize the results of flood risk predictions. For example, it can quickly provide prediction results if there is a sudden increase in emotion data. In this way, customizing prediction results based on residents' emotion data makes it easier for them to accept risks.

[0074] The proposal unit can incorporate energy consumption optimization into the facility's operation plan to reduce the environmental load. The proposal unit, for example, incorporates energy consumption optimization into the facility's operation plan to build a system that reduces the environmental load. For example, it proposes an operation schedule for highly energy-efficient equipment. The proposal unit also analyzes energy consumption data and reflects it in the facility's operation plan. For example, it proposes an operation schedule to reduce energy consumption during peak hours. The proposal unit also proposes a facility operation plan that incorporates energy consumption optimization to reduce the environmental load. For example, it proposes an operation plan that promotes the use of renewable energy. In this way, the environmental load can be reduced by incorporating energy consumption optimization.

[0075] The proposal unit can provide facility operation plans to local government employees in the form of a simulation game as training. For example, the proposal unit provides facility operation plans in the form of a simulation game and builds a system for training local government employees. For example, the facility operation plan is executed in a virtual environment and its effectiveness is confirmed. The proposal unit also uses a simulation game to develop a training program that allows local government employees to practically learn facility operation plans. For example, it simulates disaster responses. The proposal unit also provides facility operation plans in the form of a simulation game to improve the skills of local government employees. For example, it provides feedback on the success or failure of the operation plan in the game. In this way, by providing it in the form of a simulation game, it is possible to improve the practical skills of local government employees.

[0076] The proposal unit can use the emotion estimation function to analyze the stress levels of local government employees and propose work plans to reduce stress. The proposal unit, for example, uses the emotion estimation function to build a system that analyzes the stress levels of local government employees and proposes work plans to reduce stress. For example, it suggests resting during periods of high stress. The proposal unit also monitors the stress levels of local government employees in real time and proposes work plans to reduce stress. For example, it adjusts work for employees with high stress. The proposal unit also uses the emotion estimation function to analyze the stress levels of local government employees and make specific proposals to reduce stress. For example, it provides a relaxing environment. This reduces the stress of local government employees and improves work efficiency.

[0077] The proposal unit can also apply facility operation planning to infrastructure operation planning. For example, the proposal unit applies facility operation planning technology to transportation system operation planning to alleviate traffic congestion. For example, it optimizes traffic signals. The proposal unit also applies facility operation planning technology to power grid operation planning to stabilize power supply. For example, it shifts peak power demand. The proposal unit also applies facility operation planning technology to other infrastructure operation planning to improve overall efficiency. For example, it optimizes water supply and sewerage systems. In this way, by applying facility operation planning to other infrastructure, overall efficiency can be improved.

[0078] The proposal department can incorporate the facility operation plan into the company's business continuity plan (BCP) to support business continuity in the event of a disaster. For example, the proposal department may incorporate the facility operation plan into the company's business continuity plan (BCP) and build a system to support business continuity in the event of a disaster. For example, it may prioritize the operation of important equipment. The proposal department may also incorporate the facility operation plan into the company's business continuity plan (BCP) to minimize risk in the event of a disaster. For example, it may propose an operation plan for an alternative facility. The proposal department may also incorporate the facility operation plan into the company's business continuity plan (BCP) to support business continuity in the event of a disaster. For example, it may set priorities for important operations. In this way, by incorporating it into the company's business continuity plan, business continuity in the event of a disaster is supported.

[0079] The proposal unit can use the emotion estimation function to customize operation plans based on the emotion data of local government employees. The proposal unit, for example, uses the emotion estimation function to build a system that customizes operation plans based on the emotion data of local government employees. For example, it allocates work according to the employee's emotional state. The proposal unit also adjusts the operation plan based on the emotion data of local government employees. For example, it reduces the work load of employees who are highly stressed. The proposal unit also uses the emotion estimation function to collect emotion data of local government employees in real time and customize the operation plan. For example, it proposes a plan that takes into account the risks indicated by the emotion data. In this way, by customizing the operation plan based on the emotion data of local government employees, work efficiency is improved.

[0080] The suggestion unit can incorporate suggestions that take into account the special needs of pets, the elderly, and the disabled into the evacuation plan. For example, the suggestion unit incorporates evacuation sites and evacuation routes for pets into the evacuation plan to accommodate the needs of residents who have pets. For example, it specifies a pet-friendly evacuation site. The suggestion unit also proposes an evacuation plan that takes into account the special needs of the elderly and the disabled. For example, it proposes a barrier-free evacuation route and the placement of support staff. The suggestion unit also customizes the evacuation plan for residents with special needs. For example, it specifies an evacuation site for residents who require medical equipment. In this way, the safety of residents is ensured by proposing an evacuation plan that takes into account special needs.

[0081] The proposal unit can visualize evacuation plans using augmented reality (AR) and provide them to residents. The proposal unit, for example, builds a system that visualizes evacuation plans using augmented reality (AR) and provides them to residents. For example, evacuation routes are displayed in AR using a smartphone. The proposal unit also uses AR technology to visualize evacuation plans and provide them to residents. For example, evacuation locations and evacuation routes are displayed in AR so that residents can intuitively understand them. The proposal unit also visualizes evacuation plans using AR and provides them to residents, thereby deepening their understanding of evacuation. For example, AR can be used to check the actual evacuation route during an evacuation drill. In this way, visualization using AR deepens residents' understanding of the evacuation plan and enables them to take appropriate evacuation action.

[0082] The suggestion unit can use the emotion estimation function to propose an evacuation plan that reduces residents' anxiety and fear. The suggestion unit, for example, uses the emotion estimation function to build a system that proposes an evacuation plan that reduces residents' anxiety and fear. For example, the evacuation plan is explained in a manner that takes emotions into consideration. The suggestion unit also adjusts the evacuation plan based on residents' emotion data. For example, it strengthens evacuation support for residents who are highly anxious. The suggestion unit also uses the emotion estimation function to collect residents' emotion data in real time and propose an evacuation plan that reduces anxiety and fear. For example, it provides a plan that takes into account the risks indicated by the emotion data. This reduces residents' anxiety and fear, allowing evacuation actions to be carried out smoothly.

[0083] The proposal unit can also apply the evacuation plan to evacuation plans for other disasters. For example, the proposal unit applies flood evacuation planning technology to earthquake evacuation plans to propose evacuation routes and evacuation locations in the event of an earthquake. For example, it shows evacuation behavior after the earthquake shaking has subsided. The proposal unit also applies flood evacuation planning technology to fire evacuation plans to propose evacuation routes and evacuation locations in the event of a fire. For example, it shows evacuation routes to avoid smoke from a fire. The proposal unit also applies flood evacuation planning technology to evacuation plans for other disasters to address complex disaster risks. For example, it comprehensively evaluates the risks of earthquakes and fires and proposes an evacuation plan. In this way, by applying evacuation plans to other disasters, comprehensive disaster countermeasures become possible.

[0084] The proposal department can incorporate the evacuation plan into disaster prevention drills at schools and companies to provide practical training. For example, the proposal department builds a system that incorporates the evacuation plan into school disaster prevention drills and provides practical training. For example, it conducts training to actually check evacuation routes and evacuation locations. The proposal department also incorporates the evacuation plan into company disaster prevention drills and develops a training program that allows employees to learn evacuation procedures in a practical manner. For example, it provides evacuation drill scenarios. The proposal department also improves the evacuation skills of residents and employees by incorporating the evacuation plan into disaster prevention drills and providing practical training. For example, it provides feedback on the results of the evacuation drill. In this way, the provision of practical training improves the evacuation skills of residents and employees.

[0085] The suggestion unit can use the emotion estimation function to customize the evacuation plan based on the emotion data of residents. The suggestion unit, for example, uses the emotion estimation function to build a system that customizes the evacuation plan based on the emotion data of residents. For example, it suggests evacuation routes to reduce residents' anxiety and fear. The suggestion unit also adjusts the evacuation plan based on the emotion data of residents. For example, it strengthens evacuation support for residents who are highly anxious. The suggestion unit also uses the emotion estimation function to collect emotion data of residents in real time and customize the evacuation plan. For example, it provides a plan that takes into account the risks indicated by the emotion data. In this way, by customizing the evacuation plan based on the emotion data of residents, evacuation actions can be carried out smoothly.

[0086] The system provides a customized version adapted to the local language and culture for developing countries. For example, a system is constructed that provides a customized version translated into the local language for developing countries. For example, the system displays flood risk prediction results in the local language. The system also provides a customized version adapted to the local culture and customs. For example, the system suggests evacuation plans that suit local evacuation customs. The system also provides a customized version adapted to the local language and culture for developing countries to deepen user understanding. For example, the system is introduced in collaboration with local educational institutions. By providing a customized version adapted to the local language and culture, the system becomes easier to use in developing countries.

[0087] The system provides online training programs for local government officials in developing countries. For example, the system provides online training programs for local government officials in developing countries to learn how to use the flood risk prediction system. For example, it may hold video tutorials and webinars. The system also enables local government officials to learn how to operate the system and acquire data analysis techniques through online training programs. For example, it may provide interactive learning content. The system also provides online training programs for local government officials in developing countries to support the introduction and operation of the system. For example, it may accept questions and consultations in an online forum. In this way, providing online training programs will enable local government officials in developing countries to use the system effectively.

[0088] The system uses an emotion estimation function to analyze emotional data of residents in developing countries and provides the system in a culturally acceptable format. For example, the system uses the emotion estimation function to analyze emotional data of residents in developing countries and provides the system in a culturally acceptable format. For example, the system designs an interface that takes residents' emotions into consideration. The system also adjusts the way the system is provided based on the emotional data of residents in developing countries. For example, the system customizes the system according to the needs indicated by the emotional data. The system also uses the emotion estimation function to collect emotional data of residents in developing countries in real time and provides the system in a culturally acceptable format. For example, the system provides information that takes into account the risks indicated by the emotional data. In this way, by providing the system in a culturally acceptable format, residents in developing countries can use the system effectively.

[0089] The system will also provide other disaster prediction systems free of charge to developing countries. For example, the system will provide a drought prediction system free of charge to developing countries in addition to a flood prediction system. For example, it will assess drought risk based on rainfall data. The system will also provide an earthquake prediction system free of charge to developing countries in addition to a flood prediction system. For example, it will assess earthquake risk based on earthquake data. The system will also provide multiple disaster prediction systems free of charge to developing countries to support comprehensive disaster prevention measures. For example, it will comprehensively assess the risks of floods, droughts, and earthquakes. In this way, by providing other disaster prediction systems free of charge, comprehensive disaster prevention measures in developing countries will be supported.

[0090] The system collaborates with educational institutions in developing countries to provide the system as a teaching material for disaster prevention education. For example, the system collaborates with educational institutions in developing countries to provide the flood prediction system as a teaching material for disaster prevention education. For example, practical disaster prevention training is conducted using the system. The system also collaborates with educational institutions to develop disaster prevention education programs using the flood prediction system. For example, a curriculum is provided to teach how to operate the system and data analysis techniques. The system also collaborates with educational institutions in developing countries to provide the system as a teaching material for disaster prevention education and to cultivate the next generation of disaster prevention leaders. For example, students use the system to analyze actual data. In this way, by providing it as a teaching material for disaster prevention education, the next generation of disaster prevention leaders can be cultivated.

[0091] The system uses an emotion estimation function to customize how the system is used based on the emotional data of residents in developing countries. For example, the system uses the emotion estimation function to customize how the system is used based on the emotional data of residents in developing countries. For example, it designs an interface to reduce residents' anxiety and fear. The system also adjusts how the system is used based on the residents' emotional data. For example, it customizes according to the needs indicated by the emotional data. The system also uses the emotion estimation function to collect emotional data of residents in developing countries in real time and customize how the system is used. For example, it provides information that takes into account the risks indicated by the emotional data. In this way, customizing how the system is used based on residents' emotional data makes it easier for them to accept the system.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] The data collection unit, for example, collects river data as well as earthquake seismic intensity and epicenter information to analyze the correlation between flood risk and earthquake risk. For example, it evaluates the impact of earthquake damage to levees on flood risk. The data collection unit also collects geological data and rainfall data, and integrates and analyzes it with river data to analyze the risk of landslides. For example, it evaluates the risk of landslides in areas with heavy rainfall and analyzes it together with flood risk. The data collection unit also collects earthquake and landslide data in real time and builds a system that incorporates it into flood risk analysis. For example, it integrates and analyzes this data with river data immediately after an earthquake occurs, allowing for rapid risk assessment. This enables more accurate risk assessment by analyzing complex disaster risks.

[0094] The data collection unit, for example, uses a drone to collect real-time video footage from above a river and incorporates that video data into its analysis. For example, it monitors the water level and flow rate of a river from above and assesses flood risk. The data collection unit also analyzes the aerial video footage taken by the drone to evaluate the condition of the river's levees and bank protection. For example, it identifies damaged areas in the levees and analyzes their impact on flood risk. The data collection unit also uses a drone to monitor land use around the river and reflects this in flood risk analysis. For example, it evaluates the flood risk of farmland and residential areas and proposes flood prevention measures. In this way, using drones makes it possible to collect data over a wider area and in greater detail.

[0095] The data collection unit, for example, analyzes residents' social media posts to extract feelings of anxiety and fear regarding floods. For example, it collects posts containing keywords related to flooding and performs sentiment analysis. The data collection unit also uses an emotion estimation function to collect emotional data regarding flood risk from residents' social media posts and reflects this in the analysis. For example, it identifies areas where feelings of anxiety and fear are strong and incorporates this into flood risk assessments. The data collection unit also builds a system that analyzes residents' social media posts in real time and collects emotional data regarding flood risk. For example, if there is a sudden increase in posts related to flooding, it can quickly conduct risk assessments. In this way, collecting residents' emotional data enables more realistic risk assessments.

[0096] For example, the data collection unit develops a smartphone app and adds a function that allows residents to report river water levels and flow rates. For example, residents can use the app to report river conditions in real time. The data collection unit also builds a system that collects data reported by residents and reflects it in river data analysis. For example, it evaluates flood risk based on residents' reports and notifies local governments. The data collection unit also adds a function to the smartphone app that allows residents to report abnormalities in rivers, improving the accuracy of data collection. For example, residents can report levee damage and flooding conditions. This allows for more detailed data collection by accepting reports from residents.

[0097] For example, the data collection unit will build a system that optimizes agricultural water management based on the results of river data analysis. For example, it will adjust irrigation schedules based on rainfall and water level data. The data collection unit will also develop a system for water quality management using the results of river data analysis. For example, it will evaluate pollution risks based on water quality data and take measures. The data collection unit will also apply the results of river data analysis to agricultural water management and water quality management, thereby realizing sustainable water resource management. For example, it will create a water resource utilization plan based on river water level and flow rate data. In this way, sustainable water resource management will become possible by applying the results of river data analysis to other fields.

[0098] The data collection unit, for example, uses an emotion estimation function to collect residents' emotion data and build a system that reflects this in flood risk analysis. For example, it analyzes residents' emotions of anxiety and fear and incorporates them into risk assessment. The data collection unit also adjusts the results of the flood risk analysis based on the residents' emotion data. For example, it prioritizes evaluation of areas with high risk indicated by the emotion data. The data collection unit also uses the emotion estimation function to collect residents' emotion data in real time and reflect this in flood risk analysis. For example, it can quickly perform risk assessment if there is a sudden increase in emotion data. In this way, collecting residents' emotion data enables more realistic risk assessment.

[0099] The prediction unit, for example, incorporates meteorological satellite data into flood risk predictions and uses meteorological information such as rainfall and wind speed in its analysis. For example, it predicts rainfall patterns based on meteorological satellite data and assesses flood risk. The prediction unit also builds a system that collects meteorological satellite data in real time and reflects it in flood risk predictions. For example, it monitors fluctuations in flood risk in real time based on meteorological satellite data. The prediction unit also uses meteorological satellite data to improve the accuracy of flood risk predictions. For example, it analyzes fluctuations in rainfall and wind speed based on meteorological satellite data and assesses flood risk. In this way, incorporating meteorological satellite data enables more accurate flood risk predictions.

[0100] The prediction unit, for example, builds a system that visualizes the results of flood risk predictions using virtual reality (VR) and provides them to local governments and residents. For example, areas with high flood risk are displayed in VR and evacuation routes are shown. The prediction unit also uses VR technology to visually display the results of flood risk predictions and provide them to local governments and residents. For example, flood simulations are performed in VR to assess risk. The prediction unit also visualizes the results of flood risk predictions in VR and provides them to local governments and residents to deepen their understanding of risk. For example, areas with high flood risk are displayed in VR and evacuation plans are created. In this way, visualization using VR deepens understanding of flood risk and enables appropriate measures to be taken.

[0101] The prediction unit, for example, uses an emotion estimation function to analyze residents' emotional data and build a system that provides emotionally acceptable prediction results. For example, it provides prediction results that reduce residents' anxiety and fear. The prediction unit also adjusts the results of flood risk predictions based on residents' emotional data. For example, it prioritizes evaluation of high-risk areas indicated by the emotional data and provides prediction results. The prediction unit also uses the emotion estimation function to collect residents' emotional data in real time and provide emotionally acceptable prediction results. For example, it quickly provides prediction results when there is a sudden increase in emotional data. This makes it easier for residents to accept risks by providing prediction results that take into account their emotions.

[0102] The prediction unit, for example, applies flood risk prediction technology to typhoon risk prediction, predicting the path and strength of a typhoon based on meteorological data. For example, it creates an evacuation plan based on the predicted path of the typhoon. The prediction unit also applies flood risk prediction technology to tsunami risk prediction, evaluating the risk of a tsunami based on earthquake data. For example, it suggests evacuation routes based on the risk of a tsunami. The prediction unit also applies flood risk prediction technology to predicting the risk of other natural disasters, evaluating the risk of complex disasters. For example, it comprehensively evaluates the risks of typhoons and tsunamis and takes measures. In this way, comprehensive disaster prevention measures can be implemented by applying flood risk prediction technology to other natural disasters as well.

[0103] The processing flow of the second embodiment will be briefly explained below.

[0104] Step 1: The data collection unit collects river data. For example, the data collection unit collects data such as the river's water level, flow rate, and rainfall in real time. The data collection unit can also measure the river's water level using a sensor and collect that data. Furthermore, the data collection unit can collect meteorological data and integrate it with the river data for analysis. Step 2: The analysis unit analyzes the river data collected by the data collection unit. For example, the analysis unit uses generative AI (e.g., text generation AI or multimodal generation AI) to analyze the river data. The analysis unit can also compare the current situation with past data to generate data for predicting flood risk. Furthermore, the analysis unit can use data analysis algorithms to improve the accuracy of the river data analysis. Step 3: The prediction unit predicts flood risk based on the data analyzed by the analysis unit. For example, the prediction unit uses generation AI to calculate the probability of flood risk occurrence. The prediction unit can also predict flood risk based on meteorological data. Furthermore, the prediction unit can notify local governments and related organizations of the flood risk prediction results. Step 4: The proposal unit proposes facility operation plans and evacuation plans for residents to local governments based on the flood risk predicted by the prediction unit. For example, the proposal unit makes proposals to optimize dam discharge plans and drainage pump operation schedules. The proposal unit can also make specific proposals for evacuation routes, evacuation locations, and the timing of when to begin evacuation. Furthermore, the proposal unit can use generation AI to propose optimal evacuation plans.

[0105] 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.

[0106] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0107] 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.

[0108] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0109] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0118] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0133] In the headset type terminal 314, 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 headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0139] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0149] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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).

[0158] 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.

[0159] 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."

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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]

[0172] 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 river data; an analysis unit that analyzes the river data collected by the data collection unit; a prediction unit that predicts a flood risk based on the data analyzed by the analysis unit; A system characterized by comprising: a proposal unit that proposes facility operation plans and evacuation plans for residents to local governments based on the flood risk predicted by the prediction unit.

2. The data collection unit The system according to claim 1, wherein in addition to the river data, earthquake and landslide data are also collected to analyze complex disaster risks.

3. The data collection unit The system described in claim 1, characterized in that a drone is used to collect the river data and real-time footage from the sky is incorporated into the analysis.

4. The data collection unit The system according to claim 1, characterized in that it analyzes residents' feelings of anxiety and fear about floods from their social media posts and reflects this in data collection.

5. The data collection unit The system according to claim 1, further comprising a function for accepting reports from the residents using a smartphone app.

6. The data collection unit 2. The system according to claim 1, wherein the results of the analysis of the river data are also applied to agricultural water management and water quality management.

7. The data collection unit The system according to claim 1, wherein emotional data of the residents is collected and reflected in the analysis of the flood risk.

8. The prediction unit 10. The system of claim 1, wherein weather satellite data is incorporated to provide highly accurate forecasts.

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