system

Abstract

We provide the system. [Solution] A means for collecting energy usage data over time and storing it in a database, A means of training an algorithm that analyzes past energy usage patterns to forecast demand, A means of detecting abnormal values ​​and automatically sending notifications, A means of predicting renewable energy supply taking external factors into consideration, A means of analyzing electricity market data to plan the optimal electricity purchase, An energy management system that includes various means.

Description

【Technical Field】 【0001】 The technology of the present disclosure relates to a system. 【Background Art】 【0002】 Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. 【Prior Art Documents】 【Patent Documents】 【0003】 【Patent Document 1】 Japanese Patent Application Laid-Open No. 2022-180282 【Summary of the Invention】 【Problems to be Solved by the Invention】 【0004】 In homes and offices, it is important to suppress wasteful energy consumption and effectively manage energy resources. However, in conventional systems, it has been difficult to grasp the real-time energy usage status and accurately predict future energy demand. Also, it has been a problem to achieve cost-effective power purchase in the power market while coping with the variability of renewable energy. Furthermore, it has been difficult to provide energy-saving advice according to different energy usage patterns for each user. 【Means for Solving the Problems】 【0005】 This invention provides a system for collecting energy usage data over time and storing it in a database. Furthermore, it serves as a means for training algorithms to forecast demand by analyzing past energy usage patterns. This makes it possible to immediately detect and notify of anomalies. It also includes means for predicting the supply of renewable energy considering external factors, and incorporates means for formulating an optimal electricity purchase plan by analyzing electricity market data. Moreover, it solves problems by providing individual energy-saving suggestions based on each user's energy usage patterns and a visualization dashboard of energy usage data to support decision-making. 【0006】 "Energy usage data" refers to information that shows the amount and patterns of electricity consumption in homes and offices. 【0007】 A "database" is a recording medium that systematically organizes collected information, making it possible to search and process it. 【0008】 "Demand forecasting" is the process of predicting future energy consumption based on statistics and historical data. 【0009】 An "algorithm" refers to a series of computational steps or methods for solving a specific problem. 【0010】 An "outlier" refers to a value that deviates significantly from normal measurements, and in the context of energy consumption, it indicates an unexpected amount of usage. 【0011】 "Renewable energy" refers to sustainable energy sources that exist in nature and are replenished over time. 【0012】 The "electricity market" is a market where the supply and demand for electricity are traded, and prices fluctuate according to supply and demand. 【0013】 "Individualized energy-saving suggestions" refer to specific advice for efficient energy use provided based on the energy usage patterns of a particular home or office. 【0014】 A "visualization dashboard" is a tool that helps users quickly understand information and make decisions by presenting data in an easy-to-understand visual format. [Brief explanation of the drawing] 【0015】 [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when combined with an emotion engine. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when combined with an emotion engine. 【Embodiments for Carrying out the Invention】 【0016】 Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings. 【0017】 First, the terms used in the following description will be explained. 【0018】 In the following embodiments, a processor with a reference numeral (hereinafter simply referred to as "processor") may be one arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be one type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like. 【0019】 In the following embodiments, a RAM (Random Access Memory) with a reference numeral is a memory in which information is temporarily stored and is used as a work memory by the processor. 【0020】 In the following embodiments, a storage with a reference numeral is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like. 【0021】 In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark). 【0022】 In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or." 【0023】 [First Embodiment] 【0024】 Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment. 【0025】 As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server. 【0026】 The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network). 【0027】 The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52. 【0028】 The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input. 【0029】 The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor. 【0030】 Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54. 【0031】 Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14. 【0032】 As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30. 【0033】 The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. 【0034】 In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48. 【0035】 Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal". 【0036】 The energy management system of this invention mainly consists of three components: a server, a terminal, and a user. By combining these components, optimal energy management is achieved. 【0037】 System Overview 【0038】 server: 【0039】 The server is the core component of the system. It receives energy usage data transmitted from terminals and stores it in a time-series database. The data is appropriately pre-processed and used for demand forecasting. Based on historical data, the server builds and updates demand forecasting models using machine learning algorithms. It also takes immediate action when anomalies are detected and analyzes electricity market data to develop optimal electricity purchase plans. Furthermore, it analyzes energy usage data for each user and provides personalized energy-saving advice. 【0040】 Terminal: 【0041】 The terminal is a device that monitors energy usage in real time and transmits the acquired data to a server. If an abnormal value is detected, it immediately sends a notification to the server. The terminal receives control instructions from the server and controls various devices to optimize energy consumption in homes and offices. This reduces peak consumption and achieves energy savings. 【0042】 User: 【0043】 Users can review their energy consumption data and receive energy-saving advice as needed using an energy usage visualization dashboard provided by the system. The dashboard visually displays past and present energy usage patterns, helping users make informed decisions. 【0044】 Specific example 【0045】 For example, in a certain office building, energy use tends to be concentrated in the mornings and evenings on weekdays. Based on this trend, the server builds a predictive model and instructs terminals to automatically adjust the schedules of equipment and lighting to coincide with off-peak hours on weekdays when electricity costs are lower, instead of investing during weekday peaks. 【0046】 Furthermore, if a home has solar power installed, the server will take weather data into account, increasing energy storage during sunny days and optimizing electricity purchases from other sources on cloudy days. 【0047】 In this way, this system makes it possible to reduce wasteful consumption and lower costs through the prediction and optimization of energy use. 【0048】 The following describes the processing flow. 【0049】 Step 1: 【0050】 The terminal acquires energy usage data in real time from connected smart meters and sensors. After performing limited preprocessing on this data, the terminal periodically sends it to a server. 【0051】 Step 2: 【0052】 The server stores energy usage data received from terminals in a time-series database. This data undergoes a cleaning process to identify outliers and missing values. 【0053】 Step 3: 【0054】 The server trains a machine learning model based on historical energy usage data to predict energy demand. This model is continuously updated with new data to improve the accuracy of the predictions. 【0055】 Step 4: 【0056】 The terminal monitors energy usage in real time and immediately sends an anomaly alert to the server if it detects an abnormality that exceeds a set threshold. 【0057】 Step 5: 【0058】 When the server receives an anomaly alert, it quickly analyzes the anomaly and sends appropriate control instructions back to the terminal. The terminal adjusts its energy consumption according to these instructions. 【0059】 Step 6: 【0060】 The server predicts renewable energy generation based on weather data and optimizes the power supply schedule based on that information. 【0061】 Step 7: 【0062】 Users can check their energy consumption using a visual dashboard provided by the server. This dashboard displays past usage patterns and energy-saving advice. 【0063】 Step 8: 【0064】 The server analyzes trends in the electricity market and develops optimal electricity purchasing plans, thereby supporting users in using energy cost-effectively. 【0065】 (Example 1) 【0066】 Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal." 【0067】 In today's energy consumption environment, there is a need to maximize energy utilization efficiency while reducing energy waste. However, conventional energy management systems have limitations in providing real-time anomaly detection and personalized energy-saving suggestions, and lack flexibility in optimal energy procurement and equipment control. 【0068】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. 【0069】 In this invention, the server includes means for collecting energy consumption information based on the passage of time and storing it in a memory device, means for training a mathematical model that analyzes past energy usage trends and makes demand forecasts, and means for detecting abnormal data and automatically notifying. This makes it possible to efficiently optimize energy use and reduce waste. 【0070】 "Energy consumption information" refers to all data related to energy use detected within the system, including consumption amount, time, and usage trends. 【0071】 A "storage device" is a computer hardware component used to retain data for extended periods, primarily responsible for storing time-stamped energy data. 【0072】 A "mathematical model" refers to an algorithm or process that uses mathematical methods to simulate or predict actual energy use. 【0073】 "Abnormal data" refers to data that shows energy usage values ​​or patterns outside the normal range, and which may have a potential impact on the efficiency and safety of the system. 【0074】 "Automatic notification" refers to a function where the system sends information without human intervention based on set conditions or thresholds, informing users and administrators via email or SMS. 【0075】 "External environment" refers to factors that affect energy supply, and includes factors such as weather, temperature, and seasonal variations. 【0076】 "Renewable energy" refers to energy sources that exist in nature and are not depleted by human activities, and includes solar, wind, and hydroelectric power. 【0077】 "Market data" refers to economic information related to the buying and selling of energy, and typically includes electricity prices, supply, and demand. 【0078】 "Household appliances" refer to devices connected to an energy consumption system, and generally include electrical products and devices used to manage power consumption. 【0079】 An "information visualization device" refers to software or a device that displays data in a way that is easy for humans to understand, such as using graphs and charts to show trends and patterns in energy use. 【0080】 The energy management system of the present invention consists of three main components: a server, a terminal, and a user. This promotes efficient energy consumption. 【0081】 The server plays a central role in the system. It receives energy consumption information transmitted by each terminal and first stores it in a database. Specifically, it uses a general-purpose data management system, which is a time-series database. This data is cleaned using Python to remove outliers. Based on historical data, a demand forecasting model is built using a general-purpose mathematical model, a machine learning library. If abnormal data is detected, the system utilizes automated tools to automatically send notifications, ensuring that users receive information immediately. 【0082】 The terminal is connected to energy-consuming devices, collecting data in real time and sending it to a server. If an anomaly is detected in the data, the terminal immediately notifies the server. Based on instructions from the server, the terminal controls the energy devices to optimize energy consumption in homes and offices. 【0083】 Users can check their consumption patterns using an information visualization device. This makes it possible to visualize when energy consumption is high and which devices are using the most energy. A common data display library is used for visualization. Furthermore, users can receive energy-saving advice. 【0084】 Specific example 【0085】 For example, some households tend to have a high concentration of energy consumption during weekday evenings. In response, the server uses a predictive model to send instructions to the terminals to control each device to mitigate peak energy usage. Furthermore, the server generates an electricity purchase plan based on weather information to maximize the supply of renewable energy. 【0086】 Example of a prompt 【0087】 "Based on energy consumption data, please propose an optimal appliance control schedule for the home during off-peak hours." 【0088】 In this way, the entire system can achieve highly efficient energy management. 【0089】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0090】 Step 1: 【0091】 The terminal collects data in real time from energy-consuming devices. It takes energy consumption information from sensors as input. The terminal packages this data into packets and sends them to the server using a secure communication protocol. The output is energy data structured in a format that the server can process. 【0092】 Step 2: 【0093】 The server receives energy data sent from the terminal. The input is time-series energy data sent from the terminal. The server first stores the data in a database. Next, it cleans the data using a Python script to remove missing values ​​and outliers. The output is clean, analyzable energy data. 【0094】 Step 3: 【0095】 The server analyzes historical energy usage patterns using clean energy data. The input is cleaned energy data. The server trains a demand forecasting model using machine learning algorithms. Libraries such as TENSORFLOW® are used in this process. The output is a mathematical model for forecasting future energy demand. 【0096】 Step 4: 【0097】 The server monitors real-time energy data and uses a predictive model to detect anomalies. Inputs are the raw data received in real time and the predictive model generated by the server. When an anomaly occurs that exceeds a set threshold, the system triggers an automatic notification. The output is the sending of the anomaly notification. 【0098】 Step 5: 【0099】 The server optimizes energy supply and demand using external environmental information and market data. Inputs include weather information and electricity market trend data obtained from external APIs. Analysis software generates an optimal energy purchase plan. The output is an action plan for optimizing energy consumption. 【0100】 Step 6: 【0101】 The terminal controls household devices based on instructions from the server. The input is the control signal from the server. The terminal operates smart devices within the home and is optimized to reduce energy use during peak hours. The output is the optimized energy consumption profile. 【0102】 Step 7: 【0103】 Users check their energy usage using an information visualization device. The input is visualization data provided by the server. Based on the information provided by the dashboard, users make decisions about energy consumption. The output is the change or adjustment of behavior based on the decision. 【0104】 This series of processes enables efficient energy management across the entire system, resulting in cost reductions and optimized consumption. 【0105】 (Application Example 1) 【0106】 Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal." 【0107】 In recent years, there has been a growing demand for more efficient energy consumption in urban areas, and integrated energy management, particularly in smart cities, is crucial. Conventional energy management systems are inadequate in addressing the needs of individual users, making it difficult to provide real-time information or individualized energy-saving suggestions. Furthermore, building sustainable cities requires a system that maximizes the efficiency of renewable energy use and enables flexible electricity purchasing strategies tailored to energy demand. 【0108】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. 【0109】 In this invention, the server includes means for collecting energy consumption information over time and storing it in a data storage device, means for enhancing a calculation method for analyzing past energy consumption patterns and forecasting demand, and means for recognizing anomalies and automatically issuing warnings. This makes it possible to identify the different energy consumption patterns of each user and provide optimal energy-saving suggestions in a smart city. Furthermore, the provision of real-time information supports the efficient use of energy and promotes the realization of a sustainable city. 【0110】 "Energy consumption information" refers to data about the amount of energy used by each user and the system as a whole, and is collected over time. 【0111】 A "data storage device" is a system or device for storing collected energy consumption information over a long period of time. 【0112】 A "computational method" refers to an algorithm or analytical process used to predict energy consumption patterns using historical energy consumption data. 【0113】 An "outlier" is a value that deviates from the normal energy consumption pattern and suggests a system anomaly or problem. 【0114】 A "warning" is information that is sent to the user or administrator when an abnormal value is detected, in order to encourage prompt action. 【0115】 "Providing information in real time" refers to immediately providing users with the latest information regarding energy consumption. 【0116】 An "energy consumption pattern" refers to the tendencies or patterns that show how users or systems typically use energy. 【0117】 An "energy-saving suggestion" is a proposal for specific actions or setting changes recommended to users in order to save energy or improve efficiency. 【0118】 A "sustainable city" refers to a city that minimizes its environmental impact, makes effective use of resources, and provides an economically and socially stable environment. 【0119】 The server plays a central role in efficiently managing energy consumption in the city. Energy consumption information is collected in real time from sensors and terminals and transferred to the server. This information is stored long-term using data storage devices. During this process, BigQuery, a big data analytics tool from Google Cloud Platform, is used to analyze energy consumption patterns. 【0120】 The analyzed data is then incorporated into a model for predicting future energy consumption using machine learning algorithms based on TensorFlow. Based on the predictions obtained from this model, the server automatically recognizes anomalies, generates necessary warnings, and provides them to the user. 【0121】 Users can understand their own energy consumption patterns and receive real-time information via smartphones and other devices. This allows users to receive specific suggestions for energy saving and make decisions to optimize their energy use. A dashboard using React Native is used for information visualization, making cross-platform implementation easy. 【0122】 For example, when sunny weather is expected, users may be offered the option of using solar power to store energy. Such efficient energy management enhances the overall sustainability of the smart city. 【0123】 An example of a prompt to the generated AI model is: "I would like advice on efficient energy use based on urban energy consumption patterns. Please display the current energy report and suggest ways to avoid peak usage." 【0124】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0125】 Step 1: 【0126】 The terminal collects user energy consumption information in real time and transmits this data to a server. The input is energy usage data acquired from various sensors, and the output is data packets that are sent to the server. Through this process, the terminal understands the energy consumption patterns of each device in the home and office. 【0127】 Step 2: 【0128】 The server stores the received energy consumption information in a data storage device. The input is data packets sent from the terminal, and the output is a time-series organized dataset. The server then prepares this dataset for analysis using Google Cloud Platform's BigQuery. 【0129】 Step 3: 【0130】 The server uses a machine learning model based on stored data to predict future energy demand. The input is historical and current energy consumption data, and the output is an updated demand forecasting model. TensorFlow is used to train the model and achieve highly accurate predictions. 【0131】 Step 4: 【0132】 The server recognizes anomalies based on data obtained from the forecasting model. The input is the output of the demand forecasting model, and the output generates warning information when an anomaly is detected. The server immediately notifies the user of this warning and prompts them to take action. 【0133】 Step 5: 【0134】 Users receive energy-saving suggestions through an information visualization dashboard provided by the server. Inputs are warning information and forecast data from the server, and the output displays real-time energy-saving suggestions. Users can use this information to, for example, improve the efficiency of their solar power generation. 【0135】 Step 6: 【0136】 Once the user accepts the energy-saving suggestions, the terminal efficiently controls devices in the home or office again, moving on to the next step in energy management. The input is the user's selected control instructions, and the output is the execution of optimized operating patterns for the devices. 【0137】 Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions. 【0138】 This embodiment provides an energy management system that incorporates an emotion engine to recognize user emotions. This system consists of a server, a terminal, and a user, and by utilizing emotion data, it achieves a more human-like and intuitive energy management system. 【0139】 System Overview 【0140】 server: 【0141】 The server functions as a central management unit, aggregating all relevant data, including energy usage data and emotional data, into a database. The emotional engine resides on this server and recognizes emotions from user input such as facial expressions, voice, and text. It also integrates emotional data with energy usage data to determine appropriate energy-saving suggestions and interaction methods. 【0142】 Terminal: 【0143】 The terminals are placed in homes and offices and include sensors that provide elemental perception data and emotional data to a server. Based on instructions from the emotion engine, the terminals adjust their interfaces and configurations according to the user's emotional state. This allows, for example, room lighting, temperature control, and other settings to be dynamically changed according to the user's emotional state. 【0144】 User: 【0145】 Users experience emotion-based interaction through feedback provided by the system. If the user is stressed, the system refrains from displaying energy consumption information and promotes environmental adjustments that contribute to relaxation. Conversely, if the user is relaxed, it provides proactive energy-saving advice. 【0146】 Specific example 【0147】 In one household, while a user is relaxing in the living room at dusk, the system senses the user's relaxed state from their facial expression. Based on this, the server instructs the terminal to dim the living room lights slightly, play calming music, and notifies the user of casual energy-saving suggestions based on the next day's weather forecast. 【0148】 Furthermore, if the system determines that a user is experiencing stress while preparing for a meeting in an office environment, the terminal will adjust the air conditioning to the user's preference, and the server will temporarily suppress non-essential notifications. 【0149】 This invention enables dynamic adjustment of energy consumption management based on user emotions, aiming to improve user experience while simultaneously saving energy. 【0150】 The following describes the processing flow. 【0151】 Step 1: 【0152】 The device continuously acquires data such as the user's facial expressions, voice, and text from emotion sensors. This data is then transferred to the server after initial processing. 【0153】 Step 2: 【0154】 The server analyzes the received emotional data using an emotion engine to identify the user's emotional state. This emotional state is categorized into states such as "relaxed," "stressed," and "concentrated." 【0155】 Step 3: 【0156】 The server determines the optimal environment settings based on the user's emotional state, combined with energy usage data. For example, when a user is in a "stressed" state, the server may decide to reduce notifications or prioritize environmental adjustments. 【0157】 Step 4: 【0158】 The server instructs the terminal on the determined environment settings and interaction methods. These instructions may include adjusting lighting, setting the temperature, and playing music. 【0159】 Step 5: 【0160】 The device adjusts the home or office environment based on instructions from the server. For example, it might change the brightness of the lights or play soothing music. 【0161】 Step 6: 【0162】 Users experience the relaxing effects and energy-saving interactions provided by a carefully tuned environment. During this time, users can view energy-saving advice tailored to their emotional state. 【0163】 Step 7: 【0164】 The server collects data again after the interaction, uses it to further improve the algorithm, and incorporates these improvements into subsequent interactions. This feedback loop allows the system to continuously optimize the user experience. 【0165】 (Example 2) 【0166】 Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal." 【0167】 Conventional energy management systems primarily rely on predictions and energy-saving suggestions based on past energy usage patterns, and lack the flexibility to make adjustments that take into account the emotional state of users. As a result, it is difficult to properly manage the impact that users experience on energy consumption, and there are challenges in sufficiently improving energy-saving effects and user satisfaction. 【0168】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. 【0169】 In this invention, the server includes means for collecting energy usage information in a time series and storing it in an information repository, means for training a method for analyzing past energy usage patterns and making demand forecasts, and means for recognizing the emotional state of the user and dynamically adjusting energy use based on that state. This enables energy management based on the user's emotions, improving energy-saving effects and optimizing the user experience. 【0170】 "Energy usage information" refers to information about the amount and patterns of energy use, and is collected over time. 【0171】 An "information repository" is a place or system for safely and efficiently storing and preserving collected data. 【0172】 "Demand forecasting" is a method of predicting future energy use based on past data and optimizing the supply-supply balance. 【0173】 An "outlier" is a data value that deviates from the normal pattern and suggests an anomaly in the system. 【0174】 "Renewable energy supply" refers to the amount of electricity obtained from naturally derived energy sources such as wind, solar, and hydroelectric power, and is one of the sustainable energy resources. 【0175】 The "electricity market" is a market environment in which electricity is bought and sold, and it is an economic platform for adjusting the supply and demand of electricity. 【0176】 "User emotional state" refers to the state of a user's psychology and emotions, and is evaluated based on psychological and physiological data. 【0177】 "Dynamic adjustment" means changing settings and actions in real time according to the user's state. 【0178】 An "energy-saving proposal" is a set of guidelines for specific actions and settings presented to improve energy efficiency. 【0179】 An "information visualization dashboard" is an interface that visually displays complex data to support rapid decision-making. 【0180】 The energy management system according to this invention recognizes the user's emotions and optimizes energy use based on those emotions. This system mainly consists of three components: a server, a terminal, and a user. 【0181】 The server is the central management unit of this system. The server incorporates an emotion engine that uses image processing and acoustic analysis software to analyze the user's emotions in real time from their facial expressions and voice. This emotion data is integrated with energy usage information, and algorithms are applied to generate optimal energy-saving suggestions. The server stores this data in a database and performs predictive analysis that also takes historical data into account. 【0182】 The device is installed in a specific environment, such as a home or office, and uses various sensors to acquire user emotion data and environmental data. This includes cameras and microphones, and the device transmits data to a server through these devices. Based on instructions received from the server, the device adjusts environmental settings such as lighting, temperature, and music to match the user's emotions. 【0183】 Users experience environmental changes brought about by the system. The system dynamically controls energy use in response to the user's perceived stress and comfort levels. This allows users to receive energy-saving suggestions tailored to their emotional state and take necessary actions. 【0184】 As a concrete example, if a user is relaxing at dusk, the server instructs the terminal to adjust the lighting and play calming music. At the same time, the user is notified of energy-saving suggestions based on the next day's weather forecast. In this way, the system manages energy in conjunction with emotions, improving the user experience. 【0185】 An example of a prompt to a generative AI model is, "Based on the user's mood in the living room at dusk, please tell me how to adjust the lighting and music to be optimal." This prompt will result in recommendations for specific lighting and music selections. 【0186】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0187】 Step 1: 【0188】 The device collects data from the user's environment through sensors. This input data includes facial images captured by the camera and audio recorded by the microphone. The device preprocesses this data, removing noise and converting it to the required format, before transmitting it to the server in real time. 【0189】 Step 2: 【0190】 The server analyzes the data received from the terminal. At this stage, it uses image processing algorithms to analyze the user's facial expression data and voice analysis technology to extract emotional characteristics from the voice data. Through this processing of input data, the server generates output data that quantifies the user's emotional state. 【0191】 Step 3: 【0192】 The server integrates the generated emotional state data with energy usage data. It evaluates the current situation by comparing it with past energy usage patterns and, if necessary, develops an emotion-based energy-saving strategy. As an output, it creates instructional data that includes suggestions on what actions the user should take. 【0193】 Step 4: 【0194】 The device receives instruction data sent from the server. It then takes actions such as adjusting the brightness of the lighting or playing soothing music to match the user's mood. It also controls the temperature and makes other environmental adjustments to improve user comfort. 【0195】 Step 5: 【0196】 Users experience environmental feedback provided by the system. Furthermore, energy-saving suggestions are displayed as notifications on the user's screen, allowing them to make adjustments based on these suggestions. This output enables users to use their actions to reduce energy consumption. 【0197】 Through this series of steps, the system achieves flexible and efficient energy management based on user emotions, providing a comfortable user experience. 【0198】 (Application Example 2) 【0199】 Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal". 【0200】 Energy management in modern urban environments requires a balance between efficient energy use and user comfort. However, conventional systems do not take user emotions into consideration, making it difficult to achieve both improved comfort and energy efficiency. 【0201】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting energy usage data over time and storing it in a database, means for acquiring user emotional data using an intelligent function that recognizes emotional states and utilizing it for energy management, and means for dynamically making optimal energy-saving suggestions to the user based on the emotional data and energy usage data. This makes it possible to create a comfortable living space that reflects the user's emotions while enabling efficient energy use. 【0202】 "Energy usage data" refers to information on energy consumption over time and is fundamental data used for analysis and forecasting. 【0203】 A "database" is a structured information aggregation system for efficiently storing and managing information. 【0204】 An "algorithm" is a logical set of steps or computational methods that constitute a procedure for solving a specific problem. 【0205】 "Emotional state" refers to the state that indicates the user's psychological condition or mood. 【0206】 "Intelligent functions" refer to systems with advanced judgment capabilities designed to automatically perform specific tasks. 【0207】 "Prediction" is the act of anticipating future states or events based on current data and past patterns. 【0208】 "Renewable energy" refers to energy supplied from sustainable natural resources such as solar, wind, and hydroelectric power. 【0209】 "Electricity supply" refers to activities aimed at providing a stable supply of electricity to the demand side. 【0210】 "Visualized information" refers to information that is presented visually in a way that makes data easy to understand and interpret. 【0211】 "Decision-making" is the process of choosing what seems to be the best option from multiple choices and taking action accordingly. 【0212】 This invention is a system for achieving comfortable energy use in urban environments, taking into account user emotions. The system consists of three main elements: a server, a terminal, and a user. 【0213】 The server is a central management unit that collects energy usage data over time and stores it in a database. It analyzes emotional states using Google Cloud's Vision API and Speech-to-Text API to recognize user emotions. After analyzing the emotional data, it has an intelligent function that generates optimal energy-saving suggestions based on the user's state. The server sends the generated suggestions to the terminal and dynamically adjusts the environment. 【0214】 The terminal is installed in home and work environments and automatically adjusts energy management settings based on instructions received from a server. For example, if the terminal detects that the user is stressed, it adjusts the air conditioning and lighting settings to the user's preferences, contributing to stress reduction. Furthermore, the terminal provides data visualization information based on emotional data and energy usage data, offering a means for users to easily understand their energy consumption. 【0215】 Users can receive feedback on energy management through the system. This allows users to feel comfortable in an environment that adapts to their emotional state, while also becoming more conscious of efficient energy use. 【0216】 As a concrete example, if a user's emotions within a shopping mall are analyzed and it is determined that relaxation is needed, terminals within the facility will adjust the air conditioning and lighting and guide them to a relaxation area. At this time, the generating AI model will be given a prompt such as "We will notify you of suggestions for a comfortable experience in the mall" to suggest the optimal environment settings. 【0217】 Thus, the present invention makes it possible to achieve both comfort and efficient energy use by applying emotion recognition technology. 【0218】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0219】 Step 1: 【0220】 The server receives energy usage data and user facial and voice data transmitted from the terminal. The server uses Google Cloud's Vision API to analyze the facial data and the Speech-to-Text API to transcribe the voice data and recognize the user's emotional state. The input is facial and voice data, and the output is the analyzed emotional information. 【0221】 Step 2: 【0222】 The server combines analyzed emotional information with past energy usage data to generate energy-saving suggestions tailored to the user's situation. This is done using both data stored in a database and a generative AI model. The input is emotional information and energy data, and the output is energy-saving suggestions. 【0223】 Step 3: 【0224】 The server sends the generated energy-saving suggestions to the terminal. Based on these suggestions, the terminal automatically adjusts settings such as lighting and air conditioning. Specifically, it performs actions such as dimming lights and changing the air conditioning temperature. The input is the energy-saving suggestions, and the output is the actual changes to the environmental settings. 【0225】 Step 4: 【0226】 The terminal visualizes and displays the user's current energy usage and suggestions. This allows the user to intuitively understand their consumption situation and take appropriate action. The input is energy usage data and suggestions, and the output is visual feedback to the user. 【0227】 Step 5: 【0228】 Users experience the new environment created by the proposed energy-saving settings and send this information to the system as feedback, enabling further optimization. The input is user feedback, and the output is data used for future system adjustments. 【0229】 The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data. 【0230】 Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0231】 In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14. 【0232】 [Second Embodiment] 【0233】 Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment. 【0234】 As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server. 【0235】 The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network). 【0236】 The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52. 【0237】 The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46. 【0238】 Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision). 【0239】 Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner. 【0240】 Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56. 【0241】 The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30. 【0242】 The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. 【0243】 In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48. 【0244】 Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal". 【0245】 The energy management system of this invention mainly consists of three components: a server, a terminal, and a user. By combining these components, optimal energy management is achieved. 【0246】 System Overview 【0247】 server: 【0248】 The server is the core component of the system. It receives energy usage data transmitted from terminals and stores it in a time-series database. The data is appropriately pre-processed and used for demand forecasting. Based on historical data, the server builds and updates demand forecasting models using machine learning algorithms. It also takes immediate action when anomalies are detected and analyzes electricity market data to develop optimal electricity purchase plans. Furthermore, it analyzes energy usage data for each user and provides personalized energy-saving advice. 【0249】 Terminal: 【0250】 The terminal is a device that monitors energy usage in real time and transmits the acquired data to a server. If an abnormal value is detected, it immediately sends a notification to the server. The terminal receives control instructions from the server and controls various devices to optimize energy consumption in homes and offices. This reduces peak consumption and achieves energy savings. 【0251】 User: 【0252】 Users can review their energy consumption data and receive energy-saving advice as needed using an energy usage visualization dashboard provided by the system. The dashboard visually displays past and present energy usage patterns, helping users make informed decisions. 【0253】 Specific example 【0254】 For example, in a certain office building, energy use tends to be concentrated in the mornings and evenings on weekdays. Based on this trend, the server builds a predictive model and instructs terminals to automatically adjust the schedules of equipment and lighting to coincide with off-peak hours on weekdays when electricity costs are lower, instead of investing during weekday peaks. 【0255】 Furthermore, if a home has solar power installed, the server will take weather data into account, increasing energy storage during sunny days and optimizing electricity purchases from other sources on cloudy days. 【0256】 In this way, this system makes it possible to reduce wasteful consumption and lower costs through the prediction and optimization of energy use. 【0257】 The following describes the processing flow. 【0258】 Step 1: 【0259】 The terminal acquires energy usage data in real time from connected smart meters and sensors. After performing limited preprocessing on this data, the terminal periodically sends it to a server. 【0260】 Step 2: 【0261】 The server stores energy usage data received from terminals in a time-series database. This data undergoes a cleaning process to identify outliers and missing values. 【0262】 Step 3: 【0263】 The server trains a machine learning model based on historical energy usage data to predict energy demand. This model is continuously updated with new data to improve the accuracy of the predictions. 【0264】 Step 4: 【0265】 The terminal monitors energy usage in real time and immediately sends an anomaly alert to the server if it detects an abnormality that exceeds a set threshold. 【0266】 Step 5: 【0267】 When the server receives an anomaly alert, it quickly analyzes the anomaly and sends appropriate control instructions back to the terminal. The terminal adjusts its energy consumption according to these instructions. 【0268】 Step 6: 【0269】 The server predicts renewable energy generation based on weather data and optimizes the power supply schedule based on that information. 【0270】 Step 7: 【0271】 Users can check their energy consumption using a visual dashboard provided by the server. This dashboard displays past usage patterns and energy-saving advice. 【0272】 Step 8: 【0273】 The server analyzes trends in the electricity market and develops optimal electricity purchasing plans, thereby supporting users in using energy cost-effectively. 【0274】 (Example 1) 【0275】 Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal." 【0276】 In today's energy consumption environment, there is a need to maximize energy utilization efficiency while reducing energy waste. However, conventional energy management systems have limitations in providing real-time anomaly detection and personalized energy-saving suggestions, and lack flexibility in optimal energy procurement and equipment control. 【0277】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. 【0278】 In this invention, the server includes means for collecting energy consumption information based on the passage of time and storing it in a storage device, means for analyzing past energy usage trends and training a mathematical model for demand prediction, and means for detecting abnormal data and automatically notifying it. This makes it possible to efficiently optimize energy usage and reduce waste. 【0279】 "Energy consumption information" refers to all data related to energy usage detected within the system, including consumption amount, time, and usage trends. 【0280】 "Storage device" is a hardware component of a computer used to store data for a long time, mainly responsible for storing energy data with timestamps. 【0281】 "Mathematical model" refers to an algorithm or process for simulating or predicting actual energy usage using mathematical methods. 【0282】 "Abnormal data" refers to data indicating numerical values or patterns of energy usage outside the normal range, which potentially affects the efficiency and safety of the system. 【0283】 "Automatically notify" refers to the function of the system to send information without human intervention based on set conditions or thresholds, and notify users or administrators via email or SMS. 【0284】 "External environment" refers to factors that affect energy supply, including factors such as weather, temperature, and seasonal variations. 【0285】 "Renewable energy" refers to energy sources that exist in nature and are not depleted by human activities, including solar energy, wind energy, and hydro energy. 【0286】 "Market data" refers to economic information related to the trading of energy, usually including electricity prices, supply volumes, demand, etc. 【0287】 "Household equipment" refers to equipment connected to an energy consumption system, generally including electrical products and devices for managing electricity consumption. 【0288】 "Information visualization device" refers to software or a device for displaying data in a form that is easy for humans to understand, and uses graphs and charts to display trends and patterns of energy use. 【0289】 The energy management system of the present invention consists of three main components: a server, a terminal, and a user. This promotes efficient energy consumption. 【0290】 The server plays a central role in the system. The server receives the energy consumption information transmitted by each terminal and first stores it in a database. Specifically, a general data management system, which is a time-series database, is used. This data is cleaned using Python, and outliers are excluded. Based on past data, a demand prediction model is constructed using a general mathematical model, which is a machine learning library. When abnormal data is detected, the system utilizes an automation tool for automatically sending notifications so that information reaches the user immediately. 【0291】 The terminal is connected to energy consumption devices, collects data in real time, and transmits it to the server. When an abnormality is found in the data, the terminal immediately notifies the server. Upon receiving instructions from the server, the terminal controls the energy devices to optimize energy consumption within a home or office. 【0292】 Users can check their consumption patterns using an information visualization device. This makes it possible to visualize when energy consumption is high and which devices are using the most energy. A common data display library is used for visualization. Furthermore, users can receive energy-saving advice. 【0293】 Specific example 【0294】 For example, some households tend to have a high concentration of energy consumption during weekday evenings. In response, the server uses a predictive model to send instructions to the terminals to control each device to mitigate peak energy usage. Furthermore, the server generates an electricity purchase plan based on weather information to maximize the supply of renewable energy. 【0295】 Example of a prompt 【0296】 "Based on energy consumption data, please propose an optimal appliance control schedule for the home during off-peak hours." 【0297】 In this way, the entire system can achieve highly efficient energy management. 【0298】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0299】 Step 1: 【0300】 The terminal collects data in real time from energy-consuming devices. It takes energy consumption information from sensors as input. The terminal packages this data into packets and sends them to the server using a secure communication protocol. The output is energy data structured in a format that the server can process. 【0301】 Step 2: 【0302】 The server receives the energy data transmitted from the terminal. The input is the energy data in time series format transmitted from the terminal. The server first stores the data in the database. Next, it performs data cleaning using a Python script to remove missing values and outliers. The output is clean and analyzable energy data. 【0303】 Step 3: 【0304】 The server analyzes the past energy usage patterns using the clean energy data. The input is the cleaned energy data. The server trains a demand prediction model using a machine learning algorithm. In this process, libraries such as TensorFlow are used. The output is a mathematical model for predicting future energy demand. 【0305】 Step 4: 【0306】 The server monitors the real-time energy data and uses the prediction model to detect anomalies. The input is the raw data received in real time and the prediction model generated by the server. When an anomaly exceeding the set threshold occurs, the system triggers an automatic notification. The output is that an anomaly notification is sent. 【0307】 Step 5: 【0308】 The server optimizes energy supply and demand using external environmental information and market data. The input includes weather information and power market trend data obtained from an external API. An optimal energy purchase plan is generated by the analysis software. The output is an action plan for optimizing energy consumption. 【0309】 Step 6: 【0310】 The terminal controls household devices based on instructions from the server. The input is the control signal from the server. The terminal operates smart devices within the home and is optimized to reduce energy use during peak hours. The output is the optimized energy consumption profile. 【0311】 Step 7: 【0312】 Users check their energy usage using an information visualization device. The input is visualization data provided by the server. Based on the information provided by the dashboard, users make decisions about energy consumption. The output is the change or adjustment of behavior based on the decision. 【0313】 This series of processes enables efficient energy management across the entire system, resulting in cost reductions and optimized consumption. 【0314】 (Application Example 1) 【0315】 Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal." 【0316】 In recent years, there has been a growing demand for more efficient energy consumption in urban areas, and integrated energy management, particularly in smart cities, is crucial. Conventional energy management systems are inadequate in addressing the needs of individual users, making it difficult to provide real-time information or individualized energy-saving suggestions. Furthermore, building sustainable cities requires a system that maximizes the efficiency of renewable energy use and enables flexible electricity purchasing strategies tailored to energy demand. 【0317】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. 【0318】 In this invention, the server includes means for collecting energy consumption information over time and storing it in a data storage device, means for enhancing a calculation method for analyzing past energy consumption patterns and forecasting demand, and means for recognizing anomalies and automatically issuing warnings. This makes it possible to identify the different energy consumption patterns of each user and provide optimal energy-saving suggestions in a smart city. Furthermore, the provision of real-time information supports the efficient use of energy and promotes the realization of a sustainable city. 【0319】 "Energy consumption information" refers to data about the amount of energy used by each user and the system as a whole, and is collected over time. 【0320】 A "data storage device" is a system or device for storing collected energy consumption information over a long period of time. 【0321】 A "computational method" refers to an algorithm or analytical process used to predict energy consumption patterns using historical energy consumption data. 【0322】 An "outlier" is a value that deviates from the normal energy consumption pattern and suggests a system anomaly or problem. 【0323】 A "warning" is information that is sent to the user or administrator when an abnormal value is detected, in order to encourage prompt action. 【0324】 "Providing information in real time" refers to immediately providing users with the latest information regarding energy consumption. 【0325】 An "energy consumption pattern" refers to the tendencies or patterns that show how users or systems typically use energy. 【0326】 An "energy-saving suggestion" is a proposal for specific actions or setting changes recommended to users in order to save energy or improve efficiency. 【0327】 A "sustainable city" refers to a city that minimizes its environmental impact, makes effective use of resources, and provides an economically and socially stable environment. 【0328】 The server plays a central role in efficiently managing energy consumption in the city. Energy consumption information is collected in real time from sensors and terminals and transferred to the server. This information is stored long-term using data storage devices. During this process, BigQuery, a big data analytics tool from Google Cloud Platform, is used to analyze energy consumption patterns. 【0329】 The analyzed data is then incorporated into a model for predicting future energy consumption using machine learning algorithms based on TensorFlow. Based on the predictions obtained from this model, the server automatically recognizes anomalies, generates necessary warnings, and provides them to the user. 【0330】 Users can understand their own energy consumption patterns and receive real-time information via smartphones and other devices. This allows users to receive specific suggestions for energy saving and make decisions to optimize their energy use. A dashboard using React Native is used for information visualization, making cross-platform implementation easy. 【0331】 For example, when sunny weather is expected, users may be offered the option of using solar power to store energy. Such efficient energy management enhances the overall sustainability of the smart city. 【0332】 An example of a prompt to the generated AI model is: "I would like advice on efficient energy use based on urban energy consumption patterns. Please display the current energy report and suggest ways to avoid peak usage." 【0333】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0334】 Step 1: 【0335】 The terminal collects user energy consumption information in real time and transmits this data to a server. The input is energy usage data acquired from various sensors, and the output is data packets that are sent to the server. Through this process, the terminal understands the energy consumption patterns of each device in the home and office. 【0336】 Step 2: 【0337】 The server stores the received energy consumption information in a data storage device. The input is data packets sent from the terminal, and the output is a time-series organized dataset. The server then prepares this dataset for analysis using Google Cloud Platform's BigQuery. 【0338】 Step 3: 【0339】 The server uses a machine learning model based on stored data to predict future energy demand. The input is historical and current energy consumption data, and the output is an updated demand forecasting model. TensorFlow is used to train the model and achieve highly accurate predictions. 【0340】 Step 4: 【0341】 The server recognizes anomalies based on data obtained from the forecasting model. The input is the output of the demand forecasting model, and the output generates warning information when an anomaly is detected. The server immediately notifies the user of this warning and prompts them to take action. 【0342】 Step 5: 【0343】 Users receive energy-saving suggestions through an information visualization dashboard provided by the server. Inputs are warning information and forecast data from the server, and the output displays real-time energy-saving suggestions. Users can use this information to, for example, improve the efficiency of their solar power generation. 【0344】 Step 6: 【0345】 Once the user accepts the energy-saving suggestions, the terminal efficiently controls devices in the home or office again, moving on to the next step in energy management. The input is the user's selected control instructions, and the output is the execution of optimized operating patterns for the devices. 【0346】 Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions. 【0347】 This embodiment provides an energy management system that incorporates an emotion engine to recognize user emotions. This system consists of a server, a terminal, and a user, and by utilizing emotion data, it achieves a more human-like and intuitive energy management system. 【0348】 System Overview 【0349】 server: 【0350】 The server functions as a central management unit, aggregating all relevant data, including energy usage data and emotional data, into a database. The emotional engine resides on this server and recognizes emotions from user input such as facial expressions, voice, and text. It also integrates emotional data with energy usage data to determine appropriate energy-saving suggestions and interaction methods. 【0351】 Terminal: 【0352】 The terminals are placed in homes and offices and include sensors that provide elemental perception data and emotional data to a server. Based on instructions from the emotion engine, the terminals adjust their interfaces and configurations according to the user's emotional state. This allows, for example, room lighting, temperature control, and other settings to be dynamically changed according to the user's emotional state. 【0353】 User: 【0354】 Users experience emotion-based interaction through feedback provided by the system. If the user is stressed, the system refrains from displaying energy consumption information and promotes environmental adjustments that contribute to relaxation. Conversely, if the user is relaxed, it provides proactive energy-saving advice. 【0355】 Specific example 【0356】 In one household, while a user is relaxing in the living room at dusk, the system senses the user's relaxed state from their facial expression. Based on this, the server instructs the terminal to dim the living room lights slightly, play calming music, and notifies the user of casual energy-saving suggestions based on the next day's weather forecast. 【0357】 Furthermore, if the system determines that a user is experiencing stress while preparing for a meeting in an office environment, the terminal will adjust the air conditioning to the user's preference, and the server will temporarily suppress non-essential notifications. 【0358】 This invention enables dynamic adjustment of energy consumption management based on user emotions, aiming to improve user experience while simultaneously saving energy. 【0359】 The following describes the processing flow. 【0360】 Step 1: 【0361】 The device continuously acquires data such as the user's facial expressions, voice, and text from emotion sensors. This data is then transferred to the server after initial processing. 【0362】 Step 2: 【0363】 The server analyzes the received emotional data using an emotion engine to identify the user's emotional state. This emotional state is categorized into states such as "relaxed," "stressed," and "concentrated." 【0364】 Step 3: 【0365】 The server determines the optimal environment settings based on the user's emotional state, combined with energy usage data. For example, when a user is in a "stressed" state, the server may decide to reduce notifications or prioritize environmental adjustments. 【0366】 Step 4: 【0367】 The server instructs the terminal on the determined environment settings and interaction methods. These instructions may include adjusting lighting, setting the temperature, and playing music. 【0368】 Step 5: 【0369】 The device adjusts the home or office environment based on instructions from the server. For example, it might change the brightness of the lights or play soothing music. 【0370】 Step 6: 【0371】 Users experience the relaxing effects and energy-saving interactions provided by a carefully tuned environment. During this time, users can view energy-saving advice tailored to their emotional state. 【0372】 Step 7: 【0373】 The server collects data again after the interaction, uses it to further improve the algorithm, and incorporates these improvements into subsequent interactions. This feedback loop allows the system to continuously optimize the user experience. 【0374】 (Example 2) 【0375】 Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal". 【0376】 Conventional energy management systems primarily rely on predictions and energy-saving suggestions based on past energy usage patterns, and lack the flexibility to make adjustments that take into account the emotional state of users. As a result, it is difficult to properly manage the impact that users experience on energy consumption, and there are challenges in sufficiently improving energy-saving effects and user satisfaction. 【0377】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. 【0378】 In this invention, the server includes means for collecting energy usage information in a time series and storing it in an information repository, means for training a method for analyzing past energy usage patterns and making demand forecasts, and means for recognizing the emotional state of the user and dynamically adjusting energy use based on that state. This enables energy management based on the user's emotions, improving energy-saving effects and optimizing the user experience. 【0379】 "Energy usage information" refers to information about the amount and patterns of energy use, and is collected over time. 【0380】 An "information repository" is a place or system for safely and efficiently storing and preserving collected data. 【0381】 "Demand forecasting" is a method of predicting future energy use based on past data and optimizing the supply-supply balance. 【0382】 An "outlier" is a data value that deviates from the normal pattern and suggests an anomaly in the system. 【0383】 "Renewable energy supply" refers to the amount of electricity obtained from naturally derived energy sources such as wind, solar, and hydroelectric power, and is one of the sustainable energy resources. 【0384】 The "electricity market" is a market environment in which electricity is bought and sold, and it is an economic platform for adjusting the supply and demand of electricity. 【0385】 "User emotional state" refers to the state of a user's psychology and emotions, and is evaluated based on psychological and physiological data. 【0386】 "Dynamic adjustment" means changing settings and actions in real time according to the user's state. 【0387】 An "energy-saving proposal" is a set of guidelines for specific actions and settings presented to improve energy efficiency. 【0388】 An "information visualization dashboard" is an interface that visually displays complex data to support rapid decision-making. 【0389】 The energy management system according to this invention recognizes the user's emotions and optimizes energy use based on those emotions. This system mainly consists of three components: a server, a terminal, and a user. 【0390】 The server is the central management unit of this system. The server incorporates an emotion engine that uses image processing and acoustic analysis software to analyze the user's emotions in real time from their facial expressions and voice. This emotion data is integrated with energy usage information, and algorithms are applied to generate optimal energy-saving suggestions. The server stores this data in a database and performs predictive analysis that also takes historical data into account. 【0391】 The device is installed in a specific environment, such as a home or office, and uses various sensors to acquire user emotion data and environmental data. This includes cameras and microphones, and the device transmits data to a server through these devices. Based on instructions received from the server, the device adjusts environmental settings such as lighting, temperature, and music to match the user's emotions. 【0392】 Users experience environmental changes brought about by the system. The system dynamically controls energy use in response to the user's perceived stress and comfort levels. This allows users to receive energy-saving suggestions tailored to their emotional state and take necessary actions. 【0393】 As a concrete example, if a user is relaxing at dusk, the server instructs the terminal to adjust the lighting and play calming music. At the same time, the user is notified of energy-saving suggestions based on the next day's weather forecast. In this way, the system manages energy in conjunction with emotions, improving the user experience. 【0394】 An example of a prompt to a generative AI model is, "Based on the user's mood in the living room at dusk, please tell me how to adjust the lighting and music to be optimal." This prompt will result in recommendations for specific lighting and music selections. 【0395】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0396】 Step 1: 【0397】 The device collects data from the user's environment through sensors. This input data includes facial images captured by the camera and audio recorded by the microphone. The device preprocesses this data, removing noise and converting it to the required format, before transmitting it to the server in real time. 【0398】 Step 2: 【0399】 The server analyzes the data received from the terminal. At this stage, it uses image processing algorithms to analyze the user's facial expression data and voice analysis technology to extract emotional characteristics from the voice data. Through this processing of input data, the server generates output data that quantifies the user's emotional state. 【0400】 Step 3: 【0401】 The server integrates the generated emotional state data with energy usage data. It evaluates the current situation by comparing it with past energy usage patterns and, if necessary, develops an emotion-based energy-saving strategy. As an output, it creates instructional data that includes suggestions on what actions the user should take. 【0402】 Step 4: 【0403】 The device receives instruction data sent from the server. It then takes actions such as adjusting the brightness of the lighting or playing soothing music to match the user's mood. It also controls the temperature and makes other environmental adjustments to improve user comfort. 【0404】 Step 5: 【0405】 Users experience environmental feedback provided by the system. Furthermore, energy-saving suggestions are displayed as notifications on the user's screen, allowing them to make adjustments based on these suggestions. This output enables users to use their actions to reduce energy consumption. 【0406】 Through this series of steps, the system achieves flexible and efficient energy management based on user emotions, providing a comfortable user experience. 【0407】 (Application Example 2) 【0408】 Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal." 【0409】 Energy management in modern urban environments requires a balance between efficient energy use and user comfort. However, conventional systems do not take user emotions into consideration, making it difficult to achieve both improved comfort and energy efficiency. 【0410】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting energy usage data over time and storing it in a database, means for acquiring user emotional data using an intelligent function that recognizes emotional states and utilizing it for energy management, and means for dynamically making optimal energy-saving suggestions to the user based on the emotional data and energy usage data. This makes it possible to create a comfortable living space that reflects the user's emotions while enabling efficient energy use. 【0411】 "Energy usage data" refers to information on energy consumption over time and is fundamental data used for analysis and forecasting. 【0412】 A "database" is a structured information aggregation system for efficiently storing and managing information. 【0413】 An "algorithm" is a logical set of steps or computational methods that constitute a procedure for solving a specific problem. 【0414】 "Emotional state" refers to the state that indicates the user's psychological condition or mood. 【0415】 "Intelligent functions" refer to systems with advanced judgment capabilities designed to automatically perform specific tasks. 【0416】 "Prediction" is the act of anticipating future states or events based on current data and past patterns. 【0417】 "Renewable energy" refers to energy supplied from sustainable natural resources such as solar, wind, and hydroelectric power. 【0418】 "Electricity supply" refers to activities aimed at providing a stable supply of electricity to the demand side. 【0419】 "Visualized information" refers to information that is presented visually in a way that makes data easy to understand and interpret. 【0420】 "Decision-making" is the process of choosing what seems to be the best option from multiple choices and taking action accordingly. 【0421】 This invention is a system for achieving comfortable energy use in urban environments, taking into account user emotions. The system consists of three main elements: a server, a terminal, and a user. 【0422】 The server is a central management unit that collects energy usage data over time and stores it in a database. It analyzes emotional states using Google Cloud's Vision API and Speech-to-Text API to recognize user emotions. After analyzing the emotional data, it has an intelligent function that generates optimal energy-saving suggestions based on the user's state. The server sends the generated suggestions to the terminal and dynamically adjusts the environment. 【0423】 The terminal is installed in home and work environments and automatically adjusts energy management settings based on instructions received from a server. For example, if the terminal detects that the user is stressed, it adjusts the air conditioning and lighting settings to the user's preferences, contributing to stress reduction. Furthermore, the terminal provides data visualization information based on emotional data and energy usage data, offering a means for users to easily understand their energy consumption. 【0424】 Users can receive feedback on energy management through the system. This allows users to feel comfortable in an environment that adapts to their emotional state, while also becoming more conscious of efficient energy use. 【0425】 As a concrete example, if a user's emotions within a shopping mall are analyzed and it is determined that relaxation is needed, terminals within the facility will adjust the air conditioning and lighting and guide them to a relaxation area. At this time, the generating AI model will be given a prompt such as "We will notify you of suggestions for a comfortable experience in the mall" to suggest the optimal environment settings. 【0426】 Thus, the present invention makes it possible to achieve both comfort and efficient energy use by applying emotion recognition technology. 【0427】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0428】 Step 1: 【0429】 The server receives energy usage data and user facial and voice data transmitted from the terminal. The server uses Google Cloud's Vision API to analyze the facial data and the Speech-to-Text API to transcribe the voice data and recognize the user's emotional state. The input is facial and voice data, and the output is the analyzed emotional information. 【0430】 Step 2: 【0431】 The server combines analyzed emotional information with past energy usage data to generate energy-saving suggestions tailored to the user's situation. This is done using both data stored in a database and a generative AI model. The input is emotional information and energy data, and the output is energy-saving suggestions. 【0432】 Step 3: 【0433】 The server sends the generated energy-saving suggestions to the terminal. Based on these suggestions, the terminal automatically adjusts settings such as lighting and air conditioning. Specifically, it performs actions such as dimming lights and changing the air conditioning temperature. The input is the energy-saving suggestions, and the output is the actual changes to the environmental settings. 【0434】 Step 4: 【0435】 The terminal visualizes and displays the user's current energy usage and suggestions. This allows the user to intuitively understand their consumption situation and take appropriate action. The input is energy usage data and suggestions, and the output is visual feedback to the user. 【0436】 Step 5: 【0437】 Users experience the new environment created by the proposed energy-saving settings and send this information to the system as feedback, enabling further optimization. The input is user feedback, and the output is data used for future system adjustments. 【0438】 The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data. 【0439】 Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0440】 In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214. 【0441】 [Third Embodiment] 【0442】 Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment. 【0443】 As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server. 【0444】 The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network). 【0445】 The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52. 【0446】 The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46. 【0447】 Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision). 【0448】 Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner. 【0449】 Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56. 【0450】 The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30. 【0451】 The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. 【0452】 In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48. 【0453】 Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal". 【0454】 The energy management system of this invention mainly consists of three components: a server, a terminal, and a user. By combining these components, optimal energy management is achieved. 【0455】 System Overview 【0456】 server: 【0457】 The server is the core component of the system. It receives energy usage data transmitted from terminals and stores it in a time-series database. The data is appropriately pre-processed and used for demand forecasting. Based on historical data, the server builds and updates demand forecasting models using machine learning algorithms. It also takes immediate action when anomalies are detected and analyzes electricity market data to develop optimal electricity purchase plans. Furthermore, it analyzes energy usage data for each user and provides personalized energy-saving advice. 【0458】 Terminal: 【0459】 The terminal is a device that monitors energy usage in real time and transmits the acquired data to a server. If an abnormal value is detected, it immediately sends a notification to the server. The terminal receives control instructions from the server and controls various devices to optimize energy consumption in homes and offices. This reduces peak consumption and achieves energy savings. 【0460】 User: 【0461】 Users can review their energy consumption data and receive energy-saving advice as needed using an energy usage visualization dashboard provided by the system. The dashboard visually displays past and present energy usage patterns, helping users make informed decisions. 【0462】 Specific example 【0463】 For example, in a certain office building, energy use tends to be concentrated in the mornings and evenings on weekdays. Based on this trend, the server builds a predictive model and instructs terminals to automatically adjust the schedules of equipment and lighting to coincide with off-peak hours on weekdays when electricity costs are lower, instead of investing during weekday peaks. 【0464】 Furthermore, if a home has solar power installed, the server will take weather data into account, increasing energy storage during sunny days and optimizing electricity purchases from other sources on cloudy days. 【0465】 In this way, this system makes it possible to reduce wasteful consumption and lower costs through the prediction and optimization of energy use. 【0466】 The following describes the processing flow. 【0467】 Step 1: 【0468】 The terminal acquires energy usage data in real time from connected smart meters and sensors. After performing limited preprocessing on this data, the terminal periodically sends it to a server. 【0469】 Step 2: 【0470】 The server stores energy usage data received from terminals in a time-series database. This data undergoes a cleaning process to identify outliers and missing values. 【0471】 Step 3: 【0472】 The server trains a machine learning model based on historical energy usage data to predict energy demand. This model is continuously updated with new data to improve the accuracy of the predictions. 【0473】 Step 4: 【0474】 The terminal monitors energy usage in real time and immediately sends an anomaly alert to the server if it detects an abnormality that exceeds a set threshold. 【0475】 Step 5: 【0476】 When the server receives an anomaly alert, it quickly analyzes the anomaly and sends appropriate control instructions back to the terminal. The terminal adjusts its energy consumption according to these instructions. 【0477】 Step 6: 【0478】 The server predicts renewable energy generation based on weather data and optimizes the power supply schedule based on that information. 【0479】 Step 7: 【0480】 Users can check their energy consumption using a visual dashboard provided by the server. This dashboard displays past usage patterns and energy-saving advice. 【0481】 Step 8: 【0482】 The server analyzes trends in the electricity market and develops optimal electricity purchasing plans, thereby supporting users in using energy cost-effectively. 【0483】 (Example 1) 【0484】 Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal." 【0485】 In today's energy consumption environment, there is a need to maximize energy utilization efficiency while reducing energy waste. However, conventional energy management systems have limitations in providing real-time anomaly detection and personalized energy-saving suggestions, and lack flexibility in optimal energy procurement and equipment control. 【0486】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. 【0487】 In this invention, the server includes means for collecting energy consumption information based on the passage of time and storing it in a memory device, means for training a mathematical model that analyzes past energy usage trends and makes demand forecasts, and means for detecting abnormal data and automatically notifying. This makes it possible to efficiently optimize energy use and reduce waste. 【0488】 "Energy consumption information" refers to all data related to energy use detected within the system, including consumption amount, time, and usage trends. 【0489】 A "storage device" is a computer hardware component used to retain data for extended periods, primarily responsible for storing time-stamped energy data. 【0490】 A "mathematical model" refers to an algorithm or process that uses mathematical methods to simulate or predict actual energy use. 【0491】 "Abnormal data" refers to data that shows energy usage values ​​or patterns outside the normal range, and which may have a potential impact on the efficiency and safety of the system. 【0492】 "Automatic notification" refers to a function where the system sends information without human intervention based on set conditions or thresholds, informing users and administrators via email or SMS. 【0493】 "External environment" refers to factors that affect energy supply, and includes factors such as weather, temperature, and seasonal variations. 【0494】 "Renewable energy" refers to energy sources that exist in nature and are not depleted by human activities, and includes solar, wind, and hydroelectric power. 【0495】 "Market data" refers to economic information related to the buying and selling of energy, and typically includes electricity prices, supply, and demand. 【0496】 "Household appliances" refer to devices connected to an energy consumption system, and generally include electrical products and devices used to manage power consumption. 【0497】 An "information visualization device" refers to software or a device that displays data in a way that is easy for humans to understand, such as using graphs and charts to show trends and patterns in energy use. 【0498】 The energy management system of the present invention consists of three main components: a server, a terminal, and a user. This promotes efficient energy consumption. 【0499】 The server plays a central role in the system. It receives energy consumption information transmitted by each terminal and first stores it in a database. Specifically, it uses a general-purpose data management system, which is a time-series database. This data is cleaned using Python to remove outliers. Based on historical data, a demand forecasting model is built using a general-purpose mathematical model, a machine learning library. If abnormal data is detected, the system utilizes automated tools to automatically send notifications, ensuring that users receive information immediately. 【0500】 The terminal is connected to energy-consuming devices, collecting data in real time and sending it to a server. If an anomaly is detected in the data, the terminal immediately notifies the server. Based on instructions from the server, the terminal controls the energy devices to optimize energy consumption in homes and offices. 【0501】 Users can check their consumption patterns using an information visualization device. This makes it possible to visualize when energy consumption is high and which devices are using the most energy. A common data display library is used for visualization. Furthermore, users can receive energy-saving advice. 【0502】 Specific example 【0503】 For example, some households tend to have a high concentration of energy consumption during weekday evenings. In response, the server uses a predictive model to send instructions to the terminals to control each device to mitigate peak energy usage. Furthermore, the server generates an electricity purchase plan based on weather information to maximize the supply of renewable energy. 【0504】 Example of a prompt 【0505】 "Based on energy consumption data, please propose an optimal appliance control schedule for the home during off-peak hours." 【0506】 In this way, the entire system can achieve highly efficient energy management. 【0507】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0508】 Step 1: 【0509】 The terminal collects data in real time from energy-consuming devices. It takes energy consumption information from sensors as input. The terminal packages this data into packets and sends them to the server using a secure communication protocol. The output is energy data structured in a format that the server can process. 【0510】 Step 2: 【0511】 The server receives energy data sent from the terminal. The input is time-series energy data sent from the terminal. The server first stores the data in a database. Next, it cleans the data using a Python script to remove missing values ​​and outliers. The output is clean, analyzable energy data. 【0512】 Step 3: 【0513】 The server analyzes past energy usage patterns using clean energy data. The input is cleaned energy data. The server trains a demand forecasting model using machine learning algorithms. Libraries such as TensorFlow are used in this process. The output is a mathematical model for forecasting future energy demand. 【0514】 Step 4: 【0515】 The server monitors real-time energy data and uses a predictive model to detect anomalies. Inputs are the raw data received in real time and the predictive model generated by the server. When an anomaly occurs that exceeds a set threshold, the system triggers an automatic notification. The output is the sending of the anomaly notification. 【0516】 Step 5: 【0517】 The server optimizes energy supply and demand using external environmental information and market data. Inputs include weather information and electricity market trend data obtained from external APIs. Analysis software generates an optimal energy purchase plan. The output is an action plan for optimizing energy consumption. 【0518】 Step 6: 【0519】 The terminal controls household devices based on instructions from the server. The input is the control signal from the server. The terminal operates smart devices within the home and is optimized to reduce energy use during peak hours. The output is the optimized energy consumption profile. 【0520】 Step 7: 【0521】 Users check their energy usage using an information visualization device. The input is visualization data provided by the server. Based on the information provided by the dashboard, users make decisions about energy consumption. The output is the change or adjustment of behavior based on the decision. 【0522】 This series of processes enables efficient energy management across the entire system, resulting in cost reductions and optimized consumption. 【0523】 (Application Example 1) 【0524】 Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal." 【0525】 In recent years, there has been a growing demand for more efficient energy consumption in urban areas, and integrated energy management, particularly in smart cities, is crucial. Conventional energy management systems are inadequate in addressing the needs of individual users, making it difficult to provide real-time information or individualized energy-saving suggestions. Furthermore, building sustainable cities requires a system that maximizes the efficiency of renewable energy use and enables flexible electricity purchasing strategies tailored to energy demand. 【0526】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. 【0527】 In this invention, the server includes means for collecting energy consumption information over time and storing it in a data storage device, means for enhancing a calculation method for analyzing past energy consumption patterns and forecasting demand, and means for recognizing anomalies and automatically issuing warnings. This makes it possible to identify the different energy consumption patterns of each user and provide optimal energy-saving suggestions in a smart city. Furthermore, the provision of real-time information supports the efficient use of energy and promotes the realization of a sustainable city. 【0528】 "Energy consumption information" refers to data about the amount of energy used by each user and the system as a whole, and is collected over time. 【0529】 A "data storage device" is a system or device for storing collected energy consumption information over a long period of time. 【0530】 A "computational method" refers to an algorithm or analytical process used to predict energy consumption patterns using historical energy consumption data. 【0531】 An "outlier" is a value that deviates from the normal energy consumption pattern and suggests a system anomaly or problem. 【0532】 A "warning" is information that is sent to the user or administrator when an abnormal value is detected, in order to encourage prompt action. 【0533】 "Providing information in real time" refers to immediately providing users with the latest information regarding energy consumption. 【0534】 An "energy consumption pattern" refers to the tendencies or patterns that show how users or systems typically use energy. 【0535】 An "energy-saving suggestion" is a proposal for specific actions or setting changes recommended to users in order to save energy or improve efficiency. 【0536】 A "sustainable city" refers to a city that minimizes its environmental impact, makes effective use of resources, and provides an economically and socially stable environment. 【0537】 The server plays a central role in efficiently managing energy consumption in the city. Energy consumption information is collected in real time from sensors and terminals and transferred to the server. This information is stored long-term using data storage devices. During this process, BigQuery, a big data analytics tool from Google Cloud Platform, is used to analyze energy consumption patterns. 【0538】 The analyzed data is then incorporated into a model for predicting future energy consumption using machine learning algorithms based on TensorFlow. Based on the predictions obtained from this model, the server automatically recognizes anomalies, generates necessary warnings, and provides them to the user. 【0539】 Users can understand their own energy consumption patterns and receive real-time information via smartphones and other devices. This allows users to receive specific suggestions for energy saving and make decisions to optimize their energy use. A dashboard using React Native is used for information visualization, making cross-platform implementation easy. 【0540】 For example, when sunny weather is expected, users may be offered the option of using solar power to store energy. Such efficient energy management enhances the overall sustainability of the smart city. 【0541】 An example of a prompt to the generated AI model is: "I would like advice on efficient energy use based on urban energy consumption patterns. Please display the current energy report and suggest ways to avoid peak usage." 【0542】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0543】 Step 1: 【0544】 The terminal collects user energy consumption information in real time and transmits this data to a server. The input is energy usage data acquired from various sensors, and the output is data packets that are sent to the server. Through this process, the terminal understands the energy consumption patterns of each device in the home and office. 【0545】 Step 2: 【0546】 The server stores the received energy consumption information in a data storage device. The input is data packets sent from the terminal, and the output is a time-series organized dataset. The server then prepares this dataset for analysis using Google Cloud Platform's BigQuery. 【0547】 Step 3: 【0548】 The server uses a machine learning model based on stored data to predict future energy demand. The input is historical and current energy consumption data, and the output is an updated demand forecasting model. TensorFlow is used to train the model and achieve highly accurate predictions. 【0549】 Step 4: 【0550】 The server recognizes anomalies based on data obtained from the forecasting model. The input is the output of the demand forecasting model, and the output generates warning information when an anomaly is detected. The server immediately notifies the user of this warning and prompts them to take action. 【0551】 Step 5: 【0552】 Users receive energy-saving suggestions through an information visualization dashboard provided by the server. Inputs are warning information and forecast data from the server, and the output displays real-time energy-saving suggestions. Users can use this information to, for example, improve the efficiency of their solar power generation. 【0553】 Step 6: 【0554】 Once the user accepts the energy-saving suggestions, the terminal efficiently controls devices in the home or office again, moving on to the next step in energy management. The input is the user's selected control instructions, and the output is the execution of optimized operating patterns for the devices. 【0555】 Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions. 【0556】 This embodiment provides an energy management system that incorporates an emotion engine to recognize user emotions. This system consists of a server, a terminal, and a user, and by utilizing emotion data, it achieves a more human-like and intuitive energy management system. 【0557】 System Overview 【0558】 server: 【0559】 The server functions as a central management unit, aggregating all relevant data, including energy usage data and emotional data, into a database. The emotional engine resides on this server and recognizes emotions from user input such as facial expressions, voice, and text. It also integrates emotional data with energy usage data to determine appropriate energy-saving suggestions and interaction methods. 【0560】 Terminal: 【0561】 The terminals are placed in homes and offices and include sensors that provide elemental perception data and emotional data to a server. Based on instructions from the emotion engine, the terminals adjust their interfaces and configurations according to the user's emotional state. This allows, for example, room lighting, temperature control, and other settings to be dynamically changed according to the user's emotional state. 【0562】 User: 【0563】 Users experience emotion-based interaction through feedback provided by the system. If the user is stressed, the system refrains from displaying energy consumption information and promotes environmental adjustments that contribute to relaxation. Conversely, if the user is relaxed, it provides proactive energy-saving advice. 【0564】 Specific example 【0565】 In one household, while a user is relaxing in the living room at dusk, the system senses the user's relaxed state from their facial expression. Based on this, the server instructs the terminal to dim the living room lights slightly, play calming music, and notifies the user of casual energy-saving suggestions based on the next day's weather forecast. 【0566】 Furthermore, if the system determines that a user is experiencing stress while preparing for a meeting in an office environment, the terminal will adjust the air conditioning to the user's preference, and the server will temporarily suppress non-essential notifications. 【0567】 This invention enables dynamic adjustment of energy consumption management based on user emotions, aiming to improve user experience while simultaneously saving energy. 【0568】 The following describes the processing flow. 【0569】 Step 1: 【0570】 The device continuously acquires data such as the user's facial expressions, voice, and text from emotion sensors. This data is then transferred to the server after initial processing. 【0571】 Step 2: 【0572】 The server analyzes the received emotional data using an emotion engine to identify the user's emotional state. This emotional state is categorized into states such as "relaxed," "stressed," and "concentrated." 【0573】 Step 3: 【0574】 The server determines the optimal environment settings based on the user's emotional state, combined with energy usage data. For example, when a user is in a "stressed" state, the server may decide to reduce notifications or prioritize environmental adjustments. 【0575】 Step 4: 【0576】 The server instructs the terminal on the determined environment settings and interaction methods. These instructions may include adjusting lighting, setting the temperature, and playing music. 【0577】 Step 5: 【0578】 The device adjusts the home or office environment based on instructions from the server. For example, it might change the brightness of the lights or play soothing music. 【0579】 Step 6: 【0580】 Users experience the relaxing effects and energy-saving interactions provided by a carefully tuned environment. During this time, users can view energy-saving advice tailored to their emotional state. 【0581】 Step 7: 【0582】 The server collects data again after the interaction, uses it to further improve the algorithm, and incorporates these improvements into subsequent interactions. This feedback loop allows the system to continuously optimize the user experience. 【0583】 (Example 2) 【0584】 Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal." 【0585】 Conventional energy management systems primarily rely on predictions and energy-saving suggestions based on past energy usage patterns, and lack the flexibility to make adjustments that take into account the emotional state of users. As a result, it is difficult to properly manage the impact that users experience on energy consumption, and there are challenges in sufficiently improving energy-saving effects and user satisfaction. 【0586】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. 【0587】 In this invention, the server includes means for collecting energy usage information in a time series and storing it in an information repository, means for training a method for analyzing past energy usage patterns and making demand forecasts, and means for recognizing the emotional state of the user and dynamically adjusting energy use based on that state. This enables energy management based on the user's emotions, improving energy-saving effects and optimizing the user experience. 【0588】 "Energy usage information" refers to information about the amount and patterns of energy use, and is collected over time. 【0589】 An "information repository" is a place or system for safely and efficiently storing and preserving collected data. 【0590】 "Demand forecasting" is a method of predicting future energy use based on past data and optimizing the supply-supply balance. 【0591】 An "outlier" is a data value that deviates from the normal pattern and suggests an anomaly in the system. 【0592】 "Renewable energy supply" refers to the amount of electricity obtained from naturally derived energy sources such as wind, solar, and hydroelectric power, and is one of the sustainable energy resources. 【0593】 The "electricity market" is a market environment in which electricity is bought and sold, and it is an economic platform for adjusting the supply and demand of electricity. 【0594】 "User emotional state" refers to the state of a user's psychology and emotions, and is evaluated based on psychological and physiological data. 【0595】 "Dynamic adjustment" means changing settings and actions in real time according to the user's state. 【0596】 An "energy-saving proposal" is a set of guidelines for specific actions and settings presented to improve energy efficiency. 【0597】 An "information visualization dashboard" is an interface that visually displays complex data to support rapid decision-making. 【0598】 The energy management system according to this invention recognizes the user's emotions and optimizes energy use based on those emotions. This system mainly consists of three components: a server, a terminal, and a user. 【0599】 The server is the central management unit of this system. The server incorporates an emotion engine that uses image processing and acoustic analysis software to analyze the user's emotions in real time from their facial expressions and voice. This emotion data is integrated with energy usage information, and algorithms are applied to generate optimal energy-saving suggestions. The server stores this data in a database and performs predictive analysis that also takes historical data into account. 【0600】 The device is installed in a specific environment, such as a home or office, and uses various sensors to acquire user emotion data and environmental data. This includes cameras and microphones, and the device transmits data to a server through these devices. Based on instructions received from the server, the device adjusts environmental settings such as lighting, temperature, and music to match the user's emotions. 【0601】 Users experience environmental changes brought about by the system. The system dynamically controls energy use in response to the user's perceived stress and comfort levels. This allows users to receive energy-saving suggestions tailored to their emotional state and take necessary actions. 【0602】 As a concrete example, if a user is relaxing at dusk, the server instructs the terminal to adjust the lighting and play calming music. At the same time, the user is notified of energy-saving suggestions based on the next day's weather forecast. In this way, the system manages energy in conjunction with emotions, improving the user experience. 【0603】 An example of a prompt to a generative AI model is, "Based on the user's mood in the living room at dusk, please tell me how to adjust the lighting and music to be optimal." This prompt will result in recommendations for specific lighting and music selections. 【0604】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0605】 Step 1: 【0606】 The device collects data from the user's environment through sensors. This input data includes facial images captured by the camera and audio recorded by the microphone. The device preprocesses this data, removing noise and converting it to the required format, before transmitting it to the server in real time. 【0607】 Step 2: 【0608】 The server analyzes the data received from the terminal. At this stage, it uses image processing algorithms to analyze the user's facial expression data and voice analysis technology to extract emotional characteristics from the voice data. Through this processing of input data, the server generates output data that quantifies the user's emotional state. 【0609】 Step 3: 【0610】 The server integrates the generated emotional state data with energy usage data. It evaluates the current situation by comparing it with past energy usage patterns and, if necessary, develops an emotion-based energy-saving strategy. As an output, it creates instructional data that includes suggestions on what actions the user should take. 【0611】 Step 4: 【0612】 The device receives instruction data sent from the server. It then takes actions such as adjusting the brightness of the lighting or playing soothing music to match the user's mood. It also controls the temperature and makes other environmental adjustments to improve user comfort. 【0613】 Step 5: 【0614】 Users experience environmental feedback provided by the system. Furthermore, energy-saving suggestions are displayed as notifications on the user's screen, allowing them to make adjustments based on these suggestions. This output enables users to use their actions to reduce energy consumption. 【0615】 Through this series of steps, the system achieves flexible and efficient energy management based on user emotions, providing a comfortable user experience. 【0616】 (Application Example 2) 【0617】 Next, we will explain Application Example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal." 【0618】 Energy management in modern urban environments requires a balance between efficient energy use and user comfort. However, conventional systems do not take user emotions into consideration, making it difficult to achieve both improved comfort and energy efficiency. 【0619】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting energy usage data over time and storing it in a database, means for acquiring user emotional data using an intelligent function that recognizes emotional states and utilizing it for energy management, and means for dynamically making optimal energy-saving suggestions to the user based on the emotional data and energy usage data. This makes it possible to create a comfortable living space that reflects the user's emotions while enabling efficient energy use. 【0620】 "Energy usage data" refers to information on energy consumption over time and is fundamental data used for analysis and forecasting. 【0621】 A "database" is a structured information aggregation system for efficiently storing and managing information. 【0622】 An "algorithm" is a logical set of steps or computational methods that constitute a procedure for solving a specific problem. 【0623】 "Emotional state" refers to the state that indicates the user's psychological condition or mood. 【0624】 "Intelligent functions" refer to systems with advanced judgment capabilities designed to automatically perform specific tasks. 【0625】 "Prediction" is the act of anticipating future states or events based on current data and past patterns. 【0626】 "Renewable energy" refers to energy supplied from sustainable natural resources such as solar, wind, and hydroelectric power. 【0627】 "Electricity supply" refers to activities aimed at providing a stable supply of electricity to the demand side. 【0628】 "Visualized information" refers to information that is presented visually in a way that makes data easy to understand and interpret. 【0629】 "Decision-making" is the process of choosing what seems to be the best option from multiple choices and taking action accordingly. 【0630】 This invention is a system for achieving comfortable energy use in urban environments, taking into account user emotions. The system consists of three main elements: a server, a terminal, and a user. 【0631】 The server is a central management unit that collects energy usage data over time and stores it in a database. It analyzes emotional states using Google Cloud's Vision API and Speech-to-Text API to recognize user emotions. After analyzing the emotional data, it has an intelligent function that generates optimal energy-saving suggestions based on the user's state. The server sends the generated suggestions to the terminal and dynamically adjusts the environment. 【0632】 The terminal is installed in home and work environments and automatically adjusts energy management settings based on instructions received from a server. For example, if the terminal detects that the user is stressed, it adjusts the air conditioning and lighting settings to the user's preferences, contributing to stress reduction. Furthermore, the terminal provides data visualization information based on emotional data and energy usage data, offering a means for users to easily understand their energy consumption. 【0633】 Users can receive feedback on energy management through the system. This allows users to feel comfortable in an environment that adapts to their emotional state, while also becoming more conscious of efficient energy use. 【0634】 As a concrete example, if a user's emotions within a shopping mall are analyzed and it is determined that relaxation is needed, terminals within the facility will adjust the air conditioning and lighting and guide them to a relaxation area. At this time, the generating AI model will be given a prompt such as "We will notify you of suggestions for a comfortable experience in the mall" to suggest the optimal environment settings. 【0635】 Thus, the present invention makes it possible to achieve both comfort and efficient energy use by applying emotion recognition technology. 【0636】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0637】 Step 1: 【0638】 The server receives energy usage data and user facial and voice data transmitted from the terminal. The server uses Google Cloud's Vision API to analyze the facial data and the Speech-to-Text API to transcribe the voice data and recognize the user's emotional state. The input is facial and voice data, and the output is the analyzed emotional information. 【0639】 Step 2: 【0640】 The server combines analyzed emotional information with past energy usage data to generate energy-saving suggestions tailored to the user's situation. This is done using both data stored in a database and a generative AI model. The input is emotional information and energy data, and the output is energy-saving suggestions. 【0641】 Step 3: 【0642】 The server sends the generated energy-saving suggestions to the terminal. Based on these suggestions, the terminal automatically adjusts settings such as lighting and air conditioning. Specifically, it performs actions such as dimming lights and changing the air conditioning temperature. The input is the energy-saving suggestions, and the output is the actual changes to the environmental settings. 【0643】 Step 4: 【0644】 The terminal visualizes and displays the user's current energy usage and suggestions. This allows the user to intuitively understand their consumption situation and take appropriate action. The input is energy usage data and suggestions, and the output is visual feedback to the user. 【0645】 Step 5: 【0646】 Users experience the new environment created by the proposed energy-saving settings and send this information to the system as feedback, enabling further optimization. The input is user feedback, and the output is data used for future system adjustments. 【0647】 The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data. 【0648】 Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0649】 In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314. 【0650】 [Fourth Embodiment] 【0651】 Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment. 【0652】 As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server. 【0653】 The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network). 【0654】 The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52. 【0655】 The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46. 【0656】 Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision). 【0657】 Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner. 【0658】 The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes. 【0659】 Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56. 【0660】 The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30. 【0661】 The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. 【0662】 In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48. 【0663】 Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0664】 The energy management system of this invention mainly consists of three components: a server, a terminal, and a user. By combining these components, optimal energy management is achieved. 【0665】 System Overview 【0666】 server: 【0667】 The server is the core component of the system. It receives energy usage data transmitted from terminals and stores it in a time-series database. The data is appropriately pre-processed and used for demand forecasting. Based on historical data, the server builds and updates demand forecasting models using machine learning algorithms. It also takes immediate action when anomalies are detected and analyzes electricity market data to develop optimal electricity purchase plans. Furthermore, it analyzes energy usage data for each user and provides personalized energy-saving advice. 【0668】 Terminal: 【0669】 The terminal is a device that monitors energy usage in real time and transmits the acquired data to a server. If an abnormal value is detected, it immediately sends a notification to the server. The terminal receives control instructions from the server and controls various devices to optimize energy consumption in homes and offices. This reduces peak consumption and achieves energy savings. 【0670】 User: 【0671】 Users can review their energy consumption data and receive energy-saving advice as needed using an energy usage visualization dashboard provided by the system. The dashboard visually displays past and present energy usage patterns, helping users make informed decisions. 【0672】 Specific example 【0673】 For example, in a certain office building, energy use tends to be concentrated in the mornings and evenings on weekdays. Based on this trend, the server builds a predictive model and instructs terminals to automatically adjust the schedules of equipment and lighting to coincide with off-peak hours on weekdays when electricity costs are lower, instead of investing during weekday peaks. 【0674】 Furthermore, if a home has solar power installed, the server will take weather data into account, increasing energy storage during sunny days and optimizing electricity purchases from other sources on cloudy days. 【0675】 In this way, this system makes it possible to reduce wasteful consumption and lower costs through the prediction and optimization of energy use. 【0676】 The following describes the processing flow. 【0677】 Step 1: 【0678】 The terminal acquires energy usage data in real time from connected smart meters and sensors. After performing limited preprocessing on this data, the terminal periodically sends it to a server. 【0679】 Step 2: 【0680】 The server stores energy usage data received from terminals in a time-series database. This data undergoes a cleaning process to identify outliers and missing values. 【0681】 Step 3: 【0682】 The server trains a machine learning model based on historical energy usage data to predict energy demand. This model is continuously updated with new data to improve the accuracy of the predictions. 【0683】 Step 4: 【0684】 The terminal monitors energy usage in real time and immediately sends an anomaly alert to the server if it detects an abnormality that exceeds a set threshold. 【0685】 Step 5: 【0686】 When the server receives an anomaly alert, it quickly analyzes the anomaly and sends appropriate control instructions back to the terminal. The terminal adjusts its energy consumption according to these instructions. 【0687】 Step 6: 【0688】 The server predicts renewable energy generation based on weather data and optimizes the power supply schedule based on that information. 【0689】 Step 7: 【0690】 Users can check their energy consumption using a visual dashboard provided by the server. This dashboard displays past usage patterns and energy-saving advice. 【0691】 Step 8: 【0692】 The server analyzes trends in the electricity market and develops optimal electricity purchasing plans, thereby supporting users in using energy cost-effectively. 【0693】 (Example 1) 【0694】 Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0695】 In today's energy consumption environment, there is a need to maximize energy utilization efficiency while reducing energy waste. However, conventional energy management systems have limitations in providing real-time anomaly detection and personalized energy-saving suggestions, and lack flexibility in optimal energy procurement and equipment control. 【0696】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. 【0697】 In this invention, the server includes means for collecting energy consumption information based on the passage of time and storing it in a memory device, means for training a mathematical model that analyzes past energy usage trends and makes demand forecasts, and means for detecting abnormal data and automatically notifying. This makes it possible to efficiently optimize energy use and reduce waste. 【0698】 "Energy consumption information" refers to all data related to energy use detected within the system, including consumption amount, time, and usage trends. 【0699】 A "storage device" is a computer hardware component used to retain data for extended periods, primarily responsible for storing time-stamped energy data. 【0700】 A "mathematical model" refers to an algorithm or process that uses mathematical methods to simulate or predict actual energy use. 【0701】 "Abnormal data" refers to data that shows energy usage values ​​or patterns outside the normal range, and which may have a potential impact on the efficiency and safety of the system. 【0702】 "Automatic notification" refers to a function where the system sends information without human intervention based on set conditions or thresholds, informing users and administrators via email or SMS. 【0703】 "External environment" refers to factors that affect energy supply, and includes factors such as weather, temperature, and seasonal variations. 【0704】 "Renewable energy" refers to energy sources that exist in nature and are not depleted by human activities, and includes solar, wind, and hydroelectric power. 【0705】 "Market data" refers to economic information related to the buying and selling of energy, and typically includes electricity prices, supply, and demand. 【0706】 "Household appliances" refer to devices connected to an energy consumption system, and generally include electrical products and devices used to manage power consumption. 【0707】 An "information visualization device" refers to software or a device that displays data in a way that is easy for humans to understand, such as using graphs and charts to show trends and patterns in energy use. 【0708】 The energy management system of the present invention consists of three main components: a server, a terminal, and a user. This promotes efficient energy consumption. 【0709】 The server plays a central role in the system. It receives energy consumption information transmitted by each terminal and first stores it in a database. Specifically, it uses a general-purpose data management system, which is a time-series database. This data is cleaned using Python to remove outliers. Based on historical data, a demand forecasting model is built using a general-purpose mathematical model, a machine learning library. If abnormal data is detected, the system utilizes automated tools to automatically send notifications, ensuring that users receive information immediately. 【0710】 The terminal is connected to energy-consuming devices, collecting data in real time and sending it to a server. If an anomaly is detected in the data, the terminal immediately notifies the server. Based on instructions from the server, the terminal controls the energy devices to optimize energy consumption in homes and offices. 【0711】 Users can check their consumption patterns using an information visualization device. This makes it possible to visualize when energy consumption is high and which devices are using the most energy. A common data display library is used for visualization. Furthermore, users can receive energy-saving advice. 【0712】 Specific example 【0713】 For example, some households tend to have a high concentration of energy consumption during weekday evenings. In response, the server uses a predictive model to send instructions to the terminals to control each device to mitigate peak energy usage. Furthermore, the server generates an electricity purchase plan based on weather information to maximize the supply of renewable energy. 【0714】 Example of a prompt 【0715】 "Based on energy consumption data, please propose an optimal appliance control schedule for the home during off-peak hours." 【0716】 In this way, the entire system can achieve highly efficient energy management. 【0717】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0718】 Step 1: 【0719】 The terminal collects data in real time from energy-consuming devices. It takes energy consumption information from sensors as input. The terminal packages this data into packets and sends them to the server using a secure communication protocol. The output is energy data structured in a format that the server can process. 【0720】 Step 2: 【0721】 The server receives energy data sent from the terminal. The input is time-series energy data sent from the terminal. The server first stores the data in a database. Next, it cleans the data using a Python script to remove missing values ​​and outliers. The output is clean, analyzable energy data. 【0722】 Step 3: 【0723】 The server analyzes past energy usage patterns using clean energy data. The input is cleaned energy data. The server trains a demand forecasting model using machine learning algorithms. Libraries such as TensorFlow are used in this process. The output is a mathematical model for forecasting future energy demand. 【0724】 Step 4: 【0725】 The server monitors real-time energy data and uses a predictive model to detect anomalies. Inputs are the raw data received in real time and the predictive model generated by the server. When an anomaly occurs that exceeds a set threshold, the system triggers an automatic notification. The output is the sending of the anomaly notification. 【0726】 Step 5: 【0727】 The server optimizes energy supply and demand using external environmental information and market data. Inputs include weather information and electricity market trend data obtained from external APIs. Analysis software generates an optimal energy purchase plan. The output is an action plan for optimizing energy consumption. 【0728】 Step 6: 【0729】 The terminal controls household devices based on instructions from the server. The input is the control signal from the server. The terminal operates smart devices within the home and is optimized to reduce energy use during peak hours. The output is the optimized energy consumption profile. 【0730】 Step 7: 【0731】 Users check their energy usage using an information visualization device. The input is visualization data provided by the server. Based on the information provided by the dashboard, users make decisions about energy consumption. The output is the change or adjustment of behavior based on the decision. 【0732】 This series of processes enables efficient energy management across the entire system, resulting in cost reductions and optimized consumption. 【0733】 (Application Example 1) 【0734】 Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0735】 In recent years, there has been a growing demand for more efficient energy consumption in urban areas, and integrated energy management, particularly in smart cities, is crucial. Conventional energy management systems are inadequate in addressing the needs of individual users, making it difficult to provide real-time information or individualized energy-saving suggestions. Furthermore, building sustainable cities requires a system that maximizes the efficiency of renewable energy use and enables flexible electricity purchasing strategies tailored to energy demand. 【0736】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. 【0737】 In this invention, the server includes means for collecting energy consumption information over time and storing it in a data storage device, means for enhancing a calculation method for analyzing past energy consumption patterns and forecasting demand, and means for recognizing anomalies and automatically issuing warnings. This makes it possible to identify the different energy consumption patterns of each user and provide optimal energy-saving suggestions in a smart city. Furthermore, the provision of real-time information supports the efficient use of energy and promotes the realization of a sustainable city. 【0738】 "Energy consumption information" refers to data about the amount of energy used by each user and the system as a whole, and is collected over time. 【0739】 A "data storage device" is a system or device for storing collected energy consumption information over a long period of time. 【0740】 A "computational method" refers to an algorithm or analytical process used to predict energy consumption patterns using historical energy consumption data. 【0741】 An "outlier" is a value that deviates from the normal energy consumption pattern and suggests a system anomaly or problem. 【0742】 A "warning" is information that is sent to the user or administrator when an abnormal value is detected, in order to encourage prompt action. 【0743】 "Providing information in real time" refers to immediately providing users with the latest information regarding energy consumption. 【0744】 An "energy consumption pattern" refers to the tendencies or patterns that show how users or systems typically use energy. 【0745】 An "energy-saving suggestion" is a proposal for specific actions or setting changes recommended to users in order to save energy or improve efficiency. 【0746】 A "sustainable city" refers to a city that minimizes its environmental impact, makes effective use of resources, and provides an economically and socially stable environment. 【0747】 The server plays a central role in efficiently managing energy consumption in the city. Energy consumption information is collected in real time from sensors and terminals and transferred to the server. This information is stored long-term using data storage devices. During this process, BigQuery, a big data analytics tool from Google Cloud Platform, is used to analyze energy consumption patterns. 【0748】 The analyzed data is then incorporated into a model for predicting future energy consumption using machine learning algorithms based on TensorFlow. Based on the predictions obtained from this model, the server automatically recognizes anomalies, generates necessary warnings, and provides them to the user. 【0749】 Users can understand their own energy consumption patterns and receive real-time information via smartphones and other devices. This allows users to receive specific suggestions for energy saving and make decisions to optimize their energy use. A dashboard using React Native is used for information visualization, making cross-platform implementation easy. 【0750】 For example, when sunny weather is expected, users may be offered the option of using solar power to store energy. Such efficient energy management enhances the overall sustainability of the smart city. 【0751】 An example of a prompt to the generated AI model is: "I would like advice on efficient energy use based on urban energy consumption patterns. Please display the current energy report and suggest ways to avoid peak usage." 【0752】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0753】 Step 1: 【0754】 The terminal collects user energy consumption information in real time and transmits this data to a server. The input is energy usage data acquired from various sensors, and the output is data packets that are sent to the server. Through this process, the terminal understands the energy consumption patterns of each device in the home and office. 【0755】 Step 2: 【0756】 The server stores the received energy consumption information in a data storage device. The input is data packets sent from the terminal, and the output is a time-series organized dataset. The server then prepares this dataset for analysis using Google Cloud Platform's BigQuery. 【0757】 Step 3: 【0758】 The server uses a machine learning model based on stored data to predict future energy demand. The input is historical and current energy consumption data, and the output is an updated demand forecasting model. TensorFlow is used to train the model and achieve highly accurate predictions. 【0759】 Step 4: 【0760】 The server recognizes anomalies based on data obtained from the forecasting model. The input is the output of the demand forecasting model, and the output generates warning information when an anomaly is detected. The server immediately notifies the user of this warning and prompts them to take action. 【0761】 Step 5: 【0762】 Users receive energy-saving suggestions through an information visualization dashboard provided by the server. Inputs are warning information and forecast data from the server, and the output displays real-time energy-saving suggestions. Users can use this information to, for example, improve the efficiency of their solar power generation. 【0763】 Step 6: 【0764】 Once the user accepts the energy-saving suggestions, the terminal efficiently controls devices in the home or office again, moving on to the next step in energy management. The input is the user's selected control instructions, and the output is the execution of optimized operating patterns for the devices. 【0765】 Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions. 【0766】 This embodiment provides an energy management system that incorporates an emotion engine to recognize user emotions. This system consists of a server, a terminal, and a user, and by utilizing emotion data, it achieves a more human-like and intuitive energy management system. 【0767】 System Overview 【0768】 server: 【0769】 The server functions as a central management unit, aggregating all relevant data, including energy usage data and emotional data, into a database. The emotional engine resides on this server and recognizes emotions from user input such as facial expressions, voice, and text. It also integrates emotional data with energy usage data to determine appropriate energy-saving suggestions and interaction methods. 【0770】 Terminal: 【0771】 The terminals are placed in homes and offices and include sensors that provide elemental perception data and emotional data to a server. Based on instructions from the emotion engine, the terminals adjust their interfaces and configurations according to the user's emotional state. This allows, for example, room lighting, temperature control, and other settings to be dynamically changed according to the user's emotional state. 【0772】 User: 【0773】 Users experience emotion-based interaction through feedback provided by the system. If the user is stressed, the system refrains from displaying energy consumption information and promotes environmental adjustments that contribute to relaxation. Conversely, if the user is relaxed, it provides proactive energy-saving advice. 【0774】 Specific example 【0775】 In one household, while a user is relaxing in the living room at dusk, the system senses the user's relaxed state from their facial expression. Based on this, the server instructs the terminal to dim the living room lights slightly, play calming music, and notifies the user of casual energy-saving suggestions based on the next day's weather forecast. 【0776】 Furthermore, if the system determines that a user is experiencing stress while preparing for a meeting in an office environment, the terminal will adjust the air conditioning to the user's preference, and the server will temporarily suppress non-essential notifications. 【0777】 This invention enables dynamic adjustment of energy consumption management based on user emotions, aiming to improve user experience while simultaneously saving energy. 【0778】 The following describes the processing flow. 【0779】 Step 1: 【0780】 The device continuously acquires data such as the user's facial expressions, voice, and text from emotion sensors. This data is then transferred to the server after initial processing. 【0781】 Step 2: 【0782】 The server analyzes the received emotional data using an emotion engine to identify the user's emotional state. This emotional state is categorized into states such as "relaxed," "stressed," and "concentrated." 【0783】 Step 3: 【0784】 The server determines the optimal environment settings based on the user's emotional state, combined with energy usage data. For example, when a user is in a "stressed" state, the server may decide to reduce notifications or prioritize environmental adjustments. 【0785】 Step 4: 【0786】 The server instructs the terminal on the determined environment settings and interaction methods. These instructions may include adjusting lighting, setting the temperature, and playing music. 【0787】 Step 5: 【0788】 The device adjusts the home or office environment based on instructions from the server. For example, it might change the brightness of the lights or play soothing music. 【0789】 Step 6: 【0790】 Users experience the relaxing effects and energy-saving interactions provided by a carefully tuned environment. During this time, users can view energy-saving advice tailored to their emotional state. 【0791】 Step 7: 【0792】 The server collects data again after the interaction, uses it to further improve the algorithm, and incorporates these improvements into subsequent interactions. This feedback loop allows the system to continuously optimize the user experience. 【0793】 (Example 2) 【0794】 Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0795】 Conventional energy management systems primarily rely on predictions and energy-saving suggestions based on past energy usage patterns, and lack the flexibility to make adjustments that take into account the emotional state of users. As a result, it is difficult to properly manage the impact that users experience on energy consumption, and there are challenges in sufficiently improving energy-saving effects and user satisfaction. 【0796】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. 【0797】 In this invention, the server includes means for collecting energy usage information in a time series and storing it in an information repository, means for training a method for analyzing past energy usage patterns and making demand forecasts, and means for recognizing the emotional state of the user and dynamically adjusting energy use based on that state. This enables energy management based on the user's emotions, improving energy-saving effects and optimizing the user experience. 【0798】 "Energy usage information" refers to information about the amount and patterns of energy use, and is collected over time. 【0799】 An "information repository" is a place or system for safely and efficiently storing and preserving collected data. 【0800】 "Demand forecasting" is a method of predicting future energy use based on past data and optimizing the supply-supply balance. 【0801】 An "outlier" is a data value that deviates from the normal pattern and suggests an anomaly in the system. 【0802】 "Renewable energy supply" refers to the amount of electricity obtained from naturally derived energy sources such as wind, solar, and hydroelectric power, and is one of the sustainable energy resources. 【0803】 The "electricity market" is a market environment in which electricity is bought and sold, and it is an economic platform for adjusting the supply and demand of electricity. 【0804】 "User emotional state" refers to the state of a user's psychology and emotions, and is evaluated based on psychological and physiological data. 【0805】 "Dynamic adjustment" means changing settings and actions in real time according to the user's state. 【0806】 An "energy-saving proposal" is a set of guidelines for specific actions and settings presented to improve energy efficiency. 【0807】 An "information visualization dashboard" is an interface that visually displays complex data to support rapid decision-making. 【0808】 The energy management system according to this invention recognizes the user's emotions and optimizes energy use based on those emotions. This system mainly consists of three components: a server, a terminal, and a user. 【0809】 The server is the central management unit of this system. The server incorporates an emotion engine that uses image processing and acoustic analysis software to analyze the user's emotions in real time from their facial expressions and voice. This emotion data is integrated with energy usage information, and algorithms are applied to generate optimal energy-saving suggestions. The server stores this data in a database and performs predictive analysis that also takes historical data into account. 【0810】 The device is installed in a specific environment, such as a home or office, and uses various sensors to acquire user emotion data and environmental data. This includes cameras and microphones, and the device transmits data to a server through these devices. Based on instructions received from the server, the device adjusts environmental settings such as lighting, temperature, and music to match the user's emotions. 【0811】 Users experience environmental changes brought about by the system. The system dynamically controls energy use in response to the user's perceived stress and comfort levels. This allows users to receive energy-saving suggestions tailored to their emotional state and take necessary actions. 【0812】 As a concrete example, if a user is relaxing at dusk, the server instructs the terminal to adjust the lighting and play calming music. At the same time, the user is notified of energy-saving suggestions based on the next day's weather forecast. In this way, the system manages energy in conjunction with emotions, improving the user experience. 【0813】 An example of a prompt to a generative AI model is, "Based on the user's mood in the living room at dusk, please tell me how to adjust the lighting and music to be optimal." This prompt will result in recommendations for specific lighting and music selections. 【0814】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0815】 Step 1: 【0816】 The device collects data from the user's environment through sensors. This input data includes facial images captured by the camera and audio recorded by the microphone. The device preprocesses this data, removing noise and converting it to the required format, before transmitting it to the server in real time. 【0817】 Step 2: 【0818】 The server analyzes the data received from the terminal. At this stage, it uses image processing algorithms to analyze the user's facial expression data and voice analysis technology to extract emotional characteristics from the voice data. Through this processing of input data, the server generates output data that quantifies the user's emotional state. 【0819】 Step 3: 【0820】 The server integrates the generated emotional state data with energy usage data. It evaluates the current situation by comparing it with past energy usage patterns and, if necessary, develops an emotion-based energy-saving strategy. As an output, it creates instructional data that includes suggestions on what actions the user should take. 【0821】 Step 4: 【0822】 The device receives instruction data sent from the server. It then takes actions such as adjusting the brightness of the lighting or playing soothing music to match the user's mood. It also controls the temperature and makes other environmental adjustments to improve user comfort. 【0823】 Step 5: 【0824】 Users experience environmental feedback provided by the system. Furthermore, energy-saving suggestions are displayed as notifications on the user's screen, allowing them to make adjustments based on these suggestions. This output enables users to use their actions to reduce energy consumption. 【0825】 Through this series of steps, the system achieves flexible and efficient energy management based on user emotions, providing a comfortable user experience. 【0826】 (Application Example 2) 【0827】 Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0828】 Energy management in modern urban environments requires a balance between efficient energy use and user comfort. However, conventional systems do not take user emotions into consideration, making it difficult to achieve both improved comfort and energy efficiency. 【0829】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting energy usage data over time and storing it in a database, means for acquiring user emotional data using an intelligent function that recognizes emotional states and utilizing it for energy management, and means for dynamically making optimal energy-saving suggestions to the user based on the emotional data and energy usage data. This makes it possible to create a comfortable living space that reflects the user's emotions while enabling efficient energy use. 【0830】 "Energy usage data" refers to information on energy consumption over time and is fundamental data used for analysis and forecasting. 【0831】 A "database" is a structured information aggregation system for efficiently storing and managing information. 【0832】 An "algorithm" is a logical set of steps or computational methods that constitute a procedure for solving a specific problem. 【0833】 "Emotional state" refers to the state that indicates the user's psychological condition or mood. 【0834】 "Intelligent functions" refer to systems with advanced judgment capabilities designed to automatically perform specific tasks. 【0835】 "Prediction" is the act of anticipating future states or events based on current data and past patterns. 【0836】 "Renewable energy" refers to energy supplied from sustainable natural resources such as solar, wind, and hydroelectric power. 【0837】 "Electricity supply" refers to activities aimed at providing a stable supply of electricity to the demand side. 【0838】 "Visualized information" refers to information that is presented visually in a way that makes data easy to understand and interpret. 【0839】 "Decision-making" is the process of choosing what seems to be the best option from multiple choices and taking action accordingly. 【0840】 This invention is a system for achieving comfortable energy use in urban environments, taking into account user emotions. The system consists of three main elements: a server, a terminal, and a user. 【0841】 The server is a central management unit that collects energy usage data over time and stores it in a database. It analyzes emotional states using Google Cloud's Vision API and Speech-to-Text API to recognize user emotions. After analyzing the emotional data, it has an intelligent function that generates optimal energy-saving suggestions based on the user's state. The server sends the generated suggestions to the terminal and dynamically adjusts the environment. 【0842】 The terminal is installed in home and work environments and automatically adjusts energy management settings based on instructions received from a server. For example, if the terminal detects that the user is stressed, it adjusts the air conditioning and lighting settings to the user's preferences, contributing to stress reduction. Furthermore, the terminal provides data visualization information based on emotional data and energy usage data, offering a means for users to easily understand their energy consumption. 【0843】 Users can receive feedback on energy management through the system. This allows users to feel comfortable in an environment that adapts to their emotional state, while also becoming more conscious of efficient energy use. 【0844】 As a concrete example, if a user's emotions within a shopping mall are analyzed and it is determined that relaxation is needed, terminals within the facility will adjust the air conditioning and lighting and guide them to a relaxation area. At this time, the generating AI model will be given a prompt such as "We will notify you of suggestions for a comfortable experience in the mall" to suggest the optimal environment settings. 【0845】 Thus, the present invention makes it possible to achieve both comfort and efficient energy use by applying emotion recognition technology. 【0846】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0847】 Step 1: 【0848】 The server receives energy usage data and user facial and voice data transmitted from the terminal. The server uses Google Cloud's Vision API to analyze the facial data and the Speech-to-Text API to transcribe the voice data and recognize the user's emotional state. The input is facial and voice data, and the output is the analyzed emotional information. 【0849】 Step 2: 【0850】 The server combines analyzed emotional information with past energy usage data to generate energy-saving suggestions tailored to the user's situation. This is done using both data stored in a database and a generative AI model. The input is emotional information and energy data, and the output is energy-saving suggestions. 【0851】 Step 3: 【0852】 The server sends the generated energy-saving suggestions to the terminal. Based on these suggestions, the terminal automatically adjusts settings such as lighting and air conditioning. Specifically, it performs actions such as dimming lights and changing the air conditioning temperature. The input is the energy-saving suggestions, and the output is the actual changes to the environmental settings. 【0853】 Step 4: 【0854】 The terminal visualizes and displays the user's current energy usage and suggestions. This allows the user to intuitively understand their consumption situation and take appropriate action. The input is energy usage data and suggestions, and the output is visual feedback to the user. 【0855】 Step 5: 【0856】 Users experience the new environment created by the proposed energy-saving settings and send this information to the system as feedback, enabling further optimization. The input is user feedback, and the output is data used for future system adjustments. 【0857】 The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data. 【0858】 Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0859】 In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414. 【0860】 Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion. 【0861】 Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together. 【0862】 These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression. 【0863】 The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become. 【0864】 Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant. 【0865】 The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more." 【0866】 The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values. 【0867】 The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format. 【0868】 In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data. 【0869】 In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56. 【0870】 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. 【0871】 Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56. 【0872】 The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory. 【0873】 The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor. 【0874】 Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources. 【0875】 Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose. 【0876】 The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above. 【0877】 All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference. 【0878】 The following is further disclosed regarding the embodiments described above. 【0879】 (Claim 1) 【0880】 A means for collecting energy usage data over time and storing it in a database, 【0881】 A means of training an algorithm that analyzes past energy usage patterns to forecast demand, 【0882】 A means of detecting abnormal values ​​and automatically sending notifications, 【0883】 A means of predicting renewable energy supply taking external factors into consideration, 【0884】 A means of analyzing electricity market data to plan the optimal electricity purchase, 【0885】 An energy management system that includes various means. 【0886】 (Claim 2) 【0887】 The system according to claim 1, further comprising means for making individual energy-saving suggestions based on each user's energy usage patterns. 【0888】 (Claim 3) 【0889】 The system according to claim 1, further comprising means to provide a data visualization dashboard based on energy usage data and to support decision-making. 【0890】 "Example 1" 【0891】 (Claim 1) 【0892】 A means for collecting energy consumption information based on the passage of time and storing it in a memory device, 【0893】 A means of training mathematical models that analyze past energy usage trends and forecast demand, 【0894】 A means of detecting abnormal data and automatically notifying, 【0895】 A means of predicting the amount of renewable energy supply considering the external environment, 【0896】 A means of analyzing market data to plan optimal energy procurement, 【0897】 A means of observing in real time events that exceed a set threshold when an anomaly is detected, 【0898】 A means of controlling household appliances to optimize energy consumption, 【0899】 A system that includes this. 【0900】 (Claim 2) 【0901】 The system according to claim 1, further comprising means for making individual energy-saving suggestions based on each user's energy consumption trends. 【0902】 (Claim 3) 【0903】 The system according to claim 1, further comprising a means for providing an information visualization device based on energy usage information and supporting decision-making. 【0904】 "Application Example 1" 【0905】 (Claim 1) 【0906】 A means for collecting energy consumption information over time and storing it in a data storage device, 【0907】 A means to enhance calculation methods for forecasting demand by analyzing past energy consumption patterns, 【0908】 A means of recognizing abnormal values ​​and automatically issuing warnings, 【0909】 A means of estimating the amount of renewable energy supply considering external factors, 【0910】 A means of analyzing market data to plan the optimal electricity purchase, 【0911】 A means of identifying energy consumption patterns based on user signage information and providing information in real time, 【0912】 A system that includes each of the means. 【0913】 (Claim 2) 【0914】 The system according to claim 1, further comprising means for proposing individual energy-saving measures based on each user's energy consumption pattern. 【0915】 (Claim 3) 【0916】 The system according to claim 1, further comprising means for providing an information visualization screen based on energy consumption information to support decision-making. 【0917】 "Example 2 of combining an emotion engine" 【0918】 (Claim 1) 【0919】 A means of collecting energy usage information in chronological order and storing it in an information repository, 【0920】 A means of training a method for forecasting demand by analyzing past energy use patterns, 【0921】 A means of detecting abnormal values ​​and automatically sending notifications, 【0922】 A means of predicting renewable energy supply taking external factors into consideration, 【0923】 A means of analyzing electricity market information to plan the optimal electricity purchase, 【0924】 A means of recognizing the user's emotional state and dynamically adjusting energy use based on that state, 【0925】 A means of integrating emotional data and energy usage data to implement emotion-based energy saving suggestions, 【0926】 A system that includes this. 【0927】 (Claim 2) 【0928】 The system according to claim 1, further comprising means for making individual energy-saving suggestions based on the emotional state of each user. 【0929】 (Claim 3) 【0930】 The system according to claim 1, further comprising means for providing an information visualization dashboard based on energy usage data and emotional data to support decision-making. 【0931】 "Application example 2 when combining with an emotional engine" 【0932】 (Claim 1) 【0933】 A means for collecting energy usage data over time and storing it in a database, 【0934】 A means of training an algorithm that analyzes past energy usage patterns to forecast demand, 【0935】 A means of acquiring user emotional data using an intelligent function that recognizes emotional states and utilizing it for energy management, 【0936】 A means of detecting abnormal values ​​and automatically sending notifications, 【0937】 A means of predicting renewable energy supply taking external factors into consideration, 【0938】 A means of analyzing electricity market data to plan the optimal power supply, 【0939】 A system that includes this. 【0940】 (Claim 2) 【0941】 The system according to claim 1, further comprising means for dynamically providing optimal energy-saving suggestions to the user based on emotional data and energy usage data. 【0942】 (Claim 3) 【0943】 The system according to claim 1, further comprising means for integrating energy usage data and emotional data to provide data visualization information and support decision-making. [Explanation of symbols] 【0944】 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

[Claim 1] A means for collecting energy usage data over time and storing it in a database, A means of training an algorithm that analyzes past energy usage patterns to forecast demand, A means of detecting abnormal values ​​and automatically sending notifications, A means of predicting renewable energy supply taking external factors into consideration, A means of analyzing electricity market data to plan the optimal electricity purchase, An energy management system that includes various means. [Claim 2] The system according to claim 1, further comprising means for making individual energy-saving suggestions based on each user's energy usage pattern. [Claim 3] The system according to claim 1, further comprising means for providing a data visualization dashboard based on energy usage data to support decision-making.

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