system

Abstract

The system according to this embodiment aims to assess the risk of flooding and provide users with appropriate warning information. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, an evaluation unit, and a provision unit. The collection unit collects weather data and water level sensor information. The analysis unit analyzes the information collected by the collection unit. The evaluation unit evaluates the risk of flooding based on the information analyzed by the analysis unit. The provision unit provides warning information to the user based on the risk evaluated by the evaluation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, the risk of floods has not been sufficiently evaluated in real time and appropriate warning information has not been provided, leaving room for improvement.

[0005] The system according to the embodiment aims to evaluate the risk of floods and provide appropriate warning information to users.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, an evaluation unit, and a provision unit. The collection unit collects weather data and water level sensor information. The analysis unit analyzes the information collected by the collection unit. The evaluation unit evaluates the risk of flooding based on the information analyzed by the analysis unit. The provision unit provides warning information to the user based on the risk evaluated by the evaluation unit. [Effects of the Invention]

[0007] The system according to this embodiment can assess the risk of flooding and provide users with appropriate warning information. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of 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), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The flood prediction system according to an embodiment of the present invention is a system that utilizes meteorological data and water level sensor information to predict the occurrence and spread of floods and provide appropriate warnings and countermeasures. This flood prediction system collects and analyzes real-time meteorological data and water level sensor information using AI. The AI ​​analyzes this data and predicts the possibility of flood occurrence and spread. Specifically, it evaluates the degree of flood risk by considering factors such as weather patterns and water level fluctuations. When a flood is predicted, early warning information is provided to the user. The user has time to understand the possibility and degree of flooding and take appropriate countermeasures. For example, residents are provided with messages urging them to prepare for evacuation and guidance on evacuation routes and shelters. Local governments and related organizations are provided with flood risk maps and advice on safety measures, supporting the planning and implementation of disaster countermeasures. Furthermore, the AI ​​constantly collects the latest data and builds a feedback loop to improve the flood prediction model. This allows for the evaluation of the effectiveness of flood countermeasures and the updating of data analysis and algorithms to improve the system. This flood prediction system plays an important role as a tool to minimize damage caused by floods. By providing real-time information and support for appropriate countermeasures, it minimizes damage and ensures the safety of residents and local governments. This allows flood prediction systems to forecast the occurrence and spread of floods and provide appropriate warnings and countermeasures.

[0029] The flood prediction system according to the embodiment comprises a collection unit, an analysis unit, an evaluation unit, and a provision unit. The collection unit collects meteorological data and information from water level sensors. For example, the collection unit can collect meteorological data such as precipitation, temperature, and wind speed. The collection unit can also collect information from water level sensors such as water level and flow velocity. For example, the collection unit collects meteorological data in real time and stores it in a database. Furthermore, the collection unit periodically collects data from water level sensors and provides it to the analysis unit. The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit can analyze the collected data using statistical analysis of the data or machine learning models. The analysis unit evaluates the degree of flood risk, taking into account factors such as weather patterns and water level fluctuations. For example, the analysis unit can compare past weather data with current data to detect the occurrence of extreme weather events. The evaluation unit evaluates the risk of flooding based on the information analyzed by the analysis unit. For example, the evaluation unit can predict the possibility of flood occurrence and expansion and evaluate the level of risk. The evaluation unit sets criteria for providing warning information according to the level of risk. The provision unit provides warning information to users based on the risk evaluated by the evaluation unit. For example, the provision unit can provide users with messages urging them to prepare for evacuation, and guidance on evacuation routes and shelters. The provision unit can also provide flood risk maps and safety advice to local governments and related organizations. For example, the provision unit can create a flood risk map and provide it to local governments. As a result, the flood prediction system according to this embodiment can utilize meteorological data and water level sensor information to predict the occurrence and spread of floods and provide appropriate warnings and countermeasures.

[0030] The data collection unit collects meteorological data and information from water level sensors. Specifically, it can collect detailed meteorological data such as precipitation, temperature, wind speed, humidity, and atmospheric pressure. This data is acquired in real time from various devices such as weather stations, satellites, and drones. The data collection unit can also collect information from water level sensors, such as water level, flow velocity, flow rate, and river water quality data. Water level sensors are installed in bodies of water such as rivers, lakes, and dams, and collect data periodically, transmitting it to a central database via wireless communication or the internet. The data collection unit has an interface for centrally managing this data and providing it to the analysis unit. Furthermore, the data collection unit can flexibly respond to specific situations and conditions by adjusting the data collection frequency and accuracy. For example, if heavy rainfall is predicted, the collection frequency can be increased to update data in real time and support a rapid response. The data collection unit also has a function to detect outliers and missing data, and can perform filtering and imputation processing to maintain data quality. As a result, the data collection unit can collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit analyzes the information collected by the data collection unit. Specifically, it can analyze the collected data from multiple perspectives using statistical analysis and machine learning models. For example, statistical analysis analyzes time-series data of precipitation and water levels to detect anomalies and understand trends. Machine learning models learn from past weather data and water level data to predict future weather patterns and water level fluctuations. Based on these analysis results, the analysis unit evaluates the degree of flood risk. Specifically, it evaluates the probability of flood occurrence and the extent of impact by considering factors such as weather patterns, water level fluctuations, topographic information, and land use information. For example, the analysis unit can compare past weather data with current data to detect the occurrence of extreme weather events. Furthermore, the analysis unit can use simulation technology to create multiple flood progression scenarios and identify the most likely scenario. In addition, the analysis unit can continuously revise its analysis results based on real-time updated data to respond to the latest situation. This allows the analysis unit to quickly and accurately assess flood risk and improve the reliability and safety of the entire system.

[0032] The evaluation unit assesses flood risk based on information analyzed by the analysis unit. Specifically, it predicts the likelihood of flood occurrence and expansion, and evaluates the level of risk. The evaluation unit can comprehensively evaluate factors such as the probability of flood occurrence, the scope of impact, and the extent of damage, and quantify the level of risk. The evaluation unit sets criteria for providing warning information according to the level of risk. For example, if the risk level is high, it issues warning information urging immediate evacuation, and if the risk level is moderate, it provides information urging preparation for evacuation. Furthermore, the evaluation unit can perform more accurate risk assessments by considering the characteristics of each region and past disaster history. For example, stricter criteria can be set for areas that have frequently experienced flood damage in the past or areas with high flood risk due to their topography. In addition, the evaluation unit can continuously update its risk assessment based on real-time data provided by the analysis unit, enabling it to respond to the latest situation. This allows the evaluation unit to always provide highly accurate risk assessments based on the latest information, supporting a quick and appropriate response.

[0033] The information provision department provides users with warning information based on the risks assessed by the evaluation department. Specifically, it can provide users with messages urging them to prepare for evacuation, as well as guidance on evacuation routes and shelters. The information provision department can quickly and reliably transmit information to users using various communication methods, such as smartphone apps, SMS, email, and voice calls. The information provision department can also provide flood risk maps and safety advice to local governments and related organizations. For example, by creating and providing flood risk maps to local governments, the department can support the development of regional disaster prevention plans and evacuation plans. Furthermore, the information provision department can collect feedback from users and continuously improve the accuracy and effectiveness of the information it provides. For example, based on feedback from users who have received evacuation orders, it can revise evacuation routes and improve the content of the orders. The information provision department also has a function to automatically send out warning information in emergencies, supporting a rapid response. In this way, the information provision department can quickly and reliably provide warning information to users and minimize the risk of disaster.

[0034] The service provider can provide users with messages urging them to prepare for evacuation, as well as information on evacuation routes and shelters. For example, when the risk of flooding increases, the service provider can send a message urging users to prepare for evacuation. For example, the service provider can send a message such as, "The risk of flooding is increasing. Please begin preparing for evacuation." The service provider can also provide users with information on evacuation routes and shelters. For example, the service provider can guide users to the optimal evacuation route based on their current location. For example, the service provider can provide information such as, "The nearest shelter is XX. Please take XX Street as your evacuation route." This allows the service provider to provide users with information to take appropriate measures against the risk of flooding.

[0035] The service provider can provide local governments and related organizations with flood risk maps and advice on safety measures. For example, the service provider can create and provide flood risk maps to local governments. For example, the service provider can create risk maps that color-code areas with a high flood risk. The service provider can also provide advice on safety measures to local governments and related organizations. For example, the service provider can provide advice on developing evacuation plans and emergency response methods. For example, the service provider can advise, "Consider increasing the number of evacuation shelters in areas with a high flood risk." This provides local governments and related organizations with information to support the planning and implementation of flood control measures.

[0036] The data collection unit can continuously collect the latest data and build a feedback loop to improve the flood prediction model. For example, the data collection unit collects weather data and water level sensor information in real time and stores it in a database. The data collection unit provides the collected data to the analysis unit, which analyzes the data to assess flood risk. The assessment unit performs a risk assessment based on the analysis results, and the provision unit provides warning information to users. The provision unit collects feedback from users and provides it to the data collection unit. Based on the user feedback, the data collection unit improves its data collection methods and updates the prediction model. This allows the data collection unit to continuously collect the latest data and build a feedback loop to improve the flood prediction model. For example, the data collection unit can adjust the frequency of weather data collection to collect more accurate data. The data collection unit can also optimize the placement of water level sensors to improve the accuracy of data collection. This allows for continuous improvement of the flood prediction model and improved prediction accuracy.

[0037] The analysis unit can assess the degree of flood risk by considering factors such as weather patterns and water level fluctuations. For example, the analysis unit analyzes weather data such as precipitation, temperature, and wind speed to identify weather patterns. The analysis unit can compare past weather data with current data to detect the occurrence of extreme weather events. The analysis unit can also analyze water level sensor data to evaluate water level fluctuations. For example, the analysis unit analyzes fluctuations in water level height and flow velocity to assess the degree of flood risk. The analysis unit comprehensively considers factors such as weather patterns and water level fluctuations to assess the degree of flood risk. This allows the analysis unit to accurately assess the degree of flood risk. For example, the analysis unit analyzes weather data and water level data in combination to assess flood risk. The analysis unit can also use machine learning models to assess the degree of flood risk. This allows the analysis unit to accurately assess the degree of flood risk and provide appropriate warning information.

[0038] The evaluation unit can predict the likelihood of flood occurrence and spread. For example, the evaluation unit predicts the likelihood of flood occurrence and spread based on meteorological data and water level data analyzed by the analysis unit. The evaluation unit can also assess the likelihood of flood occurrence and spread by referring to past flood data and meteorological patterns. For example, the evaluation unit predicts the likelihood of flood occurrence and spread by comparing meteorological data from past floods with current data. The evaluation unit can also use machine learning models to predict the likelihood of flood occurrence and spread. This allows the evaluation unit to predict the likelihood of flood occurrence and spread and provide early warning information. For example, the evaluation unit provides warning information to users when the risk of flooding increases. This gives users time to take appropriate measures against the risk of flooding.

[0039] The data collection unit can analyze past flood data and determine the optimal sensor placement. For example, the data collection unit can determine areas where sensors should be concentrated based on past flood locations. For example, the data collection unit can analyze data from past flood locations to identify areas with a high risk of flooding. The data collection unit can also adjust the sensor density considering the frequency and magnitude of floods. For example, the data collection unit can densely place sensors in areas with high flood frequency and sparsely place sensors in areas with low frequency. Furthermore, the data collection unit can analyze flood occurrence patterns from past data and place sensors to improve prediction accuracy. For example, the data collection unit can identify flood occurrence patterns based on past flood data and place sensors based on those patterns. This allows the data collection unit to perform optimal sensor placement based on past flood data and improve the accuracy of data collection. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past flood data into a generating AI and have the generating AI determine the optimal sensor placement.

[0040] The data collection unit can be equipped with a function to automatically detect and correct anomalies in real time during data collection. For example, if the data from a sensor contains anomalies, the AI ​​can automatically detect and correct them. For example, the data collection unit can monitor the data from the sensor in real time and use an algorithm to detect anomalies. The data collection unit can also correct anomalies by comparing them with data from other sensors. For example, the data collection unit can compare the data in which anomalies were detected with data from other sensors and correct it to the correct value. Furthermore, if anomalies occur frequently, the data collection unit can suggest sensor relocation or maintenance. For example, the data collection unit can re-evaluate the location of sensors where anomalies frequently occur and relocate them as needed. This allows the data collection unit to improve data reliability by automatically detecting and correcting anomalies during data collection. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data from sensors into a generating AI and have the generating AI perform anomaly detection and correction.

[0041] The data collection unit can optimize sensor placement by considering geographical conditions during data collection. For example, the unit can place sensors in areas with a high risk of flooding, taking into account topography and geology. For example, the unit can analyze topographic and geological data to identify areas with a high risk of flooding and place sensors in those areas. The unit can also place sensors near rivers and lakes to monitor water level fluctuations in real time. For example, the unit can install sensors near rivers and lakes to monitor water level fluctuations. Furthermore, the unit can adjust sensor placement by considering the characteristics of urban and rural areas. For example, the unit can densely place sensors in urban areas and sparsely place sensors in rural areas. This allows the unit to optimize sensor placement by considering geographical conditions and improve the accuracy of data collection. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input geographical condition data into a generating AI and have the generating AI perform the optimization of sensor placement.

[0042] The data collection unit can simultaneously collect data on other disasters (e.g., earthquakes and typhoons) when collecting data. For example, if an earthquake occurs simultaneously with a flood, the data collection unit can also collect earthquake data to perform a comprehensive risk assessment. For example, the data collection unit can collect earthquake data when an earthquake occurs and provide it to the analysis unit. The data collection unit can also collect wind speed and rainfall data when a typhoon approaches to assess flood risk. For example, the data collection unit can collect wind speed and rainfall data when a typhoon approaches and provide it to the analysis unit. Furthermore, by collecting data on other disasters, the data collection unit can assess the risk of complex disasters and propose appropriate countermeasures. For example, the data collection unit can assess the risk of complex disasters by collecting earthquake and typhoon data and providing it to the analysis unit. This allows the data collection unit to assess the risk of complex disasters and propose appropriate countermeasures by simultaneously collecting data on other disasters. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input other disaster data into a generating AI and have the generating AI perform data collection and risk assessment.

[0043] The analysis unit can be equipped with a function to detect extreme weather events by comparing them with past weather patterns during analysis. For example, the analysis unit can detect abnormal rainfall or temperature fluctuations by comparing them with past weather data. For example, the analysis unit can use an algorithm to detect the occurrence of extreme weather events based on past weather data. The analysis unit can also re-evaluate flood risk when extreme weather events are detected. For example, when extreme weather events are detected, the analysis unit adjusts the flood risk assessment criteria and re-evaluates the risk. Furthermore, the analysis unit can analyze patterns of extreme weather events and predict future risks. For example, the analysis unit identifies patterns of extreme weather events and predicts future flood risks based on those patterns. This allows the analysis unit to detect extreme weather events by comparing them with past weather patterns and re-evaluate flood risks. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past weather data into a generating AI and have the generating AI perform the detection of extreme weather events and the re-evaluation of risk assessments.

[0044] The analysis unit can add a function to evaluate the reliability of data and exclude unreliable data during analysis. For example, the analysis unit can evaluate the reliability of data from sensors and exclude outliers and missing values. For example, the analysis unit can monitor data from sensors in real time and use an algorithm to detect unreliable data. The analysis unit can also suggest sensor relocation or maintenance if there is a large amount of unreliable data. For example, the analysis unit can re-evaluate the locations of sensors where unreliable data frequently occurs and relocate them as needed. Furthermore, the analysis unit can improve the accuracy of the analysis results by using only highly reliable data. For example, the analysis unit can assess flood risk based on highly reliable data. This allows the analysis unit to improve the accuracy of the analysis results by evaluating the reliability of data and excluding unreliable data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input data from sensors into a generating AI and have the generating AI perform data reliability evaluation and exclusion of unreliable data.

[0045] The analysis unit can perform a comprehensive risk assessment by considering other disaster data (e.g., earthquakes and typhoons) during the analysis. For example, if an earthquake occurs simultaneously with a flood, the analysis unit will also consider earthquake data to perform a comprehensive risk assessment. For example, the analysis unit will collect earthquake data when an earthquake occurs and evaluate it together with the flood risk. The analysis unit can also evaluate flood risk by considering wind speed and rainfall data when a typhoon approaches. For example, the analysis unit will collect wind speed and rainfall data when a typhoon approaches and evaluate it together with the flood risk. Furthermore, by considering other disaster data, the analysis unit can assess the risk of complex disasters and propose appropriate countermeasures. For example, the analysis unit will collect earthquake and typhoon data and evaluate them together with the flood risk to assess the risk of complex disasters. This allows the analysis unit to perform a comprehensive risk assessment by considering other disaster data and propose appropriate countermeasures. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input other disaster data into a generating AI and have the generating AI perform a comprehensive risk assessment.

[0046] The analysis unit can integrate information from different data sources (e.g., social media and news) during analysis. For example, the analysis unit can integrate real-time information from social media into its analysis to assess flood risk. For example, the analysis unit can collect social media posts and use them to assess flood risk. The analysis unit can also integrate information from news sources to understand the status of flood occurrence. For example, the analysis unit can collect news articles and use them to assess flood risk. Furthermore, the analysis unit can improve the accuracy of its analysis results by integrating information from different data sources. For example, the analysis unit can integrate information from social media and news sources and use it to assess flood risk. This allows the analysis unit to integrate information from different data sources and improve the accuracy of its analysis results. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information from social media and news sources into a generating AI and have the generating AI perform information integration and risk assessment.

[0047] The evaluation unit can improve the accuracy of risk assessment by referring to past flood damage data during the evaluation process. For example, the evaluation unit can adjust the risk assessment criteria based on past flood damage data. For example, the evaluation unit can set risk assessment criteria considering the scale and frequency of past flood damage. The evaluation unit can also improve the accuracy of risk assessment by considering the frequency and scale of flood damage. For example, the evaluation unit can analyze past flood damage data and adjust the flood risk assessment criteria. Furthermore, the evaluation unit can analyze flood occurrence patterns from past data to improve the accuracy of risk assessment. For example, the evaluation unit can identify past flood occurrence patterns and perform risk assessments based on those patterns. In this way, the evaluation unit can improve the accuracy of risk assessment by referring to past flood damage data. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without using AI. For example, the evaluation unit can input past flood damage data into a generating AI and have the generating AI perform the improvement of risk assessment accuracy.

[0048] The evaluation unit can update the risk assessment in real time during the evaluation and adjust the alert level based on the latest information. For example, the evaluation unit updates the risk assessment based on data collected in real time. For example, the evaluation unit collects weather data and water level sensor information in real time and performs a risk assessment. The evaluation unit can also adjust the alert level based on the latest information. For example, the evaluation unit adjusts the flood risk assessment criteria based on the latest data and sets the alert level. Furthermore, the evaluation unit can enable a rapid response by updating the risk assessment in real time. For example, the evaluation unit updates the flood risk assessment in real time and provides warning information. This enables a rapid response by updating the risk assessment in real time and adjusting the alert level based on the latest information. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input data collected in real time into a generating AI and have the generating AI perform the risk assessment update and alert level adjustment.

[0049] The evaluation unit can simultaneously evaluate other disaster risks (e.g., earthquakes and typhoons) during the evaluation process. For example, if an earthquake occurs simultaneously with a flood, the evaluation unit will also evaluate the earthquake risk. For example, the evaluation unit can evaluate the earthquake risk when an earthquake occurs and perform a comprehensive risk assessment in conjunction with the flood risk. The evaluation unit can also evaluate typhoon risk based on wind speed and rainfall data when a typhoon approaches. For example, the evaluation unit can collect wind speed and rainfall data when a typhoon approaches and evaluate it in conjunction with the flood risk. Furthermore, the evaluation unit can perform a comprehensive risk assessment by simultaneously evaluating other disaster risks. For example, the evaluation unit can collect earthquake and typhoon data and evaluate them in conjunction with the flood risk to perform a comprehensive risk assessment. This enables the evaluation unit to perform a comprehensive risk assessment by simultaneously evaluating other disaster risks. Some or all of the above-described processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input other disaster data into a generating AI and have the generating AI perform a comprehensive risk assessment.

[0050] The evaluation unit can perform risk assessments while considering regional characteristics (e.g., topography and population density) during the evaluation process. For example, the evaluation unit can assess areas with high flood risk by considering topography and geology. For example, the evaluation unit can analyze topographic and geological data to identify areas with high flood risk. The evaluation unit can also assess the difficulty of evacuation by considering population density. For example, the evaluation unit can analyze population density data to assess the difficulty of evacuation. Furthermore, the evaluation unit can improve the accuracy of risk assessments by considering regional characteristics. For example, the evaluation unit can comprehensively analyze topographic, geological, and population density data to assess flood risk. In this way, the evaluation unit can improve the accuracy of risk assessments by considering regional characteristics. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input regional characteristic data into a generating AI and have the generating AI perform the risk assessment.

[0051] The service provider can, at the time of service provision, refer to the user's past evacuation history to propose the optimal evacuation route. For example, the service provider can propose the optimal evacuation route based on the evacuation route the user has used in the past. For example, the service provider can analyze the user's past evacuation history to identify the optimal evacuation route. The service provider can also propose an evacuation route that avoids congestion based on the user's past evacuation history. For example, the service provider can propose an evacuation route to avoid congestion based on past evacuation history. Furthermore, the service provider can analyze the user's past evacuation history and propose the most efficient evacuation route. For example, the service provider can identify and propose the most efficient evacuation route based on past evacuation history. In this way, the service provider can propose the optimal evacuation route by referring to the user's past evacuation history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's past evacuation history into a generating AI and have the generating AI propose the optimal evacuation route.

[0052] The service provider can add a function to display the real-time congestion status of evacuation shelters at the time of service provision. For example, the service provider can display the real-time congestion status of evacuation shelters and suggest the most suitable evacuation shelter to the user. For example, the service provider can collect the real-time congestion status of evacuation shelters and display it to the user. The service provider can also suggest alternative evacuation shelters to avoid crowded ones. For example, the service provider can suggest alternative evacuation shelters based on information about crowded shelters. Furthermore, the service provider can update the real-time congestion status of evacuation shelters and provide the user with the latest information. For example, the service provider can monitor the real-time congestion status of evacuation shelters and provide the user with the latest information. This allows the service provider to display the real-time congestion status of evacuation shelters and suggest the most suitable evacuation shelter to the user. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input data on the congestion status of evacuation shelters into a generating AI and have the generating AI display the congestion status and suggest evacuation shelters.

[0053] The information provider can provide other disaster information (e.g., earthquakes and typhoons) at the same time as providing information. For example, if an earthquake occurs simultaneously with a flood, the information provider can also provide earthquake information at the same time. For example, the information provider can collect earthquake information when an earthquake occurs and provide it to the user. The information provider can also provide wind speed and rainfall information at the same time when a typhoon is approaching. For example, the information provider can collect wind speed and rainfall information when a typhoon is approaching and provide it to the user. Furthermore, the information provider can also perform a comprehensive risk assessment by providing other disaster information at the same time. For example, the information provider can collect earthquake and typhoon information and perform a comprehensive risk assessment in conjunction with flood risk. This enables the information provider to perform a comprehensive risk assessment by providing other disaster information at the same time. Some or all of the above processing in the information provider may be performed using AI, for example, or without using AI. For example, the information provider can input other disaster information into a generating AI and have the generating AI perform the information provision and risk assessment.

[0054] The information provider can select the optimal information delivery method considering the user's device information at the time of delivery. For example, if the user is using a smartphone, the information provider can provide an information delivery method that matches the screen size. For example, the information provider can provide information optimized for the smartphone screen size. Also, if the user is using a tablet, the information provider can provide an information delivery method optimized for a larger screen. For example, the information provider can provide information optimized for the tablet screen size. Furthermore, if the user is using a smartwatch, the information provider can provide a concise and highly visible information delivery method. For example, the information provider can provide information optimized for the smartwatch screen size. This allows the information provider to select the optimal information delivery method considering the user's device information, enabling more appropriate information delivery. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the user's device information into a generating AI and have the generating AI select the information delivery method.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The data collection unit can collect geological and land-use data in addition to meteorological data and water level sensor information. For example, the data collection unit can collect soil permeability and ground stability as geological data, and the degree of urbanization and the distribution of agricultural land as land-use data. This allows the influence of geology and land use to be considered in the assessment of flood risk. Furthermore, the data collection unit provides this data to the analysis unit, which can integrate the geological and land-use data to assess flood risk. For example, the analysis unit can predict that flood risk will increase in areas with low permeability soil or in areas with advanced urbanization. This allows the flood prediction system to perform more accurate risk assessments.

[0057] The analysis unit can consider geographical features in addition to meteorological data and water level sensor information when assessing flood risk. For example, the analysis unit can use topographic data to simulate flood flow and inundation extent. This allows for more accurate identification of areas at high flood risk. The analysis unit can also analyze river flow data to assess the likelihood of flood occurrence and expansion. For example, the analysis unit can predict an increase in flood risk if river flow increases rapidly. Furthermore, the analysis unit can improve the accuracy of flood risk assessment by considering geographical features. For example, the analysis unit can integrate and analyze topographic data and meteorological data to assess flood risk. This allows the analysis unit to perform flood risk assessments that take geographical features into account.

[0058] The assessment unit can improve the accuracy of its risk assessment by referring to past flood data in evaluating flood risk. For example, the assessment unit can analyze meteorological data and water level data from past floods to set flood risk assessment criteria. This allows it to perform current risk assessments based on past flood data. The assessment unit can also perform risk assessments that take into account the scale and frequency of damage by referring to past flood damage data. For example, the assessment unit can adjust the risk assessment criteria based on the scale and frequency of past flood damage. Furthermore, the assessment unit can analyze flood occurrence patterns from past data and predict future risks. For example, the assessment unit can identify past flood occurrence patterns and perform risk assessments based on those patterns. This allows the assessment unit to improve the accuracy of its risk assessments by referring to past flood data.

[0059] The data collection unit can collect real-time information from social media in addition to weather data and water level sensor information. For example, the data collection unit can collect social media posts to understand the occurrence of floods and the extent of damage. This allows for the consideration of real-time local information in flood risk assessment. The data collection unit also provides social media information to the analysis unit, which can integrate this information to assess flood risk. For example, the analysis unit can analyze social media posts to identify areas at high flood risk. Furthermore, the data collection unit can predict the likelihood of flood occurrence and spread based on social media information. In this way, the data collection unit can improve the accuracy of flood risk assessment by collecting information from social media.

[0060] The analysis unit can integrate information from different data sources in assessing flood risk. For example, it can integrate information from news sources in addition to meteorological data and water level sensor data. This allows for the consideration of more information in assessing flood risk. The analysis unit can also use news information to understand the occurrence of floods and the extent of damage. For example, the analysis unit can collect news articles and use them to assess flood risk. Furthermore, the analysis unit can improve the accuracy of its analysis results by integrating information from different data sources. For example, the analysis unit can integrate and analyze meteorological data, water level sensor data, and news information to assess flood risk. This allows the analysis unit to integrate information from different data sources and improve the accuracy of its analysis results.

[0061] The service provider can add a function to display the real-time congestion status of evacuation shelters at the time of provision. For example, the service provider can display the real-time congestion status of evacuation shelters and suggest the most suitable evacuation shelter to the user. For example, the service provider can collect the real-time congestion status of evacuation shelters and display it to the user. The service provider can also suggest alternative evacuation shelters to avoid crowded ones. For example, the service provider can suggest alternative evacuation shelters based on information about crowded shelters. Furthermore, the service provider can update the real-time congestion status of evacuation shelters and provide the user with the latest information. For example, the service provider can monitor the real-time congestion status of evacuation shelters and provide the user with the latest information. As a result, the service provider can display the real-time congestion status of evacuation shelters and suggest the most suitable evacuation shelter to the user.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The collection unit collects weather data and water level sensor information. For example, it collects precipitation, temperature, wind speed, etc. as weather data, and water level height, flow velocity, etc. as water level sensor information. The collection unit collects weather data in real time and stores it in a database. It also periodically collects data from water level sensors and provides it to the analysis unit. Step 2: The analysis unit analyzes the information collected by the collection unit. For example, it analyzes the collected data using statistical analysis or machine learning models. The analysis unit assesses the degree of flood risk by considering factors such as weather patterns and water level fluctuations. It can also detect the occurrence of extreme weather events by comparing past weather data with current data. Step 3: The evaluation unit assesses flood risk based on the information analyzed by the analysis unit. For example, it predicts the likelihood of flood occurrence and expansion and assesses the level of risk. The evaluation unit sets criteria for providing warning information according to the level of risk. Step 4: The provisioning department provides warning information to users based on the risks assessed by the evaluation department. For example, it provides users with messages urging them to prepare for evacuation, and information on evacuation routes and shelters. It can also provide flood risk maps and safety advice to local governments and related organizations.

[0064] (Example of form 2) The flood prediction system according to an embodiment of the present invention is a system that utilizes meteorological data and water level sensor information to predict the occurrence and spread of floods and provide appropriate warnings and countermeasures. This flood prediction system collects and analyzes real-time meteorological data and water level sensor information using AI. The AI ​​analyzes this data and predicts the possibility of flood occurrence and spread. Specifically, it evaluates the degree of flood risk by considering factors such as weather patterns and water level fluctuations. When a flood is predicted, early warning information is provided to the user. The user has time to understand the possibility and degree of flooding and take appropriate countermeasures. For example, residents are provided with messages urging them to prepare for evacuation and guidance on evacuation routes and shelters. Local governments and related organizations are provided with flood risk maps and advice on safety measures, supporting the planning and implementation of disaster countermeasures. Furthermore, the AI ​​constantly collects the latest data and builds a feedback loop to improve the flood prediction model. This allows for the evaluation of the effectiveness of flood countermeasures and the updating of data analysis and algorithms to improve the system. This flood prediction system plays an important role as a tool to minimize damage caused by floods. By providing real-time information and support for appropriate countermeasures, it minimizes damage and ensures the safety of residents and local governments. This allows flood prediction systems to forecast the occurrence and spread of floods and provide appropriate warnings and countermeasures.

[0065] The flood prediction system according to the embodiment comprises a collection unit, an analysis unit, an evaluation unit, and a provision unit. The collection unit collects meteorological data and information from water level sensors. For example, the collection unit can collect meteorological data such as precipitation, temperature, and wind speed. The collection unit can also collect information from water level sensors such as water level and flow velocity. For example, the collection unit collects meteorological data in real time and stores it in a database. Furthermore, the collection unit periodically collects data from water level sensors and provides it to the analysis unit. The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit can analyze the collected data using statistical analysis of the data or machine learning models. The analysis unit evaluates the degree of flood risk, taking into account factors such as weather patterns and water level fluctuations. For example, the analysis unit can compare past weather data with current data to detect the occurrence of extreme weather events. The evaluation unit evaluates the risk of flooding based on the information analyzed by the analysis unit. For example, the evaluation unit can predict the possibility of flood occurrence and expansion and evaluate the level of risk. The evaluation unit sets criteria for providing warning information according to the level of risk. The provision unit provides warning information to users based on the risk evaluated by the evaluation unit. For example, the provision unit can provide users with messages urging them to prepare for evacuation, and guidance on evacuation routes and shelters. The provision unit can also provide flood risk maps and safety advice to local governments and related organizations. For example, the provision unit can create a flood risk map and provide it to local governments. As a result, the flood prediction system according to this embodiment can utilize meteorological data and water level sensor information to predict the occurrence and spread of floods and provide appropriate warnings and countermeasures.

[0066] The data collection unit collects meteorological data and information from water level sensors. Specifically, it can collect detailed meteorological data such as precipitation, temperature, wind speed, humidity, and atmospheric pressure. This data is acquired in real time from various devices such as weather stations, satellites, and drones. The data collection unit can also collect information from water level sensors, such as water level, flow velocity, flow rate, and river water quality data. Water level sensors are installed in bodies of water such as rivers, lakes, and dams, and collect data periodically, transmitting it to a central database via wireless communication or the internet. The data collection unit has an interface for centrally managing this data and providing it to the analysis unit. Furthermore, the data collection unit can flexibly respond to specific situations and conditions by adjusting the data collection frequency and accuracy. For example, if heavy rainfall is predicted, the collection frequency can be increased to update data in real time and support a rapid response. The data collection unit also has a function to detect outliers and missing data, and can perform filtering and imputation processing to maintain data quality. As a result, the data collection unit can collect data efficiently and effectively, improving the overall system performance.

[0067] The analysis unit analyzes the information collected by the data collection unit. Specifically, it can analyze the collected data from multiple perspectives using statistical analysis and machine learning models. For example, statistical analysis analyzes time-series data of precipitation and water levels to detect anomalies and understand trends. Machine learning models learn from past weather data and water level data to predict future weather patterns and water level fluctuations. Based on these analysis results, the analysis unit evaluates the degree of flood risk. Specifically, it evaluates the probability of flood occurrence and the extent of impact by considering factors such as weather patterns, water level fluctuations, topographic information, and land use information. For example, the analysis unit can compare past weather data with current data to detect the occurrence of extreme weather events. Furthermore, the analysis unit can use simulation technology to create multiple flood progression scenarios and identify the most likely scenario. In addition, the analysis unit can continuously revise its analysis results based on real-time updated data to respond to the latest situation. This allows the analysis unit to quickly and accurately assess flood risk and improve the reliability and safety of the entire system.

[0068] The evaluation unit assesses flood risk based on information analyzed by the analysis unit. Specifically, it predicts the likelihood of flood occurrence and expansion, and evaluates the level of risk. The evaluation unit can comprehensively evaluate factors such as the probability of flood occurrence, the scope of impact, and the extent of damage, and quantify the level of risk. The evaluation unit sets criteria for providing warning information according to the level of risk. For example, if the risk level is high, it issues warning information urging immediate evacuation, and if the risk level is moderate, it provides information urging preparation for evacuation. Furthermore, the evaluation unit can perform more accurate risk assessments by considering the characteristics of each region and past disaster history. For example, stricter criteria can be set for areas that have frequently experienced flood damage in the past or areas with high flood risk due to their topography. In addition, the evaluation unit can continuously update its risk assessment based on real-time data provided by the analysis unit, enabling it to respond to the latest situation. This allows the evaluation unit to always provide highly accurate risk assessments based on the latest information, supporting a quick and appropriate response.

[0069] The information provision department provides users with warning information based on the risks assessed by the evaluation department. Specifically, it can provide users with messages urging them to prepare for evacuation, as well as guidance on evacuation routes and shelters. The information provision department can quickly and reliably transmit information to users using various communication methods, such as smartphone apps, SMS, email, and voice calls. The information provision department can also provide flood risk maps and safety advice to local governments and related organizations. For example, by creating and providing flood risk maps to local governments, the department can support the development of regional disaster prevention plans and evacuation plans. Furthermore, the information provision department can collect feedback from users and continuously improve the accuracy and effectiveness of the information it provides. For example, based on feedback from users who have received evacuation orders, it can revise evacuation routes and improve the content of the orders. The information provision department also has a function to automatically send out warning information in emergencies, supporting a rapid response. In this way, the information provision department can quickly and reliably provide warning information to users and minimize the risk of disaster.

[0070] The service provider can provide users with messages urging them to prepare for evacuation, as well as information on evacuation routes and shelters. For example, when the risk of flooding increases, the service provider can send a message urging users to prepare for evacuation. For example, the service provider can send a message such as, "The risk of flooding is increasing. Please begin preparing for evacuation." The service provider can also provide users with information on evacuation routes and shelters. For example, the service provider can guide users to the optimal evacuation route based on their current location. For example, the service provider can provide information such as, "The nearest shelter is XX. Please take XX Street as your evacuation route." This allows the service provider to provide users with information to take appropriate measures against the risk of flooding.

[0071] The service provider can provide local governments and related organizations with flood risk maps and advice on safety measures. For example, the service provider can create and provide flood risk maps to local governments. For example, the service provider can create risk maps that color-code areas with a high flood risk. The service provider can also provide advice on safety measures to local governments and related organizations. For example, the service provider can provide advice on developing evacuation plans and emergency response methods. For example, the service provider can advise, "Consider increasing the number of evacuation shelters in areas with a high flood risk." This provides local governments and related organizations with information to support the planning and implementation of flood control measures.

[0072] The data collection unit can continuously collect the latest data and build a feedback loop to improve the flood prediction model. For example, the data collection unit collects weather data and water level sensor information in real time and stores it in a database. The data collection unit provides the collected data to the analysis unit, which analyzes the data to assess flood risk. The assessment unit performs a risk assessment based on the analysis results, and the provision unit provides warning information to users. The provision unit collects feedback from users and provides it to the data collection unit. Based on the user feedback, the data collection unit improves its data collection methods and updates the prediction model. This allows the data collection unit to continuously collect the latest data and build a feedback loop to improve the flood prediction model. For example, the data collection unit can adjust the frequency of weather data collection to collect more accurate data. The data collection unit can also optimize the placement of water level sensors to improve the accuracy of data collection. This allows for continuous improvement of the flood prediction model and improved prediction accuracy.

[0073] The analysis unit can assess the degree of flood risk by considering factors such as weather patterns and water level fluctuations. For example, the analysis unit analyzes weather data such as precipitation, temperature, and wind speed to identify weather patterns. The analysis unit can compare past weather data with current data to detect the occurrence of extreme weather events. The analysis unit can also analyze water level sensor data to evaluate water level fluctuations. For example, the analysis unit analyzes fluctuations in water level height and flow velocity to assess the degree of flood risk. The analysis unit comprehensively considers factors such as weather patterns and water level fluctuations to assess the degree of flood risk. This allows the analysis unit to accurately assess the degree of flood risk. For example, the analysis unit analyzes weather data and water level data in combination to assess flood risk. The analysis unit can also use machine learning models to assess the degree of flood risk. This allows the analysis unit to accurately assess the degree of flood risk and provide appropriate warning information.

[0074] The evaluation unit can predict the likelihood of flood occurrence and spread. For example, the evaluation unit predicts the likelihood of flood occurrence and spread based on meteorological data and water level data analyzed by the analysis unit. The evaluation unit can also assess the likelihood of flood occurrence and spread by referring to past flood data and meteorological patterns. For example, the evaluation unit predicts the likelihood of flood occurrence and spread by comparing meteorological data from past floods with current data. The evaluation unit can also use machine learning models to predict the likelihood of flood occurrence and spread. This allows the evaluation unit to predict the likelihood of flood occurrence and spread and provide early warning information. For example, the evaluation unit provides warning information to users when the risk of flooding increases. This gives users time to take appropriate measures against the risk of flooding.

[0075] The data collection unit can estimate the user's emotions and adjust the frequency of data collection based on the estimated emotions. For example, if the user is feeling anxious, the data collection unit can increase the frequency of data collection to provide information in real time. For example, the data collection unit can collect weather data and water level sensor information more frequently and provide it to the analysis unit. Conversely, if the user is feeling at ease, the data collection unit can also reduce the frequency of data collection to conserve resources. For example, the data collection unit can periodically collect weather data and water level sensor information and provide it to the analysis unit. Furthermore, if the user is facing an emergency, the data collection unit can maximize the frequency of data collection to enable immediate response. For example, the data collection unit can collect weather data and water level sensor information in real time and provide it to the analysis unit. This allows the data collection unit to adjust the frequency of data collection according to the user's emotions and provide more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into a generating AI and have the generating AI adjust the frequency of data collection.

[0076] The data collection unit can analyze past flood data and determine the optimal sensor placement. For example, the data collection unit can determine areas where sensors should be concentrated based on past flood locations. For example, the data collection unit can analyze data from past flood locations to identify areas with a high risk of flooding. The data collection unit can also adjust the sensor density considering the frequency and magnitude of floods. For example, the data collection unit can densely place sensors in areas with high flood frequency and sparsely place sensors in areas with low frequency. Furthermore, the data collection unit can analyze flood occurrence patterns from past data and place sensors to improve prediction accuracy. For example, the data collection unit can identify flood occurrence patterns based on past flood data and place sensors based on those patterns. This allows the data collection unit to perform optimal sensor placement based on past flood data and improve the accuracy of data collection. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past flood data into a generating AI and have the generating AI determine the optimal sensor placement.

[0077] The data collection unit can be equipped with a function to automatically detect and correct anomalies in real time during data collection. For example, if the data from a sensor contains anomalies, the AI ​​can automatically detect and correct them. For example, the data collection unit can monitor the data from the sensor in real time and use an algorithm to detect anomalies. The data collection unit can also correct anomalies by comparing them with data from other sensors when anomalies are detected. For example, the data collection unit can compare the data in which anomalies were detected with data from other sensors and correct it to the correct value. Furthermore, if anomalies occur frequently, the data collection unit can suggest sensor relocation or maintenance. For example, the data collection unit can re-evaluate the location of sensors where anomalies frequently occur and relocate them as needed. This allows the data collection unit to improve data reliability by automatically detecting and correcting anomalies during data collection. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data from sensors into a generating AI and have the generating AI perform anomaly detection and correction.

[0078] The data collection unit can estimate the user's emotions and prioritize the data to collect based on those emotions. For example, if the user is feeling anxious, the data collection unit will prioritize collecting data from areas at high flood risk. For instance, it will prioritize collecting weather data and water level sensor information from high flood risk areas and provide it to the analysis unit. Furthermore, if the user is feeling at ease, the data collection unit can perform normal data collection, conserving resources. For example, it will periodically collect weather data and water level sensor information and provide it to the analysis unit. Additionally, if the user is facing an emergency, the data collection unit can prioritize collecting the most important data. For example, it will collect weather data and water level sensor information in real time and provide it to the analysis unit. This allows the data collection unit to prioritize data collection based on the user's emotions, prioritizing the collection of more important data. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into a generating AI and have the generating AI determine the priority of the data.

[0079] The data collection unit can optimize sensor placement by considering geographical conditions during data collection. For example, the data collection unit can place sensors in areas with a high risk of flooding, taking into account topography and geology. For example, the data collection unit can analyze topographic and geological data to identify areas with a high risk of flooding and place sensors in those areas. The data collection unit can also place sensors near rivers and lakes to monitor water level fluctuations in real time. For example, the data collection unit can install sensors near rivers and lakes to monitor water level fluctuations. Furthermore, the data collection unit can adjust sensor placement by considering the characteristics of urban and rural areas. For example, the data collection unit can densely place sensors in urban areas and sparsely place sensors in rural areas. This allows the data collection unit to optimize sensor placement by considering geographical conditions and improve the accuracy of data collection. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input geographical condition data into a generating AI and have the generating AI perform the optimization of sensor placement.

[0080] The data collection unit can simultaneously collect data on other disasters (e.g., earthquakes and typhoons) when collecting data. For example, if an earthquake occurs simultaneously with a flood, the data collection unit can also collect earthquake data to perform a comprehensive risk assessment. For example, the data collection unit can collect earthquake data when an earthquake occurs and provide it to the analysis unit. The data collection unit can also collect wind speed and rainfall data when a typhoon approaches to assess flood risk. For example, the data collection unit can collect wind speed and rainfall data when a typhoon approaches and provide it to the analysis unit. Furthermore, by collecting data on other disasters, the data collection unit can assess the risk of complex disasters and propose appropriate countermeasures. For example, the data collection unit can assess the risk of complex disasters by collecting earthquake and typhoon data and providing it to the analysis unit. This allows the data collection unit to assess the risk of complex disasters and propose appropriate countermeasures by simultaneously collecting data on other disasters. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input other disaster data into a generating AI and have the generating AI perform data collection and risk assessment.

[0081] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is feeling anxious, the analysis unit can provide a simple and highly visible display method. For example, the analysis unit can provide a risk map that color-codes areas with high flood risk. The analysis unit can also display detailed analysis results if the user is feeling at ease. For example, the analysis unit can display detailed flood risk assessment results. Furthermore, if the user is facing an emergency, the analysis unit can provide a concise and rapid display method. For example, the analysis unit can highlight areas with high flood risk. This allows the analysis unit to adjust the display method of the analysis results according to the user's emotions, enabling the provision of more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generating AI and have the generating AI adjust how the analysis results are displayed.

[0082] The analysis unit can be equipped with a function to detect extreme weather events by comparing them with past weather patterns during analysis. For example, the analysis unit can detect abnormal rainfall or temperature fluctuations by comparing them with past weather data. For example, the analysis unit can use an algorithm to detect the occurrence of extreme weather events based on past weather data. The analysis unit can also re-evaluate flood risk when extreme weather events are detected. For example, when extreme weather events are detected, the analysis unit adjusts the flood risk assessment criteria and re-evaluates the risk. Furthermore, the analysis unit can analyze extreme weather patterns and predict future risks. For example, the analysis unit identifies extreme weather patterns and predicts future flood risks based on those patterns. This allows the analysis unit to detect extreme weather events by comparing them with past weather patterns and re-evaluate flood risks. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past weather data into a generating AI and have the generating AI perform extreme weather detection and risk assessment re-evaluation.

[0083] The analysis unit can add a function to evaluate the reliability of data and exclude unreliable data during analysis. For example, the analysis unit can evaluate the reliability of data from sensors and exclude outliers and missing values. For example, the analysis unit can monitor data from sensors in real time and use an algorithm to detect unreliable data. The analysis unit can also suggest sensor relocation or maintenance if there is a large amount of unreliable data. For example, the analysis unit can re-evaluate the locations of sensors where unreliable data frequently occurs and relocate them as needed. Furthermore, the analysis unit can improve the accuracy of the analysis results by using only highly reliable data. For example, the analysis unit can assess flood risk based on highly reliable data. This allows the analysis unit to improve the accuracy of the analysis results by evaluating the reliability of data and excluding unreliable data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input data from sensors into a generating AI and have the generating AI perform data reliability evaluation and exclusion of unreliable data.

[0084] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated emotions. For example, if the user is feeling anxious, the analysis unit will prioritize the analysis of areas with a high flood risk. For instance, it will prioritize the analysis of weather data and water level sensor information starting from areas with a high flood risk. Conversely, if the user is feeling at ease, the analysis unit can perform normal analysis to conserve resources. For example, it will periodically analyze weather data and water level sensor information. Furthermore, if the user is facing an emergency, the analysis unit can prioritize the most important analysis. For example, it will analyze weather data and water level sensor information in real time to assess flood risk. This allows the analysis unit to determine the priority of analysis according to the user's emotions and prioritize the most important analysis. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generating AI and have the generating AI determine the priority of the analysis.

[0085] The analysis unit can perform a comprehensive risk assessment by considering other disaster data (e.g., earthquakes and typhoons) during the analysis. For example, if an earthquake occurs simultaneously with a flood, the analysis unit will also consider earthquake data to perform a comprehensive risk assessment. For example, the analysis unit will collect earthquake data when an earthquake occurs and evaluate it together with the flood risk. The analysis unit can also evaluate flood risk by considering wind speed and rainfall data when a typhoon approaches. For example, the analysis unit will collect wind speed and rainfall data when a typhoon approaches and evaluate it together with the flood risk. Furthermore, by considering other disaster data, the analysis unit can assess the risk of complex disasters and propose appropriate countermeasures. For example, the analysis unit will collect earthquake and typhoon data and evaluate them together with the flood risk to assess the risk of complex disasters. This allows the analysis unit to perform a comprehensive risk assessment by considering other disaster data and propose appropriate countermeasures. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input other disaster data into a generating AI and have the generating AI perform a comprehensive risk assessment.

[0086] The analysis unit can integrate information from different data sources (e.g., social media and news) during analysis. For example, the analysis unit can integrate real-time information from social media into its analysis to assess flood risk. For example, the analysis unit can collect social media posts and use them to assess flood risk. The analysis unit can also integrate information from news sources to understand the status of flood occurrence. For example, the analysis unit can collect news articles and use them to assess flood risk. Furthermore, the analysis unit can improve the accuracy of its analysis results by integrating information from different data sources. For example, the analysis unit can integrate information from social media and news sources and use it to assess flood risk. This allows the analysis unit to integrate information from different data sources and improve the accuracy of its analysis results. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information from social media and news sources into a generating AI and have the generating AI perform information integration and risk assessment.

[0087] The evaluation unit can estimate the user's emotions and adjust the risk assessment criteria based on the estimated user emotions. For example, if the user is feeling anxious, the evaluation unit can tighten the risk assessment criteria and raise the alert level. For example, the evaluation unit can adjust the flood risk assessment criteria and raise the alert level. The evaluation unit can also apply the normal risk assessment criteria if the user is feeling at ease. For example, the evaluation unit can revert the flood risk assessment criteria to the normal criteria. Furthermore, if the user is facing an emergency, the evaluation unit can tighten the risk assessment criteria to the maximum extent. For example, the evaluation unit can tighten the flood risk assessment criteria to the maximum extent and set the alert level to the highest level. This allows the evaluation unit to adjust the risk assessment criteria according to the user's emotions, enabling a more appropriate risk assessment. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input user emotion data into a generating AI and have the generating AI adjust the risk assessment criteria.

[0088] The evaluation unit can improve the accuracy of risk assessment by referring to past flood damage data during the evaluation process. For example, the evaluation unit can adjust the risk assessment criteria based on past flood damage data. For example, the evaluation unit can set risk assessment criteria considering the scale and frequency of past flood damage. The evaluation unit can also improve the accuracy of risk assessment by considering the frequency and scale of flood damage. For example, the evaluation unit can analyze past flood damage data and adjust the flood risk assessment criteria. Furthermore, the evaluation unit can analyze flood occurrence patterns from past data to improve the accuracy of risk assessment. For example, the evaluation unit can identify past flood occurrence patterns and perform risk assessments based on those patterns. In this way, the evaluation unit can improve the accuracy of risk assessment by referring to past flood damage data. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without using AI. For example, the evaluation unit can input past flood damage data into a generating AI and have the generating AI perform the improvement of risk assessment accuracy.

[0089] The evaluation unit can update the risk assessment in real time during the evaluation and adjust the alert level based on the latest information. For example, the evaluation unit updates the risk assessment based on data collected in real time. For example, the evaluation unit collects weather data and water level sensor information in real time and performs a risk assessment. The evaluation unit can also adjust the alert level based on the latest information. For example, the evaluation unit adjusts the flood risk assessment criteria based on the latest data and sets the alert level. Furthermore, the evaluation unit can enable a rapid response by updating the risk assessment in real time. For example, the evaluation unit updates the flood risk assessment in real time and provides warning information. This enables a rapid response by updating the risk assessment in real time and adjusting the alert level based on the latest information. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input data collected in real time into a generating AI and have the generating AI perform the risk assessment update and alert level adjustment.

[0090] The evaluation unit can estimate the user's emotions and adjust the order in which the risk assessment results are displayed based on the estimated user emotions. For example, if the user is feeling anxious, the evaluation unit will prioritize displaying the most important risk assessment results. For example, the evaluation unit will display areas with a high flood risk first. The evaluation unit can also display the risk assessment results in the normal order if the user is feeling at ease. For example, the evaluation unit will display the flood risk assessment results in the normal order. Furthermore, if the user is facing an emergency, the evaluation unit can also display the most important information first. For example, the evaluation unit will highlight areas with a high flood risk. This allows the evaluation unit to adjust the order in which the risk assessment results are displayed according to the user's emotions, enabling the provision of more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input user emotion data into the generating AI and have the generating AI adjust the display order of the risk assessment results.

[0091] The evaluation unit can simultaneously evaluate other disaster risks (e.g., earthquakes and typhoons) during the evaluation process. For example, if an earthquake occurs simultaneously with a flood, the evaluation unit will also evaluate the earthquake risk. For example, the evaluation unit can evaluate the earthquake risk when an earthquake occurs and perform a comprehensive risk assessment in conjunction with the flood risk. The evaluation unit can also evaluate typhoon risk based on wind speed and rainfall data when a typhoon approaches. For example, the evaluation unit can collect wind speed and rainfall data when a typhoon approaches and evaluate it in conjunction with the flood risk. Furthermore, the evaluation unit can perform a comprehensive risk assessment by simultaneously evaluating other disaster risks. For example, the evaluation unit can collect earthquake and typhoon data and evaluate them in conjunction with the flood risk to perform a comprehensive risk assessment. This enables the evaluation unit to perform a comprehensive risk assessment by simultaneously evaluating other disaster risks. Some or all of the above-described processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input other disaster data into a generating AI and have the generating AI perform a comprehensive risk assessment.

[0092] The evaluation unit can perform risk assessments while considering regional characteristics (e.g., topography and population density) during the evaluation process. For example, the evaluation unit can assess areas with high flood risk by considering topography and geology. For example, the evaluation unit can analyze topographic and geological data to identify areas with high flood risk. The evaluation unit can also assess the difficulty of evacuation by considering population density. For example, the evaluation unit can analyze population density data to assess the difficulty of evacuation. Furthermore, the evaluation unit can improve the accuracy of risk assessments by considering regional characteristics. For example, the evaluation unit can comprehensively analyze topographic, geological, and population density data to assess flood risk. In this way, the evaluation unit can improve the accuracy of risk assessments by considering regional characteristics. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input regional characteristic data into a generating AI and have the generating AI perform the risk assessment.

[0093] The service provider can estimate the user's emotions and adjust the way warning information is presented based on the estimated emotions. For example, if the user is feeling anxious, the service provider can provide simple and highly visible warning information. For example, the service provider can provide warning information that color-codes areas with a high flood risk. The service provider can also provide detailed warning information if the user is feeling at ease. For example, the service provider can display the flood risk assessment results in detail. Furthermore, if the user is facing an emergency, the service provider can provide concise and rapid warning information. For example, the service provider can highlight areas with a high flood risk. This allows the service provider to adjust the way warning information is presented according to the user's emotions, enabling the provision of more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input user emotion data into a generating AI and have the AI ​​adjust how warning information is expressed.

[0094] The service provider can, at the time of service provision, refer to the user's past evacuation history to propose the optimal evacuation route. For example, the service provider can propose the optimal evacuation route based on the evacuation route the user has used in the past. For example, the service provider can analyze the user's past evacuation history to identify the optimal evacuation route. The service provider can also propose an evacuation route that avoids congestion based on the user's past evacuation history. For example, the service provider can propose an evacuation route to avoid congestion based on past evacuation history. Furthermore, the service provider can analyze the user's past evacuation history and propose the most efficient evacuation route. For example, the service provider can identify and propose the most efficient evacuation route based on past evacuation history. In this way, the service provider can propose the optimal evacuation route by referring to the user's past evacuation history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's past evacuation history into a generating AI and have the generating AI propose the optimal evacuation route.

[0095] The service provider can add a function to display the real-time congestion status of evacuation shelters at the time of service provision. For example, the service provider can display the real-time congestion status of evacuation shelters and suggest the most suitable evacuation shelter to the user. For example, the service provider can collect the real-time congestion status of evacuation shelters and display it to the user. The service provider can also suggest alternative evacuation shelters to avoid crowded ones. For example, the service provider can suggest alternative evacuation shelters based on information about crowded shelters. Furthermore, the service provider can update the real-time congestion status of evacuation shelters and provide the user with the latest information. For example, the service provider can monitor the real-time congestion status of evacuation shelters and provide the user with the latest information. This allows the service provider to display the real-time congestion status of evacuation shelters and suggest the most suitable evacuation shelter to the user. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input data on the congestion status of evacuation shelters into a generating AI and have the generating AI display the congestion status and suggest evacuation shelters.

[0096] The information provider can estimate the user's emotions and prioritize warning information based on the estimated emotions. For example, if the user is feeling anxious, the information provider will prioritize providing the most important warning information. For example, the information provider will first provide information on areas with a high risk of flooding. The information provider can also provide warning information in the usual order if the user is feeling at ease. For example, the information provider will provide the flood risk assessment results in the usual order. Furthermore, if the user is facing an emergency, the information provider can also provide the most important information first. For example, the information provider will highlight information on areas with a high risk of flooding. This allows the information provider to prioritize warning information according to the user's emotions and provide more important information preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the information provider may be performed using AI, for example, or without AI. For example, the service provider can input user emotion data into a generating AI and have the AI ​​determine the priority of warning information.

[0097] The information provider can provide other disaster information (e.g., earthquakes and typhoons) at the same time as providing information. For example, if an earthquake occurs simultaneously with a flood, the information provider can also provide earthquake information at the same time. For example, the information provider can collect earthquake information when an earthquake occurs and provide it to the user. The information provider can also provide wind speed and rainfall information at the same time when a typhoon is approaching. For example, the information provider can collect wind speed and rainfall information when a typhoon is approaching and provide it to the user. Furthermore, the information provider can also perform a comprehensive risk assessment by providing other disaster information at the same time. For example, the information provider can collect earthquake and typhoon information and perform a comprehensive risk assessment in conjunction with flood risk. This enables the information provider to perform a comprehensive risk assessment by providing other disaster information at the same time. Some or all of the above processing in the information provider may be performed using AI, for example, or without using AI. For example, the information provider can input other disaster information into a generating AI and have the generating AI perform the information provision and risk assessment.

[0098] The information provider can select the optimal information delivery method by considering the user's device information at the time of delivery. For example, if the user is using a smartphone, the information provider can provide an information delivery method that matches the screen size. For example, the information provider can provide information optimized for the smartphone screen size. Also, if the user is using a tablet, the information provider can provide an information delivery method optimized for a larger screen. For example, the information provider can provide information optimized for the tablet screen size. Furthermore, if the user is using a smartwatch, the information provider can provide a concise and highly visible information delivery method. For example, the information provider can provide information optimized for the smartwatch screen size. This allows the information provider to select the optimal information delivery method by considering the user's device information, enabling more appropriate information delivery. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the user's device information into a generating AI and have the generating AI select the information delivery method.

[0099] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0100] The data collection unit can collect geological and land-use data in addition to meteorological data and water level sensor information. For example, the data collection unit can collect soil permeability and ground stability as geological data, and the degree of urbanization and the distribution of agricultural land as land-use data. This allows the influence of geology and land use to be considered in the assessment of flood risk. Furthermore, the data collection unit provides this data to the analysis unit, which can integrate the geological and land-use data to assess flood risk. For example, the analysis unit can predict that flood risk will increase in areas with low permeability soil or in areas with advanced urbanization. This allows the flood prediction system to perform more accurate risk assessments.

[0101] The system can estimate the user's emotions and adjust the evacuation route guidance based on those emotions. For example, if the user is feeling anxious, the system can provide evacuation route guidance in a simple and highly visible format. For instance, it might color-code the evacuation route and highlight important points. If the user is feeling reassured, it can also provide detailed evacuation route guidance. For example, it might display detailed explanations at each point along the evacuation route. Furthermore, if the user is facing an emergency, it can prioritize guiding them to the fastest evacuation route. For example, it might highlight the fastest route among the evacuation options. This allows the system to adjust the evacuation route guidance according to the user's emotions, enabling it to provide more appropriate information.

[0102] The analysis unit can consider geographical features in addition to meteorological data and water level sensor information when assessing flood risk. For example, the analysis unit can use topographic data to simulate flood flow and inundation extent. This allows for more accurate identification of areas at high flood risk. The analysis unit can also analyze river flow data to assess the likelihood of flood occurrence and expansion. For example, the analysis unit can predict an increase in flood risk if river flow increases rapidly. Furthermore, the analysis unit can improve the accuracy of flood risk assessment by considering geographical features. For example, the analysis unit can integrate and analyze topographic data and meteorological data to assess flood risk. This allows the analysis unit to perform flood risk assessments that take geographical features into account.

[0103] The assessment unit can improve the accuracy of its risk assessment by referring to past flood data in evaluating flood risk. For example, the assessment unit can analyze meteorological data and water level data from past floods to set flood risk assessment criteria. This allows it to perform current risk assessments based on past flood data. The assessment unit can also perform risk assessments that take into account the scale and frequency of damage by referring to past flood damage data. For example, the assessment unit can adjust the risk assessment criteria based on the scale and frequency of past flood damage. Furthermore, the assessment unit can analyze flood occurrence patterns from past data and predict future risks. For example, the assessment unit can identify past flood occurrence patterns and perform risk assessments based on those patterns. This allows the assessment unit to improve the accuracy of its risk assessments by referring to past flood data.

[0104] The information provider can estimate the user's emotions and adjust the way warning information is presented based on those emotions. For example, if the user is feeling anxious, it can provide simple and highly visible warning information. For instance, it can provide warning information that color-codes areas at high flood risk. If the user is feeling reassured, it can also provide detailed warning information. For example, it can display the flood risk assessment results in detail. Furthermore, if the user is facing an emergency, it can provide concise and timely warning information. For example, it can highlight areas at high flood risk. In this way, the information provider can adjust the way warning information is presented according to the user's emotions, enabling it to provide more appropriate information.

[0105] The data collection unit can collect real-time information from social media in addition to weather data and water level sensor information. For example, the data collection unit can collect social media posts to understand the occurrence of floods and the extent of damage. This allows for the consideration of real-time local information in flood risk assessment. The data collection unit also provides social media information to the analysis unit, which can integrate this information to assess flood risk. For example, the analysis unit can analyze social media posts to identify areas at high flood risk. Furthermore, the data collection unit can predict the likelihood of flood occurrence and spread based on social media information. In this way, the data collection unit can improve the accuracy of flood risk assessment by collecting information from social media.

[0106] The analysis unit can integrate information from different data sources in assessing flood risk. For example, it can integrate information from news sources in addition to meteorological data and water level sensor data. This allows for the consideration of more information in assessing flood risk. The analysis unit can also use news information to understand the occurrence of floods and the extent of damage. For example, the analysis unit can collect news articles and use them to assess flood risk. Furthermore, the analysis unit can improve the accuracy of its analysis results by integrating information from different data sources. For example, the analysis unit can integrate and analyze meteorological data, water level sensor data, and news information to assess flood risk. This allows the analysis unit to integrate information from different data sources and improve the accuracy of its analysis results.

[0107] The evaluation unit can estimate the user's emotions and adjust the risk assessment criteria based on those emotions. For example, if the user is feeling anxious, the risk assessment criteria can be tightened to raise the alert level. For instance, the evaluation unit can adjust the flood risk assessment criteria to raise the alert level. Conversely, if the user is feeling at ease, the normal risk assessment criteria can be applied. For example, the evaluation unit can revert the flood risk assessment criteria back to the normal criteria. Furthermore, if the user is facing an emergency, the risk assessment criteria can be made as strict as possible. For example, the evaluation unit can make the flood risk assessment criteria as strict as possible and set the alert level to the highest level. This allows the evaluation unit to adjust the risk assessment criteria according to the user's emotions, enabling more appropriate risk assessments.

[0108] The service provider can add a function to display the real-time congestion status of evacuation shelters at the time of provision. For example, the service provider can display the real-time congestion status of evacuation shelters and suggest the most suitable evacuation shelter to the user. For example, the service provider can collect the real-time congestion status of evacuation shelters and display it to the user. The service provider can also suggest alternative evacuation shelters to avoid crowded ones. For example, the service provider can suggest alternative evacuation shelters based on information about crowded shelters. Furthermore, the service provider can update the real-time congestion status of evacuation shelters and provide the user with the latest information. For example, the service provider can monitor the real-time congestion status of evacuation shelters and provide the user with the latest information. As a result, the service provider can display the real-time congestion status of evacuation shelters and suggest the most suitable evacuation shelter to the user.

[0109] The information provider can estimate the user's emotions and prioritize warning information based on those emotions. For example, if the user is feeling anxious, the most important warning information will be provided first. For instance, the information provider will provide information on areas at high flood risk first. Conversely, if the user is feeling reassured, the information can be provided in the usual order. For example, the information provider will provide the flood risk assessment results in the usual order. Furthermore, if the user is facing an emergency, the most important information can be provided first. For example, the information provider will highlight information on areas at high flood risk. This allows the information provider to prioritize warning information according to the user's emotions and provide more important information first.

[0110] The following briefly describes the processing flow for example form 2.

[0111] Step 1: The collection unit collects weather data and water level sensor information. For example, it collects precipitation, temperature, wind speed, etc. as weather data, and water level height, flow velocity, etc. as water level sensor information. The collection unit collects weather data in real time and stores it in a database. It also periodically collects data from water level sensors and provides it to the analysis unit. Step 2: The analysis unit analyzes the information collected by the collection unit. For example, it analyzes the collected data using statistical analysis or machine learning models. The analysis unit assesses the degree of flood risk by considering factors such as weather patterns and water level fluctuations. It can also detect the occurrence of extreme weather events by comparing past weather data with current data. Step 3: The evaluation unit assesses flood risk based on the information analyzed by the analysis unit. For example, it predicts the likelihood of flood occurrence and expansion and assesses the level of risk. The evaluation unit sets criteria for providing warning information according to the level of risk. Step 4: The provisioning department provides warning information to users based on the risks assessed by the evaluation department. For example, it provides users with messages urging them to prepare for evacuation, and information on evacuation routes and shelters. It can also provide flood risk maps and safety advice to local governments and related organizations.

[0112] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0113] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0114] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0115] Each of the multiple elements described above, including the collection unit, analysis unit, evaluation unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects weather data and water level sensor information using the camera 42 and sensors of the smart device 14, and transmits the data to the data processing unit 12 via the control unit 46A. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The evaluation unit is implemented in the specific processing unit 290 of the data processing unit 12 and evaluates the risk of flooding based on the analysis results. The provision unit is implemented in the specific processing unit 46A of the smart device 14 and provides warning information to the user. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0116] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0117] As shown in Figure 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.

[0118] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0119] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0120] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0122] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0123] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0124] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0125] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0126] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0127] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0128] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0129] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0130] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0131] Each of the multiple elements described above, including the collection unit, analysis unit, evaluation unit, and provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects weather data and water level sensor information using the camera 42 and sensors of the smart glasses 214, and transmits the data to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and analyzes the collected data. The evaluation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and evaluates the risk of flooding based on the analysis results. The provision unit is implemented, for example, by the control unit 46A of the smart glasses 214, and provides warning information to the user. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0132] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0133] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0134] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0135] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0136] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0137] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0138] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0139] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0140] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0141] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0142] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0143] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0144] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0145] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0146] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0147] Each of the multiple elements described above, including the collection unit, analysis unit, evaluation unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects weather data and water level sensor information using the camera 42 and sensors of the headset terminal 314, and transmits the data to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and analyzes the collected data. The evaluation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and evaluates the risk of flooding based on the analysis results. The provision unit is implemented, for example, by the control unit 46A of the headset terminal 314, and provides warning information to the user. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0148] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0149] As shown in Figure 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.

[0150] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0151] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0152] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0153] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0154] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0155] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0156] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0157] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0158] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0159] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0160] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0161] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0162] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0163] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0164] Each of the multiple elements described above, including the collection unit, analysis unit, evaluation unit, and provision unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects weather data and water level sensor information using the camera 42 and sensors of the robot 414, and transmits the data to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and analyzes the collected data. The evaluation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and evaluates the risk of flooding based on the analysis results. The provision unit is implemented, for example, by the control unit 46A of the robot 414, and provides warning information to the user. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0165] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0166] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0167] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0168] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0169] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0170] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0171] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0172] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0173] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0174] 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.

[0175] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0176] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0177] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0178] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0179] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0180] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0181] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0182] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0183] (Note 1) A collection unit that collects weather data and water level sensor information, An analysis unit analyzes the information collected by the aforementioned collection unit, An evaluation unit that evaluates the risk of flooding based on the information analyzed by the aforementioned analysis unit, The system includes a provisioning unit that provides warning information to the user based on the risk evaluated by the evaluation unit. A system characterized by the following features. (Note 2) The aforementioned supply unit is, Provides users with messages urging them to prepare for evacuation, as well as information on evacuation routes and shelters. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned supply unit is, Providing local governments and related organizations with flood risk maps and advice on safety measures. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is We constantly collect the latest data and build a feedback loop to improve flood prediction models. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit, The degree of flood risk is assessed by considering factors such as weather patterns and water level fluctuations. The system described in Appendix 1, characterized by the features described herein. (Note 6) The evaluation unit, Predicting the likelihood of flood occurrence and spread. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is It estimates the user's emotions and adjusts the frequency of data collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze past flood data to determine the optimal sensor placement. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is Add a function to automatically detect and correct anomalies in real time during data collection. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting data, optimize the placement of sensors considering geographical conditions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting data, data from other disasters will also be collected simultaneously. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, add a function to detect extreme weather events by comparing them with past weather patterns. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, add a function to evaluate the reliability of the data and exclude unreliable data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, The system estimates the user's emotions and determines the priority of analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, a comprehensive risk assessment is performed by considering other disaster data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, information from different data sources is integrated. The system described in Appendix 1, characterized by the features described herein. (Note 19) The evaluation unit, We estimate user sentiment and adjust risk assessment criteria based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 20) The evaluation unit, During the evaluation process, we improve the accuracy of risk assessments by referring to past flood damage data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The evaluation unit, During the assessment, the risk assessment is updated in real time, and the alert level is adjusted based on the latest information. The system described in Appendix 1, characterized by the features described herein. (Note 22) The evaluation unit, It estimates the user's emotions and adjusts the order in which the risk assessment results are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The evaluation unit, During the assessment, other disaster risks should also be evaluated simultaneously. The system described in Appendix 1, characterized by the features described herein. (Note 24) The evaluation unit, During the evaluation, risk assessments will be conducted taking into account the characteristics of each region. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, The system estimates the user's emotions and adjusts how warning information is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing the service, it will suggest the optimal evacuation route by referring to the user's past evacuation history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing the service, we will add a function to display the real-time congestion status of evacuation shelters. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, The system estimates the user's emotions and prioritizes warning information based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing information, other disaster-related information will also be provided at the same time. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When providing information, the optimal method of information delivery is selected, taking into account the user's device information. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0184] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that collects weather data and water level sensor information, An analysis unit analyzes the information collected by the aforementioned collection unit, An evaluation unit that evaluates the risk of flooding based on the information analyzed by the aforementioned analysis unit, The system includes a provisioning unit that provides warning information to the user based on the risk evaluated by the evaluation unit. A system characterized by the following features.

2. The aforementioned supply unit is, Provides users with messages urging them to prepare for evacuation, as well as information on evacuation routes and shelters. The system according to feature 1.

3. The aforementioned supply unit is, Providing local governments and related organizations with flood risk maps and advice on safety measures. The system according to feature 1.

4. The aforementioned collection unit is We constantly collect the latest data and build a feedback loop to improve flood prediction models. The system according to feature 1.

5. The aforementioned analysis unit, The degree of flood risk is assessed by considering factors such as weather patterns and water level fluctuations. The system according to feature 1.

6. The evaluation unit described above, Predicting the likelihood of flood occurrence and spread. The system according to feature 1.

7. The aforementioned collection unit is It estimates the user's emotions and adjusts the frequency of data collection based on the estimated user emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze past flood data to determine the optimal sensor placement. The system according to feature 1.

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