system

Abstract

We provide the system. [Solution] Means for obtaining geographical and meteorological data, A means for analyzing the aforementioned data to predict potential sites for power generation equipment, A means for displaying the aforementioned candidate installation sites on a map and presenting them to the user, A means of updating machine learning models based on user feedback, 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, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. 【Prior Art Documents】 【Patent Documents】 【0003】 【Patent Document 1】 Japanese Patent Application Laid-Open No. 2022-180282 【Summary of the Invention】 【Problems to be Solved by the Invention】 【0004】 In order to promote the spread of renewable energy, it is required to efficiently select the installation location of power generation equipment that maximally utilizes geographical and meteorological conditions. However, in the conventional method, there are problems that data collection and analysis are complicated and time-consuming, and it is difficult to select an appropriate installation location. In addition, since feedback on the selected candidate sites cannot be quickly reflected, there is a problem in the efficiency and accuracy of installation. 【Means for Solving the Problems】 【0005】 This invention provides a system that acquires geographical and meteorological data, analyzes it, and predicts potential sites for power generation facilities. This system can display the predicted sites on a map, providing a visual presentation to the user. Furthermore, by continuously updating the machine learning model based on user feedback, the accuracy of the selection process is improved. This enables the rapid and efficient selection of optimal installation locations, effectively promoting the widespread adoption of renewable energy. 【0006】 "Geographical data" refers to data that includes spatial information such as topography, land use, and elevation. 【0007】 "Weather data" refers to data related to weather conditions, such as temperature, wind speed, sunshine duration, and precipitation. 【0008】 "Power generation equipment" refers to devices that generate electricity using solar or wind power, as well as the locations where they are installed. 【0009】 A "potential installation site" refers to a location where power generation equipment could potentially be installed, and geographical and meteorological conditions are taken into consideration. 【0010】 A "machine learning model" is an algorithm that learns patterns from large amounts of data and makes predictions and decisions based on new data. 【0011】 "Feedback" refers to information provided by users through field survey results and evaluations, and is used to improve the model. 【0012】 "Displaying on a map" is a method of visually representing geographical data, enabling users to intuitively understand the information. [Brief explanation of the drawing] 【0013】 [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2]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 Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined. 【MODE FOR CARRYING OUT THE INVENTION】 【0014】 Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings. 【0015】 First, the terms used in the following description will be explained. 【0016】 In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like. 【0017】 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. 【0018】 In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc. 【0019】 In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), etc. 【0020】 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." 【0021】 [First Embodiment] 【0022】 Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment. 【0023】 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. 【0024】 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). 【0025】 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. 【0026】 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. 【0027】 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. 【0028】 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. 【0029】 Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14. 【0030】 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. 【0031】 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. 【0032】 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. 【0033】 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". 【0034】 This invention provides a system for effectively installing renewable energy power generation facilities. The system mainly consists of the following phases: data collection, analysis, site proposal, and feedback processing. 【0035】 During the data collection phase, the server acquires geographical and meteorological data from various sources. For example, topographic data is obtained from public GIS databases, while meteorological data is collected through weather information services. 【0036】 In the data analysis phase, the server preprocesses the collected data, performing interpolation and error checking as needed. Then, a machine learning algorithm analyzes the data and builds a model that predicts potential installation sites based on power generation efficiency and feasibility. 【0037】 In the candidate site proposal phase, the terminal displays the analysis results on a map, presenting the user with potential locations for power generation facilities. The map is interactive, allowing the user to zoom in / out and view detailed information. For example, the user can select a specific candidate site and check its weather conditions and expected power generation efficiency. 【0038】 In the feedback processing phase, the user provides feedback to the server regarding the results of on-site surveys and their opinions on the candidate sites they selected. The server uses this feedback to update its machine learning model and improve the accuracy of future predictions. 【0039】 This system can support the efficient and effective installation of renewable energy facilities and help optimize the use of energy resources in the region. 【0040】 The following describes the processing flow. 【0041】 Step 1: 【0042】 The server collects geographical and meteorological data from the internet and databases of specialized organizations. For example, elevation data is obtained from public GIS databases, and wind speed and sunshine duration information is downloaded from weather services. 【0043】 Step 2: 【0044】 The server preprocesses the collected data. This includes data cleansing, imputing missing values ​​using statistical methods, and detecting and removing outliers based on predefined rules. 【0045】 Step 3: 【0046】 The server uses pre-processed data to train a model by applying machine learning algorithms. Feature engineering is then performed to extract important variables for the analysis and improve the model's accuracy. 【0047】 Step 4: 【0048】 The server uses a model to predict potential locations for power generation facilities. The predicted locations are assigned evaluation scores and ranked based on conditions such as sunshine hours and wind speed. 【0049】 Step 5: 【0050】 The device visualizes the prediction results on a map and presents them to the user. The map is provided in an interactive format, allowing the user to view details using zoom and pan functions. 【0051】 Step 6: 【0052】 Users conduct on-site surveys based on the proposed locations and input their results and opinions as feedback into the device. 【0053】 Step 7: 【0054】 The server periodically updates the model based on user feedback. Newly acquired data is added to the training set, the model is retrained, and it is designed to provide more accurate predictions next time. 【0055】 (Example 1) 【0056】 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." 【0057】 The present invention aims to solve problems related to the need for data integration using geographic and climatic information, which are not considered in conventional methods, and the need to improve the accuracy of such analysis, in the candidate site selection process for the efficient installation of renewable energy power generation facilities. Specifically, it aims to promote the optimization of facility installation by accurately evaluating topographic characteristics and wind and solar power conditions, and improving the efficiency of candidate site selection based on that information. 【0058】 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. 【0059】 In this invention, the server includes means for acquiring geographic and climatic information, means for preprocessing the information and correcting outliers, and means for predicting candidate locations for power generation facilities using a machine learning algorithm. This enables efficient and effective selection of candidate sites for renewable energy facilities. 【0060】 "Geographic information" refers to information that indicates the physical attributes of a specific area, such as topography and land use. 【0061】 "Climate information" refers to information that indicates weather conditions in a specific region, such as wind power, solar power, and rainfall. 【0062】 "Preprocessing" refers to the process of organizing and processing data, such as imputing missing values ​​and correcting outliers, which is performed before data analysis. 【0063】 An "outlier" is a value in the data that differs significantly from other values ​​and can cause errors or discrepancies. 【0064】 A "machine learning algorithm" is a mathematical method that learns patterns from past data and uses them to predict and classify new data. 【0065】 A "potential location" is a place considered for installation of power generation equipment, where optimal location is predicted. 【0066】 An "interactive geographic information display system" is a digital map system that allows users to obtain information while interacting with a map. 【0067】 "Survey results" refer to information and data collected through on-site observations and verifications. 【0068】 This invention is a system for effectively installing renewable energy power generation facilities, comprising a process for accurately collecting and analyzing geographic and climatic information to select the optimal candidate site. 【0069】 The server first acquires geographic information using a GIS (Geographic Information System) database, and then collects climate information through weather information services. This includes API access using HTTP libraries such as Requests. Next, the server uses Python to preprocess the data and correct outliers in the collected data. This ensures the accuracy and consistency of the data. 【0070】 During the analysis phase, the server performs systematic data analysis using machine learning libraries such as Sci-kit Learn and TENSORFLOW®. Machine learning algorithms are used to build a model that predicts potential placement locations based on power generation efficiency and feasibility of installation. 【0071】 The device uses interactive geographic information display software such as Leaflet.js or Mapbox to visualize the analysis results. Users can view candidate locations through the displayed map and utilize zoom and detailed information display functions. For example, users can click to view detailed weather conditions such as annual sunshine hours and wind speed for a specific candidate location. 【0072】 When users provide feedback on the results and opinions of on-site surveys to the server via their terminals, this feedback is used by the server to update machine learning algorithms. This feedback process provides important data that contributes to optimizing the installation of power generation facilities and improves the accuracy of selecting candidate sites in the future. 【0073】 A concrete example of a prompt might be, "Use GIS data and weather data to predict the optimal location for solar power generation equipment and generate a map of candidate sites." This allows the generative AI model to effectively analyze the information throughout the process and derive results. 【0074】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0075】 Step 1: 【0076】 The server retrieves geographic information from a geographic information system database and collects climate information from weather information service providers. It uses the necessary API keys and query parameters as input and sends HTTP requests using the Requests library. As output, it receives JSON data containing topographic data and weather conditions (e.g., sunshine duration, wind speed). This data is stored for subsequent analysis. 【0077】 Step 2: 【0078】 The server preprocesses the data collected in the previous step. It uses the acquired geographic and climatic information as input to detect outliers and impute missing values. It creates a dataframe using the Python Pandas library and imputes missing data cells with their mean values. The output is a dataset organized in an analyzable format. 【0079】 Step 3: 【0080】 The server builds a machine learning model using preprocessed data. It receives a categorized dataset and selected features as input and runs the random forest algorithm using the Sci-kit Learn library. Once the prediction model is built, it evaluates the model's accuracy and outputs the prediction result for the optimal placement candidate location. 【0081】 Step 4: 【0082】 The terminal displays interactive geographic information based on prediction results received from the server. It receives data on potential placement locations as input and plots the location information on a map using Leaflet.js or Mapbox. Users can visually confirm the potential locations on this map and obtain detailed information using the zoom function. The output is a visual map display. 【0083】 Step 5: 【0084】 Users provide feedback to the server through their terminals, gathering information from their research. They input their research findings and opinions into a form and submit it. The server uses this feedback to update its machine learning model. Retraining the model improves its prediction accuracy for the next time. The output is the updated, more accurate model. 【0085】 (Application Example 1) 【0086】 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." 【0087】 To properly install renewable energy power generation facilities, detailed analysis based on geographical and meteorological information is necessary. However, there is a lack of systems that can effectively and efficiently identify suitable installation locations by fully utilizing this data. Furthermore, there is a lack of mechanisms for users to easily obtain information and provide feedback, which hinders the optimization of installations. This invention aims to solve these problems. 【0088】 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. 【0089】 In this invention, the server includes a device for acquiring geographical and meteorological information, a device for analyzing the information to predict locations where power generation equipment can be installed, and a device for visually displaying the locations on a map and presenting them to the user. This allows the user to check information on locations where equipment can be installed via a mobile terminal, efficiently obtain environmental conditions and expected power generation for specific locations, and provide feedback. 【0090】 "Geographical information" refers to data that describes the spatial arrangement and topographical features of a specific region. 【0091】 "Weather information" refers to data about the weather at a specific location, including information such as temperature, precipitation, wind speed, and sunshine. 【0092】 "Power generation equipment" refers to devices that generate electricity using natural energy sources, and includes solar power generation equipment and wind power generation equipment. 【0093】 A "suitable installation location" is a place that is predicted to be suitable for installing power generation equipment. 【0094】 "Map information" refers to digital or physical map data used to visually represent geographical information or information about specific locations. 【0095】 A "user" is a person who operates or refers to the system of this invention to receive geographical information and candidate installation locations. 【0096】 A "mobile device" refers to a portable communication device such as a mobile phone or tablet. 【0097】 "Environmental conditions" refer to factors that indicate the state of the natural environment in a particular area, and include weather, topography, vegetation, and so on. 【0098】 "Expected power generation" refers to the estimated amount of electricity that a power generation facility installed at a specific location is expected to be able to generate. 【0099】 "Feedback" refers to opinions and evaluation information provided by users, which is used to improve and optimize the system. 【0100】 The system for realizing this invention is primarily composed of three main actors: a server, a mobile terminal, and a user. 【0101】 The server is a device that can aggregate geographic and meteorological information. It uses "geopandas" to collect geographic data from public institutions and geographic information systems (GIS), and the "requests" library to obtain meteorological information from weather data provision services. This allows the server to collect a wide range of spatial and meteorological data. This data is analyzed using machine learning libraries such as "scikit-learn" to build a machine learning model for predicting suitable installation locations. 【0102】 The mobile device is a device for users to check candidate location information. This device displays predictive data transmitted from the server on a map, allowing users to interactively manipulate the information. The map is displayed as an interface, designed to allow users to zoom in / out on locations of interest and view detailed information. 【0103】 Users can operate their mobile devices through this system to obtain detailed information about specific candidate locations. For example, by inputting a request into the system such as "Tell me the best location for installing solar panels in a certain city," a user can receive feedback such as predicted power generation and environmental conditions for that location. 【0104】 The feedback information provided by the user in this way is returned to the server. Based on this feedback, the server incorporates suggested updates to the machine learning model to help with future predictions. This continuously improves the overall accuracy and effectiveness of the system. 【0105】 For example, if a user wants to know the most efficient placement of solar power in their area, they can use a prompt like this: 【0106】 "Please suggest the optimal locations for installing solar panels in Tokyo." 【0107】 "Please tell me the estimated power generation amount for this candidate site." 【0108】 This system provides efficient and intuitive support for the introduction of renewable energy. 【0109】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0110】 Step 1: 【0111】 The server acquires geographical and meteorological information. Geographical information is obtained from public institutions and GIS databases, while meteorological information is collected via APIs. This process uses "geopandas" to acquire and manage geographical data and "requests" to retrieve meteorological data from APIs. The input data consists of geographical and meteorological data, and the output is the collected datasets of these data. 【0112】 Step 2: 【0113】 The server preprocesses the collected geographic and meteorological data. The collected data is then processed to impute missing values ​​and remove unnecessary information, making it ready for analysis. Next, a machine learning model is created using "scikit-learn" to predict potential locations for power generation equipment. The input is the preprocessed dataset, and the output is a list of potential locations predicted by the model. Data analysis, including model training, is a crucial step in this process. 【0114】 Step 3: 【0115】 The terminal displays a list of potential installation locations received from the server on a map. Specifically, it provides an interactive map that allows the user to select and zoom in on candidate locations through a dynamic interface. The input is a list of predicted installation candidate locations, and the output is visualized map information presented to the user. 【0116】 Step 4: 【0117】 The user uses a terminal to view detailed information about potential installation locations on the interface. By selecting a location on the map, they can view information such as the environmental conditions and estimated power generation for that location. The input is the location information selected by the user, and the output is detailed data about that location. Feedback based on user input is provided here. 【0118】 Step 5: 【0119】 Users input feedback about possible installation locations via a terminal and send it to the server. The server retrieves this feedback information and uses it to improve the accuracy of its machine learning model. It incorporates this new data point into the model and uses it for training to improve future predictions. The input is user feedback information, and the output is an updated machine learning model. 【0120】 The overall processing of this system aims to improve the efficiency of renewable energy installations and optimize the use of local resources. 【0121】 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. 【0122】 This invention provides a system that combines the selection of power generation facility installation locations based on geographical and meteorological conditions with user emotion recognition. This system consists of the following phases: data collection, analysis, emotion recognition, candidate site suggestion, and feedback. 【0123】 First, in data collection, the server obtains geographical and meteorological data from the internet and public institutions. The meteorological data includes information on wind speed and sunshine intensity, which significantly impacts power generation efficiency. 【0124】 During the data analysis phase, the server preprocesses the data, including imputing missing data and removing outliers. This completes the creation of a dataset suitable for training machine learning models. 【0125】 In the emotion recognition phase, the device uses an emotion engine to recognize emotions based on user input and actions. This engine analyzes emotions from the user's language and choice patterns and adjusts the interface to suit the user. 【0126】 Next, the process moves to the candidate site proposal phase, where the server uses a machine learning model to predict suitable installation locations. The results are displayed on the terminal in an interactive map format, allowing the user to view details of the candidate sites. 【0127】 In the feedback phase, users report their feedback to the server, including sentiment data from when they selected potential locations. This feedback is used to continuously update the machine learning model and contribute to improving the accuracy of future predictions. 【0128】 For example, if a user expresses anxiety or concerns when selecting a potential site, the system can detect this and support the user's decision-making by providing additional information or alternative suggestions. In this way, the system balances technical analysis results with the user's emotional considerations, enabling more sophisticated support for the installation of renewable energy facilities. 【0129】 The following describes the processing flow. 【0130】 Step 1: 【0131】 The server collects geographical and meteorological data from the internet and databases. Specifically, it downloads topographic data from a GIS platform and uses the Japan Meteorological Agency's API to obtain historical wind speed and sunshine duration data. 【0132】 Step 2: 【0133】 The server preprocesses the collected data. Missing weather data is filled in using data from adjacent observation points, and extreme outliers are replaced with the mean or median. 【0134】 Step 3: 【0135】 The server uses the preprocessed data to train a machine learning model. For example, it applies a random forest algorithm to learn key patterns related to power generation efficiency. 【0136】 Step 4: 【0137】 The server uses a trained model to predict potential installation sites in a specified area. The prediction results include calculating a power generation efficiency score for each candidate site and ranking them. 【0138】 Step 5: 【0139】 The terminal displays predicted installation locations on a map, providing the user with a visual interface. Users can manipulate the map on the interface and view detailed information and prediction scores for the candidate locations. 【0140】 Step 6: 【0141】 The device uses nonverbal interaction patterns from the user and entered text to perform emotion recognition using an emotion engine. If the user indicates anxiety or questions, additional guidance information will be displayed on the screen. 【0142】 Step 7: 【0143】 The user selects the most suitable location based on the presented candidate locations and additional information, and then inputs their reasons for selection and their impressions into the terminal. 【0144】 Step 8: 【0145】 The server receives user feedback and sentiment data, which it uses to update its machine learning model. This improves the accuracy of future location predictions. 【0146】 This entire process allows the system to take user emotions into consideration and make more accurate and user-friendly suggestions for the installation of power generation equipment. 【0147】 (Example 2) 【0148】 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." 【0149】 Analyzing geographical and meteorological data is crucial for the efficient installation of renewable energy power generation facilities. However, conventional methods have struggled to adequately consider user emotional evaluations in addition to technical data, resulting in insufficient accuracy in suggesting potential installation sites. Furthermore, the lack of means to incorporate emotional feedback based on user input and selections into the system prevented the realization of user-friendly and flexible installation support. 【0150】 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. 【0151】 In this invention, the server includes means for acquiring geographical and meteorological data, means for preprocessing the data, supplementing missing data, and removing outliers, and means for recognizing emotions based on user operations and inputs. This makes it possible to integrate and analyze technical data and user emotional evaluations to more accurately present potential locations for power generation facilities. 【0152】 "Geographical data" refers to information about a specific location, such as elevation, topography, and location information. 【0153】 "Weather data" refers to information that shows weather conditions such as wind speed, sunshine amount, temperature, and atmospheric pressure in a specific region. 【0154】 "Preprocessing" refers to the process of filling in missing data and removing outliers in order to prepare the data for analysis. 【0155】 "Means of recognizing emotions" refers to technologies or devices used to analyze and detect a user's emotional state based on their actions or inputs. 【0156】 A "machine learning model" refers to an algorithm or system that trains a model based on a large amount of data to make predictions or classifications on new data. 【0157】 "Feedback" refers to data and opinions received from users that are used to improve or adapt the system based on their feedback and evaluations. 【0158】 A "potential installation site" refers to a location suitable for installing renewable energy power generation equipment, and is selected based on various factors. 【0159】 This invention is a system for selecting appropriate installation locations for power generation facilities based on geographical and meteorological conditions. This system mainly consists of four phases: data collection, sentiment recognition, candidate site proposal, and user feedback. 【0160】 The server first acquires geographical and meteorological data from the internet and public institutions. This includes information such as elevation, topography, wind speed, and sunshine intensity. Next, it preprocesses the data using the Python pandas library, imputing missing data and removing outliers. This process yields a dataset suitable for training machine learning models. 【0161】 The device uses natural language processing technology to recognize the user's emotions through an emotion engine, based on user actions and input. The results of this emotion analysis are used to adjust the interface for the user. 【0162】 The server uses a generated AI model based on pre-processed data to propose optimal locations for power generation facilities. This process utilizes machine learning techniques such as TensorFlow. The analysis results are displayed on the terminal in an interactive map format, allowing users to view detailed information about the candidate locations on the map. 【0163】 Users provide feedback to the system regarding any anxieties or concerns they may have when selecting a potential site. This feedback data is collected on a server, and the collected information is used to improve the accuracy of machine learning models. 【0164】 For example, if a user enters a prompt such as, "Based on current weather data and the user's sentiment, please suggest the optimal locations for power generation facilities," the system can provide the user with the best options and support their decision-making. 【0165】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0166】 Step 1: 【0167】 The server acquires geographical and meteorological data from the internet and public institutions. This includes collecting data such as elevation, topography, wind speed, and sunshine intensity for the target area. It accesses APIs and datasets as input and obtains raw data as output. 【0168】 Step 2: 【0169】 The server preprocesses the acquired raw data. It uses the Python pandas library to impute missing data and remove outliers. The input is the raw data acquired in step 1, and the output is a clean dataset suitable for machine learning models. 【0170】 Step 3: 【0171】 The device sends user actions and input data to the emotion engine. This engine uses natural language processing technology to analyze the user's emotions. Input is the user's text and action logs, and output is the analyzed emotion data. 【0172】 Step 4: 【0173】 The server uses a generative AI model based on a clean dataset to predict potential locations for power generation facilities. This process utilizes machine learning libraries such as TensorFlow. The input is the dataset generated in step 2 and the sentiment data from step 3, and the output is a list of evaluated potential locations. 【0174】 Step 5: 【0175】 The terminal displays the potential installation locations received from the server as an interactive map for the user. The input is the prediction result from step 4, and the output is a visual map display. The user can then review the details of the potential locations and make a decision. 【0176】 Step 6: 【0177】 Users provide feedback on their selected candidate locations. This feedback is provided by sending detailed reviews, including sentimental content, to the server. The server receives this feedback and uses it to improve the machine learning model. The input is user feedback information, and the output is the continuously improving performance of the model. 【0178】 (Application Example 2) 【0179】 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". 【0180】 Conventional methods for selecting locations for renewable energy facilities, which only consider geographical and meteorological conditions, have the problem of failing to adequately consider the emotional factors and decision-making anxieties of local residents. As a result, this can affect the decision on the optimal placement of facilities and potentially lead to decreased resident satisfaction. 【0181】 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. 【0182】 In this invention, the server includes means for acquiring geographical and meteorological data, means for analyzing the data to predict potential sites for power generation facilities, means for displaying the potential sites on a map and presenting them to the user, means for updating a learning model based on user feedback, and means for analyzing emotions and providing information to support the user's decision-making. This makes it possible to install facilities optimally, taking into account both the emotions of local residents and technical conditions, thereby improving resident satisfaction and enabling the efficient use of renewable energy. 【0183】 "Geographical data" refers to information about geographical location, and is a general term for data that includes details such as coordinates and topography. 【0184】 "Meteorological data" refers to information about atmospheric conditions, including weather data that encompasses specific indicators such as wind speed and sunshine amount. 【0185】 "Power generation equipment" refers to a device or system that generates electricity using natural energy sources. 【0186】 A "potential installation site" refers to a location that is considered suitable for the installation of power generation equipment based on geographical and meteorological conditions. 【0187】 "Feedback" refers to information collected from users, such as their reactions and opinions, that is used to improve a system or model. 【0188】 A "learning model" refers to a mathematical framework or algorithm used to make predictions or classifications based on data. 【0189】 "Analyzing emotions" refers to the process of measuring or estimating a user's emotional state at a given time based on their words, actions, and choice patterns. 【0190】 "Providing information" means presenting data and insights that are appropriate to the user's needs and circumstances. 【0191】 In implementing this system, the server will first acquire geographical and meteorological data from the internet and public databases. The software expected to be used is a data API. The meteorological data will include wind speed and sunshine intensity, and this information will be used to evaluate power generation efficiency. 【0192】 Next, the acquired data undergoes a cleansing process on the server, where missing data is imputed and outliers are removed. This process is performed using data preprocessing software (e.g., data analysis libraries such as Pandas). 【0193】 Subsequently, the learning model analyzes the cleansed data and predicts suitable candidate sites for power generation equipment. Machine learning algorithms (e.g., TensorFlow or Scikit-learn) can be used for this process. The predicted candidate sites are displayed on the device as an interactive map. Map rendering services such as Google® Maps API may be used to draw the map and convey information to the user. 【0194】 When users review predicted installation locations via their devices and send feedback to the server, an emotion recognition engine analyzes their actions and inputs. This engine utilizes natural language processing technologies such as the Google Cloud Natural Language API. By providing information tailored to the user's emotions, it supports their decision-making. 【0195】 Feedback data is continuously used to update the server's learning model, improving the accuracy of future predictions. Another specific example is when considering new wind power facilities; the system suggests locations with a high probability of sunny days and provides additional information if the user expresses concerns. 【0196】 An example of a prompt message would be, "Based on the following geographical and meteorological data, please suggest the optimal location for wind power generation equipment in this region." This is how the AI ​​model would be input. In this way, support for the optimal installation of power generation equipment that reflects both the sentiments of local residents and technical factors. 【0197】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0198】 Step 1: 【0199】 The server acquires geographical and meteorological data. It uses internet and public databases as input and sends the acquired data to the server via an API. The output is meteorological data (wind speed, sunshine intensity, etc.) stored on the server. 【0200】 Step 2: 【0201】 The server performs data cleansing. Here, a data analysis library (e.g., Pandas) is used to impute missing data points and remove outliers. The output is a cleansed dataset suitable for analysis. 【0202】 Step 3: 【0203】 The server runs a trained model using cleansed data to predict suitable locations for power generation facilities. This process applies machine learning algorithms (e.g., TensorFlow). The input is the cleansed dataset, and the output is information about the predicted installation locations. 【0204】 Step 4: 【0205】 The terminal receives location information from the server and displays it to the user as an interactive map. This process uses a map rendering service (e.g., Google Maps API). The input is location information, and the output is a user-interactive map display. 【0206】 Step 5: 【0207】 The user views potential locations on their device and provides emotional feedback. The device records the user's actions, and an emotion analysis engine (e.g., Google Cloud Natural Language API) analyzes the input data to determine the user's emotional state. The input is the user's action data, and the output is the analyzed emotion data. 【0208】 Step 6: 【0209】 The server receives sentiment data and feedback collected from users and updates its learning model. The input is the analyzed sentiment data and user feedback. The output is the updated learning model. This process improves the accuracy of future predictions. 【0210】 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. 【0211】 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. 【0212】 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. 【0213】 [Second Embodiment] 【0214】 Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment. 【0215】 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. 【0216】 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). 【0217】 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. 【0218】 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. 【0219】 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). 【0220】 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. 【0221】 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. 【0222】 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. 【0223】 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. 【0224】 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. 【0225】 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". 【0226】 This invention provides a system for effectively installing renewable energy power generation facilities. The system mainly consists of the following phases: data collection, analysis, site proposal, and feedback processing. 【0227】 During the data collection phase, the server acquires geographical and meteorological data from various sources. For example, topographic data is obtained from public GIS databases, while meteorological data is collected through weather information services. 【0228】 In the data analysis phase, the server preprocesses the collected data, performing interpolation and error checking as needed. Then, a machine learning algorithm analyzes the data and builds a model that predicts potential installation sites based on power generation efficiency and feasibility. 【0229】 In the candidate site proposal phase, the terminal displays the analysis results on a map, presenting the user with potential locations for power generation facilities. The map is interactive, allowing the user to zoom in / out and view detailed information. For example, the user can select a specific candidate site and check its weather conditions and expected power generation efficiency. 【0230】 In the feedback processing phase, the user provides feedback to the server regarding the results of on-site surveys and their opinions on the candidate sites they selected. The server uses this feedback to update its machine learning model and improve the accuracy of future predictions. 【0231】 This system can support the efficient and effective installation of renewable energy facilities and help optimize the use of energy resources in the region. 【0232】 The following describes the processing flow. 【0233】 Step 1: 【0234】 The server collects geographical and meteorological data from the internet and databases of specialized organizations. For example, elevation data is obtained from public GIS databases, and wind speed and sunshine duration information is downloaded from weather services. 【0235】 Step 2: 【0236】 The server preprocesses the collected data. This includes data cleansing, imputing missing values ​​using statistical methods, and detecting and removing outliers based on predefined rules. 【0237】 Step 3: 【0238】 The server uses pre-processed data to train a model by applying machine learning algorithms. Feature engineering is then performed to extract important variables for the analysis and improve the model's accuracy. 【0239】 Step 4: 【0240】 The server uses a model to predict potential locations for power generation facilities. The predicted locations are assigned evaluation scores and ranked based on conditions such as sunshine hours and wind speed. 【0241】 Step 5: 【0242】 The device visualizes the prediction results on a map and presents them to the user. The map is provided in an interactive format, allowing the user to view details using zoom and pan functions. 【0243】 Step 6: 【0244】 Users conduct on-site surveys based on the proposed locations and input their results and opinions as feedback into the device. 【0245】 Step 7: 【0246】 The server periodically updates the model based on user feedback. Newly acquired data is added to the training set, the model is retrained, and it is designed to provide more accurate predictions next time. 【0247】 (Example 1) 【0248】 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." 【0249】 The present invention aims to solve problems related to the need for data integration using geographic and climatic information, which are not considered in conventional methods, and the need to improve the accuracy of such analysis, in the candidate site selection process for the efficient installation of renewable energy power generation facilities. Specifically, it aims to promote the optimization of facility installation by accurately evaluating topographic characteristics and wind and solar power conditions, and improving the efficiency of candidate site selection based on that information. 【0250】 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. 【0251】 In this invention, the server includes means for acquiring geographic and climatic information, means for preprocessing the information and correcting outliers, and means for predicting candidate locations for power generation facilities using a machine learning algorithm. This enables efficient and effective selection of candidate sites for renewable energy facilities. 【0252】 "Geographic information" refers to information that indicates the physical attributes of a specific area, such as topography and land use. 【0253】 "Climate information" refers to information that indicates weather conditions in a specific region, such as wind power, solar power, and rainfall. 【0254】 "Preprocessing" refers to the process of organizing and processing data, such as imputing missing values ​​and correcting outliers, which is performed before data analysis. 【0255】 An "outlier" is a value in the data that differs significantly from other values ​​and can cause errors or discrepancies. 【0256】 A "machine learning algorithm" is a mathematical method that learns patterns from past data and uses them to predict and classify new data. 【0257】 A "potential location" is a place considered for installation of power generation equipment, where optimal location is predicted. 【0258】 An "interactive geographic information display system" is a digital map system that allows users to obtain information while interacting with a map. 【0259】 "Survey results" refer to information and data collected through on-site observations and verifications. 【0260】 This invention is a system for effectively installing renewable energy power generation facilities, comprising a process for accurately collecting and analyzing geographic and climatic information to select the optimal candidate site. 【0261】 The server first acquires geographic information using a GIS (Geographic Information System) database, and then collects climate information through weather information services. This includes API access using HTTP libraries such as Requests. Next, the server uses Python to preprocess the data and correct outliers in the collected data. This ensures the accuracy and consistency of the data. 【0262】 During the analysis phase, the server performs systematic data analysis using machine learning libraries such as Sci-kit Learn and TensorFlow. The machine learning algorithms are used to build a model that predicts candidate locations based on power generation efficiency and feasibility of installation. 【0263】 The device uses interactive geographic information display software such as Leaflet.js or Mapbox to visualize the analysis results. Users can view candidate locations through the displayed map and utilize zoom and detailed information display functions. For example, users can click to view detailed weather conditions such as annual sunshine hours and wind speed for a specific candidate location. 【0264】 When users provide feedback on the results and opinions of on-site surveys to the server via their terminals, this feedback is used by the server to update machine learning algorithms. This feedback process provides important data that contributes to optimizing the installation of power generation facilities and improves the accuracy of selecting candidate sites in the future. 【0265】 A concrete example of a prompt might be, "Use GIS data and weather data to predict the optimal location for solar power generation equipment and generate a map of candidate sites." This allows the generative AI model to effectively analyze the information throughout the process and derive results. 【0266】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0267】 Step 1: 【0268】 The server retrieves geographic information from a geographic information system database and collects climate information from weather information service providers. It uses the necessary API keys and query parameters as input and sends HTTP requests using the Requests library. As output, it receives JSON data containing topographic data and weather conditions (e.g., sunshine duration, wind speed). This data is stored for subsequent analysis. 【0269】 Step 2: 【0270】 The server preprocesses the data collected in the previous step. It uses the acquired geographic and climatic information as input to detect outliers and impute missing values. It creates a dataframe using the Python Pandas library and imputes missing data cells with their mean values. The output is a dataset organized in an analyzable format. 【0271】 Step 3: 【0272】 The server builds a machine learning model using preprocessed data. It receives a categorized dataset and selected features as input and runs the random forest algorithm using the Sci-kit Learn library. Once the prediction model is built, it evaluates the model's accuracy and outputs the prediction result for the optimal placement candidate location. 【0273】 Step 4: 【0274】 The terminal displays interactive geographic information based on prediction results received from the server. It receives data on potential placement locations as input and plots the location information on a map using Leaflet.js or Mapbox. Users can visually confirm the potential locations on this map and obtain detailed information using the zoom function. The output is a visual map display. 【0275】 Step 5: 【0276】 Users provide feedback to the server through their terminals, gathering information from their research. They input their research findings and opinions into a form and submit it. The server uses this feedback to update its machine learning model. Retraining the model improves its prediction accuracy for the next time. The output is the updated, more accurate model. 【0277】 (Application Example 1) 【0278】 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." 【0279】 In order to appropriately install power generation equipment using renewable energy, detailed analysis based on geographical information and meteorological information is required. However, there is no system that sufficiently utilizes these data to identify efficient and effective installation sites. In addition, there is a lack of a mechanism that allows users to easily obtain information and provide feedback, so optimization of installation has not progressed. The present invention aims to solve these problems. 【0280】 The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. 【0281】 In this invention, the server includes a device that acquires geographical information and meteorological information, a device that analyzes the information to predict possible installation sites for power generation equipment, and a device that visually displays the possible installation sites on map information and presents them to the user. As a result, the user can confirm information on possible installation sites through a mobile terminal, efficiently obtain environmental conditions and expected power generation amounts at specific sites, and also provide feedback. 【0282】 "Geographical information" is data representing the spatial arrangement and topographical features of a specific region. 【0283】 "Meteorological information" is data related to the weather at a specific point, including information such as temperature, precipitation, wind speed, and sunshine. 【0284】 "Power generation equipment" is a device that generates electricity by utilizing natural energy, including solar power generation equipment and wind power generation equipment. 【0285】 ] "Possible installation site" is a location predicted to be suitable for placing power generation equipment. 【0286】 "Map information" is digital or physical map data used to visually represent geographical information and specific point information. 【0287】 A "user" is a person who operates or refers to the system of this invention to receive geographical information and candidate installation locations. 【0288】 A "mobile device" refers to a portable communication device such as a mobile phone or tablet. 【0289】 "Environmental conditions" refer to factors that indicate the state of the natural environment in a particular area, and include weather, topography, vegetation, and so on. 【0290】 "Expected power generation" refers to the estimated amount of electricity that a power generation facility installed at a specific location is expected to be able to generate. 【0291】 "Feedback" refers to opinions and evaluation information provided by users, which is used to improve and optimize the system. 【0292】 The system for realizing this invention is primarily composed of three main actors: a server, a mobile terminal, and a user. 【0293】 The server is a device that can aggregate geographic and meteorological information. It uses "geopandas" to collect geographic data from public institutions and geographic information systems (GIS), and the "requests" library to obtain meteorological information from weather data provision services. This allows the server to collect a wide range of spatial and meteorological data. This data is analyzed using machine learning libraries such as "scikit-learn" to build a machine learning model for predicting suitable installation locations. 【0294】 The mobile device is a device for users to check candidate location information. This device displays predictive data transmitted from the server on a map, allowing users to interactively manipulate the information. The map is displayed as an interface, designed to allow users to zoom in / out on locations of interest and view detailed information. 【0295】 Users can operate their mobile devices through this system to obtain detailed information about specific candidate locations. For example, by inputting a request into the system such as "Tell me the best location for installing solar panels in a certain city," a user can receive feedback such as predicted power generation and environmental conditions for that location. 【0296】 The feedback information provided by the user in this way is returned to the server. Based on this feedback, the server incorporates suggested updates to the machine learning model to help with future predictions. This continuously improves the overall accuracy and effectiveness of the system. 【0297】 For example, if a user wants to know the most efficient placement of solar power in their area, they can use a prompt like this: 【0298】 "Please suggest the optimal locations for installing solar panels in Tokyo." 【0299】 "Please tell me the estimated power generation amount for this candidate site." 【0300】 This system provides efficient and intuitive support for the introduction of renewable energy. 【0301】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0302】 Step 1: 【0303】 The server acquires geographical and meteorological information. Geographical information is obtained from public institutions and GIS databases, while meteorological information is collected via APIs. This process uses "geopandas" to acquire and manage geographical data and "requests" to retrieve meteorological data from APIs. The input data consists of geographical and meteorological data, and the output is the collected datasets of these data. 【0304】 Step 2: 【0305】 The server preprocesses the collected geographical information and meteorological information. The collected data is complemented for missing values and unnecessary information is removed to make it in an analyzable state. Next, a machine learning model is created using "scikit-learn" to predict the installable locations of the power generation facilities. The input is the preprocessed dataset, and the output is a list of the installable locations predicted by the model. In this process, data analysis including the training of the model is an important step. 【0306】 Step 3: 【0307】 The terminal displays the list of installable locations received from the server on the map information. Specifically, an interactive map is provided so that the user can select and expand the candidate locations through a dynamic interface. The input is the list of predicted installation candidate locations, and the output is the visualized map information presented to the user. 【0308】 Step 4: 【0309】 The user uses the terminal to check the detailed information of the installable locations on the interface. By selecting a location on the map, information such as the environmental conditions and expected power generation amount at that location can be viewed. The input is the location information selected by the user, and the output is the detailed data regarding that location. Here, feedback of information based on the user operation is provided. 【0310】 Step 5: 【0311】 The user inputs feedback regarding the installable locations on the terminal and sends it to the server. The server acquires this feedback information and uses it to improve the accuracy of the machine learning model. It is incorporated into the model as a new data point and learning is performed to be utilized in the next prediction. The input is the feedback information from the user, and the output is the updated machine learning model. 【0312】 The overall processing of this system aims to improve the efficiency of renewable energy installations and optimize the use of local resources. 【0313】 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. 【0314】 This invention provides a system that combines the selection of power generation facility installation locations based on geographical and meteorological conditions with user emotion recognition. This system consists of the following phases: data collection, analysis, emotion recognition, candidate site suggestion, and feedback. 【0315】 First, in data collection, the server obtains geographical and meteorological data from the internet and public institutions. The meteorological data includes information on wind speed and sunshine intensity, which significantly impacts power generation efficiency. 【0316】 During the data analysis phase, the server preprocesses the data, including imputing missing data and removing outliers. This completes the creation of a dataset suitable for training machine learning models. 【0317】 In the emotion recognition phase, the device uses an emotion engine to recognize emotions based on user input and actions. This engine analyzes emotions from the user's language and choice patterns and adjusts the interface to suit the user. 【0318】 Next, the process moves to the candidate site proposal phase, where the server uses a machine learning model to predict suitable installation locations. The results are displayed on the terminal in an interactive map format, allowing the user to view details of the candidate sites. 【0319】 In the feedback phase, users report their feedback to the server, including sentiment data from when they selected potential locations. This feedback is used to continuously update the machine learning model and contribute to improving the accuracy of future predictions. 【0320】 For example, if a user expresses anxiety or concerns when selecting a potential site, the system can detect this and support the user's decision-making by providing additional information or alternative suggestions. In this way, the system balances technical analysis results with the user's emotional considerations, enabling more sophisticated support for the installation of renewable energy facilities. 【0321】 The following describes the processing flow. 【0322】 Step 1: 【0323】 The server collects geographical and meteorological data from the internet and databases. Specifically, it downloads topographic data from a GIS platform and uses the Japan Meteorological Agency's API to obtain historical wind speed and sunshine duration data. 【0324】 Step 2: 【0325】 The server preprocesses the collected data. Missing weather data is filled in using data from adjacent observation points, and extreme outliers are replaced with the mean or median. 【0326】 Step 3: 【0327】 The server uses the preprocessed data to train a machine learning model. For example, it applies a random forest algorithm to learn key patterns related to power generation efficiency. 【0328】 Step 4: 【0329】 The server uses a trained model to predict potential installation sites in a specified area. The prediction results include calculating a power generation efficiency score for each candidate site and ranking them. 【0330】 Step 5: 【0331】 The terminal displays predicted installation locations on a map, providing the user with a visual interface. Users can manipulate the map on the interface and view detailed information and prediction scores for the candidate locations. 【0332】 Step 6: 【0333】 The device uses nonverbal interaction patterns from the user and entered text to perform emotion recognition using an emotion engine. If the user indicates anxiety or questions, additional guidance information will be displayed on the screen. 【0334】 Step 7: 【0335】 The user selects the most suitable location based on the presented candidate locations and additional information, and then inputs their reasons for selection and their impressions into the terminal. 【0336】 Step 8: 【0337】 The server receives user feedback and sentiment data, which it uses to update its machine learning model. This improves the accuracy of future location predictions. 【0338】 This entire process allows the system to take user emotions into consideration and make more accurate and user-friendly suggestions for the installation of power generation equipment. 【0339】 (Example 2) 【0340】 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". 【0341】 Analyzing geographical and meteorological data is crucial for the efficient installation of renewable energy power generation facilities. However, conventional methods have struggled to adequately consider user emotional evaluations in addition to technical data, resulting in insufficient accuracy in suggesting potential installation sites. Furthermore, the lack of means to incorporate emotional feedback based on user input and selections into the system prevented the realization of user-friendly and flexible installation support. 【0342】 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. 【0343】 In this invention, the server includes means for acquiring geographical and meteorological data, means for preprocessing the data, supplementing missing data, and removing outliers, and means for recognizing emotions based on user operations and inputs. This makes it possible to integrate and analyze technical data and user emotional evaluations to more accurately present potential locations for power generation facilities. 【0344】 "Geographical data" refers to information about a specific location, such as elevation, topography, and location information. 【0345】 "Weather data" refers to information that shows weather conditions such as wind speed, sunshine amount, temperature, and atmospheric pressure in a specific region. 【0346】 "Preprocessing" refers to the process of filling in missing data and removing outliers in order to prepare the data for analysis. 【0347】 "Means of recognizing emotions" refers to technologies or devices used to analyze and detect a user's emotional state based on their actions or inputs. 【0348】 A "machine learning model" refers to an algorithm or system that trains a model based on a large amount of data to make predictions or classifications on new data. 【0349】 "Feedback" refers to data and opinions received from users that are used to improve or adapt the system based on their feedback and evaluations. 【0350】 A "potential installation site" refers to a location suitable for installing renewable energy power generation equipment, and is selected based on various factors. 【0351】 This invention is a system for selecting appropriate installation locations for power generation facilities based on geographical and meteorological conditions. This system mainly consists of four phases: data collection, sentiment recognition, candidate site proposal, and user feedback. 【0352】 The server first acquires geographical and meteorological data from the internet and public institutions. This includes information such as elevation, topography, wind speed, and sunshine intensity. Next, it preprocesses the data using the Python pandas library, imputing missing data and removing outliers. This process yields a dataset suitable for training machine learning models. 【0353】 The device uses natural language processing technology to recognize the user's emotions through an emotion engine, based on user actions and input. The results of this emotion analysis are used to adjust the interface for the user. 【0354】 The server uses a generated AI model based on pre-processed data to propose optimal locations for power generation facilities. This process utilizes machine learning techniques such as TensorFlow. The analysis results are displayed on the terminal in an interactive map format, allowing users to view detailed information about the candidate locations on the map. 【0355】 Users provide feedback to the system regarding any anxieties or concerns they may have when selecting a potential site. This feedback data is collected on a server, and the collected information is used to improve the accuracy of machine learning models. 【0356】 For example, if a user enters a prompt such as, "Based on current weather data and the user's sentiment, please suggest the optimal locations for power generation facilities," the system can provide the user with the best options and support their decision-making. 【0357】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0358】 Step 1: 【0359】 The server acquires geographical and meteorological data from the internet and public institutions. This includes collecting data such as elevation, topography, wind speed, and sunshine intensity for the target area. It accesses APIs and datasets as input and obtains raw data as output. 【0360】 Step 2: 【0361】 The server preprocesses the acquired raw data. It uses the Python pandas library to impute missing data and remove outliers. The input is the raw data acquired in step 1, and the output is a clean dataset suitable for machine learning models. 【0362】 Step 3: 【0363】 The device sends user actions and input data to the emotion engine. This engine uses natural language processing technology to analyze the user's emotions. Input is the user's text and action logs, and output is the analyzed emotion data. 【0364】 Step 4: 【0365】 The server uses a generative AI model based on a clean dataset to predict potential locations for power generation facilities. This process utilizes machine learning libraries such as TensorFlow. The input is the dataset generated in step 2 and the sentiment data from step 3, and the output is a list of evaluated potential locations. 【0366】 Step 5: 【0367】 The terminal displays the potential installation locations received from the server as an interactive map for the user. The input is the prediction result from step 4, and the output is a visual map display. The user can then review the details of the potential locations and make a decision. 【0368】 Step 6: 【0369】 Users provide feedback on their selected candidate locations. This feedback is provided by sending detailed reviews, including sentimental content, to the server. The server receives this feedback and uses it to improve the machine learning model. The input is user feedback information, and the output is the continuously improving performance of the model. 【0370】 (Application Example 2) 【0371】 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." 【0372】 Conventional methods for selecting locations for renewable energy facilities, which only consider geographical and meteorological conditions, have the problem of failing to adequately consider the emotional factors and decision-making anxieties of local residents. As a result, this can affect the decision on the optimal placement of facilities and potentially lead to decreased resident satisfaction. 【0373】 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. 【0374】 In this invention, the server includes means for acquiring geographical and meteorological data, means for analyzing the data to predict potential sites for power generation facilities, means for displaying the potential sites on a map and presenting them to the user, means for updating a learning model based on user feedback, and means for analyzing emotions and providing information to support the user's decision-making. This makes it possible to install facilities optimally, taking into account both the emotions of local residents and technical conditions, thereby improving resident satisfaction and enabling the efficient use of renewable energy. 【0375】 "Geographical data" refers to information about geographical location, and is a general term for data that includes details such as coordinates and topography. 【0376】 "Meteorological data" refers to information about atmospheric conditions, including weather data that encompasses specific indicators such as wind speed and sunshine amount. 【0377】 "Power generation equipment" refers to a device or system that generates electricity using natural energy sources. 【0378】 A "potential installation site" refers to a location that is considered suitable for the installation of power generation equipment based on geographical and meteorological conditions. 【0379】 "Feedback" refers to information collected from users, such as their reactions and opinions, that is used to improve a system or model. 【0380】 A "learning model" refers to a mathematical framework or algorithm used to make predictions or classifications based on data. 【0381】 "Analyzing emotions" refers to the process of measuring or estimating a user's emotional state at a given time based on their words, actions, and choice patterns. 【0382】 "Providing information" means presenting data and insights that are appropriate to the user's needs and circumstances. 【0383】 In implementing this system, the server will first acquire geographical and meteorological data from the internet and public databases. The software expected to be used is a data API. The meteorological data will include wind speed and sunshine intensity, and this information will be used to evaluate power generation efficiency. 【0384】 Next, the acquired data undergoes a cleansing process on the server, where missing data is imputed and outliers are removed. This process is performed using data preprocessing software (e.g., data analysis libraries such as Pandas). 【0385】 Subsequently, the learning model analyzes the cleansed data and predicts suitable candidate sites for power generation equipment. Machine learning algorithms (e.g., TensorFlow or Scikit-learn) can be used for this process. The predicted candidate sites are displayed on the device as an interactive map. Map rendering services such as the Google Maps API may be used to draw the map and convey information to the user. 【0386】 When users review predicted installation locations via their devices and send feedback to the server, an emotion recognition engine analyzes their actions and inputs. This engine utilizes natural language processing technologies such as the Google Cloud Natural Language API. By providing information tailored to the user's emotions, it supports their decision-making. 【0387】 Feedback data is continuously used to update the server's learning model, improving the accuracy of future predictions. Another specific example is when considering new wind power facilities; the system suggests locations with a high probability of sunny days and provides additional information if the user expresses concerns. 【0388】 An example of a prompt message would be, "Based on the following geographical and meteorological data, please suggest the optimal location for wind power generation equipment in this region." This is how the AI ​​model would be input. In this way, support for the optimal installation of power generation equipment that reflects both the sentiments of local residents and technical factors. 【0389】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0390】 Step 1: 【0391】 The server acquires geographical and meteorological data. It uses internet and public databases as input and sends the acquired data to the server via an API. The output is meteorological data (wind speed, sunshine intensity, etc.) stored on the server. 【0392】 Step 2: 【0393】 The server performs data cleansing. Here, a data analysis library (e.g., Pandas) is used to impute missing data points and remove outliers. The output is a cleansed dataset suitable for analysis. 【0394】 Step 3: 【0395】 The server runs a trained model using cleansed data to predict suitable locations for power generation facilities. This process applies machine learning algorithms (e.g., TensorFlow). The input is the cleansed dataset, and the output is information about the predicted installation locations. 【0396】 Step 4: 【0397】 The terminal receives location information from the server and displays it to the user as an interactive map. This process uses a map rendering service (e.g., Google Maps API). The input is location information, and the output is a user-interactive map display. 【0398】 Step 5: 【0399】 The user views potential locations on their device and provides emotional feedback. The device records the user's actions, and an emotion analysis engine (e.g., Google Cloud Natural Language API) analyzes the input data to determine the user's emotional state. The input is the user's action data, and the output is the analyzed emotion data. 【0400】 Step 6: 【0401】 The server receives sentiment data and feedback collected from users and updates its learning model. The input is the analyzed sentiment data and user feedback. The output is the updated learning model. This process improves the accuracy of future predictions. 【0402】 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. 【0403】 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. 【0404】 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. 【0405】 [Third Embodiment] 【0406】 Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment. 【0407】 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. 【0408】 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). 【0409】 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. 【0410】 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. 【0411】 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). 【0412】 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. 【0413】 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. 【0414】 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. 【0415】 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. 【0416】 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. 【0417】 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". 【0418】 This invention provides a system for effectively installing renewable energy power generation facilities. The system mainly consists of the following phases: data collection, analysis, site proposal, and feedback processing. 【0419】 During the data collection phase, the server acquires geographical and meteorological data from various sources. For example, topographic data is obtained from public GIS databases, while meteorological data is collected through weather information services. 【0420】 In the data analysis phase, the server preprocesses the collected data, performing interpolation and error checking as needed. Then, a machine learning algorithm analyzes the data and builds a model that predicts potential installation sites based on power generation efficiency and feasibility. 【0421】 In the candidate site proposal phase, the terminal displays the analysis results on a map, presenting the user with potential locations for power generation facilities. The map is interactive, allowing the user to zoom in / out and view detailed information. For example, the user can select a specific candidate site and check its weather conditions and expected power generation efficiency. 【0422】 In the feedback processing phase, the user provides feedback to the server regarding the results of on-site surveys and their opinions on the candidate sites they selected. The server uses this feedback to update its machine learning model and improve the accuracy of future predictions. 【0423】 This system can support the efficient and effective installation of renewable energy facilities and help optimize the use of energy resources in the region. 【0424】 The following describes the processing flow. 【0425】 Step 1: 【0426】 The server collects geographical and meteorological data from the internet and databases of specialized organizations. For example, elevation data is obtained from public GIS databases, and wind speed and sunshine duration information is downloaded from weather services. 【0427】 Step 2: 【0428】 The server preprocesses the collected data. This includes data cleansing, imputing missing values ​​using statistical methods, and detecting and removing outliers based on predefined rules. 【0429】 Step 3: 【0430】 The server uses pre-processed data to train a model by applying machine learning algorithms. Feature engineering is then performed to extract important variables for the analysis and improve the model's accuracy. 【0431】 Step 4: 【0432】 The server uses a model to predict potential locations for power generation facilities. The predicted locations are assigned evaluation scores and ranked based on conditions such as sunshine hours and wind speed. 【0433】 Step 5: 【0434】 The device visualizes the prediction results on a map and presents them to the user. The map is provided in an interactive format, allowing the user to view details using zoom and pan functions. 【0435】 Step 6: 【0436】 Users conduct on-site surveys based on the proposed locations and input their results and opinions as feedback into the device. 【0437】 Step 7: 【0438】 The server periodically updates the model based on user feedback. Newly acquired data is added to the training set, the model is retrained, and it is designed to provide more accurate predictions next time. 【0439】 (Example 1) 【0440】 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." 【0441】 The present invention aims to solve problems related to the need for data integration using geographic and climatic information, which are not considered in conventional methods, and the need to improve the accuracy of such analysis, in the candidate site selection process for the efficient installation of renewable energy power generation facilities. Specifically, it aims to promote the optimization of facility installation by accurately evaluating topographic characteristics and wind and solar power conditions, and improving the efficiency of candidate site selection based on that information. 【0442】 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. 【0443】 In this invention, the server includes means for acquiring geographic and climatic information, means for preprocessing the information and correcting outliers, and means for predicting candidate locations for power generation facilities using a machine learning algorithm. This enables efficient and effective selection of candidate sites for renewable energy facilities. 【0444】 "Geographic information" refers to information that indicates the physical attributes of a specific area, such as topography and land use. 【0445】 "Climate information" refers to information that indicates weather conditions in a specific region, such as wind power, solar power, and rainfall. 【0446】 "Preprocessing" refers to the process of organizing and processing data, such as imputing missing values ​​and correcting outliers, which is performed before data analysis. 【0447】 An "outlier" is a value in the data that differs significantly from other values ​​and can cause errors or discrepancies. 【0448】 A "machine learning algorithm" is a mathematical method that learns patterns from past data and uses them to predict and classify new data. 【0449】 A "potential location" is a place considered for installation of power generation equipment, where optimal location is predicted. 【0450】 An "interactive geographic information display system" is a digital map system that allows users to obtain information while interacting with a map. 【0451】 "Survey results" refer to information and data collected through on-site observations and verifications. 【0452】 This invention is a system for effectively installing renewable energy power generation facilities, comprising a process for accurately collecting and analyzing geographic and climatic information to select the optimal candidate site. 【0453】 The server first acquires geographic information using a GIS (Geographic Information System) database, and then collects climate information through weather information services. This includes API access using HTTP libraries such as Requests. Next, the server uses Python to preprocess the data and correct outliers in the collected data. This ensures the accuracy and consistency of the data. 【0454】 During the analysis phase, the server performs systematic data analysis using machine learning libraries such as Sci-kit Learn and TensorFlow. The machine learning algorithms are used to build a model that predicts candidate locations based on power generation efficiency and feasibility of installation. 【0455】 The device uses interactive geographic information display software such as Leaflet.js or Mapbox to visualize the analysis results. Users can view candidate locations through the displayed map and utilize zoom and detailed information display functions. For example, users can click to view detailed weather conditions such as annual sunshine hours and wind speed for a specific candidate location. 【0456】 When users provide feedback on the results and opinions of on-site surveys to the server via their terminals, this feedback is used by the server to update machine learning algorithms. This feedback process provides important data that contributes to optimizing the installation of power generation facilities and improves the accuracy of selecting candidate sites in the future. 【0457】 A concrete example of a prompt might be, "Use GIS data and weather data to predict the optimal location for solar power generation equipment and generate a map of candidate sites." This allows the generative AI model to effectively analyze the information throughout the process and derive results. 【0458】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0459】 Step 1: 【0460】 The server retrieves geographic information from a geographic information system database and collects climate information from weather information service providers. It uses the necessary API keys and query parameters as input and sends HTTP requests using the Requests library. As output, it receives JSON data containing topographic data and weather conditions (e.g., sunshine duration, wind speed). This data is stored for subsequent analysis. 【0461】 Step 2: 【0462】 The server preprocesses the data collected in the previous step. It uses the acquired geographic and climatic information as input to detect outliers and impute missing values. It creates a dataframe using the Python Pandas library and imputes missing data cells with their mean values. The output is a dataset organized in an analyzable format. 【0463】 Step 3: 【0464】 The server builds a machine learning model using preprocessed data. It receives a categorized dataset and selected features as input and runs the random forest algorithm using the Sci-kit Learn library. Once the prediction model is built, it evaluates the model's accuracy and outputs the prediction result for the optimal placement candidate location. 【0465】 Step 4: 【0466】 The terminal displays interactive geographic information based on prediction results received from the server. It receives data on potential placement locations as input and plots the location information on a map using Leaflet.js or Mapbox. Users can visually confirm the potential locations on this map and obtain detailed information using the zoom function. The output is a visual map display. 【0467】 Step 5: 【0468】 Users provide feedback to the server through their terminals, gathering information from their research. They input their research findings and opinions into a form and submit it. The server uses this feedback to update its machine learning model. Retraining the model improves its prediction accuracy for the next time. The output is the updated, more accurate model. 【0469】 (Application Example 1) 【0470】 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." 【0471】 To properly install renewable energy power generation facilities, detailed analysis based on geographical and meteorological information is necessary. However, there is a lack of systems that can effectively and efficiently identify suitable installation locations by fully utilizing this data. Furthermore, there is a lack of mechanisms for users to easily obtain information and provide feedback, which hinders the optimization of installations. This invention aims to solve these problems. 【0472】 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. 【0473】 In this invention, the server includes a device for acquiring geographical and meteorological information, a device for analyzing the information to predict locations where power generation equipment can be installed, and a device for visually displaying the locations on a map and presenting them to the user. This allows the user to check information on locations where equipment can be installed via a mobile terminal, efficiently obtain environmental conditions and expected power generation for specific locations, and provide feedback. 【0474】 "Geographical information" refers to data that describes the spatial arrangement and topographical features of a specific region. 【0475】 "Weather information" refers to data about the weather at a specific location, including information such as temperature, precipitation, wind speed, and sunshine. 【0476】 "Power generation equipment" refers to devices that generate electricity using natural energy sources, and includes solar power generation equipment and wind power generation equipment. 【0477】 A "suitable installation location" is a place that is predicted to be suitable for installing power generation equipment. 【0478】 "Map information" refers to digital or physical map data used to visually represent geographical information or information about specific locations. 【0479】 A "user" is a person who operates or refers to the system of this invention to receive geographical information and candidate installation locations. 【0480】 A "mobile device" refers to a portable communication device such as a mobile phone or tablet. 【0481】 "Environmental conditions" refer to factors that indicate the state of the natural environment in a particular area, and include weather, topography, vegetation, and so on. 【0482】 "Expected power generation" refers to the estimated amount of electricity that a power generation facility installed at a specific location is expected to be able to generate. 【0483】 "Feedback" refers to opinions and evaluation information provided by users, which is used to improve and optimize the system. 【0484】 The system for realizing this invention is primarily composed of three main actors: a server, a mobile terminal, and a user. 【0485】 The server is a device that can aggregate geographic and meteorological information. It uses "geopandas" to collect geographic data from public institutions and geographic information systems (GIS), and the "requests" library to obtain meteorological information from weather data provision services. This allows the server to collect a wide range of spatial and meteorological data. This data is analyzed using machine learning libraries such as "scikit-learn" to build a machine learning model for predicting suitable installation locations. 【0486】 The mobile device is a device for users to check candidate location information. This device displays predictive data transmitted from the server on a map, allowing users to interactively manipulate the information. The map is displayed as an interface, designed to allow users to zoom in / out on locations of interest and view detailed information. 【0487】 Users can operate their mobile devices through this system to obtain detailed information about specific candidate locations. For example, by inputting a request into the system such as "Tell me the best location for installing solar panels in a certain city," a user can receive feedback such as predicted power generation and environmental conditions for that location. 【0488】 The feedback information provided by the user in this way is returned to the server. Based on this feedback, the server incorporates suggested updates to the machine learning model to help with future predictions. This continuously improves the overall accuracy and effectiveness of the system. 【0489】 For example, if a user wants to know the most efficient placement of solar power in their area, they can use a prompt like this: 【0490】 "Please suggest the optimal locations for installing solar panels in Tokyo." 【0491】 "Please tell me the estimated power generation amount for this candidate site." 【0492】 This system provides efficient and intuitive support for the introduction of renewable energy. 【0493】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0494】 Step 1: 【0495】 The server acquires geographical and meteorological information. Geographical information is obtained from public institutions and GIS databases, while meteorological information is collected via APIs. This process uses "geopandas" to acquire and manage geographical data and "requests" to retrieve meteorological data from APIs. The input data consists of geographical and meteorological data, and the output is the collected datasets of these data. 【0496】 Step 2: 【0497】 The server preprocesses the collected geographic and meteorological data. The collected data is then processed to impute missing values ​​and remove unnecessary information, making it ready for analysis. Next, a machine learning model is created using "scikit-learn" to predict potential locations for power generation equipment. The input is the preprocessed dataset, and the output is a list of potential locations predicted by the model. Data analysis, including model training, is a crucial step in this process. 【0498】 Step 3: 【0499】 The terminal displays a list of potential installation locations received from the server on a map. Specifically, it provides an interactive map that allows the user to select and zoom in on candidate locations through a dynamic interface. The input is a list of predicted installation candidate locations, and the output is visualized map information presented to the user. 【0500】 Step 4: 【0501】 The user uses a terminal to view detailed information about potential installation locations on the interface. By selecting a location on the map, they can view information such as the environmental conditions and estimated power generation for that location. The input is the location information selected by the user, and the output is detailed data about that location. Feedback based on user input is provided here. 【0502】 Step 5: 【0503】 Users input feedback about possible installation locations via a terminal and send it to the server. The server retrieves this feedback information and uses it to improve the accuracy of its machine learning model. It incorporates this new data point into the model and uses it for training to improve future predictions. The input is user feedback information, and the output is an updated machine learning model. 【0504】 The overall processing of this system aims to improve the efficiency of renewable energy installations and optimize the use of local resources. 【0505】 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. 【0506】 This invention provides a system that combines the selection of power generation facility installation locations based on geographical and meteorological conditions with user emotion recognition. This system consists of the following phases: data collection, analysis, emotion recognition, candidate site suggestion, and feedback. 【0507】 First, in data collection, the server obtains geographical and meteorological data from the internet and public institutions. The meteorological data includes information on wind speed and sunshine intensity, which significantly impacts power generation efficiency. 【0508】 During the data analysis phase, the server preprocesses the data, including imputing missing data and removing outliers. This completes the creation of a dataset suitable for training machine learning models. 【0509】 In the emotion recognition phase, the device uses an emotion engine to recognize emotions based on user input and actions. This engine analyzes emotions from the user's language and choice patterns and adjusts the interface to suit the user. 【0510】 Next, the process moves to the candidate site proposal phase, where the server uses a machine learning model to predict suitable installation locations. The results are displayed on the terminal in an interactive map format, allowing the user to view details of the candidate sites. 【0511】 In the feedback phase, users report their feedback to the server, including sentiment data from when they selected potential locations. This feedback is used to continuously update the machine learning model and contribute to improving the accuracy of future predictions. 【0512】 For example, if a user expresses anxiety or concerns when selecting a potential site, the system can detect this and support the user's decision-making by providing additional information or alternative suggestions. In this way, the system balances technical analysis results with the user's emotional considerations, enabling more sophisticated support for the installation of renewable energy facilities. 【0513】 The following describes the processing flow. 【0514】 Step 1: 【0515】 The server collects geographical and meteorological data from the internet and databases. Specifically, it downloads topographic data from a GIS platform and uses the Japan Meteorological Agency's API to obtain historical wind speed and sunshine duration data. 【0516】 Step 2: 【0517】 The server preprocesses the collected data. Missing weather data is filled in using data from adjacent observation points, and extreme outliers are replaced with the mean or median. 【0518】 Step 3: 【0519】 The server uses the preprocessed data to train a machine learning model. For example, it applies a random forest algorithm to learn key patterns related to power generation efficiency. 【0520】 Step 4: 【0521】 The server uses a trained model to predict potential installation sites in a specified area. The prediction results include calculating a power generation efficiency score for each candidate site and ranking them. 【0522】 Step 5: 【0523】 The terminal displays predicted installation locations on a map, providing the user with a visual interface. Users can manipulate the map on the interface and view detailed information and prediction scores for the candidate locations. 【0524】 Step 6: 【0525】 The device uses nonverbal interaction patterns from the user and entered text to perform emotion recognition using an emotion engine. If the user indicates anxiety or questions, additional guidance information will be displayed on the screen. 【0526】 Step 7: 【0527】 The user selects the most suitable location based on the presented candidate locations and additional information, and then inputs their reasons for selection and their impressions into the terminal. 【0528】 Step 8: 【0529】 The server receives user feedback and sentiment data, which it uses to update its machine learning model. This improves the accuracy of future location predictions. 【0530】 This entire process allows the system to take user emotions into consideration and make more accurate and user-friendly suggestions for the installation of power generation equipment. 【0531】 (Example 2) 【0532】 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." 【0533】 Analyzing geographical and meteorological data is crucial for the efficient installation of renewable energy power generation facilities. However, conventional methods have struggled to adequately consider user emotional evaluations in addition to technical data, resulting in insufficient accuracy in suggesting potential installation sites. Furthermore, the lack of means to incorporate emotional feedback based on user input and selections into the system prevented the realization of user-friendly and flexible installation support. 【0534】 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. 【0535】 In this invention, the server includes means for acquiring geographical and meteorological data, means for preprocessing the data, supplementing missing data, and removing outliers, and means for recognizing emotions based on user operations and inputs. This makes it possible to integrate and analyze technical data and user emotional evaluations to more accurately present potential locations for power generation facilities. 【0536】 "Geographical data" refers to information about a specific location, such as elevation, topography, and location information. 【0537】 "Weather data" refers to information that shows weather conditions such as wind speed, sunshine amount, temperature, and atmospheric pressure in a specific region. 【0538】 "Preprocessing" refers to the process of filling in missing data and removing outliers in order to prepare the data for analysis. 【0539】 "Means of recognizing emotions" refers to technologies or devices used to analyze and detect a user's emotional state based on their actions or inputs. 【0540】 A "machine learning model" refers to an algorithm or system that trains a model based on a large amount of data to make predictions or classifications on new data. 【0541】 "Feedback" refers to data and opinions received from users that are used to improve or adapt the system based on their feedback and evaluations. 【0542】 A "potential installation site" refers to a location suitable for installing renewable energy power generation equipment, and is selected based on various factors. 【0543】 This invention is a system for selecting appropriate installation locations for power generation facilities based on geographical and meteorological conditions. This system mainly consists of four phases: data collection, sentiment recognition, candidate site proposal, and user feedback. 【0544】 The server first acquires geographical and meteorological data from the internet and public institutions. This includes information such as elevation, topography, wind speed, and sunshine intensity. Next, it preprocesses the data using the Python pandas library, imputing missing data and removing outliers. This process yields a dataset suitable for training machine learning models. 【0545】 The device uses natural language processing technology to recognize the user's emotions through an emotion engine, based on user actions and input. The results of this emotion analysis are used to adjust the interface for the user. 【0546】 The server uses a generated AI model based on pre-processed data to propose optimal locations for power generation facilities. This process utilizes machine learning techniques such as TensorFlow. The analysis results are displayed on the terminal in an interactive map format, allowing users to view detailed information about the candidate locations on the map. 【0547】 Users provide feedback to the system regarding any anxieties or concerns they may have when selecting a potential site. This feedback data is collected on a server, and the collected information is used to improve the accuracy of machine learning models. 【0548】 For example, if a user enters a prompt such as, "Based on current weather data and the user's sentiment, please suggest the optimal locations for power generation facilities," the system can provide the user with the best options and support their decision-making. 【0549】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0550】 Step 1: 【0551】 The server acquires geographical and meteorological data from the internet and public institutions. This includes collecting data such as elevation, topography, wind speed, and sunshine intensity for the target area. It accesses APIs and datasets as input and obtains raw data as output. 【0552】 Step 2: 【0553】 The server preprocesses the acquired raw data. It uses the Python pandas library to impute missing data and remove outliers. The input is the raw data acquired in step 1, and the output is a clean dataset suitable for machine learning models. 【0554】 Step 3: 【0555】 The device sends user actions and input data to the emotion engine. This engine uses natural language processing technology to analyze the user's emotions. Input is the user's text and action logs, and output is the analyzed emotion data. 【0556】 Step 4: 【0557】 The server uses a generative AI model based on a clean dataset to predict potential locations for power generation facilities. This process utilizes machine learning libraries such as TensorFlow. The input is the dataset generated in step 2 and the sentiment data from step 3, and the output is a list of evaluated potential locations. 【0558】 Step 5: 【0559】 The terminal displays the potential installation locations received from the server as an interactive map for the user. The input is the prediction result from step 4, and the output is a visual map display. The user can then review the details of the potential locations and make a decision. 【0560】 Step 6: 【0561】 Users provide feedback on their selected candidate locations. This feedback is provided by sending detailed reviews, including sentimental content, to the server. The server receives this feedback and uses it to improve the machine learning model. The input is user feedback information, and the output is the continuously improving performance of the model. 【0562】 (Application Example 2) 【0563】 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." 【0564】 Conventional methods for selecting locations for renewable energy facilities, which only consider geographical and meteorological conditions, have the problem of failing to adequately consider the emotional factors and decision-making anxieties of local residents. As a result, this can affect the decision on the optimal placement of facilities and potentially lead to decreased resident satisfaction. 【0565】 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. 【0566】 In this invention, the server includes means for acquiring geographical and meteorological data, means for analyzing the data to predict potential sites for power generation facilities, means for displaying the potential sites on a map and presenting them to the user, means for updating a learning model based on user feedback, and means for analyzing emotions and providing information to support the user's decision-making. This makes it possible to install facilities optimally, taking into account both the emotions of local residents and technical conditions, thereby improving resident satisfaction and enabling the efficient use of renewable energy. 【0567】 "Geographical data" refers to information about geographical location, and is a general term for data that includes details such as coordinates and topography. 【0568】 "Meteorological data" refers to information about atmospheric conditions, including weather data that encompasses specific indicators such as wind speed and sunshine amount. 【0569】 "Power generation equipment" refers to a device or system that generates electricity using natural energy sources. 【0570】 A "potential installation site" refers to a location that is considered suitable for the installation of power generation equipment based on geographical and meteorological conditions. 【0571】 "Feedback" refers to information collected from users, such as their reactions and opinions, that is used to improve a system or model. 【0572】 A "learning model" refers to a mathematical framework or algorithm used to make predictions or classifications based on data. 【0573】 "Analyzing emotions" refers to the process of measuring or estimating a user's emotional state at a given time based on their words, actions, and choice patterns. 【0574】 "Providing information" means presenting data and insights that are appropriate to the user's needs and circumstances. 【0575】 In implementing this system, the server will first acquire geographical and meteorological data from the internet and public databases. The software expected to be used is a data API. The meteorological data will include wind speed and sunshine intensity, and this information will be used to evaluate power generation efficiency. 【0576】 Next, the acquired data undergoes a cleansing process on the server, where missing data is imputed and outliers are removed. This process is performed using data preprocessing software (e.g., data analysis libraries such as Pandas). 【0577】 Subsequently, the learning model analyzes the cleansed data and predicts suitable candidate sites for power generation equipment. Machine learning algorithms (e.g., TensorFlow or Scikit-learn) can be used for this process. The predicted candidate sites are displayed on the device as an interactive map. Map rendering services such as the Google Maps API may be used to draw the map and convey information to the user. 【0578】 When users review predicted installation locations via their devices and send feedback to the server, an emotion recognition engine analyzes their actions and inputs. This engine utilizes natural language processing technologies such as the Google Cloud Natural Language API. By providing information tailored to the user's emotions, it supports their decision-making. 【0579】 Feedback data is continuously used to update the server's learning model, improving the accuracy of future predictions. Another specific example is when considering new wind power facilities; the system suggests locations with a high probability of sunny days and provides additional information if the user expresses concerns. 【0580】 An example of a prompt message would be, "Based on the following geographical and meteorological data, please suggest the optimal location for wind power generation equipment in this region." This is how the AI ​​model would be input. In this way, support for the optimal installation of power generation equipment that reflects both the sentiments of local residents and technical factors. 【0581】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0582】 Step 1: 【0583】 The server acquires geographical and meteorological data. It uses internet and public databases as input and sends the acquired data to the server via an API. The output is meteorological data (wind speed, sunshine intensity, etc.) stored on the server. 【0584】 Step 2: 【0585】 The server performs data cleansing. Here, a data analysis library (e.g., Pandas) is used to impute missing data points and remove outliers. The output is a cleansed dataset suitable for analysis. 【0586】 Step 3: 【0587】 The server runs a trained model using cleansed data to predict suitable locations for power generation facilities. This process applies machine learning algorithms (e.g., TensorFlow). The input is the cleansed dataset, and the output is information about the predicted installation locations. 【0588】 Step 4: 【0589】 The terminal receives location information from the server and displays it to the user as an interactive map. This process uses a map rendering service (e.g., Google Maps API). The input is location information, and the output is a user-interactive map display. 【0590】 Step 5: 【0591】 The user views potential locations on their device and provides emotional feedback. The device records the user's actions, and an emotion analysis engine (e.g., Google Cloud Natural Language API) analyzes the input data to determine the user's emotional state. The input is the user's action data, and the output is the analyzed emotion data. 【0592】 Step 6: 【0593】 The server receives sentiment data and feedback collected from users and updates its learning model. The input is the analyzed sentiment data and user feedback. The output is the updated learning model. This process improves the accuracy of future predictions. 【0594】 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. 【0595】 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. 【0596】 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. 【0597】 [Fourth Embodiment] 【0598】 Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment. 【0599】 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. 【0600】 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). 【0601】 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. 【0602】 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. 【0603】 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). 【0604】 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. 【0605】 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. 【0606】 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. 【0607】 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. 【0608】 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. 【0609】 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. 【0610】 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". 【0611】 This invention provides a system for effectively installing renewable energy power generation facilities. The system mainly consists of the following phases: data collection, analysis, site proposal, and feedback processing. 【0612】 During the data collection phase, the server acquires geographical and meteorological data from various sources. For example, topographic data is obtained from public GIS databases, while meteorological data is collected through weather information services. 【0613】 In the data analysis phase, the server preprocesses the collected data, performing interpolation and error checking as needed. Then, a machine learning algorithm analyzes the data and builds a model that predicts potential installation sites based on power generation efficiency and feasibility. 【0614】 In the candidate site proposal phase, the terminal displays the analysis results on a map, presenting the user with potential locations for power generation facilities. The map is interactive, allowing the user to zoom in / out and view detailed information. For example, the user can select a specific candidate site and check its weather conditions and expected power generation efficiency. 【0615】 In the feedback processing phase, the user provides feedback to the server regarding the results of on-site surveys and their opinions on the candidate sites they selected. The server uses this feedback to update its machine learning model and improve the accuracy of future predictions. 【0616】 This system can support the efficient and effective installation of renewable energy facilities and help optimize the use of energy resources in the region. 【0617】 The following describes the processing flow. 【0618】 Step 1: 【0619】 The server collects geographical and meteorological data from the internet and databases of specialized organizations. For example, elevation data is obtained from public GIS databases, and wind speed and sunshine duration information is downloaded from weather services. 【0620】 Step 2: 【0621】 The server preprocesses the collected data. This includes data cleansing, imputing missing values ​​using statistical methods, and detecting and removing outliers based on predefined rules. 【0622】 Step 3: 【0623】 The server uses pre-processed data to train a model by applying machine learning algorithms. Feature engineering is then performed to extract important variables for the analysis and improve the model's accuracy. 【0624】 Step 4: 【0625】 The server uses a model to predict potential locations for power generation facilities. The predicted locations are assigned evaluation scores and ranked based on conditions such as sunshine hours and wind speed. 【0626】 Step 5: 【0627】 The device visualizes the prediction results on a map and presents them to the user. The map is provided in an interactive format, allowing the user to view details using zoom and pan functions. 【0628】 Step 6: 【0629】 Users conduct on-site surveys based on the proposed locations and input their results and opinions as feedback into the device. 【0630】 Step 7: 【0631】 The server periodically updates the model based on user feedback. Newly acquired data is added to the training set, the model is retrained, and it is designed to provide more accurate predictions next time. 【0632】 (Example 1) 【0633】 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". 【0634】 The present invention aims to solve problems related to the need for data integration using geographic and climatic information, which are not considered in conventional methods, and the need to improve the accuracy of such analysis, in the candidate site selection process for the efficient installation of renewable energy power generation facilities. Specifically, it aims to promote the optimization of facility installation by accurately evaluating topographic characteristics and wind and solar power conditions, and improving the efficiency of candidate site selection based on that information. 【0635】 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. 【0636】 In this invention, the server includes means for acquiring geographic and climatic information, means for preprocessing the information and correcting outliers, and means for predicting candidate locations for power generation facilities using a machine learning algorithm. This enables efficient and effective selection of candidate sites for renewable energy facilities. 【0637】 "Geographic information" refers to information that indicates the physical attributes of a specific area, such as topography and land use. 【0638】 "Climate information" refers to information that indicates weather conditions in a specific region, such as wind power, solar power, and rainfall. 【0639】 "Preprocessing" refers to the process of organizing and processing data, such as imputing missing values ​​and correcting outliers, which is performed before data analysis. 【0640】 An "outlier" is a value in the data that differs significantly from other values ​​and can cause errors or discrepancies. 【0641】 A "machine learning algorithm" is a mathematical method that learns patterns from past data and uses them to predict and classify new data. 【0642】 A "potential location" is a place considered for installation of power generation equipment, where optimal location is predicted. 【0643】 An "interactive geographic information display system" is a digital map system that allows users to obtain information while interacting with a map. 【0644】 "Survey results" refer to information and data collected through on-site observations and verifications. 【0645】 This invention is a system for effectively installing renewable energy power generation facilities, comprising a process for accurately collecting and analyzing geographic and climatic information to select the optimal candidate site. 【0646】 The server first acquires geographic information using a GIS (Geographic Information System) database, and then collects climate information through weather information services. This includes API access using HTTP libraries such as Requests. Next, the server uses Python to preprocess the data and correct outliers in the collected data. This ensures the accuracy and consistency of the data. 【0647】 During the analysis phase, the server performs systematic data analysis using machine learning libraries such as Sci-kit Learn and TensorFlow. The machine learning algorithms are used to build a model that predicts candidate locations based on power generation efficiency and feasibility of installation. 【0648】 The device uses interactive geographic information display software such as Leaflet.js or Mapbox to visualize the analysis results. Users can view candidate locations through the displayed map and utilize zoom and detailed information display functions. For example, users can click to view detailed weather conditions such as annual sunshine hours and wind speed for a specific candidate location. 【0649】 When users provide feedback on the results and opinions of on-site surveys to the server via their terminals, this feedback is used by the server to update machine learning algorithms. This feedback process provides important data that contributes to optimizing the installation of power generation facilities and improves the accuracy of selecting candidate sites in the future. 【0650】 A concrete example of a prompt might be, "Use GIS data and weather data to predict the optimal location for solar power generation equipment and generate a map of candidate sites." This allows the generative AI model to effectively analyze the information throughout the process and derive results. 【0651】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0652】 Step 1: 【0653】 The server retrieves geographic information from a geographic information system database and collects climate information from weather information service providers. It uses the necessary API keys and query parameters as input and sends HTTP requests using the Requests library. As output, it receives JSON data containing topographic data and weather conditions (e.g., sunshine duration, wind speed). This data is stored for subsequent analysis. 【0654】 Step 2: 【0655】 The server preprocesses the data collected in the previous step. It uses the acquired geographic and climatic information as input to detect outliers and impute missing values. It creates a dataframe using the Python Pandas library and imputes missing data cells with their mean values. The output is a dataset organized in an analyzable format. 【0656】 Step 3: 【0657】 The server builds a machine learning model using preprocessed data. It receives a categorized dataset and selected features as input and runs the random forest algorithm using the Sci-kit Learn library. Once the prediction model is built, it evaluates the model's accuracy and outputs the prediction result for the optimal placement candidate location. 【0658】 Step 4: 【0659】 The terminal displays interactive geographic information based on prediction results received from the server. It receives data on potential placement locations as input and plots the location information on a map using Leaflet.js or Mapbox. Users can visually confirm the potential locations on this map and obtain detailed information using the zoom function. The output is a visual map display. 【0660】 Step 5: 【0661】 Users provide feedback to the server through their terminals, gathering information from their research. They input their research findings and opinions into a form and submit it. The server uses this feedback to update its machine learning model. Retraining the model improves its prediction accuracy for the next time. The output is the updated, more accurate model. 【0662】 (Application Example 1) 【0663】 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". 【0664】 To properly install renewable energy power generation facilities, detailed analysis based on geographical and meteorological information is necessary. However, there is a lack of systems that can effectively and efficiently identify suitable installation locations by fully utilizing this data. Furthermore, there is a lack of mechanisms for users to easily obtain information and provide feedback, which hinders the optimization of installations. This invention aims to solve these problems. 【0665】 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. 【0666】 In this invention, the server includes a device for acquiring geographical and meteorological information, a device for analyzing the information to predict locations where power generation equipment can be installed, and a device for visually displaying the locations on a map and presenting them to the user. This allows the user to check information on locations where equipment can be installed via a mobile terminal, efficiently obtain environmental conditions and expected power generation for specific locations, and provide feedback. 【0667】 "Geographical information" refers to data that describes the spatial arrangement and topographical features of a specific region. 【0668】 "Weather information" refers to data about the weather at a specific location, including information such as temperature, precipitation, wind speed, and sunshine. 【0669】 "Power generation equipment" refers to devices that generate electricity using natural energy sources, and includes solar power generation equipment and wind power generation equipment. 【0670】 A "suitable installation location" is a place that is predicted to be suitable for installing power generation equipment. 【0671】 "Map information" refers to digital or physical map data used to visually represent geographical information or information about specific locations. 【0672】 A "user" is a person who operates or refers to the system of this invention to receive geographical information and candidate installation locations. 【0673】 A "mobile device" refers to a portable communication device such as a mobile phone or tablet. 【0674】 "Environmental conditions" refer to factors that indicate the state of the natural environment in a particular area, and include weather, topography, vegetation, and so on. 【0675】 "Expected power generation" refers to the estimated amount of electricity that a power generation facility installed at a specific location is expected to be able to generate. 【0676】 "Feedback" refers to opinions and evaluation information provided by users, which is used to improve and optimize the system. 【0677】 The system for realizing this invention is primarily composed of three main actors: a server, a mobile terminal, and a user. 【0678】 The server is a device that can aggregate geographic and meteorological information. It uses "geopandas" to collect geographic data from public institutions and geographic information systems (GIS), and the "requests" library to obtain meteorological information from weather data provision services. This allows the server to collect a wide range of spatial and meteorological data. This data is analyzed using machine learning libraries such as "scikit-learn" to build a machine learning model for predicting suitable installation locations. 【0679】 The mobile device is a device for users to check candidate location information. This device displays predictive data transmitted from the server on a map, allowing users to interactively manipulate the information. The map is displayed as an interface, designed to allow users to zoom in / out on locations of interest and view detailed information. 【0680】 Users can operate their mobile devices through this system to obtain detailed information about specific candidate locations. For example, by inputting a request into the system such as "Tell me the best location for installing solar panels in a certain city," a user can receive feedback such as predicted power generation and environmental conditions for that location. 【0681】 The feedback information provided by the user in this way is returned to the server. Based on this feedback, the server incorporates suggested updates to the machine learning model to help with future predictions. This continuously improves the overall accuracy and effectiveness of the system. 【0682】 For example, if a user wants to know the most efficient placement of solar power in their area, they can use a prompt like this: 【0683】 "Please suggest the optimal locations for installing solar panels in Tokyo." 【0684】 "Please tell me the estimated power generation amount for this candidate site." 【0685】 This system provides efficient and intuitive support for the introduction of renewable energy. 【0686】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0687】 Step 1: 【0688】 The server acquires geographical and meteorological information. Geographical information is obtained from public institutions and GIS databases, while meteorological information is collected via APIs. This process uses "geopandas" to acquire and manage geographical data and "requests" to retrieve meteorological data from APIs. The input data consists of geographical and meteorological data, and the output is the collected datasets of these data. 【0689】 Step 2: 【0690】 The server preprocesses the collected geographic and meteorological data. The collected data is then processed to impute missing values ​​and remove unnecessary information, making it ready for analysis. Next, a machine learning model is created using "scikit-learn" to predict potential locations for power generation equipment. The input is the preprocessed dataset, and the output is a list of potential locations predicted by the model. Data analysis, including model training, is a crucial step in this process. 【0691】 Step 3: 【0692】 The terminal displays a list of potential installation locations received from the server on a map. Specifically, it provides an interactive map that allows the user to select and zoom in on candidate locations through a dynamic interface. The input is a list of predicted installation candidate locations, and the output is visualized map information presented to the user. 【0693】 Step 4: 【0694】 The user uses a terminal to view detailed information about potential installation locations on the interface. By selecting a location on the map, they can view information such as the environmental conditions and estimated power generation for that location. The input is the location information selected by the user, and the output is detailed data about that location. Feedback based on user input is provided here. 【0695】 Step 5: 【0696】 Users input feedback about possible installation locations via a terminal and send it to the server. The server retrieves this feedback information and uses it to improve the accuracy of its machine learning model. It incorporates this new data point into the model and uses it for training to improve future predictions. The input is user feedback information, and the output is an updated machine learning model. 【0697】 The overall processing of this system aims to improve the efficiency of renewable energy installations and optimize the use of local resources. 【0698】 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. 【0699】 This invention provides a system that combines the selection of power generation facility installation locations based on geographical and meteorological conditions with user emotion recognition. This system consists of the following phases: data collection, analysis, emotion recognition, candidate site suggestion, and feedback. 【0700】 First, in data collection, the server obtains geographical and meteorological data from the internet and public institutions. The meteorological data includes information on wind speed and sunshine intensity, which significantly impacts power generation efficiency. 【0701】 During the data analysis phase, the server preprocesses the data, including imputing missing data and removing outliers. This completes the creation of a dataset suitable for training machine learning models. 【0702】 In the emotion recognition phase, the device uses an emotion engine to recognize emotions based on user input and actions. This engine analyzes emotions from the user's language and choice patterns and adjusts the interface to suit the user. 【0703】 Next, the process moves to the candidate site proposal phase, where the server uses a machine learning model to predict suitable installation locations. The results are displayed on the terminal in an interactive map format, allowing the user to view details of the candidate sites. 【0704】 In the feedback phase, users report their feedback to the server, including sentiment data from when they selected potential locations. This feedback is used to continuously update the machine learning model and contribute to improving the accuracy of future predictions. 【0705】 For example, if a user expresses anxiety or concerns when selecting a potential site, the system can detect this and support the user's decision-making by providing additional information or alternative suggestions. In this way, the system balances technical analysis results with the user's emotional considerations, enabling more sophisticated support for the installation of renewable energy facilities. 【0706】 The following describes the processing flow. 【0707】 Step 1: 【0708】 The server collects geographical and meteorological data from the internet and databases. Specifically, it downloads topographic data from a GIS platform and uses the Japan Meteorological Agency's API to obtain historical wind speed and sunshine duration data. 【0709】 Step 2: 【0710】 The server preprocesses the collected data. Missing weather data is filled in using data from adjacent observation points, and extreme outliers are replaced with the mean or median. 【0711】 Step 3: 【0712】 The server uses the preprocessed data to train a machine learning model. For example, it applies a random forest algorithm to learn key patterns related to power generation efficiency. 【0713】 Step 4: 【0714】 The server uses a trained model to predict potential installation sites in a specified area. The prediction results include calculating a power generation efficiency score for each candidate site and ranking them. 【0715】 Step 5: 【0716】 The terminal displays predicted installation locations on a map, providing the user with a visual interface. Users can manipulate the map on the interface and view detailed information and prediction scores for the candidate locations. 【0717】 Step 6: 【0718】 The device uses nonverbal interaction patterns from the user and entered text to perform emotion recognition using an emotion engine. If the user indicates anxiety or questions, additional guidance information will be displayed on the screen. 【0719】 Step 7: 【0720】 The user selects the most suitable location based on the presented candidate locations and additional information, and then inputs their reasons for selection and their impressions into the terminal. 【0721】 Step 8: 【0722】 The server receives user feedback and sentiment data, which it uses to update its machine learning model. This improves the accuracy of future location predictions. 【0723】 This entire process allows the system to take user emotions into consideration and make more accurate and user-friendly suggestions for the installation of power generation equipment. 【0724】 (Example 2) 【0725】 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". 【0726】 Analyzing geographical and meteorological data is crucial for the efficient installation of renewable energy power generation facilities. However, conventional methods have struggled to adequately consider user emotional evaluations in addition to technical data, resulting in insufficient accuracy in suggesting potential installation sites. Furthermore, the lack of means to incorporate emotional feedback based on user input and selections into the system prevented the realization of user-friendly and flexible installation support. 【0727】 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. 【0728】 In this invention, the server includes means for acquiring geographical and meteorological data, means for preprocessing the data, supplementing missing data, and removing outliers, and means for recognizing emotions based on user operations and inputs. This makes it possible to integrate and analyze technical data and user emotional evaluations to more accurately present potential locations for power generation facilities. 【0729】 "Geographical data" refers to information about a specific location, such as elevation, topography, and location information. 【0730】 "Weather data" refers to information that shows weather conditions such as wind speed, sunshine amount, temperature, and atmospheric pressure in a specific region. 【0731】 "Preprocessing" refers to the process of filling in missing data and removing outliers in order to prepare the data for analysis. 【0732】 "Means of recognizing emotions" refers to technologies or devices used to analyze and detect a user's emotional state based on their actions or inputs. 【0733】 A "machine learning model" refers to an algorithm or system that trains a model based on a large amount of data to make predictions or classifications on new data. 【0734】 "Feedback" refers to data and opinions received from users that are used to improve or adapt the system based on their feedback and evaluations. 【0735】 A "potential installation site" refers to a location suitable for installing renewable energy power generation equipment, and is selected based on various factors. 【0736】 This invention is a system for selecting appropriate installation locations for power generation facilities based on geographical and meteorological conditions. This system mainly consists of four phases: data collection, sentiment recognition, candidate site proposal, and user feedback. 【0737】 The server first acquires geographical and meteorological data from the internet and public institutions. This includes information such as elevation, topography, wind speed, and sunshine intensity. Next, it preprocesses the data using the Python pandas library, imputing missing data and removing outliers. This process yields a dataset suitable for training machine learning models. 【0738】 The device uses natural language processing technology to recognize the user's emotions through an emotion engine, based on user actions and input. The results of this emotion analysis are used to adjust the interface for the user. 【0739】 The server uses a generated AI model based on pre-processed data to propose optimal locations for power generation facilities. This process utilizes machine learning techniques such as TensorFlow. The analysis results are displayed on the terminal in an interactive map format, allowing users to view detailed information about the candidate locations on the map. 【0740】 Users provide feedback to the system regarding any anxieties or concerns they may have when selecting a potential site. This feedback data is collected on a server, and the collected information is used to improve the accuracy of machine learning models. 【0741】 For example, if a user enters a prompt such as, "Based on current weather data and the user's sentiment, please suggest the optimal locations for power generation facilities," the system can provide the user with the best options and support their decision-making. 【0742】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0743】 Step 1: 【0744】 The server acquires geographical and meteorological data from the internet and public institutions. This includes collecting data such as elevation, topography, wind speed, and sunshine intensity for the target area. It accesses APIs and datasets as input and obtains raw data as output. 【0745】 Step 2: 【0746】 The server preprocesses the acquired raw data. It uses the Python pandas library to impute missing data and remove outliers. The input is the raw data acquired in step 1, and the output is a clean dataset suitable for machine learning models. 【0747】 Step 3: 【0748】 The device sends user actions and input data to the emotion engine. This engine uses natural language processing technology to analyze the user's emotions. Input is the user's text and action logs, and output is the analyzed emotion data. 【0749】 Step 4: 【0750】 The server uses a generative AI model based on a clean dataset to predict potential locations for power generation facilities. This process utilizes machine learning libraries such as TensorFlow. The input is the dataset generated in step 2 and the sentiment data from step 3, and the output is a list of evaluated potential locations. 【0751】 Step 5: 【0752】 The terminal displays the potential installation locations received from the server as an interactive map for the user. The input is the prediction result from step 4, and the output is a visual map display. The user can then review the details of the potential locations and make a decision. 【0753】 Step 6: 【0754】 Users provide feedback on their selected candidate locations. This feedback is provided by sending detailed reviews, including sentimental content, to the server. The server receives this feedback and uses it to improve the machine learning model. The input is user feedback information, and the output is the continuously improving performance of the model. 【0755】 (Application Example 2) 【0756】 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". 【0757】 Conventional methods for selecting locations for renewable energy facilities, which only consider geographical and meteorological conditions, have the problem of failing to adequately consider the emotional factors and decision-making anxieties of local residents. As a result, this can affect the decision on the optimal placement of facilities and potentially lead to decreased resident satisfaction. 【0758】 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. 【0759】 In this invention, the server includes means for acquiring geographical and meteorological data, means for analyzing the data to predict potential sites for power generation facilities, means for displaying the potential sites on a map and presenting them to the user, means for updating a learning model based on user feedback, and means for analyzing emotions and providing information to support the user's decision-making. This makes it possible to install facilities optimally, taking into account both the emotions of local residents and technical conditions, thereby improving resident satisfaction and enabling the efficient use of renewable energy. 【0760】 "Geographical data" refers to information about geographical location, and is a general term for data that includes details such as coordinates and topography. 【0761】 "Meteorological data" refers to information about atmospheric conditions, including weather data that encompasses specific indicators such as wind speed and sunshine amount. 【0762】 "Power generation equipment" refers to a device or system that generates electricity using natural energy sources. 【0763】 A "potential installation site" refers to a location that is considered suitable for the installation of power generation equipment based on geographical and meteorological conditions. 【0764】 "Feedback" refers to information collected from users, such as their reactions and opinions, that is used to improve a system or model. 【0765】 A "learning model" refers to a mathematical framework or algorithm used to make predictions or classifications based on data. 【0766】 "Analyzing emotions" refers to the process of measuring or estimating a user's emotional state at a given time based on their words, actions, and choice patterns. 【0767】 "Providing information" means presenting data and insights that are appropriate to the user's needs and circumstances. 【0768】 In implementing this system, the server will first acquire geographical and meteorological data from the internet and public databases. The software expected to be used is a data API. The meteorological data will include wind speed and sunshine intensity, and this information will be used to evaluate power generation efficiency. 【0769】 Next, the acquired data undergoes a cleansing process on the server, where missing data is imputed and outliers are removed. This process is performed using data preprocessing software (e.g., data analysis libraries such as Pandas). 【0770】 Subsequently, the learning model analyzes the cleansed data and predicts suitable candidate sites for power generation equipment. Machine learning algorithms (e.g., TensorFlow or Scikit-learn) can be used for this process. The predicted candidate sites are displayed on the device as an interactive map. Map rendering services such as the Google Maps API may be used to draw the map and convey information to the user. 【0771】 When users review predicted installation locations via their devices and send feedback to the server, an emotion recognition engine analyzes their actions and inputs. This engine utilizes natural language processing technologies such as the Google Cloud Natural Language API. By providing information tailored to the user's emotions, it supports their decision-making. 【0772】 Feedback data is continuously used to update the server's learning model, improving the accuracy of future predictions. Another specific example is when considering new wind power facilities; the system suggests locations with a high probability of sunny days and provides additional information if the user expresses concerns. 【0773】 An example of a prompt message would be, "Based on the following geographical and meteorological data, please suggest the optimal location for wind power generation equipment in this region." This is how the AI ​​model would be input. In this way, support for the optimal installation of power generation equipment that reflects both the sentiments of local residents and technical factors. 【0774】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0775】 Step 1: 【0776】 The server acquires geographical and meteorological data. It uses internet and public databases as input and sends the acquired data to the server via an API. The output is meteorological data (wind speed, sunshine intensity, etc.) stored on the server. 【0777】 Step 2: 【0778】 The server performs data cleansing. Here, a data analysis library (e.g., Pandas) is used to impute missing data points and remove outliers. The output is a cleansed dataset suitable for analysis. 【0779】 Step 3: 【0780】 The server runs a trained model using cleansed data to predict suitable locations for power generation facilities. This process applies machine learning algorithms (e.g., TensorFlow). The input is the cleansed dataset, and the output is information about the predicted installation locations. 【0781】 Step 4: 【0782】 The terminal receives location information from the server and displays it to the user as an interactive map. This process uses a map rendering service (e.g., Google Maps API). The input is location information, and the output is a user-interactive map display. 【0783】 Step 5: 【0784】 The user views potential locations on their device and provides emotional feedback. The device records the user's actions, and an emotion analysis engine (e.g., Google Cloud Natural Language API) analyzes the input data to determine the user's emotional state. The input is the user's action data, and the output is the analyzed emotion data. 【0785】 Step 6: 【0786】 The server receives sentiment data and feedback collected from users and updates its learning model. The input is the analyzed sentiment data and user feedback. The output is the updated learning model. This process improves the accuracy of future predictions. 【0787】 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. 【0788】 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. 【0789】 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. 【0790】 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. 【0791】 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. 【0792】 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. 【0793】 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. 【0794】 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. 【0795】 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." 【0796】 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. 【0797】 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. 【0798】 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. 【0799】 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. 【0800】 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. 【0801】 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. 【0802】 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. 【0803】 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. 【0804】 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. 【0805】 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. 【0806】 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. 【0807】 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. 【0808】 The following is further disclosed regarding the embodiments described above. 【0809】 (Claim 1) 【0810】 Means for obtaining geographical and meteorological data, 【0811】 A means for analyzing the aforementioned data to predict potential sites for power generation equipment, 【0812】 A means for displaying the aforementioned candidate installation sites on a map and presenting them to the user, 【0813】 A means of updating machine learning models based on user feedback, 【0814】 A system that includes this. 【0815】 (Claim 2) 【0816】 The system according to claim 1, wherein the weather data includes wind speed data and sunshine data. 【0817】 (Claim 3) 【0818】 The system according to claim 1, wherein the machine learning model is evaluated for accuracy by cross-validation of data. 【0819】 "Example 1" 【0820】 (Claim 1) 【0821】 Means for obtaining geographic and climatic information, 【0822】 A means for preprocessing the aforementioned information and correcting abnormal values, 【0823】 A method for predicting candidate locations for power generation equipment using a machine learning algorithm, 【0824】 A means for displaying the aforementioned prediction results on an interactive geographic information display device and indicating candidate locations to the user, 【0825】 A means for receiving survey results from users and updating the machine learning algorithm, 【0826】 A system that includes this. 【0827】 (Claim 2) 【0828】 The system according to claim 1, wherein the climate information includes wind power information and solar power information. 【0829】 (Claim 3) 【0830】 The system according to claim 1, wherein the accuracy of the machine learning algorithm is measured by an information evaluation method. 【0831】 "Application Example 1" 【0832】 (Claim 1) 【0833】 A device for acquiring geographical and meteorological information, 【0834】 A device that analyzes the aforementioned information to predict possible locations for installing power generation equipment, 【0835】 A device that visually displays the aforementioned installation locations on map information and presents them to the user, 【0836】 A device that improves machine learning models based on feedback from users, 【0837】 A device that allows users to check information on possible installation locations via a mobile device and obtain environmental conditions and estimated power generation for specific locations, 【0838】 A system that includes this. 【0839】 (Claim 2) 【0840】 The system according to claim 1, wherein the weather information includes wind speed information and solar irradiation information. 【0841】 (Claim 3) 【0842】 The system according to claim 1, wherein the machine learning model is evaluated for accuracy by mutual verification of information. 【0843】 "Example 2 of combining an emotion engine" 【0844】 (Claim 1) 【0845】 Means for obtaining geographical and meteorological data, 【0846】 The means for preprocessing the aforementioned data, imputing missing data, and removing outliers, 【0847】 A means of recognizing emotions based on user actions and inputs, 【0848】 A means for predicting potential sites for power generation equipment using the processed data, 【0849】 A means for displaying the aforementioned candidate installation sites on a map and presenting them to the user, 【0850】 A means of updating machine learning models based on user feedback, 【0851】 A system that includes this. 【0852】 (Claim 2) 【0853】 The system according to claim 1, wherein the weather data includes wind speed data and sunshine data. 【0854】 (Claim 3) 【0855】 The system according to claim 1, wherein the emotion recognition means analyzes the user's emotions based on natural language processing technology. 【0856】 "Application example 2 when combining with an emotional engine" 【0857】 (Claim 1) 【0858】 Means for obtaining geographical and meteorological data, 【0859】 A means for analyzing the aforementioned data to predict potential sites for power generation equipment, 【0860】 A means of displaying the aforementioned candidate installation sites on a map and presenting them to users, 【0861】 A means of updating the learning model based on user feedback, 【0862】 A means of analyzing emotions and providing information to support users' decision-making, 【0863】 A system that includes this. 【0864】 (Claim 2) 【0865】 The system according to claim 1, wherein the weather data includes wind speed information and sunshine information. 【0866】 (Claim 3) 【0867】 The system according to claim 1, wherein the accuracy of the learning model is evaluated by cross-validation of data. [Explanation of symbols] 【0868】 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] Means for obtaining geographical and meteorological data, A means for analyzing the aforementioned data to predict potential sites for power generation equipment, A means for displaying the aforementioned candidate installation sites on a map and presenting them to the user, A means of updating machine learning models based on user feedback, A system that includes this. [Claim 2] The system according to claim 1, wherein the weather data includes wind speed data and sunshine data. [Claim 3] The system according to claim 1, wherein the machine learning model is evaluated for accuracy by cross-validation of data.

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