system

Abstract

We provide the system. [Solution] In order to conduct nationwide electricity demand and supply forecasts, a means equipped with artificial intelligence to collect and analyze historical data and weather information is used. A means for generating commands to control the discharge and charging of the battery of a communication facility based on the aforementioned prediction, A means for transmitting the aforementioned command to a communication facility and managing the energy of the storage battery, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] The present invention focuses on the fact that in a communications carrier facing the problem of soaring power costs caused by increased power demand and supply instability, means for solving these problems are required. Furthermore, in a conventional energy management system, inefficient charge and discharge based on low - accuracy prediction is being performed, which has an adverse effect on the operation efficiency of communication facilities. Therefore, it is an important issue to reduce power costs based on more accurate prediction and automatic control and ensure supply stability.

Means for Solving the Problems

[0005] This invention solves the above problems by providing a system that uses a generation AI to predict nationwide power demand and supply in real time and automatically controls the optimal discharge and charging of batteries in communication facilities based on the results. Specifically, the generation AI integrates historical data and the latest weather information to make highly accurate supply and demand forecasts. Based on this forecast, the system instructs communication facilities to manage energy efficiently, thereby reducing power costs and stabilizing supply. Furthermore, it explores methods for monetizing untapped resources by making the most of excess solar energy. In this way, this invention simultaneously aims to maximize and stabilize the efficiency of power management in communication facilities.

[0006] "Nationwide" refers to the entire region of Japan, signifying a broad area that is not limited to a specific region or area.

[0007] "Electricity demand" refers to the amount of electricity consumers need at a given time, and is an important indicator for determining the amount of energy supplied.

[0008] "Supply forecasting" is the process of predicting the amount of electricity to be supplied over a specific period in the future, and is an activity aimed at maintaining a proper balance between supply and demand.

[0009] Artificial intelligence is a technology that uses large amounts of data to perform learning and reasoning, and is a system that has the ability to mimic human intellectual activity.

[0010] "Communication facilities" refer to the equipment and infrastructure necessary for conducting communication business, and are primarily used for sending and receiving information.

[0011] A "storage battery" is a rechargeable battery that can store electricity and discharge it when needed; it is a device that stores and supplies energy.

[0012] "Discharging" refers to the process of extracting electricity stored in a battery and supplying it to the outside, and is an operation to meet electricity demand.

[0013] "Charging" refers to the process of storing electricity in a battery, a process designed to prepare for future electricity demand.

[0014] "Generating a command" means creating instructions for a system or device to perform a specific action, and these instructions are then transmitted as control signals.

[0015] "Solar energy" refers to the energy emitted from the sun and is a renewable energy source that is primarily converted into electricity using solar power generation.

[0016] "Surplus electricity" refers to electricity produced in excess of demand, and is an amount of energy that would normally be wasted. [Brief explanation of the drawing]

[0017] [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]Shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Mode for Carrying Out the Invention

[0018] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0019] First, the terms used in the following description will be described.

[0020] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of a plurality of arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of a plurality of 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.

[0021] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0022] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0023] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0024] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0025] [First Embodiment]

[0026] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0027] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0028] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0029] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0030] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0031] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0032] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0033] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0035] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0036] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0037] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0038] The present invention provides a comprehensive energy management system for optimizing battery management in communication facilities. This system utilizes generative AI to predict nationwide power supply and demand in real time, enabling optimal battery control. The program's processing is described in detail below.

[0039] ---

[0040] Server operation

[0041] The server first performs extensive data collection. It acquires nationwide power consumption data and weather information from multiple databases in real time and organizes this data into an analyzable format. This data is input into a generating AI model to perform highly accurate power demand and supply forecasts. Based on these forecasts, the server calculates the optimal battery discharge and charging schedule for each communication facility. The calculation results are transmitted to the communication facilities as commands in a timely manner.

[0042] ---

[0043] Terminal operation

[0044] The terminal receives battery control commands transmitted from the server. Following these commands, the terminal appropriately controls the battery, charging or discharging as needed. For example, during periods when a surge in power demand is predicted, the terminal completes charging in advance, ensuring stable operation by supplying power as predicted. Furthermore, to effectively utilize surplus solar power, the terminal constantly monitors the amount of power generated and chooses whether to store the excess in the battery or supply it to other facilities.

[0045] ---

[0046] User actions

[0047] Telecommunications carriers, as users, can check real-time energy supply and demand conditions and battery status through the system's dashboard. Users can adjust their energy management policies based on the forecast data to achieve optimal operation. Furthermore, if there is a large discrepancy between the forecast and actual results, the forecast accuracy can be further improved by updating the AI ​​model or fine-tuning the algorithm.

[0048] Thus, the present invention enables automatic and efficient energy management of storage batteries in communication facilities, effectively achieving reductions in power costs and stability of power supply.

[0049] The following describes the processing flow.

[0050] Server processing steps

[0051] Step 1:

[0052] The server collects electricity consumption data and weather information from across the country. This includes access from power companies and weather information providers, ensuring real-time data updates.

[0053] Step 2:

[0054] The server preprocesses the collected data. By formatting it on an hourly basis, imputing missing values, and removing outliers, it constructs a dataset suitable for input to generative AI models.

[0055] Step 3:

[0056] The server uses pre-processed data to run a generating AI that forecasts electricity demand and supply. The generated forecast data is updated hourly, identifying periods of high demand and periods of supply surplus.

[0057] Step 4:

[0058] Based on the prediction results, the server calculates discharge and charge commands for the communication facility's batteries. An optimization algorithm is used to balance power cost reduction with supply stability.

[0059] Step 5:

[0060] The server distributes the calculated control commands to each communication facility. This initiates specific energy management operations at the terminals in each facility.

[0061] ---

[0062] Terminal processing steps

[0063] Step 1:

[0064] The terminal analyzes the control commands received from the server and compares them with the current status of the facility's battery.

[0065] Step 2:

[0066] The terminal controls the battery according to instructions. For example, if instructed to charge in preparation for peak demand, it will immediately begin charging and continue until the required capacity is reached.

[0067] Step 3:

[0068] When a discharge command is issued, the terminal receives power from the battery. At this time, dynamic output adjustments are made in response to increases and decreases in demand, minimizing fluctuations in supply.

[0069] Step 4:

[0070] The terminal monitors the output of solar power generation and, if surplus power is generated, decides whether to store it in a battery or supply it externally.

[0071] Step 5:

[0072] The terminal feeds energy management information back to the server, where it is used as data necessary for generating the next command.

[0073] ---

[0074] User operation steps

[0075] Step 1:

[0076] Users access the system's dashboard to view real-time forecast data and actual values.

[0077] Step 2:

[0078] Users adjust their energy management policies based on prediction accuracy and actual performance. They apply new settings to the platform as needed.

[0079] Step 3:

[0080] Users check the utilization status of surplus solar power and evaluate possible monetization options.

[0081] Step 4:

[0082] Based on their feedback, users can request adjustments to the generation AI model to improve the system's prediction accuracy and prepare for the next operational cycle.

[0083] (Example 1)

[0084] 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."

[0085] Energy consumption in modern telecommunications facilities is leading to increased electricity costs and supply instability due to the lack of efficient and stable energy management systems. Furthermore, the effective utilization of renewable energy is insufficient, resulting in wasted surplus energy. Given these issues, there is a need to develop systems that accurately predict energy supply and demand and optimally control battery storage.

[0086] 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.

[0087] In this invention, the server includes means equipped with an information processing device that collects and organizes weather data and historical consumption statistics in order to predict energy consumption and supply nationwide; means equipped with a computing device that inputs the information into a generation model and plans the discharge and charging of energy storage devices of communication facilities based on the prediction results; and means equipped with a communication device that transmits the plan as a command to communication facilities in various locations and controls the power of the energy storage devices. This enables highly accurate energy supply and demand forecasting and efficient battery management.

[0088] "Forecasting nationwide energy consumption and supply" means estimating the demand for electricity and the available supply in each region over time, based on historical data and weather information.

[0089] "Weather data and historical consumption statistics" refers to information on weather conditions and historical data on past electricity usage, which serve as the basic information for constructing predictive models.

[0090] "Means equipped with an information processing device" refers to a configuration that includes a computer system or software for performing data collection, organization, and analysis.

[0091] "Inputting data into a generative model" refers to the process of inputting the necessary data into a prediction algorithm and performing calculations and analyses based on that data.

[0092] "Planning the discharge and charging of energy storage devices" means creating a schedule to optimize the release and storage of energy in batteries based on electricity demand forecasts.

[0093] A "means equipped with a computing device" refers to a set of hardware and software for performing complex calculations and efficiently conducting data analysis and predictions.

[0094] "Means equipped with communication devices" refers to a configuration that includes network devices capable of transmitting and receiving commands and data to and from various communication facilities.

[0095] The system of this invention is an integrated system for optimizing energy management in communication equipment. Servers, terminals, and users cooperate with each other to control battery storage and maintain a balance between energy supply and demand.

[0096] The server first collects nationwide energy consumption and supply data, along with weather information. This is done by obtaining information in real time using databases and weather APIs. The obtained data is organized into a format that can be analyzed by the information processing device and input into a generating AI model. The AI ​​model used here is designed to predict electricity demand with high accuracy, taking into account historical consumption data and weather conditions. For example, it might use a prompt such as, "What is the expected maximum electricity demand for the next 48 hours? The data will include weather information and consumption data for the past 24 hours."

[0097] Based on the generated prediction results, the server plans the discharge and charging schedules for the batteries of each communication facility. During this process, a computing device is used to efficiently perform the necessary calculations. The planned schedule is transmitted as a command to the communication facilities in each location via the communication device. This ensures that the charging and discharging of the batteries is properly managed. For example, if it is predicted that power demand will peak at a specific time, the charging of the batteries will be completed in advance to ensure power is supplied at that time.

[0098] The terminal controls the charging and discharging of the battery based on commands received from the server. It also takes measures to efficiently utilize surplus renewable energy. For example, if a surplus of solar power is predicted, it reduces energy waste by supplying the excess to other facilities or storing it in the battery.

[0099] Telecommunications carriers, as users, can monitor real-time energy supply and demand conditions and battery status through a dashboard provided by the system. This allows users to adjust their energy management policies as needed and achieve optimal operation. Furthermore, by reviewing the prediction results of the generated AI model and making adjustments as necessary, it is possible to improve the accuracy of the system's predictions.

[0100] Thus, the present invention provides a system that improves the efficiency of energy management in communication equipment and enables highly accurate power supply and demand forecasting and optimal battery operation.

[0101] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0102] Step 1:

[0103] The server collects electricity consumption data and weather information from across the country. This involves retrieving necessary information from databases and weather APIs of various partner organizations. Historical electricity consumption statistics and the latest weather data from each region are used as input. Based on these inputs, the data is organized into CSV format. The output is data in an analyzable format. The server then feeds this organized data to the next processing step.

[0104] Step 2:

[0105] The server inputs the organized data into the generating AI model. Specifically, it uses prompts such as, "Please tell me the expected maximum power demand for the next 48 hours. The data includes weather information and consumption data for the past 24 hours." The input data includes the analyzable data obtained in step 1. The AI ​​model analyzes the input data using machine learning algorithms and predicts future power demand. The output provides an estimate of predicted power consumption and supply.

[0106] Step 3:

[0107] The server calculates the discharge and charge schedules for the batteries of each communication facility based on the prediction results of the AI ​​model. The input is the prediction results from the AI ​​model, which include the predicted power demand values ​​for each time period. The server uses this data to generate the optimal battery storage schedule. The output is a specific charge and discharge timeline for each facility.

[0108] Step 4:

[0109] The server transmits the calculated schedule to the terminals of each communication device via the communication equipment. Transmission uses the internet, sending command data as packets to the network address of each terminal. The input is the schedule created in step 3. The output is that the terminals that receive the commands are ready to begin specific control.

[0110] Step 5:

[0111] The terminal receives commands from the server and initiates specific charging and discharging control. The input is a schedule command from the server. The terminal checks its internal clock and manages the battery appropriately by following the instructed schedule. Specifically, it starts charging the battery at the designated time and discharges it according to the predicted demand period. As an output, efficient energy management is achieved.

[0112] Step 6:

[0113] Users monitor real-time energy supply and demand conditions through a dashboard and adjust system settings as needed. Recorded operational and forecast data from the system are provided as input. Based on this, users can re-evaluate their energy management policies and implement necessary changes. Specifically, this involves viewing graphs and numerical displays on the dashboard and adjusting settings. As an output, the system's operational efficiency improves as a result of user adjustments.

[0114] (Application Example 1)

[0115] 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."

[0116] This invention aims to optimize and manage energy consumption in facilities, and in particular to stabilize the energy supply necessary for the operation of multiple automated equipment groups while improving the efficiency of renewable energy utilization. The goal is to achieve energy cost reduction and improved operational efficiency of the facilities.

[0117] 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.

[0118] In this invention, the server includes intelligent processing means for collecting and analyzing historical data and weather information in order to perform nationwide power demand and supply forecasts; control means for generating commands to control the discharge and charging of the facility's energy storage devices based on the forecasts; execution means for transmitting the commands to the facilities and performing energy management of the energy storage devices; and means for calculating and providing operating procedures to optimize the amount of energy required to operate a group of automated devices installed in multiple facilities. This enables more efficient energy management and more stable supply.

[0119] An "intelligent processing device" is a device that has the function of collecting and analyzing historical data and weather information in order to perform nationwide electricity demand and supply forecasts.

[0120] "Control means" refers to a system that generates commands to control the discharge and charging of the facility's energy storage devices based on predicted power demand and supply information.

[0121] An "execution means" is a device that has the function of transmitting the generated command to the equipment and performing the operations necessary to manage the energy of the energy storage device.

[0122] "Means for calculating and providing operating procedures" refers to a system that calculates an operating plan to optimize the amount of energy required to operate a group of automated devices within multiple facilities, and provides that plan to the facilities.

[0123] "Facilities" refers to a collection of devices and equipment installed for a specific purpose, including the operation of energy management and automation equipment.

[0124] An "automation system" is a collection of multiple machines and robots that are deployed in a factory or facility to perform routine tasks automatically.

[0125] A "energy storage device" is a device that stores surplus energy and can discharge or charge it according to the demand for electricity.

[0126] "Natural energy" refers to energy derived from natural forces such as solar, wind, and hydroelectric power, and is renewable energy that is generated in a sustainable manner.

[0127] A system for implementing the present invention includes intelligent processing means, control means, execution means, and means for calculating and providing operating procedures. The central server of the system is equipped with intelligent processing means that use a generated AI model to predict power demand and supply, and create an optimal charge and discharge schedule for the energy storage devices within the facility based on the results.

[0128] The server's control system generates optimal energy management commands based on prediction results obtained from the intelligent processing system, according to the operating status of the equipment. These commands are transmitted to each piece of equipment through the execution system.

[0129] Terminals installed within the facility control the charging and discharging of energy storage devices according to commands sent from the server, thereby achieving energy management. The terminals monitor energy usage in real time and make necessary adjustments to achieve energy-saving operation.

[0130] As a concrete example, energy costs can be reduced by providing operating procedures that optimize the energy use of automated equipment installed in a factory. For instance, by fully charging energy storage devices in advance during peak factory operating hours and effectively utilizing surplus electricity from renewable energy sources, external electricity demand can be suppressed.

[0131] Users can monitor energy usage and the status of energy storage devices in real time through a dashboard designed for easy facility management. Furthermore, it's possible to adjust the system's algorithms based on new data to further improve the accuracy of the generated AI models.

[0132] An example of a prompt message related to the operation of this system would be: "Based on the energy consumption patterns within the factory and the weather forecast for the next three days, predict the optimal battery charging and discharging schedule."

[0133] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0134] Step 1:

[0135] The server collects nationwide electricity consumption data and weather information in real time from an external database. This process converts the raw data obtained via API into structured data, making it easier for predictive models to process. The input consists of electricity consumption data and weather information, while the output is data organized into an analyzable format.

[0136] Step 2:

[0137] The server uses a generative AI model to forecast electricity demand and supply based on the data organized in Step 1. In this process, the forecasting algorithm calculates the fluctuations in demand and supply over the next few days and outputs them as forecast data. The input is structured electricity consumption and weather data, and the output is forecasted demand and supply data.

[0138] Step 3:

[0139] The server's control system generates an optimal charge / discharge schedule for the energy storage devices in the facility based on the prediction results from step 2. This process uses the prediction data and the current energy storage status as input to create and output an optimal energy management command. In operation, it simulates multiple scenarios to determine the most efficient operational policy.

[0140] Step 4:

[0141] The terminal controls the energy storage device based on the charge / discharge schedule sent from the server. The input is a command from the server, and the output is the actual charge / discharge operation of the energy storage device. The terminal monitors the status of the energy storage device in real time and makes adjustments to deal with unexpected situations.

[0142] Step 5:

[0143] Users monitor the overall energy usage of the facility and the operating status of energy storage devices through a dashboard. Input is energy management data provided by the server, and output is a visualization of the energy data that users can visually confirm. Users can easily adjust facility management policies.

[0144] Step 6:

[0145] The user updates the generated AI model as needed to improve prediction accuracy. This process strengthens the algorithm by incorporating new data into the model. The input is the latest energy usage data and parameters of the prediction model, and the output is the new, improved prediction model. For example, a prompt such as "Predict the optimal battery charging and discharging schedule based on the energy consumption patterns in the factory and the weather forecast for the next three days" is used.

[0146] 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.

[0147] This invention incorporates a function that considers user emotions into a system that optimizes the balance between power supply and consumption in real time. This reduces the psychological burden on the user while achieving efficient energy management. The program's processing is described in detail below.

[0148] ---

[0149] Server operation

[0150] The server features a generative AI that forecasts power demand and supply, as well as an emotion engine. First, the server forecasts power supply and demand using power consumption data and weather information collected from across the country. Based on this data, it generates optimal discharge and charging commands for the batteries of each communication facility. At the same time, it also takes the user's emotional state into consideration, and the emotion engine analyzes the user's emotions from a series of data. For example, if the user is feeling stressed, it generates commands to optimize notifications and relax energy management policies, taking into account periods of low load.

[0151] ---

[0152] Terminal operation

[0153] The terminal receives commands from the server and controls the battery storage of the communication facility. It also makes adjustments to improve the user experience based on commands from the emotion engine. For example, it optimizes the timing and content of energy usage notification messages according to the user's emotions. Furthermore, it can adjust the output of solar power generation and the use of surplus power based on user feedback, thereby increasing user satisfaction.

[0154] ---

[0155] User actions

[0156] Users can monitor energy supply and demand in real time through the dashboard, as well as understand their own emotional state. Based on the data provided by the emotion engine, users can fine-tune their energy management policies and choose an operating method that suits their emotional state. For example, if stress levels are high, notifications may be suppressed and only reminders may be displayed.

[0157] Thus, the present invention is a system that simultaneously achieves power management and user comfort. By considering emotions in energy management, it is possible to enhance user satisfaction while utilizing energy efficiently.

[0158] The following describes the processing flow.

[0159] Server processing steps

[0160] Step 1:

[0161] The server acquires nationwide electricity consumption data and weather information. This allows it to begin forecasting electricity demand and supply for the following day. Data is collected in real time from multiple sources.

[0162] Step 2:

[0163] The server refines the acquired data and cleans up outliers. This process prepares a dataset suitable for the generative AI model.

[0164] Step 3:

[0165] The server uses AI generation to predict future power supply and demand. Predictions are made over time spans of several hours to several days, and the results are stored in a database.

[0166] Step 4:

[0167] The server activates an emotion engine to evaluate the user's emotional state. During this process, specific patterns and trends are identified.

[0168] Step 5:

[0169] The server integrates power forecasts and user sentiment data to create optimal battery control commands. Based on the sentiment data, it adjusts the content and timing of notifications.

[0170] Step 6:

[0171] The server transmits the generated commands to the terminals in the communication facility, instructing them on appropriate energy management and user notifications.

[0172] ---

[0173] Terminal processing steps

[0174] Step 1:

[0175] The terminal analyzes the commands received from the server and determines the corresponding operation of the battery.

[0176] Step 2:

[0177] The terminal checks the current charge status of the battery and starts charging or discharging according to the server's instructions.

[0178] Step 3:

[0179] The terminal monitors surplus electricity from solar power generation and stores it in a battery or supplies it to other loads as needed.

[0180] Step 4:

[0181] The device optimizes notification messages to the user in response to commands from the emotion engine. This includes fine-tuning the content and timing of notifications.

[0182] Step 5:

[0183] The device feeds energy and emotional data back to the server, which is then used for subsequent calculations and optimization processes.

[0184] ---

[0185] User operation steps

[0186] Step 1:

[0187] Users log into the dashboard to check their emotional state along with their energy usage.

[0188] Step 2:

[0189] Based on recommendations from the emotion engine, users can adjust their energy management settings. For example, they can change the frequency and level of detail of notifications.

[0190] Step 3:

[0191] Users provide feedback on energy and emotions, contributing to improvements in the system's predictive accuracy and emotion recognition.

[0192] Step 4:

[0193] Users will use the provided information to consider new energy management strategies and minimize their own energy costs.

[0194] (Example 2)

[0195] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0196] With current technological capabilities, it has been difficult to not only efficiently manage the balance between power supply and demand, but also to implement energy management that takes into account the psychological state of users. In particular, when a user's emotional state influences power usage and management policies, flexible responses tailored to the situation are required. However, existing systems have failed to meet this requirement, leading to a decline in user satisfaction.

[0197] 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.

[0198] This invention includes a server equipped with artificial intelligence for collecting and analyzing historical information and weather conditions to predict nationwide energy demand and supply; a means for generating commands to adjust the discharge and charging of energy storage devices in facilities with communication capabilities based on the predictions; and an emotion engine for analyzing the user's emotional state, and a means for generating adjustment commands to improve the user experience based on the analysis results. This enables not only more efficient energy management but also flexible optimization of energy use in response to the user's emotions.

[0199] "Artificial intelligence" is a technology that analyzes past information and weather conditions to make predictions necessary for optimizing energy demand and supply.

[0200] A "facility with communication capabilities" is infrastructure that allows for remote information exchange and the reception of energy management commands.

[0201] A "power storage device" is a device that temporarily stores electricity and discharges and charges it as needed.

[0202] The "emotion engine" is a technology that analyzes the user's emotional state and generates commands to adjust energy management policies based on that state.

[0203] The "means for generating instructions" are mechanisms for creating guidelines for optimal energy use based on predicted energy supply and demand and user sentiment analysis data.

[0204] "Energy control" refers to the operation of adjusting the discharge and charging of energy storage devices to balance supply and demand.

[0205] This invention incorporates a function that takes user emotions into a system that optimizes the balance between power supply and consumption in real time. For the invention to be implemented, the server, terminal, and user each need to perform specific actions.

[0206] The server first collects energy consumption data and weather information from across the country. This data collection uses database software, and a generated AI model executes a prediction algorithm based on historical datasets. This AI model predicts power demand and supply, and based on this, discharge and charge commands are generated for the energy storage devices at each communication facility.

[0207] Simultaneously, the server uses an emotion engine to analyze the user's emotional state. This emotion engine analyzes the user's data patterns to determine whether they are experiencing stress. Based on this analysis, instructions are generated to optimize the content and timing of notifications. For example, the server might generate a prompt such as, "If the user is experiencing stress, refrain from sending notifications."

[0208] The terminal controls the energy storage devices of the communication facility based on commands received from the server. The terminal also executes applications to adjust the content and timing of user notifications according to commands from the emotion engine. This enables improved energy efficiency and a better user experience.

[0209] Users can monitor energy supply and demand in real time through the dashboard interface and fine-tune their energy management policies based on data provided by the emotion engine. For example, if a user is stressed, they can change settings to reduce notifications about energy usage.

[0210] In this way, this invention can simultaneously improve power management and user satisfaction. By utilizing a generative AI model and an emotion engine, efficient energy use is achieved while also ensuring user comfort.

[0211] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0212] Step 1:

[0213] The server collects energy consumption data and weather information from across the country. It uses historical electricity consumption data and current weather information as input data. This data is managed by database software. Based on this data, the server runs a generated AI model to predict electricity demand and supply. The output of this calculation is the predicted demand for each communication facility. Specifically, the server uses the prediction model to analyze consumption patterns and forecast future electricity supply and demand.

[0214] Step 2:

[0215] The server uses the supply and demand forecast results from Step 1 to generate commands that optimize the discharge and charging of energy storage devices. The inputs are the current supply capacity and energy storage capacity at each communication facility. Based on the generated AI model, the server calculates efficient energy distribution and discharge timing. The output is a command to be sent to each communication facility. This command includes a specific discharge and charging schedule.

[0216] Step 3:

[0217] The server activates the emotion engine to analyze the user's emotional state. Input includes the user's past behavioral data and real-time operation logs. The emotion engine processes this data and assesses how stressed the user is. As a result of the analysis, it outputs an emotional state score. Specifically, it analyzes changes in the user's activity and qualitative responses to generate the emotional score.

[0218] Step 4:

[0219] The server generates commands to improve the user experience based on the emotional state score obtained from the emotion engine. The input is the emotional evaluation and energy management policy obtained in step 3. The server generates prompts to optimize the content and timing of notifications that reflect the emotional score. The output is the energy management adjustment plan sent to the terminal. As a specific example, the server might present a prompt such as "Delay notifications to reduce stress."

[0220] Step 5:

[0221] The terminal executes commands received from the server and controls the energy storage devices of the communication facility. Inputs are discharge / charge commands from the server and user experience adjustment commands. Through these commands, the terminal operates the energy management system to provide an optimized user experience. Outputs include optimized energy consumption and an improved user notification schedule. Specifically, the terminal monitors energy usage in real time and makes user interface adjustments, such as delaying notifications.

[0222] Step 6:

[0223] Users can monitor energy supply and demand in real time via a dashboard and fine-tune their energy management policies according to their emotional state. Inputs are energy consumption data and emotional state information provided by the dashboard. Outputs are the new energy usage policies and notification settings configured by the user. Specifically, users can choose to reduce notifications during stressful situations. The goal of this process is to achieve efficient energy use while maintaining user comfort.

[0224] (Application Example 2)

[0225] 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 device 14 will be referred to as the "terminal."

[0226] Conventional power management systems fail to consider the emotional stress of users, potentially leading to decreased user satisfaction even if energy management is efficient. Furthermore, simply optimizing the balance between power consumption and supply is insufficient to guarantee comfort within the home. Moreover, adjustments to optimize energy consumption within the home are often inadequate.

[0227] 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.

[0228] In this invention, the server includes means equipped with an intelligent system for collecting and analyzing historical data and weather information to perform nationwide power demand and supply forecasts; means for generating commands to control the discharge and charging of energy storage devices in communication facilities based on the forecasts; and an analysis device for detecting the user's emotional state and adjusting the operating state of home appliances based on that emotion. This enables efficient energy management that takes the user's emotions into consideration and improves comfort within the home.

[0229] An "intelligent system" is a device that has analytical functions to predict electricity demand and supply based on past data and weather information.

[0230] "Communication facilities" refer to infrastructure equipment for receiving and executing commands for discharging and charging electricity.

[0231] A "power storage device" is an energy storage device that efficiently stores electricity and discharges it as needed.

[0232] "Means for generating commands" refers to a method for creating control commands that enable optimal energy storage and discharge based on information about power supply and demand.

[0233] An "analysis device" is a data analysis device that detects the user's emotional state and adjusts the operation of home appliances accordingly.

[0234] To implement this invention, the server, terminal, and user each play their respective roles.

[0235] The server uses an intelligent system to predict nationwide power demand and supply. This requires a program to collect and analyze historical data and weather information. Possible software options include functions built with data analysis languages ​​such as Python and R, or TENSORFLOW® and PyTorch, which are convenient for operating AI models. Based on the generated prediction data, the server issues commands to the energy storage devices of communication facilities. In particular, to perform analysis that takes into account the emotional state of users, it utilizes sentiment analysis APIs (e.g., Amazon Rekognition or Google® Cloud Vision) to optimize power management according to the user's stress level.

[0236] The terminal controls the discharge and charging of the energy storage device based on commands received from the server. Furthermore, the terminal is equipped with a function to adjust the operation of home appliances based on emotion analysis results. Smart speakers and IoT devices with internet connectivity can be used to control smart home appliances in the home. This makes it possible to take specific actions when the user's stress level is high, such as changing the lighting to a softer color or setting the air conditioner to a comfortable temperature.

[0237] Users operate the application through their smartphones. The application includes a dashboard where users can check energy supply and demand status and their own emotional state in real time. An interface is provided that allows users to fine-tune energy management policies based on their input. For example, there is a prompt message that says, "When the user is relaxed, please instruct the system to change the lighting tone to a warm color and set the air conditioner temperature to 23 degrees." This allows users to create a comfortable and efficient home environment.

[0238] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0239] Step 1:

[0240] The server performs nationwide electricity demand and supply forecasts. It receives historical electricity consumption data and weather information as input. This data is analyzed, and a generative AI model is used to perform data calculations to predict future supply and demand. The output is forecast data that guides optimal energy management policies for each communication facility.

[0241] Step 2:

[0242] The server uses an emotion analysis API to detect the user's emotional state. It receives data of the user's facial expressions and voice from the camera and microphone as input. Data processing involves image recognition and voice analysis to determine the user's emotions. The output is data indicating the user's emotional state.

[0243] Step 3:

[0244] The server integrates power forecast data and user emotional state data to generate commands for adjusting the operation of household appliances. Using a generative AI model, it creates optimal operating instructions for each appliance from the input data. The output is a control command containing specific operating instructions for each appliance.

[0245] Step 4:

[0246] The terminal executes control commands received from the server. It receives command data from the server as input and sends commands to smart home appliances in the home. It adjusts the settings of the appliances using a device control protocol. This results in a comfortable appliance state that matches the user's preferences as output.

[0247] Step 5:

[0248] The user views the dashboard through a smartphone application. The application receives data from the device regarding the current energy supply and demand situation and the user's emotional state as input. It visualizes this data and displays it to the user. Based on this, the user can fine-tune their energy management policy in real time, and the output is updated configuration information based on the user's actions.

[0249] 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.

[0250] 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.

[0251] 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.

[0252] [Second Embodiment]

[0253] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0254] 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.

[0255] 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).

[0256] 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.

[0257] 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.

[0258] 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).

[0259] 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.

[0260] 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.

[0261] 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.

[0262] 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.

[0263] 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.

[0264] 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".

[0265] The present invention provides a comprehensive energy management system for optimizing battery management in communication facilities. This system utilizes generative AI to predict nationwide power supply and demand in real time, enabling optimal battery control. The program's processing is described in detail below.

[0266] ---

[0267] Server operation

[0268] The server first performs extensive data collection. It acquires nationwide power consumption data and weather information from multiple databases in real time and organizes this data into an analyzable format. This data is input into a generating AI model to perform highly accurate power demand and supply forecasts. Based on these forecasts, the server calculates the optimal battery discharge and charging schedule for each communication facility. The calculation results are transmitted to the communication facilities as commands in a timely manner.

[0269] ---

[0270] Terminal operation

[0271] The terminal receives battery control commands transmitted from the server. Following these commands, the terminal appropriately controls the battery, charging or discharging as needed. For example, during periods when a surge in power demand is predicted, the terminal completes charging in advance, ensuring stable operation by supplying power as predicted. Furthermore, to effectively utilize surplus solar power, the terminal constantly monitors the amount of power generated and chooses whether to store the excess in the battery or supply it to other facilities.

[0272] ---

[0273] User actions

[0274] Telecommunications carriers, as users, can check real-time energy supply and demand conditions and battery status through the system's dashboard. Users can adjust their energy management policies based on the forecast data to achieve optimal operation. Furthermore, if there is a large discrepancy between the forecast and actual results, the forecast accuracy can be further improved by updating the AI ​​model or fine-tuning the algorithm.

[0275] Thus, the present invention enables automatic and efficient energy management of storage batteries in communication facilities, effectively achieving reductions in power costs and stability of power supply.

[0276] The following describes the processing flow.

[0277] Server processing steps

[0278] Step 1:

[0279] The server collects national power consumption data and weather information. This includes access from power companies and weather information providers to ensure real-time data updates.

[0280] Step 2:

[0281] The server preprocesses the collected data. By formatting it in time units, complementing missing values, and removing outliers, a dataset suitable for input to the generation AI model is constructed.

[0282] Step 3:

[0283] The server runs generative AI using the preprocessed data to predict power demand and supply. The generated prediction data is updated in time units to identify time periods of increasing demand or oversupply.

[0284] Step 4:

[0285] Based on the prediction results, the server calculates discharge and charge commands for the batteries of communication facilities. An optimization algorithm is used to balance power cost reduction and supply stability.

[0286] Step 5:

[0287] The server distributes the calculated control commands to each communication facility. This initiates specific energy management operations on the terminals of each facility.

[0288] ---

[0289] Terminal processing steps

[0290] Step 1:

[0291] The terminal analyzes the control commands received from the server and compares them with the current status of the facility's battery.

[0292] Step 2:

[0293] The terminal controls the battery according to instructions. For example, if instructed to charge in preparation for peak demand, it will immediately begin charging and continue until the required capacity is reached.

[0294] Step 3:

[0295] When a discharge command is issued, the terminal receives power from the battery. At this time, dynamic output adjustments are made in response to increases and decreases in demand, minimizing fluctuations in supply.

[0296] Step 4:

[0297] The terminal monitors the output of solar power generation and, if surplus power is generated, decides whether to store it in a battery or supply it externally.

[0298] Step 5:

[0299] The terminal feeds energy management information back to the server, where it is used as data necessary for generating the next command.

[0300] ---

[0301] User operation steps

[0302] Step 1:

[0303] Users access the system's dashboard to view real-time forecast data and actual values.

[0304] Step 2:

[0305] Users adjust their energy management policies based on prediction accuracy and actual performance. They apply new settings to the platform as needed.

[0306] Step 3:

[0307] The user checks the status of surplus power utilization of solar power generation and evaluates possible revenue options.

[0308] Step 4:

[0309] Based on the feedback, the user requests the adjustment of the generative AI model and aims to improve the prediction accuracy of the system to prepare for the next operation cycle.

[0310] (Example 1)

[0311] Next, Example 1 will be described. In the following description, the data processing device 12 is referred to as a "server", and the smart glasses 214 are referred to as a "terminal".

[0312] Energy consumption in modern communication facilities is causing an increase in power costs and supply instability due to the lack of an efficient and stable energy management system. Also, there is a problem that the effective utilization of natural energy is insufficient and its surplus is wasted. Based on these, the development of a system that can accurately predict energy supply and demand and optimally control the storage battery is required.

[0313] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0314] In this invention, the server includes means having an information processing device that collects and organizes weather data and past consumption statistics in order to predict energy consumption and supply across the country, means having a calculation device that inputs the information into a generation model and plans the discharge and charge of the energy storage device of the communication facility based on the prediction results, and means having a communication device that transmits the plan as a command to communication facilities in each region and performs power control of the energy storage device. Thereby, highly accurate energy supply and demand prediction and efficient battery management become possible.

[0315] "Forecasting nationwide energy consumption and supply" means estimating the demand for electricity and the available supply in each region over time, based on historical data and weather information.

[0316] "Weather data and historical consumption statistics" refers to information on weather conditions and historical data on past electricity usage, which serve as the basic information for constructing predictive models.

[0317] "Means equipped with an information processing device" refers to a configuration that includes a computer system or software for performing data collection, organization, and analysis.

[0318] "Inputting data into a generative model" refers to the process of inputting the necessary data into a prediction algorithm and performing calculations and analyses based on that data.

[0319] "Planning the discharge and charging of energy storage devices" means creating a schedule to optimize the release and storage of energy in batteries based on electricity demand forecasts.

[0320] A "means equipped with a computing device" refers to a set of hardware and software for performing complex calculations and efficiently conducting data analysis and predictions.

[0321] "Means equipped with communication devices" refers to a configuration that includes network devices capable of transmitting and receiving commands and data to and from various communication facilities.

[0322] The system of this invention is an integrated system for optimizing energy management in communication equipment. Servers, terminals, and users cooperate with each other to control battery storage and maintain a balance between energy supply and demand.

[0323] The server first collects nationwide energy consumption and supply data, along with weather information. This is done by obtaining information in real time using databases and weather APIs. The obtained data is organized into a format that can be analyzed by the information processing device and input into a generating AI model. The AI ​​model used here is designed to predict electricity demand with high accuracy, taking into account historical consumption data and weather conditions. For example, it might use a prompt such as, "What is the expected maximum electricity demand for the next 48 hours? The data will include weather information and consumption data for the past 24 hours."

[0324] Based on the generated prediction results, the server plans the discharge and charging schedules for the batteries of each communication facility. During this process, a computing device is used to efficiently perform the necessary calculations. The planned schedule is transmitted as a command to the communication facilities in each location via the communication device. This ensures that the charging and discharging of the batteries is properly managed. For example, if it is predicted that power demand will peak at a specific time, the charging of the batteries will be completed in advance to ensure power is supplied at that time.

[0325] The terminal controls the charging and discharging of the battery based on commands received from the server. It also takes measures to efficiently utilize surplus renewable energy. For example, if a surplus of solar power is predicted, it reduces energy waste by supplying the excess to other facilities or storing it in the battery.

[0326] Telecommunications carriers, as users, can monitor real-time energy supply and demand conditions and battery status through a dashboard provided by the system. This allows users to adjust their energy management policies as needed and achieve optimal operation. Furthermore, by reviewing the prediction results of the generated AI model and making adjustments as necessary, it is possible to improve the accuracy of the system's predictions.

[0327] Thus, the present invention provides a system that improves the efficiency of energy management in communication equipment and enables highly accurate power supply and demand forecasting and optimal battery operation.

[0328] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0329] Step 1:

[0330] The server collects electricity consumption data and weather information from across the country. This involves retrieving necessary information from databases and weather APIs of various partner organizations. Historical electricity consumption statistics and the latest weather data from each region are used as input. Based on these inputs, the data is organized into CSV format. The output is data in an analyzable format. The server then feeds this organized data to the next processing step.

[0331] Step 2:

[0332] The server inputs the organized data into the generating AI model. Specifically, it uses prompts such as, "Please tell me the expected maximum power demand for the next 48 hours. The data includes weather information and consumption data for the past 24 hours." The input data includes the analyzable data obtained in step 1. The AI ​​model analyzes the input data using machine learning algorithms and predicts future power demand. The output provides an estimate of predicted power consumption and supply.

[0333] Step 3:

[0334] The server calculates the discharge and charge schedules for the batteries of each communication facility based on the prediction results of the AI ​​model. The input is the prediction results from the AI ​​model, which include the predicted power demand values ​​for each time period. The server uses this data to generate the optimal battery storage schedule. The output is a specific charge and discharge timeline for each facility.

[0335] Step 4:

[0336] The server transmits the calculated schedule to the terminals of each communication device via the communication equipment. Transmission uses the internet, sending command data as packets to the network address of each terminal. The input is the schedule created in step 3. The output is that the terminals that receive the commands are ready to begin specific control.

[0337] Step 5:

[0338] The terminal receives commands from the server and initiates specific charging and discharging control. The input is a schedule command from the server. The terminal checks its internal clock and manages the battery appropriately by following the instructed schedule. Specifically, it starts charging the battery at the designated time and discharges it according to the predicted demand period. As an output, efficient energy management is achieved.

[0339] Step 6:

[0340] Users monitor real-time energy supply and demand conditions through a dashboard and adjust system settings as needed. Recorded operational and forecast data from the system are provided as input. Based on this, users can re-evaluate their energy management policies and implement necessary changes. Specifically, this involves viewing graphs and numerical displays on the dashboard and adjusting settings. As an output, the system's operational efficiency improves as a result of user adjustments.

[0341] (Application Example 1)

[0342] 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."

[0343] This invention aims to optimize and manage energy consumption in facilities, and in particular to stabilize the energy supply necessary for the operation of multiple automated equipment groups while improving the efficiency of renewable energy utilization. The goal is to achieve energy cost reduction and improved operational efficiency of the facilities.

[0344] 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.

[0345] In this invention, the server includes intelligent processing means for collecting and analyzing historical data and weather information in order to perform nationwide power demand and supply forecasts; control means for generating commands to control the discharge and charging of the facility's energy storage devices based on the forecasts; execution means for transmitting the commands to the facilities and performing energy management of the energy storage devices; and means for calculating and providing operating procedures to optimize the amount of energy required to operate a group of automated devices installed in multiple facilities. This enables more efficient energy management and more stable supply.

[0346] An "intelligent processing device" is a device that has the function of collecting and analyzing historical data and weather information in order to perform nationwide electricity demand and supply forecasts.

[0347] "Control means" refers to a system that generates commands to control the discharge and charging of the facility's energy storage devices based on predicted power demand and supply information.

[0348] An "execution means" is a device that has the function of transmitting the generated command to the equipment and performing the operations necessary to manage the energy of the energy storage device.

[0349] "Means for calculating and providing operating procedures" refers to a system that calculates an operating plan to optimize the amount of energy required to operate a group of automated devices within multiple facilities, and provides that plan to the facilities.

[0350] "Facilities" refers to a collection of devices and equipment installed for a specific purpose, including the operation of energy management and automation equipment.

[0351] An "automation system" is a collection of multiple machines and robots that are deployed in a factory or facility to perform routine tasks automatically.

[0352] A "energy storage device" is a device that stores surplus energy and can discharge or charge it according to the demand for electricity.

[0353] "Natural energy" refers to energy derived from natural forces such as solar, wind, and hydroelectric power, and is renewable energy that is generated in a sustainable manner.

[0354] A system for implementing the present invention includes intelligent processing means, control means, execution means, and means for calculating and providing operating procedures. The central server of the system is equipped with intelligent processing means that use a generated AI model to predict power demand and supply, and create an optimal charge and discharge schedule for the energy storage devices within the facility based on the results.

[0355] The server's control system generates optimal energy management commands based on prediction results obtained from the intelligent processing system, according to the operating status of the equipment. These commands are transmitted to each piece of equipment through the execution system.

[0356] Terminals installed within the facility control the charging and discharging of energy storage devices according to commands sent from the server, thereby achieving energy management. The terminals monitor energy usage in real time and make necessary adjustments to achieve energy-saving operation.

[0357] As a concrete example, energy costs can be reduced by providing operating procedures that optimize the energy use of automated equipment installed in a factory. For instance, by fully charging energy storage devices in advance during peak factory operating hours and effectively utilizing surplus electricity from renewable energy sources, external electricity demand can be suppressed.

[0358] Users can monitor energy usage and the status of energy storage devices in real time through a dashboard designed for easy facility management. Furthermore, it's possible to adjust the system's algorithms based on new data to further improve the accuracy of the generated AI models.

[0359] An example of a prompt message related to the operation of this system would be: "Based on the energy consumption patterns within the factory and the weather forecast for the next three days, predict the optimal battery charging and discharging schedule."

[0360] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0361] Step 1:

[0362] The server collects nationwide electricity consumption data and weather information in real time from an external database. This process converts the raw data obtained via API into structured data, making it easier for predictive models to process. The input consists of electricity consumption data and weather information, while the output is data organized into an analyzable format.

[0363] Step 2:

[0364] The server uses a generative AI model to forecast electricity demand and supply based on the data organized in Step 1. In this process, the forecasting algorithm calculates the fluctuations in demand and supply over the next few days and outputs them as forecast data. The input is structured electricity consumption and weather data, and the output is forecasted demand and supply data.

[0365] Step 3:

[0366] The server's control system generates an optimal charge / discharge schedule for the energy storage devices in the facility based on the prediction results from step 2. This process uses the prediction data and the current energy storage status as input to create and output an optimal energy management command. In operation, it simulates multiple scenarios to determine the most efficient operational policy.

[0367] Step 4:

[0368] The terminal controls the energy storage device based on the charge / discharge schedule sent from the server. The input is a command from the server, and the output is the actual charge / discharge operation of the energy storage device. The terminal monitors the status of the energy storage device in real time and makes adjustments to deal with unexpected situations.

[0369] Step 5:

[0370] Users monitor the overall energy usage of the facility and the operating status of energy storage devices through a dashboard. Input is energy management data provided by the server, and output is a visualization of the energy data that users can visually confirm. Users can easily adjust facility management policies.

[0371] Step 6:

[0372] The user updates the generated AI model as needed to improve prediction accuracy. This process strengthens the algorithm by incorporating new data into the model. The input is the latest energy usage data and parameters of the prediction model, and the output is the new, improved prediction model. For example, a prompt such as "Predict the optimal battery charging and discharging schedule based on the energy consumption patterns in the factory and the weather forecast for the next three days" is used.

[0373] 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.

[0374] This invention incorporates a function that considers user emotions into a system that optimizes the balance between power supply and consumption in real time. This reduces the psychological burden on the user while achieving efficient energy management. The program's processing is described in detail below.

[0375] ---

[0376] Server operation

[0377] The server features a generative AI that forecasts power demand and supply, as well as an emotion engine. First, the server forecasts power supply and demand using power consumption data and weather information collected from across the country. Based on this data, it generates optimal discharge and charging commands for the batteries of each communication facility. At the same time, it also takes the user's emotional state into consideration, and the emotion engine analyzes the user's emotions from a series of data. For example, if the user is feeling stressed, it generates commands to optimize notifications and relax energy management policies, taking into account periods of low load.

[0378] ---

[0379] Terminal operation

[0380] The terminal receives commands from the server and controls the battery storage of the communication facility. It also makes adjustments to improve the user experience based on commands from the emotion engine. For example, it optimizes the timing and content of energy usage notification messages according to the user's emotions. Furthermore, it can adjust the output of solar power generation and the use of surplus power based on user feedback, thereby increasing user satisfaction.

[0381] ---

[0382] User actions

[0383] Users can monitor energy supply and demand in real time through the dashboard, as well as understand their own emotional state. Based on the data provided by the emotion engine, users can fine-tune their energy management policies and choose an operating method that suits their emotional state. For example, if stress levels are high, notifications may be suppressed and only reminders may be displayed.

[0384] Thus, the present invention is a system that simultaneously achieves power management and user comfort. By considering emotions in energy management, it is possible to enhance user satisfaction while utilizing energy efficiently.

[0385] The following describes the processing flow.

[0386] Server processing steps

[0387] Step 1:

[0388] The server acquires nationwide electricity consumption data and weather information. This allows it to begin forecasting electricity demand and supply for the following day. Data is collected in real time from multiple sources.

[0389] Step 2:

[0390] The server refines the acquired data and cleans up outliers. This process prepares a dataset suitable for the generative AI model.

[0391] Step 3:

[0392] The server uses AI generation to predict future power supply and demand. Predictions are made over time spans of several hours to several days, and the results are stored in a database.

[0393] Step 4:

[0394] The server activates an emotion engine to evaluate the user's emotional state. During this process, specific patterns and trends are identified.

[0395] Step 5:

[0396] The server integrates power forecasts and user sentiment data to create optimal battery control commands. Based on the sentiment data, it adjusts the content and timing of notifications.

[0397] Step 6:

[0398] The server transmits the generated commands to the terminals in the communication facility, instructing them on appropriate energy management and user notifications.

[0399] ---

[0400] Terminal processing steps

[0401] Step 1:

[0402] The terminal analyzes the commands received from the server and determines the corresponding operation of the battery.

[0403] Step 2:

[0404] The terminal checks the current charge status of the battery and starts charging or discharging according to the server's instructions.

[0405] Step 3:

[0406] The terminal monitors surplus electricity from solar power generation and stores it in a battery or supplies it to other loads as needed.

[0407] Step 4:

[0408] The device optimizes notification messages to the user in response to commands from the emotion engine. This includes fine-tuning the content and timing of notifications.

[0409] Step 5:

[0410] The device feeds energy and emotional data back to the server, which is then used for subsequent calculations and optimization processes.

[0411] ---

[0412] User operation steps

[0413] Step 1:

[0414] Users log into the dashboard to check their emotional state along with their energy usage.

[0415] Step 2:

[0416] Based on recommendations from the emotion engine, users can adjust their energy management settings. For example, they can change the frequency and level of detail of notifications.

[0417] Step 3:

[0418] Users provide feedback on energy and emotions, contributing to improvements in the system's predictive accuracy and emotion recognition.

[0419] Step 4:

[0420] Users will use the provided information to consider new energy management strategies and minimize their own energy costs.

[0421] (Example 2)

[0422] 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".

[0423] With current technological capabilities, it has been difficult to not only efficiently manage the balance between power supply and demand, but also to implement energy management that takes into account the psychological state of users. In particular, when a user's emotional state influences power usage and management policies, flexible responses tailored to the situation are required. However, existing systems have failed to meet this requirement, leading to a decline in user satisfaction.

[0424] 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.

[0425] This invention includes a server equipped with artificial intelligence for collecting and analyzing historical information and weather conditions to predict nationwide energy demand and supply; a means for generating commands to adjust the discharge and charging of energy storage devices in facilities with communication capabilities based on the predictions; and an emotion engine for analyzing the user's emotional state, and a means for generating adjustment commands to improve the user experience based on the analysis results. This enables not only more efficient energy management but also flexible optimization of energy use in response to the user's emotions.

[0426] "Artificial intelligence" is a technology that analyzes past information and weather conditions to make predictions necessary for optimizing energy demand and supply.

[0427] A "facility with communication capabilities" is infrastructure that allows for remote information exchange and the reception of energy management commands.

[0428] A "power storage device" is a device that temporarily stores electricity and discharges and charges it as needed.

[0429] The "emotion engine" is a technology that analyzes the user's emotional state and generates commands to adjust energy management policies based on that state.

[0430] The "means for generating instructions" are mechanisms for creating guidelines for optimal energy use based on predicted energy supply and demand and user sentiment analysis data.

[0431] "Energy control" refers to the operation of adjusting the discharge and charging of energy storage devices to balance supply and demand.

[0432] This invention incorporates a function that takes user emotions into a system that optimizes the balance between power supply and consumption in real time. For the invention to be implemented, the server, terminal, and user each need to perform specific actions.

[0433] The server first collects energy consumption data and weather information from across the country. This data collection uses database software, and a generated AI model executes a prediction algorithm based on historical datasets. This AI model predicts power demand and supply, and based on this, discharge and charge commands are generated for the energy storage devices at each communication facility.

[0434] Simultaneously, the server uses an emotion engine to analyze the user's emotional state. This emotion engine analyzes the user's data patterns to determine whether they are experiencing stress. Based on this analysis, instructions are generated to optimize the content and timing of notifications. For example, the server might generate a prompt such as, "If the user is experiencing stress, refrain from sending notifications."

[0435] The terminal controls the energy storage devices of the communication facility based on commands received from the server. The terminal also executes applications to adjust the content and timing of user notifications according to commands from the emotion engine. This enables improved energy efficiency and a better user experience.

[0436] Users can monitor energy supply and demand in real time through the dashboard interface and fine-tune their energy management policies based on data provided by the emotion engine. For example, if a user is stressed, they can change settings to reduce notifications about energy usage.

[0437] In this way, this invention can simultaneously improve power management and user satisfaction. By utilizing a generative AI model and an emotion engine, efficient energy use is achieved while also ensuring user comfort.

[0438] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0439] Step 1:

[0440] The server collects energy consumption data and weather information from across the country. It uses historical electricity consumption data and current weather information as input data. This data is managed by database software. Based on this data, the server runs a generated AI model to predict electricity demand and supply. The output of this calculation is the predicted demand for each communication facility. Specifically, the server uses the prediction model to analyze consumption patterns and forecast future electricity supply and demand.

[0441] Step 2:

[0442] The server uses the supply and demand forecast results from Step 1 to generate commands that optimize the discharge and charging of energy storage devices. The inputs are the current supply capacity and energy storage capacity at each communication facility. Based on the generated AI model, the server calculates efficient energy distribution and discharge timing. The output is a command to be sent to each communication facility. This command includes a specific discharge and charging schedule.

[0443] Step 3:

[0444] The server activates the emotion engine to analyze the user's emotional state. Input includes the user's past behavioral data and real-time operation logs. The emotion engine processes this data and assesses how stressed the user is. As a result of the analysis, it outputs an emotional state score. Specifically, it analyzes changes in the user's activity and qualitative responses to generate the emotional score.

[0445] Step 4:

[0446] The server generates commands to improve the user experience based on the emotional state score obtained from the emotion engine. The input is the emotional evaluation and energy management policy obtained in step 3. The server generates prompts to optimize the content and timing of notifications that reflect the emotional score. The output is the energy management adjustment plan sent to the terminal. As a specific example, the server might present a prompt such as "Delay notifications to reduce stress."

[0447] Step 5:

[0448] The terminal executes commands received from the server and controls the energy storage devices of the communication facility. Inputs are discharge / charge commands from the server and user experience adjustment commands. Through these commands, the terminal operates the energy management system to provide an optimized user experience. Outputs include optimized energy consumption and an improved user notification schedule. Specifically, the terminal monitors energy usage in real time and makes user interface adjustments, such as delaying notifications.

[0449] Step 6:

[0450] Users can monitor energy supply and demand in real time via a dashboard and fine-tune their energy management policies according to their emotional state. Inputs are energy consumption data and emotional state information provided by the dashboard. Outputs are the new energy usage policies and notification settings configured by the user. Specifically, users can choose to reduce notifications during stressful situations. The goal of this process is to achieve efficient energy use while maintaining user comfort.

[0451] (Application Example 2)

[0452] 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."

[0453] Conventional power management systems fail to consider the emotional stress of users, potentially leading to decreased user satisfaction even if energy management is efficient. Furthermore, simply optimizing the balance between power consumption and supply is insufficient to guarantee comfort within the home. Moreover, adjustments to optimize energy consumption within the home are often inadequate.

[0454] 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.

[0455] In this invention, the server includes means equipped with an intelligent system for collecting and analyzing historical data and weather information to perform nationwide power demand and supply forecasts; means for generating commands to control the discharge and charging of energy storage devices in communication facilities based on the forecasts; and an analysis device for detecting the user's emotional state and adjusting the operating state of home appliances based on that emotion. This enables efficient energy management that takes the user's emotions into consideration and improves comfort within the home.

[0456] An "intelligent system" is a device that has analytical functions to predict electricity demand and supply based on past data and weather information.

[0457] "Communication facilities" refer to infrastructure equipment for receiving and executing commands for discharging and charging electricity.

[0458] A "power storage device" is an energy storage device that efficiently stores electricity and discharges it as needed.

[0459] "Means for generating commands" refers to a method for creating control commands that enable optimal energy storage and discharge based on information about power supply and demand.

[0460] An "analysis device" is a data analysis device that detects the user's emotional state and adjusts the operation of home appliances accordingly.

[0461] To implement this invention, the server, terminal, and user each play their respective roles.

[0462] The server uses an intelligent system to predict nationwide power demand and supply. This requires a program to collect and analyze historical data and weather information. Possible software options include functions built with data analysis languages ​​such as Python and R, or TensorFlow and PyTorch, which are convenient for operating AI models. Based on the generated prediction data, the server issues commands to the energy storage devices of communication facilities. In particular, to perform analysis that takes into account the emotional state of users, it will utilize sentiment analysis APIs (e.g., Amazon Rekognition or Google Cloud Vision) to optimize power management according to the user's stress level.

[0463] The terminal controls the discharge and charging of the energy storage device based on commands received from the server. Furthermore, the terminal is equipped with a function to adjust the operation of home appliances based on emotion analysis results. Smart speakers and IoT devices with internet connectivity can be used to control smart home appliances in the home. This makes it possible to take specific actions when the user's stress level is high, such as changing the lighting to a softer color or setting the air conditioner to a comfortable temperature.

[0464] Users operate the application through their smartphones. The application includes a dashboard where users can check energy supply and demand status and their own emotional state in real time. An interface is provided that allows users to fine-tune energy management policies based on their input. For example, there is a prompt message that says, "When the user is relaxed, please instruct the system to change the lighting tone to a warm color and set the air conditioner temperature to 23 degrees." This allows users to create a comfortable and efficient home environment.

[0465] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0466] Step 1:

[0467] The server performs nationwide electricity demand and supply forecasts. It receives historical electricity consumption data and weather information as input. This data is analyzed, and a generative AI model is used to perform data calculations to predict future supply and demand. The output is forecast data that guides optimal energy management policies for each communication facility.

[0468] Step 2:

[0469] The server uses an emotion analysis API to detect the user's emotional state. It receives data of the user's facial expressions and voice from the camera and microphone as input. Data processing involves image recognition and voice analysis to determine the user's emotions. The output is data indicating the user's emotional state.

[0470] Step 3:

[0471] The server integrates power forecast data and user emotional state data to generate commands for adjusting the operation of household appliances. Using a generative AI model, it creates optimal operating instructions for each appliance from the input data. The output is a control command containing specific operating instructions for each appliance.

[0472] Step 4:

[0473] The terminal executes control commands received from the server. It receives command data from the server as input and sends commands to smart home appliances in the home. It adjusts the settings of the appliances using a device control protocol. This results in a comfortable appliance state that matches the user's preferences as output.

[0474] Step 5:

[0475] The user views the dashboard through a smartphone application. The application receives data from the device regarding the current energy supply and demand situation and the user's emotional state as input. It visualizes this data and displays it to the user. Based on this, the user can fine-tune their energy management policy in real time, and the output is updated configuration information based on the user's actions.

[0476] 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.

[0477] 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.

[0478] 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.

[0479] [Third Embodiment]

[0480] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0481] 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.

[0482] 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).

[0483] 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.

[0484] 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.

[0485] 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).

[0486] 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.

[0487] 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.

[0488] 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.

[0489] 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.

[0490] 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.

[0491] 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".

[0492] The present invention provides a comprehensive energy management system for optimizing battery management in communication facilities. This system utilizes generative AI to predict nationwide power supply and demand in real time, enabling optimal battery control. The program's processing is described in detail below.

[0493] ---

[0494] Server operation

[0495] The server first performs extensive data collection. It acquires nationwide power consumption data and weather information from multiple databases in real time and organizes this data into an analyzable format. This data is input into a generating AI model to perform highly accurate power demand and supply forecasts. Based on these forecasts, the server calculates the optimal battery discharge and charging schedule for each communication facility. The calculation results are transmitted to the communication facilities as commands in a timely manner.

[0496] ---

[0497] Terminal operation

[0498] The terminal receives battery control commands transmitted from the server. Following these commands, the terminal appropriately controls the battery, charging or discharging as needed. For example, during periods when a surge in power demand is predicted, the terminal completes charging in advance, ensuring stable operation by supplying power as predicted. Furthermore, to effectively utilize surplus solar power, the terminal constantly monitors the amount of power generated and chooses whether to store the excess in the battery or supply it to other facilities.

[0499] ---

[0500] User actions

[0501] Telecommunications carriers, as users, can check real-time energy supply and demand conditions and battery status through the system's dashboard. Users can adjust their energy management policies based on the forecast data to achieve optimal operation. Furthermore, if there is a large discrepancy between the forecast and actual results, the forecast accuracy can be further improved by updating the AI ​​model or fine-tuning the algorithm.

[0502] Thus, the present invention enables automatic and efficient energy management of storage batteries in communication facilities, effectively achieving reductions in power costs and stability of power supply.

[0503] The following describes the processing flow.

[0504] Server processing steps

[0505] Step 1:

[0506] The server collects electricity consumption data and weather information from across the country. This includes access from power companies and weather information providers, ensuring real-time data updates.

[0507] Step 2:

[0508] The server preprocesses the collected data. By formatting it on an hourly basis, imputing missing values, and removing outliers, it constructs a dataset suitable for input to generative AI models.

[0509] Step 3:

[0510] The server uses pre-processed data to run a generating AI that forecasts electricity demand and supply. The generated forecast data is updated hourly, identifying periods of high demand and periods of supply surplus.

[0511] Step 4:

[0512] Based on the prediction results, the server calculates discharge and charge commands for the communication facility's batteries. An optimization algorithm is used to balance power cost reduction with supply stability.

[0513] Step 5:

[0514] The server distributes the calculated control commands to each communication facility. This initiates specific energy management operations at the terminals in each facility.

[0515] ---

[0516] Terminal processing steps

[0517] Step 1:

[0518] The terminal analyzes the control commands received from the server and compares them with the current status of the facility's battery.

[0519] Step 2:

[0520] The terminal controls the battery according to instructions. For example, if instructed to charge in preparation for peak demand, it will immediately begin charging and continue until the required capacity is reached.

[0521] Step 3:

[0522] When a discharge command is issued, the terminal receives power from the battery. At this time, dynamic output adjustments are made in response to increases and decreases in demand, minimizing fluctuations in supply.

[0523] Step 4:

[0524] The terminal monitors the output of solar power generation and, if surplus power is generated, decides whether to store it in a battery or supply it externally.

[0525] Step 5:

[0526] The terminal feeds energy management information back to the server, where it is used as data necessary for generating the next command.

[0527] ---

[0528] User operation steps

[0529] Step 1:

[0530] Users access the system's dashboard to view real-time forecast data and actual values.

[0531] Step 2:

[0532] Users adjust their energy management policies based on prediction accuracy and actual performance. They apply new settings to the platform as needed.

[0533] Step 3:

[0534] Users check the utilization status of surplus solar power and evaluate possible monetization options.

[0535] Step 4:

[0536] Based on their feedback, users can request adjustments to the generation AI model to improve the system's prediction accuracy and prepare for the next operational cycle.

[0537] (Example 1)

[0538] 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."

[0539] Energy consumption in modern telecommunications facilities is leading to increased electricity costs and supply instability due to the lack of efficient and stable energy management systems. Furthermore, the effective utilization of renewable energy is insufficient, resulting in wasted surplus energy. Given these issues, there is a need to develop systems that accurately predict energy supply and demand and optimally control battery storage.

[0540] 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.

[0541] In this invention, the server includes means equipped with an information processing device that collects and organizes weather data and historical consumption statistics in order to predict energy consumption and supply nationwide; means equipped with a computing device that inputs the information into a generation model and plans the discharge and charging of energy storage devices of communication facilities based on the prediction results; and means equipped with a communication device that transmits the plan as a command to communication facilities in various locations and controls the power of the energy storage devices. This enables highly accurate energy supply and demand forecasting and efficient battery management.

[0542] "Forecasting nationwide energy consumption and supply" means estimating the demand for electricity and the available supply in each region over time, based on historical data and weather information.

[0543] "Weather data and historical consumption statistics" refers to information on weather conditions and historical data on past electricity usage, which serve as the basic information for constructing predictive models.

[0544] "Means equipped with an information processing device" refers to a configuration that includes a computer system or software for performing data collection, organization, and analysis.

[0545] "Inputting data into a generative model" refers to the process of inputting the necessary data into a prediction algorithm and performing calculations and analyses based on that data.

[0546] "Planning the discharge and charging of energy storage devices" means creating a schedule to optimize the release and storage of energy in batteries based on electricity demand forecasts.

[0547] A "means equipped with a computing device" refers to a set of hardware and software for performing complex calculations and efficiently conducting data analysis and predictions.

[0548] "Means equipped with communication devices" refers to a configuration that includes network devices capable of transmitting and receiving commands and data to and from various communication facilities.

[0549] The system of this invention is an integrated system for optimizing energy management in communication equipment. Servers, terminals, and users cooperate with each other to control battery storage and maintain a balance between energy supply and demand.

[0550] The server first collects nationwide energy consumption and supply data, along with weather information. This is done by obtaining information in real time using databases and weather APIs. The obtained data is organized into a format that can be analyzed by the information processing device and input into a generating AI model. The AI ​​model used here is designed to predict electricity demand with high accuracy, taking into account historical consumption data and weather conditions. For example, it might use a prompt such as, "What is the expected maximum electricity demand for the next 48 hours? The data will include weather information and consumption data for the past 24 hours."

[0551] Based on the generated prediction results, the server plans the discharge and charging schedules for the batteries of each communication facility. During this process, a computing device is used to efficiently perform the necessary calculations. The planned schedule is transmitted as a command to the communication facilities in each location via the communication device. This ensures that the charging and discharging of the batteries is properly managed. For example, if it is predicted that power demand will peak at a specific time, the charging of the batteries will be completed in advance to ensure power is supplied at that time.

[0552] The terminal controls the charging and discharging of the battery based on commands received from the server. It also takes measures to efficiently utilize surplus renewable energy. For example, if a surplus of solar power is predicted, it reduces energy waste by supplying the excess to other facilities or storing it in the battery.

[0553] Telecommunications carriers, as users, can monitor real-time energy supply and demand conditions and battery status through a dashboard provided by the system. This allows users to adjust their energy management policies as needed and achieve optimal operation. Furthermore, by reviewing the prediction results of the generated AI model and making adjustments as necessary, it is possible to improve the accuracy of the system's predictions.

[0554] Thus, the present invention provides a system that improves the efficiency of energy management in communication equipment and enables highly accurate power supply and demand forecasting and optimal battery operation.

[0555] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0556] Step 1:

[0557] The server collects electricity consumption data and weather information from across the country. This involves retrieving necessary information from databases and weather APIs of various partner organizations. Historical electricity consumption statistics and the latest weather data from each region are used as input. Based on these inputs, the data is organized into CSV format. The output is data in an analyzable format. The server then feeds this organized data to the next processing step.

[0558] Step 2:

[0559] The server inputs the organized data into the generating AI model. Specifically, it uses prompts such as, "Please tell me the expected maximum power demand for the next 48 hours. The data includes weather information and consumption data for the past 24 hours." The input data includes the analyzable data obtained in step 1. The AI ​​model analyzes the input data using machine learning algorithms and predicts future power demand. The output provides an estimate of predicted power consumption and supply.

[0560] Step 3:

[0561] The server calculates the discharge and charge schedules for the batteries of each communication facility based on the prediction results of the AI ​​model. The input is the prediction results from the AI ​​model, which include the predicted power demand values ​​for each time period. The server uses this data to generate the optimal battery storage schedule. The output is a specific charge and discharge timeline for each facility.

[0562] Step 4:

[0563] The server transmits the calculated schedule to the terminals of each communication device via the communication equipment. Transmission uses the internet, sending command data as packets to the network address of each terminal. The input is the schedule created in step 3. The output is that the terminals that receive the commands are ready to begin specific control.

[0564] Step 5:

[0565] The terminal receives commands from the server and initiates specific charging and discharging control. The input is a schedule command from the server. The terminal checks its internal clock and manages the battery appropriately by following the instructed schedule. Specifically, it starts charging the battery at the designated time and discharges it according to the predicted demand period. As an output, efficient energy management is achieved.

[0566] Step 6:

[0567] Users monitor real-time energy supply and demand conditions through a dashboard and adjust system settings as needed. Recorded operational and forecast data from the system are provided as input. Based on this, users can re-evaluate their energy management policies and implement necessary changes. Specifically, this involves viewing graphs and numerical displays on the dashboard and adjusting settings. As an output, the system's operational efficiency improves as a result of user adjustments.

[0568] (Application Example 1)

[0569] 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."

[0570] This invention aims to optimize and manage energy consumption in facilities, and in particular to stabilize the energy supply necessary for the operation of multiple automated equipment groups while improving the efficiency of renewable energy utilization. The goal is to achieve energy cost reduction and improved operational efficiency of the facilities.

[0571] 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.

[0572] In this invention, the server includes intelligent processing means for collecting and analyzing historical data and weather information in order to perform nationwide power demand and supply forecasts; control means for generating commands to control the discharge and charging of the facility's energy storage devices based on the forecasts; execution means for transmitting the commands to the facilities and performing energy management of the energy storage devices; and means for calculating and providing operating procedures to optimize the amount of energy required to operate a group of automated devices installed in multiple facilities. This enables more efficient energy management and more stable supply.

[0573] An "intelligent processing device" is a device that has the function of collecting and analyzing historical data and weather information in order to perform nationwide electricity demand and supply forecasts.

[0574] "Control means" refers to a system that generates commands to control the discharge and charging of the facility's energy storage devices based on predicted power demand and supply information.

[0575] An "execution means" is a device that has the function of transmitting the generated command to the equipment and performing the operations necessary to manage the energy of the energy storage device.

[0576] "Means for calculating and providing operating procedures" refers to a system that calculates an operating plan to optimize the amount of energy required to operate a group of automated devices within multiple facilities, and provides that plan to the facilities.

[0577] "Facilities" refers to a collection of devices and equipment installed for a specific purpose, including the operation of energy management and automation equipment.

[0578] An "automation system" is a collection of multiple machines and robots that are deployed in a factory or facility to perform routine tasks automatically.

[0579] A "energy storage device" is a device that stores surplus energy and can discharge or charge it according to the demand for electricity.

[0580] "Natural energy" refers to energy derived from natural forces such as solar, wind, and hydroelectric power, and is renewable energy that is generated in a sustainable manner.

[0581] A system for implementing the present invention includes intelligent processing means, control means, execution means, and means for calculating and providing operating procedures. The central server of the system is equipped with intelligent processing means that use a generated AI model to predict power demand and supply, and create an optimal charge and discharge schedule for the energy storage devices within the facility based on the results.

[0582] The server's control system generates optimal energy management commands based on prediction results obtained from the intelligent processing system, according to the operating status of the equipment. These commands are transmitted to each piece of equipment through the execution system.

[0583] Terminals installed within the facility control the charging and discharging of energy storage devices according to commands sent from the server, thereby achieving energy management. The terminals monitor energy usage in real time and make necessary adjustments to achieve energy-saving operation.

[0584] As a concrete example, energy costs can be reduced by providing operating procedures that optimize the energy use of automated equipment installed in a factory. For instance, by fully charging energy storage devices in advance during peak factory operating hours and effectively utilizing surplus electricity from renewable energy sources, external electricity demand can be suppressed.

[0585] Users can monitor energy usage and the status of energy storage devices in real time through a dashboard designed for easy facility management. Furthermore, it's possible to adjust the system's algorithms based on new data to further improve the accuracy of the generated AI models.

[0586] An example of a prompt message related to the operation of this system would be: "Based on the energy consumption patterns within the factory and the weather forecast for the next three days, predict the optimal battery charging and discharging schedule."

[0587] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0588] Step 1:

[0589] The server collects nationwide electricity consumption data and weather information in real time from an external database. This process converts the raw data obtained via API into structured data, making it easier for predictive models to process. The input consists of electricity consumption data and weather information, while the output is data organized into an analyzable format.

[0590] Step 2:

[0591] The server uses a generative AI model to forecast electricity demand and supply based on the data organized in Step 1. In this process, the forecasting algorithm calculates the fluctuations in demand and supply over the next few days and outputs them as forecast data. The input is structured electricity consumption and weather data, and the output is forecasted demand and supply data.

[0592] Step 3:

[0593] The server's control system generates an optimal charge / discharge schedule for the energy storage devices in the facility based on the prediction results from step 2. This process uses the prediction data and the current energy storage status as input to create and output an optimal energy management command. In operation, it simulates multiple scenarios to determine the most efficient operational policy.

[0594] Step 4:

[0595] The terminal controls the energy storage device based on the charge / discharge schedule sent from the server. The input is a command from the server, and the output is the actual charge / discharge operation of the energy storage device. The terminal monitors the status of the energy storage device in real time and makes adjustments to deal with unexpected situations.

[0596] Step 5:

[0597] Users monitor the overall energy usage of the facility and the operating status of energy storage devices through a dashboard. Input is energy management data provided by the server, and output is a visualization of the energy data that users can visually confirm. Users can easily adjust facility management policies.

[0598] Step 6:

[0599] The user updates the generated AI model as needed to improve prediction accuracy. This process strengthens the algorithm by incorporating new data into the model. The input is the latest energy usage data and parameters of the prediction model, and the output is the new, improved prediction model. For example, a prompt such as "Predict the optimal battery charging and discharging schedule based on the energy consumption patterns in the factory and the weather forecast for the next three days" is used.

[0600] 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.

[0601] This invention incorporates a function that considers user emotions into a system that optimizes the balance between power supply and consumption in real time. This reduces the psychological burden on the user while achieving efficient energy management. The program's processing is described in detail below.

[0602] ---

[0603] Server operation

[0604] The server features a generative AI that forecasts power demand and supply, as well as an emotion engine. First, the server forecasts power supply and demand using power consumption data and weather information collected from across the country. Based on this data, it generates optimal discharge and charging commands for the batteries of each communication facility. At the same time, it also takes the user's emotional state into consideration, and the emotion engine analyzes the user's emotions from a series of data. For example, if the user is feeling stressed, it generates commands to optimize notifications and relax energy management policies, taking into account periods of low load.

[0605] ---

[0606] Terminal operation

[0607] The terminal receives commands from the server and controls the battery storage of the communication facility. It also makes adjustments to improve the user experience based on commands from the emotion engine. For example, it optimizes the timing and content of energy usage notification messages according to the user's emotions. Furthermore, it can adjust the output of solar power generation and the use of surplus power based on user feedback, thereby increasing user satisfaction.

[0608] ---

[0609] User actions

[0610] Users can monitor energy supply and demand in real time through the dashboard, as well as understand their own emotional state. Based on the data provided by the emotion engine, users can fine-tune their energy management policies and choose an operating method that suits their emotional state. For example, if stress levels are high, notifications may be suppressed and only reminders may be displayed.

[0611] Thus, the present invention is a system that simultaneously achieves power management and user comfort. By considering emotions in energy management, it is possible to enhance user satisfaction while utilizing energy efficiently.

[0612] The following describes the processing flow.

[0613] Server processing steps

[0614] Step 1:

[0615] The server acquires nationwide electricity consumption data and weather information. This allows it to begin forecasting electricity demand and supply for the following day. Data is collected in real time from multiple sources.

[0616] Step 2:

[0617] The server refines the acquired data and cleans up outliers. This process prepares a dataset suitable for the generative AI model.

[0618] Step 3:

[0619] The server uses AI generation to predict future power supply and demand. Predictions are made over time spans of several hours to several days, and the results are stored in a database.

[0620] Step 4:

[0621] The server activates an emotion engine to evaluate the user's emotional state. During this process, specific patterns and trends are identified.

[0622] Step 5:

[0623] The server integrates power forecasts and user sentiment data to create optimal battery control commands. Based on the sentiment data, it adjusts the content and timing of notifications.

[0624] Step 6:

[0625] The server transmits the generated commands to the terminals in the communication facility, instructing them on appropriate energy management and user notifications.

[0626] ---

[0627] Terminal processing steps

[0628] Step 1:

[0629] The terminal analyzes the commands received from the server and determines the corresponding operation of the battery.

[0630] Step 2:

[0631] The terminal checks the current charge status of the battery and starts charging or discharging according to the server's instructions.

[0632] Step 3:

[0633] The terminal monitors surplus electricity from solar power generation and stores it in a battery or supplies it to other loads as needed.

[0634] Step 4:

[0635] The device optimizes notification messages to the user in response to commands from the emotion engine. This includes fine-tuning the content and timing of notifications.

[0636] Step 5:

[0637] The device feeds energy and emotional data back to the server, which is then used for subsequent calculations and optimization processes.

[0638] ---

[0639] User operation steps

[0640] Step 1:

[0641] Users log into the dashboard to check their emotional state along with their energy usage.

[0642] Step 2:

[0643] Based on recommendations from the emotion engine, users can adjust their energy management settings. For example, they can change the frequency and level of detail of notifications.

[0644] Step 3:

[0645] Users provide feedback on energy and emotions, contributing to improvements in the system's predictive accuracy and emotion recognition.

[0646] Step 4:

[0647] Users will use the provided information to consider new energy management strategies and minimize their own energy costs.

[0648] (Example 2)

[0649] 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."

[0650] With current technological capabilities, it has been difficult to not only efficiently manage the balance between power supply and demand, but also to implement energy management that takes into account the psychological state of users. In particular, when a user's emotional state influences power usage and management policies, flexible responses tailored to the situation are required. However, existing systems have failed to meet this requirement, leading to a decline in user satisfaction.

[0651] 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.

[0652] This invention includes a server equipped with artificial intelligence for collecting and analyzing historical information and weather conditions to predict nationwide energy demand and supply; a means for generating commands to adjust the discharge and charging of energy storage devices in facilities with communication capabilities based on the predictions; and an emotion engine for analyzing the user's emotional state, and a means for generating adjustment commands to improve the user experience based on the analysis results. This enables not only more efficient energy management but also flexible optimization of energy use in response to the user's emotions.

[0653] "Artificial intelligence" is a technology that analyzes past information and weather conditions to make predictions necessary for optimizing energy demand and supply.

[0654] A "facility with communication capabilities" is infrastructure that allows for remote information exchange and the reception of energy management commands.

[0655] A "power storage device" is a device that temporarily stores electricity and discharges and charges it as needed.

[0656] The "emotion engine" is a technology that analyzes the user's emotional state and generates commands to adjust energy management policies based on that state.

[0657] The "means for generating instructions" are mechanisms for creating guidelines for optimal energy use based on predicted energy supply and demand and user sentiment analysis data.

[0658] "Energy control" refers to the operation of adjusting the discharge and charging of energy storage devices to balance supply and demand.

[0659] This invention incorporates a function that takes user emotions into a system that optimizes the balance between power supply and consumption in real time. For the invention to be implemented, the server, terminal, and user each need to perform specific actions.

[0660] The server first collects energy consumption data and weather information from across the country. This data collection uses database software, and a generated AI model executes a prediction algorithm based on historical datasets. This AI model predicts power demand and supply, and based on this, discharge and charge commands are generated for the energy storage devices at each communication facility.

[0661] Simultaneously, the server uses an emotion engine to analyze the user's emotional state. This emotion engine analyzes the user's data patterns to determine whether they are experiencing stress. Based on this analysis, instructions are generated to optimize the content and timing of notifications. For example, the server might generate a prompt such as, "If the user is experiencing stress, refrain from sending notifications."

[0662] The terminal controls the energy storage devices of the communication facility based on commands received from the server. The terminal also executes applications to adjust the content and timing of user notifications according to commands from the emotion engine. This enables improved energy efficiency and a better user experience.

[0663] Users can monitor energy supply and demand in real time through the dashboard interface and fine-tune their energy management policies based on data provided by the emotion engine. For example, if a user is stressed, they can change settings to reduce notifications about energy usage.

[0664] In this way, this invention can simultaneously improve power management and user satisfaction. By utilizing a generative AI model and an emotion engine, efficient energy use is achieved while also ensuring user comfort.

[0665] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0666] Step 1:

[0667] The server collects energy consumption data and weather information from across the country. It uses historical electricity consumption data and current weather information as input data. This data is managed by database software. Based on this data, the server runs a generated AI model to predict electricity demand and supply. The output of this calculation is the predicted demand for each communication facility. Specifically, the server uses the prediction model to analyze consumption patterns and forecast future electricity supply and demand.

[0668] Step 2:

[0669] The server uses the supply and demand forecast results from Step 1 to generate commands that optimize the discharge and charging of energy storage devices. The inputs are the current supply capacity and energy storage capacity at each communication facility. Based on the generated AI model, the server calculates efficient energy distribution and discharge timing. The output is a command to be sent to each communication facility. This command includes a specific discharge and charging schedule.

[0670] Step 3:

[0671] The server activates the emotion engine to analyze the user's emotional state. Input includes the user's past behavioral data and real-time operation logs. The emotion engine processes this data and assesses how stressed the user is. As a result of the analysis, it outputs an emotional state score. Specifically, it analyzes changes in the user's activity and qualitative responses to generate the emotional score.

[0672] Step 4:

[0673] The server generates commands to improve the user experience based on the emotional state score obtained from the emotion engine. The input is the emotional evaluation and energy management policy obtained in step 3. The server generates prompts to optimize the content and timing of notifications that reflect the emotional score. The output is the energy management adjustment plan sent to the terminal. As a specific example, the server might present a prompt such as "Delay notifications to reduce stress."

[0674] Step 5:

[0675] The terminal executes commands received from the server and controls the energy storage devices of the communication facility. Inputs are discharge / charge commands from the server and user experience adjustment commands. Through these commands, the terminal operates the energy management system to provide an optimized user experience. Outputs include optimized energy consumption and an improved user notification schedule. Specifically, the terminal monitors energy usage in real time and makes user interface adjustments, such as delaying notifications.

[0676] Step 6:

[0677] Users can monitor energy supply and demand in real time via a dashboard and fine-tune their energy management policies according to their emotional state. Inputs are energy consumption data and emotional state information provided by the dashboard. Outputs are the new energy usage policies and notification settings configured by the user. Specifically, users can choose to reduce notifications during stressful situations. The goal of this process is to achieve efficient energy use while maintaining user comfort.

[0678] (Application Example 2)

[0679] 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."

[0680] Conventional power management systems fail to consider the emotional stress of users, potentially leading to decreased user satisfaction even if energy management is efficient. Furthermore, simply optimizing the balance between power consumption and supply is insufficient to guarantee comfort within the home. Moreover, adjustments to optimize energy consumption within the home are often inadequate.

[0681] 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.

[0682] In this invention, the server includes means equipped with an intelligent system for collecting and analyzing historical data and weather information to perform nationwide power demand and supply forecasts; means for generating commands to control the discharge and charging of energy storage devices in communication facilities based on the forecasts; and an analysis device for detecting the user's emotional state and adjusting the operating state of home appliances based on that emotion. This enables efficient energy management that takes the user's emotions into consideration and improves comfort within the home.

[0683] An "intelligent system" is a device that has analytical functions to predict electricity demand and supply based on past data and weather information.

[0684] "Communication facilities" refer to infrastructure equipment for receiving and executing commands for discharging and charging electricity.

[0685] A "power storage device" is an energy storage device that efficiently stores electricity and discharges it as needed.

[0686] "Means for generating commands" refers to a method for creating control commands that enable optimal energy storage and discharge based on information about power supply and demand.

[0687] An "analysis device" is a data analysis device that detects the user's emotional state and adjusts the operation of home appliances accordingly.

[0688] To implement this invention, the server, terminal, and user each play their respective roles.

[0689] The server uses an intelligent system to predict nationwide power demand and supply. This requires a program to collect and analyze historical data and weather information. Possible software options include functions built with data analysis languages ​​such as Python and R, or TensorFlow and PyTorch, which are convenient for operating AI models. Based on the generated prediction data, the server issues commands to the energy storage devices of communication facilities. In particular, to perform analysis that takes into account the emotional state of users, it will utilize sentiment analysis APIs (e.g., Amazon Rekognition or Google Cloud Vision) to optimize power management according to the user's stress level.

[0690] The terminal controls the discharge and charging of the energy storage device based on commands received from the server. Furthermore, the terminal is equipped with a function to adjust the operation of home appliances based on emotion analysis results. Smart speakers and IoT devices with internet connectivity can be used to control smart home appliances in the home. This makes it possible to take specific actions when the user's stress level is high, such as changing the lighting to a softer color or setting the air conditioner to a comfortable temperature.

[0691] Users operate the application through their smartphones. The application includes a dashboard where users can check energy supply and demand status and their own emotional state in real time. An interface is provided that allows users to fine-tune energy management policies based on their input. For example, there is a prompt message that says, "When the user is relaxed, please instruct the system to change the lighting tone to a warm color and set the air conditioner temperature to 23 degrees." This allows users to create a comfortable and efficient home environment.

[0692] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0693] Step 1:

[0694] The server performs nationwide electricity demand and supply forecasts. It receives historical electricity consumption data and weather information as input. This data is analyzed, and a generative AI model is used to perform data calculations to predict future supply and demand. The output is forecast data that guides optimal energy management policies for each communication facility.

[0695] Step 2:

[0696] The server uses an emotion analysis API to detect the user's emotional state. It receives data of the user's facial expressions and voice from the camera and microphone as input. Data processing involves image recognition and voice analysis to determine the user's emotions. The output is data indicating the user's emotional state.

[0697] Step 3:

[0698] The server integrates power forecast data and user emotional state data to generate commands for adjusting the operation of household appliances. Using a generative AI model, it creates optimal operating instructions for each appliance from the input data. The output is a control command containing specific operating instructions for each appliance.

[0699] Step 4:

[0700] The terminal executes control commands received from the server. It receives command data from the server as input and sends commands to smart home appliances in the home. It adjusts the settings of the appliances using a device control protocol. This results in a comfortable appliance state that matches the user's preferences as output.

[0701] Step 5:

[0702] The user views the dashboard through a smartphone application. The application receives data from the device regarding the current energy supply and demand situation and the user's emotional state as input. It visualizes this data and displays it to the user. Based on this, the user can fine-tune their energy management policy in real time, and the output is updated configuration information based on the user's actions.

[0703] 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.

[0704] 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.

[0705] 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.

[0706] [Fourth Embodiment]

[0707] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0708] 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.

[0709] 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).

[0710] 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.

[0711] 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.

[0712] 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).

[0713] 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.

[0714] 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.

[0715] 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.

[0716] 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.

[0717] 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.

[0718] 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.

[0719] 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".

[0720] The present invention provides a comprehensive energy management system for optimizing battery management in communication facilities. This system utilizes generative AI to predict nationwide power supply and demand in real time, enabling optimal battery control. The program's processing is described in detail below.

[0721] ---

[0722] Server operation

[0723] The server first performs extensive data collection. It acquires nationwide power consumption data and weather information from multiple databases in real time and organizes this data into an analyzable format. This data is input into a generating AI model to perform highly accurate power demand and supply forecasts. Based on these forecasts, the server calculates the optimal battery discharge and charging schedule for each communication facility. The calculation results are transmitted to the communication facilities as commands in a timely manner.

[0724] ---

[0725] Terminal operation

[0726] The terminal receives battery control commands transmitted from the server. Following these commands, the terminal appropriately controls the battery, charging or discharging as needed. For example, during periods when a surge in power demand is predicted, the terminal completes charging in advance, ensuring stable operation by supplying power as predicted. Furthermore, to effectively utilize surplus solar power, the terminal constantly monitors the amount of power generated and chooses whether to store the excess in the battery or supply it to other facilities.

[0727] ---

[0728] User actions

[0729] Telecommunications carriers, as users, can check real-time energy supply and demand conditions and battery status through the system's dashboard. Users can adjust their energy management policies based on the forecast data to achieve optimal operation. Furthermore, if there is a large discrepancy between the forecast and actual results, the forecast accuracy can be further improved by updating the AI ​​model or fine-tuning the algorithm.

[0730] Thus, the present invention enables automatic and efficient energy management of storage batteries in communication facilities, effectively achieving reductions in power costs and stability of power supply.

[0731] The following describes the processing flow.

[0732] Server processing steps

[0733] Step 1:

[0734] The server collects electricity consumption data and weather information from across the country. This includes access from power companies and weather information providers, ensuring real-time data updates.

[0735] Step 2:

[0736] The server preprocesses the collected data. By formatting it on an hourly basis, imputing missing values, and removing outliers, it constructs a dataset suitable for input to generative AI models.

[0737] Step 3:

[0738] The server uses pre-processed data to run a generating AI that forecasts electricity demand and supply. The generated forecast data is updated hourly, identifying periods of high demand and periods of supply surplus.

[0739] Step 4:

[0740] Based on the prediction results, the server calculates discharge and charge commands for the communication facility's batteries. An optimization algorithm is used to balance power cost reduction with supply stability.

[0741] Step 5:

[0742] The server distributes the calculated control commands to each communication facility. This initiates specific energy management operations at the terminals in each facility.

[0743] ---

[0744] Terminal processing steps

[0745] Step 1:

[0746] The terminal analyzes the control commands received from the server and compares them with the current status of the facility's battery.

[0747] Step 2:

[0748] The terminal controls the battery according to instructions. For example, if instructed to charge in preparation for peak demand, it will immediately begin charging and continue until the required capacity is reached.

[0749] Step 3:

[0750] When a discharge command is issued, the terminal receives power from the battery. At this time, dynamic output adjustments are made in response to increases and decreases in demand, minimizing fluctuations in supply.

[0751] Step 4:

[0752] The terminal monitors the output of solar power generation and, if surplus power is generated, decides whether to store it in a battery or supply it externally.

[0753] Step 5:

[0754] The terminal feeds energy management information back to the server, where it is used as data necessary for generating the next command.

[0755] ---

[0756] User operation steps

[0757] Step 1:

[0758] Users access the system's dashboard to view real-time forecast data and actual values.

[0759] Step 2:

[0760] Users adjust their energy management policies based on prediction accuracy and actual performance. They apply new settings to the platform as needed.

[0761] Step 3:

[0762] Users check the utilization status of surplus solar power and evaluate possible monetization options.

[0763] Step 4:

[0764] Based on their feedback, users can request adjustments to the generation AI model to improve the system's prediction accuracy and prepare for the next operational cycle.

[0765] (Example 1)

[0766] 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".

[0767] Energy consumption in modern telecommunications facilities is leading to increased electricity costs and supply instability due to the lack of efficient and stable energy management systems. Furthermore, the effective utilization of renewable energy is insufficient, resulting in wasted surplus energy. Given these issues, there is a need to develop systems that accurately predict energy supply and demand and optimally control battery storage.

[0768] 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.

[0769] In this invention, the server includes means equipped with an information processing device that collects and organizes weather data and historical consumption statistics in order to predict energy consumption and supply nationwide; means equipped with a computing device that inputs the information into a generation model and plans the discharge and charging of energy storage devices of communication facilities based on the prediction results; and means equipped with a communication device that transmits the plan as a command to communication facilities in various locations and controls the power of the energy storage devices. This enables highly accurate energy supply and demand forecasting and efficient battery management.

[0770] "Forecasting nationwide energy consumption and supply" means estimating the demand for electricity and the available supply in each region over time, based on historical data and weather information.

[0771] "Weather data and historical consumption statistics" refers to information on weather conditions and historical data on past electricity usage, which serve as the basic information for constructing predictive models.

[0772] "Means equipped with an information processing device" refers to a configuration that includes a computer system or software for performing data collection, organization, and analysis.

[0773] "Inputting data into a generative model" refers to the process of inputting the necessary data into a prediction algorithm and performing calculations and analyses based on that data.

[0774] "Planning the discharge and charging of energy storage devices" means creating a schedule to optimize the release and storage of energy in batteries based on electricity demand forecasts.

[0775] A "means equipped with a computing device" refers to a set of hardware and software for performing complex calculations and efficiently conducting data analysis and predictions.

[0776] "Means equipped with communication devices" refers to a configuration that includes network devices capable of transmitting and receiving commands and data to and from various communication facilities.

[0777] The system of this invention is an integrated system for optimizing energy management in communication equipment. Servers, terminals, and users cooperate with each other to control battery storage and maintain a balance between energy supply and demand.

[0778] The server first collects nationwide energy consumption and supply data, along with weather information. This is done by obtaining information in real time using databases and weather APIs. The obtained data is organized into a format that can be analyzed by the information processing device and input into a generating AI model. The AI ​​model used here is designed to predict electricity demand with high accuracy, taking into account historical consumption data and weather conditions. For example, it might use a prompt such as, "What is the expected maximum electricity demand for the next 48 hours? The data will include weather information and consumption data for the past 24 hours."

[0779] Based on the generated prediction results, the server plans the discharge and charging schedules for the batteries of each communication facility. During this process, a computing device is used to efficiently perform the necessary calculations. The planned schedule is transmitted as a command to the communication facilities in each location via the communication device. This ensures that the charging and discharging of the batteries is properly managed. For example, if it is predicted that power demand will peak at a specific time, the charging of the batteries will be completed in advance to ensure power is supplied at that time.

[0780] The terminal controls the charging and discharging of the battery based on commands received from the server. It also takes measures to efficiently utilize surplus renewable energy. For example, if a surplus of solar power is predicted, it reduces energy waste by supplying the excess to other facilities or storing it in the battery.

[0781] Telecommunications carriers, as users, can monitor real-time energy supply and demand conditions and battery status through a dashboard provided by the system. This allows users to adjust their energy management policies as needed and achieve optimal operation. Furthermore, by reviewing the prediction results of the generated AI model and making adjustments as necessary, it is possible to improve the accuracy of the system's predictions.

[0782] Thus, the present invention provides a system that improves the efficiency of energy management in communication equipment and enables highly accurate power supply and demand forecasting and optimal battery operation.

[0783] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0784] Step 1:

[0785] The server collects electricity consumption data and weather information from across the country. This involves retrieving necessary information from databases and weather APIs of various partner organizations. Historical electricity consumption statistics and the latest weather data from each region are used as input. Based on these inputs, the data is organized into CSV format. The output is data in an analyzable format. The server then feeds this organized data to the next processing step.

[0786] Step 2:

[0787] The server inputs the organized data into the generating AI model. Specifically, it uses prompts such as, "Please tell me the expected maximum power demand for the next 48 hours. The data includes weather information and consumption data for the past 24 hours." The input data includes the analyzable data obtained in step 1. The AI ​​model analyzes the input data using machine learning algorithms and predicts future power demand. The output provides an estimate of predicted power consumption and supply.

[0788] Step 3:

[0789] The server calculates the discharge and charge schedules for the batteries of each communication facility based on the prediction results of the AI ​​model. The input is the prediction results from the AI ​​model, which include the predicted power demand values ​​for each time period. The server uses this data to generate the optimal battery storage schedule. The output is a specific charge and discharge timeline for each facility.

[0790] Step 4:

[0791] The server transmits the calculated schedule to the terminals of each communication device via the communication equipment. Transmission uses the internet, sending command data as packets to the network address of each terminal. The input is the schedule created in step 3. The output is that the terminals that receive the commands are ready to begin specific control.

[0792] Step 5:

[0793] The terminal receives commands from the server and initiates specific charging and discharging control. The input is a schedule command from the server. The terminal checks its internal clock and manages the battery appropriately by following the instructed schedule. Specifically, it starts charging the battery at the designated time and discharges it according to the predicted demand period. As an output, efficient energy management is achieved.

[0794] Step 6:

[0795] Users monitor real-time energy supply and demand conditions through a dashboard and adjust system settings as needed. Recorded operational and forecast data from the system are provided as input. Based on this, users can re-evaluate their energy management policies and implement necessary changes. Specifically, this involves viewing graphs and numerical displays on the dashboard and adjusting settings. As an output, the system's operational efficiency improves as a result of user adjustments.

[0796] (Application Example 1)

[0797] 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".

[0798] This invention aims to optimize and manage energy consumption in facilities, and in particular to stabilize the energy supply necessary for the operation of multiple automated equipment groups while improving the efficiency of renewable energy utilization. The goal is to achieve energy cost reduction and improved operational efficiency of the facilities.

[0799] 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.

[0800] In this invention, the server includes intelligent processing means for collecting and analyzing historical data and weather information in order to perform nationwide power demand and supply forecasts; control means for generating commands to control the discharge and charging of the facility's energy storage devices based on the forecasts; execution means for transmitting the commands to the facilities and performing energy management of the energy storage devices; and means for calculating and providing operating procedures to optimize the amount of energy required to operate a group of automated devices installed in multiple facilities. This enables more efficient energy management and more stable supply.

[0801] An "intelligent processing device" is a device that has the function of collecting and analyzing historical data and weather information in order to perform nationwide electricity demand and supply forecasts.

[0802] "Control means" refers to a system that generates commands to control the discharge and charging of the facility's energy storage devices based on predicted power demand and supply information.

[0803] An "execution means" is a device that has the function of transmitting the generated command to the equipment and performing the operations necessary to manage the energy of the energy storage device.

[0804] "Means for calculating and providing operating procedures" refers to a system that calculates an operating plan to optimize the amount of energy required to operate a group of automated devices within multiple facilities, and provides that plan to the facilities.

[0805] "Facilities" refers to a collection of devices and equipment installed for a specific purpose, including the operation of energy management and automation equipment.

[0806] An "automation system" is a collection of multiple machines and robots that are deployed in a factory or facility to perform routine tasks automatically.

[0807] A "energy storage device" is a device that stores surplus energy and can discharge or charge it according to the demand for electricity.

[0808] "Natural energy" refers to energy derived from natural forces such as solar, wind, and hydroelectric power, and is renewable energy that is generated in a sustainable manner.

[0809] A system for implementing the present invention includes intelligent processing means, control means, execution means, and means for calculating and providing operating procedures. The central server of the system is equipped with intelligent processing means that use a generated AI model to predict power demand and supply, and create an optimal charge and discharge schedule for the energy storage devices within the facility based on the results.

[0810] The server's control system generates optimal energy management commands based on prediction results obtained from the intelligent processing system, according to the operating status of the equipment. These commands are transmitted to each piece of equipment through the execution system.

[0811] Terminals installed within the facility control the charging and discharging of energy storage devices according to commands sent from the server, thereby achieving energy management. The terminals monitor energy usage in real time and make necessary adjustments to achieve energy-saving operation.

[0812] As a concrete example, energy costs can be reduced by providing operating procedures that optimize the energy use of automated equipment installed in a factory. For instance, by fully charging energy storage devices in advance during peak factory operating hours and effectively utilizing surplus electricity from renewable energy sources, external electricity demand can be suppressed.

[0813] Users can monitor energy usage and the status of energy storage devices in real time through a dashboard designed for easy facility management. Furthermore, it's possible to adjust the system's algorithms based on new data to further improve the accuracy of the generated AI models.

[0814] An example of a prompt message related to the operation of this system would be: "Based on the energy consumption patterns within the factory and the weather forecast for the next three days, predict the optimal battery charging and discharging schedule."

[0815] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0816] Step 1:

[0817] The server collects nationwide electricity consumption data and weather information in real time from an external database. This process converts the raw data obtained via API into structured data, making it easier for predictive models to process. The input consists of electricity consumption data and weather information, while the output is data organized into an analyzable format.

[0818] Step 2:

[0819] The server uses a generative AI model to forecast electricity demand and supply based on the data organized in Step 1. In this process, the forecasting algorithm calculates the fluctuations in demand and supply over the next few days and outputs them as forecast data. The input is structured electricity consumption and weather data, and the output is forecasted demand and supply data.

[0820] Step 3:

[0821] The server's control system generates an optimal charge / discharge schedule for the energy storage devices in the facility based on the prediction results from step 2. This process uses the prediction data and the current energy storage status as input to create and output an optimal energy management command. In operation, it simulates multiple scenarios to determine the most efficient operational policy.

[0822] Step 4:

[0823] The terminal controls the energy storage device based on the charge / discharge schedule sent from the server. The input is a command from the server, and the output is the actual charge / discharge operation of the energy storage device. The terminal monitors the status of the energy storage device in real time and makes adjustments to deal with unexpected situations.

[0824] Step 5:

[0825] Users monitor the overall energy usage of the facility and the operating status of energy storage devices through a dashboard. Input is energy management data provided by the server, and output is a visualization of the energy data that users can visually confirm. Users can easily adjust facility management policies.

[0826] Step 6:

[0827] The user updates the generated AI model as needed to improve prediction accuracy. This process strengthens the algorithm by incorporating new data into the model. The input is the latest energy usage data and parameters of the prediction model, and the output is the new, improved prediction model. For example, a prompt such as "Predict the optimal battery charging and discharging schedule based on the energy consumption patterns in the factory and the weather forecast for the next three days" is used.

[0828] 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.

[0829] This invention incorporates a function that considers user emotions into a system that optimizes the balance between power supply and consumption in real time. This reduces the psychological burden on the user while achieving efficient energy management. The program's processing is described in detail below.

[0830] ---

[0831] Server operation

[0832] The server features a generative AI that forecasts power demand and supply, as well as an emotion engine. First, the server forecasts power supply and demand using power consumption data and weather information collected from across the country. Based on this data, it generates optimal discharge and charging commands for the batteries of each communication facility. At the same time, it also takes the user's emotional state into consideration, and the emotion engine analyzes the user's emotions from a series of data. For example, if the user is feeling stressed, it generates commands to optimize notifications and relax energy management policies, taking into account periods of low load.

[0833] ---

[0834] Terminal operation

[0835] The terminal receives commands from the server and controls the battery storage of the communication facility. It also makes adjustments to improve the user experience based on commands from the emotion engine. For example, it optimizes the timing and content of energy usage notification messages according to the user's emotions. Furthermore, it can adjust the output of solar power generation and the use of surplus power based on user feedback, thereby increasing user satisfaction.

[0836] ---

[0837] User actions

[0838] Users can monitor energy supply and demand in real time through the dashboard, as well as understand their own emotional state. Based on the data provided by the emotion engine, users can fine-tune their energy management policies and choose an operating method that suits their emotional state. For example, if stress levels are high, notifications may be suppressed and only reminders may be displayed.

[0839] Thus, the present invention is a system that simultaneously achieves power management and user comfort. By considering emotions in energy management, it is possible to enhance user satisfaction while utilizing energy efficiently.

[0840] The following describes the processing flow.

[0841] Server processing steps

[0842] Step 1:

[0843] The server acquires nationwide electricity consumption data and weather information. This allows it to begin forecasting electricity demand and supply for the following day. Data is collected in real time from multiple sources.

[0844] Step 2:

[0845] The server refines the acquired data and cleans up outliers. This process prepares a dataset suitable for the generative AI model.

[0846] Step 3:

[0847] The server uses AI generation to predict future power supply and demand. Predictions are made over time spans of several hours to several days, and the results are stored in a database.

[0848] Step 4:

[0849] The server activates an emotion engine to evaluate the user's emotional state. During this process, specific patterns and trends are identified.

[0850] Step 5:

[0851] The server integrates power forecasts and user sentiment data to create optimal battery control commands. Based on the sentiment data, it adjusts the content and timing of notifications.

[0852] Step 6:

[0853] The server transmits the generated commands to the terminals in the communication facility, instructing them on appropriate energy management and user notifications.

[0854] ---

[0855] Terminal processing steps

[0856] Step 1:

[0857] The terminal analyzes the commands received from the server and determines the corresponding operation of the battery.

[0858] Step 2:

[0859] The terminal checks the current charge status of the battery and starts charging or discharging according to the server's instructions.

[0860] Step 3:

[0861] The terminal monitors surplus electricity from solar power generation and stores it in a battery or supplies it to other loads as needed.

[0862] Step 4:

[0863] The device optimizes notification messages to the user in response to commands from the emotion engine. This includes fine-tuning the content and timing of notifications.

[0864] Step 5:

[0865] The device feeds energy and emotional data back to the server, which is then used for subsequent calculations and optimization processes.

[0866] ---

[0867] User operation steps

[0868] Step 1:

[0869] Users log into the dashboard to check their emotional state along with their energy usage.

[0870] Step 2:

[0871] Based on recommendations from the emotion engine, users can adjust their energy management settings. For example, they can change the frequency and level of detail of notifications.

[0872] Step 3:

[0873] Users provide feedback on energy and emotions, contributing to improvements in the system's predictive accuracy and emotion recognition.

[0874] Step 4:

[0875] Users will use the provided information to consider new energy management strategies and minimize their own energy costs.

[0876] (Example 2)

[0877] 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".

[0878] With current technological capabilities, it has been difficult to not only efficiently manage the balance between power supply and demand, but also to implement energy management that takes into account the psychological state of users. In particular, when a user's emotional state influences power usage and management policies, flexible responses tailored to the situation are required. However, existing systems have failed to meet this requirement, leading to a decline in user satisfaction.

[0879] 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.

[0880] This invention includes a server equipped with artificial intelligence for collecting and analyzing historical information and weather conditions to predict nationwide energy demand and supply; a means for generating commands to adjust the discharge and charging of energy storage devices in facilities with communication capabilities based on the predictions; and an emotion engine for analyzing the user's emotional state, and a means for generating adjustment commands to improve the user experience based on the analysis results. This enables not only more efficient energy management but also flexible optimization of energy use in response to the user's emotions.

[0881] "Artificial intelligence" is a technology that analyzes past information and weather conditions to make predictions necessary for optimizing energy demand and supply.

[0882] A "facility with communication capabilities" is infrastructure that allows for remote information exchange and the reception of energy management commands.

[0883] A "power storage device" is a device that temporarily stores electricity and discharges and charges it as needed.

[0884] The "emotion engine" is a technology that analyzes the user's emotional state and generates commands to adjust energy management policies based on that state.

[0885] The "means for generating instructions" are mechanisms for creating guidelines for optimal energy use based on predicted energy supply and demand and user sentiment analysis data.

[0886] "Energy control" refers to the operation of adjusting the discharge and charging of energy storage devices to balance supply and demand.

[0887] This invention incorporates a function that takes user emotions into a system that optimizes the balance between power supply and consumption in real time. For the invention to be implemented, the server, terminal, and user each need to perform specific actions.

[0888] The server first collects energy consumption data and weather information from across the country. This data collection uses database software, and a generated AI model executes a prediction algorithm based on historical datasets. This AI model predicts power demand and supply, and based on this, discharge and charge commands are generated for the energy storage devices at each communication facility.

[0889] Simultaneously, the server uses an emotion engine to analyze the user's emotional state. This emotion engine analyzes the user's data patterns to determine whether they are experiencing stress. Based on this analysis, instructions are generated to optimize the content and timing of notifications. For example, the server might generate a prompt such as, "If the user is experiencing stress, refrain from sending notifications."

[0890] The terminal controls the energy storage devices of the communication facility based on commands received from the server. The terminal also executes applications to adjust the content and timing of user notifications according to commands from the emotion engine. This enables improved energy efficiency and a better user experience.

[0891] Users can monitor energy supply and demand in real time through the dashboard interface and fine-tune their energy management policies based on data provided by the emotion engine. For example, if a user is stressed, they can change settings to reduce notifications about energy usage.

[0892] In this way, this invention can simultaneously improve power management and user satisfaction. By utilizing a generative AI model and an emotion engine, efficient energy use is achieved while also ensuring user comfort.

[0893] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0894] Step 1:

[0895] The server collects energy consumption data and weather information from across the country. It uses historical electricity consumption data and current weather information as input data. This data is managed by database software. Based on this data, the server runs a generated AI model to predict electricity demand and supply. The output of this calculation is the predicted demand for each communication facility. Specifically, the server uses the prediction model to analyze consumption patterns and forecast future electricity supply and demand.

[0896] Step 2:

[0897] The server uses the supply and demand forecast results from Step 1 to generate commands that optimize the discharge and charging of energy storage devices. The inputs are the current supply capacity and energy storage capacity at each communication facility. Based on the generated AI model, the server calculates efficient energy distribution and discharge timing. The output is a command to be sent to each communication facility. This command includes a specific discharge and charging schedule.

[0898] Step 3:

[0899] The server activates the emotion engine to analyze the user's emotional state. Input includes the user's past behavioral data and real-time operation logs. The emotion engine processes this data and assesses how stressed the user is. As a result of the analysis, it outputs an emotional state score. Specifically, it analyzes changes in the user's activity and qualitative responses to generate the emotional score.

[0900] Step 4:

[0901] The server generates commands to improve the user experience based on the emotional state score obtained from the emotion engine. The input is the emotional evaluation and energy management policy obtained in step 3. The server generates prompts to optimize the content and timing of notifications that reflect the emotional score. The output is the energy management adjustment plan sent to the terminal. As a specific example, the server might present a prompt such as "Delay notifications to reduce stress."

[0902] Step 5:

[0903] The terminal executes commands received from the server and controls the energy storage devices of the communication facility. Inputs are discharge / charge commands from the server and user experience adjustment commands. Through these commands, the terminal operates the energy management system to provide an optimized user experience. Outputs include optimized energy consumption and an improved user notification schedule. Specifically, the terminal monitors energy usage in real time and makes user interface adjustments, such as delaying notifications.

[0904] Step 6:

[0905] Users can monitor energy supply and demand in real time via a dashboard and fine-tune their energy management policies according to their emotional state. Inputs are energy consumption data and emotional state information provided by the dashboard. Outputs are the new energy usage policies and notification settings configured by the user. Specifically, users can choose to reduce notifications during stressful situations. The goal of this process is to achieve efficient energy use while maintaining user comfort.

[0906] (Application Example 2)

[0907] 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".

[0908] Conventional power management systems fail to consider the emotional stress of users, potentially leading to decreased user satisfaction even if energy management is efficient. Furthermore, simply optimizing the balance between power consumption and supply is insufficient to guarantee comfort within the home. Moreover, adjustments to optimize energy consumption within the home are often inadequate.

[0909] 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.

[0910] In this invention, the server includes means equipped with an intelligent system for collecting and analyzing historical data and weather information to perform nationwide power demand and supply forecasts; means for generating commands to control the discharge and charging of energy storage devices in communication facilities based on the forecasts; and an analysis device for detecting the user's emotional state and adjusting the operating state of home appliances based on that emotion. This enables efficient energy management that takes the user's emotions into consideration and improves comfort within the home.

[0911] An "intelligent system" is a device that has analytical functions to predict electricity demand and supply based on past data and weather information.

[0912] "Communication facilities" refer to infrastructure equipment for receiving and executing commands for discharging and charging electricity.

[0913] A "power storage device" is an energy storage device that efficiently stores electricity and discharges it as needed.

[0914] "Means for generating commands" refers to a method for creating control commands that enable optimal energy storage and discharge based on information about power supply and demand.

[0915] An "analysis device" is a data analysis device that detects the user's emotional state and adjusts the operation of home appliances accordingly.

[0916] To implement this invention, the server, terminal, and user each play their respective roles.

[0917] The server uses an intelligent system to predict nationwide power demand and supply. This requires a program to collect and analyze historical data and weather information. Possible software options include functions built with data analysis languages ​​such as Python and R, or TensorFlow and PyTorch, which are convenient for operating AI models. Based on the generated prediction data, the server issues commands to the energy storage devices of communication facilities. In particular, to perform analysis that takes into account the emotional state of users, it will utilize sentiment analysis APIs (e.g., Amazon Rekognition or Google Cloud Vision) to optimize power management according to the user's stress level.

[0918] The terminal controls the discharge and charging of the energy storage device based on commands received from the server. Furthermore, the terminal is equipped with a function to adjust the operation of home appliances based on emotion analysis results. Smart speakers and IoT devices with internet connectivity can be used to control smart home appliances in the home. This makes it possible to take specific actions when the user's stress level is high, such as changing the lighting to a softer color or setting the air conditioner to a comfortable temperature.

[0919] Users operate the application through their smartphones. The application includes a dashboard where users can check energy supply and demand status and their own emotional state in real time. An interface is provided that allows users to fine-tune energy management policies based on their input. For example, there is a prompt message that says, "When the user is relaxed, please instruct the system to change the lighting tone to a warm color and set the air conditioner temperature to 23 degrees." This allows users to create a comfortable and efficient home environment.

[0920] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0921] Step 1:

[0922] The server performs nationwide electricity demand and supply forecasts. It receives historical electricity consumption data and weather information as input. This data is analyzed, and a generative AI model is used to perform data calculations to predict future supply and demand. The output is forecast data that guides optimal energy management policies for each communication facility.

[0923] Step 2:

[0924] The server uses an emotion analysis API to detect the user's emotional state. It receives data of the user's facial expressions and voice from the camera and microphone as input. Data processing involves image recognition and voice analysis to determine the user's emotions. The output is data indicating the user's emotional state.

[0925] Step 3:

[0926] The server integrates power forecast data and user emotional state data to generate commands for adjusting the operation of household appliances. Using a generative AI model, it creates optimal operating instructions for each appliance from the input data. The output is a control command containing specific operating instructions for each appliance.

[0927] Step 4:

[0928] The terminal executes control commands received from the server. It receives command data from the server as input and sends commands to smart home appliances in the home. It adjusts the settings of the appliances using a device control protocol. This results in a comfortable appliance state that matches the user's preferences as output.

[0929] Step 5:

[0930] The user views the dashboard through a smartphone application. The application receives data from the device regarding the current energy supply and demand situation and the user's emotional state as input. It visualizes this data and displays it to the user. Based on this, the user can fine-tune their energy management policy in real time, and the output is updated configuration information based on the user's actions.

[0931] 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.

[0932] 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.

[0933] 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 robot 414.

[0934] 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.

[0935] 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.

[0936] 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.

[0937] 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.

[0938] 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.

[0939] 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."

[0940] 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.

[0941] 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.

[0942] 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.

[0943] 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.

[0944] 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.

[0945] 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.

[0946] 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.

[0947] 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.

[0948] 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.

[0949] 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.

[0950] 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.

[0951] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0952] The following is further disclosed regarding the embodiments described above.

[0953] (Claim 1)

[0954] In order to conduct nationwide electricity demand and supply forecasts, a means equipped with artificial intelligence to collect and analyze historical data and weather information is used.

[0955] A means for generating commands to control the discharge and charging of the battery of a communication facility based on the aforementioned prediction,

[0956] A means for transmitting the aforementioned command to a communication facility and managing the energy of the storage battery,

[0957] A system that includes this.

[0958] (Claim 2)

[0959] The system according to claim 1, wherein the communication facility includes means for managing the generation of excess solar energy and utilizing the surplus power.

[0960] (Claim 3)

[0961] The system according to claim 1, wherein the artificial intelligence uses an algorithm to improve the accuracy of demand and supply forecasts in order to optimize the energy balance of multiple communication facilities.

[0962] "Example 1"

[0963] (Claim 1)

[0964] A means equipped with an information processing device that collects and organizes weather data and historical consumption statistics in order to forecast energy consumption and supply nationwide,

[0965] A means comprising a computing device for inputting the aforementioned information into a generation model and planning the discharge and charging of the energy storage device of the communication equipment based on the prediction results,

[0966] A means equipped with a communication device that transmits the aforementioned plan as a command to communication facilities in various locations and controls the power of the energy storage device,

[0967] A system that includes this.

[0968] (Claim 2)

[0969] The system according to claim 1, wherein the communication equipment includes means for managing excess renewable energy production and efficiently utilizing surplus electricity.

[0970] (Claim 3)

[0971] The system according to claim 1, wherein the generation model utilizes an algorithm to improve the accuracy of consumption and supply predictions in order to optimize energy operation between various communication facilities.

[0972] "Application Example 1"

[0973] (Claim 1)

[0974] An intelligent processing system for collecting and analyzing historical data and weather information in order to conduct nationwide electricity demand and supply forecasts,

[0975] Based on the aforementioned prediction, a control means generates commands for controlling the discharge and charging of the energy storage device of the facility,

[0976] An execution means that transmits the aforementioned command to the equipment and manages the energy of the energy storage device,

[0977] A means for calculating and providing an operating procedure to optimize the amount of energy required to operate a group of automated devices installed in multiple facilities,

[0978] A system that includes this.

[0979] (Claim 2)

[0980] The system according to claim 1, wherein the equipment includes means for managing the generation of excess renewable energy and using the surplus electricity.

[0981] (Claim 3)

[0982] The system according to claim 1, wherein the intelligent processing means uses procedures to improve the accuracy of demand and supply forecasts in order to optimize the energy balance of a plurality of facilities.

[0983] "Example 2 of combining an emotion engine"

[0984] (Claim 1)

[0985] To forecast energy demand and supply nationwide, a means equipped with artificial intelligence that collects and analyzes historical data and weather conditions,

[0986] Based on the aforementioned prediction, means for generating commands to adjust the discharge and charging of energy storage devices in facilities with communication functions,

[0987] Means for transmitting the aforementioned command and controlling the energy of the energy storage device,

[0988] It includes an emotion engine for analyzing the user's emotional state, and means for generating adjustment commands to improve the user experience based on the analysis results.

[0989] A system that includes this.

[0990] (Claim 2)

[0991] The system according to claim 1, wherein the facility having the communication function includes means for managing the production of excess solar energy and utilizing surplus electricity.

[0992] (Claim 3)

[0993] The system according to claim 1, wherein the artificial intelligence uses an algorithm to improve the accuracy of demand and supply forecasts in order to optimize the energy balance of a facility having multiple communication functions.

[0994] "Application example 2 when combining with an emotional engine"

[0995] (Claim 1)

[0996] In order to conduct nationwide electricity demand and supply forecasts, a means equipped with an intelligent system that collects and analyzes historical data and weather information,

[0997] Based on the aforementioned prediction, means for generating commands to control the discharge and charging of the energy storage device of the communication facility,

[0998] A means for transmitting the aforementioned command to a communication facility and managing the energy of the energy storage device,

[0999] An analysis device for detecting the user's emotional state and adjusting the operating state of home appliances based on that emotion,

[1000] A system that includes this.

[1001] (Claim 2)

[1002] The system according to claim 1, wherein the communication facility includes means for managing the generation of excess solar energy and utilizing the surplus energy.

[1003] (Claim 3)

[1004] The system according to claim 1, wherein the intelligent system includes computational means for improving the accuracy of demand and supply forecasts in order to optimize the energy balance of a plurality of communication facilities. [Explanation of symbols]

[1005] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. In order to conduct nationwide electricity demand and supply forecasts, a means equipped with artificial intelligence to collect and analyze historical data and weather information is required. Based on the aforementioned prediction, means for generating commands to control the discharge and charging of the battery of the communication facility, A means for transmitting the aforementioned command to a communication facility and managing the energy of the storage battery, A system that includes this.

2. The system according to claim 1, wherein the communication facility includes means for managing the generation of excess solar energy and utilizing the surplus power.

3. The system according to claim 1, wherein the artificial intelligence uses an algorithm to improve the accuracy of demand and supply forecasts in order to optimize the energy balance of multiple communication facilities.

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