system

Abstract

We provide the system. [Solution] Means for collecting regional information data, wind power information data, and ecosystem information data, Methods for preprocessing collected data, such as imputing missing values, removing outliers, and standardizing data shape, A means for applying a generative AI model to evaluate candidate locations using preprocessed data and calculating a score for each candidate location, A means for generating a list of construction candidate sites sorted in order of priority based on the calculated score, and transmitting the list to the user's terminal, 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 persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Conventionally, the selection of a construction site for a wind power plant has taken a huge amount of time and labor, and on-site surveys and wind measurements considering the ecosystem and bird flight routes have been required. This process is very time-consuming and usually takes about 10 years. There is a need to provide a system that can solve such problems and quickly and accurately select a construction site for a wind power plant.

Means for Solving the Problems

[0005] This invention utilizes means for collecting regional information data, wind power information data, and ecosystem information data, and further includes means for preprocessing the collected data, imputing missing values ​​and removing outliers, and unifying the data shape. This allows for the application of a generative AI model to evaluate each candidate site and calculate a score. Based on the scores, a list of construction candidate sites is generated and sorted in order of priority, and this list is transmitted to the user's terminal, thereby providing a system that solves conventional problems. Furthermore, the generative AI model analyzes wind speed, wind direction, and impact on the local ecosystem as features, and the model can be improved based on feedback from the user.

[0006] "Regional information data" refers to information about the geographical, administrative, and environmental characteristics of each region.

[0007] "Wind information data" refers to information about meteorological conditions such as wind speed, wind direction, and wind variability in a specific region.

[0008] "Ecosystem information data" refers to information about the distribution, habitats, and environmental conditions of plants and animals in a specific region.

[0009] "Preprocessing" refers to the process of preparing data to be analyzable by imputing missing values, removing outliers, and standardizing data formats.

[0010] A "generative AI model" refers to a type of artificial intelligence that uses machine learning and deep learning algorithms to analyze given data and generate results.

[0011] "Evaluation" refers to the process of determining superiority, inferiority, or suitability of a subject based on specific criteria, either quantitatively or qualitatively.

[0012] A "score" refers to an index that quantitatively represents the evaluation results of candidate locations or targets.

[0013] "Feedback" refers to opinions and suggestions for improvement provided by users, and is used to improve the system. [Brief explanation of the drawing]

[0014] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying Out the Invention

[0015] 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.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, a 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.

[0018] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, a 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.

[0020] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0021] 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."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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".

[0035] This invention is a system for quickly and accurately selecting a construction site for a wind power plant, and its embodiment is configured in which a server, terminal, and user elements work together.

[0036] Server Role

[0037] The server collects large amounts of relevant data, such as regional information data, wind power information data, and ecosystem information data, from the internet and various data providers. The server integrates this data and performs preprocessing, such as imputing missing values ​​and removing outliers. This results in a clean dataset in a format that is easy for AI models to process.

[0038] The server applies a generative AI model to a pre-processed dataset. The generative AI model uses wind speed, wind direction, and impact on the local ecosystem as features and calculates an evaluation score for each candidate site. Based on the evaluation scores, it generates a list of construction candidate sites in order of priority and sends this list to the terminal.

[0039] Terminal role

[0040] The terminal displays a list of candidate locations received from the server in a user-friendly format. It shows candidate locations on a map and provides their respective evaluation scores and ecological impacts as pop-up information. Filtering and sorting functions are also included to allow users to easily obtain detailed information.

[0041] The device also features an interface for users to provide feedback on potential locations. This feedback is sent to a server to help improve the accuracy of the AI ​​model.

[0042] User roles

[0043] Users make decisions regarding the selection of a wind power plant construction site based on candidate site information provided through their devices. Users can utilize filtering functions based on their own requirements and preferences to search for the most suitable candidate site.

[0044] Furthermore, users input feedback on candidate locations through a provided interface and send it to the server. This feedback information is used to improve the generated AI model, thereby enhancing the overall accuracy and effectiveness of the system.

[0045] As a concrete example, if a user wishes to construct a wind power plant in a northern region, the server collects data on that region and uses a generative AI model to evaluate a series of candidate sites. The terminal receives these results and presents them to the user in a visually easy-to-understand format, allowing the user to decide on a candidate site based on that information. In this way, the present invention provides a system that can select a construction site for a wind power plant more efficiently and accurately than conventional methods.

[0046] The following describes the processing flow.

[0047] Step 1:

[0048] The server collects regional information data, wind power information data, and ecosystem information data from various data providers. This collection is performed using API calls and database access, and is designed to provide up-to-date information in real time.

[0049] Step 2:

[0050] The server performs preprocessing on the collected data. This process involves imputing missing values ​​using appropriate methods, eliminating outliers, and maintaining data integrity. Furthermore, it standardizes the data format and converts it into a form that is easily processed by AI models.

[0051] Step 3:

[0052] The server inputs the preprocessed dataset into a generating AI model for analysis. The AI ​​model considers features such as wind speed, wind direction, and impact on the local ecosystem to calculate an evaluation score for each candidate site. High-performance algorithms are used to enable rapid analysis.

[0053] Step 4:

[0054] The server evaluates potential sites based on scores and generates a list of construction sites sorted by priority. This list includes data that takes into account the power generation potential and environmental impact of each region.

[0055] Step 5:

[0056] The server sends the generated list of candidate locations to the terminal. This transmission is performed securely using a secure protocol.

[0057] Step 6:

[0058] The terminal visually displays a list of candidate locations received from the server. It has the ability to place candidate locations on a map and display detailed information such as the score and ecosystem impact of each location in a pop-up window.

[0059] Step 7:

[0060] Users compare and evaluate potential locations based on the information displayed on their devices. They can use the device's built-in filtering function to narrow down the list of potential locations based on specific criteria.

[0061] Step 8:

[0062] Users select candidate locations based on the evaluation results and send feedback to the server via their device. This feedback information is used to continuously improve the AI ​​model.

[0063] Step 9:

[0064] The server receives feedback from users and incorporates it into the AI ​​model. This process improves the overall accuracy and adaptability of the system, thereby enhancing the accuracy of future construction site selection.

[0065] (Example 1)

[0066] 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."

[0067] Selecting suitable sites for wind power plants quickly and accurately requires careful consideration of geographical conditions and ecosystems, as well as precise analysis of a wide range of information. Traditional methods often rely on manual information gathering and analysis, which is inefficient and prone to errors. This has led to delays in site selection and difficulties in finding the optimal location.

[0068] 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.

[0069] In this invention, the server includes means for acquiring regional information, weather information, and ecosystem information; means for preprocessing the acquired information, supplementing missing information, eliminating anomalies, and unifying the format of the information; and means for applying a generative AI algorithm to evaluate candidate sites using the preprocessed information and calculating an evaluation value for each candidate site. This makes it possible to efficiently select the optimal candidate site for wind power plant construction by integrating diverse information and analyzing it with AI.

[0070] "Regional information" refers to information such as topography, administrative divisions, and demographics related to a specific geographical area.

[0071] "Weather information" refers to data related to weather, such as wind speed, wind direction, temperature, and humidity.

[0072] "Ecosystem information" refers to information about the habitat status of plants and animals and the state of the natural environment in a specific area.

[0073] "Preprocessing" refers to the process of preparing data to make it easier to analyze, and mainly includes imputing missing values, removing outliers, and standardizing the data shape.

[0074] A "generative AI algorithm" refers to a series of computational processes that use machine learning models to perform evaluations and predictions based on specified data.

[0075] A "candidate site" refers to a geographical location where the construction of a wind power plant is being considered.

[0076] The "evaluation value" is an index that quantifies the suitability of a candidate site, and it shows the results after considering factors such as wind speed, wind direction, and impact on the ecosystem.

[0077] "User device" refers to a device that receives a list of potential construction sites and provides information to the user.

[0078] An "interface" refers to a means by which a user interacts with a system, and in particular, a mechanism that enables the input of feedback.

[0079] This invention is a system for quickly and accurately selecting a site for a wind power plant. The following describes embodiments for carrying out the invention.

[0080] The system is configured so that the server, terminal, and user elements work together. The server collects data from the internet and various information providers to obtain regional information, weather information, and ecosystem information. The data is first preprocessed, including imputing missing values ​​and removing outliers. Database software and statistical analysis tools are often used for this processing.

[0081] The pre-processed data is supplied to a generating AI algorithm on the server. This AI algorithm calculates evaluation values ​​for candidate sites based on features such as wind speed, wind direction, and impact on the ecosystem. The evaluation values ​​are then ranked in order of priority as scores for the construction candidate sites. Next, this list of construction candidate sites is sent to the terminal.

[0082] The terminal visually presents the user with a list of candidate locations received from the server. Digital map software is used for display, and the evaluation value and ecosystem impact of each candidate location are provided as pop-up information. The terminal also has functions that allow the user to filter and sort based on specific criteria. The user can use this to search for the optimal candidate location and make a decision.

[0083] Furthermore, users can send feedback on potential locations to the server via an interface provided through their device. This feedback is used to improve the generating AI algorithm, thereby enhancing the system's accuracy and efficiency.

[0084] For example, when a user wishes to build a wind power plant in a northern region, the server collects region-specific data and uses a generated AI algorithm to evaluate potential sites. The terminal displays the evaluation results visually, allowing the user to select the most suitable site based on that information.

[0085] An example of a prompt message is: "Use AI to evaluate suitable candidate sites for a wind power plant in the specified region X, and display the list on the map."

[0086] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0087] Step 1:

[0088] The server collects regional, weather, and ecosystem information from the internet and data providers. It stores the raw data obtained as input. Specifically, it retrieves data via APIs and records it in a database. The output is a well-organized dataset.

[0089] Step 2:

[0090] The server performs preprocessing on the collected data. The input is the raw data collected in step 1. Missing data points are imputed using statistical methods and machine learning, and outliers are detected and removed. As a specific example, missing wind speed data points are imputed with the average of surrounding data. The output is a clean dataset with missing and outlier data removed.

[0091] Step 3:

[0092] The server integrates the preprocessed dataset and inputs it into the generating AI model. The output data from Step 2 is used. Wind speed, wind direction, and ecosystem information are extracted as features, and the AI ​​analyzes the information based on these. Specifically, the model is trained to calculate an evaluation value for each candidate location. The output is a list containing the evaluation value for each candidate location.

[0093] Step 4:

[0094] The server determines the priority of candidate sites and generates a list of construction candidate sites based on the evaluation values ​​calculated by the generated AI model. The input is the list of evaluation values ​​obtained in step 3. Locations with high scores are placed higher, and the list is arranged in order of priority. The output is a list of candidate sites sorted by priority.

[0095] Step 5:

[0096] The server sends the created list of candidate locations to the terminal. The input is the list of candidate locations prepared in step 4. The server sends the list to the terminal using the HTTP protocol. The output is the list data displayed on the terminal.

[0097] Step 6:

[0098] The terminal visually displays a list of candidate locations received from the server to the user. The input is the list data sent from the server. Map software is used to map the candidate locations and displays evaluation values ​​and ecological impacts in a pop-up format. The output is candidate location information visualized on a map.

[0099] Step 7:

[0100] The terminal provides filtering and sorting functions based on user requirements. The input consists of user-specified conditions. The displayed list is dynamically updated based on these conditions. The output consists of candidate location information that matches the user-defined conditions.

[0101] Step 8:

[0102] The user provides feedback on potential locations via a terminal and sends the feedback information to the server. The input is the feedback data entered by the user. The terminal converts this into a data format and transfers it to the server. The output is the feedback information stored on the server.

[0103] Step 9:

[0104] The server analyzes user feedback and uses it to improve the generated AI model. The input is the feedback data from step 8. This is used to update the AI ​​model's parameters and training data. The output is the improved AI model.

[0105] (Application Example 1)

[0106] 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."

[0107] The problem that this invention aims to solve is the efficient and accurate selection of the optimal placement of machinery and equipment not only at wind power plant construction sites but also within factories. This is required to improve production efficiency, minimize energy consumption, and reduce environmental impact.

[0108] 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.

[0109] In this invention, the server includes means for collecting regional information data, wind power information data, ecosystem information data, and environmental data; means for preprocessing the collected data, including imputing missing values ​​and removing outliers, and unifying the data shape; and means for applying a generative AI model to evaluate candidate sites and machine layouts using the preprocessed data, and calculating a score for each candidate site or layout proposal. This makes it possible to determine rational and effective construction sites for wind power plants and machine layouts within factories.

[0110] "Regional information data" refers to data that includes the geographical characteristics, demographics, and land use of a region, and is used to understand the environmental and socioeconomic conditions of a specific region.

[0111] "Wind power information data" refers to information describing wind speed, wind direction, and annual wind patterns in a specific region, and is used to evaluate the efficiency of wind power generation.

[0112] "Ecosystem information data" refers to information about the habitat status of plants and animals and the natural environment in a specific area, and is important indicator data from the perspective of environmental protection and sustainable development.

[0113] A "generative AI model" is an artificial intelligence algorithm that performs predictions and analyses based on various input data. It is designed to learn the complex correlations between data and support decision-making.

[0114] "Means of calculating scores" refers to the process of calculating specific evaluation metrics based on input data and presenting the results as numerical values. It is a means used to clarify the criteria for evaluation.

[0115] A "user terminal" is a device that receives information transmitted from a server and can display and operate it for the user, and includes devices such as smartphones and computers.

[0116] This invention provides a system for optimizing the construction site of a wind power plant and the layout of machinery within a factory. This system mainly consists of three elements: a server, a terminal, and a user.

[0117] The server collects and preprocesses regional information data, wind power information data, ecosystem information data, and environmental data within the factory. This preprocessing includes imputing missing values, removing outliers, and standardizing data shapes. Based on the preprocessed data, a generating AI model analyzes wind speed, wind direction, and the impact on the regional ecosystem and the factory environment as features. The server then calculates scores for candidate wind power plant locations and proposed machine layouts within the factory, and lists them in order of priority. This list is sent to the user's terminal.

[0118] The terminal displays a list of candidate locations sent from the server in an easy-to-understand manner for the user. It shows candidate locations on a map and provides pop-up information such as scores and ecological impacts to aid visual understanding. Furthermore, users can easily search for candidate locations that meet specific criteria using filtering and sorting functions. This enables users to make decisions efficiently.

[0119] Through the provided interface, users can input feedback on potential locations for wind farms and factory machinery under various conditions. This feedback is sent to the server and used to further improve the generated AI model.

[0120] The hardware used includes sensors for data collection and smart devices to display information and provide interfaces. The software uses Python and scikit-learn libraries for data preprocessing and AI analysis.

[0121] For example, if a user is selecting the optimal location for a wind farm during strong winds, the server will collect the latest wind data for that area and use a generated AI model to suggest the best candidate locations. An example of a prompt message would be, "Based on the factory's environmental data (temperature, humidity, vibration), please propose an optimal machine layout to maximize efficiency."

[0122] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0123] Step 1:

[0124] The server collects regional information data, wind power information data, ecosystem information data, and factory environment data. This process retrieves the necessary data from data providers and online databases. The input is the raw data from the source, and the output is these datasets.

[0125] Step 2:

[0126] The server performs preprocessing on the collected data. Specifically, it imputes missing values, removes outliers, and standardizes the data shape. The input is the raw dataset, and the output is a preprocessed, clean dataset.

[0127] Step 3:

[0128] The server performs analysis using a generative AI model with preprocessed data. The input is a preprocessed dataset, and this data is used to analyze features such as wind speed, wind direction, and impact on ecosystems. The output is a score for candidate locations or proposed placements.

[0129] Step 4:

[0130] The server generates a list of candidate locations in order of priority based on scores calculated by the generative AI model. This list is sorted according to the evaluation score. The input is a scored dataset, and the output is an ordered list of candidate locations.

[0131] Step 5:

[0132] The server sends the generated list of candidate locations to the user's terminal. The input received by the terminal is the list of candidate locations, and it uses this information to prepare for display to the user. The output is an internal data structure for visualization.

[0133] Step 6:

[0134] The terminal displays a list of candidate locations received from the server on a map, showing the evaluation score and ecosystem impact for each candidate. The input is data received from the server, and the output is information presented visually to the user.

[0135] Step 7:

[0136] The user uses the provided interface to filter and sort the list of potential locations to find those that meet specific criteria. The input is a list of potential locations displayed on the terminal, and the output is the best-fitting location that meets the criteria.

[0137] Step 8:

[0138] The user provides feedback on the candidate locations they have selected, and the device sends this information to the server. The input is the user's feedback, and the output is the data sent to the server.

[0139] Step 9:

[0140] The server uses user feedback to improve the generated AI model. The input is feedback data, and the output is the refined AI model.

[0141] 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.

[0142] This invention provides a system for efficiently selecting construction sites for wind power plants, and combines it with an emotion engine that recognizes user emotions. The embodiments thereof are described below.

[0143] Server Role

[0144] The server collects regional information data, wind power information data, and ecosystem information data, preprocesses them, and integrates them into a dataset to be input into the AI ​​model. Data preprocessing includes imputing missing values, removing outliers, and standardizing the format. The AI ​​model analyzes this data and calculates an evaluation score for each candidate site. Based on this score, the server generates a list of potential construction sites and sends it to the user's terminal.

[0145] Terminal role

[0146] The terminal visually displays a list of candidate locations provided by the server and is equipped with an emotion engine to provide information in a user-friendly format. This emotion engine can analyze emotional information from the user's text and voice input and dynamically adjust the interface.

[0147] The emotion engine can change the interface's color scheme and layout to reduce user stress and distrust, and to support smoother decision-making. This feature aims to improve user satisfaction with the system.

[0148] User roles

[0149] Users review the provided site information using their devices and evaluate it based on their own requirements and preferences. They can narrow down the candidate sites using the interface's filtering function and select the optimal construction site. Furthermore, they can contribute to system improvement by utilizing the feedback function provided by the emotion engine.

[0150] For example, if a user is emotionally exhausted, the emotion engine will detect this and change the way information is presented, such as by using softer colors, to provide a comfortable interface for the user. In this way, user emotions can be reflected in the operation of the system, providing a more intuitive and user-friendly system. By incorporating user emotions and feedback into the technical selection process, this invention enables the selection of wind power plant construction sites in a more efficient and user-friendly manner than conventional methods.

[0151] The following describes the processing flow.

[0152] Step 1:

[0153] The server collects regional information data, wind power information data, and ecosystem information data via the internet or through dedicated data channels. This allows for the integrated collection of all the information necessary for the construction of wind power plants.

[0154] Step 2:

[0155] The server performs preprocessing on the collected data. This preprocessing involves imputing missing data with appropriate estimates and removing obviously abnormal data points. It also converts all data into a unified format that the AI ​​model can understand.

[0156] Step 3:

[0157] The server inputs pre-processed data into a generating AI model for analysis. This AI model primarily uses wind speed, wind direction, and impact on the local ecosystem as features to calculate an evaluation score for each candidate location.

[0158] Step 4:

[0159] The server evaluates potential construction sites based on scoring by an AI model and creates a list sorted by priority. This list takes into account power generation efficiency and environmental impact.

[0160] Step 5:

[0161] The server securely transmits the generated list of candidate locations to the terminal.

[0162] Step 6:

[0163] The terminal visually displays the list of potential locations received from the server and generates an interface that includes maps and detailed information. This display makes it easy for users to intuitively understand the information.

[0164] Step 7:

[0165] The device utilizes an emotion engine to analyze the user's emotions through text or voice input. This allows it to adjust the interface tone and information presentation method according to the user's state.

[0166] Step 8:

[0167] Users compare and evaluate potential locations based on the information provided on their devices, and narrow down their final choices according to their own conditions and requirements. They can also use the device's filtering function to select locations based on specific criteria.

[0168] Step 9:

[0169] Users provide feedback on selection results and interfaces via an emotion engine to their devices, and this information is sent to a server. This feedback is used to improve the AI ​​model and interface.

[0170] Step 10:

[0171] The server receives feedback from users and re-inputs that information into the generative AI model and emotion engine as a feedback loop, improving the accuracy of subsequent analyses. This process allows the entire system to continuously evolve.

[0172] (Example 2)

[0173] 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 as the "terminal".

[0174] In the process of selecting sites for wind power plants, conventional methods make it difficult to efficiently evaluate environmental impacts and regional characteristics while fully considering them. Furthermore, the lack of consideration for emotional factors in user decision-making leads to decreased satisfaction.

[0175] 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.

[0176] In this invention, the server includes means for collecting geographic data, meteorological data, and biodiversity data; means for processing the collected data, performing value interpolation and removal of abnormal values, and standardizing the data format; and means for applying an artificial intelligence model that evaluates candidate locations using the processed data and calculating an evaluation value for each candidate location. This makes it possible to make decisions in selecting construction sites for wind power plants that take into account the environment and regional characteristics, as well as the feelings of users.

[0177] "Geographic data" refers to information about the location, topography, and features of a specific region.

[0178] "Weather data" refers to information about weather conditions in a specific area, such as wind speed, wind direction, temperature, and humidity.

[0179] "Biological data" refers to information about the distribution, types, and habitat status of plants and animals in a specific region.

[0180] An "artificial intelligence model" refers to an algorithm that uses large amounts of data for analysis and performs evaluations and predictions based on specific conditions and indicators.

[0181] An "evaluation value" is a numerical value calculated based on specific conditions or criteria, and refers to an indicator that shows the suitability and priority of candidate locations.

[0182] "User interface" refers to the means by which a user interacts with a system, and includes methods for displaying and inputting information.

[0183] An "emotional analysis engine" refers to a processing unit that analyzes the user's emotions and psychological state and reflects this in the system's operation and interface.

[0184] This invention is a system for efficiently selecting construction sites for wind power plants, combining data processing and sentiment recognition to support user-friendly decision-making. Specific embodiments are described below.

[0185] Server role:

[0186] The server collects geographical, meteorological, and biodiversity data from external databases and sensors. This data is first processed using a dedicated data preprocessing program to impute missing values ​​and remove anomalous values, then standardized into a format suitable for use in the AI ​​model. This preprocessed data is then input into the AI ​​model as an integrated dataset. The AI ​​model evaluates the suitability of each candidate site for wind power plant construction and calculates an evaluation value. This evaluation value is used to generate a ranking of the candidate sites, which the server then transmits to user terminals.

[0187] Terminal role:

[0188] The terminal visually displays a ranking list of candidate locations received from the server to the user. The terminal is equipped with an emotion analysis engine to analyze the user's emotions and dynamically adjusts the interface based on the user's text and voice input. For example, if it is detected that the user is feeling stressed, the background color will be changed to a calmer shade to facilitate information processing.

[0189] User roles:

[0190] Users can view the candidate site rankings displayed on their devices and further refine their search based on their own evaluation criteria. For example, users can select candidate sites based on the score of environmental impacts they consider important. Users can also contribute to system improvements using the feedback function provided by the interface.

[0191] Examples of specific cases and prompt statements:

[0192] For example, if a user is emotionally exhausted, the emotion analysis engine detects this state and adjusts the interface accordingly. This user-friendly approach allows users to make comfortable and efficient decisions.

[0193] An example of a prompt message would be: "Design an AI model to efficiently select a site for a wind power plant while minimizing environmental impact, in order to evaluate potential construction sites."

[0194] In this way, servers, terminals, and users collaborate to realize a configuration that improves both environmental considerations in technical selection and user satisfaction.

[0195] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0196] Step 1:

[0197] The server collects geographic, weather, and biodiversity data from various sensors and external databases. This acquired data is sent to a data processing pipeline within the server. The output data is a raw, unprocessed dataset.

[0198] Step 2:

[0199] The server performs data cleaning on the collected data. At this stage, missing values ​​are imputed using methods such as averaging, and outliers are removed using statistical techniques. This ensures data consistency and generates a pre-processed dataset as output.

[0200] Step 3:

[0201] The server integrates data based on pre-processed datasets, combining information from different data sources to create a single unified dataset. This unified dataset is then prepared as input for the generative AI model.

[0202] Step 4:

[0203] The server inputs the integrated dataset into a generating AI model and calculates an evaluation value for each candidate site. The AI ​​model analyzes the data received as input and uses a calculation model to evaluate the optimal location for wind power plant construction. The output is the evaluation score for each candidate site.

[0204] Step 5:

[0205] The server creates a prioritized list of potential construction sites based on the calculated scores. This organizes information that is useful for supporting decisions regarding wind power plant construction. The output is in the form of a prioritized list of potential sites.

[0206] Step 6:

[0207] The terminal visually displays a list of candidate locations received from the server to the user. The terminal also utilizes an emotion analysis engine to analyze the user's emotional state. This analysis allows for dynamic adjustments to the interface, optimizing the display method according to the user's emotional state. The output is candidate location information in an optimized interface format.

[0208] Step 7:

[0209] Users evaluate potential sites based on the information displayed on their devices and narrow down the list of candidates using the filtering function. User input data considers requirements such as environmental impact and efficiency. The final output is the construction site candidates selected by the user. User feedback is also used to improve the system.

[0210] (Application Example 2)

[0211] 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".

[0212] Conventional systems struggled to efficiently process diverse information, such as regional data, weather data, and biodiversity data, to select suitable sites for logistics centers and other facilities. Furthermore, they lacked the means to support user decision-making more smoothly by providing an interface that considered user emotions. Moreover, mechanisms for improving the system based on feedback were insufficient.

[0213] 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.

[0214] This invention includes a server comprising means for collecting regional data, weather data, and biota data; means for preprocessing the collected data, including imputing missing values ​​and removing outliers, and standardizing the data format; means for applying a generative AI algorithm to evaluate candidate sites using the preprocessed data and calculating a score for each candidate site; means for an information terminal equipped with an emotion analysis engine that analyzes the user's emotions and dynamically changes the interface; and means for the user to narrow down candidate sites using a filtering function. This enables more efficient and user-friendly candidate site selection.

[0215] "Regional data" refers to various types of information related to a specific region, including geographical information and land use information.

[0216] "Weather data" refers to information about weather conditions such as wind speed, wind direction, temperature, and humidity.

[0217] "Biological community data" refers to information about the ecosystems of plants and animals in a specific region.

[0218] "Preprocessing" is the process of filling in missing data values, removing outliers, and standardizing the data format.

[0219] A "generative AI algorithm" is an algorithm that uses machine learning to analyze input data and calculate an evaluation score.

[0220] An "information terminal" is a device used by users to view and manipulate information, and primarily refers to smartphones and computers.

[0221] An "emotion analysis engine" is a system that analyzes the user's emotional state from their display and voice, and dynamically adjusts the interface accordingly.

[0222] A "filtering function" is a function that selects and displays data based on the conditions desired by the user.

[0223] In the system for implementing this invention, a server collects regional data, weather data, and biota data, and preprocesses this data. Specifically, it performs data imputation, removes outliers, and standardizes the data format. This is done using a data cleaning library in Python (e.g., Pandas). The server then runs a generative AI algorithm to calculate evaluation scores and generate a list of candidate locations. This process uses a machine learning framework (e.g., Tensorflow® or PyTorch).

[0224] The server sends the generated list of candidate locations to an information terminal. This terminal is equipped with an emotion analysis engine that dynamically adjusts the interface by analyzing the user's facial expressions and voice. This analysis utilizes APIs provided by cloud services (e.g., Google Cloud's Natural Language API and Microsoft Azure's Face API). The user can use this terminal to narrow down the candidate locations through a filtering function.

[0225] To give a concrete example, when a logistics company representative decides on the location of a new center, this system can be used to efficiently find land that meets the requirements. If she is emotionally exhausted, the interface automatically changes to a relaxing color scheme, making the selection process more comfortable. In this way, the user experience is improved. An example of a prompt would be, "Explain how to calculate an evaluation score based on candidate site information for selecting a logistics center construction site in the Kanto region, taking into account transportation access, proximity to major cities, and environmental impact, and how to provide a UI that responds to the user's emotions."

[0226] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0227] Step 1:

[0228] The server collects regional data, weather data, and biota data from various APIs. For example, regional data is obtained from a Geographic Information System (GIS), and weather data is obtained from the Japan Meteorological Agency's API. The input is raw data from each API, and the output is an integrated dataset combining them.

[0229] Step 2:

[0230] The server performs preprocessing on the integrated dataset. Specifically, it uses Python to impute missing values, remove outliers, and standardize data formats. This process results in cleaned data being output. The input is the raw integrated dataset, and the output is the preprocessed data.

[0231] Step 3:

[0232] The server inputs pre-processed data into a generating AI algorithm to calculate an evaluation score for each candidate site. Here, a machine learning model using TensorFlow is applied. The input is pre-processed data, and the output is the evaluation score for each candidate site.

[0233] Step 4:

[0234] The server generates a list of candidate locations in order of priority based on the calculated evaluation score and sends it to the terminal. The input is the evaluation score, and the output is a list of candidate locations in order of priority.

[0235] Step 5:

[0236] The terminal displays a list of candidate locations and activates an emotion analysis engine to analyze the user's emotional state. The terminal is equipped with a camera that recognizes emotions using facial expression data as input. The output is the analyzed emotion information.

[0237] Step 6:

[0238] The device dynamically adjusts the color scheme and layout of the user interface based on analyzed emotional information. The input is emotional information, and the output is the adjusted interface.

[0239] Step 7:

[0240] The user reviews a list of potential locations on a customized interface and uses a filtering function to narrow down the options. The input is the user's selection criteria, and the output is the narrowed-down list of potential locations.

[0241] Step 8:

[0242] The user selects the optimal construction site based on a list of candidate sites provided by the system. The input is a narrowed-down list of candidate sites, and the output is information on the construction site deemed optimal.

[0243] 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.

[0244] 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.

[0245] 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.

[0246] [Second Embodiment]

[0247] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0248] 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.

[0249] 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).

[0250] 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.

[0251] 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.

[0252] 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).

[0253] 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.

[0254] 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.

[0255] 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.

[0256] 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.

[0257] 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.

[0258] 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".

[0259] This invention is a system for quickly and accurately selecting a construction site for a wind power plant, and its embodiment is configured in which a server, terminal, and user elements work together.

[0260] Server Role

[0261] The server collects large amounts of relevant data, such as regional information data, wind power information data, and ecosystem information data, from the internet and various data providers. The server integrates this data and performs preprocessing, such as imputing missing values ​​and removing outliers. This results in a clean dataset in a format that is easy for AI models to process.

[0262] The server applies a generative AI model to a pre-processed dataset. The generative AI model uses wind speed, wind direction, and impact on the local ecosystem as features and calculates an evaluation score for each candidate site. Based on the evaluation scores, it generates a list of construction candidate sites in order of priority and sends this list to the terminal.

[0263] Terminal role

[0264] The terminal displays a list of candidate locations received from the server in a user-friendly format. It shows candidate locations on a map and provides their respective evaluation scores and ecological impacts as pop-up information. Filtering and sorting functions are also included to allow users to easily obtain detailed information.

[0265] The device also features an interface for users to provide feedback on potential locations. This feedback is sent to a server to help improve the accuracy of the AI ​​model.

[0266] User roles

[0267] Users make decisions regarding the selection of a wind power plant construction site based on candidate site information provided through their devices. Users can utilize filtering functions based on their own requirements and preferences to search for the most suitable candidate site.

[0268] Furthermore, users input feedback on candidate locations through a provided interface and send it to the server. This feedback information is used to improve the generated AI model, thereby enhancing the overall accuracy and effectiveness of the system.

[0269] As a concrete example, if a user wishes to construct a wind power plant in a northern region, the server collects data on that region and uses a generative AI model to evaluate a series of candidate sites. The terminal receives these results and presents them to the user in a visually easy-to-understand format, allowing the user to decide on a candidate site based on that information. In this way, the present invention provides a system that can select a construction site for a wind power plant more efficiently and accurately than conventional methods.

[0270] The following describes the processing flow.

[0271] Step 1:

[0272] The server collects regional information data, wind power information data, and ecosystem information data from various data providers. This collection is performed using API calls and database access, and is designed to provide up-to-date information in real time.

[0273] Step 2:

[0274] The server performs preprocessing on the collected data. This process involves imputing missing values ​​using appropriate methods, eliminating outliers, and maintaining data integrity. Furthermore, it standardizes the data format and converts it into a form that is easily processed by AI models.

[0275] Step 3:

[0276] The server inputs the preprocessed dataset into a generating AI model for analysis. The AI ​​model considers features such as wind speed, wind direction, and impact on the local ecosystem to calculate an evaluation score for each candidate site. High-performance algorithms are used to enable rapid analysis.

[0277] Step 4:

[0278] The server evaluates potential sites based on scores and generates a list of construction sites sorted by priority. This list includes data that takes into account the power generation potential and environmental impact of each region.

[0279] Step 5:

[0280] The server sends the generated candidate site list to the terminal. The transmission is performed securely using a security - considered protocol.

[0281] Step 6:

[0282] The terminal visually displays the candidate site list received from the server. It has a function of placing candidate sites on a map and showing detailed information such as the score and ecosystem impact of each location in a pop - up.

[0283] Step 7:

[0284] The user compares and evaluates the candidate sites based on the information displayed on the terminal. Using the filtering function provided on the terminal, the candidate sites can be narrowed down based on specific conditions.

[0285] Step 8:

[0286] The user selects a candidate site based on the evaluation results and sends feedback to the server via the terminal. This feedback information is utilized for the continuous improvement of the AI model.

[0287] Step 9:

[0288] The server receives the feedback from the user and reflects it in the AI model. Through this process, the accuracy and adaptability of the entire system are improved, and the accuracy of future construction site selection can be enhanced.

[0289] )]] (Example 1)

[0290] Next, Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0291] Selecting suitable sites for wind power plants quickly and accurately requires careful consideration of geographical conditions and ecosystems, as well as precise analysis of a wide range of information. Traditional methods often rely on manual information gathering and analysis, which is inefficient and prone to errors. This has led to delays in site selection and difficulties in finding the optimal location.

[0292] 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.

[0293] In this invention, the server includes means for acquiring regional information, weather information, and ecosystem information; means for preprocessing the acquired information, supplementing missing information, eliminating anomalies, and unifying the format of the information; and means for applying a generative AI algorithm to evaluate candidate sites using the preprocessed information and calculating an evaluation value for each candidate site. This makes it possible to efficiently select the optimal candidate site for wind power plant construction by integrating diverse information and analyzing it with AI.

[0294] "Regional information" refers to information such as topography, administrative divisions, and demographics related to a specific geographical area.

[0295] "Weather information" refers to data related to weather, such as wind speed, wind direction, temperature, and humidity.

[0296] "Ecosystem information" refers to information about the habitat status of plants and animals and the state of the natural environment in a specific area.

[0297] "Preprocessing" refers to the process of preparing data to make it easier to analyze, and mainly includes imputing missing values, removing outliers, and standardizing the data shape.

[0298] A "generative AI algorithm" refers to a series of computational processes that use machine learning models to perform evaluations and predictions based on specified data.

[0299] A "candidate site" refers to a geographical location where the construction of a wind power plant is being considered.

[0300] The "evaluation value" is an index that quantifies the suitability of a candidate site, and it shows the results after considering factors such as wind speed, wind direction, and impact on the ecosystem.

[0301] "User device" refers to a device that receives a list of potential construction sites and provides information to the user.

[0302] An "interface" refers to a means by which a user interacts with a system, and in particular, a mechanism that enables the input of feedback.

[0303] This invention is a system for quickly and accurately selecting a site for a wind power plant. The following describes embodiments for carrying out the invention.

[0304] The system is configured so that the server, terminal, and user elements work together. The server collects data from the internet and various information providers to obtain regional information, weather information, and ecosystem information. The data is first preprocessed, including imputing missing values ​​and removing outliers. Database software and statistical analysis tools are often used for this processing.

[0305] The pre-processed data is supplied to a generating AI algorithm on the server. This AI algorithm calculates evaluation values ​​for candidate sites based on features such as wind speed, wind direction, and impact on the ecosystem. The evaluation values ​​are then ranked in order of priority as scores for the construction candidate sites. Next, this list of construction candidate sites is sent to the terminal.

[0306] The terminal visually presents the candidate site list received from the server to the user. Digital map software is used for display, and the evaluation value of each candidate site and its impact on the ecosystem are provided as pop-up information. In addition, the terminal has a function that allows the user to perform filtering and sorting based on specific conditions. The user can use this to search for the optimal candidate site and make a decision.

[0307] Furthermore, the user can send feedback on the candidate sites to the server via the interface provided through the terminal. This feedback is utilized to improve the generative AI algorithm, aiming to enhance the accuracy and efficiency of the system.

[0308] As a specific example, when the user hopes to build a wind power plant in the northern region, the server collects data specialized for that region and evaluates candidate sites using the generative AI algorithm. The terminal visually displays the evaluation results, and the user can select the optimal candidate site based on them.

[0309] An example of a prompt sentence is "Please use AI to evaluate candidate sites suitable for a wind power plant in the designated region X and display the list on the map."

[0310] The flow of the specific process in Example 1 will be described using FIG. 11.

[0311] Step 1:

[0312] The server collects regional-related information, meteorological information, and ecosystem information from the Internet or data providers. It accumulates the raw data obtained as input. As a specific operation, it acquires data through the API and records it in the database. The output is an orderly dataset.

[0313] Step 2:

[0314] The server performs preprocessing on the collected data. The input is the raw data collected in step 1. Missing data points are imputed using statistical methods and machine learning, and outliers are detected and removed. As a specific example, missing wind speed data points are imputed with the average of surrounding data. The output is a clean dataset with missing and outlier data removed.

[0315] Step 3:

[0316] The server integrates the preprocessed dataset and inputs it into the generating AI model. The output data from Step 2 is used. Wind speed, wind direction, and ecosystem information are extracted as features, and the AI ​​analyzes the information based on these. Specifically, the model is trained to calculate an evaluation value for each candidate location. The output is a list containing the evaluation value for each candidate location.

[0317] Step 4:

[0318] The server determines the priority of candidate sites and generates a list of construction candidate sites based on the evaluation values ​​calculated by the generated AI model. The input is the list of evaluation values ​​obtained in step 3. Locations with high scores are placed higher, and the list is arranged in order of priority. The output is a list of candidate sites sorted by priority.

[0319] Step 5:

[0320] The server sends the created list of candidate locations to the terminal. The input is the list of candidate locations prepared in step 4. The server sends the list to the terminal using the HTTP protocol. The output is the list data displayed on the terminal.

[0321] Step 6:

[0322] The terminal visually displays a list of candidate locations received from the server to the user. The input is the list data sent from the server. Map software is used to map the candidate locations and displays evaluation values ​​and ecological impacts in a pop-up format. The output is candidate location information visualized on a map.

[0323] Step 7:

[0324] The terminal provides filtering and sorting functions based on user requirements. The input consists of user-specified conditions. The displayed list is dynamically updated based on these conditions. The output consists of candidate location information that matches the user-defined conditions.

[0325] Step 8:

[0326] The user provides feedback on potential locations via a terminal and sends the feedback information to the server. The input is the feedback data entered by the user. The terminal converts this into a data format and transfers it to the server. The output is the feedback information stored on the server.

[0327] Step 9:

[0328] The server analyzes user feedback and uses it to improve the generated AI model. The input is the feedback data from step 8. This is used to update the AI ​​model's parameters and training data. The output is the improved AI model.

[0329] (Application Example 1)

[0330] 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."

[0331] The problem that this invention aims to solve is the efficient and accurate selection of the optimal placement of machinery and equipment not only at wind power plant construction sites but also within factories. This is required to improve production efficiency, minimize energy consumption, and reduce environmental impact.

[0332] 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.

[0333] In this invention, the server includes means for collecting regional information data, wind power information data, ecosystem information data, and environmental data; means for preprocessing the collected data, including imputing missing values ​​and removing outliers, and unifying the data shape; and means for applying a generative AI model to evaluate candidate sites and machine layouts using the preprocessed data, and calculating a score for each candidate site or layout proposal. This makes it possible to determine rational and effective construction sites for wind power plants and machine layouts within factories.

[0334] "Regional information data" refers to data that includes the geographical characteristics, demographics, and land use of a region, and is used to understand the environmental and socioeconomic conditions of a specific region.

[0335] "Wind power information data" refers to information describing wind speed, wind direction, and annual wind patterns in a specific region, and is used to evaluate the efficiency of wind power generation.

[0336] "Ecosystem information data" refers to information about the habitat status of plants and animals and the natural environment in a specific area, and is important indicator data from the perspective of environmental protection and sustainable development.

[0337] A "generative AI model" is an artificial intelligence algorithm that performs predictions and analyses based on various input data. It is designed to learn the complex correlations between data and support decision-making.

[0338] "Means of calculating scores" refers to the process of calculating specific evaluation metrics based on input data and presenting the results as numerical values. It is a means used to clarify the criteria for evaluation.

[0339] A "user terminal" is a device that receives information transmitted from a server and can display and operate it for the user, and includes devices such as smartphones and computers.

[0340] This invention provides a system for optimizing the construction site of a wind power plant and the layout of machinery within a factory. This system mainly consists of three elements: a server, a terminal, and a user.

[0341] The server collects and preprocesses regional information data, wind power information data, ecosystem information data, and environmental data within the factory. This preprocessing includes imputing missing values, removing outliers, and standardizing data shapes. Based on the preprocessed data, a generating AI model analyzes wind speed, wind direction, and the impact on the regional ecosystem and the factory environment as features. The server then calculates scores for candidate wind power plant locations and proposed machine layouts within the factory, and lists them in order of priority. This list is sent to the user's terminal.

[0342] The terminal displays a list of candidate locations sent from the server in an easy-to-understand manner for the user. It shows candidate locations on a map and provides pop-up information such as scores and ecological impacts to aid visual understanding. Furthermore, users can easily search for candidate locations that meet specific criteria using filtering and sorting functions. This enables users to make decisions efficiently.

[0343] Through the provided interface, users can input feedback on potential locations for wind farms and factory machinery under various conditions. This feedback is sent to the server and used to further improve the generated AI model.

[0344] The hardware used includes sensors for data collection and smart devices to display information and provide interfaces. The software uses Python and scikit-learn libraries for data preprocessing and AI analysis.

[0345] For example, if a user is selecting the optimal location for a wind farm during strong winds, the server will collect the latest wind data for that area and use a generated AI model to suggest the best candidate locations. An example of a prompt message would be, "Based on the factory's environmental data (temperature, humidity, vibration), please propose an optimal machine layout to maximize efficiency."

[0346] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0347] Step 1:

[0348] The server collects regional information data, wind power information data, ecosystem information data, and factory environment data. This process retrieves the necessary data from data providers and online databases. The input is the raw data from the source, and the output is these datasets.

[0349] Step 2:

[0350] The server performs preprocessing on the collected data. Specifically, it imputes missing values, removes outliers, and standardizes the data shape. The input is the raw dataset, and the output is a preprocessed, clean dataset.

[0351] Step 3:

[0352] The server performs analysis using a generative AI model with preprocessed data. The input is a preprocessed dataset, and this data is used to analyze features such as wind speed, wind direction, and impact on ecosystems. The output is a score for candidate locations or proposed placements.

[0353] Step 4:

[0354] The server generates a list of candidate locations in order of priority based on scores calculated by the generative AI model. This list is sorted according to the evaluation score. The input is a scored dataset, and the output is an ordered list of candidate locations.

[0355] Step 5:

[0356] The server sends the generated list of candidate locations to the user's terminal. The input received by the terminal is the list of candidate locations, and it uses this information to prepare for display to the user. The output is an internal data structure for visualization.

[0357] Step 6:

[0358] The terminal displays a list of candidate locations received from the server on a map, showing the evaluation score and ecosystem impact for each candidate. The input is data received from the server, and the output is information presented visually to the user.

[0359] Step 7:

[0360] The user uses the provided interface to filter and sort the list of potential locations to find those that meet specific criteria. The input is a list of potential locations displayed on the terminal, and the output is the best-fitting location that meets the criteria.

[0361] Step 8:

[0362] The user provides feedback on the candidate locations they have selected, and the device sends this information to the server. The input is the user's feedback, and the output is the data sent to the server.

[0363] Step 9:

[0364] The server uses user feedback to improve the generated AI model. The input is feedback data, and the output is the refined AI model.

[0365] 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.

[0366] This invention provides a system for efficiently selecting construction sites for wind power plants, and combines it with an emotion engine that recognizes user emotions. The embodiments thereof are described below.

[0367] Server Role

[0368] The server collects regional information data, wind power information data, and ecosystem information data, preprocesses them, and integrates them into a dataset to be input into the AI ​​model. Data preprocessing includes imputing missing values, removing outliers, and standardizing the format. The AI ​​model analyzes this data and calculates an evaluation score for each candidate site. Based on this score, the server generates a list of potential construction sites and sends it to the user's terminal.

[0369] Terminal role

[0370] The terminal visually displays a list of candidate locations provided by the server and is equipped with an emotion engine to provide information in a user-friendly format. This emotion engine can analyze emotional information from the user's text and voice input and dynamically adjust the interface.

[0371] The emotion engine can change the interface's color scheme and layout to reduce user stress and distrust, and to support smoother decision-making. This feature aims to improve user satisfaction with the system.

[0372] User roles

[0373] Users review the provided site information using their devices and evaluate it based on their own requirements and preferences. They can narrow down the candidate sites using the interface's filtering function and select the optimal construction site. Furthermore, they can contribute to system improvement by utilizing the feedback function provided by the emotion engine.

[0374] For example, if a user is emotionally exhausted, the emotion engine will detect this and change the way information is presented, such as by using softer colors, to provide a comfortable interface for the user. In this way, user emotions can be reflected in the operation of the system, providing a more intuitive and user-friendly system. By incorporating user emotions and feedback into the technical selection process, this invention enables the selection of wind power plant construction sites in a more efficient and user-friendly manner than conventional methods.

[0375] The following describes the processing flow.

[0376] Step 1:

[0377] The server collects regional information data, wind power information data, and ecosystem information data via the internet or through dedicated data channels. This allows for the integrated collection of all the information necessary for the construction of wind power plants.

[0378] Step 2:

[0379] The server performs preprocessing on the collected data. This preprocessing involves imputing missing data with appropriate estimates and removing obviously abnormal data points. It also converts all data into a unified format that the AI ​​model can understand.

[0380] Step 3:

[0381] The server inputs pre-processed data into a generating AI model for analysis. This AI model primarily uses wind speed, wind direction, and impact on the local ecosystem as features to calculate an evaluation score for each candidate location.

[0382] Step 4:

[0383] The server evaluates potential construction sites based on scoring by an AI model and creates a list sorted by priority. This list takes into account power generation efficiency and environmental impact.

[0384] Step 5:

[0385] The server securely transmits the generated list of candidate locations to the terminal.

[0386] Step 6:

[0387] The terminal visually displays the list of potential locations received from the server and generates an interface that includes maps and detailed information. This display makes it easy for users to intuitively understand the information.

[0388] Step 7:

[0389] The device utilizes an emotion engine to analyze the user's emotions through text or voice input. This allows it to adjust the interface tone and information presentation method according to the user's state.

[0390] Step 8:

[0391] Users compare and evaluate potential locations based on the information provided on their devices, and narrow down their final choices according to their own conditions and requirements. They can also use the device's filtering function to select locations based on specific criteria.

[0392] Step 9:

[0393] Users provide feedback on selection results and interfaces via an emotion engine to their devices, and this information is sent to a server. This feedback is used to improve the AI ​​model and interface.

[0394] Step 10:

[0395] The server receives feedback from users and re-inputs that information into the generative AI model and emotion engine as a feedback loop, improving the accuracy of subsequent analyses. This process allows the entire system to continuously evolve.

[0396] (Example 2)

[0397] 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".

[0398] In the process of selecting sites for wind power plants, conventional methods make it difficult to efficiently evaluate environmental impacts and regional characteristics while fully considering them. Furthermore, the lack of consideration for emotional factors in user decision-making leads to decreased satisfaction.

[0399] 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.

[0400] In this invention, the server includes means for collecting geographic data, meteorological data, and biodiversity data; means for processing the collected data, performing value interpolation and removal of abnormal values, and standardizing the data format; and means for applying an artificial intelligence model that evaluates candidate locations using the processed data and calculating an evaluation value for each candidate location. This makes it possible to make decisions in selecting construction sites for wind power plants that take into account the environment and regional characteristics, as well as the feelings of users.

[0401] "Geographic data" refers to information about the location, topography, and features of a specific region.

[0402] "Weather data" refers to information about weather conditions in a specific area, such as wind speed, wind direction, temperature, and humidity.

[0403] "Biological data" refers to information about the distribution, types, and habitat status of plants and animals in a specific region.

[0404] An "artificial intelligence model" refers to an algorithm that uses large amounts of data for analysis and performs evaluations and predictions based on specific conditions and indicators.

[0405] An "evaluation value" is a numerical value calculated based on specific conditions or criteria, and refers to an indicator that shows the suitability and priority of candidate locations.

[0406] "User interface" refers to the means by which a user interacts with a system, and includes methods for displaying and inputting information.

[0407] An "emotional analysis engine" refers to a processing unit that analyzes the user's emotions and psychological state and reflects this in the system's operation and interface.

[0408] This invention is a system for efficiently selecting construction sites for wind power plants, combining data processing and sentiment recognition to support user-friendly decision-making. Specific embodiments are described below.

[0409] Server role:

[0410] The server collects geographical, meteorological, and biodiversity data from external databases and sensors. This data is first processed using a dedicated data preprocessing program to impute missing values ​​and remove anomalous values, then standardized into a format suitable for use in the AI ​​model. This preprocessed data is then input into the AI ​​model as an integrated dataset. The AI ​​model evaluates the suitability of each candidate site for wind power plant construction and calculates an evaluation value. This evaluation value is used to generate a ranking of the candidate sites, which the server then transmits to user terminals.

[0411] Terminal role:

[0412] The terminal visually displays a ranking list of candidate locations received from the server to the user. The terminal is equipped with an emotion analysis engine to analyze the user's emotions and dynamically adjusts the interface based on the user's text and voice input. For example, if it is detected that the user is feeling stressed, the background color will be changed to a calmer shade to facilitate information processing.

[0413] User roles:

[0414] Users can view the candidate site rankings displayed on their devices and further refine their search based on their own evaluation criteria. For example, users can select candidate sites based on the score of environmental impacts they consider important. Users can also contribute to system improvements using the feedback function provided by the interface.

[0415] Examples of specific cases and prompt statements:

[0416] For example, if a user is emotionally exhausted, the emotion analysis engine detects this state and adjusts the interface accordingly. This user-friendly approach allows users to make comfortable and efficient decisions.

[0417] An example of a prompt message would be: "Design an AI model to efficiently select a site for a wind power plant while minimizing environmental impact, in order to evaluate potential construction sites."

[0418] In this way, servers, terminals, and users collaborate to realize a configuration that improves both environmental considerations in technical selection and user satisfaction.

[0419] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0420] Step 1:

[0421] The server collects geographic, weather, and biodiversity data from various sensors and external databases. This acquired data is sent to a data processing pipeline within the server. The output data is a raw, unprocessed dataset.

[0422] Step 2:

[0423] The server performs data cleaning on the collected data. At this stage, missing values ​​are imputed using methods such as averaging, and outliers are removed using statistical techniques. This ensures data consistency and generates a pre-processed dataset as output.

[0424] Step 3:

[0425] The server integrates data based on pre-processed datasets, combining information from different data sources to create a single unified dataset. This unified dataset is then prepared as input for the generative AI model.

[0426] Step 4:

[0427] The server inputs the integrated dataset into a generating AI model and calculates an evaluation value for each candidate site. The AI ​​model analyzes the data received as input and uses a calculation model to evaluate the optimal location for wind power plant construction. The output is the evaluation score for each candidate site.

[0428] Step 5:

[0429] The server creates a prioritized list of potential construction sites based on the calculated scores. This organizes information that is useful for supporting decisions regarding wind power plant construction. The output is in the form of a prioritized list of potential sites.

[0430] Step 6:

[0431] The terminal visually displays a list of candidate locations received from the server to the user. The terminal also utilizes an emotion analysis engine to analyze the user's emotional state. This analysis allows for dynamic adjustments to the interface, optimizing the display method according to the user's emotional state. The output is candidate location information in an optimized interface format.

[0432] Step 7:

[0433] Users evaluate potential sites based on the information displayed on their devices and narrow down the list of candidates using the filtering function. User input data considers requirements such as environmental impact and efficiency. The final output is the construction site candidates selected by the user. User feedback is also used to improve the system.

[0434] (Application Example 2)

[0435] 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."

[0436] Conventional systems struggled to efficiently process diverse information, such as regional data, weather data, and biodiversity data, to select suitable sites for logistics centers and other facilities. Furthermore, they lacked the means to support user decision-making more smoothly by providing an interface that considered user emotions. Moreover, mechanisms for improving the system based on feedback were insufficient.

[0437] 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.

[0438] This invention includes a server comprising means for collecting regional data, weather data, and biota data; means for preprocessing the collected data, including imputing missing values ​​and removing outliers, and standardizing the data format; means for applying a generative AI algorithm to evaluate candidate sites using the preprocessed data and calculating a score for each candidate site; means for an information terminal equipped with an emotion analysis engine that analyzes the user's emotions and dynamically changes the interface; and means for the user to narrow down candidate sites using a filtering function. This enables more efficient and user-friendly candidate site selection.

[0439] "Regional data" refers to various types of information related to a specific region, including geographical information and land use information.

[0440] "Weather data" refers to information about weather conditions such as wind speed, wind direction, temperature, and humidity.

[0441] "Biological community data" refers to information about the ecosystems of plants and animals in a specific region.

[0442] "Preprocessing" is the process of filling in missing data values, removing outliers, and standardizing the data format.

[0443] A "generative AI algorithm" is an algorithm that uses machine learning to analyze input data and calculate an evaluation score.

[0444] An "information terminal" is a device used by users to view and manipulate information, and primarily refers to smartphones and computers.

[0445] An "emotion analysis engine" is a system that analyzes the user's emotional state from their display and voice, and dynamically adjusts the interface accordingly.

[0446] A "filtering function" is a function that selects and displays data based on the conditions desired by the user.

[0447] In the system for implementing this invention, a server collects regional data, weather data, and biota data, and preprocesses this data. Specifically, it performs data imputation, removes outliers, and standardizes the data format. This is done using a data cleaning library in Python (e.g., Pandas). The server then runs a generative AI algorithm to calculate evaluation scores and generate a list of candidate locations. This process uses a machine learning framework (e.g., TensorFlow or PyTorch).

[0448] The server sends the generated list of candidate locations to an information terminal. This terminal is equipped with an emotion analysis engine that dynamically adjusts the interface by analyzing the user's facial expressions and voice. This analysis utilizes APIs provided by cloud services (e.g., Google Cloud's Natural Language API and Microsoft Azure's Face API). The user can use this terminal to narrow down the candidate locations through a filtering function.

[0449] To give a concrete example, when a logistics company representative decides on the location of a new center, this system can be used to efficiently find land that meets the requirements. If she is emotionally exhausted, the interface automatically changes to a relaxing color scheme, making the selection process more comfortable. In this way, the user experience is improved. An example of a prompt would be, "Explain how to calculate an evaluation score based on candidate site information for selecting a logistics center construction site in the Kanto region, taking into account transportation access, proximity to major cities, and environmental impact, and how to provide a UI that responds to the user's emotions."

[0450] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0451] Step 1:

[0452] The server collects regional data, weather data, and biota data from various APIs. For example, regional data is obtained from a Geographic Information System (GIS), and weather data is obtained from the Japan Meteorological Agency's API. The input is raw data from each API, and the output is an integrated dataset combining them.

[0453] Step 2:

[0454] The server performs preprocessing on the integrated dataset. Specifically, it uses Python to impute missing values, remove outliers, and standardize data formats. This process results in cleaned data being output. The input is the raw integrated dataset, and the output is the preprocessed data.

[0455] Step 3:

[0456] The server inputs pre-processed data into a generating AI algorithm to calculate an evaluation score for each candidate site. Here, a machine learning model using TensorFlow is applied. The input is pre-processed data, and the output is the evaluation score for each candidate site.

[0457] Step 4:

[0458] The server generates a list of candidate locations in order of priority based on the calculated evaluation score and sends it to the terminal. The input is the evaluation score, and the output is a list of candidate locations in order of priority.

[0459] Step 5:

[0460] The terminal displays a list of candidate locations and activates an emotion analysis engine to analyze the user's emotional state. The terminal is equipped with a camera that recognizes emotions using facial expression data as input. The output is the analyzed emotion information.

[0461] Step 6:

[0462] The device dynamically adjusts the color scheme and layout of the user interface based on analyzed emotional information. The input is emotional information, and the output is the adjusted interface.

[0463] Step 7:

[0464] The user reviews a list of potential locations on a customized interface and uses a filtering function to narrow down the options. The input is the user's selection criteria, and the output is the narrowed-down list of potential locations.

[0465] Step 8:

[0466] The user selects the optimal construction site based on a list of candidate sites provided by the system. The input is a narrowed-down list of candidate sites, and the output is information on the construction site deemed optimal.

[0467] 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.

[0468] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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.

[0469] 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.

[0470] [Third Embodiment]

[0471] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0472] 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.

[0473] 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).

[0474] 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.

[0475] 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.

[0476] 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).

[0477] 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.

[0478] 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.

[0479] 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.

[0480] 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.

[0481] 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.

[0482] 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".

[0483] This invention is a system for quickly and accurately selecting a construction site for a wind power plant, and its embodiment is configured in which a server, terminal, and user elements work together.

[0484] Server Role

[0485] The server collects large amounts of relevant data, such as regional information data, wind power information data, and ecosystem information data, from the internet and various data providers. The server integrates this data and performs preprocessing, such as imputing missing values ​​and removing outliers. This results in a clean dataset in a format that is easy for AI models to process.

[0486] The server applies a generative AI model to a pre-processed dataset. The generative AI model uses wind speed, wind direction, and impact on the local ecosystem as features and calculates an evaluation score for each candidate site. Based on the evaluation scores, it generates a list of construction candidate sites in order of priority and sends this list to the terminal.

[0487] Terminal role

[0488] The terminal displays a list of candidate locations received from the server in a user-friendly format. It shows candidate locations on a map and provides their respective evaluation scores and ecological impacts as pop-up information. Filtering and sorting functions are also included to allow users to easily obtain detailed information.

[0489] The device also features an interface for users to provide feedback on potential locations. This feedback is sent to a server to help improve the accuracy of the AI ​​model.

[0490] User roles

[0491] Users make decisions regarding the selection of a wind power plant construction site based on candidate site information provided through their devices. Users can utilize filtering functions based on their own requirements and preferences to search for the most suitable candidate site.

[0492] Furthermore, users input feedback on candidate locations through a provided interface and send it to the server. This feedback information is used to improve the generated AI model, thereby enhancing the overall accuracy and effectiveness of the system.

[0493] As a concrete example, if a user wishes to construct a wind power plant in a northern region, the server collects data on that region and uses a generative AI model to evaluate a series of candidate sites. The terminal receives these results and presents them to the user in a visually easy-to-understand format, allowing the user to decide on a candidate site based on that information. In this way, the present invention provides a system that can select a construction site for a wind power plant more efficiently and accurately than conventional methods.

[0494] The following describes the processing flow.

[0495] Step 1:

[0496] The server collects regional information data, wind power information data, and ecosystem information data from various data providers. This collection is performed using API calls and database access, and is designed to provide up-to-date information in real time.

[0497] Step 2:

[0498] The server performs preprocessing on the collected data. This process involves imputing missing values ​​using appropriate methods, eliminating outliers, and maintaining data integrity. Furthermore, it standardizes the data format and converts it into a form that is easily processed by AI models.

[0499] Step 3:

[0500] The server inputs the preprocessed dataset into a generating AI model for analysis. The AI ​​model considers features such as wind speed, wind direction, and impact on the local ecosystem to calculate an evaluation score for each candidate site. High-performance algorithms are used to enable rapid analysis.

[0501] Step 4:

[0502] The server evaluates potential sites based on scores and generates a list of construction sites sorted by priority. This list includes data that takes into account the power generation potential and environmental impact of each region.

[0503] Step 5:

[0504] The server sends the generated list of candidate locations to the terminal. This transmission is performed securely using a secure protocol.

[0505] Step 6:

[0506] The terminal visually displays a list of candidate locations received from the server. It has the ability to place candidate locations on a map and display detailed information such as the score and ecosystem impact of each location in a pop-up window.

[0507] Step 7:

[0508] Users compare and evaluate potential locations based on the information displayed on their devices. They can use the device's built-in filtering function to narrow down the list of potential locations based on specific criteria.

[0509] Step 8:

[0510] Users select candidate locations based on evaluation results and send feedback to the server via their device. This feedback information is used to continuously improve the AI ​​model.

[0511] Step 9:

[0512] The server receives feedback from users and incorporates it into the AI ​​model. This process improves the overall accuracy and adaptability of the system, thereby enhancing the accuracy of future construction site selection.

[0513] (Example 1)

[0514] 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."

[0515] Selecting suitable sites for wind power plants quickly and accurately requires careful consideration of geographical conditions and ecosystems, as well as precise analysis of a wide range of information. Traditional methods often rely on manual information gathering and analysis, which is inefficient and prone to errors. This has led to delays in site selection and difficulties in finding the optimal location.

[0516] 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.

[0517] In this invention, the server includes means for acquiring regional information, weather information, and ecosystem information; means for preprocessing the acquired information, supplementing missing information, eliminating anomalies, and unifying the format of the information; and means for applying a generative AI algorithm to evaluate candidate sites using the preprocessed information and calculating an evaluation value for each candidate site. This makes it possible to efficiently select the optimal candidate site for wind power plant construction by integrating diverse information and analyzing it with AI.

[0518] "Regional information" refers to information such as topography, administrative divisions, and demographics related to a specific geographical area.

[0519] "Weather information" refers to data related to weather, such as wind speed, wind direction, temperature, and humidity.

[0520] "Ecosystem information" refers to information about the habitat status of plants and animals and the state of the natural environment in a specific area.

[0521] "Preprocessing" refers to the process of preparing data to make it easier to analyze, and mainly includes imputing missing values, removing outliers, and standardizing the data shape.

[0522] A "generative AI algorithm" refers to a series of computational processes that use machine learning models to perform evaluations and predictions based on specified data.

[0523] A "candidate site" refers to a geographical location where the construction of a wind power plant is being considered.

[0524] The "evaluation value" is an index that quantifies the suitability of a candidate site, and it shows the results after considering factors such as wind speed, wind direction, and impact on the ecosystem.

[0525] "User device" refers to a device that receives a list of potential construction sites and provides information to the user.

[0526] An "interface" refers to a means by which a user interacts with a system, and in particular, a mechanism that enables the input of feedback.

[0527] This invention is a system for quickly and accurately selecting a site for a wind power plant. The following describes embodiments for carrying out the invention.

[0528] The system is configured so that the server, terminal, and user elements work together. The server collects data from the internet and various information providers to obtain regional information, weather information, and ecosystem information. The data is first preprocessed, including imputing missing values ​​and removing outliers. Database software and statistical analysis tools are often used for this processing.

[0529] The pre-processed data is supplied to a generating AI algorithm on the server. This AI algorithm calculates evaluation values ​​for candidate sites based on features such as wind speed, wind direction, and impact on the ecosystem. The evaluation values ​​are then ranked in order of priority as scores for the construction candidate sites. Next, this list of construction candidate sites is sent to the terminal.

[0530] The terminal visually presents the user with a list of candidate locations received from the server. Digital map software is used for display, and the evaluation value and ecosystem impact of each candidate location are provided as pop-up information. The terminal also has functions that allow the user to filter and sort based on specific criteria. The user can use this to search for the optimal candidate location and make a decision.

[0531] Furthermore, users can send feedback on potential locations to the server via an interface provided through their device. This feedback is used to improve the generating AI algorithm, thereby enhancing the system's accuracy and efficiency.

[0532] For example, when a user wishes to build a wind power plant in a northern region, the server collects region-specific data and uses a generated AI algorithm to evaluate potential sites. The terminal displays the evaluation results visually, allowing the user to select the most suitable site based on that information.

[0533] An example of a prompt message is: "Use AI to evaluate suitable candidate sites for a wind power plant in the specified region X, and display the list on the map."

[0534] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0535] Step 1:

[0536] The server collects regional, weather, and ecosystem information from the internet and data providers. It stores the raw data obtained as input. Specifically, it retrieves data via APIs and records it in a database. The output is a well-organized dataset.

[0537] Step 2:

[0538] The server performs preprocessing on the collected data. The input is the raw data collected in step 1. Missing data points are imputed using statistical methods and machine learning, and outliers are detected and removed. As a specific example, missing wind speed data points are imputed with the average of surrounding data. The output is a clean dataset with missing and outlier data removed.

[0539] Step 3:

[0540] The server integrates the preprocessed dataset and inputs it into the generating AI model. The output data from Step 2 is used. Wind speed, wind direction, and ecosystem information are extracted as features, and the AI ​​analyzes the information based on these. Specifically, the model is trained to calculate an evaluation value for each candidate location. The output is a list containing the evaluation value for each candidate location.

[0541] Step 4:

[0542] The server determines the priority of candidate sites and generates a list of construction candidate sites based on the evaluation values ​​calculated by the generated AI model. The input is the list of evaluation values ​​obtained in step 3. Locations with high scores are placed higher, and the list is arranged in order of priority. The output is a list of candidate sites sorted by priority.

[0543] Step 5:

[0544] The server sends the created list of candidate locations to the terminal. The input is the list of candidate locations prepared in step 4. The server sends the list to the terminal using the HTTP protocol. The output is the list data displayed on the terminal.

[0545] Step 6:

[0546] The terminal visually displays a list of candidate locations received from the server to the user. The input is the list data sent from the server. Map software is used to map the candidate locations and displays evaluation values ​​and ecological impacts in a pop-up format. The output is candidate location information visualized on a map.

[0547] Step 7:

[0548] The terminal provides filtering and sorting functions based on user requirements. The input consists of user-specified conditions. The displayed list is dynamically updated based on these conditions. The output consists of candidate location information that matches the user-defined conditions.

[0549] Step 8:

[0550] The user provides feedback on potential locations via a terminal and sends the feedback information to the server. The input is the feedback data entered by the user. The terminal converts this into a data format and transfers it to the server. The output is the feedback information stored on the server.

[0551] Step 9:

[0552] The server analyzes user feedback and uses it to improve the generated AI model. The input is the feedback data from step 8. This is used to update the AI ​​model's parameters and training data. The output is the improved AI model.

[0553] (Application Example 1)

[0554] 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."

[0555] The problem that this invention aims to solve is the efficient and accurate selection of the optimal placement of machinery and equipment not only at wind power plant construction sites but also within factories. This is required to improve production efficiency, minimize energy consumption, and reduce environmental impact.

[0556] 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.

[0557] In this invention, the server includes means for collecting regional information data, wind power information data, ecosystem information data, and environmental data; means for preprocessing the collected data, including imputing missing values ​​and removing outliers, and unifying the data shape; and means for applying a generative AI model to evaluate candidate sites and machine layouts using the preprocessed data, and calculating a score for each candidate site or layout proposal. This makes it possible to determine rational and effective construction sites for wind power plants and machine layouts within factories.

[0558] "Regional information data" refers to data that includes the geographical characteristics, demographics, and land use of a region, and is used to understand the environmental and socioeconomic conditions of a specific region.

[0559] "Wind power information data" refers to information describing wind speed, wind direction, and annual wind patterns in a specific region, and is used to evaluate the efficiency of wind power generation.

[0560] "Ecosystem information data" refers to information about the habitat status of plants and animals and the natural environment in a specific area, and is important indicator data from the perspective of environmental protection and sustainable development.

[0561] A "generative AI model" is an artificial intelligence algorithm that performs predictions and analyses based on various input data. It is designed to learn the complex correlations between data and support decision-making.

[0562] "Means of calculating scores" refers to the process of calculating specific evaluation metrics based on input data and presenting the results as numerical values. It is a means used to clarify the criteria for evaluation.

[0563] A "user terminal" is a device that receives information transmitted from a server and can display and operate it for the user, and includes devices such as smartphones and computers.

[0564] This invention provides a system for optimizing the construction site of a wind power plant and the layout of machinery within a factory. This system mainly consists of three elements: a server, a terminal, and a user.

[0565] The server collects and preprocesses regional information data, wind power information data, ecosystem information data, and environmental data within the factory. This preprocessing includes imputing missing values, removing outliers, and standardizing data shapes. Based on the preprocessed data, a generating AI model analyzes wind speed, wind direction, and the impact on the regional ecosystem and the factory environment as features. The server then calculates scores for candidate wind power plant locations and proposed machine layouts within the factory, and lists them in order of priority. This list is sent to the user's terminal.

[0566] The terminal displays a list of candidate locations sent from the server in an easy-to-understand manner for the user. It shows candidate locations on a map and provides pop-up information such as scores and ecological impacts to aid visual understanding. Furthermore, users can easily search for candidate locations that meet specific criteria using filtering and sorting functions. This enables users to make decisions efficiently.

[0567] Through the provided interface, users can input feedback on potential locations for wind farms and factory machinery under various conditions. This feedback is sent to the server and used to further improve the generated AI model.

[0568] The hardware used includes sensors for data collection and smart devices to display information and provide interfaces. The software uses Python and scikit-learn libraries for data preprocessing and AI analysis.

[0569] For example, if a user is selecting the optimal location for a wind farm during strong winds, the server will collect the latest wind data for that area and use a generated AI model to suggest the best candidate locations. An example of a prompt message would be, "Based on the factory's environmental data (temperature, humidity, vibration), please propose an optimal machine layout to maximize efficiency."

[0570] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0571] Step 1:

[0572] The server collects regional information data, wind power information data, ecosystem information data, and factory environment data. This process retrieves the necessary data from data providers and online databases. The input is the raw data from the source, and the output is these datasets.

[0573] Step 2:

[0574] The server performs preprocessing on the collected data. Specifically, it imputes missing values, removes outliers, and standardizes the data shape. The input is the raw dataset, and the output is a preprocessed, clean dataset.

[0575] Step 3:

[0576] The server performs analysis using a generative AI model with preprocessed data. The input is a preprocessed dataset, and this data is used to analyze features such as wind speed, wind direction, and impact on ecosystems. The output is a score for candidate locations or proposed placements.

[0577] Step 4:

[0578] The server generates a list of candidate locations in order of priority based on scores calculated by the generative AI model. This list is sorted according to the evaluation score. The input is a scored dataset, and the output is an ordered list of candidate locations.

[0579] Step 5:

[0580] The server sends the generated list of candidate locations to the user's terminal. The input received by the terminal is the list of candidate locations, and it uses this information to prepare for display to the user. The output is an internal data structure for visualization.

[0581] Step 6:

[0582] The terminal displays a list of candidate locations received from the server on a map, showing the evaluation score and ecosystem impact for each candidate. The input is data received from the server, and the output is information presented visually to the user.

[0583] Step 7:

[0584] The user uses the provided interface to filter and sort the list of potential locations to find those that meet specific criteria. The input is a list of potential locations displayed on the terminal, and the output is the best-fitting location that meets the criteria.

[0585] Step 8:

[0586] The user provides feedback on the candidate locations they have selected, and the device sends this information to the server. The input is the user's feedback, and the output is the data sent to the server.

[0587] Step 9:

[0588] The server uses user feedback to improve the generated AI model. The input is feedback data, and the output is the refined AI model.

[0589] 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.

[0590] This invention provides a system for efficiently selecting construction sites for wind power plants, and combines it with an emotion engine that recognizes user emotions. The embodiments thereof are described below.

[0591] Server Role

[0592] The server collects regional information data, wind power information data, and ecosystem information data, preprocesses them, and integrates them into a dataset to be input into the AI ​​model. Data preprocessing includes imputing missing values, removing outliers, and standardizing the format. The AI ​​model analyzes this data and calculates an evaluation score for each candidate site. Based on this score, the server generates a list of potential construction sites and sends it to the user's terminal.

[0593] Terminal role

[0594] The terminal visually displays a list of candidate locations provided by the server and is equipped with an emotion engine to provide information in a user-friendly format. This emotion engine can analyze emotional information from the user's text and voice input and dynamically adjust the interface.

[0595] The emotion engine can change the interface's color scheme and layout to reduce user stress and distrust, and to support smoother decision-making. This feature aims to improve user satisfaction with the system.

[0596] User roles

[0597] Users review the provided site information using their devices and evaluate it based on their own requirements and preferences. They can narrow down the candidate sites using the interface's filtering function and select the optimal construction site. Furthermore, they can contribute to system improvement by utilizing the feedback function provided by the emotion engine.

[0598] For example, if a user is emotionally exhausted, the emotion engine will detect this and change the way information is presented, such as by using softer colors, to provide a comfortable interface for the user. In this way, user emotions can be reflected in the operation of the system, providing a more intuitive and user-friendly system. By incorporating user emotions and feedback into the technical selection process, this invention enables the selection of wind power plant construction sites in a more efficient and user-friendly manner than conventional methods.

[0599] The following describes the processing flow.

[0600] Step 1:

[0601] The server collects regional information data, wind power information data, and ecosystem information data via the internet or through dedicated data channels. This allows for the integrated collection of all the information necessary for the construction of wind power plants.

[0602] Step 2:

[0603] The server performs preprocessing on the collected data. This preprocessing involves imputing missing data with appropriate estimates and removing obviously abnormal data points. It also converts all data into a unified format that the AI ​​model can understand.

[0604] Step 3:

[0605] The server inputs pre-processed data into a generating AI model for analysis. This AI model primarily uses wind speed, wind direction, and impact on the local ecosystem as features to calculate an evaluation score for each candidate location.

[0606] Step 4:

[0607] The server evaluates potential construction sites based on scoring by an AI model and creates a list sorted by priority. This list takes into account power generation efficiency and environmental impact.

[0608] Step 5:

[0609] The server securely transmits the generated list of candidate locations to the terminal.

[0610] Step 6:

[0611] The terminal visually displays the list of potential locations received from the server and generates an interface that includes maps and detailed information. This display makes it easy for users to intuitively understand the information.

[0612] Step 7:

[0613] The device utilizes an emotion engine to analyze the user's emotions through text or voice input. This allows it to adjust the interface tone and information presentation method according to the user's state.

[0614] Step 8:

[0615] Users compare and evaluate potential locations based on the information provided on their devices, and narrow down their final choices according to their own conditions and requirements. They can also use the device's filtering function to select locations based on specific criteria.

[0616] Step 9:

[0617] Users provide feedback on selection results and interfaces via an emotion engine to their devices, and this information is sent to a server. This feedback is used to improve the AI ​​model and interface.

[0618] Step 10:

[0619] The server receives feedback from users and re-inputs that information into the generative AI model and emotion engine as a feedback loop, improving the accuracy of subsequent analyses. This process allows the entire system to continuously evolve.

[0620] (Example 2)

[0621] 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."

[0622] In the process of selecting sites for wind power plants, conventional methods make it difficult to efficiently evaluate environmental impacts and regional characteristics while fully considering them. Furthermore, the lack of consideration for emotional factors in user decision-making leads to decreased satisfaction.

[0623] 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.

[0624] In this invention, the server includes means for collecting geographic data, meteorological data, and biodiversity data; means for processing the collected data, performing value interpolation and removal of abnormal values, and standardizing the data format; and means for applying an artificial intelligence model that evaluates candidate locations using the processed data and calculating an evaluation value for each candidate location. This makes it possible to make decisions in selecting construction sites for wind power plants that take into account the environment and regional characteristics, as well as the feelings of users.

[0625] "Geographic data" refers to information about the location, topography, and features of a specific region.

[0626] "Weather data" refers to information about weather conditions in a specific area, such as wind speed, wind direction, temperature, and humidity.

[0627] "Biological data" refers to information about the distribution, types, and habitat status of plants and animals in a specific region.

[0628] An "artificial intelligence model" refers to an algorithm that uses large amounts of data for analysis and performs evaluations and predictions based on specific conditions and indicators.

[0629] An "evaluation value" is a numerical value calculated based on specific conditions or criteria, and refers to an indicator that shows the suitability and priority of candidate locations.

[0630] "User interface" refers to the means by which a user interacts with a system, and includes methods for displaying and inputting information.

[0631] An "emotional analysis engine" refers to a processing unit that analyzes the user's emotions and psychological state and reflects this in the system's operation and interface.

[0632] This invention is a system for efficiently selecting construction sites for wind power plants, combining data processing and sentiment recognition to support user-friendly decision-making. Specific embodiments are described below.

[0633] Server role:

[0634] The server collects geographical, meteorological, and biodiversity data from external databases and sensors. This data is first processed using a dedicated data preprocessing program to impute missing values ​​and remove anomalous values, then standardized into a format suitable for use in the AI ​​model. This preprocessed data is then input into the AI ​​model as an integrated dataset. The AI ​​model evaluates the suitability of each candidate site for wind power plant construction and calculates an evaluation value. This evaluation value is used to generate a ranking of the candidate sites, which the server then transmits to user terminals.

[0635] Terminal role:

[0636] The terminal visually displays a ranking list of candidate locations received from the server to the user. The terminal is equipped with an emotion analysis engine to analyze the user's emotions and dynamically adjusts the interface based on the user's text and voice input. For example, if it is detected that the user is feeling stressed, the background color will be changed to a calmer shade to facilitate information processing.

[0637] User roles:

[0638] Users can view the candidate site rankings displayed on their devices and further refine their search based on their own evaluation criteria. For example, users can select candidate sites based on the score of environmental impacts they consider important. Users can also contribute to system improvements using the feedback function provided by the interface.

[0639] Examples of specific cases and prompt statements:

[0640] For example, if a user is emotionally exhausted, the emotion analysis engine detects this state and adjusts the interface accordingly. This user-friendly approach allows users to make comfortable and efficient decisions.

[0641] An example of a prompt message would be: "Design an AI model to efficiently select a site for a wind power plant while minimizing environmental impact, in order to evaluate potential construction sites."

[0642] In this way, servers, terminals, and users collaborate to realize a configuration that improves both environmental considerations in technical selection and user satisfaction.

[0643] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0644] Step 1:

[0645] The server collects geographic, weather, and biodiversity data from various sensors and external databases. This acquired data is sent to a data processing pipeline within the server. The output data is a raw, unprocessed dataset.

[0646] Step 2:

[0647] The server performs data cleaning on the collected data. At this stage, missing values ​​are imputed using methods such as averaging, and outliers are removed using statistical techniques. This ensures data consistency and generates a pre-processed dataset as output.

[0648] Step 3:

[0649] The server integrates data based on pre-processed datasets, combining information from different data sources to create a single unified dataset. This unified dataset is then prepared as input for the generative AI model.

[0650] Step 4:

[0651] The server inputs the integrated dataset into a generating AI model and calculates an evaluation value for each candidate site. The AI ​​model analyzes the data received as input and uses a calculation model to evaluate the optimal location for wind power plant construction. The output is the evaluation score for each candidate site.

[0652] Step 5:

[0653] The server creates a prioritized list of potential construction sites based on the calculated scores. This organizes information that is useful for supporting decisions regarding wind power plant construction. The output is in the form of a prioritized list of potential sites.

[0654] Step 6:

[0655] The terminal visually displays a list of candidate locations received from the server to the user. The terminal also utilizes an emotion analysis engine to analyze the user's emotional state. This analysis allows for dynamic adjustments to the interface, optimizing the display method according to the user's emotional state. The output is candidate location information in an optimized interface format.

[0656] Step 7:

[0657] Users evaluate potential sites based on the information displayed on their devices and narrow down the list of candidates using the filtering function. User input data considers requirements such as environmental impact and efficiency. The final output is the construction site candidates selected by the user. User feedback is also used to improve the system.

[0658] (Application Example 2)

[0659] 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."

[0660] Conventional systems struggled to efficiently process diverse information, such as regional data, weather data, and biodiversity data, to select suitable sites for logistics centers and other facilities. Furthermore, they lacked the means to support user decision-making more smoothly by providing an interface that considered user emotions. Moreover, mechanisms for improving the system based on feedback were insufficient.

[0661] 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.

[0662] This invention includes a server comprising means for collecting regional data, weather data, and biota data; means for preprocessing the collected data, including imputing missing values ​​and removing outliers, and standardizing the data format; means for applying a generative AI algorithm to evaluate candidate sites using the preprocessed data and calculating a score for each candidate site; means for an information terminal equipped with an emotion analysis engine that analyzes the user's emotions and dynamically changes the interface; and means for the user to narrow down candidate sites using a filtering function. This enables more efficient and user-friendly candidate site selection.

[0663] "Regional data" refers to various types of information related to a specific region, including geographical information and land use information.

[0664] "Weather data" refers to information about weather conditions such as wind speed, wind direction, temperature, and humidity.

[0665] "Biological community data" refers to information about the ecosystems of plants and animals in a specific region.

[0666] "Preprocessing" is the process of filling in missing data values, removing outliers, and standardizing the data format.

[0667] A "generative AI algorithm" is an algorithm that uses machine learning to analyze input data and calculate an evaluation score.

[0668] An "information terminal" is a device used by users to view and manipulate information, and primarily refers to smartphones and computers.

[0669] An "emotion analysis engine" is a system that analyzes the user's emotional state from their display and voice, and dynamically adjusts the interface accordingly.

[0670] A "filtering function" is a function that selects and displays data based on the conditions desired by the user.

[0671] In the system for implementing this invention, a server collects regional data, weather data, and biota data, and preprocesses this data. Specifically, it performs data imputation, removes outliers, and standardizes the data format. This is done using a data cleaning library in Python (e.g., Pandas). The server then runs a generative AI algorithm to calculate evaluation scores and generate a list of candidate locations. This process uses a machine learning framework (e.g., TensorFlow or PyTorch).

[0672] The server sends the generated list of candidate locations to an information terminal. This terminal is equipped with an emotion analysis engine that dynamically adjusts the interface by analyzing the user's facial expressions and voice. This analysis utilizes APIs provided by cloud services (e.g., Google Cloud's Natural Language API and Microsoft Azure's Face API). The user can use this terminal to narrow down the candidate locations through a filtering function.

[0673] To give a concrete example, when a logistics company representative decides on the location of a new center, this system can be used to efficiently find land that meets the requirements. If she is emotionally exhausted, the interface automatically changes to a relaxing color scheme, making the selection process more comfortable. In this way, the user experience is improved. An example of a prompt would be, "Explain how to calculate an evaluation score based on candidate site information for selecting a logistics center construction site in the Kanto region, taking into account transportation access, proximity to major cities, and environmental impact, and how to provide a UI that responds to the user's emotions."

[0674] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0675] Step 1:

[0676] The server collects regional data, weather data, and biota data from various APIs. For example, regional data is obtained from a Geographic Information System (GIS), and weather data is obtained from the Japan Meteorological Agency's API. The input is raw data from each API, and the output is an integrated dataset combining them.

[0677] Step 2:

[0678] The server performs preprocessing on the integrated dataset. Specifically, it uses Python to impute missing values, remove outliers, and standardize data formats. This process results in cleaned data being output. The input is the raw integrated dataset, and the output is the preprocessed data.

[0679] Step 3:

[0680] The server inputs pre-processed data into a generating AI algorithm to calculate an evaluation score for each candidate site. Here, a machine learning model using TensorFlow is applied. The input is pre-processed data, and the output is the evaluation score for each candidate site.

[0681] Step 4:

[0682] The server generates a list of candidate locations in order of priority based on the calculated evaluation score and sends it to the terminal. The input is the evaluation score, and the output is a list of candidate locations in order of priority.

[0683] Step 5:

[0684] The terminal displays a list of candidate locations and activates an emotion analysis engine to analyze the user's emotional state. The terminal is equipped with a camera that recognizes emotions using facial expression data as input. The output is the analyzed emotion information.

[0685] Step 6:

[0686] The device dynamically adjusts the color scheme and layout of the user interface based on analyzed emotional information. The input is emotional information, and the output is the adjusted interface.

[0687] Step 7:

[0688] The user reviews a list of potential locations on a customized interface and uses a filtering function to narrow down the options. The input is the user's selection criteria, and the output is the narrowed-down list of potential locations.

[0689] Step 8:

[0690] The user selects the optimal construction site based on a list of candidate sites provided by the system. The input is a narrowed-down list of candidate sites, and the output is information on the construction site deemed optimal.

[0691] 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.

[0692] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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.

[0693] 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.

[0694] [Fourth Embodiment]

[0695] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0696] 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.

[0697] 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).

[0698] 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.

[0699] 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.

[0700] 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).

[0701] 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.

[0702] 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.

[0703] 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.

[0704] 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.

[0705] 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.

[0706] 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.

[0707] 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".

[0708] This invention is a system for quickly and accurately selecting a construction site for a wind power plant, and its embodiment is configured in which a server, terminal, and user elements work together.

[0709] Server Role

[0710] The server collects large amounts of relevant data, such as regional information data, wind power information data, and ecosystem information data, from the internet and various data providers. The server integrates this data and performs preprocessing, such as imputing missing values ​​and removing outliers. This results in a clean dataset in a format that is easy for AI models to process.

[0711] The server applies a generative AI model to a pre-processed dataset. The generative AI model uses wind speed, wind direction, and impact on the local ecosystem as features and calculates an evaluation score for each candidate site. Based on the evaluation scores, it generates a list of construction candidate sites in order of priority and sends this list to the terminal.

[0712] Terminal role

[0713] The terminal displays a list of candidate locations received from the server in a user-friendly format. It shows candidate locations on a map and provides their respective evaluation scores and ecological impacts as pop-up information. Filtering and sorting functions are also included to allow users to easily obtain detailed information.

[0714] The device also features an interface for users to provide feedback on potential locations. This feedback is sent to a server to help improve the accuracy of the AI ​​model.

[0715] User roles

[0716] Users make decisions regarding the selection of a wind power plant construction site based on candidate site information provided through their devices. Users can utilize filtering functions based on their own requirements and preferences to search for the most suitable candidate site.

[0717] Furthermore, users input feedback on candidate locations through a provided interface and send it to the server. This feedback information is used to improve the generated AI model, thereby enhancing the overall accuracy and effectiveness of the system.

[0718] As a concrete example, if a user wishes to construct a wind power plant in a northern region, the server collects data on that region and uses a generative AI model to evaluate a series of candidate sites. The terminal receives these results and presents them to the user in a visually easy-to-understand format, allowing the user to decide on a candidate site based on that information. In this way, the present invention provides a system that can select a construction site for a wind power plant more efficiently and accurately than conventional methods.

[0719] The following describes the processing flow.

[0720] Step 1:

[0721] The server collects regional information data, wind power information data, and ecosystem information data from various data providers. This collection is performed using API calls and database access, and is designed to provide up-to-date information in real time.

[0722] Step 2:

[0723] The server performs preprocessing on the collected data. This process involves imputing missing values ​​using appropriate methods, eliminating outliers, and maintaining data integrity. Furthermore, it standardizes the data format and converts it into a form that is easily processed by AI models.

[0724] Step 3:

[0725] The server inputs the preprocessed dataset into a generating AI model for analysis. The AI ​​model considers features such as wind speed, wind direction, and impact on the local ecosystem to calculate an evaluation score for each candidate site. High-performance algorithms are used to enable rapid analysis.

[0726] Step 4:

[0727] The server evaluates potential sites based on scores and generates a list of construction sites sorted by priority. This list includes data that takes into account the power generation potential and environmental impact of each region.

[0728] Step 5:

[0729] The server sends the generated list of candidate locations to the terminal. This transmission is performed securely using a secure protocol.

[0730] Step 6:

[0731] The terminal visually displays a list of candidate locations received from the server. It has the ability to place candidate locations on a map and display detailed information such as the score and ecosystem impact of each location in a pop-up window.

[0732] Step 7:

[0733] Users compare and evaluate potential locations based on the information displayed on their devices. They can use the device's built-in filtering function to narrow down the list of potential locations based on specific criteria.

[0734] Step 8:

[0735] Users select candidate locations based on evaluation results and send feedback to the server via their device. This feedback information is used to continuously improve the AI ​​model.

[0736] Step 9:

[0737] The server receives feedback from users and incorporates it into the AI ​​model. This process improves the overall accuracy and adaptability of the system, thereby enhancing the accuracy of future construction site selection.

[0738] (Example 1)

[0739] 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".

[0740] Selecting suitable sites for wind power plants quickly and accurately requires careful consideration of geographical conditions and ecosystems, as well as precise analysis of a wide range of information. Traditional methods often rely on manual information gathering and analysis, which is inefficient and prone to errors. This has led to delays in site selection and difficulties in finding the optimal location.

[0741] 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.

[0742] In this invention, the server includes means for acquiring regional information, weather information, and ecosystem information; means for preprocessing the acquired information, supplementing missing information, eliminating anomalies, and unifying the format of the information; and means for applying a generative AI algorithm to evaluate candidate sites using the preprocessed information and calculating an evaluation value for each candidate site. This makes it possible to efficiently select the optimal candidate site for wind power plant construction by integrating diverse information and analyzing it with AI.

[0743] "Regional information" refers to information such as topography, administrative divisions, and demographics related to a specific geographical area.

[0744] "Weather information" refers to data related to weather, such as wind speed, wind direction, temperature, and humidity.

[0745] "Ecosystem information" refers to information about the habitat status of plants and animals and the state of the natural environment in a specific area.

[0746] "Preprocessing" refers to the process of preparing data to make it easier to analyze, and mainly includes imputing missing values, removing outliers, and standardizing the data shape.

[0747] A "generative AI algorithm" refers to a series of computational processes that use machine learning models to perform evaluations and predictions based on specified data.

[0748] A "candidate site" refers to a geographical location where the construction of a wind power plant is being considered.

[0749] The "evaluation value" is an index that quantifies the suitability of a candidate site, and it shows the results after considering factors such as wind speed, wind direction, and impact on the ecosystem.

[0750] "User device" refers to a device that receives a list of potential construction sites and provides information to the user.

[0751] An "interface" refers to a means by which a user interacts with a system, and in particular, a mechanism that enables the input of feedback.

[0752] This invention is a system for quickly and accurately selecting a site for a wind power plant. The following describes embodiments for carrying out the invention.

[0753] The system is configured so that the server, terminal, and user elements work together. The server collects data from the internet and various information providers to obtain regional information, weather information, and ecosystem information. The data is first preprocessed, including imputing missing values ​​and removing outliers. Database software and statistical analysis tools are often used for this processing.

[0754] The pre-processed data is supplied to a generating AI algorithm on the server. This AI algorithm calculates evaluation values ​​for candidate sites based on features such as wind speed, wind direction, and impact on the ecosystem. The evaluation values ​​are then ranked in order of priority as scores for the construction candidate sites. Next, this list of construction candidate sites is sent to the terminal.

[0755] The terminal visually presents the user with a list of candidate locations received from the server. Digital map software is used for display, and the evaluation value and ecosystem impact of each candidate location are provided as pop-up information. The terminal also has functions that allow the user to filter and sort based on specific criteria. The user can use this to search for the optimal candidate location and make a decision.

[0756] Furthermore, users can send feedback on potential locations to the server via an interface provided through their device. This feedback is used to improve the generating AI algorithm, thereby enhancing the system's accuracy and efficiency.

[0757] For example, when a user wishes to build a wind power plant in a northern region, the server collects region-specific data and uses a generated AI algorithm to evaluate potential sites. The terminal displays the evaluation results visually, allowing the user to select the most suitable site based on that information.

[0758] An example of a prompt message is: "Use AI to evaluate suitable candidate sites for a wind power plant in the specified region X, and display the list on the map."

[0759] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0760] Step 1:

[0761] The server collects regional, weather, and ecosystem information from the internet and data providers. It stores the raw data obtained as input. Specifically, it retrieves data via APIs and records it in a database. The output is a well-organized dataset.

[0762] Step 2:

[0763] The server performs preprocessing on the collected data. The input is the raw data collected in step 1. Missing data points are imputed using statistical methods and machine learning, and outliers are detected and removed. As a specific example, missing wind speed data points are imputed with the average of surrounding data. The output is a clean dataset with missing and outlier data removed.

[0764] Step 3:

[0765] The server integrates the preprocessed dataset and inputs it into the generating AI model. The output data from Step 2 is used. Wind speed, wind direction, and ecosystem information are extracted as features, and the AI ​​analyzes the information based on these. Specifically, the model is trained to calculate an evaluation value for each candidate location. The output is a list containing the evaluation value for each candidate location.

[0766] Step 4:

[0767] The server determines the priority of candidate sites and generates a list of construction candidate sites based on the evaluation values ​​calculated by the generated AI model. The input is the list of evaluation values ​​obtained in step 3. Locations with high scores are placed higher, and the list is arranged in order of priority. The output is a list of candidate sites sorted by priority.

[0768] Step 5:

[0769] The server sends the created list of candidate locations to the terminal. The input is the list of candidate locations prepared in step 4. The server sends the list to the terminal using the HTTP protocol. The output is the list data displayed on the terminal.

[0770] Step 6:

[0771] The terminal visually displays a list of candidate locations received from the server to the user. The input is the list data sent from the server. Map software is used to map the candidate locations and displays evaluation values ​​and ecological impacts in a pop-up format. The output is candidate location information visualized on a map.

[0772] Step 7:

[0773] The terminal provides filtering and sorting functions based on user requirements. The input consists of user-specified conditions. The displayed list is dynamically updated based on these conditions. The output consists of candidate location information that matches the user-defined conditions.

[0774] Step 8:

[0775] The user provides feedback on potential locations via a terminal and sends the feedback information to the server. The input is the feedback data entered by the user. The terminal converts this into a data format and transfers it to the server. The output is the feedback information stored on the server.

[0776] Step 9:

[0777] The server analyzes user feedback and uses it to improve the generated AI model. The input is the feedback data from step 8. This is used to update the AI ​​model's parameters and training data. The output is the improved AI model.

[0778] (Application Example 1)

[0779] 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".

[0780] The problem that this invention aims to solve is the efficient and accurate selection of the optimal placement of machinery and equipment not only at wind power plant construction sites but also within factories. This is required to improve production efficiency, minimize energy consumption, and reduce environmental impact.

[0781] 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.

[0782] In this invention, the server includes means for collecting regional information data, wind power information data, ecosystem information data, and environmental data; means for preprocessing the collected data, including imputing missing values ​​and removing outliers, and unifying the data shape; and means for applying a generative AI model to evaluate candidate sites and machine layouts using the preprocessed data, and calculating a score for each candidate site or layout proposal. This makes it possible to determine rational and effective construction sites for wind power plants and machine layouts within factories.

[0783] "Regional information data" refers to data that includes the geographical characteristics, demographics, and land use of a region, and is used to understand the environmental and socioeconomic conditions of a specific region.

[0784] "Wind power information data" refers to information describing wind speed, wind direction, and annual wind patterns in a specific region, and is used to evaluate the efficiency of wind power generation.

[0785] "Ecosystem information data" refers to information about the habitat status of plants and animals and the natural environment in a specific area, and is important indicator data from the perspective of environmental protection and sustainable development.

[0786] A "generative AI model" is an artificial intelligence algorithm that performs predictions and analyses based on various input data. It is designed to learn the complex correlations between data and support decision-making.

[0787] "Means of calculating scores" refers to the process of calculating specific evaluation metrics based on input data and presenting the results as numerical values. It is a means used to clarify the criteria for evaluation.

[0788] A "user terminal" is a device that receives information transmitted from a server and can display and operate it for the user, and includes devices such as smartphones and computers.

[0789] This invention provides a system for optimizing the construction site of a wind power plant and the layout of machinery within a factory. This system mainly consists of three elements: a server, a terminal, and a user.

[0790] The server collects and preprocesses regional information data, wind power information data, ecosystem information data, and environmental data within the factory. This preprocessing includes imputing missing values, removing outliers, and standardizing data shapes. Based on the preprocessed data, a generating AI model analyzes wind speed, wind direction, and the impact on the regional ecosystem and the factory environment as features. The server then calculates scores for candidate wind power plant locations and proposed machine layouts within the factory, and lists them in order of priority. This list is sent to the user's terminal.

[0791] The terminal displays a list of candidate locations sent from the server in an easy-to-understand manner for the user. It shows candidate locations on a map and provides pop-up information such as scores and ecological impacts to aid visual understanding. Furthermore, users can easily search for candidate locations that meet specific criteria using filtering and sorting functions. This enables users to make decisions efficiently.

[0792] Through the provided interface, users can input feedback on potential locations for wind farms and factory machinery under various conditions. This feedback is sent to the server and used to further improve the generated AI model.

[0793] The hardware used includes sensors for data collection and smart devices to display information and provide interfaces. The software uses Python and scikit-learn libraries for data preprocessing and AI analysis.

[0794] For example, if a user is selecting the optimal location for a wind farm during strong winds, the server will collect the latest wind data for that area and use a generated AI model to suggest the best candidate locations. An example of a prompt message would be, "Based on the factory's environmental data (temperature, humidity, vibration), please propose an optimal machine layout to maximize efficiency."

[0795] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0796] Step 1:

[0797] The server collects regional information data, wind power information data, ecosystem information data, and factory environment data. This process retrieves the necessary data from data providers and online databases. The input is the raw data from the source, and the output is these datasets.

[0798] Step 2:

[0799] The server performs preprocessing on the collected data. Specifically, it imputes missing values, removes outliers, and standardizes the data shape. The input is the raw dataset, and the output is a preprocessed, clean dataset.

[0800] Step 3:

[0801] The server performs analysis using a generative AI model with preprocessed data. The input is a preprocessed dataset, and this data is used to analyze features such as wind speed, wind direction, and impact on ecosystems. The output is a score for candidate locations or proposed placements.

[0802] Step 4:

[0803] The server generates a list of candidate locations in order of priority based on scores calculated by the generative AI model. This list is sorted according to the evaluation score. The input is a scored dataset, and the output is an ordered list of candidate locations.

[0804] Step 5:

[0805] The server sends the generated list of candidate locations to the user's terminal. The input received by the terminal is the list of candidate locations, and it uses this information to prepare for display to the user. The output is an internal data structure for visualization.

[0806] Step 6:

[0807] The terminal displays a list of candidate locations received from the server on a map, showing the evaluation score and ecosystem impact for each candidate. The input is data received from the server, and the output is information presented visually to the user.

[0808] Step 7:

[0809] The user uses the provided interface to filter and sort the list of potential locations to find those that meet specific criteria. The input is a list of potential locations displayed on the terminal, and the output is the best-fitting location that meets the criteria.

[0810] Step 8:

[0811] The user provides feedback on the candidate locations they have selected, and the device sends this information to the server. The input is the user's feedback, and the output is the data sent to the server.

[0812] Step 9:

[0813] The server uses user feedback to improve the generated AI model. The input is feedback data, and the output is the refined AI model.

[0814] 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.

[0815] This invention provides a system for efficiently selecting construction sites for wind power plants, and combines it with an emotion engine that recognizes user emotions. The embodiments thereof are described below.

[0816] Server Role

[0817] The server collects regional information data, wind power information data, and ecosystem information data, preprocesses them, and integrates them into a dataset to be input into the AI ​​model. Data preprocessing includes imputing missing values, removing outliers, and standardizing the format. The AI ​​model analyzes this data and calculates an evaluation score for each candidate site. Based on this score, the server generates a list of potential construction sites and sends it to the user's terminal.

[0818] Terminal role

[0819] The terminal visually displays a list of candidate locations provided by the server and is equipped with an emotion engine to provide information in a user-friendly format. This emotion engine can analyze emotional information from the user's text and voice input and dynamically adjust the interface.

[0820] The emotion engine can change the interface's color scheme and layout to reduce user stress and distrust, and to support smoother decision-making. This feature aims to improve user satisfaction with the system.

[0821] User roles

[0822] Users review the provided site information using their devices and evaluate it based on their own requirements and preferences. They can narrow down the candidate sites using the interface's filtering function and select the optimal construction site. Furthermore, they can contribute to system improvement by utilizing the feedback function provided by the emotion engine.

[0823] For example, if a user is emotionally exhausted, the emotion engine will detect this and change the way information is presented, such as by using softer colors, to provide a comfortable interface for the user. In this way, user emotions can be reflected in the operation of the system, providing a more intuitive and user-friendly system. By incorporating user emotions and feedback into the technical selection process, this invention enables the selection of wind power plant construction sites in a more efficient and user-friendly manner than conventional methods.

[0824] The following describes the processing flow.

[0825] Step 1:

[0826] The server collects regional information data, wind power information data, and ecosystem information data via the internet or through dedicated data channels. This allows for the integrated collection of all the information necessary for the construction of wind power plants.

[0827] Step 2:

[0828] The server performs preprocessing on the collected data. This preprocessing involves imputing missing data with appropriate estimates and removing obviously abnormal data points. It also converts all data into a unified format that the AI ​​model can understand.

[0829] Step 3:

[0830] The server inputs pre-processed data into a generating AI model for analysis. This AI model primarily uses wind speed, wind direction, and impact on the local ecosystem as features to calculate an evaluation score for each candidate location.

[0831] Step 4:

[0832] The server evaluates potential construction sites based on scoring by an AI model and creates a list sorted by priority. This list takes into account power generation efficiency and environmental impact.

[0833] Step 5:

[0834] The server securely transmits the generated list of candidate locations to the terminal.

[0835] Step 6:

[0836] The terminal visually displays the list of potential locations received from the server and generates an interface that includes maps and detailed information. This display makes it easy for users to intuitively understand the information.

[0837] Step 7:

[0838] The device utilizes an emotion engine to analyze the user's emotions through text or voice input. This allows it to adjust the interface tone and information presentation method according to the user's state.

[0839] Step 8:

[0840] Users compare and evaluate potential locations based on the information provided on their devices, and narrow down their final choices according to their own conditions and requirements. They can also use the device's filtering function to select locations based on specific criteria.

[0841] Step 9:

[0842] Users provide feedback on selection results and interfaces via an emotion engine to their devices, and this information is sent to a server. This feedback is used to improve the AI ​​model and interface.

[0843] Step 10:

[0844] The server receives feedback from users and re-inputs that information into the generative AI model and emotion engine as a feedback loop, improving the accuracy of subsequent analyses. This process allows the entire system to continuously evolve.

[0845] (Example 2)

[0846] 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".

[0847] In the process of selecting sites for wind power plants, conventional methods make it difficult to efficiently evaluate environmental impacts and regional characteristics while fully considering them. Furthermore, the lack of consideration for emotional factors in user decision-making leads to decreased satisfaction.

[0848] 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.

[0849] In this invention, the server includes means for collecting geographic data, meteorological data, and biodiversity data; means for processing the collected data, performing value interpolation and removal of abnormal values, and standardizing the data format; and means for applying an artificial intelligence model that evaluates candidate locations using the processed data and calculating an evaluation value for each candidate location. This makes it possible to make decisions in selecting construction sites for wind power plants that take into account the environment and regional characteristics, as well as the feelings of users.

[0850] "Geographic data" refers to information about the location, topography, and features of a specific region.

[0851] "Weather data" refers to information about weather conditions in a specific area, such as wind speed, wind direction, temperature, and humidity.

[0852] "Biological data" refers to information about the distribution, types, and habitat status of plants and animals in a specific region.

[0853] An "artificial intelligence model" refers to an algorithm that uses large amounts of data for analysis and performs evaluations and predictions based on specific conditions and indicators.

[0854] An "evaluation value" is a numerical value calculated based on specific conditions or criteria, and refers to an indicator that shows the suitability and priority of candidate locations.

[0855] "User interface" refers to the means by which a user interacts with a system, and includes methods for displaying and inputting information.

[0856] An "emotional analysis engine" refers to a processing unit that analyzes the user's emotions and psychological state and reflects this in the system's operation and interface.

[0857] This invention is a system for efficiently selecting construction sites for wind power plants, combining data processing and sentiment recognition to support user-friendly decision-making. Specific embodiments are described below.

[0858] Server role:

[0859] The server collects geographical, meteorological, and biodiversity data from external databases and sensors. This data is first processed using a dedicated data preprocessing program to impute missing values ​​and remove anomalous values, then standardized into a format suitable for use in the AI ​​model. This preprocessed data is then input into the AI ​​model as an integrated dataset. The AI ​​model evaluates the suitability of each candidate site for wind power plant construction and calculates an evaluation value. This evaluation value is used to generate a ranking of the candidate sites, which the server then transmits to user terminals.

[0860] Terminal role:

[0861] The terminal visually displays a ranking list of candidate locations received from the server to the user. The terminal is equipped with an emotion analysis engine to analyze the user's emotions and dynamically adjusts the interface based on the user's text and voice input. For example, if it is detected that the user is feeling stressed, the background color will be changed to a calmer shade to facilitate information processing.

[0862] User roles:

[0863] Users can view the candidate site rankings displayed on their devices and further refine their search based on their own evaluation criteria. For example, users can select candidate sites based on the score of environmental impacts they consider important. Users can also contribute to system improvements using the feedback function provided by the interface.

[0864] Examples of specific cases and prompt statements:

[0865] For example, if a user is emotionally exhausted, the emotion analysis engine detects this state and adjusts the interface accordingly. This user-friendly approach allows users to make comfortable and efficient decisions.

[0866] An example of a prompt message would be: "Design an AI model to efficiently select a site for a wind power plant while minimizing environmental impact, in order to evaluate potential construction sites."

[0867] In this way, servers, terminals, and users collaborate to realize a configuration that improves both environmental considerations in technical selection and user satisfaction.

[0868] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0869] Step 1:

[0870] The server collects geographic, weather, and biodiversity data from various sensors and external databases. This acquired data is sent to a data processing pipeline within the server. The output data is a raw, unprocessed dataset.

[0871] Step 2:

[0872] The server performs data cleaning on the collected data. At this stage, missing values ​​are imputed using methods such as averaging, and outliers are removed using statistical techniques. This ensures data consistency and generates a pre-processed dataset as output.

[0873] Step 3:

[0874] The server integrates data based on pre-processed datasets, combining information from different data sources to create a single unified dataset. This unified dataset is then prepared as input for the generative AI model.

[0875] Step 4:

[0876] The server inputs the integrated dataset into a generating AI model and calculates an evaluation value for each candidate site. The AI ​​model analyzes the data received as input and uses a calculation model to evaluate the optimal location for wind power plant construction. The output is the evaluation score for each candidate site.

[0877] Step 5:

[0878] The server creates a prioritized list of potential construction sites based on the calculated scores. This organizes information that is useful for supporting decisions regarding wind power plant construction. The output is in the form of a prioritized list of potential sites.

[0879] Step 6:

[0880] The terminal visually displays a list of candidate locations received from the server to the user. The terminal also utilizes an emotion analysis engine to analyze the user's emotional state. This analysis allows for dynamic adjustments to the interface, optimizing the display method according to the user's emotional state. The output is candidate location information in an optimized interface format.

[0881] Step 7:

[0882] Users evaluate potential sites based on the information displayed on their devices and narrow down the list of candidates using the filtering function. User input data considers requirements such as environmental impact and efficiency. The final output is the construction site candidates selected by the user. User feedback is also used to improve the system.

[0883] (Application Example 2)

[0884] 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".

[0885] Conventional systems struggled to efficiently process diverse information, such as regional data, weather data, and biodiversity data, to select suitable sites for logistics centers and other facilities. Furthermore, they lacked the means to support user decision-making more smoothly by providing an interface that considered user emotions. Moreover, mechanisms for improving the system based on feedback were insufficient.

[0886] 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.

[0887] This invention includes a server comprising means for collecting regional data, weather data, and biota data; means for preprocessing the collected data, including imputing missing values ​​and removing outliers, and standardizing the data format; means for applying a generative AI algorithm to evaluate candidate sites using the preprocessed data and calculating a score for each candidate site; means for an information terminal equipped with an emotion analysis engine that analyzes the user's emotions and dynamically changes the interface; and means for the user to narrow down candidate sites using a filtering function. This enables more efficient and user-friendly candidate site selection.

[0888] "Regional data" refers to various types of information related to a specific region, including geographical information and land use information.

[0889] "Weather data" refers to information about weather conditions such as wind speed, wind direction, temperature, and humidity.

[0890] "Biological community data" refers to information about the ecosystems of plants and animals in a specific region.

[0891] "Preprocessing" is the process of filling in missing data values, removing outliers, and standardizing the data format.

[0892] A "generative AI algorithm" is an algorithm that uses machine learning to analyze input data and calculate an evaluation score.

[0893] An "information terminal" is a device used by users to view and manipulate information, and primarily refers to smartphones and computers.

[0894] An "emotion analysis engine" is a system that analyzes the user's emotional state from their display and voice, and dynamically adjusts the interface accordingly.

[0895] A "filtering function" is a function that selects and displays data based on the conditions desired by the user.

[0896] In the system for implementing this invention, a server collects regional data, weather data, and biota data, and preprocesses this data. Specifically, it performs data imputation, removes outliers, and standardizes the data format. This is done using a data cleaning library in Python (e.g., Pandas). The server then runs a generative AI algorithm to calculate evaluation scores and generate a list of candidate locations. This process uses a machine learning framework (e.g., TensorFlow or PyTorch).

[0897] The server sends the generated list of candidate locations to an information terminal. This terminal is equipped with an emotion analysis engine that dynamically adjusts the interface by analyzing the user's facial expressions and voice. This analysis utilizes APIs provided by cloud services (e.g., Google Cloud's Natural Language API and Microsoft Azure's Face API). The user can use this terminal to narrow down the candidate locations through a filtering function.

[0898] To give a concrete example, when a logistics company representative decides on the location of a new center, this system can be used to efficiently find land that meets the requirements. If she is emotionally exhausted, the interface automatically changes to a relaxing color scheme, making the selection process more comfortable. In this way, the user experience is improved. An example of a prompt would be, "Explain how to calculate an evaluation score based on candidate site information for selecting a logistics center construction site in the Kanto region, taking into account transportation access, proximity to major cities, and environmental impact, and how to provide a UI that responds to the user's emotions."

[0899] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0900] Step 1:

[0901] The server collects regional data, weather data, and biota data from various APIs. For example, regional data is obtained from a Geographic Information System (GIS), and weather data is obtained from the Japan Meteorological Agency's API. The input is raw data from each API, and the output is an integrated dataset combining them.

[0902] Step 2:

[0903] The server performs preprocessing on the integrated dataset. Specifically, it uses Python to impute missing values, remove outliers, and standardize data formats. This process results in cleaned data being output. The input is the raw integrated dataset, and the output is the preprocessed data.

[0904] Step 3:

[0905] The server inputs pre-processed data into a generating AI algorithm to calculate an evaluation score for each candidate site. Here, a machine learning model using TensorFlow is applied. The input is pre-processed data, and the output is the evaluation score for each candidate site.

[0906] Step 4:

[0907] The server generates a list of candidate locations in order of priority based on the calculated evaluation score and sends it to the terminal. The input is the evaluation score, and the output is a list of candidate locations in order of priority.

[0908] Step 5:

[0909] The terminal displays a list of candidate locations and activates an emotion analysis engine to analyze the user's emotional state. The terminal is equipped with a camera that recognizes emotions using facial expression data as input. The output is the analyzed emotion information.

[0910] Step 6:

[0911] The device dynamically adjusts the color scheme and layout of the user interface based on analyzed emotional information. The input is emotional information, and the output is the adjusted interface.

[0912] Step 7:

[0913] The user reviews a list of potential locations on a customized interface and uses a filtering function to narrow down the options. The input is the user's selection criteria, and the output is the narrowed-down list of potential locations.

[0914] Step 8:

[0915] The user selects the optimal construction site based on a list of candidate sites provided by the system. The input is a narrowed-down list of candidate sites, and the output is information on the construction site deemed optimal.

[0916] 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.

[0917] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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.

[0918] 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.

[0919] 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.

[0920] 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.

[0921] 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.

[0922] 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.

[0923] 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.

[0924] 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."

[0925] 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.

[0926] 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.

[0927] 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.

[0928] 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.

[0929] 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.

[0930] 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.

[0931] 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.

[0932] 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.

[0933] 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.

[0934] 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.

[0935] 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.

[0936] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0937] The following is further disclosed regarding the embodiments described above.

[0938] (Claim 1)

[0939] Means for collecting regional information data, wind power information data, and ecosystem information data,

[0940] Methods for preprocessing collected data, such as imputing missing values, removing outliers, and standardizing data shape,

[0941] A means for applying a generative AI model to evaluate candidate locations using preprocessed data and calculating a score for each candidate location,

[0942] A means for generating a list of construction candidate sites sorted in order of priority based on the calculated score, and transmitting the list to the user's terminal,

[0943] A system that includes this.

[0944] (Claim 2)

[0945] The system according to claim 1, wherein the generating AI model analyzes wind speed, wind direction, and impact on the local ecosystem as features.

[0946] (Claim 3)

[0947] The system according to claim 1, comprising an interface for providing user feedback and means for improving the generated AI model based on user feedback.

[0948] "Example 1"

[0949] (Claim 1)

[0950] Means for obtaining local information, weather information, and ecosystem information,

[0951] A means of preprocessing acquired information, supplementing missing information, eliminating anomalies, and standardizing the format of the information,

[0952] A means for applying a generative AI algorithm to evaluate candidate locations using preprocessed information and calculating an evaluation value for each candidate location,

[0953] A means for generating a list of construction candidate sites sorted in order of priority based on the calculated evaluation values, and transmitting the list to the user device,

[0954] A means by which the user device visually presents candidate locations through map display,

[0955] A system that includes this.

[0956] (Claim 2)

[0957] The system according to claim 1, wherein the generating AI algorithm performs analysis using weather conditions, geographical features, and impacts on local ecosystems as indicators.

[0958] (Claim 3)

[0959] The system according to claim 1, comprising an interface for providing user feedback and means for improving the generated AI algorithm based on user feedback.

[0960] "Application Example 1"

[0961] (Claim 1)

[0962] Means for collecting regional information data, wind power information data, ecosystem information data, and environmental data,

[0963] Methods for preprocessing collected data, such as imputing missing values, removing outliers, and standardizing data shape,

[0964] A means for applying a generative AI model to evaluate candidate locations and machine placements using preprocessed data, and calculating a score for each candidate location or placement proposal,

[0965] A means for generating a list of construction candidate sites and optimization proposals sorted in order of priority based on the calculated score, and transmitting the list to the user's terminal,

[0966] A system that includes this.

[0967] (Claim 2)

[0968] The system according to claim 1, wherein the generating AI model analyzes wind speed, wind direction, and impact on ecosystems and the environment as features, and provides a layout plan that maximizes the efficiency of deployment within the factory.

[0969] (Claim 3)

[0970] The system according to claim 1, comprising an interface for providing feedback from users, and means for improving the generated AI model and layout plan based on user feedback.

[0971] "Example 2 of combining an emotion engine"

[0972] (Claim 1)

[0973] Means for collecting geographic data, meteorological data, and biodiversity data,

[0974] A means of processing the collected data, performing value imputation and removal of abnormal values, and standardizing the data format,

[0975] A means for applying an artificial intelligence model that evaluates candidate locations using processed data and calculating an evaluation value for each candidate location,

[0976] A means for generating a list of location candidates with priority based on the calculated evaluation value and transmitting the list to the user's terminal,

[0977] A means equipped with an emotion analysis engine for recognizing the user's emotions and adjusting the interface,

[0978] A system that includes this.

[0979] (Claim 2)

[0980] The system according to claim 1, wherein the artificial intelligence model analyzes wind speed, wind direction, and impact on the local ecosystem as its features.

[0981] (Claim 3)

[0982] The system according to claim 1, comprising a user interface for providing user feedback and means for improving an artificial intelligence model based on user feedback.

[0983] "Application example 2 when combining with an emotional engine"

[0984] (Claim 1)

[0985] Means for collecting regional data, weather data, and biota data,

[0986] The collected data is preprocessed to impute missing values, remove outliers, and standardize the data format.

[0987] A means for applying a generative AI algorithm to evaluate candidate sites using preprocessed data and calculating a score for each candidate site,

[0988] A means for generating a list of construction candidate sites sorted in order of priority based on the calculated score, and transmitting the list to an information terminal,

[0989] A means for an information terminal to be equipped with an emotion analysis engine that analyzes the user's emotions and dynamically changes the interface,

[0990] A means by which users can narrow down candidate locations using a filtering function,

[0991] A system that includes this.

[0992] (Claim 2)

[0993] The system according to claim 1, wherein the generative AI algorithm analyzes weather data and its impact on ecosystems as features.

[0994] (Claim 3)

[0995] The system according to claim 1, comprising an interface for providing user feedback and means for improving the generated AI algorithm based on user feedback. [Explanation of symbols]

[0996] 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

1. Means for collecting regional information data, wind power information data, and ecosystem information data, Methods for preprocessing collected data, such as imputing missing values, removing outliers, and standardizing data shape, A means for applying a generative AI model to evaluate candidate locations using preprocessed data and calculating a score for each candidate location, A means for generating a list of construction candidate sites sorted in order of priority based on the calculated score, and transmitting the list to the user's terminal, A system that includes this.

2. The system according to claim 1, wherein the generating AI model analyzes wind speed, wind direction, and impact on the local ecosystem as features.

3. The system according to claim 1, comprising an interface for providing feedback from users, and means for improving the generated AI model based on user feedback.

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