system

Abstract

We provide the system. [Solution] A data acquisition means for acquiring solar wind data in real time and performing normalization and analysis of said data, A visualization means that generates and visualizes a 3D model based on the analyzed data, A summarization method that uses natural language processing to summarize research literature and extract important information, A hypothesis generation means that generates new hypotheses using past analysis data and summary information, A computation execution means that performs high-speed simulations using quantum computing technology, A system that includes this.

Description

【Technical Field】 【0001】 The technology of the present disclosure relates to a system. 【Background Art】 【0002】 Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. 【Prior Art Documents】 【Patent Documents】 【0003】 【Patent Document 1】 Japanese Patent Application Laid-Open No. 2022-180282 【Summary of the Invention】 【Problems to be Solved by the Invention】 【0004】 In many solar wind-related studies, it is required to efficiently analyze a huge amount of observational data. Conventional methods require a great deal of time and specialized knowledge to analyze these data, imposing a heavy burden on researchers. In addition, it is difficult to predict the solar wind and visualize its trends, which has become a factor hindering the progress of research. Furthermore, it is not easy to quickly grasp the latest research results and generate new hypotheses. By solving these problems, it is necessary to improve the efficiency and accuracy of solar wind research. 【Means for Solving the Problems】 【0005】 This invention provides a system that comprehensively supports the acquisition and analysis of solar wind data, from 3D visualization to automatic summarization of research papers and generation of new hypotheses. First, it acquires data in real time from external observation institutions and performs normalization and analysis using AI. Next, it generates a 3D model based on the analysis results and provides this model in an interactively operable form on the user's terminal. Furthermore, it uses natural language processing to automatically summarize research literature and extract important information, enabling researchers to quickly obtain the information they need. Finally, it uses historical data and summary information to generate new research hypotheses and uses quantum computing technology to perform high-speed simulations, making it possible to predict the complex behavior of the solar wind. This effectively solves conventional problems and improves the efficiency and depth of research. 【0006】 "Solar wind data" refers to observational information about the flow of charged particles emitted from the sun in outer space. 【0007】 "Real-time acquisition" refers to the process of continuously acquiring and updating data in its most up-to-date state. 【0008】 "Normalization" refers to the process of standardizing data into a form that can be compared. 【0009】 "Analysis" is the process of identifying patterns and anomalies based on collected data. 【0010】 A "3D model" is a visual representation that depicts the spatial movement of solar wind in three dimensions. 【0011】 "Visualization" is a technique that visually represents data and information to make it easier to understand. 【0012】 "Natural language processing" is a field of technology that uses computers to analyze and understand human language. 【0013】 "Automatic summarization" is the process of extracting the important parts of a text or document and summarizing them concisely. 【0014】 "Hypothesis generation" is an act of constructing new scientific questions or predictions based on existing information. 【0015】 "Quantum computing technology" is an advanced computing technology that performs data processing based on the principles of quantum mechanics. 【0016】 "Simulation" is a method of modeling real-world phenomena and virtually conducting experiments and observations. 【Brief Description of Drawings】 【0017】 [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when combined with an emotion engine. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when combined with an emotion engine. 【Mode for Carrying Out the Invention】 【0018】 Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings. 【0019】 First, the terms used in the following description will be explained. 【0020】 In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be one arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be one type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), etc. 【0021】 In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor. 【0022】 In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes. 【0023】 In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark). 【0024】 In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or." 【0025】 [First Embodiment] 【0026】 Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment. 【0027】 As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server. 【0028】 The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network). 【0029】 The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52. 【0030】 The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input. 【0031】 The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor. 【0032】 Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54. 【0033】 Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14. 【0034】 As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30. 【0035】 The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. 【0036】 In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48. 【0037】 Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal". 【0038】 The system of this invention integrates the acquisition, analysis, and visualization of solar wind data, the summarization of research literature, the generation of new hypotheses, and simulations using quantum computing technology. Each element is described below. 【0039】 First, the server connects to an API of an external observation agency to acquire solar wind data in real time. The acquired data is sent to a data analysis engine for normalization and analysis. The analysis engine processes the data using machine learning algorithms to identify solar wind trends. 【0040】 Based on these analysis results, the server generates a 3D model. The generated model is sent to the terminal in an interactive format and displayed. Users can manipulate the model on their terminal and visually examine the effects of solar wind. 【0041】 Next, the server accesses research databases on the internet and searches for relevant solar wind research literature. The collected papers are summarized using natural language processing, and the summary information is provided to the terminal. The user can refer to this summary to quickly grasp the latest research trends. 【0042】 Furthermore, the server generates new hypotheses based on the accumulated data and summary information. A generative AI model assists in this process, shaping the questions and hypotheses that researchers wish to present. Users can then use these hypotheses to develop new research. 【0043】 Finally, the terminal connects to the quantum computing platform and sends a request to run the simulation. The server leverages this platform to efficiently process complex calculations and generate simulation results regarding the solar wind. The generated results are sent back to the terminal, allowing the user to perform further analysis and make decisions based on them. 【0044】 This system streamlines each process in solar wind research, reducing the burden on researchers and deepening insights into the solar wind. For example, if a user suspects an unusual pattern in solar activity and wants to quickly verify it, this system allows for a consistent workflow from data acquisition to analysis and verification of simulation results, enabling early verification of that suspicion. 【0045】 The following describes the processing flow. 【0046】 Step 1: 【0047】 The server connects to an API provided by a solar wind observation agency to acquire solar wind data in real time. The acquired data includes information such as the velocity and density of charged particles and magnetic field. 【0048】 Step 2: 【0049】 The server normalizes the acquired data and sends it to the analysis engine. The analysis engine uses machine learning algorithms to identify trends and anomaly patterns in the data. 【0050】 Step 3: 【0051】 The server generates a 3D model based on the analysis results. This model constructs the spatial data necessary to visualize the dynamics of the solar wind. 【0052】 Step 4: 【0053】 The server renders the generated 3D model and sends it to the terminal. The terminal displays the model in real time, allowing the user to interact with it. 【0054】 Step 5: 【0055】 The server queries publicly available online research databases to search for and collect relevant solar wind research papers. 【0056】 Step 6: 【0057】 The server automatically summarizes collected papers using natural language processing and extracts important information. 【0058】 Step 7: 【0059】 The server sends the summary results to the terminal, allowing users to quickly access the latest research findings. 【0060】 Step 8: 【0061】 The server uses data analysis results and paper summaries as input to generate new research hypotheses. A generative AI model assists in this process. 【0062】 Step 9: 【0063】 The server presents the generated hypotheses to the user, providing them with new research ideas and inspiration. 【0064】 Step 10: 【0065】 The device accesses the quantum computing platform and makes a request to run a solar wind simulation. 【0066】 Step 11: 【0067】 The server leverages quantum computing to run simulations at high speed and generate results that predict the behavior of the solar wind. 【0068】 Step 12: 【0069】 The server sends the simulation results back to the terminal, providing users with the information to perform detailed analysis and make decisions. 【0070】 (Example 1) 【0071】 Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal." 【0072】 Traditional solar wind research has involved dispersed processes such as data acquisition, analysis, visualization, and literature review, each requiring significant time and effort. Furthermore, limited technology was used to support new hypothesis generation and high-speed simulations, making efficient research difficult. This has hindered the acquisition of rapid and in-depth insights into the solar wind. 【0073】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. 【0074】 In this invention, the server includes information acquisition means for acquiring and analyzing solar wind information in real time, model generation means for generating visualization models based on the analyzed information, and information summarization means for summarizing important information from literature using natural language processing. This enables researchers to comprehensively analyze solar wind data, quickly generate hypotheses, and perform high-speed simulations. 【0075】 "Solar wind information" refers to data about the flow of plasma emitted from the sun in space, including information about its characteristics such as velocity, density, and temperature. 【0076】 "Information acquisition means" refers to a series of processes or devices for acquiring data in real time from external observation agencies or centers, and for normalizing or analyzing that data as needed. 【0077】 A "model generation means" is a process or apparatus for creating a three-dimensional visualization model based on analyzed information and constructing it in a form that can be interactively manipulated by the user. 【0078】 An "information summarization tool" is a process or device that utilizes natural language processing technology to extract and summarize only the important information from relevant literature and reports. 【0079】 A "hypothesis generation method" is a process or device that creates new hypotheses based on previously analyzed and summarized information. This process includes a suggestion function that utilizes a generative AI model. 【0080】 "Computational means" refers to a process or apparatus for performing highly accurate and high-speed simulations using quantum computing technology. 【0081】 "Operation-providing means" refers to a process or apparatus that provides an interface and functions to enable a user to interactively manipulate a visualized model. 【0082】 A "hypothesis support tool" is a process or device that uses generative AI models to support researchers in formulating questions and assists in the development of hypotheses. 【0083】 Modes for carrying out the invention 【0084】 The system of this invention integrates the acquisition, analysis, visualization, and hypothesis generation of data related to solar wind. This system consists of three elements: a server, a terminal, and a user, each playing a specific role. 【0085】 First, the server acquires real-time solar wind information from external observation agencies. Specifically, it uses data provision APIs to collect information on solar wind speed and density. This acquired data is normalized and analyzed using Python or other data processing libraries. The server then uses machine learning libraries such as Scikit-learn to analyze this data and predict solar wind trends. 【0086】 Next, the server generates a 3D model based on the analysis results. This modeling process utilizes visualization libraries such as Three.js to construct an interactive 3D model via WebGL. The generated model is sent to the terminal and displayed through the user interface. 【0087】 The terminal displays this interactive 3D model, allowing user manipulation. Users can use this to observe the effects of solar wind in detail. Furthermore, the server accesses research databases on the internet to collect relevant literature information. Using NLP technology, the server summarizes this literature and provides the user with key information. 【0088】 The server also provides functionality to generate new hypotheses by utilizing past analysis data and summary information. This process uses natural language generation with generative AI models to support hypothesis generation. Specifically, users can input prompt sentences to generate hypotheses using generative AI models such as OpenAI's GPT-3. 【0089】 Prompt example: "Analyze new patterns from recent solar wind data and propose related hypotheses." 【0090】 Furthermore, the terminal connects to a quantum computing platform and sends requests to the server to run high-speed simulations. These simulations utilize quantum computing technologies such as IBM Q. The server returns the simulation results to the terminal, providing the user with material for further analysis. 【0091】 This system enables comprehensive data analysis and hypothesis generation regarding solar wind, significantly streamlining the research process. 【0092】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0093】 Step 1: 【0094】 The server obtains solar wind information in real time from external observation agencies. It uses the observation agencies' API endpoints as input. The server sends HTTP requests and receives data such as solar wind speed, density, and temperature in JSON format. The acquired data is stored in a local database. Specifically, the server executes a script that automatically makes API requests at regular intervals. 【0095】 Step 2: 【0096】 The server normalizes the acquired solar wind information and outputs it as preprocessed data. The input is the already acquired raw data. The server uses Python's Pandas and NumPy to scale this data and filter out unnecessary values. Specifically, the server runs a script to remove outliers from the dataset and standardize the data. 【0097】 Step 3: 【0098】 The server performs analysis using pre-processed data. It uses normalized data as input and generates analysis results as output. The server utilizes Scikit-learn and applies machine learning algorithms to analyze solar wind trends and anomalies. Specifically, it performs regression analysis and clustering to detect anomalies. 【0099】 Step 4: 【0100】 The server generates a 3D model based on the analysis results. The input is the data based on the analysis, and the output is the visualized 3D model. The 3D model is generated using the Three.js library, and an interactively viewable model is built using WebGL. Specifically, the analysis results are mapped to each axis of the model, making it operable in a browser. 【0101】 Step 5: 【0102】 The terminal receives 3D models sent from the server and displays them on the user interface. The input is 3D model data, and the output is a visualized interface. The user manipulates the model using a mouse or touch panel to observe specific solar wind phenomena. Specifically, the terminal dynamically renders the model and supports rotation and zooming in response to user input. 【0103】 Step 6: 【0104】 The server accesses research databases to search for relevant literature and generates summaries. The input is a search query, such as keywords, and the output is summarized literature information. Natural language processing algorithms are used to summarize the extracted literature. Specifically, the server uses an NLP library to summarize the main points of the papers and provides them to the user. 【0105】 Step 7: 【0106】 The server generates new hypotheses based on the analyzed data and summary information. The input is the accumulated analyzed data and summary information, and the output is hypothesis information. A generative AI model is used to generate hypotheses based on user prompts. 【0107】 Specifically, when generating a hypothesis, a prompt message such as "Analyze new patterns from recent solar wind data and present related hypotheses" is entered, and the AI ​​generates a new hypothesis. 【0108】 Step 8: 【0109】 The terminal sends a request to the server to connect to the quantum computing platform and run a simulation. The input is the simulation conditions, and the output is the simulation result. Complex calculations are performed using quantum computing technology, and results are generated. Specifically, the terminal sends simulation configuration information to the server, the server receives the quantum computation results and sends them to the terminal, and provides them to the user. 【0110】 (Application Example 1) 【0111】 Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal." 【0112】 There is a need for a system that can quickly predict the impact of solar activity on communication infrastructure, power supply systems, and the environment in urban areas, and help residents and managers take appropriate measures. However, current technology does not enable a unified process from data analysis to hypothesis generation and simulation, making rapid response difficult. 【0113】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. 【0114】 In this invention, the server includes data acquisition means for acquiring data related to solar activity in real time and for standardizing and analyzing the data; display means for generating and visualizing a three-dimensional model; summarization means for summarizing research information using natural language processing and extracting important content; hypothesis generation means for generating new hypotheses using past analysis data and summarized content; computation execution means for performing high-speed simulations using quantum computing technology; and application means for providing environmental predictions in urban development. This makes it possible to quickly and comprehensively analyze the impact of solar activity on urban infrastructure and to obtain information for taking appropriate countermeasures. 【0115】 "Solar activity-related data" refers to information about phenomena such as winds and flares originating from the sun, and is necessary data for evaluating the impact these have on the Earth's environment. 【0116】 "Standardization and analysis" is the process of organizing acquired data into a consistent format and interpreting it as meaningful information using machine learning and statistical methods. 【0117】 A "three-dimensional model" is a three-dimensional representation method for visually grasping information, and it is a model that makes it possible to intuitively understand the effects of solar activity. 【0118】 "Display means" refers to devices or software that allow users to visually confirm information, and functions as an interface. 【0119】 "Natural language processing" is a technology that enables computers to understand and process human language, and it is a method for summarizing documents and extracting information. 【0120】 A "summarization tool" is a system function that extracts important content from literature and information, summarizes it concisely, and provides it to the user. 【0121】 A "hypothesis generation tool" is a technology that automatically generates new research approaches and hypotheses to deepen understanding from accumulated data and analysis results. 【0122】 "Quantum computing technology" is a technology that uses the principles of quantum mechanics to perform calculations that are difficult for ordinary computers to do, quickly and efficiently. 【0123】 "High-speed simulation" refers to the process of rapidly performing simulations that would normally take a long time using quantum computing. 【0124】 "Application methods" refer to specific techniques and systems that use the obtained analysis and simulation results to predict and improve urban environments. 【0125】 The system that implements this application runs on an integrated hardware and software platform, as shown below. 【0126】 The server acquires solar activity-related data in real time from external observation agencies using API communication. This data is formatted into an appropriate format by a standardization and analysis program developed in Python, and then analyzed using the TENSORFLOW® machine learning algorithm. The analysis results are visualized as a three-dimensional model for intuitive understanding on the user's terminal. WebGL or a 3D graphics engine can be used to display the visualization. 【0127】 The terminal further uses natural language processing technology to summarize relevant research literature and extracts key information using software such as KeyBERT. This summarized information is displayed to allow the user to quickly grasp research trends. The user then uses this information to refer to simulation results performed at high speed using quantum computing technology with Qiskit. 【0128】 Generative AI models are used to optimize simulations and generate hypotheses. These AI models assist in generating new research hypotheses based on user prompts. For example, by inputting "Analyze the impact of solar wind on the urban environment in detail and generate a new hypothesis," the AI ​​model will suggest an appropriate hypothesis. In this way, the entire system is applied to urban development and environmental forecasting, helping users make quick and accurate decisions. 【0129】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0130】 Step 1: 【0131】 The server acquires real-time data on solar activity from external observation agencies via API communication. The input is raw data from the observation agencies, and the output is data converted into a format necessary for standardization. Specifically, API requests are sent, and response data is received and recorded. 【0132】 Step 2: 【0133】 The server analyzes the acquired standardized data using a machine learning algorithm based on TensorFlow. The input is the standardized data obtained in step 1, and the output is the analysis result that captures the characteristics of solar activity. Specifically, the server loads a machine learning model, inputs the data into the model, and generates the results. 【0134】 Step 3: 【0135】 The terminal generates a three-dimensional model using WebGL based on the analysis results and displays it visually to the user. The input is the analysis results from step 2, and the output is the three-dimensional visualization model on the terminal screen. Specifically, it uses a three-dimensional graphics engine to render the model and performs processes that enable interactive operation. 【0136】 Step 4: 【0137】 The device utilizes natural language processing technology to collect and summarize relevant research literature. The input is research literature on solar activity obtained from the internet, and the output is summarized key information. Specifically, it uses algorithms such as KeyBERT to extract important parts of the literature text and generate a summary. 【0138】 Step 5: 【0139】 The user generates a new hypothesis using a generative AI model. The input here is a prompt sentence, and the output is the generated hypothesis. Specifically, the AI ​​model generates a hypothesis based on the prompt sentence from the user and presents it to the user. 【0140】 Step 6: 【0141】 The terminal uses Qiskit to perform high-speed quantum computation simulations based on analysis results and hypotheses. The input is the results from steps 2 and 5, and the output is a predictive model based on the quantum simulation. Specifically, it connects to a quantum computing platform, sends the necessary computation requests, and receives and processes the results. 【0142】 Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions. 【0143】 This invention is a system that integrates solar wind data acquisition, analysis, visualization, research literature summarization, new hypothesis generation, and simulation using quantum computing technology, in addition to an emotion engine that recognizes user emotions. The following describes a specific embodiment of the system combining each of these elements. 【0144】 The server uses API communication to acquire solar wind data in real time from external observation agencies. The acquired data is immediately normalized and analyzed using machine learning algorithms. Based on the analysis results, a 3D model is generated and sent to the user's terminal. The terminal displays this model in real time, providing an environment where the user can interactively manipulate the model. 【0145】 Furthermore, the server searches publicly available research databases on the internet and collects relevant solar wind research papers. The collected literature is automatically summarized using natural language processing, and important information is extracted. This information is sent to the terminal to help users quickly obtain the latest academic information. 【0146】 The server generates new research hypotheses based on the simultaneously accumulated data and summary information. These generated hypotheses are presented to the user, providing guidance for further research. 【0147】 The emotion engine, a key feature of this invention, infers emotions from the user's voice, facial expressions, keyboard input, etc., and feeds the results back into the analysis engine and hypothesis generation in real time. This enables the provision of optimal information and hypothesis suggestions tailored to the user's emotional state. Furthermore, in 3D model display, the operability is adjusted based on the user's emotions, and an approach is taken to deepen visual understanding. 【0148】 For example, if a user is feeling anxious about solar activity, the emotion engine recognizes this emotion, and the analysis engine prioritizes providing more detailed information about stability. Furthermore, if the user is interested in a particular situation, the hypothesis generation will present answers to more challenging questions. In this way, the system is capable of flexibly responding to the user's situation. 【0149】 The following describes the processing flow. 【0150】 Step 1: 【0151】 The server sends requests to APIs provided by external observation organizations to obtain solar wind data in real time. 【0152】 Step 2: 【0153】 The server normalizes the acquired data and sends it to the data analysis engine. Here, missing data is imputed and the data is converted into a parseable format. 【0154】 Step 3: 【0155】 The analysis engine on the server uses machine learning algorithms to analyze the data and extract solar wind trends and anomaly patterns. 【0156】 Step 4: 【0157】 The server generates a 3D model based on the analysis results and sends it to the user's terminal as visualization data. 【0158】 Step 5: 【0159】 The device displays the received 3D model and provides a screen that allows the user to interact with it. The viewpoint can be zoomed and moved, allowing for a deeper understanding. 【0160】 Step 6: 【0161】 The server queries the internet for and collects research papers related to solar wind. 【0162】 Step 7: 【0163】 The server uses a natural language processing engine to summarize the collected papers and extract important information. 【0164】 Step 8: 【0165】 The server sends the summary results to the terminal, allowing the user to quickly grasp the necessary information. 【0166】 Step 9: 【0167】 The server generates new research hypotheses based on past analysis data and summary information. These generated hypotheses are then presented to the user for reference. 【0168】 Step 10: 【0169】 The emotion engine module operates on the device and analyzes the user's emotions in real time from voice, facial recognition, keyboard input, and other sources. 【0170】 Step 11: 【0171】 The server receives feedback from the emotion engine and incorporates it into the analysis and hypothesis generation process. As a result, information is prioritized and hypotheses are adjusted to correspond to the user's emotional state. 【0172】 Step 12: 【0173】 The device provides an interface that responds to the user's emotions, based on the results from the emotion engine, improving usability and the quality of visualization. 【0174】 (Example 2) 【0175】 Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal." 【0176】 In the modern era, solar wind activity is a crucial phenomenon that affects Earth, but real-time data acquisition, easily understandable visualization, and the rapid generation of new hypotheses based on that information are not adequately addressed. Furthermore, flexible systems that dynamically change information delivery according to the user's emotional state do not exist. As a result, users face the challenge of not being able to receive the necessary information immediately. 【0177】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. 【0178】 In this invention, the server includes an information acquisition means for acquiring information on solar wind in real time and normalizing and analyzing the information; a visualization means for generating a three-dimensional model based on the analyzed information and visualizing it; a summarization means for summarizing research literature using natural language processing and extracting important information; a hypothesis generation means for generating new hypotheses using past analysis information and summary information; and an emotion recognition means for identifying the user's emotional state and providing feedback to the analysis and hypothesis presentation. This enables real-time visualization based on normalized analysis information, provision of summary information on relevant research results, and optimization of information provision according to the user's emotional state. 【0179】 "Information about the solar wind" refers to data about the flow of charged particles emitted from the sun in outer space. 【0180】 "Real-time acquisition" refers to the ability to acquire the target information almost instantly. 【0181】 "Normalization" refers to the process of transforming data into a unified format or range. 【0182】 "Analysis" refers to methods used to clarify the meaning and trends of data and deepen our understanding of it. 【0183】 A "three-dimensional model" refers to a digital object that is represented three-dimensionally on a computer. 【0184】 "Visualization" refers to a method of representing information visually to aid human understanding. 【0185】 "Natural language processing" refers to the technology that enables computers to understand and process human language. 【0186】 "Summarizing" refers to extracting important information from a text or data and putting it into a concise summary. 【0187】 "Hypothesis generation" refers to the process of devising new theories or explanations based on given data and information. 【0188】 "User's emotional state" refers to the user's current feelings and mood. 【0189】 "Emotion recognition" refers to the use of technology to judge and evaluate various human emotions. 【0190】 This invention is a system that acquires, analyzes, and visualizes information related to solar wind, and further generates new research hypotheses based on that information. This system recognizes the user's emotions in real time and utilizes this information in providing information and generating hypotheses, thereby meeting the user's needs. 【0191】 The server acquires real-time information on solar wind from external observation agencies using API communication. During this process, data is continuously acquired via the observation agencies' APIs, and this information is normalized and analyzed by programs developed in Python and other languages. Machine learning algorithms are used, particularly for detecting anomalies and abnormalities. 【0192】 Based on the analyzed information, the server generates a three-dimensional model using a 3D graphics library such as Three.js. This model is transmitted to the terminal via the internet. A dedicated application is launched on the terminal, allowing the user to interactively manipulate the model and visually view detailed data. 【0193】 Furthermore, the server collects relevant research literature from digital libraries and online research databases and summarizes it using natural language processing techniques. This allows users to quickly obtain important research information. Generative AI models such as OpenAI's GPT model are used to generate new hypotheses based on the summarized information using prompt sentences. 【0194】 A possible example of a specific prompt for generating new hypotheses is, "Please generate a new hypothesis for the next forecast based on solar wind data." The generated hypothesis would then be presented to the user, serving as a guide for further research and analysis. 【0195】 The user's emotional state is analyzed by an emotion recognition engine based on voice, facial expressions, and keyboard input. The results are immediately fed back to the server and reflected in the analysis and hypothesis generation process. For example, when a user is feeling anxious, information that provides a greater sense of security is prioritized and provided. 【0196】 For example, if the system recognizes that a user is experiencing a certain level of anxiety, the analysis engine, in conjunction with the emotion engine, will prioritize providing detailed information regarding the stability of solar activity. In this way, the system of the present invention enables flexible information provision that responds to the user's emotional needs. 【0197】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0198】 Step 1: 【0199】 The server acquires real-time solar wind data using the observation agency's API. The input is raw data sent from the API. This raw data is immediately normalized and converted to a unified format using a script written in Python. The output is normalized data in a parseable format. 【0200】 Step 2: 【0201】 The server applies machine learning algorithms to analyze normalized data. The input is solar wind data normalized in step 1. Specifically, it analyzes patterns of solar activity using an anomaly detection model on the data. The output is a report including conclusions based on the analysis results and whether or not there are potential anomalies. 【0202】 Step 3: 【0203】 The server generates a three-dimensional model based on the analysis results. The input is the analysis results obtained in step 2. 3D graphics are generated using the Three.js library to make the model visually easy to understand. The output is the visualized three-dimensional model sent to the user. 【0204】 Step 4: 【0205】 The terminal displays the 3D model received from the server. The input is the 3D model data generated in step 3. The application on the terminal uses this data to perform interactive operations and visualizations. The output is a user-operable display screen. 【0206】 Step 5: 【0207】 The server searches scientific databases on the web and collects relevant literature. The input consists of keywords related to solar wind. Natural language processing is used to summarize the collected literature and extract important information. The output is the summarized research information. 【0208】 Step 6: 【0209】 The server generates a new research hypothesis based on the summary information. The input is the summary information obtained in step 5. Using the generation AI model, it generates a prompt message such as, "Generate a new hypothesis for the next prediction based on solar wind data." The output is the presentation of the hypothesis. 【0210】 Step 7: 【0211】 The system analyzes the user's emotions using an emotion engine. Inputs include voice, facial expressions, and keyboard input data. Emotion recognition software analyzes this data to determine the user's emotional state. The output is an evaluation of the user's emotional state. 【0212】 Step 8: 【0213】 The server considers the user's emotional state and provides optimal information. The input is the emotional state evaluation result obtained in step 7. The analysis engine constructs and provides information appropriate to the user based on the emotional evaluation. The output is customized information delivery according to the emotion. 【0214】 (Application Example 2) 【0215】 Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal". 【0216】 Solar wind data is a crucial element of space weather information, and its accurate and rapid analysis and visualization are needed in many fields. However, systems that acquire solar wind data in real time and provide information tailored to the user's situation and emotions based on that data are not yet well established. Furthermore, conventional systems often require manual summarization of research information and hypothesis generation, which poses challenges in terms of effort and time. In addition, flexible information provision that takes into account the user's emotional state has not been achieved. 【0217】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. 【0218】 In this invention, the server includes means for acquiring, normalizing, and analyzing solar wind data in real time; means for generating and visualizing a three-dimensional model based on the analyzed data; means for summarizing research literature and extracting important information using natural language processing; and means for inferring the user's emotional state and optimizing information provision using sentiment analysis technology. As a result, users can interact with a three-dimensional model based on the latest solar wind data and receive optimal information based on their emotions. 【0219】 "Solar wind data" refers to information about plasma and magnetic fields emitted from the sun, and is important data for understanding variations in space weather. 【0220】 "Real-time" refers to processing data and information instantly and providing it to users without delay. 【0221】 "Normalization" is the process of standardizing data according to certain criteria, making it easier to analyze. 【0222】 "Analysis" is the process of examining acquired data in detail and extracting meaningful information. 【0223】 A "three-dimensional model" is a computer-generated representation of an object or phenomenon in three-dimensional space, presented in a form that users can visually understand. 【0224】 "Visualization" is the process of representing data and information graphically, making it easier for humans to understand. 【0225】 "Natural language processing" is a technology that allows computers to understand and process human language, and is used for extracting and summarizing information. 【0226】 A "summary" is a concise compilation of key information extracted from a document or data. 【0227】 "Hypothesis generation" is the process of proposing new perspectives and ways of thinking based on existing data, which can serve as a guide for further research and analysis. 【0228】 "Quantum computing technology" is a technology that applies the principles of quantum mechanics to perform calculations at a much faster speed than conventional computers. 【0229】 "Simulation" is the process of modeling real-world phenomena and events on a computer and virtually reproducing them. 【0230】 "Emotion analysis technology" is a technology that infers emotions from a user's voice, facial expressions, input, etc., and processes that information as data. 【0231】 "User terminal" refers to an electronic device that a user directly operates or views, including smartphones and computers. 【0232】 An "interface" refers to the user interface or method of operation that allows a user and a computer system to interact with each other. 【0233】 "Optimizing information provision" means presenting information in the most appropriate format, tailored to the user's needs and circumstances. 【0234】 In order to implement the present invention, it is necessary to construct a system that enables real-time analysis and visualization of solar wind data, as well as the provision of information based on sentiment analysis. 【0235】 The server first acquires solar wind data from external observation agencies. This data is retrieved in real time via API communication, and communication is managed using frameworks such as Flask as needed. The acquired data is normalized using Python and analyzed by machine learning algorithms. Based on the analysis results, a 3D model is generated and visualized using Unity or Three.js. 【0236】 The device provides users with a visualized three-dimensional model. The model is interactive and the interface is adjusted based on the user's emotional state to enhance their understanding. The device uses OpenCV and TensorFlow to analyze the user's voice and facial expressions to infer their emotional state. This allows it to provide research information summarized using natural language processing techniques, tailored to the user's emotions. 【0237】 Users can make situation-appropriate decisions based on the information ultimately provided. This information provision process utilizes a generative AI model, which presents new hypotheses based on historical data and summarized information. Data calculations are automated using DataRobot and TensorFlow to generate hypotheses, which are then presented to the user. 【0238】 As a specific use case, if solar activity could potentially affect communications or power supply, the device would display a notification to the user explaining the impact. A prompt such as, "Please propose a strategy for optimizing public notifications in preparation for communication disruptions caused by solar winds," would be used as input to the generating AI model. 【0239】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0240】 Step 1: 【0241】 The server acquires solar wind data in real time from external observation agencies via APIs. The input is raw data from the API, and the output is normalized data. This process uses Python to standardize the data format and perform data cleaning. 【0242】 Step 2: 【0243】 The server inputs normalized data into a machine learning algorithm for analysis. The input is normalized data, and the output is the analysis result. TensorFlow is used to perform pattern recognition and anomaly detection to generate the analysis result. 【0244】 Step 3: 【0245】 The server generates a three-dimensional model based on the analysis results. The input is the analysis results, and the output is the three-dimensional model. A visually manipulable model is constructed using Unity or Three.js. 【0246】 Step 4: 【0247】 The terminal provides the user with a generated 3D model and enables interactive manipulation. The input is the 3D model, and the output is an interactive interface. The model is updated in real time in response to user input. 【0248】 Step 5: 【0249】 The device acquires the user's voice and facial expressions and performs emotion analysis. The input is the user's visual and audio data, and the output is their emotional state. OpenCV and TensorFlow are used to analyze the data and infer emotions. 【0250】 Step 6: 【0251】 The server optimizes information delivery based on the user's emotional state. The input is the emotional state and analysis results, while the output is optimized information. Natural language processing is used to present summaries and hypotheses tailored to the user's emotions. 【0252】 Step 7: 【0253】 The user receives optimized information and makes situational decisions. The input is information from the server, and the output is the user's decision. The user takes necessary actions, considering hypotheses and actions proposed by the generative AI model. 【0254】 This series of processes creates a system that allows users to receive information tailored to their emotions based on real-time analyzed data, enabling them to make accurate decisions. 【0255】 The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data. 【0256】 Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0257】 In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14. 【0258】 [Second Embodiment] 【0259】 Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment. 【0260】 As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server. 【0261】 The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network). 【0262】 The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52. 【0263】 The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46. 【0264】 Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision). 【0265】 Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner. 【0266】 Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56. 【0267】 The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30. 【0268】 The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. 【0269】 In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48. 【0270】 Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal". 【0271】 The system of this invention integrates the acquisition, analysis, and visualization of solar wind data, the summarization of research literature, the generation of new hypotheses, and simulations using quantum computing technology. Each element is described below. 【0272】 First, the server connects to an API of an external observation agency to acquire solar wind data in real time. The acquired data is sent to a data analysis engine for normalization and analysis. The analysis engine processes the data using machine learning algorithms to identify solar wind trends. 【0273】 Based on these analysis results, the server generates a 3D model. The generated model is sent to the terminal in an interactive format and displayed. Users can manipulate the model on their terminal and visually examine the effects of solar wind. 【0274】 Next, the server accesses research databases on the internet and searches for relevant solar wind research literature. The collected papers are summarized using natural language processing, and the summary information is provided to the terminal. The user can refer to this summary to quickly grasp the latest research trends. 【0275】 Furthermore, the server generates new hypotheses based on the accumulated data and summary information. A generative AI model assists in this process, shaping the questions and hypotheses that researchers wish to present. Users can then use these hypotheses to develop new research. 【0276】 Finally, the terminal connects to the quantum computing platform and sends a request to run the simulation. The server leverages this platform to efficiently process complex calculations and generate simulation results regarding the solar wind. The generated results are sent back to the terminal, allowing the user to perform further analysis and make decisions based on them. 【0277】 This system streamlines each process in solar wind research, reducing the burden on researchers and deepening insights into the solar wind. For example, if a user suspects an unusual pattern in solar activity and wants to quickly verify it, this system allows for a consistent workflow from data acquisition to analysis and verification of simulation results, enabling early verification of that suspicion. 【0278】 The following describes the processing flow. 【0279】 Step 1: 【0280】 The server connects to the API provided by the solar wind observation institution and retrieves solar wind data in real time. The acquired data includes information such as the velocity, density of charged particles, and magnetic field. 【0281】 Step 2: 【0282】 The server normalizes the acquired data and sends it to the analysis engine. In the analysis engine, machine learning algorithms are used to identify data trends and abnormal patterns. 【0283】 Step 3: 【0284】 The server generates a 3D model based on the analysis results. This model constructs the spatial data necessary for visualizing the dynamics of the solar wind. 【0285】 Step 4: 【0286】 The server renders the generated 3D model and sends it to the terminal. The terminal displays the model in real time and enables the user to interactively operate it. 【0287】 Step 5: 【0288】 The server executes a query on the online research database and searches for and collects relevant solar wind research papers. 【0289】 Step 6: 【0290】 The server automatically summarizes the collected papers using natural language processing and extracts important information. 【0291】 Step 7: 【0292】 The server sends the summary results to the terminal so that the user can quickly check the latest research findings. 【0293】 Step 8: 【0294】 The server uses data analysis results and paper summaries as input to generate new research hypotheses. A generative AI model assists in this process. 【0295】 Step 9: 【0296】 The server presents the generated hypotheses to the user, providing them with new research ideas and inspiration. 【0297】 Step 10: 【0298】 The device accesses the quantum computing platform and makes a request to run a solar wind simulation. 【0299】 Step 11: 【0300】 The server leverages quantum computing to run simulations at high speed and generate results that predict the behavior of the solar wind. 【0301】 Step 12: 【0302】 The server sends the simulation results back to the terminal, providing users with the information to perform detailed analysis and make decisions. 【0303】 (Example 1) 【0304】 Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal." 【0305】 Traditional solar wind research has involved dispersed processes such as data acquisition, analysis, visualization, and literature review, each requiring significant time and effort. Furthermore, limited technology was used to support new hypothesis generation and high-speed simulations, making efficient research difficult. This has hindered the acquisition of rapid and in-depth insights into the solar wind. 【0306】 The specific processing by the specific processing unit 290 of the data processing device 12 in Embodiment 1 is realized by the following means. 【0307】 In this invention, the server includes information acquisition means for acquiring and analyzing solar wind information in real time, model generation means for generating a visualization model based on the analyzed information, and information summarization means for summarizing important information from documents using natural language processing. As a result, researchers can integratively analyze solar wind data, quickly generate hypotheses, and execute high-speed simulations. 【0308】 "Solar wind information" is data related to the flow of plasma emitted from the sun in the universe, and includes information such as velocity, density, and temperature. 【0309】 "Information acquisition means" is a series of processes or devices for acquiring data from external observation institutions or centers in real time and normalizing or analyzing the data as necessary. 【0310】 "Model generation means" is a process or device for creating a three-dimensional visualization model based on the analyzed information and constructing it in a form that allows users to interactively operate. 【0311】 "Information summarization means" is a process or device that utilizes natural language processing technology to extract and summarize only important information from related documents and reports. 【0312】 "Hypothesis generation means" is a process or device for creating new hypotheses based on information analyzed and summarized in the past. This process includes a proposal function using a generated AI model. 【0313】 "Calculation means" is a process or device for executing highly accurate and high-speed simulations using quantum computing technology. 【0314】 "Operation-providing means" refers to a process or apparatus that provides an interface and functions to enable a user to interactively manipulate a visualized model. 【0315】 A "hypothesis support tool" is a process or device that uses generative AI models to support researchers in formulating questions and assists in the development of hypotheses. 【0316】 Modes for carrying out the invention 【0317】 The system of this invention integrates the acquisition, analysis, visualization, and hypothesis generation of data related to solar wind. This system consists of three elements: a server, a terminal, and a user, each playing a specific role. 【0318】 First, the server acquires real-time solar wind information from external observation agencies. Specifically, it uses data provision APIs to collect information on solar wind speed and density. This acquired data is normalized and analyzed using Python or other data processing libraries. The server then uses machine learning libraries such as Scikit-learn to analyze this data and predict solar wind trends. 【0319】 Next, the server generates a 3D model based on the analysis results. This modeling process utilizes visualization libraries such as Three.js to construct an interactive 3D model via WebGL. The generated model is sent to the terminal and displayed through the user interface. 【0320】 The terminal displays this interactive 3D model, allowing user manipulation. Users can use this to observe the effects of solar wind in detail. Furthermore, the server accesses research databases on the internet to collect relevant literature information. Using NLP technology, the server summarizes this literature and provides the user with key information. 【0321】 The server also provides functionality to generate new hypotheses by utilizing past analysis data and summary information. This process uses natural language generation with generative AI models to support hypothesis generation. Specifically, users can input prompt sentences to generate hypotheses using generative AI models such as OpenAI's GPT-3. 【0322】 Prompt example: "Analyze new patterns from recent solar wind data and propose related hypotheses." 【0323】 Furthermore, the terminal connects to a quantum computing platform and sends requests to the server to run high-speed simulations. These simulations utilize quantum computing technologies such as IBM Q. The server returns the simulation results to the terminal, providing the user with material for further analysis. 【0324】 This system enables comprehensive data analysis and hypothesis generation regarding solar wind, significantly streamlining the research process. 【0325】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0326】 Step 1: 【0327】 The server obtains solar wind information in real time from external observation agencies. It uses the observation agencies' API endpoints as input. The server sends HTTP requests and receives data such as solar wind speed, density, and temperature in JSON format. The acquired data is stored in a local database. Specifically, the server executes a script that automatically makes API requests at regular intervals. 【0328】 Step 2: 【0329】 The server normalizes the acquired solar wind information and outputs it as preprocessed data. The input is the already acquired raw data. The server uses Python's Pandas and NumPy to scale this data and filter out unnecessary values. Specifically, the server runs a script to remove outliers from the dataset and standardize the data. 【0330】 Step 3: 【0331】 The server performs analysis using pre-processed data. It uses normalized data as input and generates analysis results as output. The server utilizes Scikit-learn and applies machine learning algorithms to analyze solar wind trends and anomalies. Specifically, it performs regression analysis and clustering to detect anomalies. 【0332】 Step 4: 【0333】 The server generates a 3D model based on the analysis results. The input is the data based on the analysis, and the output is the visualized 3D model. The 3D model is generated using the Three.js library, and an interactively viewable model is built using WebGL. Specifically, the analysis results are mapped to each axis of the model, making it operable in a browser. 【0334】 Step 5: 【0335】 The terminal receives 3D models sent from the server and displays them on the user interface. The input is 3D model data, and the output is a visualized interface. The user manipulates the model using a mouse or touch panel to observe specific solar wind phenomena. Specifically, the terminal dynamically renders the model and supports rotation and zooming in response to user input. 【0336】 Step 6: 【0337】 The server accesses research databases to search for relevant literature and generates summaries. The input is a search query, such as keywords, and the output is summarized literature information. Natural language processing algorithms are used to summarize the extracted literature. Specifically, the server uses an NLP library to summarize the main points of the papers and provides them to the user. 【0338】 Step 7: 【0339】 The server generates new hypotheses based on the analyzed data and summary information. The input is the accumulated analyzed data and summary information, and the output is hypothesis information. A generative AI model is used to generate hypotheses based on user prompts. 【0340】 Specifically, when generating a hypothesis, the user is prompted with a message such as, "Analyze new patterns from recent solar wind data and present relevant hypotheses," and the AI ​​then generates new hypotheses. 【0341】 Step 8: 【0342】 The terminal sends a request to the server to connect to the quantum computing platform and run a simulation. The input is the simulation conditions, and the output is the simulation result. Complex calculations are performed using quantum computing technology, and results are generated. Specifically, the terminal sends simulation configuration information to the server, the server receives the quantum computation results and sends them to the terminal, and provides them to the user. 【0343】 (Application Example 1) 【0344】 Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal." 【0345】 There is a need for a system that can quickly predict the impact of solar activity on communication infrastructure, power supply systems, and the environment in urban areas, and help residents and managers take appropriate measures. However, current technology does not enable a unified process from data analysis to hypothesis generation and simulation, making rapid response difficult. 【0346】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. 【0347】 In this invention, the server includes data acquisition means for acquiring data related to solar activity in real time and for standardizing and analyzing the data; display means for generating and visualizing a three-dimensional model; summarization means for summarizing research information using natural language processing and extracting important content; hypothesis generation means for generating new hypotheses using past analysis data and summarized content; computation execution means for performing high-speed simulations using quantum computing technology; and application means for providing environmental predictions in urban development. This makes it possible to quickly and comprehensively analyze the impact of solar activity on urban infrastructure and to obtain information for taking appropriate countermeasures. 【0348】 "Solar activity-related data" refers to information about phenomena such as winds and flares originating from the sun, and is necessary data for evaluating the impact these have on the Earth's environment. 【0349】 "Standardization and analysis" is the process of organizing acquired data into a consistent format and interpreting it as meaningful information using machine learning and statistical methods. 【0350】 A "three-dimensional model" is a three-dimensional representation method for visually grasping information, and it is a model that makes it possible to intuitively understand the effects of solar activity. 【0351】 "Display means" refers to devices or software that allow users to visually confirm information, and functions as an interface. 【0352】 "Natural language processing" is a technology that enables computers to understand and process human language, and it is a method for summarizing documents and extracting information. 【0353】 A "summarization tool" is a system function that extracts important content from literature and information, summarizes it concisely, and provides it to the user. 【0354】 A "hypothesis generation tool" is a technology that automatically generates new research approaches and hypotheses to deepen understanding from accumulated data and analysis results. 【0355】 "Quantum computing technology" is a technology that uses the principles of quantum mechanics to perform calculations that are difficult for ordinary computers to do, quickly and efficiently. 【0356】 "High-speed simulation" refers to the process of rapidly performing simulations that would normally take a long time using quantum computing. 【0357】 "Application methods" refer to specific techniques and systems that use the obtained analysis and simulation results to predict and improve urban environments. 【0358】 The system that implements this application runs on an integrated hardware and software platform, as shown below. 【0359】 The server acquires solar activity-related data in real time from external observation agencies using API communication. This data is formatted into an appropriate format by a standardization and analysis program developed in Python, and then analyzed using TensorFlow machine learning algorithms. The analysis results are visualized as a three-dimensional model for intuitive understanding on the user's terminal. WebGL or a 3D graphics engine can be used to display the visualization. 【0360】 The terminal further uses natural language processing technology to summarize relevant research literature and extracts key information using software such as KeyBERT. This summarized information is displayed to allow the user to quickly grasp research trends. The user then uses this information to refer to simulation results performed at high speed using quantum computing technology with Qiskit. 【0361】 Generative AI models are used to optimize simulations and generate hypotheses. These AI models assist in generating new research hypotheses based on user prompts. For example, by inputting "Analyze the impact of solar wind on the urban environment in detail and generate a new hypothesis," the AI ​​model will suggest an appropriate hypothesis. In this way, the entire system is applied to urban development and environmental forecasting, helping users make quick and accurate decisions. 【0362】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0363】 Step 1: 【0364】 The server acquires real-time data on solar activity from external observation agencies via API communication. The input is raw data from the observation agencies, and the output is data converted into a format necessary for standardization. Specifically, API requests are sent, and response data is received and recorded. 【0365】 Step 2: 【0366】 The server analyzes the acquired standardized data using a machine learning algorithm based on TensorFlow. The input is the standardized data obtained in step 1, and the output is the analysis result that captures the characteristics of solar activity. Specifically, the server loads a machine learning model, inputs the data into the model, and generates the results. 【0367】 Step 3: 【0368】 The terminal generates a three-dimensional model using WebGL based on the analysis results and displays it visually to the user. The input is the analysis results from step 2, and the output is the three-dimensional visualization model on the terminal screen. Specifically, it uses a three-dimensional graphics engine to render the model and performs processes that enable interactive operation. 【0369】 Step 4: 【0370】 The device utilizes natural language processing technology to collect and summarize relevant research literature. The input is research literature on solar activity obtained from the internet, and the output is summarized key information. Specifically, it uses algorithms such as KeyBERT to extract important parts of the literature text and generate a summary. 【0371】 Step 5: 【0372】 The user generates a new hypothesis using a generative AI model. The input here is a prompt sentence, and the output is the generated hypothesis. Specifically, the AI ​​model generates a hypothesis based on the prompt sentence from the user and presents it to the user. 【0373】 Step 6: 【0374】 The terminal uses Qiskit to perform high-speed quantum computation simulations based on analysis results and hypotheses. The input is the results from steps 2 and 5, and the output is a predictive model based on the quantum simulation. Specifically, it connects to a quantum computing platform, sends the necessary computation requests, and receives and processes the results. 【0375】 Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions. 【0376】 This invention is a system that integrates solar wind data acquisition, analysis, visualization, research literature summarization, new hypothesis generation, and simulation using quantum computing technology, in addition to an emotion engine that recognizes user emotions. The following describes a specific embodiment of the system combining each of these elements. 【0377】 The server uses API communication to acquire solar wind data in real time from external observation agencies. The acquired data is immediately normalized and analyzed using machine learning algorithms. Based on the analysis results, a 3D model is generated and sent to the user's terminal. The terminal displays this model in real time, providing an environment where the user can interactively manipulate the model. 【0378】 Furthermore, the server searches publicly available research databases on the internet and collects relevant solar wind research papers. The collected literature is automatically summarized using natural language processing, and important information is extracted. This information is sent to the terminal to help users quickly obtain the latest academic information. 【0379】 The server generates new research hypotheses based on the simultaneously accumulated data and summary information. These generated hypotheses are presented to the user, providing guidance for further research. 【0380】 The emotion engine, a key feature of this invention, infers emotions from the user's voice, facial expressions, keyboard input, etc., and feeds the results back into the analysis engine and hypothesis generation in real time. This enables the provision of optimal information and hypothesis suggestions tailored to the user's emotional state. Furthermore, in 3D model display, the operability is adjusted based on the user's emotions, and an approach is taken to deepen visual understanding. 【0381】 For example, if a user is feeling anxious about solar activity, the emotion engine recognizes this emotion, and the analysis engine prioritizes providing more detailed information about stability. Furthermore, if the user is interested in a particular situation, the hypothesis generation will present answers to more challenging questions. In this way, the system is capable of flexibly responding to the user's situation. 【0382】 The following describes the processing flow. 【0383】 Step 1: 【0384】 The server sends requests to APIs provided by external observation organizations to obtain solar wind data in real time. 【0385】 Step 2: 【0386】 The server normalizes the acquired data and sends it to the data analysis engine. Here, missing data is imputed and the data is converted into a parseable format. 【0387】 Step 3: 【0388】 The analysis engine on the server uses machine learning algorithms to analyze the data and extract solar wind trends and anomaly patterns. 【0389】 Step 4: 【0390】 The server generates a 3D model based on the analysis results and sends it to the user's terminal as visualization data. 【0391】 Step 5: 【0392】 The device displays the received 3D model and provides a screen that allows the user to interact with it. The viewpoint can be zoomed and moved, allowing for a deeper understanding. 【0393】 Step 6: 【0394】 The server queries the internet for and collects research papers related to solar wind. 【0395】 Step 7: 【0396】 The server uses a natural language processing engine to summarize the collected papers and extract important information. 【0397】 Step 8: 【0398】 The server sends the summary results to the terminal, allowing the user to quickly grasp the necessary information. 【0399】 Step 9: 【0400】 The server generates new research hypotheses based on past analysis data and summary information. These generated hypotheses are then presented to the user for reference. 【0401】 Step 10: 【0402】 The emotion engine module operates on the device and analyzes the user's emotions in real time from voice, facial recognition, keyboard input, and other sources. 【0403】 Step 11: 【0404】 The server receives feedback from the emotion engine and incorporates it into the analysis and hypothesis generation process. As a result, information is prioritized and hypotheses are adjusted to correspond to the user's emotional state. 【0405】 Step 12: 【0406】 The device provides an interface that responds to the user's emotions, based on the results from the emotion engine, improving usability and the quality of visualization. 【0407】 (Example 2) 【0408】 Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal". 【0409】 In the modern era, solar wind activity is a crucial phenomenon that affects Earth, but real-time data acquisition, easily understandable visualization, and the rapid generation of new hypotheses based on that information are not adequately addressed. Furthermore, flexible systems that dynamically change information delivery according to the user's emotional state do not exist. As a result, users face the challenge of not being able to receive the necessary information immediately. 【0410】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. 【0411】 In this invention, the server includes an information acquisition means for acquiring information on solar wind in real time and normalizing and analyzing the information; a visualization means for generating a three-dimensional model based on the analyzed information and visualizing it; a summarization means for summarizing research literature using natural language processing and extracting important information; a hypothesis generation means for generating new hypotheses using past analysis information and summary information; and an emotion recognition means for identifying the user's emotional state and providing feedback to the analysis and hypothesis presentation. This enables real-time visualization based on normalized analysis information, provision of summary information on relevant research results, and optimization of information provision according to the user's emotional state. 【0412】 "Information about the solar wind" refers to data about the flow of charged particles emitted from the sun in outer space. 【0413】 "Real-time acquisition" refers to the ability to acquire the target information almost instantly. 【0414】 "Normalization" refers to the process of transforming data into a unified format or range. 【0415】 "Analysis" refers to methods used to clarify the meaning and trends of data and deepen our understanding of it. 【0416】 A "three-dimensional model" refers to a digital object that is represented three-dimensionally on a computer. 【0417】 "Visualization" refers to a method of representing information visually to aid human understanding. 【0418】 "Natural language processing" refers to the technology that enables computers to understand and process human language. 【0419】 "Summarizing" refers to extracting important information from a text or data and putting it into a concise summary. 【0420】 "Hypothesis generation" refers to the process of devising new theories or explanations based on given data and information. 【0421】 "User's emotional state" refers to the user's current feelings and mood. 【0422】 "Emotion recognition" refers to the use of technology to judge and evaluate various human emotions. 【0423】 This invention is a system that acquires, analyzes, and visualizes information related to solar wind, and further generates new research hypotheses based on that information. This system recognizes the user's emotions in real time and utilizes this information in providing information and generating hypotheses, thereby meeting the user's needs. 【0424】 The server acquires real-time information on solar wind from external observation agencies using API communication. During this process, data is continuously acquired via the observation agencies' APIs, and this information is normalized and analyzed by programs developed in Python and other languages. Machine learning algorithms are used, particularly for detecting anomalies and abnormalities. 【0425】 Based on the analyzed information, the server generates a three-dimensional model using a 3D graphics library such as Three.js. This model is transmitted to the terminal via the internet. A dedicated application is launched on the terminal, allowing the user to interactively manipulate the model and visually view detailed data. 【0426】 Furthermore, the server collects relevant research literature from digital libraries and online research databases and summarizes it using natural language processing techniques. This allows users to quickly obtain important research information. Generative AI models such as OpenAI's GPT model are used to generate new hypotheses based on the summarized information using prompt sentences. 【0427】 A possible example of a specific prompt for generating new hypotheses is, "Please generate a new hypothesis for the next forecast based on solar wind data." The generated hypothesis would then be presented to the user, serving as a guide for further research and analysis. 【0428】 The user's emotional state is analyzed by an emotion recognition engine based on voice, facial expressions, and keyboard input. The results are immediately fed back to the server and reflected in the analysis and hypothesis generation process. For example, when a user is feeling anxious, information that provides a greater sense of security is prioritized and provided. 【0429】 For example, if the system recognizes that a user is experiencing a certain level of anxiety, the analysis engine, in conjunction with the emotion engine, will prioritize providing detailed information regarding the stability of solar activity. In this way, the system of the present invention enables flexible information provision that responds to the user's emotional needs. 【0430】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0431】 Step 1: 【0432】 The server acquires real-time solar wind data using the observation agency's API. The input is raw data sent from the API. This raw data is immediately normalized and converted to a unified format using a script written in Python. The output is normalized data in a parseable format. 【0433】 Step 2: 【0434】 The server applies machine learning algorithms to analyze normalized data. The input is solar wind data normalized in step 1. Specifically, it analyzes patterns of solar activity using an anomaly detection model on the data. The output is a report including conclusions based on the analysis results and whether or not there are potential anomalies. 【0435】 Step 3: 【0436】 The server generates a three-dimensional model based on the analysis results. The input is the analysis results obtained in step 2. 3D graphics are generated using the Three.js library to make the model visually easy to understand. The output is the visualized three-dimensional model sent to the user. 【0437】 Step 4: 【0438】 The terminal displays the 3D model received from the server. The input is the 3D model data generated in step 3. The application on the terminal uses this data to perform interactive operations and visualizations. The output is a user-operable display screen. 【0439】 Step 5: 【0440】 The server searches scientific databases on the web and collects relevant literature. The input consists of keywords related to solar wind. Natural language processing is used to summarize the collected literature and extract important information. The output is the summarized research information. 【0441】 Step 6: 【0442】 The server generates a new research hypothesis based on the summary information. The input is the summary information obtained in step 5. Using the generation AI model, it generates a prompt message such as, "Generate a new hypothesis for the next prediction based on solar wind data." The output is the presentation of the hypothesis. 【0443】 Step 7: 【0444】 The system analyzes the user's emotions using an emotion engine. Inputs include voice, facial expressions, and keyboard input data. Emotion recognition software analyzes this data to determine the user's emotional state. The output is an evaluation of the user's emotional state. 【0445】 Step 8: 【0446】 The server considers the user's emotional state and provides optimal information. The input is the emotional state evaluation result obtained in step 7. The analysis engine constructs and provides information appropriate to the user based on the emotional evaluation. The output is customized information delivery according to the emotion. 【0447】 (Application Example 2) 【0448】 Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal." 【0449】 Solar wind data is a crucial element of space weather information, and its accurate and rapid analysis and visualization are needed in many fields. However, systems that acquire solar wind data in real time and provide information tailored to the user's situation and emotions based on that data are not yet well established. Furthermore, conventional systems often require manual summarization of research information and hypothesis generation, which poses challenges in terms of effort and time. In addition, flexible information provision that takes into account the user's emotional state has not been achieved. 【0450】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. 【0451】 In this invention, the server includes means for acquiring, normalizing, and analyzing solar wind data in real time; means for generating and visualizing a three-dimensional model based on the analyzed data; means for summarizing research literature and extracting important information using natural language processing; and means for inferring the user's emotional state and optimizing information provision using sentiment analysis technology. As a result, users can interact with a three-dimensional model based on the latest solar wind data and receive optimal information based on their emotions. 【0452】 "Solar wind data" refers to information about plasma and magnetic fields emitted from the sun, and is important data for understanding variations in space weather. 【0453】 "Real-time" refers to processing data and information instantly and providing it to users without delay. 【0454】 "Normalization" is the process of standardizing data according to certain criteria, making it easier to analyze. 【0455】 "Analysis" is the process of examining acquired data in detail and extracting meaningful information. 【0456】 A "three-dimensional model" is a computer-generated representation of an object or phenomenon in three-dimensional space, presented in a form that users can visually understand. 【0457】 "Visualization" is the process of representing data and information graphically, making it easier for humans to understand. 【0458】 "Natural language processing" is a technology that allows computers to understand and process human language, and is used for extracting and summarizing information. 【0459】 A "summary" is a concise compilation of key information extracted from a document or data. 【0460】 "Hypothesis generation" is the process of proposing new perspectives and ways of thinking based on existing data, which can serve as a guide for further research and analysis. 【0461】 "Quantum computing technology" is a technology that applies the principles of quantum mechanics to perform calculations at a much faster speed than conventional computers. 【0462】 "Simulation" is the process of modeling real-world phenomena and events on a computer and virtually reproducing them. 【0463】 "Emotion analysis technology" is a technology that infers emotions from a user's voice, facial expressions, input, etc., and processes that information as data. 【0464】 "User terminal" refers to an electronic device that a user directly operates or views, including smartphones and computers. 【0465】 An "interface" refers to the user interface or method of operation that allows a user and a computer system to interact with each other. 【0466】 "Optimizing information provision" means presenting information in the most appropriate format, tailored to the user's needs and circumstances. 【0467】 In order to implement the present invention, it is necessary to construct a system that enables real-time analysis and visualization of solar wind data, as well as the provision of information based on sentiment analysis. 【0468】 The server first acquires solar wind data from external observation agencies. This data is retrieved in real time via API communication, and communication is managed using frameworks such as Flask as needed. The acquired data is normalized using Python and analyzed by machine learning algorithms. Based on the analysis results, a 3D model is generated and visualized using Unity or Three.js. 【0469】 The device provides users with a visualized three-dimensional model. The model is interactive and the interface is adjusted based on the user's emotional state to enhance their understanding. The device uses OpenCV and TensorFlow to analyze the user's voice and facial expressions to infer their emotional state. This allows it to provide research information summarized using natural language processing techniques, tailored to the user's emotions. 【0470】 Users can make situation-appropriate decisions based on the information ultimately provided. This information provision process utilizes a generative AI model, which presents new hypotheses based on historical data and summarized information. Data calculations are automated using DataRobot and TensorFlow to generate hypotheses, which are then presented to the user. 【0471】 As a specific use case, if solar activity could potentially affect communications or power supply, the device would display a notification to the user explaining the impact. A prompt such as, "Please propose a strategy for optimizing public notifications in preparation for communication disruptions caused by solar winds," would be used as input to the generating AI model. 【0472】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0473】 Step 1: 【0474】 The server acquires solar wind data in real time from external observation agencies via APIs. The input is raw data from the API, and the output is normalized data. This process uses Python to standardize the data format and perform data cleaning. 【0475】 Step 2: 【0476】 The server inputs normalized data into a machine learning algorithm for analysis. The input is normalized data, and the output is the analysis result. TensorFlow is used to perform pattern recognition and anomaly detection to generate the analysis result. 【0477】 Step 3: 【0478】 The server generates a three-dimensional model based on the analysis results. The input is the analysis results, and the output is the three-dimensional model. A visually manipulable model is constructed using Unity or Three.js. 【0479】 Step 4: 【0480】 The terminal provides the user with a generated 3D model and enables interactive manipulation. The input is the 3D model, and the output is an interactive interface. The model is updated in real time in response to user input. 【0481】 Step 5: 【0482】 The device acquires the user's voice and facial expressions and performs emotion analysis. The input is the user's visual and audio data, and the output is their emotional state. OpenCV and TensorFlow are used to analyze the data and infer emotions. 【0483】 Step 6: 【0484】 The server optimizes information delivery based on the user's emotional state. The input is the emotional state and analysis results, while the output is optimized information. Natural language processing is used to present summaries and hypotheses tailored to the user's emotions. 【0485】 Step 7: 【0486】 The user receives optimized information and makes situational decisions. The input is information from the server, and the output is the user's decision. The user takes necessary actions, considering hypotheses and actions proposed by the generative AI model. 【0487】 This series of processes creates a system that allows users to receive information tailored to their emotions based on real-time analyzed data, enabling them to make accurate decisions. 【0488】 The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data. 【0489】 Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0490】 In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214. 【0491】 [Third Embodiment] 【0492】 Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment. 【0493】 As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server. 【0494】 The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network). 【0495】 The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52. 【0496】 The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46. 【0497】 Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision). 【0498】 Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner. 【0499】 Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56. 【0500】 The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30. 【0501】 The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. 【0502】 In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48. 【0503】 Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal". 【0504】 The system of this invention integrates the acquisition, analysis, and visualization of solar wind data, the summarization of research literature, the generation of new hypotheses, and simulations using quantum computing technology. Each element is described below. 【0505】 First, the server connects to an API of an external observation agency to acquire solar wind data in real time. The acquired data is sent to a data analysis engine for normalization and analysis. The analysis engine processes the data using machine learning algorithms to identify solar wind trends. 【0506】 Based on these analysis results, the server generates a 3D model. The generated model is sent to the terminal in an interactive format and displayed. Users can manipulate the model on their terminal and visually examine the effects of solar wind. 【0507】 Next, the server accesses research databases on the internet and searches for relevant solar wind research literature. The collected papers are summarized using natural language processing, and the summary information is provided to the terminal. The user can refer to this summary to quickly grasp the latest research trends. 【0508】 Furthermore, the server generates new hypotheses based on the accumulated data and summary information. A generative AI model assists in this process, shaping the questions and hypotheses that researchers wish to present. Users can then use these hypotheses to develop new research. 【0509】 Finally, the terminal connects to the quantum computing platform and sends a request to run the simulation. The server leverages this platform to efficiently process complex calculations and generate simulation results regarding the solar wind. The generated results are sent back to the terminal, allowing the user to perform further analysis and make decisions based on them. 【0510】 This system streamlines each process in solar wind research, reducing the burden on researchers and deepening insights into the solar wind. For example, if a user suspects an unusual pattern in solar activity and wants to quickly verify it, this system allows for a consistent workflow from data acquisition to analysis and verification of simulation results, enabling early verification of that suspicion. 【0511】 The following describes the processing flow. 【0512】 Step 1: 【0513】 The server connects to an API provided by a solar wind observation agency to acquire solar wind data in real time. The acquired data includes information such as the velocity and density of charged particles and magnetic field. 【0514】 Step 2: 【0515】 The server normalizes the acquired data and sends it to the analysis engine. The analysis engine uses machine learning algorithms to identify trends and anomaly patterns in the data. 【0516】 Step 3: 【0517】 The server generates a 3D model based on the analysis results. This model constructs the spatial data necessary to visualize the dynamics of the solar wind. 【0518】 Step 4: 【0519】 The server renders the generated 3D model and sends it to the terminal. The terminal displays the model in real time, allowing the user to interact with it. 【0520】 Step 5: 【0521】 The server queries publicly available online research databases to search for and collect relevant solar wind research papers. 【0522】 Step 6: 【0523】 The server automatically summarizes collected papers using natural language processing and extracts important information. 【0524】 Step 7: 【0525】 The server sends the summary results to the terminal, allowing users to quickly access the latest research findings. 【0526】 Step 8: 【0527】 The server uses data analysis results and paper summaries as input to generate new research hypotheses. A generative AI model assists in this process. 【0528】 Step 9: 【0529】 The server presents the generated hypotheses to the user, providing them with new research ideas and inspiration. 【0530】 Step 10: 【0531】 The device accesses the quantum computing platform and makes a request to run a solar wind simulation. 【0532】 Step 11: 【0533】 The server leverages quantum computing to run simulations at high speed and generate results that predict the behavior of the solar wind. 【0534】 Step 12: 【0535】 The server sends the simulation results back to the terminal, providing users with the information to perform detailed analysis and make decisions. 【0536】 (Example 1) 【0537】 Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal." 【0538】 Traditional solar wind research has involved dispersed processes such as data acquisition, analysis, visualization, and literature review, each requiring significant time and effort. Furthermore, limited technology was used to support new hypothesis generation and high-speed simulations, making efficient research difficult. This has hindered the acquisition of rapid and in-depth insights into the solar wind. 【0539】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. 【0540】 In this invention, the server includes information acquisition means for acquiring and analyzing solar wind information in real time, model generation means for generating visualization models based on the analyzed information, and information summarization means for summarizing important information from literature using natural language processing. This enables researchers to comprehensively analyze solar wind data, quickly generate hypotheses, and perform high-speed simulations. 【0541】 "Solar wind information" refers to data about the flow of plasma emitted from the sun in space, including information about its characteristics such as velocity, density, and temperature. 【0542】 "Information acquisition means" refers to a series of processes or devices for acquiring data in real time from external observation agencies or centers, and for normalizing or analyzing that data as needed. 【0543】 A "model generation means" is a process or apparatus for creating a three-dimensional visualization model based on analyzed information and constructing it in a form that can be interactively manipulated by the user. 【0544】 An "information summarization tool" is a process or device that utilizes natural language processing technology to extract and summarize only the important information from relevant literature and reports. 【0545】 A "hypothesis generation method" is a process or device that creates new hypotheses based on previously analyzed and summarized information. This process includes a suggestion function that utilizes a generative AI model. 【0546】 "Computational means" refers to a process or apparatus for performing highly accurate and high-speed simulations using quantum computing technology. 【0547】 "Operation-providing means" refers to a process or apparatus that provides an interface and functions to enable a user to interactively manipulate a visualized model. 【0548】 A "hypothesis support tool" is a process or device that uses generative AI models to support researchers in formulating questions and assists in the development of hypotheses. 【0549】 Modes for carrying out the invention 【0550】 The system of this invention integrates the acquisition, analysis, visualization, and hypothesis generation of data related to solar wind. This system consists of three elements: a server, a terminal, and a user, each playing a specific role. 【0551】 First, the server acquires real-time solar wind information from external observation agencies. Specifically, it uses data provision APIs to collect information on solar wind speed and density. This acquired data is normalized and analyzed using Python or other data processing libraries. The server then uses machine learning libraries such as Scikit-learn to analyze this data and predict solar wind trends. 【0552】 Next, the server generates a 3D model based on the analysis results. This modeling process utilizes visualization libraries such as Three.js to construct an interactive 3D model via WebGL. The generated model is sent to the terminal and displayed through the user interface. 【0553】 The terminal displays this interactive 3D model, allowing user manipulation. Users can use this to observe the effects of solar wind in detail. Furthermore, the server accesses research databases on the internet to collect relevant literature information. Using NLP technology, the server summarizes this literature and provides the user with key information. 【0554】 The server also provides functionality to generate new hypotheses by utilizing past analysis data and summary information. This process uses natural language generation with generative AI models to support hypothesis generation. Specifically, users can input prompt sentences to generate hypotheses using generative AI models such as OpenAI's GPT-3. 【0555】 Prompt example: "Analyze new patterns from recent solar wind data and propose related hypotheses." 【0556】 Furthermore, the terminal connects to a quantum computing platform and sends requests to the server to run high-speed simulations. These simulations utilize quantum computing technologies such as IBM Q. The server returns the simulation results to the terminal, providing the user with material for further analysis. 【0557】 This system enables comprehensive data analysis and hypothesis generation regarding solar wind, significantly streamlining the research process. 【0558】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0559】 Step 1: 【0560】 The server obtains solar wind information in real time from external observation agencies. It uses the observation agencies' API endpoints as input. The server sends HTTP requests and receives data such as solar wind speed, density, and temperature in JSON format. The acquired data is stored in a local database. Specifically, the server executes a script that automatically makes API requests at regular intervals. 【0561】 Step 2: 【0562】 The server normalizes the acquired solar wind information and outputs it as preprocessed data. The input is the already acquired raw data. The server uses Python's Pandas and NumPy to scale this data and filter out unnecessary values. Specifically, the server runs a script to remove outliers from the dataset and standardize the data. 【0563】 Step 3: 【0564】 The server performs analysis using pre-processed data. It uses normalized data as input and generates analysis results as output. The server utilizes Scikit-learn and applies machine learning algorithms to analyze solar wind trends and anomalies. Specifically, it performs regression analysis and clustering to detect anomalies. 【0565】 Step 4: 【0566】 The server generates a 3D model based on the analysis results. The input is the data based on the analysis, and the output is the visualized 3D model. The 3D model is generated using the Three.js library, and an interactively viewable model is built using WebGL. Specifically, the analysis results are mapped to each axis of the model, making it operable in a browser. 【0567】 Step 5: 【0568】 The terminal receives 3D models sent from the server and displays them on the user interface. The input is 3D model data, and the output is a visualized interface. The user manipulates the model using a mouse or touch panel to observe specific solar wind phenomena. Specifically, the terminal dynamically renders the model and supports rotation and zooming in response to user input. 【0569】 Step 6: 【0570】 The server accesses research databases to search for relevant literature and generates summaries. The input is a search query, such as keywords, and the output is summarized literature information. Natural language processing algorithms are used to summarize the extracted literature. Specifically, the server uses an NLP library to summarize the main points of the papers and provides them to the user. 【0571】 Step 7: 【0572】 The server generates new hypotheses based on the analyzed data and summary information. The input is the accumulated analyzed data and summary information, and the output is hypothesis information. A generative AI model is used to generate hypotheses based on user prompts. 【0573】 Specifically, when generating a hypothesis, a prompt message such as "Analyze new patterns from recent solar wind data and present related hypotheses" is entered, and the AI ​​generates a new hypothesis. 【0574】 Step 8: 【0575】 The terminal sends a request to the server to connect to the quantum computing platform and run a simulation. The input is the simulation conditions, and the output is the simulation result. Complex calculations are performed using quantum computing technology, and results are generated. Specifically, the terminal sends simulation configuration information to the server, the server receives the quantum computation results and sends them to the terminal, and provides them to the user. 【0576】 (Application Example 1) 【0577】 Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal." 【0578】 There is a need for a system that can quickly predict the impact of solar activity on communication infrastructure, power supply systems, and the environment in urban areas, and help residents and managers take appropriate measures. However, current technology does not enable a unified process from data analysis to hypothesis generation and simulation, making rapid response difficult. 【0579】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. 【0580】 In this invention, the server includes data acquisition means for acquiring data related to solar activity in real time and for standardizing and analyzing the data; display means for generating and visualizing a three-dimensional model; summarization means for summarizing research information using natural language processing and extracting important content; hypothesis generation means for generating new hypotheses using past analysis data and summarized content; computation execution means for performing high-speed simulations using quantum computing technology; and application means for providing environmental predictions in urban development. This makes it possible to quickly and comprehensively analyze the impact of solar activity on urban infrastructure and to obtain information for taking appropriate countermeasures. 【0581】 "Solar activity-related data" refers to information about phenomena such as winds and flares originating from the sun, and is necessary data for evaluating the impact these have on the Earth's environment. 【0582】 "Standardization and analysis" is the process of organizing acquired data into a consistent format and interpreting it as meaningful information using machine learning and statistical methods. 【0583】 A "three-dimensional model" is a three-dimensional representation method for visually grasping information, and it is a model that makes it possible to intuitively understand the effects of solar activity. 【0584】 "Display means" refers to devices or software that allow users to visually confirm information, and functions as an interface. 【0585】 "Natural language processing" is a technology that enables computers to understand and process human language, and it is a method for summarizing documents and extracting information. 【0586】 A "summarization tool" is a system function that extracts important content from literature and information, summarizes it concisely, and provides it to the user. 【0587】 A "hypothesis generation tool" is a technology that automatically generates new research approaches and hypotheses to deepen understanding from accumulated data and analysis results. 【0588】 "Quantum computing technology" is a technology that uses the principles of quantum mechanics to perform calculations that are difficult for ordinary computers to do, quickly and efficiently. 【0589】 "High-speed simulation" refers to the process of rapidly performing simulations that would normally take a long time using quantum computing. 【0590】 "Application methods" refer to specific techniques and systems that use the obtained analysis and simulation results to predict and improve urban environments. 【0591】 The system that implements this application runs on an integrated hardware and software platform, as shown below. 【0592】 The server acquires solar activity-related data in real time from external observation agencies using API communication. This data is formatted into an appropriate format by a standardization and analysis program developed in Python, and then analyzed using TensorFlow machine learning algorithms. The analysis results are visualized as a three-dimensional model for intuitive understanding on the user's terminal. WebGL or a 3D graphics engine can be used to display the visualization. 【0593】 The terminal further uses natural language processing technology to summarize relevant research literature and extracts key information using software such as KeyBERT. This summarized information is displayed to allow the user to quickly grasp research trends. The user then uses this information to refer to simulation results performed at high speed using quantum computing technology with Qiskit. 【0594】 Generative AI models are used to optimize simulations and generate hypotheses. These AI models assist in generating new research hypotheses based on user prompts. For example, by inputting "Analyze the impact of solar wind on the urban environment in detail and generate a new hypothesis," the AI ​​model will suggest an appropriate hypothesis. In this way, the entire system is applied to urban development and environmental forecasting, helping users make quick and accurate decisions. 【0595】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0596】 Step 1: 【0597】 The server acquires real-time data on solar activity from external observation agencies via API communication. The input is raw data from the observation agencies, and the output is data converted into a format necessary for standardization. Specifically, API requests are sent, and response data is received and recorded. 【0598】 Step 2: 【0599】 The server analyzes the acquired standardized data using a machine learning algorithm based on TensorFlow. The input is the standardized data obtained in step 1, and the output is the analysis result that captures the characteristics of solar activity. Specifically, the server loads a machine learning model, inputs the data into the model, and generates the results. 【0600】 Step 3: 【0601】 The terminal generates a three-dimensional model using WebGL based on the analysis results and displays it visually to the user. The input is the analysis results from step 2, and the output is the three-dimensional visualization model on the terminal screen. Specifically, it uses a three-dimensional graphics engine to render the model and performs processes that enable interactive operation. 【0602】 Step 4: 【0603】 The device utilizes natural language processing technology to collect and summarize relevant research literature. The input is research literature on solar activity obtained from the internet, and the output is summarized key information. Specifically, it uses algorithms such as KeyBERT to extract important parts of the literature text and generate a summary. 【0604】 Step 5: 【0605】 The user generates a new hypothesis using a generative AI model. The input here is a prompt sentence, and the output is the generated hypothesis. Specifically, the AI ​​model generates a hypothesis based on the prompt sentence from the user and presents it to the user. 【0606】 Step 6: 【0607】 The terminal uses Qiskit to perform high-speed quantum computation simulations based on analysis results and hypotheses. The input is the results from steps 2 and 5, and the output is a predictive model based on the quantum simulation. Specifically, it connects to a quantum computing platform, sends the necessary computation requests, and receives and processes the results. 【0608】 Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions. 【0609】 This invention is a system that integrates solar wind data acquisition, analysis, visualization, research literature summarization, new hypothesis generation, and simulation using quantum computing technology, in addition to an emotion engine that recognizes user emotions. The following describes a specific embodiment of the system combining each of these elements. 【0610】 The server uses API communication to acquire solar wind data in real time from external observation agencies. The acquired data is immediately normalized and analyzed using machine learning algorithms. Based on the analysis results, a 3D model is generated and sent to the user's terminal. The terminal displays this model in real time, providing an environment where the user can interactively manipulate the model. 【0611】 Furthermore, the server searches publicly available research databases on the internet and collects relevant solar wind research papers. The collected literature is automatically summarized using natural language processing, and important information is extracted. This information is sent to the terminal to help users quickly obtain the latest academic information. 【0612】 The server generates new research hypotheses based on the simultaneously accumulated data and summary information. These generated hypotheses are presented to the user, providing guidance for further research. 【0613】 The emotion engine, a key feature of this invention, infers emotions from the user's voice, facial expressions, keyboard input, etc., and feeds the results back into the analysis engine and hypothesis generation in real time. This enables the provision of optimal information and hypothesis suggestions tailored to the user's emotional state. Furthermore, in 3D model display, the operability is adjusted based on the user's emotions, and an approach is taken to deepen visual understanding. 【0614】 For example, if a user is feeling anxious about solar activity, the emotion engine recognizes this emotion, and the analysis engine prioritizes providing more detailed information about stability. Furthermore, if the user is interested in a particular situation, the hypothesis generation will present answers to more challenging questions. In this way, the system is capable of flexibly responding to the user's situation. 【0615】 The following describes the processing flow. 【0616】 Step 1: 【0617】 The server sends requests to APIs provided by external observation organizations to obtain solar wind data in real time. 【0618】 Step 2: 【0619】 The server normalizes the acquired data and sends it to the data analysis engine. Here, missing data is imputed and the data is converted into a parseable format. 【0620】 Step 3: 【0621】 The analysis engine on the server uses machine learning algorithms to analyze the data and extract solar wind trends and anomaly patterns. 【0622】 Step 4: 【0623】 The server generates a 3D model based on the analysis results and sends it to the user's terminal as visualization data. 【0624】 Step 5: 【0625】 The device displays the received 3D model and provides a screen that allows the user to interact with it. The viewpoint can be zoomed and moved, allowing for a deeper understanding. 【0626】 Step 6: 【0627】 The server queries the internet for and collects research papers related to solar wind. 【0628】 Step 7: 【0629】 The server uses a natural language processing engine to summarize the collected papers and extract important information. 【0630】 Step 8: 【0631】 The server sends the summary results to the terminal, allowing the user to quickly grasp the necessary information. 【0632】 Step 9: 【0633】 The server generates new research hypotheses based on past analysis data and summary information. These generated hypotheses are then presented to the user for reference. 【0634】 Step 10: 【0635】 The emotion engine module operates on the device and analyzes the user's emotions in real time from voice, facial recognition, keyboard input, and other sources. 【0636】 Step 11: 【0637】 The server receives feedback from the emotion engine and incorporates it into the analysis and hypothesis generation process. As a result, information is prioritized and hypotheses are adjusted to correspond to the user's emotional state. 【0638】 Step 12: 【0639】 The device provides an interface that responds to the user's emotions, based on the results from the emotion engine, improving usability and the quality of visualization. 【0640】 (Example 2) 【0641】 Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal." 【0642】 In the modern era, solar wind activity is a crucial phenomenon that affects Earth, but real-time data acquisition, easily understandable visualization, and the rapid generation of new hypotheses based on that information are not adequately addressed. Furthermore, flexible systems that dynamically change information delivery according to the user's emotional state do not exist. As a result, users face the challenge of not being able to receive the necessary information immediately. 【0643】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. 【0644】 In this invention, the server includes an information acquisition means for acquiring information on solar wind in real time and normalizing and analyzing the information; a visualization means for generating a three-dimensional model based on the analyzed information and visualizing it; a summarization means for summarizing research literature using natural language processing and extracting important information; a hypothesis generation means for generating new hypotheses using past analysis information and summary information; and an emotion recognition means for identifying the user's emotional state and providing feedback to the analysis and hypothesis presentation. This enables real-time visualization based on normalized analysis information, provision of summary information on relevant research results, and optimization of information provision according to the user's emotional state. 【0645】 "Information about the solar wind" refers to data about the flow of charged particles emitted from the sun in outer space. 【0646】 "Real-time acquisition" refers to the ability to acquire the target information almost instantly. 【0647】 "Normalization" refers to the process of transforming data into a unified format or range. 【0648】 "Analysis" refers to methods used to clarify the meaning and trends of data and deepen our understanding of it. 【0649】 A "three-dimensional model" refers to a digital object that is represented three-dimensionally on a computer. 【0650】 "Visualization" refers to a method of representing information visually to aid human understanding. 【0651】 "Natural language processing" refers to the technology that enables computers to understand and process human language. 【0652】 "Summarizing" refers to extracting important information from a text or data and putting it into a concise summary. 【0653】 "Hypothesis generation" refers to the process of devising new theories or explanations based on given data and information. 【0654】 "User's emotional state" refers to the user's current feelings and mood. 【0655】 "Emotion recognition" refers to the use of technology to judge and evaluate various human emotions. 【0656】 This invention is a system that acquires, analyzes, and visualizes information related to solar wind, and further generates new research hypotheses based on that information. This system recognizes the user's emotions in real time and utilizes this information in providing information and generating hypotheses, thereby meeting the user's needs. 【0657】 The server acquires real-time information on solar wind from external observation agencies using API communication. During this process, data is continuously acquired via the observation agencies' APIs, and this information is normalized and analyzed by programs developed in Python and other languages. Machine learning algorithms are used, particularly for detecting anomalies and abnormalities. 【0658】 Based on the analyzed information, the server generates a three-dimensional model using a 3D graphics library such as Three.js. This model is transmitted to the terminal via the internet. A dedicated application is launched on the terminal, allowing the user to interactively manipulate the model and visually view detailed data. 【0659】 Furthermore, the server collects relevant research literature from digital libraries and online research databases and summarizes it using natural language processing techniques. This allows users to quickly obtain important research information. Generative AI models such as OpenAI's GPT model are used to generate new hypotheses based on the summarized information using prompt sentences. 【0660】 A possible example of a specific prompt for generating new hypotheses is, "Please generate a new hypothesis for the next forecast based on solar wind data." The generated hypothesis would then be presented to the user, serving as a guide for further research and analysis. 【0661】 The user's emotional state is analyzed by an emotion recognition engine based on voice, facial expressions, and keyboard input. The results are immediately fed back to the server and reflected in the analysis and hypothesis generation process. For example, when a user is feeling anxious, information that provides a greater sense of security is prioritized and provided. 【0662】 For example, if the system recognizes that a user is experiencing a certain level of anxiety, the analysis engine, in conjunction with the emotion engine, will prioritize providing detailed information regarding the stability of solar activity. In this way, the system of the present invention enables flexible information provision that responds to the user's emotional needs. 【0663】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0664】 Step 1: 【0665】 The server acquires real-time solar wind data using the observation agency's API. The input is raw data sent from the API. This raw data is immediately normalized and converted to a unified format using a script written in Python. The output is normalized data in a parseable format. 【0666】 Step 2: 【0667】 The server applies machine learning algorithms to analyze normalized data. The input is solar wind data normalized in step 1. Specifically, it analyzes patterns of solar activity using an anomaly detection model on the data. The output is a report including conclusions based on the analysis results and whether or not there are potential anomalies. 【0668】 Step 3: 【0669】 The server generates a three-dimensional model based on the analysis results. The input is the analysis results obtained in step 2. 3D graphics are generated using the Three.js library to make the model visually easy to understand. The output is the visualized three-dimensional model sent to the user. 【0670】 Step 4: 【0671】 The terminal displays the 3D model received from the server. The input is the 3D model data generated in step 3. The application on the terminal uses this data to perform interactive operations and visualizations. The output is a user-operable display screen. 【0672】 Step 5: 【0673】 The server searches scientific databases on the web and collects relevant literature. The input consists of keywords related to solar wind. Natural language processing is used to summarize the collected literature and extract important information. The output is the summarized research information. 【0674】 Step 6: 【0675】 The server generates a new research hypothesis based on the summary information. The input is the summary information obtained in step 5. Using the generation AI model, it generates a prompt message such as, "Generate a new hypothesis for the next prediction based on solar wind data." The output is the presentation of the hypothesis. 【0676】 Step 7: 【0677】 The system analyzes the user's emotions using an emotion engine. Inputs include voice, facial expressions, and keyboard input data. Emotion recognition software analyzes this data to determine the user's emotional state. The output is an evaluation of the user's emotional state. 【0678】 Step 8: 【0679】 The server considers the user's emotional state and provides optimal information. The input is the emotional state evaluation result obtained in step 7. The analysis engine constructs and provides information appropriate to the user based on the emotional evaluation. The output is customized information delivery according to the emotion. 【0680】 (Application Example 2) 【0681】 Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal." 【0682】 Solar wind data is a crucial element of space weather information, and its accurate and rapid analysis and visualization are needed in many fields. However, systems that acquire solar wind data in real time and provide information tailored to the user's situation and emotions based on that data are not yet well established. Furthermore, conventional systems often require manual summarization of research information and hypothesis generation, which poses challenges in terms of effort and time. In addition, flexible information provision that takes into account the user's emotional state has not been achieved. 【0683】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. 【0684】 In this invention, the server includes means for acquiring, normalizing, and analyzing solar wind data in real time; means for generating and visualizing a three-dimensional model based on the analyzed data; means for summarizing research literature and extracting important information using natural language processing; and means for inferring the user's emotional state and optimizing information provision using sentiment analysis technology. As a result, users can interact with a three-dimensional model based on the latest solar wind data and receive optimal information based on their emotions. 【0685】 "Solar wind data" refers to information about plasma and magnetic fields emitted from the sun, and is important data for understanding variations in space weather. 【0686】 "Real-time" refers to processing data and information instantly and providing it to users without delay. 【0687】 "Normalization" is the process of standardizing data according to certain criteria, making it easier to analyze. 【0688】 "Analysis" is the process of examining acquired data in detail and extracting meaningful information. 【0689】 A "three-dimensional model" is a computer-generated representation of an object or phenomenon in three-dimensional space, presented in a form that users can visually understand. 【0690】 "Visualization" is the process of representing data and information graphically, making it easier for humans to understand. 【0691】 "Natural language processing" is a technology that allows computers to understand and process human language, and is used for extracting and summarizing information. 【0692】 A "summary" is a concise compilation of key information extracted from a document or data. 【0693】 "Hypothesis generation" is the process of proposing new perspectives and ways of thinking based on existing data, which can serve as a guide for further research and analysis. 【0694】 "Quantum computing technology" is a technology that applies the principles of quantum mechanics to perform calculations at a much faster speed than conventional computers. 【0695】 "Simulation" is the process of modeling real-world phenomena and events on a computer and virtually reproducing them. 【0696】 "Emotion analysis technology" is a technology that infers emotions from a user's voice, facial expressions, input, etc., and processes that information as data. 【0697】 "User terminal" refers to an electronic device that a user directly operates or views, including smartphones and computers. 【0698】 An "interface" refers to the user interface or method of operation that allows a user and a computer system to interact with each other. 【0699】 "Optimizing information provision" means presenting information in the most appropriate format, tailored to the user's needs and circumstances. 【0700】 In order to implement the present invention, it is necessary to construct a system that enables real-time analysis and visualization of solar wind data, as well as the provision of information based on sentiment analysis. 【0701】 The server first acquires solar wind data from external observation agencies. This data is retrieved in real time via API communication, and communication is managed using frameworks such as Flask as needed. The acquired data is normalized using Python and analyzed by machine learning algorithms. Based on the analysis results, a 3D model is generated and visualized using Unity or Three.js. 【0702】 The device provides users with a visualized three-dimensional model. The model is interactive and the interface is adjusted based on the user's emotional state to enhance their understanding. The device uses OpenCV and TensorFlow to analyze the user's voice and facial expressions to infer their emotional state. This allows it to provide research information summarized using natural language processing techniques, tailored to the user's emotions. 【0703】 Users can make situation-appropriate decisions based on the information ultimately provided. This information provision process utilizes a generative AI model, which presents new hypotheses based on historical data and summarized information. Data calculations are automated using DataRobot and TensorFlow to generate hypotheses, which are then presented to the user. 【0704】 As a specific use case, if solar activity could potentially affect communications or power supply, the device would display a notification to the user explaining the impact. A prompt such as, "Please propose a strategy for optimizing public notifications in preparation for communication disruptions caused by solar winds," would be used as input to the generating AI model. 【0705】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0706】 Step 1: 【0707】 The server acquires solar wind data in real time from external observation agencies via APIs. The input is raw data from the API, and the output is normalized data. This process uses Python to standardize the data format and perform data cleaning. 【0708】 Step 2: 【0709】 The server inputs normalized data into a machine learning algorithm for analysis. The input is normalized data, and the output is the analysis result. TensorFlow is used to perform pattern recognition and anomaly detection to generate the analysis result. 【0710】 Step 3: 【0711】 The server generates a three-dimensional model based on the analysis results. The input is the analysis results, and the output is the three-dimensional model. A visually manipulable model is constructed using Unity or Three.js. 【0712】 Step 4: 【0713】 The terminal provides the user with a generated 3D model and enables interactive manipulation. The input is the 3D model, and the output is an interactive interface. The model is updated in real time in response to user input. 【0714】 Step 5: 【0715】 The device acquires the user's voice and facial expressions and performs emotion analysis. The input is the user's visual and audio data, and the output is their emotional state. OpenCV and TensorFlow are used to analyze the data and infer emotions. 【0716】 Step 6: 【0717】 The server optimizes information delivery based on the user's emotional state. The input is the emotional state and analysis results, while the output is optimized information. Natural language processing is used to present summaries and hypotheses tailored to the user's emotions. 【0718】 Step 7: 【0719】 The user receives optimized information and makes situational decisions. The input is information from the server, and the output is the user's decision. The user takes necessary actions, considering hypotheses and actions proposed by the generative AI model. 【0720】 This series of processes creates a system that allows users to receive information tailored to their emotions based on real-time analyzed data, enabling them to make accurate decisions. 【0721】 The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data. 【0722】 Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0723】 In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314. 【0724】 [Fourth Embodiment] 【0725】 Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment. 【0726】 As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server. 【0727】 The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network). 【0728】 The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52. 【0729】 The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46. 【0730】 Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision). 【0731】 Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner. 【0732】 The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes. 【0733】 Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56. 【0734】 The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30. 【0735】 The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. 【0736】 In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48. 【0737】 Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0738】 The system of this invention integrates the acquisition, analysis, and visualization of solar wind data, the summarization of research literature, the generation of new hypotheses, and simulations using quantum computing technology. Each element is described below. 【0739】 First, the server connects to an API of an external observation agency to acquire solar wind data in real time. The acquired data is sent to a data analysis engine for normalization and analysis. The analysis engine processes the data using machine learning algorithms to identify solar wind trends. 【0740】 Based on these analysis results, the server generates a 3D model. The generated model is sent to the terminal in an interactive format and displayed. Users can manipulate the model on their terminal and visually examine the effects of solar wind. 【0741】 Next, the server accesses research databases on the internet and searches for relevant solar wind research literature. The collected papers are summarized using natural language processing, and the summary information is provided to the terminal. The user can refer to this summary to quickly grasp the latest research trends. 【0742】 Furthermore, the server generates new hypotheses based on the accumulated data and summary information. A generative AI model assists in this process, shaping the questions and hypotheses that researchers wish to present. Users can then use these hypotheses to develop new research. 【0743】 Finally, the terminal connects to the quantum computing platform and sends a request to run the simulation. The server leverages this platform to efficiently process complex calculations and generate simulation results regarding the solar wind. The generated results are sent back to the terminal, allowing the user to perform further analysis and make decisions based on them. 【0744】 This system streamlines each process in solar wind research, reducing the burden on researchers and deepening insights into the solar wind. For example, if a user suspects an unusual pattern in solar activity and wants to quickly verify it, this system allows for a consistent workflow from data acquisition to analysis and verification of simulation results, enabling early verification of that suspicion. 【0745】 The following describes the processing flow. 【0746】 Step 1: 【0747】 The server connects to an API provided by a solar wind observation agency to acquire solar wind data in real time. The acquired data includes information such as the velocity and density of charged particles and magnetic field. 【0748】 Step 2: 【0749】 The server normalizes the acquired data and sends it to the analysis engine. The analysis engine uses machine learning algorithms to identify trends and anomaly patterns in the data. 【0750】 Step 3: 【0751】 The server generates a 3D model based on the analysis results. This model constructs the spatial data necessary to visualize the dynamics of the solar wind. 【0752】 Step 4: 【0753】 The server renders the generated 3D model and sends it to the terminal. The terminal displays the model in real time, allowing the user to interact with it. 【0754】 Step 5: 【0755】 The server queries publicly available online research databases to search for and collect relevant solar wind research papers. 【0756】 Step 6: 【0757】 The server automatically summarizes collected papers using natural language processing and extracts important information. 【0758】 Step 7: 【0759】 The server sends the summary results to the terminal, allowing users to quickly access the latest research findings. 【0760】 Step 8: 【0761】 The server uses data analysis results and paper summaries as input to generate new research hypotheses. A generative AI model assists in this process. 【0762】 Step 9: 【0763】 The server presents the generated hypotheses to the user, providing them with new research ideas and inspiration. 【0764】 Step 10: 【0765】 The device accesses the quantum computing platform and makes a request to run a solar wind simulation. 【0766】 Step 11: 【0767】 The server leverages quantum computing to run simulations at high speed and generate results that predict the behavior of the solar wind. 【0768】 Step 12: 【0769】 The server sends the simulation results back to the terminal, providing users with the information to perform detailed analysis and make decisions. 【0770】 (Example 1) 【0771】 Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0772】 Traditional solar wind research has involved dispersed processes such as data acquisition, analysis, visualization, and literature review, each requiring significant time and effort. Furthermore, limited technology was used to support new hypothesis generation and high-speed simulations, making efficient research difficult. This has hindered the acquisition of rapid and in-depth insights into the solar wind. 【0773】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. 【0774】 In this invention, the server includes information acquisition means for acquiring and analyzing solar wind information in real time, model generation means for generating visualization models based on the analyzed information, and information summarization means for summarizing important information from literature using natural language processing. This enables researchers to comprehensively analyze solar wind data, quickly generate hypotheses, and perform high-speed simulations. 【0775】 "Solar wind information" refers to data about the flow of plasma emitted from the sun in space, including information about its characteristics such as velocity, density, and temperature. 【0776】 "Information acquisition means" refers to a series of processes or devices for acquiring data in real time from external observation agencies or centers, and for normalizing or analyzing that data as needed. 【0777】 A "model generation means" is a process or apparatus for creating a three-dimensional visualization model based on analyzed information and constructing it in a form that can be interactively manipulated by the user. 【0778】 An "information summarization tool" is a process or device that utilizes natural language processing technology to extract and summarize only the important information from relevant literature and reports. 【0779】 A "hypothesis generation method" is a process or device that creates new hypotheses based on previously analyzed and summarized information. This process includes a suggestion function that utilizes a generative AI model. 【0780】 "Computational means" refers to a process or apparatus for performing highly accurate and high-speed simulations using quantum computing technology. 【0781】 "Operation-providing means" refers to a process or apparatus that provides an interface and functions to enable a user to interactively manipulate a visualized model. 【0782】 A "hypothesis support tool" is a process or device that uses generative AI models to support researchers in formulating questions and assists in the development of hypotheses. 【0783】 Modes for carrying out the invention 【0784】 The system of this invention integrates the acquisition, analysis, visualization, and hypothesis generation of data related to solar wind. This system consists of three elements: a server, a terminal, and a user, each playing a specific role. 【0785】 First, the server acquires real-time solar wind information from external observation agencies. Specifically, it uses data provision APIs to collect information on solar wind speed and density. This acquired data is normalized and analyzed using Python or other data processing libraries. The server then uses machine learning libraries such as Scikit-learn to analyze this data and predict solar wind trends. 【0786】 Next, the server generates a 3D model based on the analysis results. This modeling process utilizes visualization libraries such as Three.js to construct an interactive 3D model via WebGL. The generated model is sent to the terminal and displayed through the user interface. 【0787】 The terminal displays this interactive 3D model, allowing user manipulation. Users can use this to observe the effects of solar wind in detail. Furthermore, the server accesses research databases on the internet to collect relevant literature information. Using NLP technology, the server summarizes this literature and provides the user with key information. 【0788】 The server also provides functionality to generate new hypotheses by utilizing past analysis data and summary information. This process uses natural language generation with generative AI models to support hypothesis generation. Specifically, users can input prompt sentences to generate hypotheses using generative AI models such as OpenAI's GPT-3. 【0789】 Prompt example: "Analyze new patterns from recent solar wind data and propose related hypotheses." 【0790】 Furthermore, the terminal connects to a quantum computing platform and sends requests to the server to run high-speed simulations. These simulations utilize quantum computing technologies such as IBM Q. The server returns the simulation results to the terminal, providing the user with material for further analysis. 【0791】 This system enables comprehensive data analysis and hypothesis generation regarding solar wind, significantly streamlining the research process. 【0792】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0793】 Step 1: 【0794】 The server obtains solar wind information in real time from external observation agencies. It uses the observation agencies' API endpoints as input. The server sends HTTP requests and receives data such as solar wind speed, density, and temperature in JSON format. The acquired data is stored in a local database. Specifically, the server executes a script that automatically makes API requests at regular intervals. 【0795】 Step 2: 【0796】 The server normalizes the acquired solar wind information and outputs it as preprocessed data. The input is the already acquired raw data. The server uses Python's Pandas and NumPy to scale this data and filter out unnecessary values. Specifically, the server runs a script to remove outliers from the dataset and standardize the data. 【0797】 Step 3: 【0798】 The server performs analysis using pre-processed data. It uses normalized data as input and generates analysis results as output. The server utilizes Scikit-learn and applies machine learning algorithms to analyze solar wind trends and anomalies. Specifically, it performs regression analysis and clustering to detect anomalies. 【0799】 Step 4: 【0800】 The server generates a 3D model based on the analysis results. The input is the data based on the analysis, and the output is the visualized 3D model. The 3D model is generated using the Three.js library, and an interactively viewable model is built using WebGL. Specifically, the analysis results are mapped to each axis of the model, making it operable in a browser. 【0801】 Step 5: 【0802】 The terminal receives 3D models sent from the server and displays them on the user interface. The input is 3D model data, and the output is a visualized interface. The user manipulates the model using a mouse or touch panel to observe specific solar wind phenomena. Specifically, the terminal dynamically renders the model and supports rotation and zooming in response to user input. 【0803】 Step 6: 【0804】 The server accesses research databases to search for relevant literature and generates summaries. The input is a search query, such as keywords, and the output is summarized literature information. Natural language processing algorithms are used to summarize the extracted literature. Specifically, the server uses an NLP library to summarize the main points of the papers and provides them to the user. 【0805】 Step 7: 【0806】 The server generates new hypotheses based on the analyzed data and summary information. The input is the accumulated analyzed data and summary information, and the output is hypothesis information. A generative AI model is used to generate hypotheses based on user prompts. 【0807】 Specifically, when generating a hypothesis, a prompt message such as "Analyze new patterns from recent solar wind data and present related hypotheses" is entered, and the AI ​​generates a new hypothesis. 【0808】 Step 8: 【0809】 The terminal sends a request to the server to connect to the quantum computing platform and run a simulation. The input is the simulation conditions, and the output is the simulation result. Complex calculations are performed using quantum computing technology, and results are generated. Specifically, the terminal sends simulation configuration information to the server, the server receives the quantum computation results and sends them to the terminal, and provides them to the user. 【0810】 (Application Example 1) 【0811】 Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0812】 There is a need for a system that can quickly predict the impact of solar activity on communication infrastructure, power supply systems, and the environment in urban areas, and help residents and managers take appropriate measures. However, current technology does not enable a unified process from data analysis to hypothesis generation and simulation, making rapid response difficult. 【0813】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. 【0814】 In this invention, the server includes data acquisition means for acquiring data related to solar activity in real time and for standardizing and analyzing the data; display means for generating and visualizing a three-dimensional model; summarization means for summarizing research information using natural language processing and extracting important content; hypothesis generation means for generating new hypotheses using past analysis data and summarized content; computation execution means for performing high-speed simulations using quantum computing technology; and application means for providing environmental predictions in urban development. This makes it possible to quickly and comprehensively analyze the impact of solar activity on urban infrastructure and to obtain information for taking appropriate countermeasures. 【0815】 "Solar activity-related data" refers to information about phenomena such as winds and flares originating from the sun, and is necessary data for evaluating the impact these have on the Earth's environment. 【0816】 "Standardization and analysis" is the process of organizing acquired data into a consistent format and interpreting it as meaningful information using machine learning and statistical methods. 【0817】 A "three-dimensional model" is a three-dimensional representation method for visually grasping information, and it is a model that makes it possible to intuitively understand the effects of solar activity. 【0818】 "Display means" refers to devices or software that allow users to visually confirm information, and functions as an interface. 【0819】 "Natural language processing" is a technology that enables computers to understand and process human language, and it is a method for summarizing documents and extracting information. 【0820】 A "summarization tool" is a system function that extracts important content from literature and information, summarizes it concisely, and provides it to the user. 【0821】 A "hypothesis generation tool" is a technology that automatically generates new research approaches and hypotheses to deepen understanding from accumulated data and analysis results. 【0822】 "Quantum computing technology" is a technology that uses the principles of quantum mechanics to perform calculations that are difficult for ordinary computers to do, quickly and efficiently. 【0823】 "High-speed simulation" refers to the process of rapidly performing simulations that would normally take a long time using quantum computing. 【0824】 "Application methods" refer to specific techniques and systems that use the obtained analysis and simulation results to predict and improve urban environments. 【0825】 The system that implements this application runs on an integrated hardware and software platform, as shown below. 【0826】 The server acquires solar activity-related data in real time from external observation agencies using API communication. This data is formatted into an appropriate format by a standardization and analysis program developed in Python, and then analyzed using TensorFlow machine learning algorithms. The analysis results are visualized as a three-dimensional model for intuitive understanding on the user's terminal. WebGL or a 3D graphics engine can be used to display the visualization. 【0827】 The terminal further uses natural language processing technology to summarize relevant research literature and extracts key information using software such as KeyBERT. This summarized information is displayed to allow the user to quickly grasp research trends. The user then uses this information to refer to simulation results performed at high speed using quantum computing technology with Qiskit. 【0828】 Generative AI models are used to optimize simulations and generate hypotheses. These AI models assist in generating new research hypotheses based on user prompts. For example, by inputting "Analyze the impact of solar wind on the urban environment in detail and generate a new hypothesis," the AI ​​model will suggest an appropriate hypothesis. In this way, the entire system is applied to urban development and environmental forecasting, helping users make quick and accurate decisions. 【0829】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0830】 Step 1: 【0831】 The server acquires real-time data on solar activity from external observation agencies via API communication. The input is raw data from the observation agencies, and the output is data converted into a format necessary for standardization. Specifically, API requests are sent, and response data is received and recorded. 【0832】 Step 2: 【0833】 The server analyzes the acquired standardized data using a machine learning algorithm based on TensorFlow. The input is the standardized data obtained in step 1, and the output is the analysis result that captures the characteristics of solar activity. Specifically, the server loads a machine learning model, inputs the data into the model, and generates the results. 【0834】 Step 3: 【0835】 The terminal generates a three-dimensional model using WebGL based on the analysis results and displays it visually to the user. The input is the analysis results from step 2, and the output is the three-dimensional visualization model on the terminal screen. Specifically, it uses a three-dimensional graphics engine to render the model and performs processes that enable interactive operation. 【0836】 Step 4: 【0837】 The device utilizes natural language processing technology to collect and summarize relevant research literature. The input is research literature on solar activity obtained from the internet, and the output is summarized key information. Specifically, it uses algorithms such as KeyBERT to extract important parts of the literature text and generate a summary. 【0838】 Step 5: 【0839】 The user generates a new hypothesis using a generative AI model. The input here is a prompt sentence, and the output is the generated hypothesis. Specifically, the AI ​​model generates a hypothesis based on the prompt sentence from the user and presents it to the user. 【0840】 Step 6: 【0841】 The terminal uses Qiskit to perform high-speed quantum computation simulations based on analysis results and hypotheses. The input is the results from steps 2 and 5, and the output is a predictive model based on the quantum simulation. Specifically, it connects to a quantum computing platform, sends the necessary computation requests, and receives and processes the results. 【0842】 Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions. 【0843】 This invention is a system that integrates solar wind data acquisition, analysis, visualization, research literature summarization, new hypothesis generation, and simulation using quantum computing technology, in addition to an emotion engine that recognizes user emotions. The following describes a specific embodiment of the system combining each of these elements. 【0844】 The server uses API communication to acquire solar wind data in real time from external observation agencies. The acquired data is immediately normalized and analyzed using machine learning algorithms. Based on the analysis results, a 3D model is generated and sent to the user's terminal. The terminal displays this model in real time, providing an environment where the user can interactively manipulate the model. 【0845】 Furthermore, the server searches publicly available research databases on the internet and collects relevant solar wind research papers. The collected literature is automatically summarized using natural language processing, and important information is extracted. This information is sent to the terminal to help users quickly obtain the latest academic information. 【0846】 The server generates new research hypotheses based on the simultaneously accumulated data and summary information. These generated hypotheses are presented to the user, providing guidance for further research. 【0847】 The emotion engine, a key feature of this invention, infers emotions from the user's voice, facial expressions, keyboard input, etc., and feeds the results back into the analysis engine and hypothesis generation in real time. This enables the provision of optimal information and hypothesis suggestions tailored to the user's emotional state. Furthermore, in 3D model display, the operability is adjusted based on the user's emotions, and an approach is taken to deepen visual understanding. 【0848】 For example, if a user is feeling anxious about solar activity, the emotion engine recognizes this emotion, and the analysis engine prioritizes providing more detailed information about stability. Furthermore, if the user is interested in a particular situation, the hypothesis generation will present answers to more challenging questions. In this way, the system is capable of flexibly responding to the user's situation. 【0849】 The following describes the processing flow. 【0850】 Step 1: 【0851】 The server sends requests to APIs provided by external observation organizations to obtain solar wind data in real time. 【0852】 Step 2: 【0853】 The server normalizes the acquired data and sends it to the data analysis engine. Here, missing data is imputed and the data is converted into a parseable format. 【0854】 Step 3: 【0855】 The analysis engine on the server uses machine learning algorithms to analyze the data and extract solar wind trends and anomaly patterns. 【0856】 Step 4: 【0857】 The server generates a 3D model based on the analysis results and sends it to the user's terminal as visualization data. 【0858】 Step 5: 【0859】 The device displays the received 3D model and provides a screen that allows the user to interact with it. The viewpoint can be zoomed and moved, allowing for a deeper understanding. 【0860】 Step 6: 【0861】 The server queries the internet for and collects research papers related to solar wind. 【0862】 Step 7: 【0863】 The server uses a natural language processing engine to summarize the collected papers and extract important information. 【0864】 Step 8: 【0865】 The server sends the summary results to the terminal, allowing the user to quickly grasp the necessary information. 【0866】 Step 9: 【0867】 The server generates new research hypotheses based on past analysis data and summary information. These generated hypotheses are then presented to the user for reference. 【0868】 Step 10: 【0869】 The emotion engine module operates on the device and analyzes the user's emotions in real time from voice, facial recognition, keyboard input, and other sources. 【0870】 Step 11: 【0871】 The server receives feedback from the emotion engine and incorporates it into the analysis and hypothesis generation process. As a result, information is prioritized and hypotheses are adjusted to correspond to the user's emotional state. 【0872】 Step 12: 【0873】 The device provides an interface that responds to the user's emotions, based on the results from the emotion engine, improving usability and the quality of visualization. 【0874】 (Example 2) 【0875】 Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0876】 In the modern era, solar wind activity is a crucial phenomenon that affects Earth, but real-time data acquisition, easily understandable visualization, and the rapid generation of new hypotheses based on that information are not adequately addressed. Furthermore, flexible systems that dynamically change information delivery according to the user's emotional state do not exist. As a result, users face the challenge of not being able to receive the necessary information immediately. 【0877】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. 【0878】 In this invention, the server includes an information acquisition means for acquiring information on solar wind in real time and normalizing and analyzing the information; a visualization means for generating a three-dimensional model based on the analyzed information and visualizing it; a summarization means for summarizing research literature using natural language processing and extracting important information; a hypothesis generation means for generating new hypotheses using past analysis information and summary information; and an emotion recognition means for identifying the user's emotional state and providing feedback to the analysis and hypothesis presentation. This enables real-time visualization based on normalized analysis information, provision of summary information on relevant research results, and optimization of information provision according to the user's emotional state. 【0879】 "Information about the solar wind" refers to data about the flow of charged particles emitted from the sun in outer space. 【0880】 "Real-time acquisition" refers to the ability to acquire the target information almost instantly. 【0881】 "Normalization" refers to the process of transforming data into a unified format or range. 【0882】 "Analysis" refers to methods used to clarify the meaning and trends of data and deepen our understanding of it. 【0883】 A "three-dimensional model" refers to a digital object that is represented three-dimensionally on a computer. 【0884】 "Visualization" refers to a method of representing information visually to aid human understanding. 【0885】 "Natural language processing" refers to the technology that enables computers to understand and process human language. 【0886】 "Summarizing" refers to extracting important information from a text or data and putting it into a concise summary. 【0887】 "Hypothesis generation" refers to the process of devising new theories or explanations based on given data and information. 【0888】 "User's emotional state" refers to the user's current feelings and mood. 【0889】 "Emotion recognition" refers to the use of technology to judge and evaluate various human emotions. 【0890】 This invention is a system that acquires, analyzes, and visualizes information related to solar wind, and further generates new research hypotheses based on that information. This system recognizes the user's emotions in real time and utilizes this information in providing information and generating hypotheses, thereby meeting the user's needs. 【0891】 The server acquires real-time information on solar wind from external observation agencies using API communication. During this process, data is continuously acquired via the observation agencies' APIs, and this information is normalized and analyzed by programs developed in Python and other languages. Machine learning algorithms are used, particularly for detecting anomalies and abnormalities. 【0892】 Based on the analyzed information, the server generates a three-dimensional model using a 3D graphics library such as Three.js. This model is transmitted to the terminal via the internet. A dedicated application is launched on the terminal, allowing the user to interactively manipulate the model and visually view detailed data. 【0893】 Furthermore, the server collects relevant research literature from digital libraries and online research databases and summarizes it using natural language processing techniques. This allows users to quickly obtain important research information. Generative AI models such as OpenAI's GPT model are used to generate new hypotheses based on the summarized information using prompt sentences. 【0894】 A possible example of a specific prompt for generating new hypotheses is, "Please generate a new hypothesis for the next forecast based on solar wind data." The generated hypothesis would then be presented to the user, serving as a guide for further research and analysis. 【0895】 The user's emotional state is analyzed by an emotion recognition engine based on voice, facial expressions, and keyboard input. The results are immediately fed back to the server and reflected in the analysis and hypothesis generation process. For example, when a user is feeling anxious, information that provides a greater sense of security is prioritized and provided. 【0896】 For example, if the system recognizes that a user is experiencing a certain level of anxiety, the analysis engine, in conjunction with the emotion engine, will prioritize providing detailed information regarding the stability of solar activity. In this way, the system of the present invention enables flexible information provision that responds to the user's emotional needs. 【0897】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0898】 Step 1: 【0899】 The server acquires real-time solar wind data using the observation agency's API. The input is raw data sent from the API. This raw data is immediately normalized and converted to a unified format using a script written in Python. The output is normalized data in a parseable format. 【0900】 Step 2: 【0901】 The server applies machine learning algorithms to analyze normalized data. The input is solar wind data normalized in step 1. Specifically, it analyzes patterns of solar activity using an anomaly detection model on the data. The output is a report including conclusions based on the analysis results and whether or not there are potential anomalies. 【0902】 Step 3: 【0903】 The server generates a three-dimensional model based on the analysis results. The input is the analysis results obtained in step 2. 3D graphics are generated using the Three.js library to make the model visually easy to understand. The output is the visualized three-dimensional model sent to the user. 【0904】 Step 4: 【0905】 The terminal displays the 3D model received from the server. The input is the 3D model data generated in step 3. The application on the terminal uses this data to perform interactive operations and visualizations. The output is a user-operable display screen. 【0906】 Step 5: 【0907】 The server searches scientific databases on the web and collects relevant literature. The input consists of keywords related to solar wind. Natural language processing is used to summarize the collected literature and extract important information. The output is the summarized research information. 【0908】 Step 6: 【0909】 The server generates a new research hypothesis based on the summary information. The input is the summary information obtained in step 5. Using the generation AI model, it generates a prompt message such as, "Generate a new hypothesis for the next prediction based on solar wind data." The output is the presentation of the hypothesis. 【0910】 Step 7: 【0911】 The system analyzes the user's emotions using an emotion engine. Inputs include voice, facial expressions, and keyboard input data. Emotion recognition software analyzes this data to determine the user's emotional state. The output is an evaluation of the user's emotional state. 【0912】 Step 8: 【0913】 The server considers the user's emotional state and provides optimal information. The input is the emotional state evaluation result obtained in step 7. The analysis engine constructs and provides information appropriate to the user based on the emotional evaluation. The output is customized information delivery according to the emotion. 【0914】 (Application Example 2) 【0915】 Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0916】 Solar wind data is a crucial element of space weather information, and its accurate and rapid analysis and visualization are needed in many fields. However, systems that acquire solar wind data in real time and provide information tailored to the user's situation and emotions based on that data are not yet well established. Furthermore, conventional systems often require manual summarization of research information and hypothesis generation, which poses challenges in terms of effort and time. In addition, flexible information provision that takes into account the user's emotional state has not been achieved. 【0917】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. 【0918】 In this invention, the server includes means for acquiring, normalizing, and analyzing solar wind data in real time; means for generating and visualizing a three-dimensional model based on the analyzed data; means for summarizing research literature and extracting important information using natural language processing; and means for inferring the user's emotional state and optimizing information provision using sentiment analysis technology. As a result, users can interact with a three-dimensional model based on the latest solar wind data and receive optimal information based on their emotions. 【0919】 "Solar wind data" refers to information about plasma and magnetic fields emitted from the sun, and is important data for understanding variations in space weather. 【0920】 "Real-time" refers to processing data and information instantly and providing it to users without delay. 【0921】 "Normalization" is the process of standardizing data according to certain criteria, making it easier to analyze. 【0922】 "Analysis" is the process of examining acquired data in detail and extracting meaningful information. 【0923】 A "three-dimensional model" is a computer-generated representation of an object or phenomenon in three-dimensional space, presented in a form that users can visually understand. 【0924】 "Visualization" is the process of representing data and information graphically, making it easier for humans to understand. 【0925】 "Natural language processing" is a technology that allows computers to understand and process human language, and is used for extracting and summarizing information. 【0926】 A "summary" is a concise compilation of key information extracted from a document or data. 【0927】 "Hypothesis generation" is the process of proposing new perspectives and ways of thinking based on existing data, which can serve as a guide for further research and analysis. 【0928】 "Quantum computing technology" is a technology that applies the principles of quantum mechanics to perform calculations at a much faster speed than conventional computers. 【0929】 "Simulation" is the process of modeling real-world phenomena and events on a computer and virtually reproducing them. 【0930】 "Emotion analysis technology" is a technology that infers emotions from a user's voice, facial expressions, input, etc., and processes that information as data. 【0931】 "User terminal" refers to an electronic device that a user directly operates or views, including smartphones and computers. 【0932】 An "interface" refers to the user interface or method of operation that allows a user and a computer system to interact with each other. 【0933】 "Optimizing information provision" means presenting information in the most appropriate format, tailored to the user's needs and circumstances. 【0934】 In order to implement the present invention, it is necessary to construct a system that enables real-time analysis and visualization of solar wind data, as well as the provision of information based on sentiment analysis. 【0935】 The server first acquires solar wind data from external observation agencies. This data is retrieved in real time via API communication, and communication is managed using frameworks such as Flask as needed. The acquired data is normalized using Python and analyzed by machine learning algorithms. Based on the analysis results, a 3D model is generated and visualized using Unity or Three.js. 【0936】 The device provides users with a visualized three-dimensional model. The model is interactive and the interface is adjusted based on the user's emotional state to enhance their understanding. The device uses OpenCV and TensorFlow to analyze the user's voice and facial expressions to infer their emotional state. This allows it to provide research information summarized using natural language processing techniques, tailored to the user's emotions. 【0937】 Users can make situation-appropriate decisions based on the information ultimately provided. This information provision process utilizes a generative AI model, which presents new hypotheses based on historical data and summarized information. Data calculations are automated using DataRobot and TensorFlow to generate hypotheses, which are then presented to the user. 【0938】 As a specific use case, if solar activity could potentially affect communications or power supply, the device would display a notification to the user explaining the impact. A prompt such as, "Please propose a strategy for optimizing public notifications in preparation for communication disruptions caused by solar winds," would be used as input to the generating AI model. 【0939】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0940】 Step 1: 【0941】 The server acquires solar wind data in real time from external observation agencies via APIs. The input is raw data from the API, and the output is normalized data. This process uses Python to standardize the data format and perform data cleaning. 【0942】 Step 2: 【0943】 The server inputs normalized data into a machine learning algorithm for analysis. The input is normalized data, and the output is the analysis result. TensorFlow is used to perform pattern recognition and anomaly detection to generate the analysis result. 【0944】 Step 3: 【0945】 The server generates a three-dimensional model based on the analysis results. The input is the analysis results, and the output is the three-dimensional model. A visually manipulable model is constructed using Unity or Three.js. 【0946】 Step 4: 【0947】 The terminal provides the user with a generated 3D model and enables interactive manipulation. The input is the 3D model, and the output is an interactive interface. The model is updated in real time in response to user input. 【0948】 Step 5: 【0949】 The device acquires the user's voice and facial expressions and performs emotion analysis. The input is the user's visual and audio data, and the output is their emotional state. OpenCV and TensorFlow are used to analyze the data and infer emotions. 【0950】 Step 6: 【0951】 The server optimizes information delivery based on the user's emotional state. The input is the emotional state and analysis results, while the output is optimized information. Natural language processing is used to present summaries and hypotheses tailored to the user's emotions. 【0952】 Step 7: 【0953】 The user receives optimized information and makes situational decisions. The input is information from the server, and the output is the user's decision. The user takes necessary actions, considering hypotheses and actions proposed by the generative AI model. 【0954】 This series of processes creates a system that allows users to receive information tailored to their emotions based on real-time analyzed data, enabling them to make accurate decisions. 【0955】 The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data. 【0956】 Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0957】 In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414. 【0958】 Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion. 【0959】 Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together. 【0960】 These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression. 【0961】 The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become. 【0962】 Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant. 【0963】 The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more." 【0964】 The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values. 【0965】 The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format. 【0966】 In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data. 【0967】 In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56. 【0968】 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. 【0969】 Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56. 【0970】 The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory. 【0971】 The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor. 【0972】 Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources. 【0973】 Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose. 【0974】 The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above. 【0975】 All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference. 【0976】 The following is further disclosed regarding the embodiments described above. 【0977】 (Claim 1) 【0978】 A data acquisition means for acquiring solar wind data in real time and performing normalization and analysis of said data, 【0979】 A visualization means that generates and visualizes a 3D model based on the analyzed data, 【0980】 A summarization method that uses natural language processing to summarize research literature and extract important information, 【0981】 A hypothesis generation means that generates new hypotheses using past analysis data and summary information, 【0982】 A computation execution means that performs high-speed simulations using quantum computing technology, 【0983】 A system that includes this. 【0984】 (Claim 2) 【0985】 The system according to claim 1, wherein the data acquisition means further comprises an API communication means for directly acquiring data from an external observation agency. 【0986】 (Claim 3) 【0987】 The system according to claim 1, wherein the 3D model generation means provides an interface that enables interactive operation on a user terminal. 【0988】 "Example 1" 【0989】 (Claim 1) 【0990】 An information acquisition means for acquiring solar wind information in real time and normalizing and analyzing said information, 【0991】 A model generation means that generates a visualization model based on the analyzed information and performs visualization, 【0992】 An information summarization means that uses natural language processing to summarize literature and extract important information, 【0993】 A hypothesis generation means that generates new hypotheses using past analysis information and summary information, 【0994】 A computing method that performs high-speed simulations using quantum computing technology, 【0995】 An operation-providing means that enables manipulation of a visualization model through a user interface, 【0996】 A hypothesis support tool that uses generative AI models to assist researchers in proposing questions, 【0997】 A system that includes this. 【0998】 (Claim 2) 【0999】 The system according to claim 1, wherein the information acquisition means further comprises a communication means for acquiring information directly from an external observation agency. 【1000】 (Claim 3) 【1001】 The system according to claim 1, wherein the model generation means provides an interface that enables interactive operation in the user device. 【1002】 "Application Example 1" 【1003】 (Claim 1) 【1004】 A data acquisition means for acquiring data related to solar activity in real time, and for standardizing and analyzing said data, 【1005】 A display means for generating and visualizing a three-dimensional model based on the analyzed information, 【1006】 A summarization method that uses natural language processing to summarize research information and extract important content, 【1007】 A hypothesis generation means that generates new hypotheses using past analysis data and summary content, 【1008】 A computation execution means that performs high-speed simulations using quantum computing technology, 【1009】 Application methods for providing environmental prediction in urban development, 【1010】 A system that includes this. 【1011】 (Claim 2) 【1012】 The system according to claim 1, wherein the data acquisition means further comprises communication means for acquiring information directly from an external information source via an intuitive interface. 【1013】 (Claim 3) 【1014】 The system according to claim 1, wherein the three-dimensional model generation means provides an interface that enables operation on a user device. 【1015】 "Example 2 of combining an emotion engine" 【1016】 (Claim 1) 【1017】 Information acquisition means for acquiring information on solar wind in real time, and for normalizing and analyzing said information, 【1018】 A visualization means that generates and visualizes a three-dimensional model based on the analyzed information, 【1019】 A summarization method that uses natural language processing to summarize research literature and extract important information, 【1020】 A hypothesis generation means that generates new hypotheses using past analysis information and summary information, 【1021】 An emotion recognition means that identifies the user's emotional state and provides feedback for analysis and hypothesis generation, 【1022】 A system that includes this. 【1023】 (Claim 2) 【1024】 The system according to claim 1, wherein the information acquisition means further comprises a communication means for acquiring information directly from an external observation agency. 【1025】 (Claim 3) 【1026】 The system according to claim 1, wherein the three-dimensional model generation means provides an interface that enables operation on a user device. 【1027】 "Application example 2 when combining with an emotional engine" 【1028】 (Claim 1) 【1029】 A data acquisition means for acquiring solar wind data in real time and performing normalization and analysis of said data, 【1030】 A visualization means for generating and visualizing a three-dimensional model based on the analyzed data, 【1031】 A summarization method that uses natural language processing to summarize research literature and extract important information, 【1032】 A hypothesis generation means that generates new hypotheses using past analysis data and summary information, 【1033】 A computation execution means that performs high-speed simulations using quantum computing technology, 【1034】 An emotion recognition means that uses emotion analysis technology to infer the user's emotional state and optimize information provision, 【1035】 A system that includes this. 【1036】 (Claim 2) 【1037】 The system according to claim 1, wherein the data acquisition means further comprises a communication means for directly acquiring data from an external observation agency. 【1038】 (Claim 3) 【1039】 The system according to claim 1, wherein the three-dimensional model generation means provides an interface that enables interactive operation on the user terminal and adjusts the ease of operation according to the user's emotions. [Explanation of Symbols] 【1040】 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

[Claim 1] A data acquisition means for acquiring solar wind data in real time and performing normalization and analysis of said data, A visualization means that generates and visualizes a 3D model based on the analyzed data, A summarization method that uses natural language processing to summarize research literature and extract important information, A hypothesis generation means that generates new hypotheses using past analysis data and summary information, A computation execution means that performs high-speed simulations using quantum computing technology, A system that includes this. [Claim 2] The system according to claim 1, wherein the data acquisition means further comprises an API communication means for directly acquiring data from an external observation agency. [Claim 3] The system according to claim 1, wherein the 3D model generation means provides an interface that enables interactive operation on a user terminal.

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