system

Abstract

The system according to this embodiment aims to assess flood risk in real time and issue warnings quickly. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, and a warning unit. The data collection unit collects meteorological data, topographic data, and hydrological data. The analysis unit analyzes the data collected by the data collection unit and evaluates the risk of flooding. The warning unit issues warnings to high-risk areas based on the risk evaluated by the analysis 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 character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the flood risk has not been sufficiently evaluated in real time and warnings have not been issued promptly, leaving room for improvement.

[0005] The system according to the embodiment aims to evaluate the flood risk in real time and issue warnings promptly.

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a warning unit. The collection unit collects meteorological data, terrain data, and hydrological data. The analysis unit analyzes the data collected by the collection unit and evaluates the flood risk. The warning unit issues warnings to high-risk areas based on the risk evaluated by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can assess flood risk in real time and issue warnings quickly. [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 signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are 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 risk analysis system according to an embodiment of the present invention is a system that uses AI technology to analyze flood risk in real time and provide predictive alerts. This flood risk analysis system integrates meteorological data, topographic data, and hydrological data, performs risk assessment using an AI algorithm, and quickly issues warnings to high-risk areas. For example, the flood risk analysis system collects rainfall and wind speed as meteorological data, elevation and slope as topographic data, and river water level and flow rate as hydrological data. This data is collected in real time from sensors, satellites, ground observation devices, etc. Next, the flood risk analysis system analyzes the collected data using an AI algorithm. The AI ​​algorithm compares past data with current data and predicts future flood risk. For example, it compares past rainfall patterns with current rainfall to predict how much river water levels will rise. Based on this prediction, a risk assessment is performed. Based on the assessment results, a warning is quickly issued to high-risk areas. For example, if there is a possibility that river water levels in a particular area may reach dangerous levels, a warning is issued to residents and local governments in that area. This warning is provided in real time through smartphone apps, email, social media, etc. This system aims to protect lives and mitigate economic losses. For example, early warnings allow residents to evacuate quickly, and local governments can take appropriate measures. Businesses can also quickly implement measures to protect infrastructure and ensure business continuity. Furthermore, this system is ideal for local governments, disaster response organizations, and infrastructure management companies. By providing precise risk assessment and a real-time warning system powered by AI, it minimizes flood damage and enables rapid response. For example, local governments can use this system to issue rapid evacuation orders to residents. Disaster response organizations can take effective measures based on risk assessments. Infrastructure management companies can quickly carry out infrastructure protection and restoration work. Thus, this AI-powered real-time flood risk analysis and predictive alert system is a next-generation disaster response system that protects the safety and security of society.This allows flood risk analysis systems to protect lives and mitigate economic losses.

[0029] The flood risk analysis system according to this embodiment comprises a data collection unit, an analysis unit, and a warning unit. The data collection unit collects meteorological data, topographic data, and hydrological data. For example, the data collection unit collects rainfall and wind speed as meteorological data, elevation and slope as topographic data, and river water level and flow rate as hydrological data. The data collection unit can collect data in real time from sensors, satellites, and ground observation devices. For example, the data collection unit measures rainfall and wind speed using meteorological sensors and elevation and slope using topographic sensors. The data collection unit can also use hydrological sensors to measure river water level and flow rate. The analysis unit analyzes the data collected by the data collection unit and evaluates the flood risk. The analysis unit uses an AI algorithm to compare past data with current data and predict future flood risk. For example, the analysis unit compares past rainfall patterns with current rainfall to predict how much the river water level will rise. The analysis unit can also evaluate the risk of flooding by considering wind speed and topographic data. The analysis unit performs data analysis using an AI algorithm. For example, the analysis unit uses machine learning algorithms to learn patterns from past data and predict future risks. The warning unit issues warnings to high-risk areas based on the risks assessed by the analysis unit. The warning unit can issue warnings via smartphone apps, email, and social media. For example, if river levels in a particular area are likely to reach dangerous levels, the warning unit will issue a warning to residents and local governments in that area. The warning unit can also customize warning messages and provide information tailored to specific risks. For example, the warning unit can provide evacuation orders and safety information to areas at high risk of flooding. As a result, the flood risk analysis system according to this embodiment can integrate meteorological data, topographic data, and hydrological data, perform risk assessments using AI algorithms, and quickly issue warnings to high-risk areas.

[0030] The data collection unit collects meteorological, topographic, and hydrological data. Specifically, it collects rainfall and wind speed as meteorological data, elevation and slope as topographic data, and river water level and flow rate as hydrological data. The data collection unit can collect data in real time from sensors, satellites, and ground observation devices. For example, meteorological sensors measure rainfall in millimeters and record wind speed in meters per second. This allows for a detailed understanding of changes in meteorological conditions. Topographic sensors measure elevation in centimeters and record terrain slope in degrees. This allows for accurate capture of subtle changes in terrain and the effects of slope. Hydrological sensors measure river water level in millimeters and record flow rate in cubic meters per second. This allows for real-time monitoring of rising river levels and changes in flow rate. Furthermore, the data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data is stored on a cloud server and made accessible to the analysis and warning units. In addition, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0031] The analysis unit analyzes the data collected by the collection unit to assess flood risk. Using AI algorithms, the analysis unit compares past and current data to predict future flood risk. Specifically, it uses machine learning algorithms to compare past rainfall patterns with current rainfall to predict how much river levels will rise. For example, using a model learned from past data, it identifies which past patterns the current rainfall resembles and predicts river level rise based on that pattern. It can also assess flood risk by considering wind speed and topographic data. For example, strong winds tend to concentrate rainfall in specific areas, thus increasing the flood risk in those areas. Furthermore, topographic data is used to assess whether low-lying or sloping areas are susceptible to flooding. The analysis unit integrates this data to conduct a comprehensive risk assessment. For example, it combines rainfall, wind speed, topography, and water level data to comprehensively assess flood risk in a specific area. This allows the analysis unit to quickly and accurately analyze collected data and grasp the surrounding risk situation in real time. Furthermore, the analysis unit can utilize historical data and statistical information to perform long-term risk assessments and trend analyses. For example, it can predict risk fluctuations in specific areas and time periods based on past flood data and formulate future countermeasures. The analysis unit can also use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing early warnings. As a result, the analysis unit can not only grasp the situation in real time but also handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0032] The warning unit issues warnings to high-risk areas based on the risks assessed by the analysis unit. Specifically, warnings can be sent via smartphone apps, email, and social media. For example, if river levels in a particular area are likely to reach dangerous levels, the warning unit will issue a warning to residents and local governments in that area. Warning messages can include specific risk information and evacuation orders. For example, it may send a message such as, "River levels in your area are rising rapidly. Please evacuate immediately." The warning unit can also customize warning messages to provide information tailored to specific risks. For example, it can provide evacuation orders and safety information to areas at high risk of flooding. Furthermore, the warning unit can reliably transmit information using multiple communication methods. For example, it can reliably deliver important information by using not only smartphone notifications but also voice calls, SMS, and email. This allows the warning unit to provide users with quick and reliable instructions, minimizing the risk of disaster. In addition, the warning unit can collect user feedback and continuously improve the accuracy and effectiveness of its warnings. For example, it can revise evacuation routes and improve warning content based on feedback from users who have received evacuation orders. This allows the warning unit to provide highly accurate risk predictions based on the latest information at all times, supporting a quick and appropriate response.

[0033] The data collection unit can collect data in real time from sensors, satellites, and ground observation equipment. For example, the data collection unit can measure rainfall and wind speed using weather sensors, and measure elevation and slope using terrain sensors. The data collection unit can also use hydrological sensors to measure river water levels and flow rates. For example, the data collection unit can measure rainfall in real time using weather sensors and collect data. The data collection unit can also collect wide-area terrain data using satellite data. Furthermore, the data collection unit can measure river water levels and flow rates using ground observation equipment and collect data. This allows for risk assessment based on the latest information by collecting data in real time. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input data acquired from sensors into a generating AI and have the generating AI perform data analysis.

[0034] The analysis unit can compare past and present data to predict future flood risk. For example, it can compare past rainfall patterns with current rainfall to predict how much river levels will rise. The analysis unit can also assess flood risk by considering wind speed and topographic data. For example, it can use past data to model the relationship between rainfall and river levels and predict future water levels from current rainfall. The analysis unit can also use wind speed data to assess flood risk due to strong winds. Furthermore, it can use topographic data to identify areas susceptible to flooding. This allows for highly accurate prediction of future flood risk by comparing past and present data. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input past and present data into a generating AI and have the generating AI perform risk prediction.

[0035] The warning unit can issue warnings via smartphone apps, email, and social media. For example, if river levels in a particular area are likely to reach dangerous levels, the warning unit can issue warnings to residents and local governments in that area. The warning unit can also customize warning messages and provide information tailored to specific risks. For example, the warning unit can provide evacuation orders and safety information to areas at high risk of flooding. The warning unit can also issue warnings widely via social media, quickly disseminating information to many people. Furthermore, the warning unit can issue warnings to specific groups or individuals using email. This allows for rapid and widespread warning dissemination using diverse communication methods. Some or all of the above processes in the warning unit may be performed using AI or not. For example, the warning unit can input risk assessment results into a generating AI and have the generating AI generate warning messages.

[0036] The analysis unit can analyze data such as rainfall, wind speed, elevation, slope, river water level, and flow rate. For example, the analysis unit can use rainfall data to evaluate the intensity and duration of rainfall. It can also use wind speed data to evaluate the risk of flooding due to strong winds. Furthermore, it can use elevation data to identify low-lying areas susceptible to flooding. It can also use slope data to identify areas prone to flooding. Furthermore, it can use river water level data to evaluate the risk of river flooding. It can also use flow rate data to evaluate the strength and velocity of river flow. By analyzing diverse data in this way, a more precise risk assessment becomes possible. Some or all of the above-described processes in the analysis unit may be performed using AI, or they may not be performed using AI. For example, the analysis unit can input data such as rainfall, wind speed, elevation, slope, river water level, and flow rate into a generating AI, and have the generating AI perform the risk assessment.

[0037] The warning unit can issue warnings to residents and local governments in specific areas when river levels are likely to reach dangerous levels. For example, if river levels rise rapidly and the risk of flooding increases, the warning unit can issue evacuation orders to residents in that area. The warning unit can also issue warnings to local governments to encourage a swift response. Furthermore, the warning unit can customize warning messages and provide information tailored to specific risks. For example, the warning unit can provide information on evacuation routes and shelters to areas at high risk of flooding. The warning unit can also disseminate warnings widely via social media, quickly reaching many people. This allows residents and local governments to respond quickly by issuing warnings to specific areas rapidly. Some or all of the above processes in the warning unit may be performed using AI or not. For example, the warning unit can input risk assessment results into a generating AI and have the generating AI generate warning messages.

[0038] The data collection unit can analyze past collected data and select the optimal collection method. For example, the data collection unit can analyze past data collection patterns to determine the most effective sensor placement. Furthermore, the data collection unit can optimize the collection frequency and timing based on past data collection results. In addition, the data collection unit can compare successful and unsuccessful past data collection examples to select the optimal collection method. This allows for efficient data collection by analyzing past data and selecting the optimal collection method. Some or all of the above-described processes in the data collection unit may be performed using AI, or they may not. For example, the data collection unit can input past collected data into a generating AI and have the generating AI select the optimal collection method.

[0039] The data collection unit can filter data based on specific weather and terrain conditions during data collection. For example, the data collection unit can collect data only under specific weather conditions such as heavy rain or strong winds. Furthermore, the data collection unit can filter data based on specific terrain conditions such as mountainous areas or lowlands. In addition, the data collection unit can determine the priority of data to collect based on combinations of weather and terrain conditions. This allows for the efficient collection of only the necessary data by filtering it based on specific conditions. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input specific weather and terrain conditions into a generating AI and have the generating AI perform data filtering.

[0040] The data collection unit can prioritize the collection of highly relevant data by considering geographical location information during data collection. For example, the data collection unit can prioritize the collection of data from high-risk areas. Furthermore, the data collection unit can filter highly relevant data based on geographical location information. In addition, the data collection unit can combine geographical location information with other data to formulate an optimal data collection strategy. This enables efficient data collection by prioritizing the collection of highly relevant data based on geographical location information. Some or all of the above-described processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input geographical location information into a generating AI and have the generating AI collect highly relevant data.

[0041] The data collection unit can analyze social media activity and collect relevant data during data collection. For example, the data collection unit can analyze posts on social media to collect real-time flood information. Furthermore, the data collection unit can prioritize the collection of data from specific regions based on location information on social media. In addition, the data collection unit can analyze trends on social media and collect relevant data. This allows for the collection of real-time flood information by analyzing social media activity. Some or all of the above-described processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input social media data into a generating AI and have the generating AI collect relevant data.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on important data and a simplified analysis on less important data. The analysis unit can also determine the priority of the analysis based on the importance of the data. Furthermore, the analysis unit can apply multiple analysis methods to important data to improve accuracy. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0043] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a meteorological analysis algorithm to meteorological data. It can also apply a topographic analysis algorithm to topographic data. Furthermore, it can apply a hydrological analysis algorithm to hydrological data. This allows for more accurate analysis by applying different analysis algorithms depending on the data category. Some or all of the above-described processes in the analysis unit may be performed using AI, or they may be performed without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI select and apply an appropriate analysis algorithm.

[0044] The analysis unit can determine the priority of analysis based on the data collection timing during the analysis. For example, the analysis unit can prioritize the analysis of the latest data to perform real-time risk assessment. Alternatively, the analysis unit can perform analysis while referencing past data, with emphasis on current data. Furthermore, the analysis unit can dynamically adjust the analysis priority based on the data collection timing. This enables real-time risk assessment by determining the analysis priority based on the data collection timing. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the data collection timing into a generating AI and have the generating AI determine the analysis priority.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant data to perform a rapid risk assessment. The analysis unit can also dynamically adjust the order of analysis based on the relevance of the data. Furthermore, the analysis unit can perform a detailed analysis based on the highly relevant data. This enables a rapid risk assessment by adjusting the order of analysis based on the relevance of the data. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0046] The warning unit can adjust the level of detail of a warning based on the severity of the risk when a warning is issued. For example, in the case of high risk, the warning unit can provide detailed warning information. In the case of medium risk, the warning unit can provide concise warning information. Furthermore, in the case of low risk, the warning unit can provide simplified warning information. In this way, appropriate warning information can be provided by adjusting the level of detail of the warning based on the severity of the risk. Some or all of the above processing in the warning unit may be performed using AI or not. For example, the warning unit can input the severity of the risk into a generating AI and have the generating AI perform the adjustment of the level of detail of the warning.

[0047] The warning unit can apply different warning algorithms depending on the risk category when a warning is issued. For example, the warning unit can apply a weather-specific warning algorithm to weather risks. It can also apply a topographic-specific warning algorithm to topographic risks. Furthermore, it can apply a hydrological-specific warning algorithm to hydrological risks. This allows for the provision of appropriate warnings according to the risk category. Some or all of the above processing in the warning unit may be performed using AI or not. For example, the warning unit can input the risk category into a generating AI and have the generating AI select and apply an appropriate warning algorithm.

[0048] The warning unit can determine the priority of warnings based on when the risk occurred. For example, the warning unit will issue a priority warning for immediate risks. The warning unit can also dynamically adjust the warning priority based on when the risk occurred. Furthermore, the warning unit can issue planned warnings for long-term risks. This allows warnings to be provided at the appropriate time by determining the priority of warnings based on when the risk occurred. Some or all of the above processing in the warning unit may be performed using AI or not. For example, the warning unit can input the risk occurrence time into a generating AI and have the generating AI perform the determination of the warning priority.

[0049] The warning unit can adjust the order of warnings based on the relevance of the risks when a warning is issued. For example, the warning unit will issue warnings preferentially for highly relevant risks. The warning unit can also dynamically adjust the order of warnings based on the relevance of the risks. Furthermore, the warning unit can issue detailed warnings for highly relevant risks. This allows for the provision of quick and appropriate warnings by adjusting the order of warnings based on the relevance of the risks. Some or all of the above processing in the warning unit may be performed using AI or not. For example, the warning unit can input the relevance of risks into a generating AI and have the generating AI perform the adjustment of the warning order.

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

[0051] The data collection unit can analyze past collected data and select the optimal collection method. For example, the data collection unit can analyze past data collection patterns to determine the most effective sensor placement. Furthermore, the data collection unit can optimize the collection frequency and timing based on past data collection results. In addition, the data collection unit can compare successful and unsuccessful past data collection examples to select the optimal collection method. This allows for efficient data collection by analyzing past data and selecting the optimal collection method. Some or all of the above-described processes in the data collection unit may be performed using AI, or they may not. For example, the data collection unit can input past collected data into a generating AI and have the generating AI select the optimal collection method.

[0052] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on important data and a simplified analysis on less important data. The analysis unit can also determine the priority of the analysis based on the importance of the data. Furthermore, the analysis unit can apply multiple analysis methods to important data to improve accuracy. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0053] The warning unit can adjust the level of detail of a warning based on the severity of the risk when a warning is issued. For example, in the case of high risk, the warning unit can provide detailed warning information. In the case of medium risk, the warning unit can provide concise warning information. Furthermore, in the case of low risk, the warning unit can provide simplified warning information. In this way, appropriate warning information can be provided by adjusting the level of detail of the warning based on the severity of the risk. Some or all of the above processing in the warning unit may be performed using AI or not. For example, the warning unit can input the severity of the risk into a generating AI and have the generating AI perform the adjustment of the level of detail of the warning.

[0054] The data collection unit can filter data based on specific weather and terrain conditions during data collection. For example, the data collection unit can collect data only under specific weather conditions such as heavy rain or strong winds. Furthermore, the data collection unit can filter data based on specific terrain conditions such as mountainous areas or lowlands. In addition, the data collection unit can determine the priority of data to collect based on combinations of weather and terrain conditions. This allows for the efficient collection of only the necessary data by filtering it based on specific conditions. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input specific weather and terrain conditions into a generating AI and have the generating AI perform data filtering.

[0055] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a meteorological analysis algorithm to meteorological data. It can also apply a topographic analysis algorithm to topographic data. Furthermore, it can apply a hydrological analysis algorithm to hydrological data. This allows for more accurate analysis by applying different analysis algorithms depending on the data category. Some or all of the above-described processes in the analysis unit may be performed using AI, or they may be performed without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI select and apply an appropriate analysis algorithm.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The data collection unit collects meteorological data, topographic data, and hydrological data. Specifically, it collects rainfall and wind speed as meteorological data, elevation and slope as topographic data, and river water level and flow rate as hydrological data. The data collection unit can collect data in real time from sensors, satellites, and ground observation equipment. For example, it can measure rainfall and wind speed using meteorological sensors, elevation and slope using topographic sensors, and river water level and flow rate using hydrological sensors. Step 2: The analysis unit analyzes the data collected by the collection unit and assesses flood risk. The analysis unit uses AI algorithms to compare past and current data and predict future flood risk. For example, it compares past rainfall patterns with current rainfall to predict how much river levels will rise. It can also assess the risk of flooding by considering wind speed and topographic data. The analysis unit uses machine learning algorithms to analyze the data. Step 3: The warning unit issues warnings to high-risk areas based on the risks assessed by the analysis unit. The warning unit can issue warnings via smartphone apps, email, and social media. For example, if river levels in a particular area are likely to reach dangerous levels, it will issue warnings to residents and local governments in that area. It can also customize warning messages to provide information tailored to specific risks. For example, it can provide evacuation orders and safety information to areas at high risk of flooding.

[0058] (Example of form 2) The flood risk analysis system according to an embodiment of the present invention is a system that uses AI technology to analyze flood risk in real time and provide predictive alerts. This flood risk analysis system integrates meteorological data, topographic data, and hydrological data, performs risk assessment using an AI algorithm, and quickly issues warnings to high-risk areas. For example, the flood risk analysis system collects rainfall and wind speed as meteorological data, elevation and slope as topographic data, and river water level and flow rate as hydrological data. This data is collected in real time from sensors, satellites, ground observation devices, etc. Next, the flood risk analysis system analyzes the collected data using an AI algorithm. The AI ​​algorithm compares past data with current data and predicts future flood risk. For example, it compares past rainfall patterns with current rainfall to predict how much river water levels will rise. Based on this prediction, a risk assessment is performed. Based on the assessment results, a warning is quickly issued to high-risk areas. For example, if there is a possibility that river water levels in a particular area may reach dangerous levels, a warning is issued to residents and local governments in that area. This warning is provided in real time through smartphone apps, email, social media, etc. This system aims to protect lives and mitigate economic losses. For example, early warnings allow residents to evacuate quickly, and local governments can take appropriate measures. Businesses can also quickly implement measures to protect infrastructure and ensure business continuity. Furthermore, this system is ideal for local governments, disaster response organizations, and infrastructure management companies. By providing precise risk assessment and a real-time warning system powered by AI, it minimizes flood damage and enables rapid response. For example, local governments can use this system to issue rapid evacuation orders to residents. Disaster response organizations can take effective measures based on risk assessments. Infrastructure management companies can quickly carry out infrastructure protection and restoration work. Thus, this AI-powered real-time flood risk analysis and predictive alert system is a next-generation disaster response system that protects the safety and security of society.This allows flood risk analysis systems to protect lives and mitigate economic losses.

[0059] The flood risk analysis system according to this embodiment comprises a data collection unit, an analysis unit, and a warning unit. The data collection unit collects meteorological data, topographic data, and hydrological data. For example, the data collection unit collects rainfall and wind speed as meteorological data, elevation and slope as topographic data, and river water level and flow rate as hydrological data. The data collection unit can collect data in real time from sensors, satellites, and ground observation devices. For example, the data collection unit measures rainfall and wind speed using meteorological sensors and elevation and slope using topographic sensors. The data collection unit can also use hydrological sensors to measure river water level and flow rate. The analysis unit analyzes the data collected by the data collection unit and evaluates the flood risk. The analysis unit uses an AI algorithm to compare past data with current data and predict future flood risk. For example, the analysis unit compares past rainfall patterns with current rainfall to predict how much the river water level will rise. The analysis unit can also evaluate the risk of flooding by considering wind speed and topographic data. The analysis unit performs data analysis using an AI algorithm. For example, the analysis unit uses machine learning algorithms to learn patterns from past data and predict future risks. The warning unit issues warnings to high-risk areas based on the risks assessed by the analysis unit. The warning unit can issue warnings via smartphone apps, email, and social media. For example, if river levels in a particular area are likely to reach dangerous levels, the warning unit will issue a warning to residents and local governments in that area. The warning unit can also customize warning messages and provide information tailored to specific risks. For example, the warning unit can provide evacuation orders and safety information to areas at high risk of flooding. As a result, the flood risk analysis system according to this embodiment can integrate meteorological data, topographic data, and hydrological data, perform risk assessments using AI algorithms, and quickly issue warnings to high-risk areas.

[0060] The data collection unit collects meteorological, topographic, and hydrological data. Specifically, it collects rainfall and wind speed as meteorological data, elevation and slope as topographic data, and river water level and flow rate as hydrological data. The data collection unit can collect data in real time from sensors, satellites, and ground observation devices. For example, meteorological sensors measure rainfall in millimeters and record wind speed in meters per second. This allows for a detailed understanding of changes in meteorological conditions. Topographic sensors measure elevation in centimeters and record terrain slope in degrees. This allows for accurate capture of subtle changes in terrain and the effects of slope. Hydrological sensors measure river water level in millimeters and record flow rate in cubic meters per second. This allows for real-time monitoring of rising river levels and changes in flow rate. Furthermore, the data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data is stored on a cloud server and made accessible to the analysis and warning units. In addition, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0061] The analysis unit analyzes the data collected by the collection unit to assess flood risk. Using AI algorithms, the analysis unit compares past and current data to predict future flood risk. Specifically, it uses machine learning algorithms to compare past rainfall patterns with current rainfall to predict how much river levels will rise. For example, using a model learned from past data, it identifies which past patterns the current rainfall resembles and predicts river level rise based on that pattern. It can also assess flood risk by considering wind speed and topographic data. For example, strong winds tend to concentrate rainfall in specific areas, thus increasing the flood risk in those areas. Furthermore, topographic data is used to assess whether low-lying or sloping areas are susceptible to flooding. The analysis unit integrates this data to conduct a comprehensive risk assessment. For example, it combines rainfall, wind speed, topography, and water level data to comprehensively assess flood risk in a specific area. This allows the analysis unit to quickly and accurately analyze collected data and grasp the surrounding risk situation in real time. Furthermore, the analysis unit can utilize historical data and statistical information to perform long-term risk assessments and trend analyses. For example, it can predict risk fluctuations in specific areas and time periods based on past flood data and formulate future countermeasures. The analysis unit can also use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing early warnings. As a result, the analysis unit can not only grasp the situation in real time but also handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0062] The warning unit issues warnings to high-risk areas based on the risks assessed by the analysis unit. Specifically, warnings can be sent via smartphone apps, email, and social media. For example, if river levels in a particular area are likely to reach dangerous levels, the warning unit will issue a warning to residents and local governments in that area. Warning messages can include specific risk information and evacuation orders. For example, it may send a message such as, "River levels in your area are rising rapidly. Please evacuate immediately." The warning unit can also customize warning messages to provide information tailored to specific risks. For example, it can provide evacuation orders and safety information to areas at high risk of flooding. Furthermore, the warning unit can reliably transmit information using multiple communication methods. For example, it can reliably deliver important information by using not only smartphone notifications but also voice calls, SMS, and email. This allows the warning unit to provide users with quick and reliable instructions, minimizing the risk of disaster. In addition, the warning unit can collect user feedback and continuously improve the accuracy and effectiveness of its warnings. For example, it can revise evacuation routes and improve warning content based on feedback from users who have received evacuation orders. This allows the warning unit to provide highly accurate risk predictions based on the latest information at all times, supporting a quick and appropriate response.

[0063] The data collection unit can collect data in real time from sensors, satellites, and ground observation equipment. For example, the data collection unit can measure rainfall and wind speed using weather sensors, and measure elevation and slope using terrain sensors. The data collection unit can also use hydrological sensors to measure river water levels and flow rates. For example, the data collection unit can measure rainfall in real time using weather sensors and collect data. The data collection unit can also collect wide-area terrain data using satellite data. Furthermore, the data collection unit can measure river water levels and flow rates using ground observation equipment and collect data. This allows for risk assessment based on the latest information by collecting data in real time. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input data acquired from sensors into a generating AI and have the generating AI perform data analysis.

[0064] The analysis unit can compare past and present data to predict future flood risk. For example, it can compare past rainfall patterns with current rainfall to predict how much river levels will rise. The analysis unit can also assess flood risk by considering wind speed and topographic data. For example, it can use past data to model the relationship between rainfall and river levels and predict future water levels from current rainfall. The analysis unit can also use wind speed data to assess flood risk due to strong winds. Furthermore, it can use topographic data to identify areas susceptible to flooding. This allows for highly accurate prediction of future flood risk by comparing past and present data. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input past and present data into a generating AI and have the generating AI perform risk prediction.

[0065] The warning unit can issue warnings via smartphone apps, email, and social media. For example, if river levels in a particular area are likely to reach dangerous levels, the warning unit can issue warnings to residents and local governments in that area. The warning unit can also customize warning messages and provide information tailored to specific risks. For example, the warning unit can provide evacuation orders and safety information to areas at high risk of flooding. The warning unit can also issue warnings widely via social media, quickly disseminating information to many people. Furthermore, the warning unit can issue warnings to specific groups or individuals using email. This allows for rapid and widespread warning dissemination using diverse communication methods. Some or all of the above processes in the warning unit may be performed using AI or not. For example, the warning unit can input risk assessment results into a generating AI and have the generating AI generate warning messages.

[0066] The analysis unit can analyze data such as rainfall, wind speed, elevation, slope, river water level, and flow rate. For example, the analysis unit can use rainfall data to evaluate the intensity and duration of rainfall. It can also use wind speed data to evaluate the risk of flooding due to strong winds. Furthermore, it can use elevation data to identify low-lying areas susceptible to flooding. It can also use slope data to identify areas prone to flooding. Furthermore, it can use river water level data to evaluate the risk of river flooding. It can also use flow rate data to evaluate the strength and velocity of river flow. By analyzing diverse data in this way, a more precise risk assessment becomes possible. Some or all of the above-described processes in the analysis unit may be performed using AI, or they may not be performed using AI. For example, the analysis unit can input data such as rainfall, wind speed, elevation, slope, river water level, and flow rate into a generating AI, and have the generating AI perform the risk assessment.

[0067] The warning unit can issue warnings to residents and local governments in specific areas when river levels are likely to reach dangerous levels. For example, if river levels rise rapidly and the risk of flooding increases, the warning unit can issue evacuation orders to residents in that area. The warning unit can also issue warnings to local governments to encourage a swift response. Furthermore, the warning unit can customize warning messages and provide information tailored to specific risks. For example, the warning unit can provide information on evacuation routes and shelters to areas at high risk of flooding. The warning unit can also disseminate warnings widely via social media, quickly reaching many people. This allows residents and local governments to respond quickly by issuing warnings to specific areas rapidly. Some or all of the above processes in the warning unit may be performed using AI or not. For example, the warning unit can input risk assessment results into a generating AI and have the generating AI generate warning messages.

[0068] The data collection unit can estimate the user's emotions and adjust the timing 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 and update it in real time. If the user is relaxed, the data collection unit can return to the normal frequency of data collection and collect only the necessary information. Furthermore, if the user is facing an emergency, the data collection unit can immediately start data collection, enabling a rapid response. This allows for more appropriate data collection by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI adjust the timing of data collection.

[0069] The data collection unit can analyze past collected data and select the optimal collection method. For example, the data collection unit can analyze past data collection patterns to determine the most effective sensor placement. Furthermore, the data collection unit can optimize the collection frequency and timing based on past data collection results. In addition, the data collection unit can compare successful and unsuccessful past data collection examples to select the optimal collection method. This allows for efficient data collection by analyzing past data and selecting the optimal collection method. Some or all of the above-described processes in the data collection unit may be performed using AI, or they may not. For example, the data collection unit can input past collected data into a generating AI and have the generating AI select the optimal collection method.

[0070] The data collection unit can filter data based on specific weather and terrain conditions during data collection. For example, the data collection unit can collect data only under specific weather conditions such as heavy rain or strong winds. Furthermore, the data collection unit can filter data based on specific terrain conditions such as mountainous areas or lowlands. In addition, the data collection unit can determine the priority of data to collect based on combinations of weather and terrain conditions. This allows for the efficient collection of only the necessary data by filtering it based on specific conditions. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input specific weather and terrain conditions into a generating AI and have the generating AI perform data filtering.

[0071] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is feeling anxious, the data collection unit will prioritize collecting important data. Conversely, if the user is relaxed, the data collection unit can perform normal data collection. Furthermore, if the user is facing an emergency, the data collection unit can immediately collect important data, enabling a rapid response. This allows for the priority collection of important data by determining data priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the data priorities.

[0072] The data collection unit can prioritize the collection of highly relevant data by considering geographical location information during data collection. For example, the data collection unit can prioritize the collection of data from high-risk areas. Furthermore, the data collection unit can filter highly relevant data based on geographical location information. In addition, the data collection unit can combine geographical location information with other data to formulate an optimal data collection strategy. This enables efficient data collection by prioritizing the collection of highly relevant data based on geographical location information. Some or all of the above-described processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input geographical location information into a generating AI and have the generating AI collect highly relevant data.

[0073] The data collection unit can analyze social media activity and collect relevant data during data collection. For example, the data collection unit can analyze posts on social media to collect real-time flood information. Furthermore, the data collection unit can prioritize the collection of data from specific regions based on location information on social media. In addition, the data collection unit can analyze trends on social media and collect relevant data. This allows for the collection of real-time flood information by analyzing social media activity. Some or all of the above-described processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input social media data into a generating AI and have the generating AI collect relevant data.

[0074] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is feeling anxious, the analysis unit can provide the analysis results in a simple and easy-to-understand format. If the user is relaxed, the analysis unit can provide detailed analysis results. Furthermore, if the user is facing an emergency, the analysis unit can provide the analysis results in a format that can be quickly understood. In this way, by adjusting the presentation of the analysis according to the user's emotions, it is possible to provide analysis results that are easier to understand. 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 or not. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the presentation of the analysis.

[0075] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on important data and a simplified analysis on less important data. The analysis unit can also determine the priority of the analysis based on the importance of the data. Furthermore, the analysis unit can apply multiple analysis methods to important data to improve accuracy. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0076] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a meteorological analysis algorithm to meteorological data. It can also apply a topographic analysis algorithm to topographic data. Furthermore, it can apply a hydrological analysis algorithm to hydrological data. This allows for more accurate analysis by applying different analysis algorithms depending on the data category. Some or all of the above-described processes in the analysis unit may be performed using AI, or they may be performed without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI select and apply an appropriate analysis algorithm.

[0077] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is feeling anxious, the analysis unit can provide a short, concise analysis result. If the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is facing an emergency, the analysis unit can provide the analysis result in a format that can be quickly understood. By adjusting the length of the analysis according to the user's emotions, more appropriate analysis results can be provided. 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 or not. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the length of the analysis.

[0078] The analysis unit can determine the priority of analysis based on the data collection timing during the analysis. For example, the analysis unit can prioritize the analysis of the latest data to perform real-time risk assessment. Alternatively, the analysis unit can perform analysis while referencing past data, with emphasis on current data. Furthermore, the analysis unit can dynamically adjust the analysis priority based on the data collection timing. This enables real-time risk assessment by determining the analysis priority based on the data collection timing. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the data collection timing into a generating AI and have the generating AI determine the analysis priority.

[0079] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant data to perform a rapid risk assessment. The analysis unit can also dynamically adjust the order of analysis based on the relevance of the data. Furthermore, the analysis unit can perform a detailed analysis based on the highly relevant data. This enables a rapid risk assessment by adjusting the order of analysis based on the relevance of the data. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0080] The warning unit can estimate the user's emotions and adjust the way the warning is displayed based on the estimated emotions. For example, if the user is feeling anxious, the warning unit can provide a simple and highly visible display method. If the user is relaxed, the warning unit can provide a display method that includes detailed information. Furthermore, if the user is facing an emergency, the warning unit can display the warning in a format that can be quickly understood. This allows for more effective warnings by adjusting the way the warning is displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the warning unit may be performed using AI or not. For example, the warning unit can input user emotion data into the generative AI and have the generative AI adjust the way the warning is displayed.

[0081] The warning unit can adjust the level of detail of a warning based on the severity of the risk when a warning is issued. For example, in the case of high risk, the warning unit can provide detailed warning information. In the case of medium risk, the warning unit can provide concise warning information. Furthermore, in the case of low risk, the warning unit can provide simplified warning information. In this way, appropriate warning information can be provided by adjusting the level of detail of the warning based on the severity of the risk. Some or all of the above processing in the warning unit may be performed using AI or not. For example, the warning unit can input the severity of the risk into a generating AI and have the generating AI perform the adjustment of the level of detail of the warning.

[0082] The warning unit can apply different warning algorithms depending on the risk category when a warning is issued. For example, the warning unit can apply a weather-specific warning algorithm to weather risks. It can also apply a topographic-specific warning algorithm to topographic risks. Furthermore, it can apply a hydrological-specific warning algorithm to hydrological risks. This allows for the provision of appropriate warnings according to the risk category. Some or all of the above processing in the warning unit may be performed using AI or not. For example, the warning unit can input the risk category into a generating AI and have the generating AI select and apply an appropriate warning algorithm.

[0083] The warning unit can estimate the user's emotions and adjust the length of the warning based on the estimated emotions. For example, if the user is feeling anxious, the warning unit can provide a short, concise warning. If the user is relaxed, the warning unit can provide a detailed warning. Furthermore, if the user is facing an emergency, the warning unit can provide a warning in a format that can be quickly understood. By adjusting the length of the warning according to the user's emotions, a more effective warning can be provided. 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 warning unit may be performed using AI or not. For example, the warning unit can input user emotion data into a generative AI and have the generative AI adjust the length of the warning.

[0084] The warning unit can determine the priority of warnings based on when the risk occurred. For example, the warning unit will issue a priority warning for immediate risks. The warning unit can also dynamically adjust the warning priority based on when the risk occurred. Furthermore, the warning unit can issue planned warnings for long-term risks. This allows warnings to be provided at the appropriate time by determining the priority of warnings based on when the risk occurred. Some or all of the above processing in the warning unit may be performed using AI or not. For example, the warning unit can input the risk occurrence time into a generating AI and have the generating AI perform the determination of the warning priority.

[0085] The warning unit can adjust the order of warnings based on the relevance of the risks when a warning is issued. For example, the warning unit will issue warnings preferentially for highly relevant risks. The warning unit can also dynamically adjust the order of warnings based on the relevance of the risks. Furthermore, the warning unit can issue detailed warnings for highly relevant risks. This allows for the provision of quick and appropriate warnings by adjusting the order of warnings based on the relevance of the risks. Some or all of the above processing in the warning unit may be performed using AI or not. For example, the warning unit can input the relevance of risks into a generating AI and have the generating AI perform the adjustment of the warning order.

[0086] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0087] The data collection unit can estimate the user's emotions and adjust the timing 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 and update it in real time. If the user is relaxed, the data collection unit can return to the normal frequency of data collection and collect only the necessary information. Furthermore, if the user is facing an emergency, the data collection unit can immediately start data collection, enabling a rapid response. This allows for more appropriate data collection by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI adjust the timing of data collection.

[0088] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is feeling anxious, the analysis unit can provide the analysis results in a simple and easy-to-understand format. If the user is relaxed, the analysis unit can provide detailed analysis results. Furthermore, if the user is facing an emergency, the analysis unit can provide the analysis results in a format that can be quickly understood. In this way, by adjusting the presentation of the analysis according to the user's emotions, it is possible to provide analysis results that are easier to understand. 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 or not. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the presentation of the analysis.

[0089] The warning unit can estimate the user's emotions and adjust the way the warning is displayed based on the estimated emotions. For example, if the user is feeling anxious, the warning unit can provide a simple and highly visible display method. If the user is relaxed, the warning unit can provide a display method that includes detailed information. Furthermore, if the user is facing an emergency, the warning unit can display the warning in a format that can be quickly understood. This allows for more effective warnings by adjusting the way the warning is displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the warning unit may be performed using AI or not. For example, the warning unit can input user emotion data into the generative AI and have the generative AI adjust the way the warning is displayed.

[0090] The data collection unit can analyze past collected data and select the optimal collection method. For example, the data collection unit can analyze past data collection patterns to determine the most effective sensor placement. Furthermore, the data collection unit can optimize the collection frequency and timing based on past data collection results. In addition, the data collection unit can compare successful and unsuccessful past data collection examples to select the optimal collection method. This allows for efficient data collection by analyzing past data and selecting the optimal collection method. Some or all of the above-described processes in the data collection unit may be performed using AI, or they may not. For example, the data collection unit can input past collected data into a generating AI and have the generating AI select the optimal collection method.

[0091] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on important data and a simplified analysis on less important data. The analysis unit can also determine the priority of the analysis based on the importance of the data. Furthermore, the analysis unit can apply multiple analysis methods to important data to improve accuracy. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0092] The warning unit can adjust the level of detail of a warning based on the severity of the risk when a warning is issued. For example, in the case of high risk, the warning unit can provide detailed warning information. In the case of medium risk, the warning unit can provide concise warning information. Furthermore, in the case of low risk, the warning unit can provide simplified warning information. In this way, appropriate warning information can be provided by adjusting the level of detail of the warning based on the severity of the risk. Some or all of the above processing in the warning unit may be performed using AI or not. For example, the warning unit can input the severity of the risk into a generating AI and have the generating AI perform the adjustment of the level of detail of the warning.

[0093] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is feeling anxious, the analysis unit can provide a short, concise analysis result. If the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is facing an emergency, the analysis unit can provide the analysis result in a format that can be quickly understood. By adjusting the length of the analysis according to the user's emotions, more appropriate analysis results can be provided. 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 or not. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the length of the analysis.

[0094] The data collection unit can filter data based on specific weather and terrain conditions during data collection. For example, the data collection unit can collect data only under specific weather conditions such as heavy rain or strong winds. Furthermore, the data collection unit can filter data based on specific terrain conditions such as mountainous areas or lowlands. In addition, the data collection unit can determine the priority of data to collect based on combinations of weather and terrain conditions. This allows for the efficient collection of only the necessary data by filtering it based on specific conditions. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input specific weather and terrain conditions into a generating AI and have the generating AI perform data filtering.

[0095] The warning unit can estimate the user's emotions and adjust the length of the warning based on the estimated emotions. For example, if the user is feeling anxious, the warning unit can provide a short, concise warning. If the user is relaxed, the warning unit can provide a detailed warning. Furthermore, if the user is facing an emergency, the warning unit can provide a warning in a format that can be quickly understood. By adjusting the length of the warning according to the user's emotions, a more effective warning can be provided. 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 warning unit may be performed using AI or not. For example, the warning unit can input user emotion data into a generative AI and have the generative AI adjust the length of the warning.

[0096] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a meteorological analysis algorithm to meteorological data. It can also apply a topographic analysis algorithm to topographic data. Furthermore, it can apply a hydrological analysis algorithm to hydrological data. This allows for more accurate analysis by applying different analysis algorithms depending on the data category. Some or all of the above-described processes in the analysis unit may be performed using AI, or they may be performed without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI select and apply an appropriate analysis algorithm.

[0097] The following briefly describes the processing flow for example form 2.

[0098] Step 1: The data collection unit collects meteorological data, topographic data, and hydrological data. Specifically, it collects rainfall and wind speed as meteorological data, elevation and slope as topographic data, and river water level and flow rate as hydrological data. The data collection unit can collect data in real time from sensors, satellites, and ground observation equipment. For example, it can measure rainfall and wind speed using meteorological sensors, elevation and slope using topographic sensors, and river water level and flow rate using hydrological sensors. Step 2: The analysis unit analyzes the data collected by the collection unit and assesses flood risk. The analysis unit uses AI algorithms to compare past and current data and predict future flood risk. For example, it compares past rainfall patterns with current rainfall to predict how much river levels will rise. It can also assess the risk of flooding by considering wind speed and topographic data. The analysis unit uses machine learning algorithms to analyze the data. Step 3: The warning unit issues warnings to high-risk areas based on the risks assessed by the analysis unit. The warning unit can issue warnings via smartphone apps, email, and social media. For example, if river levels in a particular area are likely to reach dangerous levels, it will issue warnings to residents and local governments in that area. It can also customize warning messages to provide information tailored to specific risks. For example, it can provide evacuation orders and safety information to areas at high risk of flooding.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] Each of the multiple elements described above, including the data collection unit, analysis unit, and warning unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects meteorological data, topographic data, and hydrological data using the sensors and camera 42 of the smart device 14. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and analyzes the data using an AI algorithm to assess flood risk. The warning unit is implemented in the control unit 46A of the smart device 14, for example, and issues warnings via smartphone apps, email, and social media. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0103] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] Each of the multiple elements described above, including the data collection unit, analysis unit, and warning unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects weather data, topographic data, and hydrological data using the sensors and camera 42 of the smart glasses 214. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12, which analyzes the data using an AI algorithm and assesses the risk of flooding. The warning unit is implemented in the control unit 46A of the smart glasses 214, which issues warnings via a smartphone app, email, or social media. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

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

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.).

[0131] 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.

[0132] 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.

[0133] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is 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.

[0134] Each of the multiple elements described above, including the data collection unit, analysis unit, and warning unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects meteorological data, topographic data, and hydrological data using the sensors and camera 42 of the headset terminal 314. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and analyzes the data using an AI algorithm to assess flood risk. The warning unit is implemented in the control unit 46A of the headset terminal 314, for example, and issues warnings via smartphone apps, email, and social media. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various modifications are possible.

[0135] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] Each of the multiple elements described above, including the data collection unit, analysis unit, and warning unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the data collection unit collects meteorological data, topographic data, and hydrological data using the sensors and camera 42 of the robot 414. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and analyzes the data using an AI algorithm to assess flood risk. The warning unit is implemented in the control unit 46A of the robot 414, for example, and issues warnings via smartphone apps, email, and social media. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] (Note 1) A data collection unit that collects meteorological data, topographic data, and hydrological data, An analysis unit analyzes the data collected by the aforementioned collection unit and evaluates the risk of flooding, The system includes a warning unit that issues warnings to high-risk areas based on the risk evaluated by the analysis unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is It collects data in real time from sensors, satellites, and ground observation equipment. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, By comparing past and present data, we predict future flood risk. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned warning unit is Warnings are issued via smartphone apps, email, and social media. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit, We analyze data such as rainfall, wind speed, elevation, slope, river water level, and flow rate. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned warning unit is If river levels in a particular area are likely to reach dangerous levels, a warning will be issued to residents and local authorities in that area. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze past collected data and select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting data, filtering is performed based on specific weather and topographic conditions. 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, prioritize the collection of highly relevant data, taking geographical location information into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During data collection, social media activity is analyzed and relevant data is gathered. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis 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, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned warning unit is It estimates the user's emotions and adjusts how warnings are displayed based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned warning unit is When a warning is issued, adjust the level of detail in the warning based on the severity of the risk. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned warning unit is When a warning is issued, different warning algorithms are applied depending on the risk category. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned warning unit is It estimates the user's emotions and adjusts the length of the warning based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned warning unit is When a warning is issued, the priority of the warning is determined based on when the risk occurred. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned warning unit is When a warning is issued, the order of warnings will be adjusted based on the relevance of the risk. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0171] 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 data collection unit that collects meteorological data, topographic data, and hydrological data, An analysis unit analyzes the data collected by the aforementioned collection unit and evaluates the risk of flooding, The system includes a warning unit that issues warnings to high-risk areas based on the risk evaluated by the analysis unit. A system characterized by the following features.

2. The aforementioned collection unit is It collects data in real time from sensors, satellites, and ground observation equipment. The system according to feature 1.

3. The aforementioned analysis unit, By comparing past and present data, we predict future flood risk. The system according to feature 1.

4. The aforementioned warning unit is Warnings are issued via smartphone apps, email, and social media. The system according to feature 1.

5. The aforementioned analysis unit, We analyze data such as rainfall, wind speed, elevation, slope, river water level, and flow rate. The system according to feature 1.

6. The aforementioned warning unit is If river levels in a particular area are likely to reach dangerous levels, a warning will be issued to residents and local authorities in that area. The system according to feature 1.

7. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze past collected data and select the optimal collection method. The system according to feature 1.

9. The aforementioned collection unit is When collecting data, filtering is performed based on specific weather and topographic conditions. The system according to feature 1.

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 according to feature 1.

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