System

Abstract

An object of a system according to an embodiment is to integrate data from earth observation satellites and perform highly accurate disaster prediction.SOLUTION: A system according to an embodiment includes a collection unit, an integration unit, and a generation unit. The collection unit collects data from an earth observation satellite. The integration unit constructs a virtual earth environment by integrating the data collected by the collection unit. The generation unit performs disaster prediction based on the virtual earth environment constructed by the integration unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult to comprehensively utilize data from Earth observation satellites, which limited the ability to improve the accuracy of disaster predictions.

[0005] The system according to the embodiment aims to integrate data from earth observation satellites and perform highly accurate disaster prediction. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an integration unit, and a generation unit. The collection unit collects data from Earth observation satellites. The integration unit integrates the data collected by the collection unit to construct a virtual Earth environment. The generation unit performs disaster prediction based on the virtual Earth environment constructed by the integration unit. [Effects of the Invention]

[0007] The system according to the embodiment can integrate data from earth observation satellites and perform highly accurate disaster predictions. [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) A system according to an embodiment of the present invention utilizes observation images and data acquired from Earth observation satellites to build a Metaverse Earth, thereby achieving high-precision disaster forecasting. This system collects observation images and data acquired from Earth observation satellites and uses the collected data as training data to build a Metaverse Earth. Metaverse Earth recreates a virtual Earth environment, integrating differences in observation technologies and operator differences. Furthermore, a generative AI based on conventional forecasting methods is used to improve the accuracy of disaster forecasts, such as earthquakes. For example, observation images and data acquired from Earth observation satellites are collected. Data from multiple Earth observation satellites is integrated to create a comprehensive dataset. For example, data from meteorological and environmental observation satellites can be collected and integrated to obtain more detailed information about the Earth's environment. Next, a Metaverse Earth is built using the collected data as training data. Metaverse Earth recreates a virtual Earth environment, integrating differences in observation technologies and operator differences. For example, meteorological data and topographical data can be integrated and reproduced in a virtual environment to create a detailed Earth model. Furthermore, a generative AI based on conventional forecasting methods is used to improve the accuracy of disaster forecasts, such as earthquakes. The Generative AI performs simulations on Metaverse Earth to predict the occurrence of disasters such as earthquakes. For example, it can analyze the mechanisms that cause earthquakes and predict the probability of earthquakes with high accuracy. This is expected to contribute to the contributions of local governments and the safety and security of citizens. This will enable the system to achieve higher accuracy in disaster forecasts, which will contribute to the contributions of local governments and the safety and security of citizens. For example, higher accuracy in disaster forecasts will enable local governments to respond more quickly and citizens to strengthen their disaster preparedness. Furthermore, by utilizing Metaverse Earth, differences in observation technology and differences between operators can be integrated, making it possible to utilize comprehensive data.

[0029] A disaster forecasting system according to an embodiment includes a collection unit, an integration unit, and a generation unit. The collection unit collects data from earth observation satellites. The collection unit can collect data from, for example, meteorological satellites and environmental observation satellites. The collection unit can also integrate data from multiple earth observation satellites to create a comprehensive data set. For example, the collection unit can collect temperature data and precipitation data from meteorological satellites and topography data and vegetation data from environmental observation satellites. The integration unit integrates the data collected by the collection unit to construct a virtual earth environment. The integration unit can, for example, integrate meteorological data and topography data and reproduce them in the virtual environment. The integration unit can also integrate differences in observation technology and operator differences to create a detailed earth model. For example, the integration unit can perform weather simulations based on meteorological data and create a topography model based on topography data. The generation unit performs disaster predictions based on the virtual earth environment constructed by the integration unit. The generation unit uses generation AI to accurately predict earthquakes and other disasters. For example, the generation unit can analyze the mechanisms behind earthquakes and predict the probability of earthquake occurrence. The generation unit can also predict the occurrence of typhoons and floods based on meteorological data. For example, the generation unit can predict the path of a typhoon based on meteorological data and evaluate the risk of flooding. This allows the disaster forecasting system according to the embodiment to integrate data from Earth observation satellites and achieve highly accurate disaster forecasts. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can perform disaster predictions using a generation AI model that receives the virtual Earth environment constructed by the integration unit as input and outputs a disaster prediction.

[0030] The collection unit can collect data from multiple earth observation satellites. The collection unit can collect data from, for example, meteorological satellites and environmental observation satellites. For example, the collection unit collects temperature data and precipitation data from meteorological satellites. The collection unit can also collect topography data and vegetation data from environmental observation satellites. For example, the collection unit integrates data from multiple earth observation satellites to create a comprehensive data set. In this way, a comprehensive data set can be created by collecting data from multiple earth observation satellites. 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 data from multiple earth observation satellites into AI and have the AI ​​collect the data.

[0031] The integrating unit can integrate the collected data and recreate a virtual Earth environment. The integrating unit can integrate, for example, weather data and terrain data and recreate the environment in a virtual environment. For example, the integrating unit performs a weather simulation based on the weather data and creates a terrain model based on the terrain data. The integrating unit can also integrate differences in observation technology and differences between operators to create a detailed Earth model. For example, the integrating unit performs a weather simulation based on the weather data and creates a terrain model based on the terrain data. In this way, a detailed Earth model can be created by integrating the collected data and recreating a virtual Earth environment. Some or all of the above-described processing in the integrating unit can be performed using, for example, AI, or without AI. For example, the integrating unit can input the collected data into AI and have the AI ​​integrate the data.

[0032] The generation unit can use the generation AI to perform highly accurate predictions of earthquakes and other disasters. The generation unit can, for example, use the generation AI to perform highly accurate predictions of earthquakes and other disasters. For example, the generation unit analyzes the mechanism of earthquake occurrence and predicts the probability of an earthquake occurring. The generation unit can also predict the occurrence of typhoons and floods based on meteorological data. For example, the generation unit predicts the path of a typhoon based on meteorological data and evaluates the risk of flood occurrence. In this way, the use of the generation AI improves the accuracy of disaster forecasts. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the generation unit can perform disaster predictions using a generation AI model that inputs the virtual Earth environment constructed by the integration unit and outputs a disaster prediction.

[0033] The generation unit can analyze the mechanism by which an earthquake occurs and predict the probability of an earthquake occurring. The generation unit, for example, analyzes the mechanism by which an earthquake occurs and predicts the probability of an earthquake occurring. For example, the generation unit analyzes the mechanism by which an earthquake occurs based on geological data and fault data and predicts the probability of an earthquake occurring. The generation unit can also select the most accurate forecasting algorithm based on the mechanism by which an earthquake occurs. For example, the generation unit improves the forecasting algorithm by referring to the mechanism by which an earthquake occurs. In this way, by analyzing the mechanism by which an earthquake occurs, the probability of an earthquake occurring can be predicted with high accuracy. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input geological data and fault data into the generation AI and cause the generation AI to predict the probability of an earthquake occurring.

[0034] The integration unit can integrate weather data and terrain data and reproduce them in a virtual environment. The integration unit can, for example, integrate weather data and terrain data and reproduce them in a virtual environment. For example, the integration unit performs a weather simulation based on the weather data and creates a terrain model based on the terrain data. The integration unit can also integrate differences in observation technology and differences between operators to create a detailed earth model. For example, the integration unit performs a weather simulation based on the weather data and creates a terrain model based on the terrain data. In this way, by integrating the weather data and terrain data, a more detailed virtual environment can be reproduced. Some or all of the above-mentioned processing in the integration unit may be performed using, for example, AI, or may be performed without using AI. For example, the integration unit can input weather data and terrain data into AI and have the AI ​​integrate the data.

[0035] The collection unit can analyze past observation data and select an efficient data collection method. The collection unit, for example, selects the most efficient data collection method based on past observation data. For example, the collection unit selects the optimal data collection method for a specific time period from past observation data. The collection unit can also analyze past observation data and select the most accurate data collection method. For example, the collection unit selects the most efficient data collection method based on past observation data. In this way, the most efficient data collection method can be selected by analyzing past observation 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 past observation data into AI and have the AI ​​select an efficient data collection method.

[0036] The collection unit can perform filtering based on the type and location of the observation satellite when collecting data. The collection unit, for example, collects only specific data based on the type of observation satellite. For example, the collection unit collects only data from a specific region based on the location of the observation satellite. The collection unit can also collect the most relevant data based on the type and location of the observation satellite. For example, the collection unit collects only specific data based on the type of observation satellite. In this way, by filtering based on the type and location of the observation satellite, the most relevant data can be collected. 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 data on the type and location of the observation satellite into AI and have the AI ​​perform the filtering.

[0037] The collection unit can select an efficient collection means according to the operational status of the observation satellite when collecting data. The collection unit selects the optimal data collection means based on, for example, the operational status of the observation satellite. For example, the collection unit monitors the operational status of the observation satellite in real time and selects the optimal collection means. The collection unit can also dynamically change the data collection means according to the operational status of the observation satellite. For example, the collection unit selects the optimal data collection means based on the operational status of the observation satellite. This enables efficient data collection by selecting the optimal collection means according to the operational status of the observation satellite. 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 data on the operational status of the observation satellite into AI and have the AI ​​select an efficient collection means.

[0038] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the geographical location information of the observation satellite. The collection unit, for example, prioritizes collecting data for a specific region based on the geographical location information of the observation satellite. For example, the collection unit prioritizes collecting the most relevant data by taking into account the geographical location information of the observation satellite. The collection unit can also prioritize collecting highly relevant data during a specific time period based on the geographical location information of the observation satellite. For example, the collection unit prioritizes collecting data for a specific region based on the geographical location information of the observation satellite. This makes it possible to prioritize collecting highly relevant data by taking into account the geographical location information of the observation satellite. 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 data on the geographical location information of the observation satellite into AI and cause the AI ​​to collect highly relevant data.

[0039] The collection unit can analyze the operation history of the observation satellite and collect relevant data when collecting data. The collection unit, for example, collects the most relevant data based on the operation history of the observation satellite. For example, the collection unit analyzes the operation history of the observation satellite and collects data related to a specific time period. The collection unit can also select the optimal data collection method by referring to the operation history of the observation satellite. For example, the collection unit collects the most relevant data based on the operation history of the observation satellite. In this way, the most relevant data can be collected by analyzing the operation history of the observation satellite. 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 data on the operation history of the observation satellite into AI and have the AI ​​collect the relevant data.

[0040] The collection unit can customize the collection method by reflecting past feedback when collecting data. The collection unit, for example, customizes the optimal data collection method based on past feedback. For example, the collection unit adjusts the timing of data collection by reflecting past feedback. The collection unit can also determine the priority of data collection by referring to past feedback. For example, the collection unit customizes the optimal data collection method based on past feedback. In this way, the optimal data collection method can be customized by reflecting past feedback. 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 data of past feedback into AI and have the AI ​​customize the collection method.

[0041] The integration unit can adjust the level of integration detail based on the importance of the data when integrating data. For example, the integration unit prioritizes integration of data with high importance and provides detailed information. The integration unit can also simplify and integrate data with low importance. For example, the integration unit dynamically adjusts the level of integration detail based on the importance of the data. As a result, important data can be integrated in detail by adjusting the level of integration detail based on the importance of the data. Some or all of the above-mentioned processing in the integration unit may be performed using, for example, AI, or may be performed without using AI. For example, the integration unit can input the importance of the data to AI and have the AI ​​adjust the level of integration detail.

[0042] When integrating data, the integration unit can apply different integration algorithms depending on the data category. For example, the integration unit integrates meteorological data and topographical data using different algorithms. The integration unit can also integrate observation data and environmental data using different algorithms. For example, the integration unit selects an optimal integration algorithm depending on the data category. This enables optimal integration by applying different integration algorithms depending on the data category. Some or all of the above-mentioned processing in the integration unit may be performed using, for example, AI, or may be performed without using AI. For example, the integration unit can input the data category into AI and have the AI ​​select the integration algorithm.

[0043] When integrating data, the integration unit can improve the accuracy of the integration by referring to past integration results. The integration unit, for example, improves the accuracy of the integration based on past integration results. The integration unit can also analyze past integration results and select an optimal integration method. For example, the integration unit improves the integration algorithm by referring to past integration results. In this way, the accuracy of the integration can be improved by referring to past integration results. Some or all of the above-mentioned processing in the integration unit may be performed using, for example, AI, or may be performed without using AI. For example, the integration unit can input data of past integration results into AI and have the AI ​​improve the accuracy of the integration.

[0044] When integrating data, the integration unit can determine the integration priority based on the time when the data was collected. For example, the integration unit prioritizes integration of the most recent data. The integration unit can also determine the integration priority by referring to past data. For example, the integration unit selects an optimal integration method based on the time when the data was collected. In this way, by determining the integration priority based on the time when the data was collected, the most recent data can be integrated preferentially. Some or all of the above-mentioned processing in the integration unit may be performed using, for example, AI, or may be performed without using AI. For example, the integration unit can input data from the time when the data was collected into AI and have the AI ​​determine the integration priority.

[0045] The integration unit can adjust the order of integration based on the relevance of the data when integrating the data. For example, the integration unit prioritizes integration of highly relevant data. The integration unit can also prioritize integration of less relevant data. For example, the integration unit dynamically adjusts the order of integration based on the relevance of the data. In this way, by adjusting the order of integration based on the relevance of the data, highly relevant data can be integrated preferentially. Some or all of the above-described processing in the integration unit may be performed using, for example, AI, or may be performed without using AI. For example, the integration unit can input the relevance of the data into AI and have the AI ​​adjust the order of integration.

[0046] When integrating data, the integration unit can perform the integration while taking into account attribute information of the data provider. The integration unit determines the priority of integration based on, for example, the reliability of the data provider. The integration unit can also select the optimal integration method by referring to the attribute information of the data provider. For example, the integration unit improves the accuracy of integration by taking into account the attribute information of the data provider. In this way, by taking into account the attribute information of the data provider, highly reliable data can be preferentially integrated. Some or all of the above-mentioned processing in the integration unit may be performed using, for example, AI, or may be performed without using AI. For example, the integration unit can input the attribute information of the data provider into AI and have the AI ​​perform the integration.

[0047] When forecasting a disaster, the generation unit can improve the efficiency of the forecasting algorithm by referring to past disaster data. The generation unit, for example, optimizes the forecasting algorithm based on past disaster data. The generation unit can also analyze past disaster data and select the most accurate forecasting algorithm. For example, the generation unit improves the forecasting algorithm by referring to past disaster data. In this way, the accuracy of the forecasting algorithm can be improved by referring to past disaster data. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input past disaster data into the generation AI and cause the generation AI to optimize the forecasting algorithm.

[0048] The generation unit can analyze the mechanism by which an earthquake occurs and predict the probability of occurrence with high accuracy when forecasting a disaster. The generation unit, for example, analyzes the mechanism by which an earthquake occurs and predicts the probability of occurrence with high accuracy. The generation unit can also select the most accurate forecasting algorithm based on the mechanism by which an earthquake occurs. For example, the generation unit improves the forecasting algorithm by referring to the mechanism by which an earthquake occurs. In this way, by analyzing the mechanism by which an earthquake occurs, the probability of occurrence can be predicted with high accuracy. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input data on the mechanism by which an earthquake occurs into the generation AI and have the generation AI predict the probability of occurrence.

[0049] When forecasting a disaster, the generation unit can weight the forecast based on the frequency of earthquakes. The generation unit weights the forecast based on, for example, the frequency of earthquakes. The generation unit can also select the most accurate forecast algorithm by referring to the frequency of earthquakes. For example, the generation unit improves the forecast algorithm based on the frequency of earthquakes. In this way, by weighting the forecast based on the frequency of earthquakes, the accuracy of the forecast can be improved. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input data on the frequency of earthquakes into the generation AI and have the generation AI perform the weighting of the forecast.

[0050] The generation unit can make a disaster forecast taking into account the geographical distribution of earthquakes. For example, the generation unit makes a disaster forecast for a specific region based on the geographical distribution of earthquakes. The generation unit can also make a forecast for the region that will be most affected by referring to the geographical distribution of earthquakes. For example, the generation unit improves the accuracy of the forecast based on the geographical distribution of earthquakes. In this way, by taking the geographical distribution of earthquakes into consideration, a disaster forecast for a specific region can be made with high accuracy. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the generation unit can input data on the geographical distribution of earthquakes into the generation AI and have the generation AI execute the forecast.

[0051] The generation unit can improve the accuracy of the forecast by referring to related literature when forecasting a disaster. The generation unit, for example, optimizes the forecast algorithm based on the related literature. The generation unit can also select the most accurate forecast algorithm by referring to the related literature. For example, the generation unit improves the forecast algorithm by referring to the related literature. In this way, the accuracy of the forecast algorithm can be improved by referring to the related literature. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the generation unit can input data from the related literature into the generation AI and cause the generation AI to optimize the forecast algorithm.

[0052] The generation unit can make a forecast taking into account the market value of an earthquake when making a disaster forecast. The generation unit, for example, determines the priority of the forecast based on the market value of the earthquake. The generation unit can also make a forecast for the most affected area by referring to the market value of the earthquake. For example, the generation unit improves the forecast algorithm based on the market value of the earthquake. This allows for prioritized forecasting of the most affected area by taking the market value of the earthquake into consideration. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the generation unit can input data on the market value of the earthquake into the generation AI and have the generation AI execute the forecast.

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

[0054] When collecting data from an Earth observation satellite, the collection unit can evaluate the reliability of the data and prioritize collection of highly reliable data. For example, the collection unit evaluates reliability based on the operation history of the observation satellite and the consistency of the data. The collection unit can also adjust the frequency of data collection based on the reliability of the data. For example, highly reliable data is collected more frequently, and less reliable data is collected less frequently. This allows the collection unit to prioritize collection of highly reliable data, thereby improving the accuracy of the entire system.

[0055] When collecting data from an Earth observation satellite, the collection unit can apply different collection methods depending on the type of data. For example, meteorological data is collected in real time, and topographical data is collected periodically. The collection unit can also determine collection priorities depending on the type of data. For example, meteorological data is collected with priority when a disaster occurs, and topographical data is collected with priority during normal times. This allows the collection unit to achieve efficient data collection by applying the optimal collection method depending on the type of data.

[0056] When integrating collected data, the integration unit can evaluate the accuracy of the data and prioritize integration of data with high accuracy. For example, the integration unit evaluates accuracy based on the source and method of data acquisition. The integration unit can also adjust the level of detail of integration based on the accuracy of the data. For example, data with high accuracy is integrated in detail, and data with low accuracy is integrated in a simplified manner. In this way, the integration unit can create a detailed Earth model by prioritizing integration of data with high accuracy.

[0057] When making a disaster prediction, the generation unit can improve the accuracy of the prediction by referring to past disaster data. For example, the generation unit analyzes the mechanism of earthquake occurrence based on past earthquake data and predicts the probability of occurrence. The generation unit can also evaluate the risk of typhoons and floods occurring based on past weather data. In this way, the generation unit can improve the accuracy of disaster prediction by referring to past disaster data.

[0058] When making disaster predictions, the generation unit analyzes the mechanism by which earthquakes occur and can predict the probability of occurrence with high accuracy. For example, the generation unit analyzes the mechanism by which earthquakes occur based on geological data and fault data and predicts the probability of earthquakes occurring. The generation unit can also select the most accurate forecasting algorithm based on the mechanism by which earthquakes occur. In this way, the generation unit can predict the probability of earthquakes occurring with high accuracy by analyzing the mechanism by which earthquakes occur.

[0059] The processing flow of the first embodiment will be briefly explained below.

[0060] Step 1: The collection unit collects data from Earth observation satellites. For example, it collects data from meteorological and environmental observation satellites, and integrates data from multiple Earth observation satellites to create a comprehensive data set. Specifically, it collects temperature and precipitation data from meteorological satellites, and topography and vegetation data from environmental observation satellites. Step 2: The integration unit integrates the data collected by the collection unit and creates a virtual Earth environment. For example, it integrates meteorological data and topographical data and recreates them in the virtual environment. It also integrates differences in observation technology and operators to create a detailed Earth model. Specifically, it performs meteorological simulations based on meteorological data and creates a topographical model based on topographical data. Step 3: The Generation Unit performs disaster predictions based on the virtual Earth environment constructed by the Integration Unit. The Generation Unit uses generation AI to accurately predict earthquakes and other disasters. Specifically, it analyzes the mechanisms by which earthquakes occur and predicts the probability of earthquakes occurring. It also predicts the occurrence of typhoons and floods based on meteorological data, predicts the path of typhoons, and assesses the risk of floods.

[0061] (Example 2) A system according to an embodiment of the present invention utilizes observation images and data acquired from Earth observation satellites to build a Metaverse Earth, thereby achieving high-precision disaster forecasting. This system collects observation images and data acquired from Earth observation satellites and uses the collected data as training data to build a Metaverse Earth. Metaverse Earth recreates a virtual Earth environment, integrating differences in observation technologies and operator differences. Furthermore, a generative AI based on conventional forecasting methods is used to improve the accuracy of disaster forecasts, such as earthquakes. For example, observation images and data acquired from Earth observation satellites are collected. Data from multiple Earth observation satellites is integrated to create a comprehensive dataset. For example, data from meteorological and environmental observation satellites can be collected and integrated to obtain more detailed information about the Earth's environment. Next, a Metaverse Earth is built using the collected data as training data. Metaverse Earth recreates a virtual Earth environment, integrating differences in observation technologies and operator differences. For example, meteorological data and topographical data can be integrated and reproduced in a virtual environment to create a detailed Earth model. Furthermore, a generative AI based on conventional forecasting methods is used to improve the accuracy of disaster forecasts, such as earthquakes. The Generative AI performs simulations on Metaverse Earth to predict the occurrence of disasters such as earthquakes. For example, it can analyze the mechanisms that cause earthquakes and predict the probability of earthquakes with high accuracy. This is expected to contribute to the contributions of local governments and the safety and security of citizens. This will enable the system to achieve higher accuracy in disaster forecasts, which will contribute to the contributions of local governments and the safety and security of citizens. For example, higher accuracy in disaster forecasts will enable local governments to respond more quickly and citizens to strengthen their disaster preparedness. Furthermore, by utilizing Metaverse Earth, differences in observation technology and differences between operators can be integrated, making it possible to utilize comprehensive data.

[0062] A disaster forecasting system according to an embodiment includes a collection unit, an integration unit, and a generation unit. The collection unit collects data from earth observation satellites. The collection unit can collect data from, for example, meteorological satellites and environmental observation satellites. The collection unit can also integrate data from multiple earth observation satellites to create a comprehensive data set. For example, the collection unit can collect temperature data and precipitation data from meteorological satellites and topography data and vegetation data from environmental observation satellites. The integration unit integrates the data collected by the collection unit to construct a virtual earth environment. The integration unit can, for example, integrate meteorological data and topography data and reproduce them in the virtual environment. The integration unit can also integrate differences in observation technology and operator differences to create a detailed earth model. For example, the integration unit can perform weather simulations based on meteorological data and create a topography model based on topography data. The generation unit performs disaster predictions based on the virtual earth environment constructed by the integration unit. The generation unit uses generation AI to accurately predict earthquakes and other disasters. For example, the generation unit can analyze the mechanisms behind earthquakes and predict the probability of earthquake occurrence. The generation unit can also predict the occurrence of typhoons and floods based on meteorological data. For example, the generation unit can predict the path of a typhoon based on meteorological data and evaluate the risk of flooding. This allows the disaster forecasting system according to the embodiment to integrate data from Earth observation satellites and achieve highly accurate disaster forecasts. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can perform disaster predictions using a generation AI model that receives the virtual Earth environment constructed by the integration unit as input and outputs a disaster prediction.

[0063] The collection unit can collect data from multiple earth observation satellites. The collection unit can collect data from, for example, meteorological satellites and environmental observation satellites. For example, the collection unit collects temperature data and precipitation data from meteorological satellites. The collection unit can also collect topography data and vegetation data from environmental observation satellites. For example, the collection unit integrates data from multiple earth observation satellites to create a comprehensive data set. In this way, a comprehensive data set can be created by collecting data from multiple earth observation satellites. 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 data from multiple earth observation satellites into AI and have the AI ​​collect the data.

[0064] The integrating unit can integrate the collected data and recreate a virtual Earth environment. The integrating unit can integrate, for example, weather data and terrain data and recreate the environment in a virtual environment. For example, the integrating unit performs a weather simulation based on the weather data and creates a terrain model based on the terrain data. The integrating unit can also integrate differences in observation technology and differences between operators to create a detailed Earth model. For example, the integrating unit performs a weather simulation based on the weather data and creates a terrain model based on the terrain data. In this way, a detailed Earth model can be created by integrating the collected data and recreating a virtual Earth environment. Some or all of the above-described processing in the integrating unit can be performed using, for example, AI, or without AI. For example, the integrating unit can input the collected data into AI and have the AI ​​integrate the data.

[0065] The generation unit can use the generation AI to perform highly accurate predictions of earthquakes and other disasters. The generation unit can, for example, use the generation AI to perform highly accurate predictions of earthquakes and other disasters. For example, the generation unit analyzes the mechanism of earthquake occurrence and predicts the probability of an earthquake occurring. The generation unit can also predict the occurrence of typhoons and floods based on meteorological data. For example, the generation unit predicts the path of a typhoon based on meteorological data and evaluates the risk of flood occurrence. In this way, the use of the generation AI improves the accuracy of disaster forecasts. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the generation unit can perform disaster predictions using a generation AI model that inputs the virtual Earth environment constructed by the integration unit and outputs a disaster prediction.

[0066] The generation unit can analyze the mechanism by which an earthquake occurs and predict the probability of an earthquake occurring. The generation unit, for example, analyzes the mechanism by which an earthquake occurs and predicts the probability of an earthquake occurring. For example, the generation unit analyzes the mechanism by which an earthquake occurs based on geological data and fault data and predicts the probability of an earthquake occurring. The generation unit can also select the most accurate forecasting algorithm based on the mechanism by which an earthquake occurs. For example, the generation unit improves the forecasting algorithm by referring to the mechanism by which an earthquake occurs. In this way, by analyzing the mechanism by which an earthquake occurs, the probability of an earthquake occurring can be predicted with high accuracy. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input geological data and fault data into the generation AI and cause the generation AI to predict the probability of an earthquake occurring.

[0067] The integration unit can integrate weather data and terrain data and reproduce them in a virtual environment. The integration unit can, for example, integrate weather data and terrain data and reproduce them in a virtual environment. For example, the integration unit performs a weather simulation based on the weather data and creates a terrain model based on the terrain data. The integration unit can also integrate differences in observation technology and differences between operators to create a detailed earth model. For example, the integration unit performs a weather simulation based on the weather data and creates a terrain model based on the terrain data. In this way, by integrating the weather data and terrain data, a more detailed virtual environment can be reproduced. Some or all of the above-mentioned processing in the integration unit may be performed using, for example, AI, or may be performed without using AI. For example, the integration unit can input weather data and terrain data into AI and have the AI ​​integrate the data.

[0068] The collection unit can estimate the user's emotional state and adjust the timing of data collection based on the estimated user's emotional state. For example, if the user is feeling stressed, the collection unit can reduce the frequency of data collection to reduce the load on the system. Furthermore, if the user is relaxed, the collection unit can increase the frequency of data collection to acquire more detailed data. For example, if the user is in a hurry, the collection unit can speed up the timing of data collection and immediately acquire the necessary data. This can reduce the load on the system by adjusting the timing of data collection 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, 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 emotional data into an AI and have the AI ​​adjust the timing of data collection.

[0069] The collection unit can analyze past observation data and select an efficient data collection method. The collection unit, for example, selects the most efficient data collection method based on past observation data. For example, the collection unit selects the optimal data collection method for a specific time period from past observation data. The collection unit can also analyze past observation data and select the most accurate data collection method. For example, the collection unit selects the most efficient data collection method based on past observation data. In this way, the most efficient data collection method can be selected by analyzing past observation 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 past observation data into AI and have the AI ​​select an efficient data collection method.

[0070] The collection unit can perform filtering based on the type and location of the observation satellite when collecting data. The collection unit, for example, collects only specific data based on the type of observation satellite. For example, the collection unit collects only data from a specific region based on the location of the observation satellite. The collection unit can also collect the most relevant data based on the type and location of the observation satellite. For example, the collection unit collects only specific data based on the type of observation satellite. In this way, by filtering based on the type and location of the observation satellite, the most relevant data can be collected. 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 data on the type and location of the observation satellite into AI and have the AI ​​perform the filtering.

[0071] The collection unit can select an efficient collection means according to the operational status of the observation satellite when collecting data. The collection unit selects the optimal data collection means based on, for example, the operational status of the observation satellite. For example, the collection unit monitors the operational status of the observation satellite in real time and selects the optimal collection means. The collection unit can also dynamically change the data collection means according to the operational status of the observation satellite. For example, the collection unit selects the optimal data collection means based on the operational status of the observation satellite. This enables efficient data collection by selecting the optimal collection means according to the operational status of the observation satellite. 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 data on the operational status of the observation satellite into AI and have the AI ​​select an efficient collection means.

[0072] The collection unit can estimate the user's emotional state and determine the priority of data to be collected based on the estimated emotional state of the user. For example, when the user is feeling stressed, the collection unit prioritizes collecting important data. Furthermore, when the user is relaxed, the collection unit can also prioritize collecting detailed data. For example, when the user is in a hurry, the collection unit prioritizes collecting data that can be collected quickly. Thus, by determining the priority of data to be collected according to the user's emotions, important data can be collected preferentially. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may 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 may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotional data into an AI and have the AI ​​determine the priority of the data.

[0073] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the geographical location information of the observation satellite. The collection unit, for example, prioritizes collecting data for a specific region based on the geographical location information of the observation satellite. For example, the collection unit prioritizes collecting the most relevant data by taking into account the geographical location information of the observation satellite. The collection unit can also prioritize collecting highly relevant data during a specific time period based on the geographical location information of the observation satellite. For example, the collection unit prioritizes collecting data for a specific region based on the geographical location information of the observation satellite. This makes it possible to prioritize collecting highly relevant data by taking into account the geographical location information of the observation satellite. 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 data on the geographical location information of the observation satellite into AI and cause the AI ​​to collect highly relevant data.

[0074] The collection unit can analyze the operation history of the observation satellite and collect relevant data when collecting data. The collection unit, for example, collects the most relevant data based on the operation history of the observation satellite. For example, the collection unit analyzes the operation history of the observation satellite and collects data related to a specific time period. The collection unit can also select the optimal data collection method by referring to the operation history of the observation satellite. For example, the collection unit collects the most relevant data based on the operation history of the observation satellite. In this way, the most relevant data can be collected by analyzing the operation history of the observation satellite. 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 data on the operation history of the observation satellite into AI and have the AI ​​collect the relevant data.

[0075] The collection unit can customize the collection method by reflecting past feedback when collecting data. The collection unit, for example, customizes the optimal data collection method based on past feedback. For example, the collection unit adjusts the timing of data collection by reflecting past feedback. The collection unit can also determine the priority of data collection by referring to past feedback. For example, the collection unit customizes the optimal data collection method based on past feedback. In this way, the optimal data collection method can be customized by reflecting past feedback. 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 data of past feedback into AI and have the AI ​​customize the collection method.

[0076] The integration unit can estimate the user's emotional state and adjust the data integration method based on the estimated emotional state of the user. For example, if the user is stressed, the integration unit can provide a simple integration method. Alternatively, if the user is relaxed, the integration unit can provide a detailed integration method. For example, if the user is in a hurry, the integration unit can provide a quick integration method. This allows the data integration method to be adjusted according to the user's emotions, thereby providing an integration method suitable for the user. 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 integration unit can be performed using, for example, an AI, or without an AI. For example, the integration unit can input the user's emotional data into an AI and have the AI ​​adjust the data integration method.

[0077] The integration unit can adjust the level of integration detail based on the importance of the data when integrating data. For example, the integration unit prioritizes integration of data with high importance and provides detailed information. The integration unit can also simplify and integrate data with low importance. For example, the integration unit dynamically adjusts the level of integration detail based on the importance of the data. As a result, important data can be integrated in detail by adjusting the level of integration detail based on the importance of the data. Some or all of the above-mentioned processing in the integration unit may be performed using, for example, AI, or may be performed without using AI. For example, the integration unit can input the importance of the data to AI and have the AI ​​adjust the level of integration detail.

[0078] When integrating data, the integration unit can apply different integration algorithms depending on the data category. For example, the integration unit integrates meteorological data and topographical data using different algorithms. The integration unit can also integrate observation data and environmental data using different algorithms. For example, the integration unit selects an optimal integration algorithm depending on the data category. This enables optimal integration by applying different integration algorithms depending on the data category. Some or all of the above-mentioned processing in the integration unit may be performed using, for example, AI, or may be performed without using AI. For example, the integration unit can input the data category into AI and have the AI ​​select the integration algorithm.

[0079] When integrating data, the integration unit can improve the accuracy of the integration by referring to past integration results. The integration unit, for example, improves the accuracy of the integration based on past integration results. The integration unit can also analyze past integration results and select an optimal integration method. For example, the integration unit improves the integration algorithm by referring to past integration results. In this way, the accuracy of the integration can be improved by referring to past integration results. Some or all of the above-mentioned processing in the integration unit may be performed using, for example, AI, or may be performed without using AI. For example, the integration unit can input data of past integration results into AI and have the AI ​​improve the accuracy of the integration.

[0080] The integration unit can estimate the user's emotional state and adjust the display method of the integrated data based on the estimated emotional state of the user. For example, if the user is feeling stressed, the integration unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the integration unit can also provide a display method that includes detailed information. For example, if the user is in a hurry, the integration unit can provide a display method that focuses on the main points. This allows the display method of the integrated data to be adjusted according to the user's emotions, thereby providing a display method that is suitable for the user. 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 integration unit can be performed using, for example, an AI, or without an AI. For example, the integration unit can input the user's emotional data into an AI and have the AI ​​adjust the display method.

[0081] When integrating data, the integration unit can determine the integration priority based on the time when the data was collected. For example, the integration unit prioritizes integration of the most recent data. The integration unit can also determine the integration priority by referring to past data. For example, the integration unit selects an optimal integration method based on the time when the data was collected. In this way, by determining the integration priority based on the time when the data was collected, the most recent data can be integrated preferentially. Some or all of the above-mentioned processing in the integration unit may be performed using, for example, AI, or may be performed without using AI. For example, the integration unit can input data from the time when the data was collected into AI and have the AI ​​determine the integration priority.

[0082] The integration unit can adjust the order of integration based on the relevance of the data when integrating the data. For example, the integration unit prioritizes integration of highly relevant data. The integration unit can also prioritize integration of less relevant data. For example, the integration unit dynamically adjusts the order of integration based on the relevance of the data. In this way, by adjusting the order of integration based on the relevance of the data, highly relevant data can be integrated preferentially. Some or all of the above-described processing in the integration unit may be performed using, for example, AI, or may be performed without using AI. For example, the integration unit can input the relevance of the data into AI and have the AI ​​adjust the order of integration.

[0083] When integrating data, the integration unit can perform the integration while taking into account attribute information of the data provider. The integration unit determines the priority of integration based on, for example, the reliability of the data provider. The integration unit can also select the optimal integration method by referring to the attribute information of the data provider. For example, the integration unit improves the accuracy of integration by taking into account the attribute information of the data provider. In this way, by taking into account the attribute information of the data provider, highly reliable data can be preferentially integrated. Some or all of the above-mentioned processing in the integration unit may be performed using, for example, AI, or may be performed without using AI. For example, the integration unit can input the attribute information of the data provider into AI and have the AI ​​perform the integration.

[0084] The generation unit can estimate the user's emotional state and adjust the display method of the disaster forecast based on the estimated emotional state of the user. For example, if the user is feeling stressed, the generation unit provides a simple, highly visible display method. Furthermore, if the user is relaxed, the generation unit can also provide a display method that includes detailed information. For example, if the user is in a hurry, the generation unit provides a display method that focuses on the main points. This allows the display method of the disaster forecast to be adjusted according to the user's emotions, thereby providing a display method that is suitable for the user. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may 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 generation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the generation unit can input the user's emotional data into the generation AI and have the generation AI adjust the display method.

[0085] When forecasting a disaster, the generation unit can improve the efficiency of the forecasting algorithm by referring to past disaster data. The generation unit, for example, optimizes the forecasting algorithm based on past disaster data. The generation unit can also analyze past disaster data and select the most accurate forecasting algorithm. For example, the generation unit improves the forecasting algorithm by referring to past disaster data. In this way, the accuracy of the forecasting algorithm can be improved by referring to past disaster data. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input past disaster data into the generation AI and cause the generation AI to optimize the forecasting algorithm.

[0086] The generation unit can analyze the mechanism by which an earthquake occurs and predict the probability of occurrence with high accuracy when forecasting a disaster. The generation unit, for example, analyzes the mechanism by which an earthquake occurs and predicts the probability of occurrence with high accuracy. The generation unit can also select the most accurate forecasting algorithm based on the mechanism by which an earthquake occurs. For example, the generation unit improves the forecasting algorithm by referring to the mechanism by which an earthquake occurs. In this way, by analyzing the mechanism by which an earthquake occurs, the probability of occurrence can be predicted with high accuracy. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input data on the mechanism by which an earthquake occurs into the generation AI and have the generation AI predict the probability of occurrence.

[0087] When forecasting a disaster, the generation unit can weight the forecast based on the frequency of earthquakes. The generation unit weights the forecast based on, for example, the frequency of earthquakes. The generation unit can also select the most accurate forecast algorithm by referring to the frequency of earthquakes. For example, the generation unit improves the forecast algorithm based on the frequency of earthquakes. In this way, by weighting the forecast based on the frequency of earthquakes, the accuracy of the forecast can be improved. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input data on the frequency of earthquakes into the generation AI and have the generation AI perform the weighting of the forecast.

[0088] The generation unit can estimate the user's emotional state and determine the priority of disaster forecasts based on the estimated emotional state of the user. For example, if the user is feeling stressed, the generation unit can prioritize displaying disaster forecasts with high importance. Furthermore, if the user is relaxed, the generation unit can also prioritize displaying detailed disaster forecasts. For example, if the user is in a hurry, the generation unit can prioritize displaying disaster forecasts that can be checked quickly. Thus, by determining the priority of disaster forecasts according to the user's emotions, important disaster forecasts can be prioritized and displayed. 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 generation unit can be performed using, for example, the generation AI. For example, the generation unit can input the user's emotional data into the generation AI and have the generation AI determine the priority.

[0089] The generation unit can make a disaster forecast taking into account the geographical distribution of earthquakes. For example, the generation unit makes a disaster forecast for a specific region based on the geographical distribution of earthquakes. The generation unit can also make a forecast for the region that will be most affected by referring to the geographical distribution of earthquakes. For example, the generation unit improves the accuracy of the forecast based on the geographical distribution of earthquakes. In this way, by taking the geographical distribution of earthquakes into consideration, a disaster forecast for a specific region can be made with high accuracy. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the generation unit can input data on the geographical distribution of earthquakes into the generation AI and have the generation AI execute the forecast.

[0090] The generation unit can improve the accuracy of the forecast by referring to related literature when forecasting a disaster. The generation unit, for example, optimizes the forecast algorithm based on the related literature. The generation unit can also select the most accurate forecast algorithm by referring to the related literature. For example, the generation unit improves the forecast algorithm by referring to the related literature. In this way, the accuracy of the forecast algorithm can be improved by referring to the related literature. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the generation unit can input data from the related literature into the generation AI and cause the generation AI to optimize the forecast algorithm.

[0091] The generation unit can make a forecast taking into account the market value of an earthquake when making a disaster forecast. The generation unit, for example, determines the priority of the forecast based on the market value of the earthquake. The generation unit can also make a forecast for the most affected area by referring to the market value of the earthquake. For example, the generation unit improves the forecast algorithm based on the market value of the earthquake. This allows for prioritized forecasting of the most affected area by taking the market value of the earthquake into consideration. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the generation unit can input data on the market value of the earthquake into the generation AI and have the generation AI execute the forecast. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, integration unit, and generation unit described above may be realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit may collect data from an Earth observation satellite using the camera 42 and communication I / F 44 of the smart device 14. The integration unit may be realized by the specific processing unit 290 of the data processing device 12, and integrates the collected data to construct a virtual Earth environment. The generation unit may be realized, for example, by the specific processing unit 290 of the data processing device 12, and performs disaster prediction using a generation AI. Each of the collection unit, integration unit, and generation unit may also be realized, for example, by the control unit 46A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, integration unit, and generation unit described above may be realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit may collect data from Earth observation satellites using the camera 42 and communication I / F 44 of the smart glasses 214. The integration unit may be realized by the specific processing unit 290 of the data processing device 12, and integrates the collected data to construct a virtual Earth environment. The generation unit may be realized, for example, by the specific processing unit 290 of the data processing device 12, and performs disaster prediction using a generation AI. Each of the collection unit, integration unit, and generation unit may also be realized, for example, by the control unit 46A of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, integration unit, and generation unit described above is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit can collect data from Earth observation satellites using the camera 42 and communication I / F 44 of the headset terminal 314. The integration unit is realized by the specific processing unit 290 of the data processing device 12 and integrates the collected data to construct a virtual Earth environment. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and performs disaster prediction using a generation AI. Each of the collection unit, integration unit, and generation unit may also be realized, for example, by the control unit 46A of the headset terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, integration unit, and generation unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit can collect data from an Earth observation satellite using the camera 42 and communication I / F 44 of the robot 414. The integration unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and integrates the collected data to construct a virtual Earth environment. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and performs disaster prediction using a generation AI. Each of the collection unit, integration unit, and generation unit may also be realized, for example, by the control unit 46A of the robot 414.

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

[0093] When collecting data from an Earth observation satellite, the collection unit can evaluate the reliability of the data and prioritize collection of highly reliable data. For example, the collection unit evaluates reliability based on the operation history of the observation satellite and the consistency of the data. The collection unit can also adjust the frequency of data collection based on the reliability of the data. For example, highly reliable data is collected more frequently, and less reliable data is collected less frequently. This allows the collection unit to prioritize collection of highly reliable data, thereby improving the accuracy of the entire system.

[0094] When collecting data from an Earth observation satellite, the collection unit can apply different collection methods depending on the type of data. For example, meteorological data is collected in real time, and topographical data is collected periodically. The collection unit can also determine collection priorities depending on the type of data. For example, meteorological data is collected with priority when a disaster occurs, and topographical data is collected with priority during normal times. This allows the collection unit to achieve efficient data collection by applying the optimal collection method depending on the type of data.

[0095] When integrating collected data, the integration unit can evaluate the accuracy of the data and prioritize integration of data with high accuracy. For example, the integration unit evaluates accuracy based on the source and method of data acquisition. The integration unit can also adjust the level of detail of integration based on the accuracy of the data. For example, data with high accuracy is integrated in detail, and data with low accuracy is integrated in a simplified manner. In this way, the integration unit can create a detailed Earth model by prioritizing integration of data with high accuracy.

[0096] When making a disaster prediction, the generation unit can improve the accuracy of the prediction by referring to past disaster data. For example, the generation unit analyzes the mechanism of earthquake occurrence based on past earthquake data and predicts the probability of occurrence. The generation unit can also evaluate the risk of typhoons and floods occurring based on past weather data. In this way, the generation unit can improve the accuracy of disaster prediction by referring to past disaster data.

[0097] When making disaster predictions, the generation unit analyzes the mechanism by which earthquakes occur and can predict the probability of occurrence with high accuracy. For example, the generation unit analyzes the mechanism by which earthquakes occur based on geological data and fault data and predicts the probability of earthquakes occurring. The generation unit can also select the most accurate forecasting algorithm based on the mechanism by which earthquakes occur. In this way, the generation unit can predict the probability of earthquakes occurring with high accuracy by analyzing the mechanism by which earthquakes occur.

[0098] The collection unit can estimate the user's emotional state and adjust the timing of data collection based on the estimated user's emotional state. For example, if the user is feeling stressed, the frequency of data collection can be reduced to reduce the load on the system. Alternatively, if the user is relaxed, the frequency of data collection can be increased to obtain more detailed data. In this way, the collection unit can adjust the timing of data collection according to the user's emotions, thereby efficiently collecting necessary data while reducing the load on the system.

[0099] The integration unit can estimate the user's emotional state and adjust the data integration method based on the estimated emotional state of the user. For example, if the user is feeling stressed, a simple integration method can be provided. Alternatively, if the user is relaxed, a detailed integration method can be provided. In this way, the integration unit can adjust the data integration method according to the user's emotions and provide an integration method that is suitable for the user.

[0100] The generation unit can estimate the user's emotional state and adjust the display method of the disaster forecast based on the estimated emotional state of the user. For example, if the user is feeling stressed, a simple, highly visible display method can be provided. Alternatively, if the user is relaxed, a display method including detailed information can be provided. In this way, the generation unit can provide a display method suitable for the user by adjusting the display method of the disaster forecast according to the user's emotions.

[0101] The collection unit can estimate the user's emotional state and determine the priority of data to be collected based on the estimated emotional state of the user. For example, if the user is feeling stressed, it can prioritize collecting data of high importance. Also, if the user is relaxed, it can prioritize collecting detailed data. In this way, the collection unit can prioritize collecting important data by determining the priority of data to be collected according to the user's emotions.

[0102] The generation unit can estimate the user's emotional state and determine the priority of disaster forecasts based on the estimated emotional state of the user. For example, if the user is feeling stressed, disaster forecasts with high importance can be displayed with priority. Also, if the user is relaxed, detailed disaster forecasts can be displayed with priority. In this way, the generation unit can determine the priority of disaster forecasts according to the user's emotions, thereby allowing important disaster forecasts to be displayed with priority.

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

[0104] Step 1: The collection unit collects data from Earth observation satellites. For example, it collects data from meteorological and environmental observation satellites, and integrates data from multiple Earth observation satellites to create a comprehensive data set. Specifically, it collects temperature and precipitation data from meteorological satellites, and topography and vegetation data from environmental observation satellites. Step 2: The integration unit integrates the data collected by the collection unit and creates a virtual Earth environment. For example, it integrates meteorological data and topographical data and recreates them in the virtual environment. It also integrates differences in observation technology and operators to create a detailed Earth model. Specifically, it performs meteorological simulations based on meteorological data and creates a topographical model based on topographical data. Step 3: The Generation Unit performs disaster predictions based on the virtual Earth environment constructed by the Integration Unit. The Generation Unit uses generation AI to accurately predict earthquakes and other disasters. Specifically, it analyzes the mechanisms by which earthquakes occur and predicts the probability of earthquakes occurring. It also predicts the occurrence of typhoons and floods based on meteorological data, predicts the path of typhoons, and assesses the risk of floods.

[0105] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0107] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0108] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0109] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0123] 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.

[0124] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0125] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0139] 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.

[0140] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0141] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0156] 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.

[0157] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] 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."

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] [Explanation of symbols]

[0177] 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 for collecting data from an Earth observation satellite; an integration unit that integrates the data collected by the collection unit to construct a virtual earth environment; a generation unit that performs disaster prediction based on the virtual global environment constructed by the integration unit; Equipped with A system characterized by:

2. The collecting unit Collecting data from multiple Earth observation satellites 2. The system of claim 1.

3. The integration unit Integrating collected data to recreate a virtual Earth environment 2. The system of claim 1.

4. The generation unit Using generative AI to accurately predict earthquakes and other disasters 2. The system of claim 1.

5. The generation unit Analyzing the mechanisms behind earthquakes and predicting their occurrence 2. The system of claim 1.

6. The integration unit Integrating meteorological and terrain data and recreating it in a virtual environment 2. The system of claim 1.

7. The collecting unit Estimating a user's emotional state and adjusting the timing of data collection based on the estimated user's emotional state 2. The system of claim 1.

8. The collecting unit Analyze past observation data and select an efficient data collection method 2. The system of claim 1.

9. The collecting unit When collecting data, filtering is performed based on the type and location of the observation satellite.

2. The system of claim 1.

10. The collecting unit When collecting data, select the most efficient collection method according to the operational status of the observation satellite.

2. The system of claim 1.

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