system

Abstract

The system according to the embodiment aims to assess disaster risk and provide individual evacuation instructions in real time. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, an evaluation unit, and an instruction generation unit. The collection unit collects meteorological data, river water level data, or topographical data. The analysis unit analyzes the data collected by the collection unit. The evaluation unit evaluates disaster risk based on the data analyzed by the analysis unit. The instruction generation unit generates individual evacuation instructions based on the disaster risk evaluated by the evaluation unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not assess disaster risk and provide individual evacuation instructions in real time, leaving room for improvement.

[0005] The system according to the embodiment aims to assess disaster risk and provide individual evacuation instructions in real time. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, an evaluation unit, and an instruction generation unit. The collection unit collects meteorological data, river water level data, or topographical data. The analysis unit analyzes the data collected by the collection unit. The evaluation unit evaluates disaster risk based on the data analyzed by the analysis unit. The instruction generation unit generates individual evacuation instructions based on the disaster risk evaluated by the evaluation unit. [Effects of the Invention]

[0007] Embodiments of the system can assess disaster risk and provide personalized evacuation instructions in real time. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An evacuation direction system according to an embodiment of the present invention is a system for supporting individual evacuation instructions during floods, such as river flooding or heavy rain. This evacuation direction system prioritizes the safety and lives of individuals and utilizes comprehensive information to provide disaster risk and evacuation instructions in real time. Specifically, the system comprises the following steps: First, comprehensive information, such as meteorological data, river water level data, and topographical data, is collected. Next, the collected data is analyzed to assess disaster risk. Based on the assessed disaster risk, individual evacuation instructions are generated and provided to users. This system enables prompt and appropriate evacuation instructions to protect individual safety and lives. For example, comprehensive information, such as meteorological data, river water level data, and topographical data, is collected. In this case, meteorological data is obtained from the Japan Meteorological Agency or weather observation stations, and river water level data is obtained from river administrators. Topographical data is also obtained using a geographic information system (GIS). This provides basic data for assessing disaster risk. Next, the collected data is analyzed to assess disaster risk. For example, meteorological data is analyzed to predict heavy rain, and river water level data is analyzed to assess flood risk. The system also analyzes topographical data to identify areas at high risk of flooding. This allows for a comprehensive disaster risk assessment. Based on the assessed disaster risk, the system then generates and provides individual evacuation instructions to users. For example, for users living in areas at high risk of flooding, the system generates instructions encouraging early evacuation. Information on evacuation routes and evacuation locations is also provided. This allows users to take prompt and appropriate evacuation action. This system enables prompt and appropriate evacuation instructions to protect personal safety and lives. For example, if heavy rain is predicted, the system issues an evacuation instruction early and notifies the user. Furthermore, if river water levels are rising, the system assesses the risk of flooding and generates appropriate evacuation instructions. This allows users to understand disaster risks in advance and take prompt evacuation action. The evacuation instruction system can provide prompt and appropriate evacuation instructions to protect personal safety and lives.

[0029] An evacuation direction system according to an embodiment includes a collection unit, an analysis unit, an evaluation unit, and an instruction generation unit. The collection unit collects meteorological data, river water level data, or topographical data. The meteorological data includes, but is not limited to, temperature, precipitation, wind speed, and the like. The collection unit acquires meteorological data from, for example, the Japan Meteorological Agency or a weather observation station. The collection unit can also acquire river water level data from a river administrator. The collection unit can also acquire topographical data using a geographic information system (GIS). For example, the collection unit acquires data such as temperature, precipitation, and wind speed from the Japan Meteorological Agency, acquires river water level data from a river administrator, and acquires topographical data using the GIS. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes meteorological data to predict heavy rain. The analysis unit can also analyze river water level data to assess the risk of flooding. The analysis unit can also analyze topographical data to identify areas at high risk of flooding. For example, the analysis unit analyzes weather data to predict heavy rain, analyzes river water level data to assess flood risk, and analyzes topographical data to identify areas at high risk of flooding. The evaluation unit evaluates disaster risk based on the data analyzed by the analysis unit. The evaluation unit, for example, comprehensively evaluates weather data, river water level data, and topographical data to assess disaster risk. The instruction generation unit generates individual evacuation instructions based on the disaster risk assessed by the evaluation unit. For example, the instruction generation unit generates instructions encouraging early evacuation for users living in areas at high risk of flooding. The instruction generation unit can also provide information on evacuation routes and evacuation locations. For example, the instruction generation unit generates instructions encouraging early evacuation for users living in areas at high risk of flooding, and provides information on evacuation routes and evacuation locations. As a result, the evacuation direction system according to the embodiment can provide prompt and appropriate evacuation instructions to protect personal safety and lives.

[0030] The collection unit can acquire weather data from a meteorological agency or a weather observation station. Weather data includes, but is not limited to, temperature, precipitation, wind speed, etc. For example, the collection unit acquires data such as temperature, precipitation, and wind speed from a meteorological agency. The collection unit can also acquire weather data from a weather observation station. For example, the collection unit acquires real-time weather data from a weather observation station and provides it to the analysis unit. This enables accurate collection of weather data. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input weather data acquired from a meteorological agency to a generation AI and have the generation AI perform preprocessing of the data.

[0031] The collection unit can acquire river water level data from the river administrator. The river water level data includes, for example, the installation location of a water level meter and the measurement frequency, but is not limited to these examples. The collection unit, for example, acquires water level meter data from the river administrator. The collection unit can also acquire real-time water level data from the river administrator. For example, the collection unit provides the water level data acquired from the river administrator to the analysis unit. This enables accurate collection of river water level data. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the water level data acquired from the river administrator to the generation AI and have the generation AI perform preprocessing of the data.

[0032] The collection unit can acquire topographical data using a geographical information system. Topographical data includes, but is not limited to, elevation data, topographical maps, and the like. For example, the collection unit acquires elevation data using a geographical information system (GIS). The collection unit can also acquire topographical maps using a geographical information system. For example, the collection unit acquires topographical data using a GIS and provides the data to the analysis unit. This enables accurate collection of topographical data. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the topographical data acquired from the GIS into the generation AI and have the generation AI perform preprocessing of the data.

[0033] The analysis unit can analyze weather data to predict heavy rain. Heavy rain prediction includes, but is not limited to, for example, a prediction model, a data preprocessing method, and the like. The analysis unit can, for example, analyze weather data to predict heavy rain. The analysis unit can also improve the accuracy of heavy rain predictions by using multiple prediction models. For example, the analysis unit analyzes weather data, predicts heavy rain, and provides the prediction results to the evaluation unit. This improves the accuracy of heavy rain predictions. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input weather data to a generation AI and cause the generation AI to predict heavy rain.

[0034] The analysis unit can analyze river water level data to assess the risk of flooding. Examples of flood risk assessment include, but are not limited to, risk scoring methods and assessment models. For example, the analysis unit can analyze river water level data to assess the risk of flooding. The analysis unit can also perform risk assessment by referring to past flood data. For example, the analysis unit can analyze river water level data, assess flood risk, and provide the assessment results to the assessment unit. This improves the accuracy of flood risk assessment. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input river water level data to the generation AI and cause the generation AI to perform a flood risk assessment.

[0035] The analysis unit can analyze the topographical data to identify areas at high risk of flooding. Examples of methods for identifying the risk of flooding include, but are not limited to, risk scoring methods and assessment models. For example, the analysis unit can analyze the topographical data to identify areas at high risk of flooding. The analysis unit can also combine geological data to evaluate the flood risk in more detail. For example, the analysis unit can analyze the topographical data to identify areas at high risk of flooding and provide the identification results to the evaluation unit. This improves the accuracy of identifying the flood risk. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input the topographical data into the generation AI and cause the generation AI to identify the flood risk.

[0036] The evaluation unit can evaluate the comprehensive disaster risk. Examples of the comprehensive disaster risk evaluation include, but are not limited to, risk scoring methods and evaluation models. For example, the evaluation unit comprehensively evaluates meteorological data, river water level data, and topographical data to evaluate the disaster risk. The evaluation unit can also improve the accuracy of the evaluation by referring to past disaster data. For example, the evaluation unit comprehensively evaluates meteorological data, river water level data, and topographical data to evaluate the disaster risk and provides the evaluation result to the instruction generation unit. This improves the accuracy of the comprehensive disaster risk evaluation. Some or all of the above-described processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input meteorological data, river water level data, and topographical data into the generation AI and cause the generation AI to perform a comprehensive disaster risk evaluation.

[0037] The instruction generation unit can generate individual evacuation instructions based on the assessed disaster risk. Examples of the generation of individual evacuation instructions include, but are not limited to, evacuation routes and evacuation site designation methods. The instruction generation unit can also generate individual evacuation instructions based on, for example, the assessed disaster risk. The instruction generation unit can also provide optimal instructions by referring to past evacuation history. For example, the instruction generation unit generates individual evacuation instructions based on the assessed disaster risk and provides them to the user. This improves the accuracy of generating individual evacuation instructions. Some or all of the above-described processing in the instruction generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the instruction generation unit can input the assessed disaster risk into a generation AI and cause the generation AI to generate individual evacuation instructions.

[0038] The instruction generation unit can provide information on evacuation routes and evacuation locations. Examples of information provided on evacuation routes and evacuation locations include, but are not limited to, map information and real-time updates. The instruction generation unit can also provide, for example, information on evacuation routes and evacuation locations. The instruction generation unit can also propose an optimal evacuation route by combining real-time traffic information. For example, the instruction generation unit provides information on evacuation routes and evacuation locations and notifies the user. This makes it possible to provide information on evacuation routes and evacuation locations. Some or all of the above-described processing in the instruction generation unit may be performed, for example, using AI or without AI. For example, the instruction generation unit can input information on evacuation routes and evacuation locations into a generation AI and have the generation AI provide the information.

[0039] When collecting weather data, the collection unit can improve the accuracy of collection by referring to past weather patterns. Referencing past weather patterns includes, but is not limited to, database construction methods and reference algorithms. For example, the collection unit can refer to weather data from the past 10 years to identify abnormal weather patterns and improve collection accuracy. The collection unit can also strengthen data collection in specific areas based on past heavy rain data. Furthermore, the collection unit can analyze past weather patterns and optimize collection accuracy for each season. For example, the collection unit can refer to past weather data to improve collection accuracy. By referring to past weather patterns, collection accuracy is improved. Some or all of the above-described processing in the collection unit may be performed, for example, using AI, or may be performed without AI. For example, the collection unit can input past weather data into the generation AI and cause the generation AI to improve collection accuracy.

[0040] When collecting river water level data, the collection unit can simultaneously collect river flow rate and flow velocity to provide more detailed data. Collection of river flow rate and flow velocity includes, for example, but is not limited to, the type of measuring equipment and the frequency of measurement. For example, the collection unit collects flow rate data along with river water level data to perform a detailed assessment of flood risk. The collection unit can also collect river flow velocity data to evaluate risks due to flow velocity. Furthermore, the collection unit can integrate river water level, flow rate, and flow velocity data to perform a comprehensive risk assessment. For example, the collection unit collects flow rate data along with river water level data and provides it to the analysis unit. By simultaneously collecting river flow rate and flow velocity, detailed data can be provided. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input river water level data, flow rate data, and flow velocity data to a generation AI and have the generation AI perform data preprocessing.

[0041] The collection unit can simultaneously collect geological data when collecting topographical data to evaluate ground stability. Examples of collected geological data include, but are not limited to, geological survey data and geological maps. For example, the collection unit can collect geological data along with topographical data to evaluate ground stability. The collection unit can also integrate topographical data and geological data to perform a detailed evaluation of flood risk. Furthermore, the collection unit can evaluate the safety of evacuation routes based on the topographical data and geological data. For example, the collection unit collects geological data along with topographical data and provides it to the analysis unit. This enables ground stability evaluation by simultaneously collecting topographical data and geological data. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input topographical data and geological data into a generation AI and have the generation AI perform data preprocessing.

[0042] When collecting weather data, the collection unit can acquire weather information over a wide area by utilizing weather satellite data. The collection of weather satellite data includes, but is not limited to, the type of satellite and the data acquisition method. For example, the collection unit can collect weather information over a wide area by utilizing weather satellite data. The collection unit can also integrate weather satellite data with ground observation data to improve collection accuracy. Furthermore, the collection unit can evaluate local weather fluctuations in detail based on the weather satellite data. For example, the collection unit can collect weather information over a wide area by utilizing weather satellite data and provide it to the analysis unit. This makes it possible to acquire weather information over a wide area by utilizing weather satellite data. Some or all of the above-described processing in the collection unit can be performed, for example, using AI, or can be performed without using AI. For example, the collection unit can input weather satellite data into a generation AI and have the generation AI perform preprocessing of the data.

[0043] The analysis unit can apply multiple weather models when analyzing weather data to improve the accuracy of the analysis. Examples of weather models that can be applied include, but are not limited to, numerical forecast models and statistical models. For example, the analysis unit can apply multiple weather models to improve the accuracy of heavy rain forecasts. The analysis unit can also compare multiple weather models to provide the most reliable forecast. Furthermore, the analysis unit can integrate multiple weather models to perform a comprehensive weather forecast. For example, the analysis unit can apply multiple weather models to improve the accuracy of the analysis. Thus, applying multiple weather models improves the accuracy of the analysis. Some or all of the above-described processing in the analysis unit can be performed, for example, using AI, or can be performed without using AI. For example, the analysis unit can input weather data to a generation AI and have the generation AI apply multiple weather models.

[0044] When analyzing river water level data, the analysis unit can perform risk assessment by referring to past flood data. Referencing past flood data includes, but is not limited to, database construction methods and reference algorithms. The analysis unit, for example, refers to past flood data and compares it with current water level data to perform risk assessment. The analysis unit can also perform a detailed risk assessment of a specific area based on the past flood data. Furthermore, the analysis unit can integrate past flood data and current water level data to perform a comprehensive risk assessment. For example, the analysis unit performs risk assessment by referring to past flood data. As a result, referring to past flood data improves the accuracy of the risk assessment. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past flood data into a generation AI and have the generation AI perform a risk assessment.

[0045] When analyzing the topographical data, the analysis unit can combine geological data to evaluate the flood risk in more detail. Examples of combinations of geological data include, but are not limited to, geological survey data and geological maps. For example, the analysis unit combines topographical data and geological data to evaluate the flood risk in detail. The analysis unit can also evaluate the flood risk of a specific area based on the topographical data and geological data. Furthermore, the analysis unit can integrate the topographical data and geological data to perform a comprehensive flood risk evaluation. For example, the analysis unit combines the topographical data and geological data to evaluate the flood risk. This enables a detailed evaluation of the flood risk by combining the topographical data and geological data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the topographical data and geological data into the generation AI and cause the generation AI to perform a flood risk evaluation.

[0046] When analyzing the weather data, the analysis unit can use weather radar data to forecast localized heavy rain. The use of weather radar data includes, but is not limited to, the type of radar and the method of acquiring the data. For example, the analysis unit can use weather radar data to forecast localized heavy rain. The analysis unit can also integrate weather radar data with ground observation data to improve forecast accuracy. Furthermore, the analysis unit can also evaluate the risk of heavy rain in a specific area in detail based on the weather radar data. For example, the analysis unit can use weather radar data to forecast localized heavy rain. This makes it possible to forecast localized heavy rain by utilizing the weather radar data. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input weather radar data into a generation AI and have the generation AI execute a heavy rain forecast.

[0047] When assessing comprehensive disaster risk, the evaluation unit can improve the accuracy of the evaluation by referring to past disaster data. Referencing past disaster data includes, but is not limited to, database construction methods, reference algorithms, and the like. For example, the evaluation unit can refer to past disaster data and compare it with the current risk assessment to improve accuracy. The evaluation unit can also evaluate the risk of a specific area in detail based on past disaster data. Furthermore, the evaluation unit can integrate past disaster data and current risk assessment to perform a comprehensive risk assessment. For example, the evaluation unit refers to past disaster data to perform risk assessment. As a result, referring to past disaster data improves the accuracy of the assessment. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input past disaster data into the generation AI and have the generation AI perform a risk assessment.

[0048] When assessing flood risk, the evaluation unit can combine river flow rate and flow velocity data to perform the assessment. Combinations of river flow rate and flow velocity data include, but are not limited to, the type of measuring equipment and the frequency of measurement. For example, the evaluation unit combines river flow rate data and flow velocity data to perform a detailed assessment of flood risk. The evaluation unit can also assess the risk of a specific area based on the river flow rate data and flow velocity data. Furthermore, the evaluation unit can integrate river flow rate data and flow velocity data to perform a comprehensive flood risk assessment. For example, the evaluation unit combines river flow rate data and flow velocity data to assess flood risk. This enables a detailed assessment of flood risk by combining river flow rate and flow velocity data. Some or all of the above-described processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input river flow rate data and flow velocity data to the generation AI and cause the generation AI to perform a flood risk assessment.

[0049] When assessing flood risk, the evaluation unit can combine geological data to evaluate ground stability. Examples of combined geological data include, but are not limited to, geological survey data and geological maps. The evaluation unit, for example, combines geological data to evaluate ground stability. The evaluation unit can also evaluate the flood risk of a specific area based on geological data and topographical data. Furthermore, the evaluation unit can integrate geological data and topographical data to perform a comprehensive flood risk assessment. For example, the evaluation unit combines geological data to evaluate ground stability. This enables ground stability assessment by combining geological data. Some or all of the above-described processing in the evaluation unit may be performed using, or without, AI. For example, the evaluation unit can input geological data and topographical data into the generation AI and cause the generation AI to perform a ground stability assessment.

[0050] When assessing comprehensive disaster risk, the evaluation unit can utilize meteorological satellite data to conduct a wide-ranging risk assessment. The utilization of meteorological satellite data includes, but is not limited to, the type of satellite and the method of data acquisition. The evaluation unit, for example, utilizes meteorological satellite data to conduct a wide-ranging risk assessment. The evaluation unit can also integrate meteorological satellite data with ground observation data to improve the accuracy of the assessment. Furthermore, the evaluation unit can also conduct a detailed assessment of the risk of a specific region based on the meteorological satellite data. For example, the evaluation unit utilizes meteorological satellite data to conduct a wide-ranging risk assessment. This enables a wide-ranging risk assessment by utilizing meteorological satellite data. Some or all of the above-described processing in the evaluation unit may be performed, for example, using AI, or may be performed without using AI. For example, the evaluation unit can input meteorological satellite data into a generation AI and have the generation AI perform a risk assessment.

[0051] When generating individual evacuation instructions, the instruction generation unit can refer to past evacuation history to provide optimal instructions. Referencing past evacuation history includes, but is not limited to, database construction methods and reference algorithms. For example, the instruction generation unit can refer to past evacuation history to provide optimal evacuation instructions. The instruction generation unit can also provide detailed evacuation instructions for a specific area based on past evacuation history. Furthermore, the instruction generation unit can integrate past evacuation history with current risk assessment to provide comprehensive evacuation instructions. For example, the instruction generation unit can refer to past evacuation history to provide optimal evacuation instructions. This makes it possible to provide optimal evacuation instructions by referring to past evacuation history. Some or all of the above-described processing in the instruction generation unit may be performed, for example, using AI or without AI. For example, the instruction generation unit can input past evacuation history into the generation AI and cause the generation AI to generate evacuation instructions.

[0052] The instruction generation unit can combine real-time traffic information to propose an optimal evacuation route when providing information on evacuation routes and evacuation locations. Examples of combinations of real-time traffic information include, but are not limited to, traffic sensors and data update frequencies. The instruction generation unit can, for example, propose an optimal evacuation route based on real-time traffic information. The instruction generation unit can also integrate real-time traffic information with topographical data to evaluate the safety of an evacuation route. Furthermore, the instruction generation unit can utilize real-time traffic information to propose an evacuation route that avoids congestion. For example, the instruction generation unit proposes an optimal evacuation route based on real-time traffic information. This makes it possible to propose an optimal evacuation route by combining real-time traffic information. Some or all of the above-described processing in the instruction generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the instruction generation unit can input real-time traffic information to the generation AI and cause the generation AI to propose an evacuation route.

[0053] When generating evacuation instructions, the instruction generation unit can combine geological data to propose evacuation sites that take into account ground stability. Examples of combinations of geological data include, but are not limited to, geological survey data and geological maps. For example, the instruction generation unit can propose evacuation sites that take into account ground stability based on the geological data. The instruction generation unit can also integrate geological data and topographical data to evaluate the safety of evacuation sites. Furthermore, the instruction generation unit can utilize the geological data to evaluate evacuation sites in detail in a specific area. For example, the instruction generation unit can propose evacuation sites that take into account ground stability based on the geological data. This makes it possible to propose evacuation sites that take into account ground stability by combining geological data. Some or all of the above-described processing in the instruction generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the instruction generation unit can input geological data into the generation AI and cause the generation AI to propose evacuation sites.

[0054] The instruction generation unit can use meteorological satellite data to issue evacuation instructions over a wide area when providing information on evacuation routes and evacuation locations. The use of meteorological satellite data includes, but is not limited to, the type of satellite and the method of acquiring data. The instruction generation unit can use meteorological satellite data to issue evacuation instructions over a wide area. The instruction generation unit can also integrate meteorological satellite data with ground observation data to improve the accuracy of evacuation instructions. Furthermore, the instruction generation unit can provide detailed evacuation instructions for a specific area based on meteorological satellite data. For example, the instruction generation unit can use meteorological satellite data to issue evacuation instructions over a wide area. This makes it possible to issue evacuation instructions over a wide area by utilizing meteorological satellite data. Some or all of the above-described processing in the instruction generation unit can be performed, for example, using AI, or can be performed without using AI. For example, the instruction generation unit can input meteorological satellite data into the generation AI and cause the generation AI to generate evacuation instructions.

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

[0056] When collecting weather data, the collection unit can improve the accuracy of collection by referring to past weather patterns. For example, by referring to weather data from the past 10 years, it can identify patterns of abnormal weather and improve collection accuracy. It can also strengthen data collection in specific areas based on past heavy rain data. Furthermore, it can analyze past weather patterns and optimize collection accuracy for each season. By referring to past weather patterns, collection accuracy is improved. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past weather data into the generation AI and have the generation AI improve collection accuracy.

[0057] When collecting river water level data, the collection unit can simultaneously collect river flow rate and flow velocity to provide more detailed data. For example, flow rate data can be collected along with river water level data to evaluate flood risk in detail. River flow velocity data can also be collected to evaluate risks due to flow velocity. Furthermore, river water level, flow rate, and flow velocity data can be integrated to perform comprehensive risk assessment. In this way, by simultaneously collecting river flow rate and flow velocity, detailed data can be provided. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input river water level data, flow rate data, and flow velocity data to a generation AI and have the generation AI perform data preprocessing.

[0058] The collection unit can simultaneously collect geological data when collecting topographical data to evaluate the stability of the ground. For example, geological data can be collected along with topographical data to evaluate the stability of the ground. Topographical data and geological data can also be integrated to evaluate the risk of flooding in detail. Furthermore, the safety of evacuation routes can be evaluated based on topographical data and geological data. In this way, by collecting topographical data and geological data simultaneously, it is possible to evaluate the stability of the ground. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input topographical data and geological data into a generation AI and have the generation AI perform preprocessing of the data.

[0059] When analyzing weather data, the analysis unit can apply multiple weather models to improve the accuracy of the analysis. For example, multiple weather models can be applied to improve the accuracy of heavy rain forecasts. Multiple weather models can also be compared to provide the most reliable forecasts. Furthermore, multiple weather models can be integrated to perform comprehensive weather forecasts. By applying multiple weather models, the analysis unit can improve the accuracy of the analysis. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input weather data into a generation AI and have the generation AI apply multiple weather models.

[0060] When assessing flood risk, the evaluation unit can combine river flow rate and flow velocity data to perform the assessment. For example, the evaluation unit can combine river flow rate data and flow velocity data to assess flood risk in detail. The risk of a specific area can also be assessed based on river flow rate data and flow velocity data. Furthermore, the evaluation unit can integrate river flow rate data and flow velocity data to perform a comprehensive flood risk assessment. This makes it possible to assess flood risk in detail by combining river flow rate and flow velocity data. Some or all of the above-mentioned processing in the evaluation unit can be performed using, for example, AI, or without AI. For example, the evaluation unit can input river flow rate data and flow velocity data into the generation AI and have the generation AI perform a flood risk assessment.

[0061] The processing flow of the first embodiment will be briefly explained below.

[0062] Step 1: The collection unit collects meteorological data, river water level data, or topographical data. Meteorological data includes temperature, precipitation, wind speed, etc. The collection unit obtains meteorological data from the Japan Meteorological Agency or weather observation stations, river water level data from river managers, and topographical data using a geographic information system (GIS). Step 2: The analysis unit analyzes the data collected by the collection unit. For example, it analyzes meteorological data to predict heavy rain, analyzes river water level data to assess the risk of flooding, and analyzes topographical data to identify areas at high risk of flooding. Step 3: The evaluation unit evaluates disaster risk based on the data analyzed by the analysis unit. For example, it comprehensively evaluates meteorological data, river water level data, and topographical data to evaluate disaster risk. Step 4: The instruction generation unit generates individual evacuation instructions based on the disaster risk assessed by the evaluation unit. For example, for users living in areas at high risk of flooding, the unit generates instructions encouraging early evacuation and provides information on evacuation routes and evacuation locations.

[0063] (Example 2) An evacuation direction system according to an embodiment of the present invention is a system for supporting individual evacuation instructions during floods, such as river flooding or heavy rain. This evacuation direction system prioritizes the safety and lives of individuals and utilizes comprehensive information to provide disaster risk and evacuation instructions in real time. Specifically, the system comprises the following steps: First, comprehensive information, such as meteorological data, river water level data, and topographical data, is collected. Next, the collected data is analyzed to assess disaster risk. Based on the assessed disaster risk, individual evacuation instructions are generated and provided to users. This system enables prompt and appropriate evacuation instructions to protect individual safety and lives. For example, comprehensive information, such as meteorological data, river water level data, and topographical data, is collected. In this case, meteorological data is obtained from the Japan Meteorological Agency or weather observation stations, and river water level data is obtained from river administrators. Topographical data is also obtained using a geographic information system (GIS). This provides basic data for assessing disaster risk. Next, the collected data is analyzed to assess disaster risk. For example, meteorological data is analyzed to predict heavy rain, and river water level data is analyzed to assess flood risk. The system also analyzes topographical data to identify areas at high risk of flooding. This allows for a comprehensive disaster risk assessment. Based on the assessed disaster risk, the system then generates and provides individual evacuation instructions to users. For example, for users living in areas at high risk of flooding, the system generates instructions encouraging early evacuation. Information on evacuation routes and evacuation locations is also provided. This allows users to take prompt and appropriate evacuation action. This system enables prompt and appropriate evacuation instructions to protect personal safety and lives. For example, if heavy rain is predicted, the system issues an evacuation instruction early and notifies the user. Furthermore, if river water levels are rising, the system assesses the risk of flooding and generates appropriate evacuation instructions. This allows users to understand disaster risks in advance and take prompt evacuation action. The evacuation instruction system can provide prompt and appropriate evacuation instructions to protect personal safety and lives.

[0064] An evacuation direction system according to an embodiment includes a collection unit, an analysis unit, an evaluation unit, and an instruction generation unit. The collection unit collects meteorological data, river water level data, or topographical data. The meteorological data includes, but is not limited to, temperature, precipitation, wind speed, and the like. The collection unit acquires meteorological data from, for example, the Japan Meteorological Agency or a weather observation station. The collection unit can also acquire river water level data from a river administrator. The collection unit can also acquire topographical data using a geographic information system (GIS). For example, the collection unit acquires data such as temperature, precipitation, and wind speed from the Japan Meteorological Agency, acquires river water level data from a river administrator, and acquires topographical data using the GIS. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes meteorological data to predict heavy rain. The analysis unit can also analyze river water level data to assess the risk of flooding. The analysis unit can also analyze topographical data to identify areas at high risk of flooding. For example, the analysis unit analyzes weather data to predict heavy rain, analyzes river water level data to assess flood risk, and analyzes topographical data to identify areas at high risk of flooding. The evaluation unit evaluates disaster risk based on the data analyzed by the analysis unit. The evaluation unit, for example, comprehensively evaluates weather data, river water level data, and topographical data to assess disaster risk. The instruction generation unit generates individual evacuation instructions based on the disaster risk assessed by the evaluation unit. For example, the instruction generation unit generates instructions encouraging early evacuation for users living in areas at high risk of flooding. The instruction generation unit can also provide information on evacuation routes and evacuation locations. For example, the instruction generation unit generates instructions encouraging early evacuation for users living in areas at high risk of flooding, and provides information on evacuation routes and evacuation locations. As a result, the evacuation direction system according to the embodiment can provide prompt and appropriate evacuation instructions to protect personal safety and lives.

[0065] The collection unit can acquire weather data from a meteorological agency or a weather observation station. Weather data includes, but is not limited to, temperature, precipitation, wind speed, etc. For example, the collection unit acquires data such as temperature, precipitation, and wind speed from a meteorological agency. The collection unit can also acquire weather data from a weather observation station. For example, the collection unit acquires real-time weather data from a weather observation station and provides it to the analysis unit. This enables accurate collection of weather data. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input weather data acquired from a meteorological agency to a generation AI and have the generation AI perform preprocessing of the data.

[0066] The collection unit can acquire river water level data from the river administrator. The river water level data includes, for example, the installation location of a water level meter and the measurement frequency, but is not limited to these examples. The collection unit, for example, acquires water level meter data from the river administrator. The collection unit can also acquire real-time water level data from the river administrator. For example, the collection unit provides the water level data acquired from the river administrator to the analysis unit. This enables accurate collection of river water level data. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the water level data acquired from the river administrator to the generation AI and have the generation AI perform preprocessing of the data.

[0067] The collection unit can acquire topographical data using a geographical information system. Topographical data includes, but is not limited to, elevation data, topographical maps, and the like. For example, the collection unit acquires elevation data using a geographical information system (GIS). The collection unit can also acquire topographical maps using a geographical information system. For example, the collection unit acquires topographical data using a GIS and provides the data to the analysis unit. This enables accurate collection of topographical data. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the topographical data acquired from the GIS into the generation AI and have the generation AI perform preprocessing of the data.

[0068] The analysis unit can analyze weather data to predict heavy rain. Heavy rain prediction includes, but is not limited to, for example, a prediction model, a data preprocessing method, and the like. The analysis unit can, for example, analyze weather data to predict heavy rain. The analysis unit can also improve the accuracy of heavy rain predictions by using multiple prediction models. For example, the analysis unit analyzes weather data, predicts heavy rain, and provides the prediction results to the evaluation unit. This improves the accuracy of heavy rain predictions. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input weather data to a generation AI and cause the generation AI to predict heavy rain.

[0069] The analysis unit can analyze river water level data to assess the risk of flooding. Examples of flood risk assessment include, but are not limited to, risk scoring methods and assessment models. For example, the analysis unit can analyze river water level data to assess the risk of flooding. The analysis unit can also perform risk assessment by referring to past flood data. For example, the analysis unit can analyze river water level data, assess flood risk, and provide the assessment results to the assessment unit. This improves the accuracy of flood risk assessment. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input river water level data to the generation AI and cause the generation AI to perform a flood risk assessment.

[0070] The analysis unit can analyze the topographical data to identify areas at high risk of flooding. Examples of methods for identifying the risk of flooding include, but are not limited to, risk scoring methods and assessment models. For example, the analysis unit can analyze the topographical data to identify areas at high risk of flooding. The analysis unit can also combine geological data to evaluate the flood risk in more detail. For example, the analysis unit can analyze the topographical data to identify areas at high risk of flooding and provide the identification results to the evaluation unit. This improves the accuracy of identifying the flood risk. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input the topographical data into the generation AI and cause the generation AI to identify the flood risk.

[0071] The evaluation unit can evaluate the comprehensive disaster risk. Examples of the comprehensive disaster risk evaluation include, but are not limited to, risk scoring methods and evaluation models. For example, the evaluation unit comprehensively evaluates meteorological data, river water level data, and topographical data to evaluate the disaster risk. The evaluation unit can also improve the accuracy of the evaluation by referring to past disaster data. For example, the evaluation unit comprehensively evaluates meteorological data, river water level data, and topographical data to evaluate the disaster risk and provides the evaluation result to the instruction generation unit. This improves the accuracy of the comprehensive disaster risk evaluation. Some or all of the above-described processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input meteorological data, river water level data, and topographical data into the generation AI and cause the generation AI to perform a comprehensive disaster risk evaluation.

[0072] The instruction generation unit can generate individual evacuation instructions based on the assessed disaster risk. Examples of the generation of individual evacuation instructions include, but are not limited to, evacuation routes and evacuation site designation methods. The instruction generation unit can also generate individual evacuation instructions based on, for example, the assessed disaster risk. The instruction generation unit can also provide optimal instructions by referring to past evacuation history. For example, the instruction generation unit generates individual evacuation instructions based on the assessed disaster risk and provides them to the user. This improves the accuracy of generating individual evacuation instructions. Some or all of the above-described processing in the instruction generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the instruction generation unit can input the assessed disaster risk into a generation AI and cause the generation AI to generate individual evacuation instructions.

[0073] The instruction generation unit can provide information on evacuation routes and evacuation locations. Examples of information provided on evacuation routes and evacuation locations include, but are not limited to, map information and real-time updates. The instruction generation unit can also provide, for example, information on evacuation routes and evacuation locations. The instruction generation unit can also propose an optimal evacuation route by combining real-time traffic information. For example, the instruction generation unit provides information on evacuation routes and evacuation locations and notifies the user. This makes it possible to provide information on evacuation routes and evacuation locations. Some or all of the above-described processing in the instruction generation unit may be performed, for example, using AI or without AI. For example, the instruction generation unit can input information on evacuation routes and evacuation locations into a generation AI and have the generation AI provide the information.

[0074] The collection unit can estimate the user's emotions and adjust the frequency of data collection based on the estimated user emotions. For example, if the user is feeling anxious, the collection unit can increase the frequency of data collection to provide the latest information. The collection unit can also maintain the normal frequency of data collection when the user is relaxed. Furthermore, the collection unit can maximize the frequency of data collection when the user is facing an emergency. For example, the collection unit can estimate the user's emotions and adjust the frequency of data collection based on the estimated emotions. This enables the frequency of data collection to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI adjust the frequency of data collection.

[0075] When collecting weather data, the collection unit can improve the accuracy of collection by referring to past weather patterns. Referencing past weather patterns includes, but is not limited to, database construction methods and reference algorithms. For example, the collection unit can refer to weather data from the past 10 years to identify abnormal weather patterns and improve collection accuracy. The collection unit can also strengthen data collection in specific areas based on past heavy rain data. Furthermore, the collection unit can analyze past weather patterns and optimize collection accuracy for each season. For example, the collection unit can refer to past weather data to improve collection accuracy. By referring to past weather patterns, collection accuracy is improved. Some or all of the above-described processing in the collection unit may be performed, for example, using AI, or may be performed without AI. For example, the collection unit can input past weather data into the generation AI and cause the generation AI to improve collection accuracy.

[0076] When collecting river water level data, the collection unit can simultaneously collect river flow rate and flow velocity to provide more detailed data. Collection of river flow rate and flow velocity includes, for example, but is not limited to, the type of measuring equipment and the frequency of measurement. For example, the collection unit collects flow rate data along with river water level data to perform a detailed assessment of flood risk. The collection unit can also collect river flow velocity data to evaluate risks due to flow velocity. Furthermore, the collection unit can integrate river water level, flow rate, and flow velocity data to perform a comprehensive risk assessment. For example, the collection unit collects flow rate data along with river water level data and provides it to the analysis unit. By simultaneously collecting river flow rate and flow velocity, detailed data can be provided. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input river water level data, flow rate data, and flow velocity data to a generation AI and have the generation AI perform data preprocessing.

[0077] The collection unit can estimate the user's emotions and prioritize the data to be collected based on the estimated user emotions. For example, if the user is feeling anxious, the collection unit can prioritize collecting weather data. Furthermore, if the user is relaxed, the collection unit can prioritize collecting river water level data. Furthermore, if the user is facing an emergency, the collection unit can simultaneously collect all data. For example, the collection unit can estimate the user's emotions and prioritize the data to be collected based on the estimated emotions. This enables prioritization of data collection according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit can be performed using, for example, an AI, or without an AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of data collection.

[0078] The collection unit can simultaneously collect geological data when collecting topographical data to evaluate ground stability. Examples of collected geological data include, but are not limited to, geological survey data and geological maps. For example, the collection unit can collect geological data along with topographical data to evaluate ground stability. The collection unit can also integrate topographical data and geological data to perform a detailed evaluation of flood risk. Furthermore, the collection unit can evaluate the safety of evacuation routes based on the topographical data and geological data. For example, the collection unit collects geological data along with topographical data and provides it to the analysis unit. This enables ground stability evaluation by simultaneously collecting topographical data and geological data. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input topographical data and geological data into a generation AI and have the generation AI perform data preprocessing.

[0079] When collecting weather data, the collection unit can acquire weather information over a wide area by utilizing weather satellite data. The collection of weather satellite data includes, but is not limited to, the type of satellite and the data acquisition method. For example, the collection unit can collect weather information over a wide area by utilizing weather satellite data. The collection unit can also integrate weather satellite data with ground observation data to improve collection accuracy. Furthermore, the collection unit can evaluate local weather fluctuations in detail based on the weather satellite data. For example, the collection unit can collect weather information over a wide area by utilizing weather satellite data and provide it to the analysis unit. This makes it possible to acquire weather information over a wide area by utilizing weather satellite data. Some or all of the above-described processing in the collection unit can be performed, for example, using AI, or can be performed without using AI. For example, the collection unit can input weather satellite data into a generation AI and have the generation AI perform preprocessing of the data.

[0080] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is feeling anxious, the analysis unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the user is facing an emergency, the analysis unit can provide a display method that focuses on the key points. For example, the analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. This enables the display method of the analysis results to be adjusted according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the display method of the analysis results.

[0081] The analysis unit can apply multiple weather models when analyzing weather data to improve the accuracy of the analysis. Examples of weather models that can be applied include, but are not limited to, numerical forecast models and statistical models. For example, the analysis unit can apply multiple weather models to improve the accuracy of heavy rain forecasts. The analysis unit can also compare multiple weather models to provide the most reliable forecast. Furthermore, the analysis unit can integrate multiple weather models to perform a comprehensive weather forecast. For example, the analysis unit can apply multiple weather models to improve the accuracy of the analysis. Thus, applying multiple weather models improves the accuracy of the analysis. Some or all of the above-described processing in the analysis unit can be performed, for example, using AI, or can be performed without using AI. For example, the analysis unit can input weather data to a generation AI and have the generation AI apply multiple weather models.

[0082] When analyzing river water level data, the analysis unit can perform risk assessment by referring to past flood data. Referencing past flood data includes, but is not limited to, database construction methods and reference algorithms. The analysis unit, for example, refers to past flood data and compares it with current water level data to perform risk assessment. The analysis unit can also perform a detailed risk assessment of a specific area based on the past flood data. Furthermore, the analysis unit can integrate past flood data and current water level data to perform a comprehensive risk assessment. For example, the analysis unit performs risk assessment by referring to past flood data. As a result, referring to past flood data improves the accuracy of the risk assessment. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past flood data into a generation AI and have the generation AI perform a risk assessment.

[0083] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated user's emotions. For example, if the user is feeling anxious, the analysis unit can prioritize the analysis of weather data. Furthermore, if the user is relaxed, the analysis unit can prioritize the analysis of river water level data. Furthermore, if the user is facing an emergency, the analysis unit can simultaneously analyze all data. For example, the analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated emotions. This enables the priority of analysis to be determined according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of analysis.

[0084] When analyzing the topographical data, the analysis unit can combine geological data to evaluate the flood risk in more detail. Examples of combinations of geological data include, but are not limited to, geological survey data and geological maps. For example, the analysis unit combines topographical data and geological data to evaluate the flood risk in detail. The analysis unit can also evaluate the flood risk of a specific area based on the topographical data and geological data. Furthermore, the analysis unit can integrate the topographical data and geological data to perform a comprehensive flood risk evaluation. For example, the analysis unit combines the topographical data and geological data to evaluate the flood risk. This enables a detailed evaluation of the flood risk by combining the topographical data and geological data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the topographical data and geological data into the generation AI and cause the generation AI to perform a flood risk evaluation.

[0085] When analyzing the weather data, the analysis unit can use weather radar data to forecast localized heavy rain. The use of weather radar data includes, but is not limited to, the type of radar and the method of acquiring the data. For example, the analysis unit can use weather radar data to forecast localized heavy rain. The analysis unit can also integrate weather radar data with ground observation data to improve forecast accuracy. Furthermore, the analysis unit can also evaluate the risk of heavy rain in a specific area in detail based on the weather radar data. For example, the analysis unit can use weather radar data to forecast localized heavy rain. This makes it possible to forecast localized heavy rain by utilizing the weather radar data. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input weather radar data into a generation AI and have the generation AI execute a heavy rain forecast.

[0086] The evaluation unit can estimate the user's emotions and adjust the risk assessment criteria based on the estimated user emotions. For example, if the user is feeling anxious, the evaluation unit can set the risk assessment criteria to be strict. Furthermore, if the user is relaxed, the evaluation unit can maintain the risk assessment criteria at a normal level. Furthermore, if the user is facing an emergency, the evaluation unit can maximize the risk assessment criteria. For example, the evaluation unit can estimate the user's emotions and adjust the risk assessment criteria based on the estimated emotions. This enables the risk assessment criteria to be adjusted according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the evaluation unit can be performed using, for example, an AI, or without an AI. For example, the evaluation unit can input the user's emotion data into the generation AI and have the generation AI adjust the risk assessment criteria.

[0087] When assessing comprehensive disaster risk, the evaluation unit can improve the accuracy of the evaluation by referring to past disaster data. Referencing past disaster data includes, but is not limited to, database construction methods, reference algorithms, and the like. For example, the evaluation unit can refer to past disaster data and compare it with the current risk assessment to improve accuracy. The evaluation unit can also evaluate the risk of a specific area in detail based on past disaster data. Furthermore, the evaluation unit can integrate past disaster data and current risk assessment to perform a comprehensive risk assessment. For example, the evaluation unit refers to past disaster data to perform risk assessment. As a result, referring to past disaster data improves the accuracy of the assessment. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input past disaster data into the generation AI and have the generation AI perform a risk assessment.

[0088] When assessing flood risk, the evaluation unit can combine river flow rate and flow velocity data to perform the assessment. Combinations of river flow rate and flow velocity data include, but are not limited to, the type of measuring equipment and the frequency of measurement. For example, the evaluation unit combines river flow rate data and flow velocity data to perform a detailed assessment of flood risk. The evaluation unit can also assess the risk of a specific area based on the river flow rate data and flow velocity data. Furthermore, the evaluation unit can integrate river flow rate data and flow velocity data to perform a comprehensive flood risk assessment. For example, the evaluation unit combines river flow rate data and flow velocity data to assess flood risk. This enables a detailed assessment of flood risk by combining river flow rate and flow velocity data. Some or all of the above-described processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input river flow rate data and flow velocity data to the generation AI and cause the generation AI to perform a flood risk assessment.

[0089] The evaluation unit can estimate the user's emotions and prioritize risk assessments based on the estimated user emotions. For example, if the user is feeling anxious, the evaluation unit can prioritize risk assessments of weather data. Furthermore, if the user is relaxed, the evaluation unit can prioritize risk assessments of river water level data. Furthermore, if the user is facing an emergency, the evaluation unit can simultaneously perform risk assessments of all data. For example, the evaluation unit can estimate the user's emotions and prioritize risk assessments based on the estimated emotions. This enables prioritization of risk assessments according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the evaluation unit can be performed using, for example, an AI, or without an AI. For example, the evaluation unit can input the user's emotion data into the generation AI and have the generation AI perform risk assessment prioritization.

[0090] When assessing flood risk, the evaluation unit can combine geological data to evaluate ground stability. Examples of combined geological data include, but are not limited to, geological survey data and geological maps. The evaluation unit, for example, combines geological data to evaluate ground stability. The evaluation unit can also evaluate the flood risk of a specific area based on geological data and topographical data. Furthermore, the evaluation unit can integrate geological data and topographical data to perform a comprehensive flood risk assessment. For example, the evaluation unit combines geological data to evaluate ground stability. This enables ground stability assessment by combining geological data. Some or all of the above-described processing in the evaluation unit may be performed using, or without, AI. For example, the evaluation unit can input geological data and topographical data into the generation AI and cause the generation AI to perform a ground stability assessment.

[0091] When assessing comprehensive disaster risk, the evaluation unit can utilize meteorological satellite data to conduct a wide-ranging risk assessment. The utilization of meteorological satellite data includes, but is not limited to, the type of satellite and the method of data acquisition. The evaluation unit, for example, utilizes meteorological satellite data to conduct a wide-ranging risk assessment. The evaluation unit can also integrate meteorological satellite data with ground observation data to improve the accuracy of the assessment. Furthermore, the evaluation unit can also conduct a detailed assessment of the risk of a specific region based on the meteorological satellite data. For example, the evaluation unit utilizes meteorological satellite data to conduct a wide-ranging risk assessment. This enables a wide-ranging risk assessment by utilizing meteorological satellite data. Some or all of the above-described processing in the evaluation unit may be performed, for example, using AI, or may be performed without using AI. For example, the evaluation unit can input meteorological satellite data into a generation AI and have the generation AI perform a risk assessment.

[0092] The instruction generation unit can estimate the user's emotions and adjust the expression of evacuation instructions based on the estimated user's emotions. For example, if the user is feeling anxious, the instruction generation unit can provide simple, highly visible evacuation instructions. Furthermore, if the user is relaxed, the instruction generation unit can provide evacuation instructions with detailed information. Furthermore, if the user is facing an emergency, the instruction generation unit can provide evacuation instructions that focus on the key points. For example, the instruction generation unit can estimate the user's emotions and adjust the expression of evacuation instructions based on the estimated emotions. This enables the expression of evacuation instructions to be adjusted according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the instruction generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the instruction generation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the expression of the evacuation instructions.

[0093] When generating individual evacuation instructions, the instruction generation unit can refer to past evacuation history to provide optimal instructions. Referencing past evacuation history includes, but is not limited to, database construction methods and reference algorithms. For example, the instruction generation unit can refer to past evacuation history to provide optimal evacuation instructions. The instruction generation unit can also provide detailed evacuation instructions for a specific area based on past evacuation history. Furthermore, the instruction generation unit can integrate past evacuation history with current risk assessment to provide comprehensive evacuation instructions. For example, the instruction generation unit can refer to past evacuation history to provide optimal evacuation instructions. This makes it possible to provide optimal evacuation instructions by referring to past evacuation history. Some or all of the above-described processing in the instruction generation unit may be performed, for example, using AI or without AI. For example, the instruction generation unit can input past evacuation history into the generation AI and cause the generation AI to generate evacuation instructions.

[0094] The instruction generation unit can combine real-time traffic information to propose an optimal evacuation route when providing information on evacuation routes and evacuation locations. Examples of combinations of real-time traffic information include, but are not limited to, traffic sensors and data update frequencies. The instruction generation unit can, for example, propose an optimal evacuation route based on real-time traffic information. The instruction generation unit can also integrate real-time traffic information with topographical data to evaluate the safety of an evacuation route. Furthermore, the instruction generation unit can utilize real-time traffic information to propose an evacuation route that avoids congestion. For example, the instruction generation unit proposes an optimal evacuation route based on real-time traffic information. This makes it possible to propose an optimal evacuation route by combining real-time traffic information. Some or all of the above-described processing in the instruction generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the instruction generation unit can input real-time traffic information to the generation AI and cause the generation AI to propose an evacuation route.

[0095] The instruction generation unit can estimate the user's emotions and determine the priority of evacuation instructions based on the estimated user emotions. For example, the instruction generation unit can increase the priority of evacuation instructions when the user is feeling anxious. Furthermore, the instruction generation unit can also provide evacuation instructions with normal priority when the user is relaxed. Furthermore, the instruction generation unit can maximize the priority of evacuation instructions when the user is facing an emergency. For example, the instruction generation unit can estimate the user's emotions and determine the priority of evacuation instructions based on the estimated emotions. This enables the priority of evacuation instructions to be determined according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the instruction generation unit can be performed using, for example, an AI, or without an AI. For example, the instruction generation unit can input the user's emotion data into the generation AI and cause the generation AI to determine the priority of evacuation instructions.

[0096] When generating evacuation instructions, the instruction generation unit can combine geological data to propose evacuation sites that take into account ground stability. Examples of combinations of geological data include, but are not limited to, geological survey data and geological maps. For example, the instruction generation unit can propose evacuation sites that take into account ground stability based on the geological data. The instruction generation unit can also integrate geological data and topographical data to evaluate the safety of evacuation sites. Furthermore, the instruction generation unit can utilize the geological data to evaluate evacuation sites in detail in a specific area. For example, the instruction generation unit can propose evacuation sites that take into account ground stability based on the geological data. This makes it possible to propose evacuation sites that take into account ground stability by combining geological data. Some or all of the above-described processing in the instruction generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the instruction generation unit can input geological data into the generation AI and cause the generation AI to propose evacuation sites.

[0097] The instruction generation unit can use meteorological satellite data to issue evacuation instructions over a wide area when providing information on evacuation routes and evacuation locations. The use of meteorological satellite data includes, but is not limited to, the type of satellite and the method of acquiring data. The instruction generation unit can use meteorological satellite data to issue evacuation instructions over a wide area. The instruction generation unit can also integrate meteorological satellite data with ground observation data to improve the accuracy of evacuation instructions. Furthermore, the instruction generation unit can provide detailed evacuation instructions for a specific area based on meteorological satellite data. For example, the instruction generation unit can use meteorological satellite data to issue evacuation instructions over a wide area. This makes it possible to issue evacuation instructions over a wide area by utilizing meteorological satellite data. Some or all of the above-described processing in the instruction generation unit can be performed, for example, using AI, or can be performed without using AI. For example, the instruction generation unit can input meteorological satellite data into the generation AI and cause the generation AI to generate evacuation instructions. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, evaluation unit, and instruction generation unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects meteorological data and river water level data using the camera 42 and microphone 38B of the smart device 14, and acquires topographical data using the control unit 46A. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data and evaluates disaster risk. The evaluation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, evaluates overall disaster risk based on the analysis results. The instruction generation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, generates individual evacuation instructions based on the evaluated disaster risk and provides them to the user via the output device 40 of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, evaluation unit, and instruction generation unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects meteorological data and river water level data using the camera 42 and microphone 238 of the smart glasses 214, and acquires topographical data using the control unit 46A. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data and evaluates disaster risk. The evaluation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, evaluates overall disaster risk based on the analysis results. The instruction generation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, generates individual evacuation instructions based on the evaluated disaster risk and provides them to the user via the speaker 240 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, evaluation unit, and instruction generation unit, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit collects meteorological data and river water level data using the camera 42 and microphone 238 of the headset terminal 314, and acquires topographical data using the control unit 46A. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data and evaluates disaster risk. The evaluation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, evaluates overall disaster risk based on the analysis results. The instruction generation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, generates individual evacuation instructions based on the evaluated disaster risk and provides them to the user via the display 343 of the headset terminal 314. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, evaluation unit, and instruction generation unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects meteorological data and river water level data using the camera 42 and microphone 238 of the robot 414, and acquires topographical data using the control unit 46A. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data and evaluates disaster risk. The evaluation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, evaluates overall disaster risk based on the analysis results. The instruction generation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, generates individual evacuation instructions based on the evaluated disaster risk and provides them to the user via the speaker 240 of the robot 414.

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

[0099] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is feeling anxious, a simple, highly visible display method can be provided. Alternatively, if the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is facing an emergency, a display method that focuses on the key points can be provided. This enables the display method of the analysis results to be adjusted according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the display method of the analysis results.

[0100] The evaluation unit can estimate the user's emotions and adjust the risk assessment criteria based on the estimated user emotions. For example, if the user is feeling anxious, the risk assessment criteria can be set stricter. Alternatively, if the user is relaxed, the risk assessment criteria can be kept normal. Furthermore, if the user is facing an emergency, the risk assessment criteria can be maximized. This enables the risk assessment criteria to be adjusted according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the evaluation unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the evaluation unit can input the user's emotion data into the generation AI and have the generation AI adjust the risk assessment criteria.

[0101] The instruction generation unit can estimate the user's emotions and adjust the expression of evacuation instructions based on the estimated user's emotions. For example, if the user is feeling anxious, simple, highly visible evacuation instructions can be provided. Furthermore, if the user is relaxed, evacuation instructions including detailed information can be provided. Furthermore, if the user is facing an emergency, evacuation instructions that focus on the key points can be provided. This enables the expression of evacuation instructions to be adjusted according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the instruction generation unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the instruction generation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the expression of the evacuation instructions.

[0102] The collection unit can estimate the user's emotions and adjust the frequency of data collection based on the estimated user emotions. For example, if the user is feeling anxious, the frequency of data collection can be increased to provide the latest information. Alternatively, if the user is relaxed, the frequency of data collection can be maintained at a normal level. Furthermore, if the user is facing an emergency, the frequency of data collection can be maximized. This enables the frequency of data collection to be adjusted according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI adjust the frequency of data collection.

[0103] The analysis unit can estimate the user's emotions and determine the analysis priorities based on the estimated user emotions. For example, if the user is feeling anxious, the analysis of weather data can be prioritized. Alternatively, if the user is relaxed, the analysis of river water level data can be prioritized. Furthermore, if the user is facing an emergency, all data can be analyzed simultaneously. This enables the prioritization of analysis according to the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI determine the analysis priorities.

[0104] When collecting weather data, the collection unit can improve the accuracy of collection by referring to past weather patterns. For example, by referring to weather data from the past 10 years, it can identify patterns of abnormal weather and improve collection accuracy. It can also strengthen data collection in specific areas based on past heavy rain data. Furthermore, it can analyze past weather patterns and optimize collection accuracy for each season. By referring to past weather patterns, collection accuracy is improved. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past weather data into the generation AI and have the generation AI improve collection accuracy.

[0105] When collecting river water level data, the collection unit can simultaneously collect river flow rate and flow velocity to provide more detailed data. For example, flow rate data can be collected along with river water level data to evaluate flood risk in detail. River flow velocity data can also be collected to evaluate risks due to flow velocity. Furthermore, river water level, flow rate, and flow velocity data can be integrated to perform comprehensive risk assessment. In this way, by simultaneously collecting river flow rate and flow velocity, detailed data can be provided. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input river water level data, flow rate data, and flow velocity data to a generation AI and have the generation AI perform data preprocessing.

[0106] The collection unit can simultaneously collect geological data when collecting topographical data to evaluate the stability of the ground. For example, geological data can be collected along with topographical data to evaluate the stability of the ground. Topographical data and geological data can also be integrated to evaluate the risk of flooding in detail. Furthermore, the safety of evacuation routes can be evaluated based on topographical data and geological data. In this way, by collecting topographical data and geological data simultaneously, it is possible to evaluate the stability of the ground. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input topographical data and geological data into a generation AI and have the generation AI perform preprocessing of the data.

[0107] When analyzing weather data, the analysis unit can apply multiple weather models to improve the accuracy of the analysis. For example, multiple weather models can be applied to improve the accuracy of heavy rain forecasts. Multiple weather models can also be compared to provide the most reliable forecasts. Furthermore, multiple weather models can be integrated to perform comprehensive weather forecasts. By applying multiple weather models, the analysis unit can improve the accuracy of the analysis. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input weather data into a generation AI and have the generation AI apply multiple weather models.

[0108] When assessing flood risk, the evaluation unit can combine river flow rate and flow velocity data to perform the assessment. For example, the evaluation unit can combine river flow rate data and flow velocity data to assess flood risk in detail. The risk of a specific area can also be assessed based on river flow rate data and flow velocity data. Furthermore, the evaluation unit can integrate river flow rate data and flow velocity data to perform a comprehensive flood risk assessment. This makes it possible to assess flood risk in detail by combining river flow rate and flow velocity data. Some or all of the above-mentioned processing in the evaluation unit can be performed using, for example, AI, or without AI. For example, the evaluation unit can input river flow rate data and flow velocity data into the generation AI and have the generation AI perform a flood risk assessment.

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

[0110] Step 1: The collection unit collects meteorological data, river water level data, or topographical data. Meteorological data includes temperature, precipitation, wind speed, etc. The collection unit obtains meteorological data from the Japan Meteorological Agency or weather observation stations, river water level data from river managers, and topographical data using a geographic information system (GIS). Step 2: The analysis unit analyzes the data collected by the collection unit. For example, it analyzes meteorological data to predict heavy rain, analyzes river water level data to assess the risk of flooding, and analyzes topographical data to identify areas at high risk of flooding. Step 3: The evaluation unit evaluates disaster risk based on the data analyzed by the analysis unit. For example, it comprehensively evaluates meteorological data, river water level data, and topographical data to evaluate disaster risk. Step 4: The instruction generation unit generates individual evacuation instructions based on the disaster risk assessed by the evaluation unit. For example, for users living in areas at high risk of flooding, the unit generates instructions encouraging early evacuation and provides information on evacuation routes and evacuation locations.

[0111] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0113] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0114] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0115] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0116] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0117] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0118] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0119] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0121] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0122] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0123] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0124] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0125] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0126] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0127] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0128] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0129] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0130] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0131] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0132] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0133] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0134] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0135] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0137] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0138] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0139] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0140] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0141] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0142] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0143] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0144] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0145] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0146] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0147] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0148] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0149] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0150] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0151] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0153] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0154] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0155] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0156] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0157] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0158] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0159] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0160] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0161] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0162] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0163] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0164] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0165] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0166] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0167] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0168] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0169] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0170] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0171] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0172] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0173] 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.

[0174] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0175] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0176] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0177] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0178] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0179] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0180] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0181] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0182] [Explanation of symbols]

[0183] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects meteorological data, river water level data, or topographical data; an analysis unit that analyzes the data collected by the collection unit; an evaluation unit that evaluates disaster risks based on the data analyzed by the analysis unit; an instruction generation unit that generates individual evacuation instructions based on the disaster risk evaluated by the evaluation unit; A system characterized by:

2. The collecting unit Obtaining weather data from a meteorological agency or weather station 2. The system of claim 1.

3. The collecting unit Obtaining river water level data from river administrators 2. The system of claim 1.

4. The collecting unit Obtaining topographical data using a geographic information system 2. The system of claim 1.

5. The analysis unit Analyzing meteorological data to predict heavy rain 2. The system of claim 1.

6. The analysis unit Analyzing river water level data to assess flood risk 2. The system of claim 1.

7. The analysis unit Analyzing topographical data to identify areas at high risk of flooding 2. The system of claim 1.

8. The evaluation unit Assessing comprehensive disaster risk 2. The system of claim 1.

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