System

Abstract

A system is provided.SOLUTION: A system comprising: means for collecting historical wind information from a weather database; means for inputting the collected historical wind information into a generative AI to predict future wind flow; means for analyzing the predicted future wind information to identify a location where wind is most stable; and means for installing a wind farm at the identified location to provide energy using wireless power transmission technology.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] The current energy supply relies mainly on nuclear power generation and fossil fuels, and there is a need to secure sustainable energy. Furthermore, there is an urgent need to reduce greenhouse gas emissions, and the effective use of renewable energy sources is necessary. Wind power is an inexhaustible energy resource, but wind fluctuations make it difficult to provide a stable energy supply. Furthermore, the costs associated with building the infrastructure to supply energy and the instability of the energy supply during disasters are also problems. [Means for solving the problem]

[0005] This invention provides a system that collects past wind data from a weather database and inputs this data into a generation AI to predict future wind flow. By analyzing the predicted future wind data and identifying the locations where wind is most stable, it becomes possible to efficiently install wind power generation facilities. Furthermore, energy is supplied using wireless power transmission technology, reducing infrastructure development costs and ensuring a stable energy supply. Specifically, a system that includes a means for collecting past wind data, a means for predicting wind using a generation AI, a means for identifying installation locations, and a means for using wireless power transmission technology enables a sustainable energy supply.

[0006] A "weather database" is a database for storing and managing past and current weather data, and for extracting and analyzing the data.

[0007] "Wind data" refers to data that includes information about wind, such as wind speed, wind direction, date and time, longitude, and latitude.

[0008] "Generative AI" is a type of artificial intelligence technology used to predict future situations based on collected data, and often refers specifically to deep learning models.

[0009] "Means for predicting future wind flow" refers to methods and technologies for predicting future wind speed and direction using collected wind data.

[0010] A "wind power generation facility" is a device or structure that converts the kinetic energy of wind into electricity.

[0011] "Wireless power transmission technology" is a technology that transmits power wirelessly without using physical cables.

[0012] The "energy supply system" is a system for supplying energy by combining wind power generation equipment and wireless power transmission technology.

[0013] "Data collection means" refers to the methods and techniques used to obtain the required data from the meteorological database.

[0014] "Data integration" is the process of centrally managing data collected from multiple data sources and converting it into an analyzable format.

[0015] "Predictive modeling" is the process of using collected data to create a model to predict future conditions and then using that model to make predictions.

[0016] "Siting" refers to the methods and techniques used to select the most efficient location for wind power generation facilities.

[0017] "Stable energy supply" refers to the continuous supply of energy of a certain quality and quantity. [Brief explanation of the drawings]

[0018] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0019] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0020] First, the terms used in the following description will be explained.

[0021] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0022] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0023] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0024] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0025] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0026] [First embodiment]

[0027] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0028] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0029] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0030] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0031] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0032] 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 of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0033] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0034] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0035] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0036] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0037] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0038] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0039] System Configuration

[0040] The system of the present invention mainly comprises the following elements:

[0041] 1. Server

[0042] Ability to collect historical wind data from weather databases

[0043] A function that uses generative AI to predict future wind flow

[0044] A function that analyzes predicted wind data and identifies optimal locations for wind power generation facilities

[0045] 2. Terminal

[0046] Ability to install and operate wind power generation facilities

[0047] A function that supplies energy using wireless power transmission technology

[0048] 3. Users

[0049] Ability to use a dashboard to monitor the system's operating status

[0050] Program processing flow

[0051] Data collection

[0052] The server collects historical wind data from a meteorological database, including information such as date, time, longitude, latitude, wind speed, and direction. The data is retrieved through a REST API and integrated into the server's database.

[0053] Examples:

[0054] The server uses the OpenWeather API to obtain wind data for the past five years.

[0055] The acquired data is stored in a local database, and missing data is complemented and data normalization is performed as necessary.

[0056] Wind prediction using generative AI

[0057] The server inputs the collected wind data into the generation AI to predict future wind flow. The generation AI uses a deep learning model (such as an LSTM model). It learns wind speed and direction patterns and predicts wind flow for the next five years.

[0058] Examples:

[0059] The server uses the LSTM model to predict future wind speed and direction based on past wind data.

[0060] The forecast results are saved as time series data and used for analysis.

[0061] Identifying the optimal installation location

[0062] The server analyzes the predicted wind data to identify the best locations for wind farms, using indicators such as average wind speed and low variability.

[0063] Examples:

[0064] The server analyzes the forecast results and identifies areas with high wind speeds and little fluctuation.

[0065] Calculate specific coordinates and plot them on a map.

[0066] Application of wireless power transmission technology

[0067] The terminal then installs the wind power generation equipment at the identified optimal installation location and supplies energy using wireless power transmission technology, which adjusts parameters to transmit power efficiently and minimizes energy loss.

[0068] Examples:

[0069] The terminal sets up the wireless power transmitting device at the specified installation location.

[0070] Verify that the configured device transmits power efficiently and has an energy loss rate of 5% or less.

[0071] System operation monitoring

[0072] Users can use a web-based dashboard to monitor the operational status of their installed wind power generation systems and wireless power transmission systems in real time, checking the amount of power generated and the stability of power transmission, and receiving alerts if any abnormalities occur.

[0073] Examples:

[0074] Users can monitor the amount of power generated through a dashboard.

[0075] If a system abnormality is detected, a notification is sent to the user.

[0076] As described above, this system provides a concrete means for efficiently generating wind power and achieving a stable energy supply using wireless power transmission technology.

[0077] The processing flow will be explained below.

[0078] Step 1:

[0079] The server collects past wind data from a weather database. Specifically, it uses OpenWeather and NOAA APIs to obtain wind data from the past five years, collecting information such as date, time, longitude, latitude, wind speed, and wind direction. The collected data is stored in a database on the server.

[0080] Step 2:

[0081] The server preprocesses the collected data, specifically by filling in missing values ​​and normalizing the data, converting it into a format that can be efficiently learned by the subsequent AI model.

[0082] Step 3:

[0083] The server inputs the preprocessed data into the generation AI to predict future wind flow. The generation AI uses a deep learning model (such as an LSTM model) to predict future wind speed and direction from past wind data. The prediction results are stored on the server.

[0084] Step 4:

[0085] The server analyzes the predicted future wind data and identifies the location with the most stable wind. Specifically, it calculates the optimal installation location using indicators such as average wind speed and minimal fluctuation, and plots the specific coordinates on a map.

[0086] Step 5:

[0087] The terminal installs the wind power generation equipment based on the installation location information received from the server. The installation work includes assembling and installing the wind turbines and developing the infrastructure to operate them.

[0088] Step 6:

[0089] The device transmits energy using wireless power transmission technology. Specifically, it configures the wireless power transmission device and adjusts parameters to transmit power efficiently, thereby minimizing energy loss.

[0090] Step 7:

[0091] Users can monitor the operation of the wind power generation system and wireless power transmission system in real time using a web-based dashboard that displays the amount of power generated and the stability of the power transmission, and sends alerts if any abnormalities occur.

[0092] Example 1

[0093] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0094] Wind power generation is a form of clean energy and plays an important role in combating climate change. However, identifying the optimal location for wind power generation facilities and stabilizing their power generation output is not easy. The application of wireless power transmission technology to efficiently utilize the generated energy is also a challenge. Furthermore, a means of monitoring the operation status in real time and quickly detecting abnormalities is also required. Therefore, a system that integrates the identification of the optimal installation location, the efficient operation of wireless power transmission technology, and the monitoring of the system's operation status is required.

[0095] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0096] In this invention, the server includes means for collecting past wind data from a weather database, means for inputting the collected wind data into a generative AI model to predict future wind flow, means for analyzing the predicted future wind data and identifying the optimal location for installing a wind power generation facility, means for installing the wind power generation facility in the identified location and supplying energy using wireless power transmission technology, and means for providing a dashboard for monitoring the operating status of the installed wind power generation facility and the wireless power transmission system, thereby enabling the identification of the optimal installation location for the wind power generation facility, the efficient operation of wireless power transmission technology, and real-time monitoring of the operating status of the system.

[0097] A "weather database" is a data storage system for recording and storing past and current weather data.

[0098] A "generative AI model" is an algorithm that uses collected data to predict future phenomena and patterns, and generally uses techniques such as machine learning and deep learning.

[0099] "Wind power generation facilities" refers to equipment and infrastructure that utilizes wind energy to generate electricity, including windmills and turbines.

[0100] "Wireless power transmission technology" is a technology that transmits power without using physical wires, and technical adjustments are made to minimize energy loss.

[0101] A "dashboard" is an interface for monitoring and visually displaying the system's operating status in real time, and often takes the form of a web application or dedicated monitoring software.

[0102] A "deep learning model" is a technology that uses multi-layered neural networks to learn complex data patterns and make future predictions and classifications.

[0103] "Energy loss" refers to the percentage of energy lost during the transmission and conversion processes, and is an important indicator for system efficiency.

[0104] "Time series data" is a collection of data recorded over time, including data points taken at specific time intervals, such as wind speed and direction.

[0105] System Configuration

[0106] The system of the present invention mainly comprises the following elements:

[0107] 1. Server

[0108] A means of collecting historical wind data from meteorological databases

[0109] A means of inputting collected wind data into a generative AI model to predict future wind flow.

[0110] A means of analyzing predicted future wind data to identify optimal locations for installing wind turbines

[0111] 2. Terminal

[0112] A method of installing wind power generation facilities in designated locations and supplying energy using wireless power transmission technology

[0113] 3. Users

[0114] A means to use dashboards to monitor the operational status of installed wind power plants and wireless power transmission systems

[0115] Server Operation

[0116] The server collects historical wind data from a meteorological database, including information such as date, time, longitude, latitude, wind speed, and direction. The data is retrieved through a REST API and integrated into the server's database.

[0117] Specific behavior:

[0118] The server uses a common weather database API to collect historical wind data, using the Python requests library.

[0119] The Pandas library in Python is used to normalize and format the acquired data, which is then stored in a PostgreSQL database.

[0120] The server then inputs the collected wind data into a generative AI model to predict future wind flow, using the deep learning technology LSTM (Long Short-Term Memory).

[0121] Specific behavior:

[0122] The server preprocesses the collected wind dataset using the TensorFlow framework and inputs it into the LSTM model.

[0123] The LSTM model learns from past data to predict future wind speed and direction.

[0124] The predicted data is again saved in the PostgreSQL database.

[0125] Once the predicted wind data is obtained, the server performs analysis based on this data to identify the best locations to install wind power generation equipment.

[0126] Specific behavior:

[0127] The server uses Python's SciPy and NumPy libraries for analysis.

[0128] The optimal installation location is determined by scoring indicators such as the average wind speed and the lack of fluctuation.

[0129] The obtained location longitude and latitude information is converted into GeoJSON format and a common map API (e.g., Google Maps API) is used to plot it on a map.

[0130] Device behavior

[0131] The terminal then installs the wind power generation equipment in the optimal location identified by the server, using wireless power transmission technology to efficiently supply energy.

[0132] Specific behavior:

[0133] The terminal arrives at the designated location and deploys the necessary installation equipment.

[0134] By configuring the wireless power transmission device and adjusting the optimal parameters, energy loss during power transmission is minimized.

[0135] User operations

[0136] Users monitor the system's operational status in real time through a dedicated web-based dashboard.

[0137] Specific behavior:

[0138] Users access the dashboard through a web browser, which is typically built with React.js and Node.js.

[0139] The dashboard displays real-time information such as wind power generation, wireless power transmission status, and system status.

[0140] If the system detects an abnormality, it will use the Twilio API to notify the user via SMS or email.

[0141] Prompt Sentence Examples

[0142] Specific examples of prompts are as follows:

[0143] "Design a generative AI model to predict wind speed and direction for the next five years using wind data from the past five years. The model should use LSTM, and input data should include date, time, longitude, latitude, wind speed, and wind direction."

[0144] In this way, the system of the present invention provides a concrete means for efficiently generating wind power and realizing a stable energy supply using wireless power transmission technology.

[0145] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0146] Step 1:

[0147] The server collects historical wind data from a weather database.

[0148] Input: Weather database API endpoint

[0149] Specific operation: The server uses Python's requests library to retrieve wind data (date, time, longitude, latitude, wind speed, and direction) for the past five years from the weather database API using an HTTP GET request.

[0150] Output: The acquired data is saved on the server in JSON format.

[0151] Step 2:

[0152] The server normalizes and formats the collected wind data.

[0153] Input: Wind data in JSON format obtained in step 1

[0154] Specific operation: The server uses Python's Pandas library to impute missing values ​​in the acquired dataset and standardize the data.

[0155] Output: A normalized dataset is generated and stored in a PostgreSQL database.

[0156] Step 3:

[0157] The server inputs the collected wind data into a generative AI model to predict future wind flow.

[0158] Input: Normalized wind dataset

[0159] How it works: The server uses the TensorFlow framework to build and train an LSTM model. It then inputs past data into the trained model to predict future wind speed and direction.

[0160] Output: Predicted future wind data (time series data) is generated in JSON format and stored in a PostgreSQL database.

[0161] Step 4:

[0162] The server analyzes the predicted future wind data and identifies the optimal location for installing wind power generation equipment.

[0163] Input: Forecast wind data obtained in step 3

[0164] How it works: The server uses Python's SciPy and NumPy libraries to calculate and score the average wind speed and the least amount of fluctuation, thereby identifying the optimal installation location.

[0165] Output: The longitude and latitude information of the identified installation location is generated in GeoJSON format.

[0166] Step 5:

[0167] Plot the locations where the servers were identified on a map.

[0168] Input: Longitude and latitude information obtained in step 4 (GeoJSON format)

[0169] Specific operation: The server uses a common map API (e.g., Google Maps API) to visually display the identified location on a map.

[0170] Output: Potential installation sites plotted on a map

[0171] Step 6:

[0172] The terminal installs the wind power generation equipment in the optimal installation location designated by the terminal.

[0173] Input: Information on the candidate installation site obtained in Step 5

[0174] Specific operations: The terminal arrives at the site, deploys and installs the wind power generation equipment, adjusts the wireless power transmission equipment, and sets the optimal parameters.

[0175] Output: Installed wind power generation equipment and wireless power transmission equipment

[0176] Step 7:

[0177] The terminal configures the wireless power transmission device and supplies energy wirelessly.

[0178] Input: Installed wind power generation equipment and wireless power transmission equipment

[0179] Specific operation: The terminal configures the wireless power transmission device, monitors the efficiency of energy transmission in real time, and minimizes energy loss.

[0180] Output: Wireless power transmission device that efficiently transmits energy

[0181] Step 8:

[0182] Users can monitor the system's status using a web-based dashboard.

[0183] Input: Operation status data from the server and terminal

[0184] Specific operation: The user accesses the dashboard from a web browser and monitors the system's operating status (power generation amount, power transmission status, system status, etc.) in real time.

[0185] Output: Visual display of system operating status and alert notification when an abnormality occurs

[0186] Through this step, a system will be created that efficiently generates wind power and provides a stable energy supply through wireless power transmission technology.

[0187] (Application example 1)

[0188] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0189] Conventional wind power generation systems have difficulty ensuring efficient and stable energy supply, and have had the problem of significant energy loss, especially when installation locations are inappropriate. Furthermore, due to a lack of systems for monitoring energy supply conditions and equipment operation status in real time, there have been many cases where abnormality detection was delayed. This invention aims to solve these issues by predicting future wind flow, identifying optimal installation locations, and monitoring operation status in real time.

[0190] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0191] In this invention, the server includes means for collecting past data from a weather database, means for inputting the collected data into a generation AI to predict future wind flow, means for analyzing the predicted future data and identifying the location where wind will be most stable, means for installing energy conversion equipment in the identified location and supplying energy using wireless power transmission technology, and means for monitoring the performance of the energy supply system and robot in real time and detecting abnormalities, thereby enabling improved efficiency and stability of energy supply and real-time abnormality detection.

[0192] A "weather database" is a database that stores past weather data and allows it to be searched and retrieved.

[0193] "Generative AI" is a type of artificial intelligence that predicts future situations based on collected data, and is achieved using techniques such as deep learning models.

[0194] A "deep learning model" is a machine learning method that uses a multi-layer neural network to learn the characteristics of data and make predictions and classifications.

[0195] "Means for predicting wind flow" refers to a method or device for predicting future wind speed and direction using meteorological data as input.

[0196] An "energy conversion facility" is a device that converts natural energy into a usable form of energy, such as electricity.

[0197] "Wireless power transmission technology" is a technology for transmitting power without using wires, and utilizes electromagnetic waves and electromagnetic induction.

[0198] An "energy supply system" is a system for comprehensively managing the generation, conversion, and supply of energy.

[0199] "Robot performance" is a measure of how efficiently and accurately a robot performs a specified task.

[0200] "Real-time monitoring" means constantly monitoring the system status and performance, and ensuring that the latest data is always available.

[0201] "Means for detecting anomalies" refers to methods or devices for detecting situations that deviate from the normal operating state of a system.

[0202] The present invention relates to a system for providing efficient utilization of wind energy and real-time monitoring capabilities, and comprises the following elements:

[0203] System Configuration

[0204] The system mainly consists of three components: a server, a terminal, and a user.

[0205] server

[0206] The server has the following functions:

[0207] 1. Meteorological data collection:

[0208] The server collects historical wind data from a weather database via the Internet. This data includes date, time, longitude, latitude, wind speed, and wind direction. The data is retrieved through a REST API and integrated into a database on the server. For example, the server retrieves historical wind data using a weather data API (e.g., OpenWeather) and stores it in a local database.

[0209] 2. Generative AI wind prediction:

[0210] The collected wind data is input into a generative AI to predict future wind flow. A deep learning model (such as an LSTM model) is used for this purpose. For example, the server uses an LSTM model to learn from past wind data and predict wind speed and direction for the next few years.

[0211] 3. Identify the installation location:

[0212] The predicted wind data is analyzed to identify the optimal location for wind energy conversion equipment based on average wind speed and wind stability. The prediction results are saved as time-series data and used for further analysis. Specifically, the coordinates with high wind speed and little fluctuation are calculated and plotted on a map.

[0213] Terminal

[0214] The terminal has the following features:

[0215] 1. Installation of energy conversion equipment:

[0216] Install wind energy conversion equipment in the identified optimal locations.

[0217] 2. Application of wireless power transmission technology:

[0218] The converted energy is supplied using wireless power transmission technology. This technology adjusts parameters to transmit power efficiently and minimizes energy loss. For example, it places energy conversion devices in optimal settings to increase power transmission efficiency.

[0219] User

[0220] The user has the following capabilities:

[0221] 1. Real-time monitoring:

[0222] A web-based dashboard allows users to monitor the system's operating status in real time, allowing them to check the amount of power generated and the stability of power transmission, and receive alerts if an abnormality occurs.Specifically, the system has the ability to monitor power generation and notify users when an abnormality is detected.

[0223] Prompt Sentence Examples

[0224] The generative AI model that implements this system uses the following prompt:

[0225] "To identify the optimal location for installing wind power generation facilities, input wind data from the past five years in Tokyo into an LSTM model and predict wind speed and direction for the next five years. Also, implement an algorithm that selects the location with the optimal wind speed and direction based on the prediction results."

[0226] This invention makes it possible to improve the efficiency and stability of energy supply and to detect abnormalities in real time.

[0227] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0228] Step 1: Collect data

[0229] The server collects historical wind data from a weather database. Specifically, it retrieves data including wind speed, wind direction, and date and time information at a specified longitude and latitude. It uses an API key and specified location information as input. It retrieves this data via a REST API and stores it in a local database. The output is the historical wind data stored in the database.

[0230] Step 2: Preprocessing the data

[0231] The server preprocesses the collected wind data. Specifically, it complements missing data and normalizes (scales) it. The input is the raw data collected in step 1, and methods such as linear interpolation are used to complement the missing data. Normalization involves scaling based on the mean and standard deviation. The output is the preprocessed wind data.

[0232] Step 3: Generative AI predicts wind

[0233] The server uses the preprocessed data to input it into a generative AI model (LSTM model) to predict future wind data. The input is preprocessed wind speed and direction data, and a deep learning model is used to learn and predict the data. Specifically, it predicts wind speed and direction for the next few years as a time series based on past data. The output is predicted future wind data.

[0234] Step 4: Identify the optimal location

[0235] The server analyzes the predicted wind data to identify optimal installation locations. It uses predicted future wind data as input, applying an algorithm that evaluates the stability of average wind speed and direction and calculates locations with minimal fluctuations. The output is the coordinates of the optimal wind energy conversion facility installation location.

[0236] Step 5: Installing the energy conversion equipment

[0237] The terminal installs the wind energy conversion equipment at the specified installation location. The input is the coordinate information of the optimal installation location received from the server. The specific operation is to perform the physical installation work and configure the energy conversion equipment so that it functions properly. The output is the installed energy conversion equipment.

[0238] Step 6: Applying wireless power transmission technology

[0239] The terminal transmits the converted energy using wireless power transmission technology. The input is the converted energy and optimal transmission parameters. Specifically, the parameters of the wireless power transmission device are adjusted to transmit power while minimizing energy loss. The output is the efficiently transmitted energy.

[0240] Step 7: Real-time system monitoring

[0241] Users can monitor the performance of the energy supply system and robots in real time through a web-based dashboard. The input is real-time data sent from the server and terminals. Specifically, it displays power generation amount, power transmission efficiency, anomaly detection information, etc. in real time, and issues an alert if an abnormality is detected. The output is status information of the monitored system and an alert to the user.

[0242] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0243] System Configuration

[0244] The system of the present invention comprises the following main components:

[0245] 1. Server

[0246] Ability to collect historical wind data from weather databases

[0247] A function that uses generative AI to predict future wind flow

[0248] A function that analyzes predicted wind data and identifies optimal locations for wind power generation facilities

[0249] 2. Terminal

[0250] Ability to install and operate wind power generation facilities

[0251] A function that supplies energy using wireless power transmission technology

[0252] 3. Users

[0253] Ability to use a dashboard to monitor the system's operating status

[0254] 4. Emotion Engine

[0255] Ability to recognize user emotions and collect emotional data

[0256] Ability to analyze emotional data and adjust the system's operating interface and energy supply performance

[0257] A function that detects system anomalies early based on emotional data and notifies users

[0258] Program processing flow

[0259] Data collection and prediction

[0260] The server first collects historical wind data from a weather database. This data includes details such as date, time, longitude, latitude, wind speed, and wind direction. The data is retrieved through a REST API and stored in a database on the server. The collected data is then preprocessed by filling in missing values ​​and normalizing the data.

[0261] The server then inputs the collected data into a generation AI to predict future wind flow. The generation AI uses a deep learning model (such as an LSTM model). It learns from past wind data and predicts wind speed and direction for the next five years. The prediction results are recorded on the server and used for subsequent analysis.

[0262] Identifying installation locations and wireless power transmission

[0263] The server analyzes the predicted future wind data and identifies the optimal location for installing wind power generation equipment. It selects the optimal location based on factors such as average wind speed and the smallest fluctuations, and identifies its specific coordinates. The identified coordinates are then sent to the device.

[0264] The terminal installs the wind power generation equipment based on the installation location information received from the server. This includes assembling the wind turbines and preparing the infrastructure. Energy is then supplied using wireless power transmission technology. The terminal configures and adjusts the wireless power transmission equipment, setting parameters to minimize energy loss.

[0265] System monitoring and user emotion recognition

[0266] Users can monitor the system's operation in real time using a web-based dashboard, which displays the amount of power generated and the stability of power transmission, and sends alerts if any abnormalities occur.

[0267] The emotion engine recognizes the user's emotions and collects emotional data. This data is used to adjust the system's operation interface and energy supply performance. For example, if the user is feeling stressed, it will provide a simpler operation interface. It also has the ability to detect system abnormalities early on based on the emotional data and notify the user.

[0268] Specific examples

[0269] 1. Data collection and prediction

[0270] The server uses the OpenWeather API to collect wind data from the past five years and store it in a local database.

[0271] The server uses an LSTM model to predict wind speed and direction for the next five years based on the collected wind data.

[0272] 2. Identifying the installation location and wireless power transmission

[0273] The server analyzes the forecast data and identifies the North Sea coastal region as the best location for installation.

[0274] The terminal installs a wind turbine at the specified location, sets up a wireless power transmission device, and starts transmitting energy.

[0275] 3. System Monitoring and Emotion Recognition

[0276] Users can check the amount of power generated and the stability of transmission in real time through the dashboard.

[0277] The emotion engine recognizes the user's emotions and simplifies the operation interface when the user is feeling stressed.

[0278] The emotion engine detects anomalies and sends an alert to the user.

[0279] This system not only efficiently generates wind power and uses wireless power transmission technology to ensure a stable energy supply, but also aims to improve the user experience by taking into account the user's emotions.

[0280] The processing flow will be explained below.

[0281] System Configuration

[0282] The system of the present invention comprises the following main components:

[0283] 1. Server

[0284] Ability to collect historical wind data from weather databases

[0285] A function that uses generative AI to predict future wind flow

[0286] A function that analyzes predicted wind data and identifies optimal locations for wind power generation facilities

[0287] 2. Terminal

[0288] Ability to install and operate wind power generation facilities

[0289] A function that supplies energy using wireless power transmission technology

[0290] 3. Users

[0291] Ability to use a dashboard to monitor the system's operating status

[0292] 4. Emotion Engine

[0293] Ability to recognize user emotions and collect emotional data

[0294] Ability to analyze emotional data and adjust the system's operating interface and energy supply performance

[0295] A function that detects system anomalies early based on emotional data and notifies users

[0296] Program processing flow

[0297] Step 1:

[0298] The server collects past wind data from a weather database. Specifically, it uses OpenWeather and NOAA APIs to obtain wind data from the past five years, collecting date, time, longitude, latitude, wind speed, wind direction, etc. The collected data is stored in a database on the server.

[0299] Step 2:

[0300] The server preprocesses the collected data, specifically by imputing missing values ​​and normalizing the data, converting it into a format that allows subsequent AI models to efficiently learn from the data.

[0301] Step 3:

[0302] The server inputs the preprocessed data into the generation AI to predict future wind flow. The generation AI uses a deep learning model (such as an LSTM model) to predict future wind speed and direction from past wind data. The prediction results are recorded on the server.

[0303] Step 4:

[0304] The server analyzes the predicted future wind data and identifies the optimal location for installing wind power generation equipment. Specifically, it selects the optimal installation location based on indicators such as average wind speed and minimal fluctuation. The identified coordinates are sent to the terminal.

[0305] Step 5:

[0306] The terminal installs the wind power generation equipment based on the installation location information received from the server. The installation work includes assembling and installing the wind turbines and preparing the infrastructure for their operation.

[0307] Step 6:

[0308] The device transmits energy using wireless power transmission technology. Specifically, it configures the wireless power transmission device and adjusts parameters to transmit power efficiently, thereby minimizing energy loss.

[0309] Step 7:

[0310] Users can monitor the operation of the wind power generation system and wireless power transmission system in real time using a web-based dashboard that displays the amount of power generated and the stability of the power transmission, and sends alerts if any abnormalities occur.

[0311] Step 8:

[0312] The emotion engine recognizes the user's emotions and collects emotional data. This data is used to adjust the system's operation interface and energy supply performance. For example, if the user is feeling stressed, a simpler operation interface will be provided. The system also has a function to detect system abnormalities early based on the emotional data and notify the user.

[0313] Examples:

[0314] 1. Data collection and prediction

[0315] The server uses the OpenWeather API to collect wind data from the past five years and store it in a local database.

[0316] The server uses an LSTM model to predict wind speed and direction for the next five years based on the collected wind data.

[0317] 2. Identifying the installation location and wireless power transmission

[0318] The server analyzes the forecast data and identifies the North Sea coastal region as the best location for installation.

[0319] The terminal installs a wind turbine at the specified location, sets up a wireless power transmission device, and starts transmitting energy.

[0320] 3. System Monitoring and Emotion Recognition

[0321] Users can check the amount of power generated and the stability of transmission in real time through the dashboard.

[0322] The emotion engine recognizes the user's emotions and simplifies the operation interface when the user is feeling stressed.

[0323] The emotion engine detects anomalies and sends an alert to the user.

[0324] This system not only efficiently generates wind power and uses wireless power transmission technology to ensure a stable energy supply, but also aims to improve the user experience by taking into account the user's emotions.

[0325] Example 2

[0326] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0327] Conventional wind power generation systems have problems such as difficulty in selecting installation locations and optimizing power generation efficiency, resulting in high energy loss. Furthermore, measures to improve the user experience by taking user emotions into consideration have not been taken. Therefore, there is a need to simultaneously achieve improved power generation efficiency and user convenience.

[0328] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0329] In this invention, the server includes a means for collecting past wind data from a weather database, a means for inputting the collected wind data into a generation AI to predict future wind flow, and a means for analyzing the predicted future wind data to identify the location where wind will be most stable. This enables the identification of the optimal installation location. The server also includes a means for installing wind power generation equipment at the identified location and supplying energy using wireless power transmission technology, a means for providing a user interface for monitoring the system's operating status, a means for recognizing user emotions and adjusting the system's operation interface based on the collected emotion data, and a means for early detection and notification of system abnormalities. This enables improved power generation efficiency, a more stable energy supply, and an improved user experience.

[0330] A "weather database" is a data management system that has the function of collecting, storing, and providing past and current weather data.

[0331] "Wind data" refers to various data related to wind, such as wind speed, wind direction, longitude, latitude, date and time, etc.

[0332] "Generative AI" is a machine learning model that uses artificial intelligence techniques to predict future data from past data.

[0333] "Future wind flow" refers to future weather conditions, such as wind speed and direction, predicted using generative AI.

[0334] "Wireless power transmission technology" is a technology that transmits electricity without using physical wiring.

[0335] A "wind power generation facility" is a device that converts the kinetic energy of wind into electricity.

[0336] A "user interface" refers to the screens and operating means that allow a user to interact with a system or device.

[0337] An "emotion engine" is a system that can recognize the user's emotional state and provide appropriate feedback and responses based on that.

[0338] "Emotion data" refers to data that indicates the user's emotional state.

[0339] "System operational status" refers to information that indicates the current operating state and performance of the system.

[0340] "Anomaly detection" is the process of detecting behaviors or conditions that deviate from a system's normal operation.

[0341] The present invention provides a system for optimizing the installation location of wind power generation facilities and improving energy efficiency and user experience during operation of the facilities. Specific embodiments of this system will be described below.

[0342] Data collection and prediction

[0343] The server first collects historical wind data from a weather database. This wind data includes detailed information such as date, time, longitude, latitude, wind speed, and wind direction. Specifically, the server uses the OpenWeather API to obtain the historical wind data and stores it in a local database on the server. The collected data is retrieved from a REST API using the Python requests library, and preprocessing such as filling in missing values ​​and normalizing the data is performed using the Pandas library. The preprocessed data obtained in this way is input into the generative AI model.

[0344] The server then inputs the preprocessed data into a generative AI model to predict future wind flow. The generative AI uses an LSTM model powered by the TensorFlow library. Based on past wind data, the server predicts wind speed and direction for the next five years and records the results.

[0345] Identifying installation locations and wireless power transmission

[0346] The server analyzes the predicted future wind data to identify the optimal location for wind power generation facilities. This analysis uses Python machine learning libraries (e.g., scikit-learn) to evaluate the average wind speed and the smallest fluctuations. As a result of the analysis, a specific region (e.g., the North Sea coast) is selected as the optimal location. This information is sent to the device.

[0347] The terminal then installs the wind power generation equipment based on the installation location information it receives. The installation process includes using GPS to transport materials to the specified coordinates, assembling the wind turbines, and preparing the infrastructure. After the wind power generation equipment is installed, the terminal configures the wireless power transmission equipment and begins transmitting energy. Using wireless power transmission technology (e.g., WiTricity), it adjusts parameters for efficient energy transmission and implements settings that minimize energy loss.

[0348] System Monitoring and Emotion Recognition

[0349] Users can monitor the system's operating status in real time through a web-based dashboard. The dashboard displays information such as power generation volume, power transmission stability, and energy consumption status. Users can receive real-time notifications if an abnormality occurs. The dashboard is built using front-end technologies such as React and Angular, and back-end frameworks such as Node.js or Python (e.g., Flask and Django).

[0350] The emotion engine recognizes the user's emotions and collects emotional data. Data collected using a facial recognition camera and biometric sensors is analyzed by an emotion recognition algorithm using the Emotion API. The emotional data is used to adjust the system's operating interface and energy supply performance. For example, if the user is feeling stressed, the emotion engine will simplify the operating interface. The emotional data can also be used to detect system abnormalities early and send an alert to the user.

[0351] Specific examples

[0352] 1. Data collection and prediction

[0353] The server uses the OpenWeather API to collect wind data from the past five years and stores it in a local database.The server then uses an LSTM model using TensorFlow to predict wind speed and direction for the next five years.

[0354] 2. Identifying the installation location and wireless power transmission

[0355] The server analyzes the forecast data and identifies the North Sea coastal area as the optimal location for installation. The device then installs the wind power generation equipment in the identified location, configures the wireless power transmission equipment, and begins transmitting energy.

[0356] 3. System Monitoring and Emotion Recognition

[0357] Users can check the amount of power generated and the stability of power transmission in real time through a dashboard. The emotion engine recognizes the user's emotions and simplifies the operation interface if the user is feeling stressed. The emotion engine also detects abnormalities and sends alerts to the user.

[0358] Prompt Sentence Examples

[0359] "Collect wind data from the past five years and predict wind speed and direction for the next five years."

[0360] "Install the wind power generation facility at the specified coordinates, set up the wireless power transmission device, and start supplying energy."

[0361] "Recognize users' emotions and adjust the control interface if they are feeling stressed."

[0362] In this way, the present invention not only efficiently generates wind power and realizes a stable energy supply using wireless power transmission technology, but also provides adjustment of the operation interface and abnormality detection based on the user's emotions.

[0363] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0364] Step 1:

[0365] The server collects historical wind data from a weather database. This process uses the OpenWeather API. It sends an API request as input and receives the resulting wind data in JSON format as output. This data includes the date, time, longitude, latitude, wind speed, and wind direction. Specifically, it uses the Python requests library to make an API request and stores the obtained data in a local database.

[0366] Step 2:

[0367] The server preprocesses the collected wind data. It uses the wind data obtained in step 1 as input and obtains preprocessed data as output. Preprocessing includes missing value imputation, data normalization, and outlier handling. Specifically, it uses the Pandas library to create a data frame, impute missing values, and normalize the data.

[0368] Step 3:

[0369] The server inputs the preprocessed data into a generative AI model to predict future wind flow. It uses the preprocessed data as input and obtains forecast data for wind speed and direction for the next five years as output. The generative AI model uses an LSTM model using TensorFlow. Specifically, it trains the LSTM model, obtains prediction results, and stores them on the server.

[0370] Step 4:

[0371] The server analyzes the predicted future wind data and identifies the optimal location for wind power generation equipment. It uses the predicted data as input and obtains the specific coordinates of the optimal installation location as output. The analysis uses Python's scikit-learn library to evaluate the average wind speed and the smallness of fluctuation. Specifically, it runs a clustering algorithm to identify the optimal area.

[0372] Step 5:

[0373] The server sends the identified coordinate information to the terminal. It uses the coordinates of the optimal installation location as input and transmits the information to the terminal as output. This process uses a REST API and a messaging queue system. Specifically, it uses the Python requests library to send the installation location information to the terminal.

[0374] Step 6:

[0375] The terminal installs the wind power generation equipment based on the received installation location information. It uses the coordinates of the installation location as input and obtains the assembled wind power generation equipment as output. Specifically, it uses GPS to transport materials to the specified coordinates and assembles the wind power generator.

[0376] Step 7:

[0377] After the wind power generation equipment is installed, the terminal configures the wireless power transmission equipment and begins transmitting energy. It uses information about the installed wind power generation equipment as input and operates the wireless power transmission system as output. It uses wireless power transmission technology (e.g., WiTricity) to adjust parameters for efficient energy transmission. Specifically, it adjusts the position and output of the wireless power transmission equipment to set it up to minimize energy loss.

[0378] Step 8:

[0379] Users monitor the system's operating status in real time using a web-based dashboard. Various system operating data is used as input, and real-time monitoring information is obtained as output. The dashboard displays information such as power generation volume, power transmission stability, and energy consumption status. In terms of specific operation, it uses front-end technologies such as React and Angular, and the back-end is built using Node.js or Python frameworks (e.g., Flask, Django).

[0380] Step 9:

[0381] The emotion engine recognizes the user's emotions and collects emotional data. It uses data indicating the user's emotional state as input and obtains analyzed emotional information as output. Emotion data is collected using a facial recognition camera and biometric sensors and analyzed by an emotion recognition algorithm using the Emotion API. Specifically, it sends the collected emotional data to a server and obtains the analysis results.

[0382] Step 10:

[0383] The emotion engine adjusts the operation interface and detects anomalies based on the analyzed emotion data. It uses emotion data as input and obtains an adjusted interface and anomaly notification as output. For example, if the user is feeling stressed, it simplifies the operation interface. It also detects system anomalies early based on emotion data and sends an alert to the user. Specifically, if stress is detected, the operation interface settings are changed, and if an anomaly is detected, it is reflected through an API that notifies the user.

[0384] (Application example 2)

[0385] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0386] Conventional wind power generation systems are limited to energy generation and supply, and do not address in-store energy management or improving customer satisfaction. In particular, there was no way to optimize the use of wind energy, utilize wireless power transmission technology, or collect customer sentiment data and use it to improve store operations. Therefore, there was a need to improve energy efficiency and optimize the customer experience.

[0387] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting past wind data from a weather database, means for inputting the collected wind data into a generation AI and predicting future wind flow, means for analyzing the predicted future wind data and identifying the location where wind will be most stable, means for installing wind power generation equipment in the identified location and supplying energy using wireless power transmission technology, means for monitoring the store's energy consumption in real time, and means for collecting and managing customer emotion data from cameras and devices within the store. This enables optimal use of wind energy, real-time management of energy supply, and optimization of store operations based on user emotion data.

[0388] A "weather database" is a database that stores and provides information about past and present climates.

[0389] "Wind data" refers to information about wind, such as wind speed, wind direction, and frequency of occurrence.

[0390] "Generative AI" is artificial intelligence that has the ability to analyze large amounts of data and generate new information.

[0391] A "deep learning model" is an algorithm that uses a multi-layer neural network to learn features from data and perform predictions and classifications.

[0392] "Wireless power transmission technology" is a technology that transmits power wirelessly and makes it available to the receiving end.

[0393] A "wind power generation facility" is a device that uses wind energy to generate electricity.

[0394] "Energy consumption status" is information indicating the energy usage status in a specific location or time period.

[0395] "Emotion data" is information that quantifies or categorizes a person's emotional state.

[0396] "Means for monitoring energy consumption status of a store in real time" refers to a system for instantly observing and recording electricity usage status within a store.

[0397] "Means for collecting and managing customer emotional data from cameras and devices within the store" refers to a system that observes customers' facial expressions and behavior, analyzes their emotional state, and records and manages it.

[0398] The system for implementing this invention comprises the following main elements: a server, a terminal, and a user. The roles of each and their specific operating methods will be described in detail below.

[0399] Server Features

[0400] The server first collects historical wind data from a weather database. In this case, it uses the OpenWeather API to obtain wind data for the past five years. This data includes date, time, longitude, latitude, wind speed, and wind direction. The collected data is then stored in a local database after missing values ​​are filled and data normalization is performed.

[0401] The server then uses a generative AI to predict future wind flow. The generative AI uses a deep learning model (such as an LSTM model) that learns from past wind data and predicts wind speed and direction for the next five years. The prediction results are recorded on the server and used for subsequent analysis.

[0402] The server also analyzes the predicted future wind data to identify the location where wind will be most stable. The server selects the optimal location based on factors such as average wind speed and minimal fluctuation, and identifies its specific coordinates. The identified coordinates are then sent to the device.

[0403] Device Features

[0404] The terminal installs the wind power generation equipment based on the installation location information received from the server. This involves assembling the wind turbines and preparing the infrastructure. Energy is then supplied using wireless power transmission technology. The wireless power transmission device is configured and adjusted, and parameters are set to minimize energy loss. For example, when supplying energy to a store, wireless power transmission technology ensures efficient energy supply.

[0405] User Roles

[0406] Users can monitor the system's operation in real time using a web-based dashboard, which displays the amount of power generated and the stability of power transmission, and sends alerts if any abnormalities occur.

[0407] Furthermore, customers' emotional data is collected using cameras and devices (e.g., smart glasses and robots) equipped with an emotion engine. The emotional data is used to adjust the system's operation interface and store operation performance. Specifically, if a customer is feeling stressed, an easier operation interface will be provided. Emotional data can also be used to detect system anomalies early on and notify users to prevent problems before they occur.

[0408] Specific examples

[0409] 1. Data collection and prediction

[0410] The server uses the OpenWeather API to collect wind data from the past five years and stores it in a local database.

[0411] The server uses an LSTM model to predict wind speed and direction for the next five years based on the collected wind data.

[0412] 2. Identifying the installation location and wireless power transmission

[0413] The server analyzes the predictive data and identifies the optimal installation location.

[0414] The terminal installs a wind turbine at the specified location, sets up a wireless power transmission device, and starts transmitting energy.

[0415] 3. System Monitoring and Emotion Recognition

[0416] Users can check the amount of power generated and the stability of transmission in real time through the dashboard.

[0417] The emotion engine recognizes the user's emotions and simplifies the operation interface when the user is feeling stressed.

[0418] The emotion engine detects anomalies and sends an alert to the user.

[0419] Examples of prompt statements

[0420] "Based on wind data from the past five years, predict wind speed and direction for the next five years."

[0421] By inputting this prompt into a generative AI model, future wind flow can be predicted and optimal wind energy utilization can be achieved, thereby improving both energy efficiency and customer satisfaction in the store.

[0422] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0423] Step 1:

[0424] The server collects historical wind data from a weather database. Specifically, the server uses the OpenWeather API to retrieve wind speed, direction, and date and time information for the past five years for a specified latitude and longitude. The input required is an API key and latitude and longitude, and the output is JSON-formatted data containing wind data. The server stores the data in a local database.

[0425] Step 2:

[0426] The server preprocesses the collected wind data. Specifically, it fills in missing values ​​and normalizes the data. The input is wind data, and the output is preprocessed wind data. The server prepares the data and creates a dataset to input into the generative AI model.

[0427] Step 3:

[0428] The server uses a generative AI model to predict future wind flow. Specifically, it inputs preprocessed wind data into the LSTM model to obtain predicted wind speed and direction for the next few years. The input requires preprocessed data, and the output is data showing future wind speed and direction. The server stores the generated data in a local database.

[0429] Step 4:

[0430] The server analyzes the predicted future wind data and identifies the optimal location for installing wind power generation equipment. Specifically, it determines the optimal installation location based on parameters such as average wind speed and the lack of fluctuation in wind speed. The input required is the predicted data, and the output is the coordinate information of the optimal installation location. The server sends this information to the terminal.

[0431] Step 5:

[0432] The terminal installs the wind power generation equipment based on the installation location information received from the server. Specifically, the wind power generator is assembled, infrastructure is prepared, and the wireless power transmission device is set up. The installation location information is required as input, and the installed wind power generation equipment and wireless power transmission device are the output.

[0433] Step 6:

[0434] The device supplies energy using wireless power transmission technology. Specifically, it configures and adjusts the wireless power transmission device and sets parameters to minimize energy loss. Energy generated by wind power is required as input, and the output is wirelessly transmitted electricity.

[0435] Step 7:

[0436] Users monitor the system's operational status using a web-based dashboard, specifically checking data such as power generation volume and transmission stability in real time. Data from the monitoring system is required as input, and the output is dashboard information displayed to the user.

[0437] Step 8:

[0438] Users collect and manage customer emotion data from cameras and devices in the store. Specifically, an emotion engine is used to analyze facial expressions and behaviors and quantify or categorize the emotional state. Image data from cameras and devices is required as input, and the output is analyzed emotion data.

[0439] Step 9:

[0440] The system adjusts the operation interface and store operations based on the emotional data collected from users. Specifically, the interface is simplified or the operation method is changed depending on specific situations, such as when a customer is feeling stressed. Emotional data is required as input, and the output is an adjusted operation interface and store operation method.

[0441] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0442] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0443] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0444] [Second embodiment]

[0445] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0446] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0447] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0448] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0449] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0450] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0451] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0452] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0453] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[0454] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0455] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0456] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0457] System Configuration

[0458] The system of the present invention mainly comprises the following elements:

[0459] 1. Server

[0460] Ability to collect historical wind data from weather databases

[0461] A function that uses generative AI to predict future wind flow

[0462] A function that analyzes predicted wind data and identifies optimal locations for wind power generation facilities

[0463] 2. Terminal

[0464] Ability to install and operate wind power generation facilities

[0465] A function that supplies energy using wireless power transmission technology

[0466] 3. Users

[0467] Ability to use a dashboard to monitor the system's operating status

[0468] Program processing flow

[0469] Data collection

[0470] The server collects historical wind data from a meteorological database, including information such as date, time, longitude, latitude, wind speed, and direction. The data is retrieved through a REST API and integrated into the server's database.

[0471] Examples:

[0472] The server uses the OpenWeather API to obtain wind data for the past five years.

[0473] The acquired data is stored in a local database, and missing data is complemented and data normalization is performed as necessary.

[0474] Wind prediction using generative AI

[0475] The server inputs the collected wind data into the generation AI to predict future wind flow. The generation AI uses a deep learning model (such as an LSTM model). It learns wind speed and direction patterns and predicts wind flow for the next five years.

[0476] Examples:

[0477] The server uses the LSTM model to predict future wind speed and direction based on past wind data.

[0478] The forecast results are saved as time series data and used for analysis.

[0479] Identifying the optimal installation location

[0480] The server analyzes the predicted wind data to identify the best locations for wind farms, using indicators such as average wind speed and low variability.

[0481] Examples:

[0482] The server analyzes the forecast results and identifies areas with high wind speeds and little fluctuation.

[0483] Calculate specific coordinates and plot them on a map.

[0484] Application of wireless power transmission technology

[0485] The terminal then installs the wind power generation equipment at the identified optimal installation location and supplies energy using wireless power transmission technology, which adjusts parameters to transmit power efficiently and minimizes energy loss.

[0486] Examples:

[0487] The terminal sets up the wireless power transmitting device at the specified installation location.

[0488] Verify that the configured device transmits power efficiently and has an energy loss rate of 5% or less.

[0489] System operation monitoring

[0490] Users can use a web-based dashboard to monitor the operational status of their installed wind power generation systems and wireless power transmission systems in real time, checking the amount of power generated and the stability of power transmission, and receiving alerts if any abnormalities occur.

[0491] Examples:

[0492] Users can monitor the amount of power generated through a dashboard.

[0493] If a system abnormality is detected, a notification is sent to the user.

[0494] As described above, this system provides a concrete means for efficiently generating wind power and achieving a stable energy supply using wireless power transmission technology.

[0495] The processing flow will be explained below.

[0496] Step 1:

[0497] The server collects past wind data from a weather database. Specifically, it uses OpenWeather and NOAA APIs to obtain wind data from the past five years, collecting information such as date, time, longitude, latitude, wind speed, and wind direction. The collected data is stored in a database on the server.

[0498] Step 2:

[0499] The server preprocesses the collected data, specifically by filling in missing values ​​and normalizing the data, converting it into a format that can be efficiently learned by the subsequent AI model.

[0500] Step 3:

[0501] The server inputs the preprocessed data into the generation AI to predict future wind flow. The generation AI uses a deep learning model (such as an LSTM model) to predict future wind speed and direction from past wind data. The prediction results are stored on the server.

[0502] Step 4:

[0503] The server analyzes the predicted future wind data and identifies the location with the most stable wind. Specifically, it calculates the optimal installation location using indicators such as average wind speed and minimal fluctuation, and plots the specific coordinates on a map.

[0504] Step 5:

[0505] The terminal installs the wind power generation equipment based on the installation location information received from the server. The installation work includes assembling and installing the wind turbines and developing the infrastructure to operate them.

[0506] Step 6:

[0507] The device transmits energy using wireless power transmission technology. Specifically, it configures the wireless power transmission device and adjusts parameters to transmit power efficiently, thereby minimizing energy loss.

[0508] Step 7:

[0509] Users can monitor the operation of the wind power generation system and wireless power transmission system in real time using a web-based dashboard that displays the amount of power generated and the stability of the power transmission, and sends alerts if any abnormalities occur.

[0510] Example 1

[0511] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0512] Wind power generation is a form of clean energy and plays an important role in combating climate change. However, identifying the optimal location for wind power generation facilities and stabilizing their power generation output is not easy. The application of wireless power transmission technology to efficiently utilize the generated energy is also a challenge. Furthermore, a means of monitoring the operation status in real time and quickly detecting abnormalities is also required. Therefore, a system that integrates the identification of the optimal installation location, the efficient operation of wireless power transmission technology, and the monitoring of the system's operation status is required.

[0513] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0514] In this invention, the server includes means for collecting past wind data from a weather database, means for inputting the collected wind data into a generative AI model to predict future wind flow, means for analyzing the predicted future wind data and identifying the optimal location for installing a wind power generation facility, means for installing the wind power generation facility in the identified location and supplying energy using wireless power transmission technology, and means for providing a dashboard for monitoring the operating status of the installed wind power generation facility and the wireless power transmission system, thereby enabling the identification of the optimal installation location for the wind power generation facility, the efficient operation of wireless power transmission technology, and real-time monitoring of the operating status of the system.

[0515] A "weather database" is a data storage system for recording and storing past and current weather data.

[0516] A "generative AI model" is an algorithm that uses collected data to predict future phenomena and patterns, and generally uses techniques such as machine learning and deep learning.

[0517] "Wind power generation facilities" refers to equipment and infrastructure that utilizes wind energy to generate electricity, including windmills and turbines.

[0518] "Wireless power transmission technology" is a technology that transmits power without using physical wires, and technical adjustments are made to minimize energy loss.

[0519] A "dashboard" is an interface for monitoring and visually displaying the system's operating status in real time, and often takes the form of a web application or dedicated monitoring software.

[0520] A "deep learning model" is a technology that uses multi-layered neural networks to learn complex data patterns and make future predictions and classifications.

[0521] "Energy loss" refers to the percentage of energy lost during the transmission and conversion processes, and is an important indicator for system efficiency.

[0522] "Time series data" is a collection of data recorded over time, including data points taken at specific time intervals, such as wind speed and direction.

[0523] System Configuration

[0524] The system of the present invention mainly comprises the following elements:

[0525] 1. Server

[0526] A means of collecting historical wind data from meteorological databases

[0527] A means of inputting collected wind data into a generative AI model to predict future wind flow.

[0528] A means of analyzing predicted future wind data to identify optimal locations for installing wind turbines

[0529] 2. Terminal

[0530] A method of installing wind power generation facilities in designated locations and supplying energy using wireless power transmission technology

[0531] 3. Users

[0532] A means to use dashboards to monitor the operational status of installed wind power plants and wireless power transmission systems

[0533] Server Operation

[0534] The server collects historical wind data from a meteorological database, including information such as date, time, longitude, latitude, wind speed, and direction. The data is retrieved through a REST API and integrated into the server's database.

[0535] Specific behavior:

[0536] The server uses a common weather database API to collect historical wind data, using the Python requests library.

[0537] The Pandas library in Python is used to normalize and format the acquired data, which is then stored in a PostgreSQL database.

[0538] The server then inputs the collected wind data into a generative AI model to predict future wind flow, using the deep learning technology LSTM (Long Short-Term Memory).

[0539] Specific behavior:

[0540] The server preprocesses the collected wind dataset using the TensorFlow framework and inputs it into the LSTM model.

[0541] The LSTM model learns from past data to predict future wind speed and direction.

[0542] The predicted data is again saved in the PostgreSQL database.

[0543] Once the predicted wind data is obtained, the server performs analysis based on this data to identify the best locations to install wind power generation equipment.

[0544] Specific behavior:

[0545] The server uses Python's SciPy and NumPy libraries for analysis.

[0546] The optimal installation location is determined by scoring indicators such as the average wind speed and the lack of fluctuation.

[0547] The obtained location longitude and latitude information is converted into GeoJSON format and a common map API (e.g., Google Maps API) is used to plot it on a map.

[0548] Device behavior

[0549] The terminal then installs the wind power generation equipment in the optimal location identified by the server, using wireless power transmission technology to efficiently supply energy.

[0550] Specific behavior:

[0551] The terminal arrives at the designated location and deploys the necessary installation equipment.

[0552] By configuring the wireless power transmission device and adjusting the optimal parameters, energy loss during power transmission is minimized.

[0553] User operations

[0554] Users monitor the system's operational status in real time through a dedicated web-based dashboard.

[0555] Specific behavior:

[0556] Users access the dashboard through a web browser, which is typically built with React.js and Node.js.

[0557] The dashboard displays real-time information such as wind power generation, wireless power transmission status, and system status.

[0558] If the system detects an abnormality, it will use the Twilio API to notify the user via SMS or email.

[0559] Prompt Sentence Examples

[0560] Specific examples of prompts are as follows:

[0561] "Design a generative AI model to predict wind speed and direction for the next five years using wind data from the past five years. The model should use LSTM, and input data should include date, time, longitude, latitude, wind speed, and wind direction."

[0562] In this way, the system of the present invention provides a concrete means for efficiently generating wind power and realizing a stable energy supply using wireless power transmission technology.

[0563] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0564] Step 1:

[0565] The server collects historical wind data from a weather database.

[0566] Input: Weather database API endpoint

[0567] Specific operation: The server uses Python's requests library to retrieve wind data (date, time, longitude, latitude, wind speed, and direction) for the past five years from the weather database API using an HTTP GET request.

[0568] Output: The acquired data is saved on the server in JSON format.

[0569] Step 2:

[0570] The server normalizes and formats the collected wind data.

[0571] Input: Wind data in JSON format obtained in step 1

[0572] Specific operation: The server uses Python's Pandas library to impute missing values ​​in the acquired dataset and standardize the data.

[0573] Output: A normalized dataset is generated and stored in a PostgreSQL database.

[0574] Step 3:

[0575] The server inputs the collected wind data into a generative AI model to predict future wind flow.

[0576] Input: Normalized wind dataset

[0577] How it works: The server uses the TensorFlow framework to build and train an LSTM model. It then inputs past data into the trained model to predict future wind speed and direction.

[0578] Output: Predicted future wind data (time series data) is generated in JSON format and stored in a PostgreSQL database.

[0579] Step 4:

[0580] The server analyzes the predicted future wind data and identifies the optimal location for installing wind power generation equipment.

[0581] Input: Forecast wind data obtained in step 3

[0582] How it works: The server uses Python's SciPy and NumPy libraries to calculate and score the average wind speed and the least amount of fluctuation, thereby identifying the optimal installation location.

[0583] Output: The longitude and latitude information of the identified installation location is generated in GeoJSON format.

[0584] Step 5:

[0585] Plot the locations where the servers were identified on a map.

[0586] Input: Longitude and latitude information obtained in step 4 (GeoJSON format)

[0587] Specific operation: The server uses a common map API (e.g., Google Maps API) to visually display the identified location on a map.

[0588] Output: Potential installation sites plotted on a map

[0589] Step 6:

[0590] The terminal installs the wind power generation equipment in the optimal installation location designated by the terminal.

[0591] Input: Information on the candidate installation site obtained in Step 5

[0592] Specific operations: The terminal arrives at the site, deploys and installs the wind power generation equipment, adjusts the wireless power transmission equipment, and sets the optimal parameters.

[0593] Output: Installed wind power generation equipment and wireless power transmission equipment

[0594] Step 7:

[0595] The terminal configures the wireless power transmission device and supplies energy wirelessly.

[0596] Input: Installed wind power generation equipment and wireless power transmission equipment

[0597] Specific operation: The terminal configures the wireless power transmission device, monitors the efficiency of energy transmission in real time, and minimizes energy loss.

[0598] Output: Wireless power transmission device that efficiently transmits energy

[0599] Step 8:

[0600] Users can monitor the system's status using a web-based dashboard.

[0601] Input: Operation status data from the server and terminal

[0602] Specific operation: The user accesses the dashboard from a web browser and monitors the system's operating status (power generation amount, power transmission status, system status, etc.) in real time.

[0603] Output: Visual display of system operating status and alert notification when an abnormality occurs

[0604] Through this step, a system will be created that efficiently generates wind power and provides a stable energy supply through wireless power transmission technology.

[0605] (Application example 1)

[0606] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0607] Conventional wind power generation systems have difficulty ensuring efficient and stable energy supply, and have had the problem of significant energy loss, especially when installation locations are inappropriate. Furthermore, due to a lack of systems for monitoring energy supply conditions and equipment operation status in real time, there have been many cases where abnormality detection was delayed. This invention aims to solve these issues by predicting future wind flow, identifying optimal installation locations, and monitoring operation status in real time.

[0608] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0609] In this invention, the server includes means for collecting past data from a weather database, means for inputting the collected data into a generation AI to predict future wind flow, means for analyzing the predicted future data and identifying the location where wind will be most stable, means for installing energy conversion equipment in the identified location and supplying energy using wireless power transmission technology, and means for monitoring the performance of the energy supply system and robot in real time and detecting abnormalities, thereby enabling improved efficiency and stability of energy supply and real-time abnormality detection.

[0610] A "weather database" is a database that stores past weather data and allows it to be searched and retrieved.

[0611] "Generative AI" is a type of artificial intelligence that predicts future situations based on collected data, and is achieved using techniques such as deep learning models.

[0612] A "deep learning model" is a machine learning method that uses a multi-layer neural network to learn the characteristics of data and make predictions and classifications.

[0613] "Means for predicting wind flow" refers to a method or device for predicting future wind speed and direction using meteorological data as input.

[0614] An "energy conversion facility" is a device that converts natural energy into a usable form of energy, such as electricity.

[0615] "Wireless power transmission technology" is a technology for transmitting power without using wires, and utilizes electromagnetic waves and electromagnetic induction.

[0616] An "energy supply system" is a system for comprehensively managing the generation, conversion, and supply of energy.

[0617] "Robot performance" is a measure of how efficiently and accurately a robot performs a specified task.

[0618] "Real-time monitoring" means constantly monitoring the system status and performance, and ensuring that the latest data is always available.

[0619] "Means for detecting anomalies" refers to methods or devices for detecting situations that deviate from the normal operating state of a system.

[0620] The present invention relates to a system for providing efficient utilization of wind energy and real-time monitoring capabilities, and comprises the following elements:

[0621] System Configuration

[0622] The system mainly consists of three components: a server, a terminal, and a user.

[0623] server

[0624] The server has the following functions:

[0625] 1. Meteorological data collection:

[0626] The server collects historical wind data from a weather database via the Internet. This data includes date, time, longitude, latitude, wind speed, and wind direction. The data is retrieved through a REST API and integrated into a database on the server. For example, the server retrieves historical wind data using a weather data API (e.g., OpenWeather) and stores it in a local database.

[0627] 2. Generative AI wind prediction:

[0628] The collected wind data is input into a generative AI to predict future wind flow. A deep learning model (such as an LSTM model) is used for this purpose. For example, the server uses an LSTM model to learn from past wind data and predict wind speed and direction for the next few years.

[0629] 3. Identify the installation location:

[0630] The predicted wind data is analyzed to identify the optimal location for wind energy conversion equipment based on average wind speed and wind stability. The prediction results are saved as time-series data and used for further analysis. Specifically, the coordinates with high wind speed and little fluctuation are calculated and plotted on a map.

[0631] Terminal

[0632] The terminal has the following features:

[0633] 1. Installation of energy conversion equipment:

[0634] Install wind energy conversion equipment in the identified optimal locations.

[0635] 2. Application of wireless power transmission technology:

[0636] The converted energy is supplied using wireless power transmission technology. This technology adjusts parameters to transmit power efficiently and minimizes energy loss. For example, it places energy conversion devices in optimal settings to increase power transmission efficiency.

[0637] User

[0638] The user has the following capabilities:

[0639] 1. Real-time monitoring:

[0640] A web-based dashboard allows users to monitor the system's operating status in real time, allowing them to check the amount of power generated and the stability of power transmission, and receive alerts if an abnormality occurs.Specifically, the system has the ability to monitor power generation and notify users when an abnormality is detected.

[0641] Prompt Sentence Examples

[0642] The generative AI model that implements this system uses the following prompt:

[0643] "To identify the optimal location for installing wind power generation facilities, input wind data from the past five years in Tokyo into an LSTM model and predict wind speed and direction for the next five years. Also, implement an algorithm that selects the location with the optimal wind speed and direction based on the prediction results."

[0644] This invention makes it possible to improve the efficiency and stability of energy supply and to detect abnormalities in real time.

[0645] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0646] Step 1: Collect data

[0647] The server collects historical wind data from a weather database. Specifically, it retrieves data including wind speed, wind direction, and date and time information at a specified longitude and latitude. It uses an API key and specified location information as input. It retrieves this data via a REST API and stores it in a local database. The output is the historical wind data stored in the database.

[0648] Step 2: Preprocessing the data

[0649] The server preprocesses the collected wind data. Specifically, it complements missing data and normalizes (scales) it. The input is the raw data collected in step 1, and methods such as linear interpolation are used to complement the missing data. Normalization involves scaling based on the mean and standard deviation. The output is the preprocessed wind data.

[0650] Step 3: Generative AI predicts wind

[0651] The server uses the preprocessed data to input it into a generative AI model (LSTM model) to predict future wind data. The input is preprocessed wind speed and direction data, and a deep learning model is used to learn and predict the data. Specifically, it predicts wind speed and direction for the next few years as a time series based on past data. The output is predicted future wind data.

[0652] Step 4: Identify the optimal location

[0653] The server analyzes the predicted wind data to identify optimal installation locations. It uses predicted future wind data as input, applying an algorithm that evaluates the stability of average wind speed and direction and calculates locations with minimal fluctuations. The output is the coordinates of the optimal wind energy conversion facility installation location.

[0654] Step 5: Installing the energy conversion equipment

[0655] The terminal installs the wind energy conversion equipment at the specified installation location. The input is the coordinate information of the optimal installation location received from the server. The specific operation is to perform the physical installation work and configure the energy conversion equipment so that it functions properly. The output is the installed energy conversion equipment.

[0656] Step 6: Applying wireless power transmission technology

[0657] The terminal transmits the converted energy using wireless power transmission technology. The input is the converted energy and optimal transmission parameters. Specifically, the parameters of the wireless power transmission device are adjusted to transmit power while minimizing energy loss. The output is the efficiently transmitted energy.

[0658] Step 7: Real-time system monitoring

[0659] Users can monitor the performance of the energy supply system and robots in real time through a web-based dashboard. The input is real-time data sent from the server and terminals. Specifically, it displays power generation amount, power transmission efficiency, anomaly detection information, etc. in real time, and issues an alert if an abnormality is detected. The output is status information of the monitored system and an alert to the user.

[0660] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0661] System Configuration

[0662] The system of the present invention comprises the following main components:

[0663] 1. Server

[0664] Ability to collect historical wind data from weather databases

[0665] A function that uses generative AI to predict future wind flow

[0666] A function that analyzes predicted wind data and identifies optimal locations for wind power generation facilities

[0667] 2. Terminal

[0668] Ability to install and operate wind power generation facilities

[0669] A function that supplies energy using wireless power transmission technology

[0670] 3. Users

[0671] Ability to use a dashboard to monitor the system's operating status

[0672] 4. Emotion Engine

[0673] Ability to recognize user emotions and collect emotional data

[0674] Ability to analyze emotional data and adjust the system's operating interface and energy supply performance

[0675] A function that detects system anomalies early based on emotional data and notifies users

[0676] Program processing flow

[0677] Data collection and prediction

[0678] The server first collects historical wind data from a weather database. This data includes details such as date, time, longitude, latitude, wind speed, and wind direction. The data is retrieved through a REST API and stored in a database on the server. The collected data is then preprocessed by filling in missing values ​​and normalizing the data.

[0679] The server then inputs the collected data into a generation AI to predict future wind flow. The generation AI uses a deep learning model (such as an LSTM model). It learns from past wind data and predicts wind speed and direction for the next five years. The prediction results are recorded on the server and used for subsequent analysis.

[0680] Identifying installation locations and wireless power transmission

[0681] The server analyzes the predicted future wind data and identifies the optimal location for installing wind power generation equipment. It selects the optimal location based on factors such as average wind speed and the smallest fluctuations, and identifies its specific coordinates. The identified coordinates are then sent to the device.

[0682] The terminal installs the wind power generation equipment based on the installation location information received from the server. This includes assembling the wind turbines and preparing the infrastructure. Energy is then supplied using wireless power transmission technology. The terminal configures and adjusts the wireless power transmission equipment, setting parameters to minimize energy loss.

[0683] System monitoring and user emotion recognition

[0684] Users can monitor the system's operation in real time using a web-based dashboard, which displays the amount of power generated and the stability of power transmission, and sends alerts if any abnormalities occur.

[0685] The emotion engine recognizes the user's emotions and collects emotional data. This data is used to adjust the system's operation interface and energy supply performance. For example, if the user is feeling stressed, it will provide a simpler operation interface. It also has the ability to detect system abnormalities early on based on the emotional data and notify the user.

[0686] Specific examples

[0687] 1. Data collection and prediction

[0688] The server uses the OpenWeather API to collect wind data from the past five years and store it in a local database.

[0689] The server uses an LSTM model to predict wind speed and direction for the next five years based on the collected wind data.

[0690] 2. Identifying the installation location and wireless power transmission

[0691] The server analyzes the forecast data and identifies the North Sea coastal region as the best location for installation.

[0692] The terminal installs a wind turbine at the specified location, sets up a wireless power transmission device, and starts transmitting energy.

[0693] 3. System Monitoring and Emotion Recognition

[0694] Users can check the amount of power generated and the stability of transmission in real time through the dashboard.

[0695] The emotion engine recognizes the user's emotions and simplifies the operation interface when the user is feeling stressed.

[0696] The emotion engine detects anomalies and sends an alert to the user.

[0697] This system not only efficiently generates wind power and uses wireless power transmission technology to ensure a stable energy supply, but also aims to improve the user experience by taking into account the user's emotions.

[0698] The processing flow will be explained below.

[0699] System Configuration

[0700] The system of the present invention comprises the following main components:

[0701] 1. Server

[0702] Ability to collect historical wind data from weather databases

[0703] A function that uses generative AI to predict future wind flow

[0704] A function that analyzes predicted wind data and identifies optimal locations for wind power generation facilities

[0705] 2. Terminal

[0706] Ability to install and operate wind power generation facilities

[0707] A function that supplies energy using wireless power transmission technology

[0708] 3. Users

[0709] Ability to use a dashboard to monitor the system's operating status

[0710] 4. Emotion Engine

[0711] Ability to recognize user emotions and collect emotional data

[0712] Ability to analyze emotional data and adjust the system's operating interface and energy supply performance

[0713] A function that detects system anomalies early based on emotional data and notifies users

[0714] Program processing flow

[0715] Step 1:

[0716] The server collects past wind data from a weather database. Specifically, it uses OpenWeather and NOAA APIs to obtain wind data from the past five years, collecting date, time, longitude, latitude, wind speed, wind direction, etc. The collected data is stored in a database on the server.

[0717] Step 2:

[0718] The server preprocesses the collected data, specifically by imputing missing values ​​and normalizing the data, converting it into a format that allows subsequent AI models to efficiently learn from the data.

[0719] Step 3:

[0720] The server inputs the preprocessed data into the generation AI to predict future wind flow. The generation AI uses a deep learning model (such as an LSTM model) to predict future wind speed and direction from past wind data. The prediction results are recorded on the server.

[0721] Step 4:

[0722] The server analyzes the predicted future wind data and identifies the optimal location for installing wind power generation equipment. Specifically, it selects the optimal installation location based on indicators such as average wind speed and minimal fluctuation. The identified coordinates are sent to the terminal.

[0723] Step 5:

[0724] The terminal installs the wind power generation equipment based on the installation location information received from the server. The installation work includes assembling and installing the wind turbines and preparing the infrastructure for their operation.

[0725] Step 6:

[0726] The device transmits energy using wireless power transmission technology. Specifically, it configures the wireless power transmission device and adjusts parameters to transmit power efficiently, thereby minimizing energy loss.

[0727] Step 7:

[0728] Users can monitor the operation of the wind power generation system and wireless power transmission system in real time using a web-based dashboard that displays the amount of power generated and the stability of the power transmission, and sends alerts if any abnormalities occur.

[0729] Step 8:

[0730] The emotion engine recognizes the user's emotions and collects emotional data. This data is used to adjust the system's operation interface and energy supply performance. For example, if the user is feeling stressed, a simpler operation interface will be provided. The system also has a function to detect system abnormalities early based on the emotional data and notify the user.

[0731] Examples:

[0732] 1. Data collection and prediction

[0733] The server uses the OpenWeather API to collect wind data from the past five years and store it in a local database.

[0734] The server uses an LSTM model to predict wind speed and direction for the next five years based on the collected wind data.

[0735] 2. Identifying the installation location and wireless power transmission

[0736] The server analyzes the forecast data and identifies the North Sea coastal region as the best location for installation.

[0737] The terminal installs a wind turbine at the specified location, sets up a wireless power transmission device, and starts transmitting energy.

[0738] 3. System Monitoring and Emotion Recognition

[0739] Users can check the amount of power generated and the stability of transmission in real time through the dashboard.

[0740] The emotion engine recognizes the user's emotions and simplifies the operation interface when the user is feeling stressed.

[0741] The emotion engine detects anomalies and sends an alert to the user.

[0742] This system not only efficiently generates wind power and uses wireless power transmission technology to ensure a stable energy supply, but also aims to improve the user experience by taking into account the user's emotions.

[0743] Example 2

[0744] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0745] Conventional wind power generation systems have problems such as difficulty in selecting installation locations and optimizing power generation efficiency, resulting in high energy loss. Furthermore, measures to improve the user experience by taking user emotions into consideration have not been taken. Therefore, there is a need to simultaneously achieve improved power generation efficiency and user convenience.

[0746] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0747] In this invention, the server includes a means for collecting past wind data from a weather database, a means for inputting the collected wind data into a generation AI to predict future wind flow, and a means for analyzing the predicted future wind data to identify the location where wind will be most stable. This enables the identification of the optimal installation location. The server also includes a means for installing wind power generation equipment at the identified location and supplying energy using wireless power transmission technology, a means for providing a user interface for monitoring the system's operating status, a means for recognizing user emotions and adjusting the system's operation interface based on the collected emotion data, and a means for early detection and notification of system abnormalities. This enables improved power generation efficiency, a more stable energy supply, and an improved user experience.

[0748] A "weather database" is a data management system that has the function of collecting, storing, and providing past and current weather data.

[0749] "Wind data" refers to various data related to wind, such as wind speed, wind direction, longitude, latitude, date and time, etc.

[0750] "Generative AI" is a machine learning model that uses artificial intelligence techniques to predict future data from past data.

[0751] "Future wind flow" refers to future weather conditions, such as wind speed and direction, predicted using generative AI.

[0752] "Wireless power transmission technology" is a technology that transmits electricity without using physical wiring.

[0753] A "wind power generation facility" is a device that converts the kinetic energy of wind into electricity.

[0754] A "user interface" refers to the screens and operating means that allow a user to interact with a system or device.

[0755] An "emotion engine" is a system that can recognize the user's emotional state and provide appropriate feedback and responses based on that.

[0756] "Emotion data" refers to data that indicates the user's emotional state.

[0757] "System operational status" refers to information that indicates the current operating state and performance of the system.

[0758] "Anomaly detection" is the process of detecting behaviors or conditions that deviate from a system's normal operation.

[0759] The present invention provides a system for optimizing the installation location of wind power generation facilities and improving energy efficiency and user experience during operation of the facilities. Specific embodiments of this system will be described below.

[0760] Data collection and prediction

[0761] The server first collects historical wind data from a weather database. This wind data includes detailed information such as date, time, longitude, latitude, wind speed, and wind direction. Specifically, the server uses the OpenWeather API to obtain the historical wind data and stores it in a local database on the server. The collected data is retrieved from a REST API using the Python requests library, and preprocessing such as filling in missing values ​​and normalizing the data is performed using the Pandas library. The preprocessed data obtained in this way is input into the generative AI model.

[0762] The server then inputs the preprocessed data into a generative AI model to predict future wind flow. The generative AI uses an LSTM model powered by the TensorFlow library. Based on past wind data, the server predicts wind speed and direction for the next five years and records the results.

[0763] Identifying installation locations and wireless power transmission

[0764] The server analyzes the predicted future wind data to identify the optimal location for wind power generation facilities. This analysis uses Python machine learning libraries (e.g., scikit-learn) to evaluate the average wind speed and the smallest fluctuations. As a result of the analysis, a specific region (e.g., the North Sea coast) is selected as the optimal location. This information is sent to the device.

[0765] The terminal then installs the wind power generation equipment based on the installation location information it receives. The installation process includes using GPS to transport materials to the specified coordinates, assembling the wind turbines, and preparing the infrastructure. After the wind power generation equipment is installed, the terminal configures the wireless power transmission equipment and begins transmitting energy. Using wireless power transmission technology (e.g., WiTricity), it adjusts parameters for efficient energy transmission and implements settings that minimize energy loss.

[0766] System Monitoring and Emotion Recognition

[0767] Users can monitor the system's operating status in real time through a web-based dashboard. The dashboard displays information such as power generation volume, power transmission stability, and energy consumption status. Users can receive real-time notifications if an abnormality occurs. The dashboard is built using front-end technologies such as React and Angular, and back-end frameworks such as Node.js or Python (e.g., Flask and Django).

[0768] The emotion engine recognizes the user's emotions and collects emotional data. Data collected using a facial recognition camera and biometric sensors is analyzed by an emotion recognition algorithm using the Emotion API. The emotional data is used to adjust the system's operating interface and energy supply performance. For example, if the user is feeling stressed, the emotion engine will simplify the operating interface. The emotional data can also be used to detect system abnormalities early and send an alert to the user.

[0769] Specific examples

[0770] 1. Data collection and prediction

[0771] The server uses the OpenWeather API to collect wind data from the past five years and stores it in a local database.The server then uses an LSTM model using TensorFlow to predict wind speed and direction for the next five years.

[0772] 2. Identifying the installation location and wireless power transmission

[0773] The server analyzes the forecast data and identifies the North Sea coastal area as the optimal location for installation. The device then installs the wind power generation equipment in the identified location, configures the wireless power transmission equipment, and begins transmitting energy.

[0774] 3. System Monitoring and Emotion Recognition

[0775] Users can check the amount of power generated and the stability of power transmission in real time through a dashboard. The emotion engine recognizes the user's emotions and simplifies the operation interface if the user is feeling stressed. The emotion engine also detects abnormalities and sends alerts to the user.

[0776] Prompt Sentence Examples

[0777] "Collect wind data from the past five years and predict wind speed and direction for the next five years."

[0778] "Install the wind power generation facility at the specified coordinates, set up the wireless power transmission device, and start supplying energy."

[0779] "Recognize users' emotions and adjust the control interface if they are feeling stressed."

[0780] In this way, the present invention not only efficiently generates wind power and realizes a stable energy supply using wireless power transmission technology, but also provides adjustment of the operation interface and abnormality detection based on the user's emotions.

[0781] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0782] Step 1:

[0783] The server collects historical wind data from a weather database. This process uses the OpenWeather API. It sends an API request as input and receives the resulting wind data in JSON format as output. This data includes the date, time, longitude, latitude, wind speed, and wind direction. Specifically, it uses the Python requests library to make an API request and stores the obtained data in a local database.

[0784] Step 2:

[0785] The server preprocesses the collected wind data. It uses the wind data obtained in step 1 as input and obtains preprocessed data as output. Preprocessing includes missing value imputation, data normalization, and outlier handling. Specifically, it uses the Pandas library to create a data frame, impute missing values, and normalize the data.

[0786] Step 3:

[0787] The server inputs the preprocessed data into a generative AI model to predict future wind flow. It uses the preprocessed data as input and obtains forecast data for wind speed and direction for the next five years as output. The generative AI model uses an LSTM model using TensorFlow. Specifically, it trains the LSTM model, obtains prediction results, and stores them on the server.

[0788] Step 4:

[0789] The server analyzes the predicted future wind data and identifies the optimal location for wind power generation equipment. It uses the predicted data as input and obtains the specific coordinates of the optimal installation location as output. The analysis uses Python's scikit-learn library to evaluate the average wind speed and the smallness of fluctuation. Specifically, it runs a clustering algorithm to identify the optimal area.

[0790] Step 5:

[0791] The server sends the identified coordinate information to the terminal. It uses the coordinates of the optimal installation location as input and transmits the information to the terminal as output. This process uses a REST API and a messaging queue system. Specifically, it uses the Python requests library to send the installation location information to the terminal.

[0792] Step 6:

[0793] The terminal installs the wind power generation equipment based on the received installation location information. It uses the coordinates of the installation location as input and obtains the assembled wind power generation equipment as output. Specifically, it uses GPS to transport materials to the specified coordinates and assembles the wind power generator.

[0794] Step 7:

[0795] After the wind power generation equipment is installed, the terminal configures the wireless power transmission equipment and begins transmitting energy. It uses information about the installed wind power generation equipment as input and operates the wireless power transmission system as output. It uses wireless power transmission technology (e.g., WiTricity) to adjust parameters for efficient energy transmission. Specifically, it adjusts the position and output of the wireless power transmission equipment to set it up to minimize energy loss.

[0796] Step 8:

[0797] Users monitor the system's operating status in real time using a web-based dashboard. Various system operating data is used as input, and real-time monitoring information is obtained as output. The dashboard displays information such as power generation volume, power transmission stability, and energy consumption status. In terms of specific operation, it uses front-end technologies such as React and Angular, and the back-end is built using Node.js or Python frameworks (e.g., Flask, Django).

[0798] Step 9:

[0799] The emotion engine recognizes the user's emotions and collects emotional data. It uses data indicating the user's emotional state as input and obtains analyzed emotional information as output. Emotion data is collected using a facial recognition camera and biometric sensors and analyzed by an emotion recognition algorithm using the Emotion API. Specifically, it sends the collected emotional data to a server and obtains the analysis results.

[0800] Step 10:

[0801] The emotion engine adjusts the operation interface and detects anomalies based on the analyzed emotion data. It uses emotion data as input and obtains an adjusted interface and anomaly notification as output. For example, if the user is feeling stressed, it simplifies the operation interface. It also detects system anomalies early based on emotion data and sends an alert to the user. Specifically, if stress is detected, the operation interface settings are changed, and if an anomaly is detected, it is reflected through an API that notifies the user.

[0802] (Application example 2)

[0803] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0804] Conventional wind power generation systems are limited to energy generation and supply, and do not address in-store energy management or improving customer satisfaction. In particular, there was no way to optimize the use of wind energy, utilize wireless power transmission technology, or collect customer sentiment data and use it to improve store operations. Therefore, there was a need to improve energy efficiency and optimize the customer experience.

[0805] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting past wind data from a weather database, means for inputting the collected wind data into a generation AI and predicting future wind flow, means for analyzing the predicted future wind data and identifying the location where wind will be most stable, means for installing wind power generation equipment in the identified location and supplying energy using wireless power transmission technology, means for monitoring the store's energy consumption in real time, and means for collecting and managing customer emotion data from cameras and devices within the store. This enables optimal use of wind energy, real-time management of energy supply, and optimization of store operations based on user emotion data.

[0806] A "weather database" is a database that stores and provides information about past and present climates.

[0807] "Wind data" refers to information about wind, such as wind speed, wind direction, and frequency of occurrence.

[0808] "Generative AI" is artificial intelligence that has the ability to analyze large amounts of data and generate new information.

[0809] A "deep learning model" is an algorithm that uses a multi-layer neural network to learn features from data and perform predictions and classifications.

[0810] "Wireless power transmission technology" is a technology that transmits power wirelessly and makes it available to the receiving end.

[0811] A "wind power generation facility" is a device that uses wind energy to generate electricity.

[0812] "Energy consumption status" is information indicating the energy usage status in a specific location or time period.

[0813] "Emotion data" is information that quantifies or categorizes a person's emotional state.

[0814] "Means for monitoring energy consumption status of a store in real time" refers to a system for instantly observing and recording electricity usage status within a store.

[0815] "Means for collecting and managing customer emotional data from cameras and devices within the store" refers to a system that observes customers' facial expressions and behavior, analyzes their emotional state, and records and manages it.

[0816] The system for implementing this invention comprises the following main elements: a server, a terminal, and a user. The roles of each and their specific operating methods will be described in detail below.

[0817] Server Features

[0818] The server first collects historical wind data from a weather database. In this case, it uses the OpenWeather API to obtain wind data for the past five years. This data includes date, time, longitude, latitude, wind speed, and wind direction. The collected data is then stored in a local database after missing values ​​are filled and data normalization is performed.

[0819] The server then uses a generative AI to predict future wind flow. The generative AI uses a deep learning model (such as an LSTM model) that learns from past wind data and predicts wind speed and direction for the next five years. The prediction results are recorded on the server and used for subsequent analysis.

[0820] The server also analyzes the predicted future wind data to identify the location where wind will be most stable. The server selects the optimal location based on factors such as average wind speed and minimal fluctuation, and identifies its specific coordinates. The identified coordinates are then sent to the device.

[0821] Device Features

[0822] The terminal installs the wind power generation equipment based on the installation location information received from the server. This involves assembling the wind turbines and preparing the infrastructure. Energy is then supplied using wireless power transmission technology. The wireless power transmission device is configured and adjusted, and parameters are set to minimize energy loss. For example, when supplying energy to a store, wireless power transmission technology ensures efficient energy supply.

[0823] User Roles

[0824] Users can monitor the system's operation in real time using a web-based dashboard, which displays the amount of power generated and the stability of power transmission, and sends alerts if any abnormalities occur.

[0825] Furthermore, customers' emotional data is collected using cameras and devices (e.g., smart glasses and robots) equipped with an emotion engine. The emotional data is used to adjust the system's operation interface and store operation performance. Specifically, if a customer is feeling stressed, an easier operation interface will be provided. Emotional data can also be used to detect system anomalies early on and notify users to prevent problems before they occur.

[0826] Specific examples

[0827] 1. Data collection and prediction

[0828] The server uses the OpenWeather API to collect wind data from the past five years and stores it in a local database.

[0829] The server uses an LSTM model to predict wind speed and direction for the next five years based on the collected wind data.

[0830] 2. Identifying the installation location and wireless power transmission

[0831] The server analyzes the predictive data and identifies the optimal installation location.

[0832] The terminal installs a wind turbine at the specified location, sets up a wireless power transmission device, and starts transmitting energy.

[0833] 3. System Monitoring and Emotion Recognition

[0834] Users can check the amount of power generated and the stability of transmission in real time through the dashboard.

[0835] The emotion engine recognizes the user's emotions and simplifies the operation interface when the user is feeling stressed.

[0836] The emotion engine detects anomalies and sends an alert to the user.

[0837] Examples of prompt statements

[0838] "Based on wind data from the past five years, predict wind speed and direction for the next five years."

[0839] By inputting this prompt into a generative AI model, future wind flow can be predicted and optimal wind energy utilization can be achieved, thereby improving both energy efficiency and customer satisfaction in the store.

[0840] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0841] Step 1:

[0842] The server collects historical wind data from a weather database. Specifically, the server uses the OpenWeather API to retrieve wind speed, direction, and date and time information for the past five years for a specified latitude and longitude. The input required is an API key and latitude and longitude, and the output is JSON-formatted data containing wind data. The server stores the data in a local database.

[0843] Step 2:

[0844] The server preprocesses the collected wind data. Specifically, it fills in missing values ​​and normalizes the data. The input is wind data, and the output is preprocessed wind data. The server prepares the data and creates a dataset to input into the generative AI model.

[0845] Step 3:

[0846] The server uses a generative AI model to predict future wind flow. Specifically, it inputs preprocessed wind data into the LSTM model to obtain predicted wind speed and direction for the next few years. The input requires preprocessed data, and the output is data showing future wind speed and direction. The server stores the generated data in a local database.

[0847] Step 4:

[0848] The server analyzes the predicted future wind data and identifies the optimal location for installing wind power generation equipment. Specifically, it determines the optimal installation location based on parameters such as average wind speed and the lack of fluctuation in wind speed. The input required is the predicted data, and the output is the coordinate information of the optimal installation location. The server sends this information to the terminal.

[0849] Step 5:

[0850] The terminal installs the wind power generation equipment based on the installation location information received from the server. Specifically, the wind power generator is assembled, infrastructure is prepared, and the wireless power transmission device is set up. The installation location information is required as input, and the installed wind power generation equipment and wireless power transmission device are the output.

[0851] Step 6:

[0852] The device supplies energy using wireless power transmission technology. Specifically, it configures and adjusts the wireless power transmission device and sets parameters to minimize energy loss. Energy generated by wind power is required as input, and the output is wirelessly transmitted electricity.

[0853] Step 7:

[0854] Users monitor the system's operational status using a web-based dashboard, specifically checking data such as power generation volume and transmission stability in real time. Data from the monitoring system is required as input, and the output is dashboard information displayed to the user.

[0855] Step 8:

[0856] Users collect and manage customer emotion data from cameras and devices in the store. Specifically, an emotion engine is used to analyze facial expressions and behaviors and quantify or categorize the emotional state. Image data from cameras and devices is required as input, and the output is analyzed emotion data.

[0857] Step 9:

[0858] The system adjusts the operation interface and store operations based on the emotional data collected from users. Specifically, the interface is simplified or the operation method is changed depending on specific situations, such as when a customer is feeling stressed. Emotional data is required as input, and the output is an adjusted operation interface and store operation method.

[0859] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0860] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0861] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0862] [Third embodiment]

[0863] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0864] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0865] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0866] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0867] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0868] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0869] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0870] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0871] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[0872] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0873] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0874] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0875] System Configuration

[0876] The system of the present invention mainly comprises the following elements:

[0877] 1. Server

[0878] Ability to collect historical wind data from weather databases

[0879] A function that uses generative AI to predict future wind flow

[0880] A function that analyzes predicted wind data and identifies optimal locations for wind power generation facilities

[0881] 2. Terminal

[0882] Ability to install and operate wind power generation facilities

[0883] A function that supplies energy using wireless power transmission technology

[0884] 3. Users

[0885] Ability to use a dashboard to monitor the system's operating status

[0886] Program processing flow

[0887] Data collection

[0888] The server collects historical wind data from a meteorological database, including information such as date, time, longitude, latitude, wind speed, and direction. The data is retrieved through a REST API and integrated into the server's database.

[0889] Examples:

[0890] The server uses the OpenWeather API to obtain wind data for the past five years.

[0891] The acquired data is stored in a local database, and missing data is complemented and data normalization is performed as necessary.

[0892] Wind prediction using generative AI

[0893] The server inputs the collected wind data into the generation AI to predict future wind flow. The generation AI uses a deep learning model (such as an LSTM model). It learns wind speed and direction patterns and predicts wind flow for the next five years.

[0894] Examples:

[0895] The server uses the LSTM model to predict future wind speed and direction based on past wind data.

[0896] The forecast results are saved as time series data and used for analysis.

[0897] Identifying the optimal installation location

[0898] The server analyzes the predicted wind data to identify the best locations for wind farms, using indicators such as average wind speed and low variability.

[0899] Examples:

[0900] The server analyzes the forecast results and identifies areas with high wind speeds and little fluctuation.

[0901] Calculate specific coordinates and plot them on a map.

[0902] Application of wireless power transmission technology

[0903] The terminal then installs the wind power generation equipment at the identified optimal installation location and supplies energy using wireless power transmission technology, which adjusts parameters to transmit power efficiently and minimizes energy loss.

[0904] Examples:

[0905] The terminal sets up the wireless power transmitting device at the specified installation location.

[0906] Verify that the configured device transmits power efficiently and has an energy loss rate of 5% or less.

[0907] System operation monitoring

[0908] Users can use a web-based dashboard to monitor the operational status of their installed wind power generation systems and wireless power transmission systems in real time, checking the amount of power generated and the stability of power transmission, and receiving alerts if any abnormalities occur.

[0909] Examples:

[0910] Users can monitor the amount of power generated through a dashboard.

[0911] If a system abnormality is detected, a notification is sent to the user.

[0912] As described above, this system provides a concrete means for efficiently generating wind power and achieving a stable energy supply using wireless power transmission technology.

[0913] The processing flow will be explained below.

[0914] Step 1:

[0915] The server collects past wind data from a weather database. Specifically, it uses OpenWeather and NOAA APIs to obtain wind data from the past five years, collecting information such as date, time, longitude, latitude, wind speed, and wind direction. The collected data is stored in a database on the server.

[0916] Step 2:

[0917] The server preprocesses the collected data, specifically by filling in missing values ​​and normalizing the data, converting it into a format that can be efficiently learned by the subsequent AI model.

[0918] Step 3:

[0919] The server inputs the preprocessed data into the generation AI to predict future wind flow. The generation AI uses a deep learning model (such as an LSTM model) to predict future wind speed and direction from past wind data. The prediction results are stored on the server.

[0920] Step 4:

[0921] The server analyzes the predicted future wind data and identifies the location with the most stable wind. Specifically, it calculates the optimal installation location using indicators such as average wind speed and minimal fluctuation, and plots the specific coordinates on a map.

[0922] Step 5:

[0923] The terminal installs the wind power generation equipment based on the installation location information received from the server. The installation work includes assembling and installing the wind turbines and developing the infrastructure to operate them.

[0924] Step 6:

[0925] The device transmits energy using wireless power transmission technology. Specifically, it configures the wireless power transmission device and adjusts parameters to transmit power efficiently, thereby minimizing energy loss.

[0926] Step 7:

[0927] Users can monitor the operation of the wind power generation system and wireless power transmission system in real time using a web-based dashboard that displays the amount of power generated and the stability of the power transmission, and sends alerts if any abnormalities occur.

[0928] Example 1

[0929] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0930] Wind power generation is a form of clean energy and plays an important role in combating climate change. However, identifying the optimal location for wind power generation facilities and stabilizing their power generation output is not easy. The application of wireless power transmission technology to efficiently utilize the generated energy is also a challenge. Furthermore, a means of monitoring the operation status in real time and quickly detecting abnormalities is also required. Therefore, a system that integrates the identification of the optimal installation location, the efficient operation of wireless power transmission technology, and the monitoring of the system's operation status is required.

[0931] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0932] In this invention, the server includes means for collecting past wind data from a weather database, means for inputting the collected wind data into a generative AI model to predict future wind flow, means for analyzing the predicted future wind data and identifying the optimal location for installing a wind power generation facility, means for installing the wind power generation facility in the identified location and supplying energy using wireless power transmission technology, and means for providing a dashboard for monitoring the operating status of the installed wind power generation facility and the wireless power transmission system, thereby enabling the identification of the optimal installation location for the wind power generation facility, the efficient operation of wireless power transmission technology, and real-time monitoring of the operating status of the system.

[0933] A "weather database" is a data storage system for recording and storing past and current weather data.

[0934] A "generative AI model" is an algorithm that uses collected data to predict future phenomena and patterns, and generally uses techniques such as machine learning and deep learning.

[0935] "Wind power generation facilities" refers to equipment and infrastructure that utilizes wind energy to generate electricity, including windmills and turbines.

[0936] "Wireless power transmission technology" is a technology that transmits power without using physical wires, and technical adjustments are made to minimize energy loss.

[0937] A "dashboard" is an interface for monitoring and visually displaying the system's operating status in real time, and often takes the form of a web application or dedicated monitoring software.

[0938] A "deep learning model" is a technology that uses multi-layered neural networks to learn complex data patterns and make future predictions and classifications.

[0939] "Energy loss" refers to the percentage of energy lost during the transmission and conversion processes, and is an important indicator for system efficiency.

[0940] "Time series data" is a collection of data recorded over time, including data points taken at specific time intervals, such as wind speed and direction.

[0941] System Configuration

[0942] The system of the present invention mainly comprises the following elements:

[0943] 1. Server

[0944] A means of collecting historical wind data from meteorological databases

[0945] A means of inputting collected wind data into a generative AI model to predict future wind flow.

[0946] A means of analyzing predicted future wind data to identify optimal locations for installing wind turbines

[0947] 2. Terminal

[0948] A method of installing wind power generation facilities in designated locations and supplying energy using wireless power transmission technology

[0949] 3. Users

[0950] A means to use dashboards to monitor the operational status of installed wind power plants and wireless power transmission systems

[0951] Server Operation

[0952] The server collects historical wind data from a meteorological database, including information such as date, time, longitude, latitude, wind speed, and direction. The data is retrieved through a REST API and integrated into the server's database.

[0953] Specific behavior:

[0954] The server uses a common weather database API to collect historical wind data, using the Python requests library.

[0955] The Pandas library in Python is used to normalize and format the acquired data, which is then stored in a PostgreSQL database.

[0956] The server then inputs the collected wind data into a generative AI model to predict future wind flow, using the deep learning technology LSTM (Long Short-Term Memory).

[0957] Specific behavior:

[0958] The server preprocesses the collected wind dataset using the TensorFlow framework and inputs it into the LSTM model.

[0959] The LSTM model learns from past data to predict future wind speed and direction.

[0960] The predicted data is again saved in the PostgreSQL database.

[0961] Once the predicted wind data is obtained, the server performs analysis based on this data to identify the best locations to install wind power generation equipment.

[0962] Specific behavior:

[0963] The server uses Python's SciPy and NumPy libraries for analysis.

[0964] The optimal installation location is determined by scoring indicators such as the average wind speed and the lack of fluctuation.

[0965] The obtained location longitude and latitude information is converted into GeoJSON format and a common map API (e.g., Google Maps API) is used to plot it on a map.

[0966] Device behavior

[0967] The terminal then installs the wind power generation equipment in the optimal location identified by the server, using wireless power transmission technology to efficiently supply energy.

[0968] Specific behavior:

[0969] The terminal arrives at the designated location and deploys the necessary installation equipment.

[0970] By configuring the wireless power transmission device and adjusting the optimal parameters, energy loss during power transmission is minimized.

[0971] User operations

[0972] Users monitor the system's operational status in real time through a dedicated web-based dashboard.

[0973] Specific behavior:

[0974] Users access the dashboard through a web browser, which is typically built with React.js and Node.js.

[0975] The dashboard displays real-time information such as wind power generation, wireless power transmission status, and system status.

[0976] If the system detects an abnormality, it will use the Twilio API to notify the user via SMS or email.

[0977] Prompt Sentence Examples

[0978] Specific examples of prompts are as follows:

[0979] "Design a generative AI model to predict wind speed and direction for the next five years using wind data from the past five years. The model should use LSTM, and input data should include date, time, longitude, latitude, wind speed, and wind direction."

[0980] In this way, the system of the present invention provides a concrete means for efficiently generating wind power and realizing a stable energy supply using wireless power transmission technology.

[0981] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0982] Step 1:

[0983] The server collects historical wind data from a weather database.

[0984] Input: Weather database API endpoint

[0985] Specific operation: The server uses Python's requests library to retrieve wind data (date, time, longitude, latitude, wind speed, and direction) for the past five years from the weather database API using an HTTP GET request.

[0986] Output: The acquired data is saved on the server in JSON format.

[0987] Step 2:

[0988] The server normalizes and formats the collected wind data.

[0989] Input: Wind data in JSON format obtained in step 1

[0990] Specific operation: The server uses Python's Pandas library to impute missing values ​​in the acquired dataset and standardize the data.

[0991] Output: A normalized dataset is generated and stored in a PostgreSQL database.

[0992] Step 3:

[0993] The server inputs the collected wind data into a generative AI model to predict future wind flow.

[0994] Input: Normalized wind dataset

[0995] How it works: The server uses the TensorFlow framework to build and train an LSTM model. It then inputs past data into the trained model to predict future wind speed and direction.

[0996] Output: Predicted future wind data (time series data) is generated in JSON format and stored in a PostgreSQL database.

[0997] Step 4:

[0998] The server analyzes the predicted future wind data and identifies the optimal location for installing wind power generation equipment.

[0999] Input: Forecast wind data obtained in step 3

[1000] How it works: The server uses Python's SciPy and NumPy libraries to calculate and score the average wind speed and the least amount of fluctuation, thereby identifying the optimal installation location.

[1001] Output: The longitude and latitude information of the identified installation location is generated in GeoJSON format.

[1002] Step 5:

[1003] Plot the locations where the servers were identified on a map.

[1004] Input: Longitude and latitude information obtained in step 4 (GeoJSON format)

[1005] Specific operation: The server uses a common map API (e.g., Google Maps API) to visually display the identified location on a map.

[1006] Output: Potential installation sites plotted on a map

[1007] Step 6:

[1008] The terminal installs the wind power generation equipment in the optimal installation location designated by the terminal.

[1009] Input: Information on the candidate installation site obtained in Step 5

[1010] Specific operations: The terminal arrives at the site, deploys and installs the wind power generation equipment, adjusts the wireless power transmission equipment, and sets the optimal parameters.

[1011] Output: Installed wind power generation equipment and wireless power transmission equipment

[1012] Step 7:

[1013] The terminal configures the wireless power transmission device and supplies energy wirelessly.

[1014] Input: Installed wind power generation equipment and wireless power transmission equipment

[1015] Specific operation: The terminal configures the wireless power transmission device, monitors the efficiency of energy transmission in real time, and minimizes energy loss.

[1016] Output: Wireless power transmission device that efficiently transmits energy

[1017] Step 8:

[1018] Users can monitor the system's status using a web-based dashboard.

[1019] Input: Operation status data from the server and terminal

[1020] Specific operation: The user accesses the dashboard from a web browser and monitors the system's operating status (power generation amount, power transmission status, system status, etc.) in real time.

[1021] Output: Visual display of system operating status and alert notification when an abnormality occurs

[1022] Through this step, a system will be created that efficiently generates wind power and provides a stable energy supply through wireless power transmission technology.

[1023] (Application example 1)

[1024] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1025] Conventional wind power generation systems have difficulty ensuring efficient and stable energy supply, and have had the problem of significant energy loss, especially when installation locations are inappropriate. Furthermore, due to a lack of systems for monitoring energy supply conditions and equipment operation status in real time, there have been many cases where abnormality detection was delayed. This invention aims to solve these issues by predicting future wind flow, identifying optimal installation locations, and monitoring operation status in real time.

[1026] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1027] In this invention, the server includes means for collecting past data from a weather database, means for inputting the collected data into a generation AI to predict future wind flow, means for analyzing the predicted future data and identifying the location where wind will be most stable, means for installing energy conversion equipment in the identified location and supplying energy using wireless power transmission technology, and means for monitoring the performance of the energy supply system and robot in real time and detecting abnormalities, thereby enabling improved efficiency and stability of energy supply and real-time abnormality detection.

[1028] A "weather database" is a database that stores past weather data and allows it to be searched and retrieved.

[1029] "Generative AI" is a type of artificial intelligence that predicts future situations based on collected data, and is achieved using techniques such as deep learning models.

[1030] A "deep learning model" is a machine learning method that uses a multi-layer neural network to learn the characteristics of data and make predictions and classifications.

[1031] "Means for predicting wind flow" refers to a method or device for predicting future wind speed and direction using meteorological data as input.

[1032] An "energy conversion facility" is a device that converts natural energy into a usable form of energy, such as electricity.

[1033] "Wireless power transmission technology" is a technology for transmitting power without using wires, and utilizes electromagnetic waves and electromagnetic induction.

[1034] An "energy supply system" is a system for comprehensively managing the generation, conversion, and supply of energy.

[1035] "Robot performance" is a measure of how efficiently and accurately a robot performs a specified task.

[1036] "Real-time monitoring" means constantly monitoring the system status and performance, and ensuring that the latest data is always available.

[1037] "Means for detecting anomalies" refers to methods or devices for detecting situations that deviate from the normal operating state of a system.

[1038] The present invention relates to a system for providing efficient utilization of wind energy and real-time monitoring capabilities, and comprises the following elements:

[1039] System Configuration

[1040] The system mainly consists of three components: a server, a terminal, and a user.

[1041] server

[1042] The server has the following functions:

[1043] 1. Meteorological data collection:

[1044] The server collects historical wind data from a weather database via the Internet. This data includes date, time, longitude, latitude, wind speed, and wind direction. The data is retrieved through a REST API and integrated into a database on the server. For example, the server retrieves historical wind data using a weather data API (e.g., OpenWeather) and stores it in a local database.

[1045] 2. Generative AI wind prediction:

[1046] The collected wind data is input into a generative AI to predict future wind flow. A deep learning model (such as an LSTM model) is used for this purpose. For example, the server uses an LSTM model to learn from past wind data and predict wind speed and direction for the next few years.

[1047] 3. Identify the installation location:

[1048] The predicted wind data is analyzed to identify the optimal location for wind energy conversion equipment based on average wind speed and wind stability. The prediction results are saved as time-series data and used for further analysis. Specifically, the coordinates with high wind speed and little fluctuation are calculated and plotted on a map.

[1049] Terminal

[1050] The terminal has the following features:

[1051] 1. Installation of energy conversion equipment:

[1052] Install wind energy conversion equipment in the identified optimal locations.

[1053] 2. Application of wireless power transmission technology:

[1054] The converted energy is supplied using wireless power transmission technology. This technology adjusts parameters to transmit power efficiently and minimizes energy loss. For example, it places energy conversion devices in optimal settings to increase power transmission efficiency.

[1055] User

[1056] The user has the following capabilities:

[1057] 1. Real-time monitoring:

[1058] A web-based dashboard allows users to monitor the system's operating status in real time, allowing them to check the amount of power generated and the stability of power transmission, and receive alerts if an abnormality occurs.Specifically, the system has the ability to monitor power generation and notify users when an abnormality is detected.

[1059] Prompt Sentence Examples

[1060] The generative AI model that implements this system uses the following prompt:

[1061] "To identify the optimal location for installing wind power generation facilities, input wind data from the past five years in Tokyo into an LSTM model and predict wind speed and direction for the next five years. Also, implement an algorithm that selects the location with the optimal wind speed and direction based on the prediction results."

[1062] This invention makes it possible to improve the efficiency and stability of energy supply and to detect abnormalities in real time.

[1063] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1064] Step 1: Collect data

[1065] The server collects historical wind data from a weather database. Specifically, it retrieves data including wind speed, wind direction, and date and time information at a specified longitude and latitude. It uses an API key and specified location information as input. It retrieves this data via a REST API and stores it in a local database. The output is the historical wind data stored in the database.

[1066] Step 2: Preprocessing the data

[1067] The server preprocesses the collected wind data. Specifically, it complements missing data and normalizes (scales) it. The input is the raw data collected in step 1, and methods such as linear interpolation are used to complement the missing data. Normalization involves scaling based on the mean and standard deviation. The output is the preprocessed wind data.

[1068] Step 3: Generative AI predicts wind

[1069] The server uses the preprocessed data to input it into a generative AI model (LSTM model) to predict future wind data. The input is preprocessed wind speed and direction data, and a deep learning model is used to learn and predict the data. Specifically, it predicts wind speed and direction for the next few years as a time series based on past data. The output is predicted future wind data.

[1070] Step 4: Identify the optimal location

[1071] The server analyzes the predicted wind data to identify optimal installation locations. It uses predicted future wind data as input, applying an algorithm that evaluates the stability of average wind speed and direction and calculates locations with minimal fluctuations. The output is the coordinates of the optimal wind energy conversion facility installation location.

[1072] Step 5: Installing the energy conversion equipment

[1073] The terminal installs the wind energy conversion equipment at the specified installation location. The input is the coordinate information of the optimal installation location received from the server. The specific operation is to perform the physical installation work and configure the energy conversion equipment so that it functions properly. The output is the installed energy conversion equipment.

[1074] Step 6: Applying wireless power transmission technology

[1075] The terminal transmits the converted energy using wireless power transmission technology. The input is the converted energy and optimal transmission parameters. Specifically, the parameters of the wireless power transmission device are adjusted to transmit power while minimizing energy loss. The output is the efficiently transmitted energy.

[1076] Step 7: Real-time system monitoring

[1077] Users can monitor the performance of the energy supply system and robots in real time through a web-based dashboard. The input is real-time data sent from the server and terminals. Specifically, it displays power generation amount, power transmission efficiency, anomaly detection information, etc. in real time, and issues an alert if an abnormality is detected. The output is status information of the monitored system and an alert to the user.

[1078] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1079] System Configuration

[1080] The system of the present invention comprises the following main components:

[1081] 1. Server

[1082] Ability to collect historical wind data from weather databases

[1083] A function that uses generative AI to predict future wind flow

[1084] A function that analyzes predicted wind data and identifies optimal locations for wind power generation facilities

[1085] 2. Terminal

[1086] Ability to install and operate wind power generation facilities

[1087] A function that supplies energy using wireless power transmission technology

[1088] 3. Users

[1089] Ability to use a dashboard to monitor the system's operating status

[1090] 4. Emotion Engine

[1091] Ability to recognize user emotions and collect emotional data

[1092] Ability to analyze emotional data and adjust the system's operating interface and energy supply performance

[1093] A function that detects system anomalies early based on emotional data and notifies users

[1094] Program processing flow

[1095] Data collection and prediction

[1096] The server first collects historical wind data from a weather database. This data includes details such as date, time, longitude, latitude, wind speed, and wind direction. The data is retrieved through a REST API and stored in a database on the server. The collected data is then preprocessed by filling in missing values ​​and normalizing the data.

[1097] The server then inputs the collected data into a generation AI to predict future wind flow. The generation AI uses a deep learning model (such as an LSTM model). It learns from past wind data and predicts wind speed and direction for the next five years. The prediction results are recorded on the server and used for subsequent analysis.

[1098] Identifying installation locations and wireless power transmission

[1099] The server analyzes the predicted future wind data and identifies the optimal location for installing wind power generation equipment. It selects the optimal location based on factors such as average wind speed and the smallest fluctuations, and identifies its specific coordinates. The identified coordinates are then sent to the device.

[1100] The terminal installs the wind power generation equipment based on the installation location information received from the server. This includes assembling the wind turbines and preparing the infrastructure. Energy is then supplied using wireless power transmission technology. The terminal configures and adjusts the wireless power transmission equipment, setting parameters to minimize energy loss.

[1101] System monitoring and user emotion recognition

[1102] Users can monitor the system's operation in real time using a web-based dashboard, which displays the amount of power generated and the stability of power transmission, and sends alerts if any abnormalities occur.

[1103] The emotion engine recognizes the user's emotions and collects emotional data. This data is used to adjust the system's operation interface and energy supply performance. For example, if the user is feeling stressed, it will provide a simpler operation interface. It also has the ability to detect system abnormalities early on based on the emotional data and notify the user.

[1104] Specific examples

[1105] 1. Data collection and prediction

[1106] The server uses the OpenWeather API to collect wind data from the past five years and store it in a local database.

[1107] The server uses an LSTM model to predict wind speed and direction for the next five years based on the collected wind data.

[1108] 2. Identifying the installation location and wireless power transmission

[1109] The server analyzes the forecast data and identifies the North Sea coastal region as the best location for installation.

[1110] The terminal installs a wind turbine at the specified location, sets up a wireless power transmission device, and starts transmitting energy.

[1111] 3. System Monitoring and Emotion Recognition

[1112] Users can check the amount of power generated and the stability of transmission in real time through the dashboard.

[1113] The emotion engine recognizes the user's emotions and simplifies the operation interface when the user is feeling stressed.

[1114] The emotion engine detects anomalies and sends an alert to the user.

[1115] This system not only efficiently generates wind power and uses wireless power transmission technology to ensure a stable energy supply, but also aims to improve the user experience by taking into account the user's emotions.

[1116] The processing flow will be explained below.

[1117] System Configuration

[1118] The system of the present invention comprises the following main components:

[1119] 1. Server

[1120] Ability to collect historical wind data from weather databases

[1121] A function that uses generative AI to predict future wind flow

[1122] A function that analyzes predicted wind data and identifies optimal locations for wind power generation facilities

[1123] 2. Terminal

[1124] Ability to install and operate wind power generation facilities

[1125] A function that supplies energy using wireless power transmission technology

[1126] 3. Users

[1127] Ability to use a dashboard to monitor the system's operating status

[1128] 4. Emotion Engine

[1129] Ability to recognize user emotions and collect emotional data

[1130] Ability to analyze emotional data and adjust the system's operating interface and energy supply performance

[1131] A function that detects system anomalies early based on emotional data and notifies users

[1132] Program processing flow

[1133] Step 1:

[1134] The server collects past wind data from a weather database. Specifically, it uses OpenWeather and NOAA APIs to obtain wind data from the past five years, collecting date, time, longitude, latitude, wind speed, wind direction, etc. The collected data is stored in a database on the server.

[1135] Step 2:

[1136] The server preprocesses the collected data, specifically by imputing missing values ​​and normalizing the data, converting it into a format that allows subsequent AI models to efficiently learn from the data.

[1137] Step 3:

[1138] The server inputs the preprocessed data into the generation AI to predict future wind flow. The generation AI uses a deep learning model (such as an LSTM model) to predict future wind speed and direction from past wind data. The prediction results are recorded on the server.

[1139] Step 4:

[1140] The server analyzes the predicted future wind data and identifies the optimal location for installing wind power generation equipment. Specifically, it selects the optimal installation location based on indicators such as average wind speed and minimal fluctuation. The identified coordinates are sent to the terminal.

[1141] Step 5:

[1142] The terminal installs the wind power generation equipment based on the installation location information received from the server. The installation work includes assembling and installing the wind turbines and preparing the infrastructure for their operation.

[1143] Step 6:

[1144] The device transmits energy using wireless power transmission technology. Specifically, it configures the wireless power transmission device and adjusts parameters to transmit power efficiently, thereby minimizing energy loss.

[1145] Step 7:

[1146] Users can monitor the operation of the wind power generation system and wireless power transmission system in real time using a web-based dashboard that displays the amount of power generated and the stability of the power transmission, and sends alerts if any abnormalities occur.

[1147] Step 8:

[1148] The emotion engine recognizes the user's emotions and collects emotional data. This data is used to adjust the system's operation interface and energy supply performance. For example, if the user is feeling stressed, a simpler operation interface will be provided. The system also has a function to detect system abnormalities early based on the emotional data and notify the user.

[1149] Examples:

[1150] 1. Data collection and prediction

[1151] The server uses the OpenWeather API to collect wind data from the past five years and store it in a local database.

[1152] The server uses an LSTM model to predict wind speed and direction for the next five years based on the collected wind data.

[1153] 2. Identifying the installation location and wireless power transmission

[1154] The server analyzes the forecast data and identifies the North Sea coastal region as the best location for installation.

[1155] The terminal installs a wind turbine at the specified location, sets up a wireless power transmission device, and starts transmitting energy.

[1156] 3. System Monitoring and Emotion Recognition

[1157] Users can check the amount of power generated and the stability of transmission in real time through the dashboard.

[1158] The emotion engine recognizes the user's emotions and simplifies the operation interface when the user is feeling stressed.

[1159] The emotion engine detects anomalies and sends an alert to the user.

[1160] This system not only efficiently generates wind power and uses wireless power transmission technology to ensure a stable energy supply, but also aims to improve the user experience by taking into account the user's emotions.

[1161] Example 2

[1162] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1163] Conventional wind power generation systems have problems such as difficulty in selecting installation locations and optimizing power generation efficiency, resulting in high energy loss. Furthermore, measures to improve the user experience by taking user emotions into consideration have not been taken. Therefore, there is a need to simultaneously achieve improved power generation efficiency and user convenience.

[1164] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1165] In this invention, the server includes a means for collecting past wind data from a weather database, a means for inputting the collected wind data into a generation AI to predict future wind flow, and a means for analyzing the predicted future wind data to identify the location where wind will be most stable. This enables the identification of the optimal installation location. The server also includes a means for installing wind power generation equipment at the identified location and supplying energy using wireless power transmission technology, a means for providing a user interface for monitoring the system's operating status, a means for recognizing user emotions and adjusting the system's operation interface based on the collected emotion data, and a means for early detection and notification of system abnormalities. This enables improved power generation efficiency, a more stable energy supply, and an improved user experience.

[1166] A "weather database" is a data management system that has the function of collecting, storing, and providing past and current weather data.

[1167] "Wind data" refers to various data related to wind, such as wind speed, wind direction, longitude, latitude, date and time, etc.

[1168] "Generative AI" is a machine learning model that uses artificial intelligence techniques to predict future data from past data.

[1169] "Future wind flow" refers to future weather conditions, such as wind speed and direction, predicted using generative AI.

[1170] "Wireless power transmission technology" is a technology that transmits electricity without using physical wiring.

[1171] A "wind power generation facility" is a device that converts the kinetic energy of wind into electricity.

[1172] A "user interface" refers to the screens and operating means that allow a user to interact with a system or device.

[1173] An "emotion engine" is a system that can recognize the user's emotional state and provide appropriate feedback and responses based on that.

[1174] "Emotion data" refers to data that indicates the user's emotional state.

[1175] "System operational status" refers to information that indicates the current operating state and performance of the system.

[1176] "Anomaly detection" is the process of detecting behaviors or conditions that deviate from a system's normal operation.

[1177] The present invention provides a system for optimizing the installation location of wind power generation facilities and improving energy efficiency and user experience during operation of the facilities. Specific embodiments of this system will be described below.

[1178] Data collection and prediction

[1179] The server first collects historical wind data from a weather database. This wind data includes detailed information such as date, time, longitude, latitude, wind speed, and wind direction. Specifically, the server uses the OpenWeather API to obtain the historical wind data and stores it in a local database on the server. The collected data is retrieved from a REST API using the Python requests library, and preprocessing such as filling in missing values ​​and normalizing the data is performed using the Pandas library. The preprocessed data obtained in this way is input into the generative AI model.

[1180] The server then inputs the preprocessed data into a generative AI model to predict future wind flow. The generative AI uses an LSTM model powered by the TensorFlow library. Based on past wind data, the server predicts wind speed and direction for the next five years and records the results.

[1181] Identifying installation locations and wireless power transmission

[1182] The server analyzes the predicted future wind data to identify the optimal location for wind power generation facilities. This analysis uses Python machine learning libraries (e.g., scikit-learn) to evaluate the average wind speed and the smallest fluctuations. As a result of the analysis, a specific region (e.g., the North Sea coast) is selected as the optimal location. This information is sent to the device.

[1183] The terminal then installs the wind power generation equipment based on the installation location information it receives. The installation process includes using GPS to transport materials to the specified coordinates, assembling the wind turbines, and preparing the infrastructure. After the wind power generation equipment is installed, the terminal configures the wireless power transmission equipment and begins transmitting energy. Using wireless power transmission technology (e.g., WiTricity), it adjusts parameters for efficient energy transmission and implements settings that minimize energy loss.

[1184] System Monitoring and Emotion Recognition

[1185] Users can monitor the system's operating status in real time through a web-based dashboard. The dashboard displays information such as power generation volume, power transmission stability, and energy consumption status. Users can receive real-time notifications if an abnormality occurs. The dashboard is built using front-end technologies such as React and Angular, and back-end frameworks such as Node.js or Python (e.g., Flask and Django).

[1186] The emotion engine recognizes the user's emotions and collects emotional data. Data collected using a facial recognition camera and biometric sensors is analyzed by an emotion recognition algorithm using the Emotion API. The emotional data is used to adjust the system's operating interface and energy supply performance. For example, if the user is feeling stressed, the emotion engine will simplify the operating interface. The emotional data can also be used to detect system abnormalities early and send an alert to the user.

[1187] Specific examples

[1188] 1. Data collection and prediction

[1189] The server uses the OpenWeather API to collect wind data from the past five years and stores it in a local database.The server then uses an LSTM model using TensorFlow to predict wind speed and direction for the next five years.

[1190] 2. Identifying the installation location and wireless power transmission

[1191] The server analyzes the forecast data and identifies the North Sea coastal area as the optimal location for installation. The device then installs the wind power generation equipment in the identified location, configures the wireless power transmission equipment, and begins transmitting energy.

[1192] 3. System Monitoring and Emotion Recognition

[1193] Users can check the amount of power generated and the stability of power transmission in real time through a dashboard. The emotion engine recognizes the user's emotions and simplifies the operation interface if the user is feeling stressed. The emotion engine also detects abnormalities and sends alerts to the user.

[1194] Prompt Sentence Examples

[1195] "Collect wind data from the past five years and predict wind speed and direction for the next five years."

[1196] "Install the wind power generation facility at the specified coordinates, set up the wireless power transmission device, and start supplying energy."

[1197] "Recognize users' emotions and adjust the control interface if they are feeling stressed."

[1198] In this way, the present invention not only efficiently generates wind power and realizes a stable energy supply using wireless power transmission technology, but also provides adjustment of the operation interface and abnormality detection based on the user's emotions.

[1199] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1200] Step 1:

[1201] The server collects historical wind data from a weather database. This process uses the OpenWeather API. It sends an API request as input and receives the resulting wind data in JSON format as output. This data includes the date, time, longitude, latitude, wind speed, and wind direction. Specifically, it uses the Python requests library to make an API request and stores the obtained data in a local database.

[1202] Step 2:

[1203] The server preprocesses the collected wind data. It uses the wind data obtained in step 1 as input and obtains preprocessed data as output. Preprocessing includes missing value imputation, data normalization, and outlier handling. Specifically, it uses the Pandas library to create a data frame, impute missing values, and normalize the data.

[1204] Step 3:

[1205] The server inputs the preprocessed data into a generative AI model to predict future wind flow. It uses the preprocessed data as input and obtains forecast data for wind speed and direction for the next five years as output. The generative AI model uses an LSTM model using TensorFlow. Specifically, it trains the LSTM model, obtains prediction results, and stores them on the server.

[1206] Step 4:

[1207] The server analyzes the predicted future wind data and identifies the optimal location for wind power generation equipment. It uses the predicted data as input and obtains the specific coordinates of the optimal installation location as output. The analysis uses Python's scikit-learn library to evaluate the average wind speed and the smallness of fluctuation. Specifically, it runs a clustering algorithm to identify the optimal area.

[1208] Step 5:

[1209] The server sends the identified coordinate information to the terminal. It uses the coordinates of the optimal installation location as input and transmits the information to the terminal as output. This process uses a REST API and a messaging queue system. Specifically, it uses the Python requests library to send the installation location information to the terminal.

[1210] Step 6:

[1211] The terminal installs the wind power generation equipment based on the received installation location information. It uses the coordinates of the installation location as input and obtains the assembled wind power generation equipment as output. Specifically, it uses GPS to transport materials to the specified coordinates and assembles the wind power generator.

[1212] Step 7:

[1213] After the wind power generation equipment is installed, the terminal configures the wireless power transmission equipment and begins transmitting energy. It uses information about the installed wind power generation equipment as input and operates the wireless power transmission system as output. It uses wireless power transmission technology (e.g., WiTricity) to adjust parameters for efficient energy transmission. Specifically, it adjusts the position and output of the wireless power transmission equipment to set it up to minimize energy loss.

[1214] Step 8:

[1215] Users monitor the system's operating status in real time using a web-based dashboard. Various system operating data is used as input, and real-time monitoring information is obtained as output. The dashboard displays information such as power generation volume, power transmission stability, and energy consumption status. In terms of specific operation, it uses front-end technologies such as React and Angular, and the back-end is built using Node.js or Python frameworks (e.g., Flask, Django).

[1216] Step 9:

[1217] The emotion engine recognizes the user's emotions and collects emotional data. It uses data indicating the user's emotional state as input and obtains analyzed emotional information as output. Emotion data is collected using a facial recognition camera and biometric sensors and analyzed by an emotion recognition algorithm using the Emotion API. Specifically, it sends the collected emotional data to a server and obtains the analysis results.

[1218] Step 10:

[1219] The emotion engine adjusts the operation interface and detects anomalies based on the analyzed emotion data. It uses emotion data as input and obtains an adjusted interface and anomaly notification as output. For example, if the user is feeling stressed, it simplifies the operation interface. It also detects system anomalies early based on emotion data and sends an alert to the user. Specifically, if stress is detected, the operation interface settings are changed, and if an anomaly is detected, it is reflected through an API that notifies the user.

[1220] (Application example 2)

[1221] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1222] Conventional wind power generation systems are limited to energy generation and supply, and do not address in-store energy management or improving customer satisfaction. In particular, there was no way to optimize the use of wind energy, utilize wireless power transmission technology, or collect customer sentiment data and use it to improve store operations. Therefore, there was a need to improve energy efficiency and optimize the customer experience.

[1223] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting past wind data from a weather database, means for inputting the collected wind data into a generation AI and predicting future wind flow, means for analyzing the predicted future wind data and identifying the location where wind will be most stable, means for installing wind power generation equipment in the identified location and supplying energy using wireless power transmission technology, means for monitoring the store's energy consumption in real time, and means for collecting and managing customer emotion data from cameras and devices within the store. This enables optimal use of wind energy, real-time management of energy supply, and optimization of store operations based on user emotion data.

[1224] A "weather database" is a database that stores and provides information about past and present climates.

[1225] "Wind data" refers to information about wind, such as wind speed, wind direction, and frequency of occurrence.

[1226] "Generative AI" is artificial intelligence that has the ability to analyze large amounts of data and generate new information.

[1227] A "deep learning model" is an algorithm that uses a multi-layer neural network to learn features from data and perform predictions and classifications.

[1228] "Wireless power transmission technology" is a technology that transmits power wirelessly and makes it available to the receiving end.

[1229] A "wind power generation facility" is a device that uses wind energy to generate electricity.

[1230] "Energy consumption status" is information indicating the energy usage status in a specific location or time period.

[1231] "Emotion data" is information that quantifies or categorizes a person's emotional state.

[1232] "Means for monitoring energy consumption status of a store in real time" refers to a system for instantly observing and recording electricity usage status within a store.

[1233] "Means for collecting and managing customer emotional data from cameras and devices within the store" refers to a system that observes customers' facial expressions and behavior, analyzes their emotional state, and records and manages it.

[1234] The system for implementing this invention comprises the following main elements: a server, a terminal, and a user. The roles of each and their specific operating methods will be described in detail below.

[1235] Server Features

[1236] The server first collects historical wind data from a weather database. In this case, it uses the OpenWeather API to obtain wind data for the past five years. This data includes date, time, longitude, latitude, wind speed, and wind direction. The collected data is then stored in a local database after missing values ​​are filled and data normalization is performed.

[1237] The server then uses a generative AI to predict future wind flow. The generative AI uses a deep learning model (such as an LSTM model) that learns from past wind data and predicts wind speed and direction for the next five years. The prediction results are recorded on the server and used for subsequent analysis.

[1238] The server also analyzes the predicted future wind data to identify the location where wind will be most stable. The server selects the optimal location based on factors such as average wind speed and minimal fluctuation, and identifies its specific coordinates. The identified coordinates are then sent to the device.

[1239] Device Features

[1240] The terminal installs the wind power generation equipment based on the installation location information received from the server. This involves assembling the wind turbines and preparing the infrastructure. Energy is then supplied using wireless power transmission technology. The wireless power transmission device is configured and adjusted, and parameters are set to minimize energy loss. For example, when supplying energy to a store, wireless power transmission technology ensures efficient energy supply.

[1241] User Roles

[1242] Users can monitor the system's operation in real time using a web-based dashboard, which displays the amount of power generated and the stability of power transmission, and sends alerts if any abnormalities occur.

[1243] Furthermore, customers' emotional data is collected using cameras and devices (e.g., smart glasses and robots) equipped with an emotion engine. The emotional data is used to adjust the system's operation interface and store operation performance. Specifically, if a customer is feeling stressed, an easier operation interface will be provided. Emotional data can also be used to detect system anomalies early on and notify users to prevent problems before they occur.

[1244] Specific examples

[1245] 1. Data collection and prediction

[1246] The server uses the OpenWeather API to collect wind data from the past five years and stores it in a local database.

[1247] The server uses an LSTM model to predict wind speed and direction for the next five years based on the collected wind data.

[1248] 2. Identifying the installation location and wireless power transmission

[1249] The server analyzes the predictive data and identifies the optimal installation location.

[1250] The terminal installs a wind turbine at the specified location, sets up a wireless power transmission device, and starts transmitting energy.

[1251] 3. System Monitoring and Emotion Recognition

[1252] Users can check the amount of power generated and the stability of transmission in real time through the dashboard.

[1253] The emotion engine recognizes the user's emotions and simplifies the operation interface when the user is feeling stressed.

[1254] The emotion engine detects anomalies and sends an alert to the user.

[1255] Examples of prompt statements

[1256] "Based on wind data from the past five years, predict wind speed and direction for the next five years."

[1257] By inputting this prompt into a generative AI model, future wind flow can be predicted and optimal wind energy utilization can be achieved, thereby improving both energy efficiency and customer satisfaction in the store.

[1258] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1259] Step 1:

[1260] The server collects historical wind data from a weather database. Specifically, the server uses the OpenWeather API to retrieve wind speed, direction, and date and time information for the past five years for a specified latitude and longitude. The input required is an API key and latitude and longitude, and the output is JSON-formatted data containing wind data. The server stores the data in a local database.

[1261] Step 2:

[1262] The server preprocesses the collected wind data. Specifically, it fills in missing values ​​and normalizes the data. The input is wind data, and the output is preprocessed wind data. The server prepares the data and creates a dataset to input into the generative AI model.

[1263] Step 3:

[1264] The server uses a generative AI model to predict future wind flow. Specifically, it inputs preprocessed wind data into the LSTM model to obtain predicted wind speed and direction for the next few years. The input requires preprocessed data, and the output is data showing future wind speed and direction. The server stores the generated data in a local database.

[1265] Step 4:

[1266] The server analyzes the predicted future wind data and identifies the optimal location for installing wind power generation equipment. Specifically, it determines the optimal installation location based on parameters such as average wind speed and the lack of fluctuation in wind speed. The input required is the predicted data, and the output is the coordinate information of the optimal installation location. The server sends this information to the terminal.

[1267] Step 5:

[1268] The terminal installs the wind power generation equipment based on the installation location information received from the server. Specifically, the wind power generator is assembled, infrastructure is prepared, and the wireless power transmission device is set up. The installation location information is required as input, and the installed wind power generation equipment and wireless power transmission device are the output.

[1269] Step 6:

[1270] The device supplies energy using wireless power transmission technology. Specifically, it configures and adjusts the wireless power transmission device and sets parameters to minimize energy loss. Energy generated by wind power is required as input, and the output is wirelessly transmitted electricity.

[1271] Step 7:

[1272] Users monitor the system's operational status using a web-based dashboard, specifically checking data such as power generation volume and transmission stability in real time. Data from the monitoring system is required as input, and the output is dashboard information displayed to the user.

[1273] Step 8:

[1274] Users collect and manage customer emotion data from cameras and devices in the store. Specifically, an emotion engine is used to analyze facial expressions and behaviors and quantify or categorize the emotional state. Image data from cameras and devices is required as input, and the output is analyzed emotion data.

[1275] Step 9:

[1276] The system adjusts the operation interface and store operations based on the emotional data collected from users. Specifically, the interface is simplified or the operation method is changed depending on specific situations, such as when a customer is feeling stressed. Emotional data is required as input, and the output is an adjusted operation interface and store operation method.

[1277] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1278] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1279] 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 the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1280] [Fourth embodiment]

[1281] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1282] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1283] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1284] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1285] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1286] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1287] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1288] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1289] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1290] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[1291] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1292] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1293] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1294] System Configuration

[1295] The system of the present invention mainly comprises the following elements:

[1296] 1. Server

[1297] Ability to collect historical wind data from weather databases

[1298] A function that uses generative AI to predict future wind flow

[1299] A function that analyzes predicted wind data and identifies optimal locations for wind power generation facilities

[1300] 2. Terminal

[1301] Ability to install and operate wind power generation facilities

[1302] A function that supplies energy using wireless power transmission technology

[1303] 3. Users

[1304] Ability to use a dashboard to monitor the system's operating status

[1305] Program processing flow

[1306] Data collection

[1307] The server collects historical wind data from a meteorological database, including information such as date, time, longitude, latitude, wind speed, and direction. The data is retrieved through a REST API and integrated into the server's database.

[1308] Examples:

[1309] The server uses the OpenWeather API to obtain wind data for the past five years.

[1310] The acquired data is stored in a local database, and missing data is complemented and data normalization is performed as necessary.

[1311] Wind prediction using generative AI

[1312] The server inputs the collected wind data into the generation AI to predict future wind flow. The generation AI uses a deep learning model (such as an LSTM model). It learns wind speed and direction patterns and predicts wind flow for the next five years.

[1313] Examples:

[1314] The server uses the LSTM model to predict future wind speed and direction based on past wind data.

[1315] The forecast results are saved as time series data and used for analysis.

[1316] Identifying the optimal installation location

[1317] The server analyzes the predicted wind data to identify the best locations for wind farms, using indicators such as average wind speed and low variability.

[1318] Examples:

[1319] The server analyzes the forecast results and identifies areas with high wind speeds and little fluctuation.

[1320] Calculate specific coordinates and plot them on a map.

[1321] Application of wireless power transmission technology

[1322] The terminal then installs the wind power generation equipment at the identified optimal installation location and supplies energy using wireless power transmission technology, which adjusts parameters to transmit power efficiently and minimizes energy loss.

[1323] Examples:

[1324] The terminal sets up the wireless power transmitting device at the specified installation location.

[1325] Verify that the configured device transmits power efficiently and has an energy loss rate of 5% or less.

[1326] System operation monitoring

[1327] Users can use a web-based dashboard to monitor the operational status of their installed wind power generation systems and wireless power transmission systems in real time, checking the amount of power generated and the stability of power transmission, and receiving alerts if any abnormalities occur.

[1328] Examples:

[1329] Users can monitor the amount of power generated through a dashboard.

[1330] If a system abnormality is detected, a notification is sent to the user.

[1331] As described above, this system provides a concrete means for efficiently generating wind power and achieving a stable energy supply using wireless power transmission technology.

[1332] The processing flow will be explained below.

[1333] Step 1:

[1334] The server collects past wind data from a weather database. Specifically, it uses OpenWeather and NOAA APIs to obtain wind data from the past five years, collecting information such as date, time, longitude, latitude, wind speed, and wind direction. The collected data is stored in a database on the server.

[1335] Step 2:

[1336] The server preprocesses the collected data, specifically by filling in missing values ​​and normalizing the data, converting it into a format that can be efficiently learned by the subsequent AI model.

[1337] Step 3:

[1338] The server inputs the preprocessed data into the generation AI to predict future wind flow. The generation AI uses a deep learning model (such as an LSTM model) to predict future wind speed and direction from past wind data. The prediction results are stored on the server.

[1339] Step 4:

[1340] The server analyzes the predicted future wind data and identifies the location with the most stable wind. Specifically, it calculates the optimal installation location using indicators such as average wind speed and minimal fluctuation, and plots the specific coordinates on a map.

[1341] Step 5:

[1342] The terminal installs the wind power generation equipment based on the installation location information received from the server. The installation work includes assembling and installing the wind turbines and developing the infrastructure to operate them.

[1343] Step 6:

[1344] The device transmits energy using wireless power transmission technology. Specifically, it configures the wireless power transmission device and adjusts parameters to transmit power efficiently, thereby minimizing energy loss.

[1345] Step 7:

[1346] Users can monitor the operation of the wind power generation system and wireless power transmission system in real time using a web-based dashboard that displays the amount of power generated and the stability of the power transmission, and sends alerts if any abnormalities occur.

[1347] Example 1

[1348] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1349] Wind power generation is a form of clean energy and plays an important role in combating climate change. However, identifying the optimal location for wind power generation facilities and stabilizing their power generation output is not easy. The application of wireless power transmission technology to efficiently utilize the generated energy is also a challenge. Furthermore, a means of monitoring the operation status in real time and quickly detecting abnormalities is also required. Therefore, a system that integrates the identification of the optimal installation location, the efficient operation of wireless power transmission technology, and the monitoring of the system's operation status is required.

[1350] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1351] In this invention, the server includes means for collecting past wind data from a weather database, means for inputting the collected wind data into a generative AI model to predict future wind flow, means for analyzing the predicted future wind data and identifying the optimal location for installing a wind power generation facility, means for installing the wind power generation facility in the identified location and supplying energy using wireless power transmission technology, and means for providing a dashboard for monitoring the operating status of the installed wind power generation facility and the wireless power transmission system, thereby enabling the identification of the optimal installation location for the wind power generation facility, the efficient operation of wireless power transmission technology, and real-time monitoring of the operating status of the system.

[1352] A "weather database" is a data storage system for recording and storing past and current weather data.

[1353] A "generative AI model" is an algorithm that uses collected data to predict future phenomena and patterns, and generally uses techniques such as machine learning and deep learning.

[1354] "Wind power generation facilities" refers to equipment and infrastructure that utilizes wind energy to generate electricity, including windmills and turbines.

[1355] "Wireless power transmission technology" is a technology that transmits power without using physical wires, and technical adjustments are made to minimize energy loss.

[1356] A "dashboard" is an interface for monitoring and visually displaying the system's operating status in real time, and often takes the form of a web application or dedicated monitoring software.

[1357] A "deep learning model" is a technology that uses multi-layered neural networks to learn complex data patterns and make future predictions and classifications.

[1358] "Energy loss" refers to the percentage of energy lost during the transmission and conversion processes, and is an important indicator for system efficiency.

[1359] "Time series data" is a collection of data recorded over time, including data points taken at specific time intervals, such as wind speed and direction.

[1360] System Configuration

[1361] The system of the present invention mainly comprises the following elements:

[1362] 1. Server

[1363] A means of collecting historical wind data from meteorological databases

[1364] A means of inputting collected wind data into a generative AI model to predict future wind flow.

[1365] A means of analyzing predicted future wind data to identify optimal locations for installing wind turbines

[1366] 2. Terminal

[1367] A method of installing wind power generation facilities in designated locations and supplying energy using wireless power transmission technology

[1368] 3. Users

[1369] A means to use dashboards to monitor the operational status of installed wind power plants and wireless power transmission systems

[1370] Server Operation

[1371] The server collects historical wind data from a meteorological database, including information such as date, time, longitude, latitude, wind speed, and direction. The data is retrieved through a REST API and integrated into the server's database.

[1372] Specific behavior:

[1373] The server uses a common weather database API to collect historical wind data, using the Python requests library.

[1374] The Pandas library in Python is used to normalize and format the acquired data, which is then stored in a PostgreSQL database.

[1375] The server then inputs the collected wind data into a generative AI model to predict future wind flow, using the deep learning technology LSTM (Long Short-Term Memory).

[1376] Specific behavior:

[1377] The server preprocesses the collected wind dataset using the TensorFlow framework and inputs it into the LSTM model.

[1378] The LSTM model learns from past data to predict future wind speed and direction.

[1379] The predicted data is again saved in the PostgreSQL database.

[1380] Once the predicted wind data is obtained, the server performs analysis based on this data to identify the best locations to install wind power generation equipment.

[1381] Specific behavior:

[1382] The server uses Python's SciPy and NumPy libraries for analysis.

[1383] The optimal installation location is determined by scoring indicators such as the average wind speed and the lack of fluctuation.

[1384] The obtained location longitude and latitude information is converted into GeoJSON format and a common map API (e.g., Google Maps API) is used to plot it on a map.

[1385] Device behavior

[1386] The terminal then installs the wind power generation equipment in the optimal location identified by the server, using wireless power transmission technology to efficiently supply energy.

[1387] Specific behavior:

[1388] The terminal arrives at the designated location and deploys the necessary installation equipment.

[1389] By configuring the wireless power transmission device and adjusting the optimal parameters, energy loss during power transmission is minimized.

[1390] User operations

[1391] Users monitor the system's operational status in real time through a dedicated web-based dashboard.

[1392] Specific behavior:

[1393] Users access the dashboard through a web browser, which is typically built with React.js and Node.js.

[1394] The dashboard displays real-time information such as wind power generation, wireless power transmission status, and system status.

[1395] If the system detects an abnormality, it will use the Twilio API to notify the user via SMS or email.

[1396] Prompt Sentence Examples

[1397] Specific examples of prompts are as follows:

[1398] "Design a generative AI model to predict wind speed and direction for the next five years using wind data from the past five years. The model should use LSTM, and input data should include date, time, longitude, latitude, wind speed, and wind direction."

[1399] In this way, the system of the present invention provides a concrete means for efficiently generating wind power and realizing a stable energy supply using wireless power transmission technology.

[1400] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1401] Step 1:

[1402] The server collects historical wind data from a weather database.

[1403] Input: Weather database API endpoint

[1404] Specific operation: The server uses Python's requests library to retrieve wind data (date, time, longitude, latitude, wind speed, and direction) for the past five years from the weather database API using an HTTP GET request.

[1405] Output: The acquired data is saved on the server in JSON format.

[1406] Step 2:

[1407] The server normalizes and formats the collected wind data.

[1408] Input: Wind data in JSON format obtained in step 1

[1409] Specific operation: The server uses Python's Pandas library to impute missing values ​​in the acquired dataset and standardize the data.

[1410] Output: A normalized dataset is generated and stored in a PostgreSQL database.

[1411] Step 3:

[1412] The server inputs the collected wind data into a generative AI model to predict future wind flow.

[1413] Input: Normalized wind dataset

[1414] How it works: The server uses the TensorFlow framework to build and train an LSTM model. It then inputs past data into the trained model to predict future wind speed and direction.

[1415] Output: Predicted future wind data (time series data) is generated in JSON format and stored in a PostgreSQL database.

[1416] Step 4:

[1417] The server analyzes the predicted future wind data and identifies the optimal location for installing wind power generation equipment.

[1418] Input: Forecast wind data obtained in step 3

[1419] How it works: The server uses Python's SciPy and NumPy libraries to calculate and score the average wind speed and the least amount of fluctuation, thereby identifying the optimal installation location.

[1420] Output: The longitude and latitude information of the identified installation location is generated in GeoJSON format.

[1421] Step 5:

[1422] Plot the locations where the servers were identified on a map.

[1423] Input: Longitude and latitude information obtained in step 4 (GeoJSON format)

[1424] Specific operation: The server uses a common map API (e.g., Google Maps API) to visually display the identified location on a map.

[1425] Output: Potential installation sites plotted on a map

[1426] Step 6:

[1427] The terminal installs the wind power generation equipment in the optimal installation location designated by the terminal.

[1428] Input: Information on the candidate installation site obtained in Step 5

[1429] Specific operations: The terminal arrives at the site, deploys and installs the wind power generation equipment, adjusts the wireless power transmission equipment, and sets the optimal parameters.

[1430] Output: Installed wind power generation equipment and wireless power transmission equipment

[1431] Step 7:

[1432] The terminal configures the wireless power transmission device and supplies energy wirelessly.

[1433] Input: Installed wind power generation equipment and wireless power transmission equipment

[1434] Specific operation: The terminal configures the wireless power transmission device, monitors the efficiency of energy transmission in real time, and minimizes energy loss.

[1435] Output: Wireless power transmission device that efficiently transmits energy

[1436] Step 8:

[1437] Users can monitor the system's status using a web-based dashboard.

[1438] Input: Operation status data from the server and terminal

[1439] Specific operation: The user accesses the dashboard from a web browser and monitors the system's operating status (power generation amount, power transmission status, system status, etc.) in real time.

[1440] Output: Visual display of system operating status and alert notification when an abnormality occurs

[1441] Through this step, a system will be created that efficiently generates wind power and provides a stable energy supply through wireless power transmission technology.

[1442] (Application example 1)

[1443] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1444] Conventional wind power generation systems have difficulty ensuring efficient and stable energy supply, and have had the problem of significant energy loss, especially when installation locations are inappropriate. Furthermore, due to a lack of systems for monitoring energy supply conditions and equipment operation status in real time, there have been many cases where abnormality detection was delayed. This invention aims to solve these issues by predicting future wind flow, identifying optimal installation locations, and monitoring operation status in real time.

[1445] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1446] In this invention, the server includes means for collecting past data from a weather database, means for inputting the collected data into a generation AI to predict future wind flow, means for analyzing the predicted future data and identifying the location where wind will be most stable, means for installing energy conversion equipment in the identified location and supplying energy using wireless power transmission technology, and means for monitoring the performance of the energy supply system and robot in real time and detecting abnormalities, thereby enabling improved efficiency and stability of energy supply and real-time abnormality detection.

[1447] A "weather database" is a database that stores past weather data and allows it to be searched and retrieved.

[1448] "Generative AI" is a type of artificial intelligence that predicts future situations based on collected data, and is achieved using techniques such as deep learning models.

[1449] A "deep learning model" is a machine learning method that uses a multi-layer neural network to learn the characteristics of data and make predictions and classifications.

[1450] "Means for predicting wind flow" refers to a method or device for predicting future wind speed and direction using meteorological data as input.

[1451] An "energy conversion facility" is a device that converts natural energy into a usable form of energy, such as electricity.

[1452] "Wireless power transmission technology" is a technology for transmitting power without using wires, and utilizes electromagnetic waves and electromagnetic induction.

[1453] An "energy supply system" is a system for comprehensively managing the generation, conversion, and supply of energy.

[1454] "Robot performance" is a measure of how efficiently and accurately a robot performs a specified task.

[1455] "Real-time monitoring" means constantly monitoring the system status and performance, and ensuring that the latest data is always available.

[1456] "Means for detecting anomalies" refers to methods or devices for detecting situations that deviate from the normal operating state of a system.

[1457] The present invention relates to a system for providing efficient utilization of wind energy and real-time monitoring capabilities, and comprises the following elements:

[1458] System Configuration

[1459] The system mainly consists of three components: a server, a terminal, and a user.

[1460] server

[1461] The server has the following functions:

[1462] 1. Meteorological data collection:

[1463] The server collects historical wind data from a weather database via the Internet. This data includes date, time, longitude, latitude, wind speed, and wind direction. The data is retrieved through a REST API and integrated into a database on the server. For example, the server retrieves historical wind data using a weather data API (e.g., OpenWeather) and stores it in a local database.

[1464] 2. Generative AI wind prediction:

[1465] The collected wind data is input into a generative AI to predict future wind flow. A deep learning model (such as an LSTM model) is used for this purpose. For example, the server uses an LSTM model to learn from past wind data and predict wind speed and direction for the next few years.

[1466] 3. Identify the installation location:

[1467] The predicted wind data is analyzed to identify the optimal location for wind energy conversion equipment based on average wind speed and wind stability. The prediction results are saved as time-series data and used for further analysis. Specifically, the coordinates with high wind speed and little fluctuation are calculated and plotted on a map.

[1468] Terminal

[1469] The terminal has the following features:

[1470] 1. Installation of energy conversion equipment:

[1471] Install wind energy conversion equipment in the identified optimal locations.

[1472] 2. Application of wireless power transmission technology:

[1473] The converted energy is supplied using wireless power transmission technology. This technology adjusts parameters to transmit power efficiently and minimizes energy loss. For example, it places energy conversion devices in optimal settings to increase power transmission efficiency.

[1474] User

[1475] The user has the following capabilities:

[1476] 1. Real-time monitoring:

[1477] A web-based dashboard allows users to monitor the system's operating status in real time, allowing them to check the amount of power generated and the stability of power transmission, and receive alerts if an abnormality occurs.Specifically, the system has the ability to monitor power generation and notify users when an abnormality is detected.

[1478] Prompt Sentence Examples

[1479] The generative AI model that implements this system uses the following prompt:

[1480] "To identify the optimal location for installing wind power generation facilities, input wind data from the past five years in Tokyo into an LSTM model and predict wind speed and direction for the next five years. Also, implement an algorithm that selects the location with the optimal wind speed and direction based on the prediction results."

[1481] This invention makes it possible to improve the efficiency and stability of energy supply and to detect abnormalities in real time.

[1482] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1483] Step 1: Collect data

[1484] The server collects historical wind data from a weather database. Specifically, it retrieves data including wind speed, wind direction, and date and time information at a specified longitude and latitude. It uses an API key and specified location information as input. It retrieves this data via a REST API and stores it in a local database. The output is the historical wind data stored in the database.

[1485] Step 2: Preprocessing the data

[1486] The server preprocesses the collected wind data. Specifically, it complements missing data and normalizes (scales) it. The input is the raw data collected in step 1, and methods such as linear interpolation are used to complement the missing data. Normalization involves scaling based on the mean and standard deviation. The output is the preprocessed wind data.

[1487] Step 3: Generative AI predicts wind

[1488] The server uses the preprocessed data to input it into a generative AI model (LSTM model) to predict future wind data. The input is preprocessed wind speed and direction data, and a deep learning model is used to learn and predict the data. Specifically, it predicts wind speed and direction for the next few years as a time series based on past data. The output is predicted future wind data.

[1489] Step 4: Identify the optimal location

[1490] The server analyzes the predicted wind data to identify optimal installation locations. It uses predicted future wind data as input, applying an algorithm that evaluates the stability of average wind speed and direction and calculates locations with minimal fluctuations. The output is the coordinates of the optimal wind energy conversion facility installation location.

[1491] Step 5: Installing the energy conversion equipment

[1492] The terminal installs the wind energy conversion equipment at the specified installation location. The input is the coordinate information of the optimal installation location received from the server. The specific operation is to perform the physical installation work and configure the energy conversion equipment so that it functions properly. The output is the installed energy conversion equipment.

[1493] Step 6: Applying wireless power transmission technology

[1494] The terminal transmits the converted energy using wireless power transmission technology. The input is the converted energy and optimal transmission parameters. Specifically, the parameters of the wireless power transmission device are adjusted to transmit power while minimizing energy loss. The output is the efficiently transmitted energy.

[1495] Step 7: Real-time system monitoring

[1496] Users can monitor the performance of the energy supply system and robots in real time through a web-based dashboard. The input is real-time data sent from the server and terminals. Specifically, it displays power generation amount, power transmission efficiency, anomaly detection information, etc. in real time, and issues an alert if an abnormality is detected. The output is status information of the monitored system and an alert to the user.

[1497] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1498] System Configuration

[1499] The system of the present invention comprises the following main components:

[1500] 1. Server

[1501] Ability to collect historical wind data from weather databases

[1502] A function that uses generative AI to predict future wind flow

[1503] A function that analyzes predicted wind data and identifies optimal locations for wind power generation facilities

[1504] 2. Terminal

[1505] Ability to install and operate wind power generation facilities

[1506] A function that supplies energy using wireless power transmission technology

[1507] 3. Users

[1508] Ability to use a dashboard to monitor the system's operating status

[1509] 4. Emotion Engine

[1510] Ability to recognize user emotions and collect emotional data

[1511] Ability to analyze emotional data and adjust the system's operating interface and energy supply performance

[1512] A function that detects system anomalies early based on emotional data and notifies users

[1513] Program processing flow

[1514] Data collection and prediction

[1515] The server first collects historical wind data from a weather database. This data includes details such as date, time, longitude, latitude, wind speed, and wind direction. The data is retrieved through a REST API and stored in a database on the server. The collected data is then preprocessed by filling in missing values ​​and normalizing the data.

[1516] The server then inputs the collected data into a generation AI to predict future wind flow. The generation AI uses a deep learning model (such as an LSTM model). It learns from past wind data and predicts wind speed and direction for the next five years. The prediction results are recorded on the server and used for subsequent analysis.

[1517] Identifying installation locations and wireless power transmission

[1518] The server analyzes the predicted future wind data and identifies the optimal location for installing wind power generation equipment. It selects the optimal location based on factors such as average wind speed and the smallest fluctuations, and identifies its specific coordinates. The identified coordinates are then sent to the device.

[1519] The terminal installs the wind power generation equipment based on the installation location information received from the server. This includes assembling the wind turbines and preparing the infrastructure. Energy is then supplied using wireless power transmission technology. The terminal configures and adjusts the wireless power transmission equipment, setting parameters to minimize energy loss.

[1520] System monitoring and user emotion recognition

[1521] Users can monitor the system's operation in real time using a web-based dashboard, which displays the amount of power generated and the stability of power transmission, and sends alerts if any abnormalities occur.

[1522] The emotion engine recognizes the user's emotions and collects emotional data. This data is used to adjust the system's operation interface and energy supply performance. For example, if the user is feeling stressed, it will provide a simpler operation interface. It also has the ability to detect system abnormalities early on based on the emotional data and notify the user.

[1523] Specific examples

[1524] 1. Data collection and prediction

[1525] The server uses the OpenWeather API to collect wind data from the past five years and store it in a local database.

[1526] The server uses an LSTM model to predict wind speed and direction for the next five years based on the collected wind data.

[1527] 2. Identifying the installation location and wireless power transmission

[1528] The server analyzes the forecast data and identifies the North Sea coastal region as the best location for installation.

[1529] The terminal installs a wind turbine at the specified location, sets up a wireless power transmission device, and starts transmitting energy.

[1530] 3. System Monitoring and Emotion Recognition

[1531] Users can check the amount of power generated and the stability of transmission in real time through the dashboard.

[1532] The emotion engine recognizes the user's emotions and simplifies the operation interface when the user is feeling stressed.

[1533] The emotion engine detects anomalies and sends an alert to the user.

[1534] This system not only efficiently generates wind power and uses wireless power transmission technology to ensure a stable energy supply, but also aims to improve the user experience by taking into account the user's emotions.

[1535] The processing flow will be explained below.

[1536] System Configuration

[1537] The system of the present invention comprises the following main components:

[1538] 1. Server

[1539] Ability to collect historical wind data from weather databases

[1540] A function that uses generative AI to predict future wind flow

[1541] A function that analyzes predicted wind data and identifies optimal locations for wind power generation facilities

[1542] 2. Terminal

[1543] Ability to install and operate wind power generation facilities

[1544] A function that supplies energy using wireless power transmission technology

[1545] 3. Users

[1546] Ability to use a dashboard to monitor the system's operating status

[1547] 4. Emotion Engine

[1548] Ability to recognize user emotions and collect emotional data

[1549] Ability to analyze emotional data and adjust the system's operating interface and energy supply performance

[1550] A function that detects system anomalies early based on emotional data and notifies users

[1551] Program processing flow

[1552] Step 1:

[1553] The server collects past wind data from a weather database. Specifically, it uses OpenWeather and NOAA APIs to obtain wind data from the past five years, collecting date, time, longitude, latitude, wind speed, wind direction, etc. The collected data is stored in a database on the server.

[1554] Step 2:

[1555] The server preprocesses the collected data, specifically by imputing missing values ​​and normalizing the data, converting it into a format that allows subsequent AI models to efficiently learn from the data.

[1556] Step 3:

[1557] The server inputs the preprocessed data into the generation AI to predict future wind flow. The generation AI uses a deep learning model (such as an LSTM model) to predict future wind speed and direction from past wind data. The prediction results are recorded on the server.

[1558] Step 4:

[1559] The server analyzes the predicted future wind data and identifies the optimal location for installing wind power generation equipment. Specifically, it selects the optimal installation location based on indicators such as average wind speed and minimal fluctuation. The identified coordinates are sent to the terminal.

[1560] Step 5:

[1561] The terminal installs the wind power generation equipment based on the installation location information received from the server. The installation work includes assembling and installing the wind turbines and preparing the infrastructure for their operation.

[1562] Step 6:

[1563] The device transmits energy using wireless power transmission technology. Specifically, it configures the wireless power transmission device and adjusts parameters to transmit power efficiently, thereby minimizing energy loss.

[1564] Step 7:

[1565] Users can monitor the operation of the wind power generation system and wireless power transmission system in real time using a web-based dashboard that displays the amount of power generated and the stability of the power transmission, and sends alerts if any abnormalities occur.

[1566] Step 8:

[1567] The emotion engine recognizes the user's emotions and collects emotional data. This data is used to adjust the system's operation interface and energy supply performance. For example, if the user is feeling stressed, a simpler operation interface will be provided. The system also has a function to detect system abnormalities early based on the emotional data and notify the user.

[1568] Examples:

[1569] 1. Data collection and prediction

[1570] The server uses the OpenWeather API to collect wind data from the past five years and store it in a local database.

[1571] The server uses an LSTM model to predict wind speed and direction for the next five years based on the collected wind data.

[1572] 2. Identifying the installation location and wireless power transmission

[1573] The server analyzes the forecast data and identifies the North Sea coastal region as the best location for installation.

[1574] The terminal installs a wind turbine at the specified location, sets up a wireless power transmission device, and starts transmitting energy.

[1575] 3. System Monitoring and Emotion Recognition

[1576] Users can check the amount of power generated and the stability of transmission in real time through the dashboard.

[1577] The emotion engine recognizes the user's emotions and simplifies the operation interface when the user is feeling stressed.

[1578] The emotion engine detects anomalies and sends an alert to the user.

[1579] This system not only efficiently generates wind power and uses wireless power transmission technology to ensure a stable energy supply, but also aims to improve the user experience by taking into account the user's emotions.

[1580] Example 2

[1581] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1582] Conventional wind power generation systems have problems such as difficulty in selecting installation locations and optimizing power generation efficiency, resulting in high energy loss. Furthermore, measures to improve the user experience by taking user emotions into consideration have not been taken. Therefore, there is a need to simultaneously achieve improved power generation efficiency and user convenience.

[1583] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1584] In this invention, the server includes a means for collecting past wind data from a weather database, a means for inputting the collected wind data into a generation AI to predict future wind flow, and a means for analyzing the predicted future wind data to identify the location where wind will be most stable. This enables the identification of the optimal installation location. The server also includes a means for installing wind power generation equipment at the identified location and supplying energy using wireless power transmission technology, a means for providing a user interface for monitoring the system's operating status, a means for recognizing user emotions and adjusting the system's operation interface based on the collected emotion data, and a means for early detection and notification of system abnormalities. This enables improved power generation efficiency, a more stable energy supply, and an improved user experience.

[1585] A "weather database" is a data management system that has the function of collecting, storing, and providing past and current weather data.

[1586] "Wind data" refers to various data related to wind, such as wind speed, wind direction, longitude, latitude, date and time, etc.

[1587] "Generative AI" is a machine learning model that uses artificial intelligence techniques to predict future data from past data.

[1588] "Future wind flow" refers to future weather conditions, such as wind speed and direction, predicted using generative AI.

[1589] "Wireless power transmission technology" is a technology that transmits electricity without using physical wiring.

[1590] A "wind power generation facility" is a device that converts the kinetic energy of wind into electricity.

[1591] A "user interface" refers to the screens and operating means that allow a user to interact with a system or device.

[1592] An "emotion engine" is a system that can recognize the user's emotional state and provide appropriate feedback and responses based on that.

[1593] "Emotion data" refers to data that indicates the user's emotional state.

[1594] "System operational status" refers to information that indicates the current operating state and performance of the system.

[1595] "Anomaly detection" is the process of detecting behaviors or conditions that deviate from a system's normal operation.

[1596] The present invention provides a system for optimizing the installation location of wind power generation facilities and improving energy efficiency and user experience during operation of the facilities. Specific embodiments of this system will be described below.

[1597] Data collection and prediction

[1598] The server first collects historical wind data from a weather database. This wind data includes detailed information such as date, time, longitude, latitude, wind speed, and wind direction. Specifically, the server uses the OpenWeather API to obtain the historical wind data and stores it in a local database on the server. The collected data is retrieved from a REST API using the Python requests library, and preprocessing such as filling in missing values ​​and normalizing the data is performed using the Pandas library. The preprocessed data obtained in this way is input into the generative AI model.

[1599] The server then inputs the preprocessed data into a generative AI model to predict future wind flow. The generative AI uses an LSTM model powered by the TensorFlow library. Based on past wind data, the server predicts wind speed and direction for the next five years and records the results.

[1600] Identifying installation locations and wireless power transmission

[1601] The server analyzes the predicted future wind data to identify the optimal location for wind power generation facilities. This analysis uses Python machine learning libraries (e.g., scikit-learn) to evaluate the average wind speed and the smallest fluctuations. As a result of the analysis, a specific region (e.g., the North Sea coast) is selected as the optimal location. This information is sent to the device.

[1602] The terminal then installs the wind power generation equipment based on the installation location information it receives. The installation process includes using GPS to transport materials to the specified coordinates, assembling the wind turbines, and preparing the infrastructure. After the wind power generation equipment is installed, the terminal configures the wireless power transmission equipment and begins transmitting energy. Using wireless power transmission technology (e.g., WiTricity), it adjusts parameters for efficient energy transmission and implements settings that minimize energy loss.

[1603] System Monitoring and Emotion Recognition

[1604] Users can monitor the system's operating status in real time through a web-based dashboard. The dashboard displays information such as power generation volume, power transmission stability, and energy consumption status. Users can receive real-time notifications if an abnormality occurs. The dashboard is built using front-end technologies such as React and Angular, and back-end frameworks such as Node.js or Python (e.g., Flask and Django).

[1605] The emotion engine recognizes the user's emotions and collects emotional data. Data collected using a facial recognition camera and biometric sensors is analyzed by an emotion recognition algorithm using the Emotion API. The emotional data is used to adjust the system's operating interface and energy supply performance. For example, if the user is feeling stressed, the emotion engine will simplify the operating interface. The emotional data can also be used to detect system abnormalities early and send an alert to the user.

[1606] Specific examples

[1607] 1. Data collection and prediction

[1608] The server uses the OpenWeather API to collect wind data from the past five years and stores it in a local database.The server then uses an LSTM model using TensorFlow to predict wind speed and direction for the next five years.

[1609] 2. Identifying the installation location and wireless power transmission

[1610] The server analyzes the forecast data and identifies the North Sea coastal area as the optimal location for installation. The device then installs the wind power generation equipment in the identified location, configures the wireless power transmission equipment, and begins transmitting energy.

[1611] 3. System Monitoring and Emotion Recognition

[1612] Users can check the amount of power generated and the stability of power transmission in real time through a dashboard. The emotion engine recognizes the user's emotions and simplifies the operation interface if the user is feeling stressed. The emotion engine also detects abnormalities and sends alerts to the user.

[1613] Prompt Sentence Examples

[1614] "Collect wind data from the past five years and predict wind speed and direction for the next five years."

[1615] "Install the wind power generation facility at the specified coordinates, set up the wireless power transmission device, and start supplying energy."

[1616] "Recognize users' emotions and adjust the control interface if they are feeling stressed."

[1617] In this way, the present invention not only efficiently generates wind power and realizes a stable energy supply using wireless power transmission technology, but also provides adjustment of the operation interface and abnormality detection based on the user's emotions.

[1618] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1619] Step 1:

[1620] The server collects historical wind data from a weather database. This process uses the OpenWeather API. It sends an API request as input and receives the resulting wind data in JSON format as output. This data includes the date, time, longitude, latitude, wind speed, and wind direction. Specifically, it uses the Python requests library to make an API request and stores the obtained data in a local database.

[1621] Step 2:

[1622] The server preprocesses the collected wind data. It uses the wind data obtained in step 1 as input and obtains preprocessed data as output. Preprocessing includes missing value imputation, data normalization, and outlier handling. Specifically, it uses the Pandas library to create a data frame, impute missing values, and normalize the data.

[1623] Step 3:

[1624] The server inputs the preprocessed data into a generative AI model to predict future wind flow. It uses the preprocessed data as input and obtains forecast data for wind speed and direction for the next five years as output. The generative AI model uses an LSTM model using TensorFlow. Specifically, it trains the LSTM model, obtains prediction results, and stores them on the server.

[1625] Step 4:

[1626] The server analyzes the predicted future wind data and identifies the optimal location for wind power generation equipment. It uses the predicted data as input and obtains the specific coordinates of the optimal installation location as output. The analysis uses Python's scikit-learn library to evaluate the average wind speed and the smallness of fluctuation. Specifically, it runs a clustering algorithm to identify the optimal area.

[1627] Step 5:

[1628] The server sends the identified coordinate information to the terminal. It uses the coordinates of the optimal installation location as input and transmits the information to the terminal as output. This process uses a REST API and a messaging queue system. Specifically, it uses the Python requests library to send the installation location information to the terminal.

[1629] Step 6:

[1630] The terminal installs the wind power generation equipment based on the received installation location information. It uses the coordinates of the installation location as input and obtains the assembled wind power generation equipment as output. Specifically, it uses GPS to transport materials to the specified coordinates and assembles the wind power generator.

[1631] Step 7:

[1632] After the wind power generation equipment is installed, the terminal configures the wireless power transmission equipment and begins transmitting energy. It uses information about the installed wind power generation equipment as input and operates the wireless power transmission system as output. It uses wireless power transmission technology (e.g., WiTricity) to adjust parameters for efficient energy transmission. Specifically, it adjusts the position and output of the wireless power transmission equipment to set it up to minimize energy loss.

[1633] Step 8:

[1634] Users monitor the system's operating status in real time using a web-based dashboard. Various system operating data is used as input, and real-time monitoring information is obtained as output. The dashboard displays information such as power generation volume, power transmission stability, and energy consumption status. In terms of specific operation, it uses front-end technologies such as React and Angular, and the back-end is built using Node.js or Python frameworks (e.g., Flask, Django).

[1635] Step 9:

[1636] The emotion engine recognizes the user's emotions and collects emotional data. It uses data indicating the user's emotional state as input and obtains analyzed emotional information as output. Emotion data is collected using a facial recognition camera and biometric sensors and analyzed by an emotion recognition algorithm using the Emotion API. Specifically, it sends the collected emotional data to a server and obtains the analysis results.

[1637] Step 10:

[1638] The emotion engine adjusts the operation interface and detects anomalies based on the analyzed emotion data. It uses emotion data as input and obtains an adjusted interface and anomaly notification as output. For example, if the user is feeling stressed, it simplifies the operation interface. It also detects system anomalies early based on emotion data and sends an alert to the user. Specifically, if stress is detected, the operation interface settings are changed, and if an anomaly is detected, it is reflected through an API that notifies the user.

[1639] (Application example 2)

[1640] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1641] Conventional wind power generation systems are limited to energy generation and supply, and do not address in-store energy management or improving customer satisfaction. In particular, there was no way to optimize the use of wind energy, utilize wireless power transmission technology, or collect customer sentiment data and use it to improve store operations. Therefore, there was a need to improve energy efficiency and optimize the customer experience.

[1642] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting past wind data from a weather database, means for inputting the collected wind data into a generation AI and predicting future wind flow, means for analyzing the predicted future wind data and identifying the location where wind will be most stable, means for installing wind power generation equipment in the identified location and supplying energy using wireless power transmission technology, means for monitoring the store's energy consumption in real time, and means for collecting and managing customer emotion data from cameras and devices within the store. This enables optimal use of wind energy, real-time management of energy supply, and optimization of store operations based on user emotion data.

[1643] A "weather database" is a database that stores and provides information about past and present climates.

[1644] "Wind data" refers to information about wind, such as wind speed, wind direction, and frequency of occurrence.

[1645] "Generative AI" is artificial intelligence that has the ability to analyze large amounts of data and generate new information.

[1646] A "deep learning model" is an algorithm that uses a multi-layer neural network to learn features from data and perform predictions and classifications.

[1647] "Wireless power transmission technology" is a technology that transmits power wirelessly and makes it available to the receiving end.

[1648] A "wind power generation facility" is a device that uses wind energy to generate electricity.

[1649] "Energy consumption status" is information indicating the energy usage status in a specific location or time period.

[1650] "Emotion data" is information that quantifies or categorizes a person's emotional state.

[1651] "Means for monitoring energy consumption status of a store in real time" refers to a system for instantly observing and recording electricity usage status within a store.

[1652] "Means for collecting and managing customer emotional data from cameras and devices within the store" refers to a system that observes customers' facial expressions and behavior, analyzes their emotional state, and records and manages it.

[1653] The system for implementing this invention comprises the following main elements: a server, a terminal, and a user. The roles of each and their specific operating methods will be described in detail below.

[1654] Server Features

[1655] The server first collects historical wind data from a weather database. In this case, it uses the OpenWeather API to obtain wind data for the past five years. This data includes date, time, longitude, latitude, wind speed, and wind direction. The collected data is then stored in a local database after missing values ​​are filled and data normalization is performed.

[1656] The server then uses a generative AI to predict future wind flow. The generative AI uses a deep learning model (such as an LSTM model) that learns from past wind data and predicts wind speed and direction for the next five years. The prediction results are recorded on the server and used for subsequent analysis.

[1657] The server also analyzes the predicted future wind data to identify the location where wind will be most stable. The server selects the optimal location based on factors such as average wind speed and minimal fluctuation, and identifies its specific coordinates. The identified coordinates are then sent to the device.

[1658] Device Features

[1659] The terminal installs the wind power generation equipment based on the installation location information received from the server. This involves assembling the wind turbines and preparing the infrastructure. Energy is then supplied using wireless power transmission technology. The wireless power transmission device is configured and adjusted, and parameters are set to minimize energy loss. For example, when supplying energy to a store, wireless power transmission technology ensures efficient energy supply.

[1660] User Roles

[1661] Users can monitor the system's operation in real time using a web-based dashboard, which displays the amount of power generated and the stability of power transmission, and sends alerts if any abnormalities occur.

[1662] Furthermore, customers' emotional data is collected using cameras and devices (e.g., smart glasses and robots) equipped with an emotion engine. The emotional data is used to adjust the system's operation interface and store operation performance. Specifically, if a customer is feeling stressed, an easier operation interface will be provided. Emotional data can also be used to detect system anomalies early on and notify users to prevent problems before they occur.

[1663] Specific examples

[1664] 1. Data collection and prediction

[1665] The server uses the OpenWeather API to collect wind data from the past five years and stores it in a local database.

[1666] The server uses an LSTM model to predict wind speed and direction for the next five years based on the collected wind data.

[1667] 2. Identifying the installation location and wireless power transmission

[1668] The server analyzes the predictive data and identifies the optimal installation location.

[1669] The terminal installs a wind turbine at the specified location, sets up a wireless power transmission device, and starts transmitting energy.

[1670] 3. System Monitoring and Emotion Recognition

[1671] Users can check the amount of power generated and the stability of transmission in real time through the dashboard.

[1672] The emotion engine recognizes the user's emotions and simplifies the operation interface when the user is feeling stressed.

[1673] The emotion engine detects anomalies and sends an alert to the user.

[1674] Examples of prompt statements

[1675] "Based on wind data from the past five years, predict wind speed and direction for the next five years."

[1676] By inputting this prompt into a generative AI model, future wind flow can be predicted and optimal wind energy utilization can be achieved, thereby improving both energy efficiency and customer satisfaction in the store.

[1677] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1678] Step 1:

[1679] The server collects historical wind data from a weather database. Specifically, the server uses the OpenWeather API to retrieve wind speed, direction, and date and time information for the past five years for a specified latitude and longitude. The input required is an API key and latitude and longitude, and the output is JSON-formatted data containing wind data. The server stores the data in a local database.

[1680] Step 2:

[1681] The server preprocesses the collected wind data. Specifically, it fills in missing values ​​and normalizes the data. The input is wind data, and the output is preprocessed wind data. The server prepares the data and creates a dataset to input into the generative AI model.

[1682] Step 3:

[1683] The server uses a generative AI model to predict future wind flow. Specifically, it inputs preprocessed wind data into the LSTM model to obtain predicted wind speed and direction for the next few years. The input requires preprocessed data, and the output is data showing future wind speed and direction. The server stores the generated data in a local database.

[1684] Step 4:

[1685] The server analyzes the predicted future wind data and identifies the optimal location for installing wind power generation equipment. Specifically, it determines the optimal installation location based on parameters such as average wind speed and the lack of fluctuation in wind speed. The input required is the predicted data, and the output is the coordinate information of the optimal installation location. The server sends this information to the terminal.

[1686] Step 5:

[1687] The terminal installs the wind power generation equipment based on the installation location information received from the server. Specifically, the wind power generator is assembled, infrastructure is prepared, and the wireless power transmission device is set up. The installation location information is required as input, and the installed wind power generation equipment and wireless power transmission device are the output.

[1688] Step 6:

[1689] The device supplies energy using wireless power transmission technology. Specifically, it configures and adjusts the wireless power transmission device and sets parameters to minimize energy loss. Energy generated by wind power is required as input, and the output is wirelessly transmitted electricity.

[1690] Step 7:

[1691] Users monitor the system's operational status using a web-based dashboard, specifically checking data such as power generation volume and transmission stability in real time. Data from the monitoring system is required as input, and the output is dashboard information displayed to the user.

[1692] Step 8:

[1693] Users collect and manage customer emotion data from cameras and devices in the store. Specifically, an emotion engine is used to analyze facial expressions and behaviors and quantify or categorize the emotional state. Image data from cameras and devices is required as input, and the output is analyzed emotion data.

[1694] Step 9:

[1695] The system adjusts the operation interface and store operations based on the emotional data collected from users. Specifically, the interface is simplified or the operation method is changed depending on specific situations, such as when a customer is feeling stressed. Emotional data is required as input, and the output is an adjusted operation interface and store operation method.

[1696] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1697] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1698] 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 the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1699] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1700] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1701] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1702] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1703] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1704] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1705] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1706] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1707] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1708] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1709] 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.

[1710] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1711] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1712] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1713] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1714] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1715] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1716] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1717] The following is further disclosed regarding the above embodiment.

[1718] (Claim 1)

[1719] means for collecting historical wind data from a meteorological database;

[1720] The collected wind data is input into the AI ​​generator to predict future wind flow.

[1721] A means of analyzing predicted future wind data to identify locations with the most stable wind conditions;

[1722] A means for installing wind power generation facilities at the specified locations and supplying energy using wireless power transmission technology;

[1723] A system including:

[1724] (Claim 2)

[1725] The system of claim 1, wherein the generating AI is a deep learning model and optimizes installation locations using predicted wind data.

[1726] (Claim 3)

[1727] 10. The system of claim 1, wherein the system uses wireless power transmission technology for configuration and adjustment to minimize energy loss.

[1728] "Example 1"

[1729] (Claim 1)

[1730] means for collecting historical wind data from a meteorological database;

[1731] A means of inputting collected wind data into a generative AI model to predict future wind flow,

[1732] A means of analyzing predicted future wind data to identify optimal locations for installing wind turbines; and

[1733] A means for installing wind power generation facilities at the specified locations and supplying energy using wireless power transmission technology;

[1734] A means for providing a dashboard for monitoring the operational status of the installed wind power generation facility and the wireless power transmission system;

[1735] A system including:

[1736] (Claim 2)

[1737] 2. The system of claim 1, wherein the generative AI model is a deep learning model that learns wind speed and direction patterns to predict future wind data.

[1738] (Claim 3)

[1739] 10. The system of claim 1, wherein the system uses wireless power transmission technology for configuration and adjustment to minimize energy loss.

[1740] "Application Example 1"

[1741] (Claim 1)

[1742] a means for collecting historical data from a meteorological database;

[1743] The collected data is input into a generation AI to predict future wind flow,

[1744] A means of analyzing predicted future data to identify the most stable wind locations;

[1745] A means for installing energy conversion equipment at the specified location and supplying energy using wireless power transmission technology;

[1746] A means of monitoring the performance of the energy supply system and robots in real time and detecting anomalies;

[1747] A system including:

[1748] (Claim 2)

[1749] The system of claim 1, wherein the generating AI is a deep learning model and uses predicted data to optimize installation locations.

[1750] (Claim 3)

[1751] 10. The system of claim 1, wherein the system uses wireless power transmission technology for configuration and adjustment to minimize energy loss.

[1752] "Example 2: Combining Emotion Engines"

[1753] (Claim 1)

[1754] means for collecting historical wind data from a meteorological database;

[1755] The collected wind data is input into the AI ​​generator to predict future wind flow.

[1756] A means of analyzing predicted future wind data to identify locations with the most stable wind conditions;

[1757] A means for installing wind power generation facilities at the specified locations and supplying energy using wireless power transmission technology;

[1758] means for providing a user interface for monitoring the operation status of the system;

[1759] means for recognizing the user's emotions and adjusting the system's operation interface based on the collected emotion data;

[1760] A means of detecting and notifying system abnormalities early on;

[1761] A system including:

[1762] (Claim 2)

[1763] The system of claim 1, wherein the generating AI is a deep learning model and optimizes installation locations using predicted wind data.

[1764] (Claim 3)

[1765] 10. The system of claim 1, wherein the system uses wireless power transmission technology for configuration and adjustment to minimize energy loss.

[1766] (Claim 4)

[1767] 10. The system of claim 1, further comprising an emotion engine for recognizing user emotions, and means for adjusting the operation interface and energy supply performance using the emotion data.

[1768] "Application example 2 when combining emotion engines"

[1769] (Claim 1)

[1770] means for collecting historical wind data from a meteorological database;

[1771] The collected wind data is input into the AI ​​generator to predict future wind flow.

[1772] A means of analyzing predicted future wind data to identify locations with the most stable wind conditions;

[1773] A means for installing wind power generation facilities at the specified locations and supplying energy using wireless power transmission technology;

[1774] A means of monitoring the store's energy consumption in real time;

[1775] A means to collect and manage customer emotion data from cameras and devices in the store;

[1776] A system including:

[1777] (Claim 2)

[1778] The system of claim 1, wherein the generating AI is a deep learning model and optimizes installation locations using predicted wind data.

[1779] (Claim 3)

[1780] 10. The system of claim 1, wherein the system uses wireless power transmission technology for configuration and adjustment to minimize energy loss. [Explanation of symbols]

[1781] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for collecting historical wind data from a meteorological database; The collected wind data is input into the AI ​​generator to predict future wind flow. A means of analyzing predicted future wind data to identify locations with the most stable wind conditions; A means for installing wind power generation facilities at the specified locations and supplying energy using wireless power transmission technology; A system including:

2. The system of claim 1, wherein the generating AI is a deep learning model and optimizes installation locations using predicted wind data.

3. 10. The system of claim 1, wherein wireless power transmission technology is used for configuration and adjustment to minimize energy loss.

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