system

Abstract

We provide the system. [Solution] Means for collecting electricity usage data, A means for analyzing the aforementioned power usage data and weather information to predict future consumption patterns, A means for adjusting the amount of power supplied from each generator, A means of securely and quickly exchanging data between a generator and a consumer using quantum communication technology, A means for optimizing energy distribution based on consumption patterns, A power management system including

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In a conventional power management system, the instability of renewable energy and the excess or shortage of supply are problems, which reduce the efficiency and stability of power supply. Also, security risks and delays in data exchange between power plants and consumers are important issues. Therefore, an object of the present invention is to solve these problems and achieve stable power supply and cost optimization.

Means for Solving the Problems

[0005] This invention comprises means for collecting power usage data and means for predicting future consumption patterns based on this data and weather information. Based on this prediction, the amount of power supplied from each generator is adjusted, and power distribution is optimized using a multi-agent system. Furthermore, by using quantum communication technology to safely and quickly exchange data between generators and consumers, and by providing means for notifying consumers' terminals of the optimized energy distribution, the efficiency and stability of power supply are improved.

[0006] "Electricity usage data" refers to information about the amount of electricity consumed in homes and businesses, and is collected through smart meters and sensors.

[0007] "Weather information" refers to meteorological data such as temperature, humidity, wind speed, and precipitation, and is used to predict power consumption patterns.

[0008] "Consumption patterns" refer to trends and patterns that indicate future electricity demand trends, analyzed from past electricity consumption data.

[0009] A "generator" is a mechanical device used to generate electricity, and it is a device that supplies electricity from various energy sources, including renewable energy.

[0010] A "multi-agent system" is a system in which multiple agents cooperate to manage and optimize power generation and distribution.

[0011] "Quantum communication technology" refers to communication technology that applies the principles of quantum mechanics to transfer information extremely securely and quickly.

[0012] A "terminal" is a device used by the user that receives notifications regarding power consumption forecasts and optimization suggestions. [Brief explanation of the drawing]

[0013] [Figure 1]This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0014] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0017] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0019] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), and the like.

[0020] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0024] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0025] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0026] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0027] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0028] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0031] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0032] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0033] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0034] This invention relates to a power management system that predicts power consumption using power usage data and weather information, and optimizes the supply of power generators. This system consists of a server, terminals, and users, and each function works together to achieve efficient power supply.

[0035] System Configuration

[0036] 1. Server

[0037] The server is responsible for collecting and analyzing power usage data and weather information. Using AI agents, it analyzes past consumption data and weather information to predict future consumption patterns. This predicted data is used to efficiently adjust the operation of power generators. It also utilizes a multi-agent system to issue power supply instructions to each power plant.

[0038] 2. Terminal

[0039] The terminal, acting as a user device, notifies the user of energy allocation optimization suggestions from the server. The terminal displays energy-saving suggestions from the AI ​​agent, allowing the user to optimize their actions based on these suggestions.

[0040] 3. User

[0041] Users receive notifications about energy usage through their devices and can follow optimization suggestions to achieve efficient use of electricity. During peak consumption periods, users are given suggestions to conserve energy, thereby leveling out electricity consumption.

[0042] Example of operation

[0043] As a concrete example, let's explain the daytime electricity management process for a household. Consider a case where a household receives a proposal to reduce electricity consumption during peak hours.

[0044] 1. In the morning, the server predicts that heater usage will increase due to a drop in temperature during the night, based on the day's weather forecast and past power usage data.

[0045] 2. The AI ​​agent analyzes this forecast data and determines that the peak in electricity demand will occur between 6:00 PM and 9:00 PM.

[0046] 3. The server adjusts the output of each generator based on its predictions and prepares backup power generation capacity if necessary.

[0047] 4. The device notifies the user based on the forecast results. A message will be displayed stating, "Peak electricity demand is expected between 18:00 and 21:00. By reducing electricity usage during this time, you can minimize energy costs."

[0048] 5. Based on this notification, users can take action such as adjusting their home heating timers or turning off unused lights, thereby avoiding peak electricity usage.

[0049] In this way, servers, terminals, and users work together to achieve a stable power supply and reduce energy costs.

[0050] The following describes the processing flow.

[0051] Step 1:

[0052] The server collects real-time electricity usage data from individual homes and businesses. Simultaneously, it obtains weather information such as temperature and humidity from external data providers and stores this information in a centrally managed database.

[0053] Step 2:

[0054] An AI agent on the server analyzes collected electricity usage data and weather information. It uses machine learning algorithms to predict future electricity demand, evaluating the relationship between past consumption patterns and weather. In particular, it precisely models demand fluctuations by time of day.

[0055] Step 3:

[0056] The server adjusts the power supply from each generator based on consumption pattern predictions obtained from AI agents. Using a multi-agent system, it provides appropriate output instructions to each power plant. In doing so, it also considers the availability of renewable energy to ensure a balanced distribution.

[0057] Step 4:

[0058] The server uses quantum communication technology to exchange data between generators and consumers. This ensures the secure delivery of proposed energy allocation instructions and change information.

[0059] Step 5:

[0060] The device notifies the user of energy-saving suggestions received from the server. Specifically, it displays information such as, "18:00 to 21:00 is peak time. Reducing your electricity usage will save you money on your electricity bill."

[0061] Step 6:

[0062] Users receive notifications from their devices and take actions to reduce power consumption. For example, they might turn off unnecessary lights or shift the use of appliances to off-peak hours to avoid peak electricity demand.

[0063] Step 7:

[0064] The server collects actual consumption data based on user behavior and feeds it back to the AI ​​agent. This improves the accuracy of demand forecasts for future use, enabling more efficient energy management.

[0065] (Example 1)

[0066] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0067] In modern society, effectively managing peak electricity demand periods is becoming increasingly important. However, conventional power management systems have insufficient demand forecasting accuracy, making it difficult to maximize the operating efficiency of power equipment. Furthermore, there are many limitations in adjusting supply levels and providing efficient information to users, resulting in insufficient optimization.

[0068] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0069] In this invention, the server includes means for analyzing usage data and weather information to predict future consumption patterns, means for dynamically managing the operation of power equipment based on the predicted demand, and means for realizing the coordinated operation of multiple devices using an agent model. This makes it possible to maximize the efficiency of power supply and optimize the operation of power equipment.

[0070] "Usage data" refers to data that shows how energy consumers are using electricity.

[0071] "Weather information" refers to information about meteorological conditions such as weather, temperature, and humidity.

[0072] "Consumption patterns" refer to trends and fluctuations in electricity usage over a certain period.

[0073] A "power device" is a mechanical device that is responsible for generating or supplying electricity.

[0074] "Communication technology" refers to a set of technologies used to safely and quickly transmit data from a sender to a receiver.

[0075] The "agent model" is a technology that enables multiple program agents to work together in a coordinated manner.

[0076] A "notification device" is a communication device or equipment used to provide information or suggestions to users.

[0077] "Means of optimizing energy distribution" refer to methods and techniques for maximizing efficiency by adjusting the amount and timing of energy supplied.

[0078] In this invention, a server, terminals, and users work together to build a power management system.

[0079] The server's primary role is to collect and analyze usage data and weather information. Usage data includes real-time acquisition of each consumer's power usage through sensor devices. Weather information is obtained using APIs from weather information providers. On the server, machine learning frameworks such as TENSORFLOW® and PyTorch are used to model consumption patterns based on this data and predict future demand.

[0080] The server dynamically manages the operation of power units based on predicted demand information. This management uses an agent model to ensure that multiple power units operate efficiently in coordination. Secure communication technology is also used for transmitting data to each power unit.

[0081] A device is a medium for providing information and notifications to users. Users can receive notifications displayed on the device and optimize their energy usage. Devices include smartphones and smart devices in the home, and notifications are delivered as push messages.

[0082] For example, a server uses weather data to predict the peak hours of electricity demand in the evening and sends a message to the terminal saying, "Electricity demand will be high between 6:00 PM and 9:00 PM. Please reduce your usage." Based on this notification, users can adjust their electricity usage, thereby reducing peak demand.

[0083] An example of a prompt using the generated AI model would be, "Based on power consumption data and weather forecasts, please predict the peak power demand for October 21, 2023." In this way, this invention achieves efficient power management and resource optimization through the cooperation of servers, terminals, and users.

[0084] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0085] Step 1:

[0086] The server collects usage data from each consumer in real time. Input data from sensor devices includes hourly records of power consumption. This data is analyzed and cleaned, anomalies are removed, and then stored in a database. The output is a formatted set of usage data.

[0087] Step 2:

[0088] The server obtains weather information from external sources via a weather information API. Inputs include weather conditions, temperature, humidity, etc., which are used to form a weather dataset. The output is a dataset integrating this weather data.

[0089] Step 3:

[0090] The server inputs usage data and weather data into a machine learning platform such as TensorFlow to predict future consumption patterns. Data processing involves fitting this data to a time-series model for training. The output is the predicted consumption pattern, representing future electricity demand numerically.

[0091] Step 4:

[0092] The server develops an operating plan for the power units based on predictive data. Specifically, it uses an agent model to determine the optimal output level for each power unit and sends operating instructions to each unit. The output is operating instruction information for each unit.

[0093] Step 5:

[0094] The terminal notifies the user of forecast information and energy usage suggestions sent from the server. The input is notification data from the server, which is displayed on the user's screen. For example, it sends a push notification such as, "Electricity demand will be high between 18:00 and 21:00." The output is a message notification to the user.

[0095] Step 6:

[0096] Users adjust their actions based on notifications received from their devices. Specifically, they might change heating timers to match the notified peak times or turn off unnecessary electrical appliances. The input is notification information from the device, and the output is the user's change in power usage patterns.

[0097] (Application Example 1)

[0098] 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."

[0099] Managing electricity in modern cities remains a major challenge, given the increasing demand for volatile demand and efficient use of energy resources. In particular, while appropriate adjustments to electricity supply in response to rapid changes in demand are required, optimizing consumption through the actions of individual residents is not sufficiently achieved in many cities. Therefore, improving energy efficiency across the entire community is necessary.

[0100] 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.

[0101] In this invention, the server includes means for collecting data for predicting power consumption, means for analyzing the data and weather information to predict demand, and means for adjusting the supply of power generation equipment. This enables efficient energy distribution and the provision of information to optimize consumption for residents.

[0102] "Data collection methods" refer to technical techniques for obtaining information about electricity consumption from multiple sources.

[0103] "Analysis means" refers to processing technology used to derive future demand patterns based on collected electricity usage data and weather information.

[0104] A "supply adjustment mechanism" is a function for optimizing the output of power generation equipment based on demand forecasts.

[0105] "Communication technology" is the infrastructure for securely and quickly exchanging necessary data between energy supply facilities and electricity consumers.

[0106] An "energy distribution optimization method" is a method for allocating energy resources most effectively based on predicted consumption patterns.

[0107] "Information provision means" refers to technologies for notifying residents of suggestions regarding optimizing their consumption.

[0108] This invention is a power management system for smart cities, realized through the interaction of servers, terminals, and residents. The server has the function of collecting power consumption data and weather information, and performing data analysis, thereby predicting future power demand.

[0109] The server operates on cloud infrastructure such as AWS® and analyzes consumption patterns using data analysis libraries such as Python, Pandas, and Scikit-learn. The server's role is to train an AI model based on the collected data and predict future electricity demand with high accuracy. Based on this prediction, the server adjusts the output of power generation equipment to meet demand.

[0110] The terminal operates on smart devices owned by residents. It has the functionality to provide residents with real-time information on optimizing their consumption through an application developed with React Native. The application displays suggestions based on consumption forecasts sent from the server, prompting consumer action. For example, it might notify users with a message such as, "You can save 20% energy by refraining from using air conditioning around 6 PM. Try to dress in cooler clothing."

[0111] Based on information received via their devices, users optimize their individual consumption behavior, contributing to overall energy efficiency improvements. They are expected to receive suggestions indicating specific actions they should take and adjust their energy consumption at appropriate times.

[0112] As a specific example, the server predicts that when high summer temperatures are forecast for a weekday afternoon, the peak demand for air conditioning will be between 6:00 PM and 9:00 PM, and adjusts energy consumption accordingly. Based on this prediction, the terminal sends the user specific energy-saving instructions.

[0113] Examples of prompts for a generative AI model include the following:

[0114] "According to weather forecasts and historical consumption data, electricity demand is expected to peak tonight between 6:00 PM and 9:00 PM. What energy-saving actions would you suggest?"

[0115] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0116] Step 1:

[0117] The server collects electricity usage data and weather information from multiple data providers on the internet. Specifically, it periodically retrieves data using APIs and stores it in a database. The input for this step is electricity usage history and weather forecast information, and the output is an analyzable dataset.

[0118] Step 2:

[0119] The server analyzes consumption patterns using the collected data and predicts future electricity demand. Here, Python and machine learning libraries (e.g., Scikit-learn) are used. The input is the dataset generated in the previous step, and the AI ​​model calculates the supply and demand forecast. The output is the future electricity demand forecast.

[0120] Step 3:

[0121] The server optimally adjusts the supply of power generation equipment based on the prediction results. This process involves sending instructions to power plants to establish an efficient energy distribution. The input is future prediction data, and the output is the adjusted supply plan.

[0122] Step 4:

[0123] The terminal notifies residents of supply and demand forecasts and energy-saving suggestions sent from the server. These notifications include specific energy-saving actions and are received by the user via a smartphone app. The input is energy-saving suggestions from the server, and the output is the notification message to the user.

[0124] Step 5:

[0125] Users adjust their daily energy usage based on information from their devices. They use the app to create concrete action plans for energy saving and optimize the use of home appliances. The input is suggestive information from the device, and the output is improved energy efficiency.

[0126] 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.

[0127] This invention combines an emotion engine with a power management system to achieve energy management that takes into account the user's emotional state. This system consists of a server, terminals, users, and an emotion engine, and provides efficient power supply while maintaining user comfort.

[0128] System Configuration

[0129] 1. Server

[0130] The server collects power usage data and weather information, and analyzes it with AI agents. It predicts consumption patterns and issues supply instructions to each generator. A multi-agent system is utilized here to ensure efficient energy distribution. It also receives input from an emotion engine and adjusts suggestions based on the user's emotional state.

[0131] 2. Terminal

[0132] The device notifies the user of energy allocation optimization suggestions from the server. Based on feedback analyzed by the emotion engine, the suggestions are automatically adjusted. For example, if the user is stressed, a gentler suggestion will be provided.

[0133] 3. User

[0134] Users receive suggestions via their devices and optimize their actions. An emotion engine operates in the background, analyzing the user's voice and facial expressions to understand their emotional state in real time.

[0135] 4. Emotional Engine

[0136] The emotion engine includes sensors that estimate the user's emotions using speech recognition and facial recognition technologies. This engine continuously learns from the user's responses in everyday life and improves the accuracy of its suggestions.

[0137] Example of operation

[0138] As a concrete example, let's explain the power management process in a user's daily life. Consider a case where a user has returned home from work and wants to relax.

[0139] 1. The server predicts that electricity demand will increase from evening to night based on past data from when the user returned home and weather information. However, the emotion engine recognizes the user's fatigue level from their voice.

[0140] 2. Based on the prediction results, energy-saving suggestions are prepared, but because the user is tired, the notification content is adjusted to "You must be tired. To relax, we recommend using appliances after peak consumption hours."

[0141] 3. The device displays these adjusted suggestions to the user. Based on this, the user can adjust the usage time of heating and lighting appliances.

[0142] 4. Users should follow the notification and use the heating slightly later to avoid peak consumption while maintaining comfort.

[0143] In this way, the server, terminal, and emotion engine work together to achieve power management that takes user emotions into consideration.

[0144] The following describes the processing flow.

[0145] Step 1:

[0146] The server collects electricity usage data from individual homes and businesses. It also obtains weather information such as temperature and humidity from external data services and stores this information in a database. Furthermore, it prepares to receive user emotion data from an emotion engine.

[0147] Step 2:

[0148] The emotion engine analyzes the user's voice and facial expressions in real time to recognize their emotions at that moment. It detects specific emotional patterns, such as stress or relaxation, and sends the results to the server.

[0149] Step 3:

[0150] An AI agent on the server analyzes collected power usage data, weather information, and sentiment data. It evaluates the relationship between past usage patterns, current sentiment states, and weather to predict future power demand.

[0151] Step 4:

[0152] The server adjusts the power supply from each generator based on consumption pattern predictions obtained from AI agents and emotion data from the emotion engine. It uses a multi-agent system to send appropriate output instructions to each power plant.

[0153] Step 5:

[0154] The server uses quantum communication technology to securely and quickly exchange data between the generator and the consumer. This data includes adjusted energy distribution and suggestions tailored to the user's emotional state.

[0155] Step 6:

[0156] The device receives information sent from the server and notifies the user of energy-saving suggestions that have been adjusted based on input from the emotion engine. For example, it might display a message such as, "Today is a day to relax. Enjoy your day comfortably while avoiding peak consumption."

[0157] Step 7:

[0158] Users receive notifications from their devices and adjust their lifestyle in a way that suits their emotional state. For example, on days when they feel stressed, they can choose a manageable energy-saving method to stabilize their emotions while simultaneously optimizing their power usage.

[0159] Step 8:

[0160] The server continuously collects user behavior data and feeds it back to the emotion engine and AI agent. This further improves the accuracy of future suggestions and enables more personalized energy management.

[0161] (Example 2)

[0162] 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".

[0163] Conventional power management systems struggled to efficiently allocate energy without considering the emotional state of users. In particular, energy recommendations when users were stressed or fatigued could worsen their condition. This resulted in a challenge in providing electricity that would improve user satisfaction and quality of life.

[0164] 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.

[0165] In this invention, the server includes means for collecting power data, means for analyzing the power data and weather data to predict future consumption patterns, and means for analyzing the user's emotional state and generating energy suggestions based on the analysis results. This enables efficient and satisfying energy management that takes the user's emotional state into consideration.

[0166] "Power data" refers to information about electricity usage, usage time, and location.

[0167] "Weather data" refers to information about weather conditions such as temperature, humidity, and precipitation.

[0168] "Consumption patterns" refer to trends and fluctuations in electricity usage derived from past electricity usage data.

[0169] "Power generation equipment" refers to devices and systems for generating electricity, and specifically includes thermal power plants and solar power generation systems.

[0170] "Communication technology" refers to methods and techniques for sending and receiving information, and specifically includes wireless communication, optical communication, and the like.

[0171] "Energy distribution" refers to the appropriate distribution of electricity supplied from multiple power sources.

[0172] "User's emotional state" refers to the emotional reactions and situations experienced by the user, and generally includes stress, fatigue, and feelings of well-being.

[0173] "Energy proposals" refer to recommendations and advice on energy use provided to users.

[0174] This invention is a system that manages energy based on the user's emotional state. A server, terminal, and emotion analysis technology work together to efficiently manage power supply from a power generator and provide energy recommendations that take the user's emotions into consideration.

[0175] The server collects real-time electricity and weather data using smart meters and weather APIs. Based on this data, it uses a generative AI model to predict future consumption patterns. This prediction makes it possible to balance energy supply and demand.

[0176] The device uses emotion analysis technology to analyze the user's voice and facial expressions. Specifically, it incorporates voice recognition and facial recognition technologies using a microphone and camera to grasp the user's current emotional state in real time. This data is sent to a server and used to provide energy recommendations tailored to the user's situation.

[0177] Users receive energy suggestions displayed on their devices to optimize their daily electricity usage. These suggestions are tailored to their emotional state; for example, a stressed user might receive suggestions to promote relaxation. This ensures user comfort while achieving efficient energy management.

[0178] As a concrete example, consider a user who has returned home from work and wants to relax. Based on past return-home data and weather conditions, the server suggests energy usage to avoid peak consumption. Furthermore, if the emotional analysis determines that the user is tired, the device will notify the user with a message such as, "You must be tired. To help you relax, we recommend using your home appliances after the peak consumption time."

[0179] As an example of a prompt, the input to the generating AI model might be text such as, "Please provide a prompt for an AI model that generates appropriate energy usage suggestions when a user is feeling tired but wants to relax." This allows the model to generate specific and appropriate energy suggestions.

[0180] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0181] Step 1:

[0182] The server collects power and weather data from smart meters and weather APIs. It receives power consumption data from homes and facilities, along with external weather information, as input. By storing this data with timestamps, it generates a time-series dataset. Specifically, it executes API calls and records real-time data streams into the database.

[0183] Step 2:

[0184] The server uses a generative AI model to predict future consumption patterns based on collected power and weather data. This process involves inputting historical consumption data into the model and applying a prediction algorithm to estimate future demand. The output is time-of-day consumption forecast data. Specifically, it calculates predicted values ​​based on the model's training results and saves them in a visualized format.

[0185] Step 3:

[0186] The device uses its built-in microphone and camera to capture the user's voice and facial expressions in real time. This input data is sent to an emotion analysis engine. The engine performs voice tone analysis and facial expression recognition, and outputs the user's stress level and emotional state. Specific operations include acquiring data from sensors and applying analysis algorithms.

[0187] Step 4:

[0188] The server integrates predictions of consumption patterns and analysis results of emotional states, and generates optimal energy recommendations using a generative AI model. This process takes integrated data as input in the form of prompts and generates adjusted energy recommendations as output. Specifically, it involves data integration and the execution of a generative algorithm.

[0189] Step 5:

[0190] The terminal notifies the user of energy recommendations received from the server. The input is the generated recommendation data, and the output is a notification via screen display or voice message. Specifically, the screen displays "Thank you for your hard work. To help you relax, we recommend using your home appliances after peak consumption hours."

[0191] Step 6:

[0192] Users adjust their electricity usage based on energy suggestions provided by their devices. They receive the suggestions as input and modify their energy consumption behavior as output. Specifically, they adjust heating and lighting usage times to optimize energy costs while maintaining comfort.

[0193] (Application Example 2)

[0194] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0195] In recent years, there has been a growing demand for both efficient power supply and user comfort. However, conventional power management systems have struggled to implement energy management that takes into account the emotional state of users, and proposals for optimizing energy consumption sometimes do not match the user's current situation. Therefore, achieving both efficient power consumption and user comfort that takes their emotions into consideration remains a challenge.

[0196] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting power usage data, means for voice and facial expression analysis for estimating the user's emotional state, and means for adjusting energy consumption optimization suggestions based on the estimated emotional state. This makes it possible to propose flexible energy consumption optimizations according to the user's emotional state.

[0197] "Electricity usage data" is a collection of information about the amount of electricity used by energy consumers, and is collected in order to understand energy consumption patterns.

[0198] "Weather information" refers to information related to the natural environment, such as weather, temperature, and humidity, and is a factor that affects electricity demand.

[0199] A "power generation device" refers to a machine or equipment used to generate electricity, and it constitutes part of the power supply system.

[0200] "Communication technology" is a general term for technical means of exchanging data between different devices, enabling safe and efficient information transmission.

[0201] "Emotional state" refers to the psychological and emotional conditions a user experiences at a particular time, and can be estimated from voice and facial expressions.

[0202] "Voice and facial expression analysis means" refers to technology that analyzes voice data and facial expression data in order to understand the user's emotional state.

[0203] "Means for adjusting energy consumption optimization suggestions" refers to a technology that modifies energy consumption suggestions based on the estimated emotional state of the user, taking into consideration the user's comfort.

[0204] This invention relates to a system aimed at flexibly optimizing energy consumption based on the user's emotional state. This system consists of a server, a terminal, a user, and an emotion analysis engine.

[0205] The server collects power usage data and weather information, and uses an AI model to predict future consumption patterns. Machine learning libraries such as TensorFlow and PyTorch are utilized for data analysis. Furthermore, by combining voice and facial expression analysis techniques, the system estimates the user's emotional state in real time. This estimation process involves processing image data using libraries such as OpenCV and analyzing voice data using the SpeechRecognition library.

[0206] The terminal notifies the user of energy consumption optimization suggestions provided by the server. The terminal includes smartphones and smart devices, and provides information through a user interface.

[0207] Users can choose whether or not to accept the suggestions provided and act accordingly. For example, if a user is feeling tired, the server might suggest, "We have detected your current stress level. We recommend listening to music and dimming the lights slightly to help you relax."

[0208] An example of a prompt to input into the generation AI model would be: "Analyze the user's emotional state and generate optimal energy consumption suggestions. Please also include specific suggestions for when the user is tired." This prompt will automatically generate appropriate suggestions that take the user's emotions into consideration.

[0209] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0210] Step 1:

[0211] The server collects user power usage data and weather information. Input data comes from power meters and weather sensors, which are stored in a database. This provides the foundational data necessary for subsequent analysis.

[0212] Step 2:

[0213] The server uses an AI model based on the collected data to predict power consumption patterns. The input consists of power usage data and weather information obtained in the previous step, which are then fed into an AI model built with TensorFlow or PyTorch. The output is predicted data showing future consumption patterns.

[0214] Step 3:

[0215] The server analyzes user voice and facial expression data obtained from the camera and microphone built into the terminal. The input consists of voice and image data transmitted from the terminal. Facial expressions are analyzed using OpenCV, and voice is analyzed using the SpeechRecognition library. The output is data indicating the user's emotional state.

[0216] Step 4:

[0217] The server generates energy consumption optimization suggestions based on predicted consumption patterns and emotional state data. The input consists of predicted consumption pattern data and emotional state data, which are used to generate suggestions using a generative AI model. The output is an optimization suggestion tailored to the user's situation.

[0218] Step 5:

[0219] The terminal notifies the user of optimization suggestions received from the server. The input is the suggestion data generated by the server, which is presented to the user visually or audibly via the user interface. The output is the energy consumption optimization notification received by the user.

[0220] Step 6:

[0221] The user evaluates suggestions from the device and takes appropriate action. The input is a notification from the device, which the user uses to adjust the usage time and settings of home appliances. The output is the actual adjusted energy consumption behavior.

[0222] 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.

[0223] 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.

[0224] 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.

[0225] [Second Embodiment]

[0226] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0227] 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.

[0228] 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).

[0229] 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.

[0230] 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.

[0231] 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).

[0232] 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.

[0233] 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.

[0234] 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.

[0235] 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.

[0236] 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.

[0237] 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".

[0238] This invention relates to a power management system that predicts power consumption using power usage data and weather information, and optimizes the supply of power generators. This system consists of a server, terminals, and users, and each function works together to achieve efficient power supply.

[0239] System Configuration

[0240] 1. Server

[0241] The server is responsible for collecting and analyzing power usage data and weather information. Using AI agents, it analyzes past consumption data and weather information to predict future consumption patterns. This predicted data is used to efficiently adjust the operation of power generators. It also utilizes a multi-agent system to issue power supply instructions to each power plant.

[0242] 2. Terminal

[0243] The terminal, acting as a user device, notifies the user of energy allocation optimization suggestions from the server. The terminal displays energy-saving suggestions from the AI ​​agent, allowing the user to optimize their actions based on these suggestions.

[0244] 3. User

[0245] Users receive notifications about energy usage through their devices and can follow optimization suggestions to achieve efficient use of electricity. During peak consumption periods, users are given suggestions to conserve energy, thereby leveling out electricity consumption.

[0246] Example of operation

[0247] As a concrete example, let's explain the daytime electricity management process for a household. Consider a case where a household receives a proposal to reduce electricity consumption during peak hours.

[0248] 1. In the morning, the server predicts that heater usage will increase due to a drop in temperature during the night, based on the day's weather forecast and past power usage data.

[0249] 2. The AI ​​agent analyzes this forecast data and determines that the peak in electricity demand will occur between 6:00 PM and 9:00 PM.

[0250] 3. The server adjusts the output of each generator based on its predictions and prepares backup power generation capacity if necessary.

[0251] 4. The device notifies the user based on the forecast results. A message will be displayed stating, "Peak electricity demand is expected between 18:00 and 21:00. By reducing electricity usage during this time, you can minimize energy costs."

[0252] 5. Based on this notification, users can take action such as adjusting their home heating timers or turning off unused lights, thereby avoiding peak electricity usage.

[0253] In this way, servers, terminals, and users work together to achieve a stable power supply and reduce energy costs.

[0254] The following describes the processing flow.

[0255] Step 1:

[0256] The server collects real-time electricity usage data from individual homes and businesses. Simultaneously, it obtains weather information such as temperature and humidity from external data providers and stores this information in a centrally managed database.

[0257] Step 2:

[0258] An AI agent on the server analyzes collected electricity usage data and weather information. It uses machine learning algorithms to predict future electricity demand, evaluating the relationship between past consumption patterns and weather. In particular, it precisely models demand fluctuations by time of day.

[0259] Step 3:

[0260] The server adjusts the power supply from each generator based on consumption pattern predictions obtained from AI agents. Using a multi-agent system, it provides appropriate output instructions to each power plant. In doing so, it also considers the availability of renewable energy to ensure a balanced distribution.

[0261] Step 4:

[0262] The server uses quantum communication technology to exchange data between generators and consumers. This ensures the secure delivery of proposed energy allocation instructions and change information.

[0263] Step 5:

[0264] The device notifies the user of energy-saving suggestions received from the server. Specifically, it displays information such as, "18:00 to 21:00 is peak time. Reducing your electricity usage will save you money on your electricity bill."

[0265] Step 6:

[0266] Users receive notifications from their devices and take actions to reduce power consumption. For example, they might turn off unnecessary lights or shift the use of appliances to off-peak hours to avoid peak electricity demand.

[0267] Step 7:

[0268] The server collects actual consumption data based on user behavior and feeds it back to the AI ​​agent. This improves the accuracy of demand forecasts for future use, enabling more efficient energy management.

[0269] (Example 1)

[0270] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0271] In modern society, effectively managing peak electricity demand periods is becoming increasingly important. However, conventional power management systems have insufficient demand forecasting accuracy, making it difficult to maximize the operating efficiency of power equipment. Furthermore, there are many limitations in adjusting supply levels and providing efficient information to users, resulting in insufficient optimization.

[0272] 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.

[0273] In this invention, the server includes means for analyzing usage data and weather information to predict future consumption patterns, means for dynamically managing the operation of power equipment based on the predicted demand, and means for realizing the coordinated operation of multiple devices using an agent model. This makes it possible to maximize the efficiency of power supply and optimize the operation of power equipment.

[0274] "Usage data" refers to data that shows how energy consumers are using electricity.

[0275] "Weather information" refers to information about meteorological conditions such as weather, temperature, and humidity.

[0276] "Consumption patterns" refer to trends and fluctuations in electricity usage over a certain period.

[0277] A "power device" is a mechanical device that is responsible for generating or supplying electricity.

[0278] "Communication technology" refers to a set of technologies used to safely and quickly transmit data from a sender to a receiver.

[0279] The "agent model" is a technology that enables multiple program agents to work together in a coordinated manner.

[0280] A "notification device" is a communication device or equipment used to provide information or suggestions to users.

[0281] "Means of optimizing energy distribution" refer to methods and techniques for maximizing efficiency by adjusting the amount and timing of energy supplied.

[0282] In this invention, the server, the terminal, and the user cooperate to operate in order to construct a power management system.

[0283] The server mainly plays a role in collecting and analyzing usage data and weather information. As usage data, the server acquires in real time the power usage status of each consumer through a sensor device. The weather information is obtained by leveraging the API of a weather information provider. On the server, machine learning frameworks such as TensorFlow and PyTorch are used to model consumption patterns based on these data and predict future demand.

[0284] The server dynamically manages the operation of power generation devices based on the predicted demand information. For this management, an agent model is used to enable multiple power generation devices to operate efficiently in cooperation. Also, for data transmission to each power generation device, secure communication technology is utilized.

[0285] The terminal is a medium for notifying the user of information. The user can receive the notifications displayed on the terminal and optimize their energy usage behavior. The terminal includes smartphones and smart devices within the home, and the notifications are provided as push messages.

[0286] As a specific example, based on weather data, the server predicts the peak time of power demand in the evening and sends a message to the terminal saying, "Power demand will increase between 18:00 and 21:00. Please refrain from using." Based on this notification, the user adjusts their own power usage, thereby reducing the peak demand.

[0287] An example of a prompt sentence using a generative AI model is, "Please predict the peak power demand on October 21, 2023, based on power consumption data and weather forecasts." Thus, this invention achieves efficient power management and resource optimization through the cooperation of the server, the terminal, and the user.

[0288] The flow of specific processing in Example 1 will be described using FIG. 11.

[0289] Step 1:

[0290] The server collects usage data from each consumer in real time. Input data from sensor devices includes hourly records of power consumption. This data is analyzed and cleaned, anomalies are removed, and then stored in a database. The output is a formatted set of usage data.

[0291] Step 2:

[0292] The server obtains weather information from external sources via a weather information API. Inputs include weather conditions, temperature, humidity, etc., which are used to form a weather dataset. The output is a dataset integrating this weather data.

[0293] Step 3:

[0294] The server inputs usage data and weather data into a machine learning platform such as TensorFlow to predict future consumption patterns. Data processing involves fitting this data to a time-series model for training. The output is the predicted consumption pattern, representing future electricity demand numerically.

[0295] Step 4:

[0296] The server develops an operating plan for the power units based on predictive data. Specifically, it uses an agent model to determine the optimal output level for each power unit and sends operating instructions to each unit. The output is operating instruction information for each unit.

[0297] Step 5:

[0298] The terminal notifies the user of forecast information and energy usage suggestions sent from the server. The input is notification data from the server, which is displayed on the user's screen. For example, it sends a push notification such as, "Electricity demand will be high between 18:00 and 21:00." The output is a message notification to the user.

[0299] Step 6:

[0300] Users adjust their actions based on notifications received from their devices. Specifically, they might change heating timers to match the notified peak times or turn off unnecessary electrical appliances. The input is notification information from the device, and the output is the user's change in power usage patterns.

[0301] (Application Example 1)

[0302] 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."

[0303] Managing electricity in modern cities remains a major challenge, given the increasing demand for volatile demand and efficient use of energy resources. In particular, while appropriate adjustments to electricity supply in response to rapid changes in demand are required, optimizing consumption through the actions of individual residents is not sufficiently achieved in many cities. Therefore, improving energy efficiency across the entire community is necessary.

[0304] 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.

[0305] In this invention, the server includes means for collecting data for predicting power consumption, means for analyzing the data and weather information to predict demand, and means for adjusting the supply of power generation equipment. This enables efficient energy distribution and the provision of information to optimize consumption for residents.

[0306] "Data collection means" is a technical method for obtaining information related to power consumption from multiple sources.

[0307] "Analysis means" is a processing technology for deriving future demand patterns based on the collected power usage data and weather information.

[0308] "Supply adjustment means" is a function for optimizing the output of power generation facilities based on demand prediction.

[0309] "Communication technology" is an infrastructure for safely and quickly exchanging necessary data between energy supply facilities and power consumers.

[0310] "Energy distribution optimization means" is a method for most effectively allocating energy resources based on the predicted consumption pattern.

[0311] "Information providing means" is a technology for notifying residents of suggestions regarding consumption optimization.

[0312] This invention is a power management system in a smart city, which is realized by the server, the terminal, and the residents cooperating with each other. The server has the function of collecting power consumption data and weather information and performing data analysis, thereby predicting future power demand.

[0313] The server operates on a cloud infrastructure such as AWS and analyzes the consumption pattern using data analysis libraries such as Python, Pandas, and Scikit-learn. The role of the server is to train an AI model based on the collected data and accurately predict future power demand. Based on this prediction, the server adjusts the output of the power generation facilities to meet the demand.

[0314] The terminal operates on smart devices owned by residents. It has the functionality to provide residents with real-time information on optimizing their consumption through an application developed with React Native. The application displays suggestions based on consumption forecasts sent from the server, prompting consumer action. For example, it might notify users with a message such as, "You can save 20% energy by refraining from using air conditioning around 6 PM. Try to dress in cooler clothing."

[0315] Based on information received via their devices, users optimize their individual consumption behavior, contributing to overall energy efficiency improvements. They are expected to receive suggestions indicating specific actions they should take and adjust their energy consumption at appropriate times.

[0316] As a specific example, the server predicts that when high summer temperatures are forecast for a weekday afternoon, the peak demand for air conditioning will be between 6:00 PM and 9:00 PM, and adjusts energy consumption accordingly. Based on this prediction, the terminal sends the user specific energy-saving instructions.

[0317] Examples of prompts for a generative AI model include the following:

[0318] "According to weather forecasts and historical consumption data, electricity demand is expected to peak tonight between 6:00 PM and 9:00 PM. What energy-saving actions would you suggest?"

[0319] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0320] Step 1:

[0321] The server collects electricity usage data and weather information from multiple data providers on the internet. Specifically, it periodically retrieves data using APIs and stores it in a database. The input for this step is electricity usage history and weather forecast information, and the output is an analyzable dataset.

[0322] Step 2:

[0323] The server analyzes consumption patterns using the collected data and predicts future electricity demand. Here, Python and machine learning libraries (e.g., Scikit-learn) are used. The input is the dataset generated in the previous step, and the AI ​​model calculates the supply and demand forecast. The output is the future electricity demand forecast.

[0324] Step 3:

[0325] The server optimally adjusts the supply of power generation equipment based on the prediction results. This process involves sending instructions to power plants to establish an efficient energy distribution. The input is future prediction data, and the output is the adjusted supply plan.

[0326] Step 4:

[0327] The terminal notifies residents of supply and demand forecasts and energy-saving suggestions sent from the server. These notifications include specific energy-saving actions and are received by the user via a smartphone app. The input is energy-saving suggestions from the server, and the output is the notification message to the user.

[0328] Step 5:

[0329] Users adjust their daily energy usage based on information from their devices. They use the app to create concrete action plans for energy saving and optimize the use of home appliances. The input is suggestive information from the device, and the output is improved energy efficiency.

[0330] 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.

[0331] This invention combines an emotion engine with a power management system to achieve energy management that takes into account the user's emotional state. This system consists of a server, terminals, users, and an emotion engine, and provides efficient power supply while maintaining user comfort.

[0332] System Configuration

[0333] 1. Server

[0334] The server collects power usage data and weather information, and analyzes it with AI agents. It predicts consumption patterns and issues supply instructions to each generator. A multi-agent system is utilized here to ensure efficient energy distribution. It also receives input from an emotion engine and adjusts suggestions based on the user's emotional state.

[0335] 2. Terminal

[0336] The device notifies the user of energy allocation optimization suggestions from the server. Based on feedback analyzed by the emotion engine, the suggestions are automatically adjusted. For example, if the user is stressed, a gentler suggestion will be provided.

[0337] 3. User

[0338] Users receive suggestions via their devices and optimize their actions. An emotion engine operates in the background, analyzing the user's voice and facial expressions to understand their emotional state in real time.

[0339] 4. Emotional Engine

[0340] The emotion engine includes sensors that estimate the user's emotions using speech recognition and facial recognition technologies. This engine continuously learns from the user's responses in everyday life and improves the accuracy of its suggestions.

[0341] Example of operation

[0342] As a concrete example, let's explain the power management process in a user's daily life. Consider a case where a user has returned home from work and wants to relax.

[0343] 1. The server predicts that electricity demand will increase from evening to night based on past data from when the user returned home and weather information. However, the emotion engine recognizes the user's fatigue level from their voice.

[0344] 2. Based on the prediction results, energy-saving suggestions are prepared, but because the user is tired, the notification content is adjusted to "You must be tired. To relax, we recommend using appliances after peak consumption hours."

[0345] 3. The device displays these adjusted suggestions to the user. Based on this, the user can adjust the usage time of heating and lighting appliances.

[0346] 4. Users should follow the notification and use the heating slightly later to avoid peak consumption while maintaining comfort.

[0347] In this way, the server, terminal, and emotion engine work together to achieve power management that takes user emotions into consideration.

[0348] The following describes the processing flow.

[0349] Step 1:

[0350] The server collects electricity usage data from individual homes and businesses. It also obtains weather information such as temperature and humidity from external data services and stores this information in a database. Furthermore, it prepares to receive user emotion data from an emotion engine.

[0351] Step 2:

[0352] The emotion engine analyzes the user's voice and facial expressions in real time to recognize their emotions at that moment. It detects specific emotional patterns, such as stress or relaxation, and sends the results to the server.

[0353] Step 3:

[0354] An AI agent on the server analyzes collected power usage data, weather information, and sentiment data. It evaluates the relationship between past usage patterns, current sentiment states, and weather to predict future power demand.

[0355] Step 4:

[0356] The server adjusts the power supply from each generator based on consumption pattern predictions obtained from AI agents and emotion data from the emotion engine. It uses a multi-agent system to send appropriate output instructions to each power plant.

[0357] Step 5:

[0358] The server uses quantum communication technology to securely and quickly exchange data between the generator and the consumer. This data includes adjusted energy distribution and suggestions tailored to the user's emotional state.

[0359] Step 6:

[0360] The device receives information sent from the server and notifies the user of energy-saving suggestions that have been adjusted based on input from the emotion engine. For example, it might display a message such as, "Today is a day to relax. Enjoy your day comfortably while avoiding peak consumption."

[0361] Step 7:

[0362] Users receive notifications from their devices and adjust their lifestyle in a way that suits their emotional state. For example, on days when they feel stressed, they can choose a manageable energy-saving method to stabilize their emotions while simultaneously optimizing their power usage.

[0363] Step 8:

[0364] The server continuously collects user behavior data and feeds it back to the emotion engine and AI agent. This further improves the accuracy of future suggestions and enables more personalized energy management.

[0365] (Example 2)

[0366] 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".

[0367] Conventional power management systems struggled to efficiently allocate energy without considering the emotional state of users. In particular, energy recommendations when users were stressed or fatigued could worsen their condition. This resulted in a challenge in providing electricity that would improve user satisfaction and quality of life.

[0368] 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.

[0369] In this invention, the server includes means for collecting power data, means for analyzing the power data and weather data to predict future consumption patterns, and means for analyzing the user's emotional state and generating energy suggestions based on the analysis results. This enables efficient and satisfying energy management that takes the user's emotional state into consideration.

[0370] "Power data" refers to information about electricity usage, usage time, and location.

[0371] "Weather data" refers to information about weather conditions such as temperature, humidity, and precipitation.

[0372] "Consumption patterns" refer to trends and fluctuations in electricity usage derived from past electricity usage data.

[0373] "Power generation equipment" refers to devices and systems for generating electricity, and specifically includes thermal power plants and solar power generation systems.

[0374] "Communication technology" refers to methods and techniques for sending and receiving information, and specifically includes wireless communication, optical communication, and the like.

[0375] "Energy distribution" refers to the appropriate distribution of electricity supplied from multiple power sources.

[0376] "User's emotional state" refers to the emotional reactions and situations experienced by the user, and generally includes stress, fatigue, and feelings of well-being.

[0377] "Energy proposals" refer to recommendations and advice on energy use provided to users.

[0378] This invention is a system that manages energy based on the user's emotional state. A server, terminal, and emotion analysis technology work together to efficiently manage power supply from a power generator and provide energy recommendations that take the user's emotions into consideration.

[0379] The server collects real-time electricity and weather data using smart meters and weather APIs. Based on this data, it uses a generative AI model to predict future consumption patterns. This prediction makes it possible to balance energy supply and demand.

[0380] The device uses emotion analysis technology to analyze the user's voice and facial expressions. Specifically, it incorporates voice recognition and facial recognition technologies using a microphone and camera to grasp the user's current emotional state in real time. This data is sent to a server and used to provide energy recommendations tailored to the user's situation.

[0381] Users receive energy suggestions displayed on their devices to optimize their daily electricity usage. These suggestions are tailored to their emotional state; for example, a stressed user might receive suggestions to promote relaxation. This ensures user comfort while achieving efficient energy management.

[0382] As a concrete example, consider a user who has returned home from work and wants to relax. Based on past return-home data and weather conditions, the server suggests energy usage to avoid peak consumption. Furthermore, if the emotional analysis determines that the user is tired, the device will notify the user with a message such as, "You must be tired. To help you relax, we recommend using your home appliances after the peak consumption time."

[0383] As an example of a prompt, the input to the generating AI model might be text such as, "Please provide a prompt for an AI model that generates appropriate energy usage suggestions when a user is feeling tired but wants to relax." This allows the model to generate specific and appropriate energy suggestions.

[0384] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0385] Step 1:

[0386] The server collects power and weather data from smart meters and weather APIs. It receives power consumption data from homes and facilities, along with external weather information, as input. By storing this data with timestamps, it generates a time-series dataset. Specifically, it executes API calls and records real-time data streams into the database.

[0387] Step 2:

[0388] The server uses a generative AI model to predict future consumption patterns based on collected power and weather data. This process involves inputting historical consumption data into the model and applying a prediction algorithm to estimate future demand. The output is time-of-day consumption forecast data. Specifically, it calculates predicted values ​​based on the model's training results and saves them in a visualized format.

[0389] Step 3:

[0390] The device uses its built-in microphone and camera to capture the user's voice and facial expressions in real time. This input data is sent to an emotion analysis engine. The engine performs voice tone analysis and facial expression recognition, and outputs the user's stress level and emotional state. Specific operations include acquiring data from sensors and applying analysis algorithms.

[0391] Step 4:

[0392] The server integrates predictions of consumption patterns and analysis results of emotional states, and generates optimal energy recommendations using a generative AI model. This process takes integrated data as input in the form of prompts and generates adjusted energy recommendations as output. Specifically, it involves data integration and the execution of a generative algorithm.

[0393] Step 5:

[0394] The terminal notifies the user of energy recommendations received from the server. The input is the generated recommendation data, and the output is a notification via screen display or voice message. Specifically, the screen displays "Thank you for your hard work. To help you relax, we recommend using your home appliances after peak consumption hours."

[0395] Step 6:

[0396] Users adjust their electricity usage based on energy suggestions provided by their devices. They receive the suggestions as input and modify their energy consumption behavior as output. Specifically, they adjust heating and lighting usage times to optimize energy costs while maintaining comfort.

[0397] (Application Example 2)

[0398] 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."

[0399] In recent years, there has been a growing demand for both efficient power supply and user comfort. However, conventional power management systems have struggled to implement energy management that takes into account the emotional state of users, and proposals for optimizing energy consumption sometimes do not match the user's current situation. Therefore, achieving both efficient power consumption and user comfort that takes their emotions into consideration remains a challenge.

[0400] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting power usage data, means for voice and facial expression analysis for estimating the user's emotional state, and means for adjusting energy consumption optimization suggestions based on the estimated emotional state. This makes it possible to propose flexible energy consumption optimizations according to the user's emotional state.

[0401] "Electricity usage data" is a collection of information about the amount of electricity used by energy consumers, and is collected in order to understand energy consumption patterns.

[0402] "Weather information" refers to information related to the natural environment, such as weather, temperature, and humidity, and is a factor that affects electricity demand.

[0403] A "power generation device" refers to a machine or equipment used to generate electricity, and it constitutes part of the power supply system.

[0404] "Communication technology" is a general term for technical means of exchanging data between different devices, enabling safe and efficient information transmission.

[0405] "Emotional state" refers to the psychological and emotional conditions a user experiences at a particular time, and can be estimated from voice and facial expressions.

[0406] "Voice and facial expression analysis means" refers to technology that analyzes voice data and facial expression data in order to understand the user's emotional state.

[0407] "Means for adjusting energy consumption optimization suggestions" refers to a technology that modifies energy consumption suggestions based on the estimated emotional state of the user, taking into consideration the user's comfort.

[0408] This invention relates to a system aimed at flexibly optimizing energy consumption based on the user's emotional state. This system consists of a server, a terminal, a user, and an emotion analysis engine.

[0409] The server collects power usage data and weather information, and uses an AI model to predict future consumption patterns. Machine learning libraries such as TensorFlow and PyTorch are utilized for data analysis. Furthermore, by combining voice and facial expression analysis techniques, the system estimates the user's emotional state in real time. This estimation process involves processing image data using libraries such as OpenCV and analyzing voice data using the SpeechRecognition library.

[0410] The terminal notifies the user of energy consumption optimization suggestions provided by the server. The terminal includes smartphones and smart devices, and provides information through a user interface.

[0411] Users can choose whether or not to accept the suggestions provided and act accordingly. For example, if a user is feeling tired, the server might suggest, "We have detected your current stress level. We recommend listening to music and dimming the lights slightly to help you relax."

[0412] An example of a prompt to input into the generation AI model would be: "Analyze the user's emotional state and generate optimal energy consumption suggestions. Please also include specific suggestions for when the user is tired." This prompt will automatically generate appropriate suggestions that take the user's emotions into consideration.

[0413] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0414] Step 1:

[0415] The server collects user power usage data and weather information. Input data comes from power meters and weather sensors, which are stored in a database. This provides the foundational data necessary for subsequent analysis.

[0416] Step 2:

[0417] The server uses an AI model based on the collected data to predict power consumption patterns. The input consists of power usage data and weather information obtained in the previous step, which are then fed into an AI model built with TensorFlow or PyTorch. The output is predicted data showing future consumption patterns.

[0418] Step 3:

[0419] The server analyzes user voice and facial expression data obtained from the camera and microphone built into the terminal. The input consists of voice and image data transmitted from the terminal. Facial expressions are analyzed using OpenCV, and voice is analyzed using the SpeechRecognition library. The output is data indicating the user's emotional state.

[0420] Step 4:

[0421] The server generates energy consumption optimization suggestions based on predicted consumption patterns and emotional state data. The input consists of predicted consumption pattern data and emotional state data, which are used to generate suggestions using a generative AI model. The output is an optimization suggestion tailored to the user's situation.

[0422] Step 5:

[0423] The terminal notifies the user of optimization suggestions received from the server. The input is the suggestion data generated by the server, which is presented to the user visually or audibly via the user interface. The output is the energy consumption optimization notification received by the user.

[0424] Step 6:

[0425] The user evaluates suggestions from the device and takes appropriate action. The input is a notification from the device, which the user uses to adjust the usage time and settings of home appliances. The output is the actual adjusted energy consumption behavior.

[0426] 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.

[0427] 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.

[0428] 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.

[0429] [Third Embodiment]

[0430] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0431] 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.

[0432] 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).

[0433] 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.

[0434] 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.

[0435] 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).

[0436] 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.

[0437] 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.

[0438] 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.

[0439] 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.

[0440] 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.

[0441] 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".

[0442] This invention relates to a power management system that predicts power consumption using power usage data and weather information, and optimizes the supply of power generators. This system consists of a server, terminals, and users, and each function works together to achieve efficient power supply.

[0443] System Configuration

[0444] 1. Server

[0445] The server is responsible for collecting and analyzing power usage data and weather information. Using AI agents, it analyzes past consumption data and weather information to predict future consumption patterns. This predicted data is used to efficiently adjust the operation of power generators. It also utilizes a multi-agent system to issue power supply instructions to each power plant.

[0446] 2. Terminal

[0447] The terminal, acting as a user device, notifies the user of energy allocation optimization suggestions from the server. The terminal displays energy-saving suggestions from the AI ​​agent, allowing the user to optimize their actions based on these suggestions.

[0448] 3. User

[0449] Users receive notifications about energy usage through their devices and can follow optimization suggestions to achieve efficient use of electricity. During peak consumption periods, users are given suggestions to conserve energy, thereby leveling out electricity consumption.

[0450] Example of operation

[0451] As a concrete example, let's explain the daytime electricity management process for a household. Consider a case where a household receives a proposal to reduce electricity consumption during peak hours.

[0452] 1. In the morning, the server predicts that heater usage will increase due to a drop in temperature during the night, based on the day's weather forecast and past power usage data.

[0453] 2. The AI ​​agent analyzes this forecast data and determines that the peak in electricity demand will occur between 6:00 PM and 9:00 PM.

[0454] 3. The server adjusts the output of each generator based on its predictions and prepares backup power generation capacity if necessary.

[0455] 4. The device notifies the user based on the forecast results. A message will be displayed stating, "Peak electricity demand is expected between 18:00 and 21:00. By reducing electricity usage during this time, you can minimize energy costs."

[0456] 5. Based on this notification, users can take action such as adjusting their home heating timers or turning off unused lights, thereby avoiding peak electricity usage.

[0457] In this way, servers, terminals, and users work together to achieve a stable power supply and reduce energy costs.

[0458] The following describes the processing flow.

[0459] Step 1:

[0460] The server collects real-time electricity usage data from individual homes and businesses. Simultaneously, it obtains weather information such as temperature and humidity from external data providers and stores this information in a centrally managed database.

[0461] Step 2:

[0462] An AI agent on the server analyzes collected electricity usage data and weather information. It uses machine learning algorithms to predict future electricity demand, evaluating the relationship between past consumption patterns and weather. In particular, it precisely models demand fluctuations by time of day.

[0463] Step 3:

[0464] The server adjusts the power supply from each generator based on consumption pattern predictions obtained from AI agents. Using a multi-agent system, it provides appropriate output instructions to each power plant. In doing so, it also considers the availability of renewable energy to ensure a balanced distribution.

[0465] Step 4:

[0466] The server uses quantum communication technology to exchange data between generators and consumers. This ensures the secure delivery of proposed energy allocation instructions and change information.

[0467] Step 5:

[0468] The device notifies the user of energy-saving suggestions received from the server. Specifically, it displays information such as, "18:00 to 21:00 is peak time. Reducing your electricity usage will save you money on your electricity bill."

[0469] Step 6:

[0470] Users receive notifications from their devices and take actions to reduce power consumption. For example, they might turn off unnecessary lights or shift the use of appliances to off-peak hours to avoid peak electricity demand.

[0471] Step 7:

[0472] The server collects actual consumption data based on user behavior and feeds it back to the AI ​​agent. This improves the accuracy of demand forecasts for future use, enabling more efficient energy management.

[0473] (Example 1)

[0474] 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."

[0475] In modern society, effectively managing peak electricity demand periods is becoming increasingly important. However, conventional power management systems have insufficient demand forecasting accuracy, making it difficult to maximize the operating efficiency of power equipment. Furthermore, there are many limitations in adjusting supply levels and providing efficient information to users, resulting in insufficient optimization.

[0476] 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.

[0477] In this invention, the server includes means for analyzing usage data and weather information to predict future consumption patterns, means for dynamically managing the operation of power equipment based on the predicted demand, and means for realizing the coordinated operation of multiple devices using an agent model. This makes it possible to maximize the efficiency of power supply and optimize the operation of power equipment.

[0478] "Usage data" refers to data that shows how energy consumers are using electricity.

[0479] "Weather information" refers to information about meteorological conditions such as weather, temperature, and humidity.

[0480] "Consumption patterns" refer to trends and fluctuations in electricity usage over a certain period.

[0481] A "power device" is a mechanical device that is responsible for generating or supplying electricity.

[0482] "Communication technology" refers to a set of technologies used to safely and quickly transmit data from a sender to a receiver.

[0483] The "agent model" is a technology that enables multiple program agents to work together in a coordinated manner.

[0484] A "notification device" is a communication device or equipment used to provide information or suggestions to users.

[0485] "Means of optimizing energy distribution" refer to methods and techniques for maximizing efficiency by adjusting the amount and timing of energy supplied.

[0486] In this invention, a server, terminals, and users work together to build a power management system.

[0487] The server's primary role is to collect and analyze usage data and weather information. Usage data includes real-time acquisition of each consumer's power usage through sensor devices. Weather information is obtained using APIs from weather information providers. On the server, machine learning frameworks such as TensorFlow and PyTorch are used to model consumption patterns based on this data and predict future demand.

[0488] The server dynamically manages the operation of power units based on predicted demand information. This management uses an agent model to ensure that multiple power units operate efficiently in coordination. Secure communication technology is also used for transmitting data to each power unit.

[0489] A device is a medium for providing information and notifications to users. Users can receive notifications displayed on the device and optimize their energy usage. Devices include smartphones and smart devices in the home, and notifications are delivered as push messages.

[0490] For example, a server uses weather data to predict the peak hours of electricity demand in the evening and sends a message to the terminal saying, "Electricity demand will be high between 6:00 PM and 9:00 PM. Please reduce your usage." Based on this notification, users can adjust their electricity usage, thereby reducing peak demand.

[0491] An example of a prompt using the generated AI model would be, "Based on power consumption data and weather forecasts, please predict the peak power demand for October 21, 2023." In this way, this invention achieves efficient power management and resource optimization through the cooperation of servers, terminals, and users.

[0492] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0493] Step 1:

[0494] The server collects usage data from each consumer in real time. Input data from sensor devices includes hourly records of power consumption. This data is analyzed and cleaned, anomalies are removed, and then stored in a database. The output is a formatted set of usage data.

[0495] Step 2:

[0496] The server obtains weather information from external sources via a weather information API. Inputs include weather conditions, temperature, humidity, etc., which are used to form a weather dataset. The output is a dataset integrating this weather data.

[0497] Step 3:

[0498] The server inputs usage data and weather data into a machine learning platform such as TensorFlow to predict future consumption patterns. Data processing involves fitting this data to a time-series model for training. The output is the predicted consumption pattern, representing future electricity demand numerically.

[0499] Step 4:

[0500] The server develops an operating plan for the power units based on predictive data. Specifically, it uses an agent model to determine the optimal output level for each power unit and sends operating instructions to each unit. The output is operating instruction information for each unit.

[0501] Step 5:

[0502] The terminal notifies the user of forecast information and energy usage suggestions sent from the server. The input is notification data from the server, which is displayed on the user's screen. For example, it sends a push notification such as, "Electricity demand will be high between 18:00 and 21:00." The output is a message notification to the user.

[0503] Step 6:

[0504] Users adjust their actions based on notifications received from their devices. Specifically, they might change heating timers to match the notified peak times or turn off unnecessary electrical appliances. The input is notification information from the device, and the output is the user's change in power usage patterns.

[0505] (Application Example 1)

[0506] 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."

[0507] Managing electricity in modern cities remains a major challenge, given the increasing demand for volatile demand and efficient use of energy resources. In particular, while appropriate adjustments to electricity supply in response to rapid changes in demand are required, optimizing consumption through the actions of individual residents is not sufficiently achieved in many cities. Therefore, improving energy efficiency across the entire community is necessary.

[0508] 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.

[0509] In this invention, the server includes means for collecting data for predicting power consumption, means for analyzing the data and weather information to predict demand, and means for adjusting the supply of power generation equipment. This enables efficient energy distribution and the provision of information to optimize consumption for residents.

[0510] "Data collection methods" refer to technical techniques for obtaining information about electricity consumption from multiple sources.

[0511] "Analysis means" refers to processing technology used to derive future demand patterns based on collected electricity usage data and weather information.

[0512] A "supply adjustment mechanism" is a function for optimizing the output of power generation equipment based on demand forecasts.

[0513] "Communication technology" is the infrastructure for securely and quickly exchanging necessary data between energy supply facilities and electricity consumers.

[0514] An "energy distribution optimization method" is a method for allocating energy resources most effectively based on predicted consumption patterns.

[0515] "Information provision means" refers to technologies for notifying residents of suggestions regarding optimizing their consumption.

[0516] This invention is a power management system for smart cities, realized through the interaction of servers, terminals, and residents. The server has the function of collecting power consumption data and weather information, and performing data analysis, thereby predicting future power demand.

[0517] The server runs on cloud infrastructure such as AWS and analyzes consumption patterns using data analysis libraries such as Python, Pandas, and Scikit-learn. The server's role is to train an AI model based on the collected data and predict future electricity demand with high accuracy. Based on this prediction, the server adjusts the output of power generation equipment to meet demand.

[0518] The terminal operates on smart devices owned by residents. It has the functionality to provide residents with real-time information on optimizing their consumption through an application developed with React Native. The application displays suggestions based on consumption forecasts sent from the server, prompting consumer action. For example, it might notify users with a message such as, "You can save 20% energy by refraining from using air conditioning around 6 PM. Try to dress in cooler clothing."

[0519] Based on information received via their devices, users optimize their individual consumption behavior, contributing to overall energy efficiency improvements. They are expected to receive suggestions indicating specific actions they should take and adjust their energy consumption at appropriate times.

[0520] As a specific example, the server predicts that when high summer temperatures are forecast for a weekday afternoon, the peak demand for air conditioning will be between 6:00 PM and 9:00 PM, and adjusts energy consumption accordingly. Based on this prediction, the terminal sends the user specific energy-saving instructions.

[0521] Examples of prompts for a generative AI model include the following:

[0522] "According to weather forecasts and historical consumption data, electricity demand is expected to peak tonight between 6:00 PM and 9:00 PM. What energy-saving actions would you suggest?"

[0523] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0524] Step 1:

[0525] The server collects electricity usage data and weather information from multiple data providers on the internet. Specifically, it periodically retrieves data using APIs and stores it in a database. The input for this step is electricity usage history and weather forecast information, and the output is an analyzable dataset.

[0526] Step 2:

[0527] The server analyzes consumption patterns using the collected data and predicts future electricity demand. Here, Python and machine learning libraries (e.g., Scikit-learn) are used. The input is the dataset generated in the previous step, and the AI ​​model calculates the supply and demand forecast. The output is the future electricity demand forecast.

[0528] Step 3:

[0529] The server optimally adjusts the supply of power generation equipment based on the prediction results. This process involves sending instructions to power plants to establish an efficient energy distribution. The input is future prediction data, and the output is the adjusted supply plan.

[0530] Step 4:

[0531] The terminal notifies residents of supply and demand forecasts and energy-saving suggestions sent from the server. These notifications include specific energy-saving actions and are received by the user via a smartphone app. The input is energy-saving suggestions from the server, and the output is the notification message to the user.

[0532] Step 5:

[0533] Users adjust their daily energy usage based on information from their devices. They use the app to create concrete action plans for energy saving and optimize the use of home appliances. The input is suggestive information from the device, and the output is improved energy efficiency.

[0534] 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.

[0535] This invention combines an emotion engine with a power management system to achieve energy management that takes into account the user's emotional state. This system consists of a server, terminals, users, and an emotion engine, and provides efficient power supply while maintaining user comfort.

[0536] System Configuration

[0537] 1. Server

[0538] The server collects power usage data and weather information, and analyzes it with AI agents. It predicts consumption patterns and issues supply instructions to each generator. A multi-agent system is utilized here to ensure efficient energy distribution. It also receives input from an emotion engine and adjusts suggestions based on the user's emotional state.

[0539] 2. Terminal

[0540] The device notifies the user of energy allocation optimization suggestions from the server. Based on feedback analyzed by the emotion engine, the suggestions are automatically adjusted. For example, if the user is stressed, a gentler suggestion will be provided.

[0541] 3. User

[0542] Users receive suggestions via their devices and optimize their actions. An emotion engine operates in the background, analyzing the user's voice and facial expressions to understand their emotional state in real time.

[0543] 4. Emotional Engine

[0544] The emotion engine includes sensors that estimate the user's emotions using speech recognition and facial recognition technologies. This engine continuously learns from the user's responses in everyday life and improves the accuracy of its suggestions.

[0545] Example of operation

[0546] As a concrete example, let's explain the power management process in a user's daily life. Consider a case where a user has returned home from work and wants to relax.

[0547] 1. The server predicts that electricity demand will increase from evening to night based on past data from when the user returned home and weather information. However, the emotion engine recognizes the user's fatigue level from their voice.

[0548] 2. Based on the prediction results, energy-saving suggestions are prepared, but because the user is tired, the notification content is adjusted to "You must be tired. To relax, we recommend using appliances after peak consumption hours."

[0549] 3. The device displays these adjusted suggestions to the user. Based on this, the user can adjust the usage time of heating and lighting appliances.

[0550] 4. Users should follow the notification and use the heating slightly later to avoid peak consumption while maintaining comfort.

[0551] In this way, the server, terminal, and emotion engine work together to achieve power management that takes user emotions into consideration.

[0552] The following describes the processing flow.

[0553] Step 1:

[0554] The server collects electricity usage data from individual homes and businesses. It also obtains weather information such as temperature and humidity from external data services and stores this information in a database. Furthermore, it prepares to receive user emotion data from an emotion engine.

[0555] Step 2:

[0556] The emotion engine analyzes the user's voice and facial expressions in real time to recognize their emotions at that moment. It detects specific emotional patterns, such as stress or relaxation, and sends the results to the server.

[0557] Step 3:

[0558] An AI agent on the server analyzes collected power usage data, weather information, and sentiment data. It evaluates the relationship between past usage patterns, current sentiment states, and weather to predict future power demand.

[0559] Step 4:

[0560] The server adjusts the power supply from each generator based on consumption pattern predictions obtained from AI agents and emotion data from the emotion engine. It uses a multi-agent system to send appropriate output instructions to each power plant.

[0561] Step 5:

[0562] The server uses quantum communication technology to securely and quickly exchange data between the generator and the consumer. This data includes adjusted energy distribution and suggestions tailored to the user's emotional state.

[0563] Step 6:

[0564] The device receives information sent from the server and notifies the user of energy-saving suggestions that have been adjusted based on input from the emotion engine. For example, it might display a message such as, "Today is a day to relax. Enjoy your day comfortably while avoiding peak consumption."

[0565] Step 7:

[0566] Users receive notifications from their devices and adjust their lifestyle in a way that suits their emotional state. For example, on days when they feel stressed, they can choose a manageable energy-saving method to stabilize their emotions while simultaneously optimizing their power usage.

[0567] Step 8:

[0568] The server continuously collects user behavior data and feeds it back to the emotion engine and AI agent. This further improves the accuracy of future suggestions and enables more personalized energy management.

[0569] (Example 2)

[0570] 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."

[0571] Conventional power management systems struggled to efficiently allocate energy without considering the emotional state of users. In particular, energy recommendations when users were stressed or fatigued could worsen their condition. This resulted in a challenge in providing electricity that would improve user satisfaction and quality of life.

[0572] 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.

[0573] In this invention, the server includes means for collecting power data, means for analyzing the power data and weather data to predict future consumption patterns, and means for analyzing the user's emotional state and generating energy suggestions based on the analysis results. This enables efficient and satisfying energy management that takes the user's emotional state into consideration.

[0574] "Power data" refers to information about electricity usage, usage time, and location.

[0575] "Weather data" refers to information about weather conditions such as temperature, humidity, and precipitation.

[0576] "Consumption patterns" refer to trends and fluctuations in electricity usage derived from past electricity usage data.

[0577] "Power generation equipment" refers to devices and systems for generating electricity, and specifically includes thermal power plants and solar power generation systems.

[0578] "Communication technology" refers to methods and techniques for sending and receiving information, and specifically includes wireless communication, optical communication, and the like.

[0579] "Energy distribution" refers to the appropriate distribution of electricity supplied from multiple power sources.

[0580] "User's emotional state" refers to the emotional reactions and situations experienced by the user, and generally includes stress, fatigue, and feelings of well-being.

[0581] "Energy proposals" refer to recommendations and advice on energy use provided to users.

[0582] This invention is a system that manages energy based on the user's emotional state. A server, terminal, and emotion analysis technology work together to efficiently manage power supply from a power generator and provide energy recommendations that take the user's emotions into consideration.

[0583] The server collects real-time electricity and weather data using smart meters and weather APIs. Based on this data, it uses a generative AI model to predict future consumption patterns. This prediction makes it possible to balance energy supply and demand.

[0584] The device uses emotion analysis technology to analyze the user's voice and facial expressions. Specifically, it incorporates voice recognition and facial recognition technologies using a microphone and camera to grasp the user's current emotional state in real time. This data is sent to a server and used to provide energy recommendations tailored to the user's situation.

[0585] Users receive energy suggestions displayed on their devices to optimize their daily electricity usage. These suggestions are tailored to their emotional state; for example, a stressed user might receive suggestions to promote relaxation. This ensures user comfort while achieving efficient energy management.

[0586] As a concrete example, consider a user who has returned home from work and wants to relax. Based on past return-home data and weather conditions, the server suggests energy usage to avoid peak consumption. Furthermore, if the emotional analysis determines that the user is tired, the device will notify the user with a message such as, "You must be tired. To help you relax, we recommend using your home appliances after the peak consumption time."

[0587] As an example of a prompt, the input to the generating AI model might be text such as, "Please provide a prompt for an AI model that generates appropriate energy usage suggestions when a user is feeling tired but wants to relax." This allows the model to generate specific and appropriate energy suggestions.

[0588] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0589] Step 1:

[0590] The server collects power and weather data from smart meters and weather APIs. It receives power consumption data from homes and facilities, along with external weather information, as input. By storing this data with timestamps, it generates a time-series dataset. Specifically, it executes API calls and records real-time data streams into the database.

[0591] Step 2:

[0592] The server uses a generative AI model to predict future consumption patterns based on collected power and weather data. This process involves inputting historical consumption data into the model and applying a prediction algorithm to estimate future demand. The output is time-of-day consumption forecast data. Specifically, it calculates predicted values ​​based on the model's training results and saves them in a visualized format.

[0593] Step 3:

[0594] The device uses its built-in microphone and camera to capture the user's voice and facial expressions in real time. This input data is sent to an emotion analysis engine. The engine performs voice tone analysis and facial expression recognition, and outputs the user's stress level and emotional state. Specific operations include acquiring data from sensors and applying analysis algorithms.

[0595] Step 4:

[0596] The server integrates predictions of consumption patterns and analysis results of emotional states, and generates optimal energy recommendations using a generative AI model. This process takes integrated data as input in the form of prompts and generates adjusted energy recommendations as output. Specifically, it involves data integration and the execution of a generative algorithm.

[0597] Step 5:

[0598] The terminal notifies the user of energy recommendations received from the server. The input is the generated recommendation data, and the output is a notification via screen display or voice message. Specifically, the screen displays "Thank you for your hard work. To help you relax, we recommend using your home appliances after peak consumption hours."

[0599] Step 6:

[0600] Users adjust their electricity usage based on energy suggestions provided by their devices. They receive the suggestions as input and modify their energy consumption behavior as output. Specifically, they adjust heating and lighting usage times to optimize energy costs while maintaining comfort.

[0601] (Application Example 2)

[0602] 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."

[0603] In recent years, there has been a growing demand for both efficient power supply and user comfort. However, conventional power management systems have struggled to implement energy management that takes into account the emotional state of users, and proposals for optimizing energy consumption sometimes do not match the user's current situation. Therefore, achieving both efficient power consumption and user comfort that takes their emotions into consideration remains a challenge.

[0604] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting power usage data, means for voice and facial expression analysis for estimating the user's emotional state, and means for adjusting energy consumption optimization suggestions based on the estimated emotional state. This makes it possible to propose flexible energy consumption optimizations according to the user's emotional state.

[0605] "Electricity usage data" is a collection of information about the amount of electricity used by energy consumers, and is collected in order to understand energy consumption patterns.

[0606] "Weather information" refers to information related to the natural environment, such as weather, temperature, and humidity, and is a factor that affects electricity demand.

[0607] A "power generation device" refers to a machine or equipment used to generate electricity, and it constitutes part of the power supply system.

[0608] "Communication technology" is a general term for technical means of exchanging data between different devices, enabling safe and efficient information transmission.

[0609] "Emotional state" refers to the psychological and emotional conditions a user experiences at a particular time, and can be estimated from voice and facial expressions.

[0610] "Voice and facial expression analysis means" refers to technology that analyzes voice data and facial expression data in order to understand the user's emotional state.

[0611] "Means for adjusting energy consumption optimization suggestions" refers to a technology that modifies energy consumption suggestions based on the estimated emotional state of the user, taking into consideration the user's comfort.

[0612] This invention relates to a system aimed at flexibly optimizing energy consumption based on the user's emotional state. This system consists of a server, a terminal, a user, and an emotion analysis engine.

[0613] The server collects power usage data and weather information, and uses an AI model to predict future consumption patterns. Machine learning libraries such as TensorFlow and PyTorch are utilized for data analysis. Furthermore, by combining voice and facial expression analysis techniques, the system estimates the user's emotional state in real time. This estimation process involves processing image data using libraries such as OpenCV and analyzing voice data using the SpeechRecognition library.

[0614] The terminal notifies the user of energy consumption optimization suggestions provided by the server. The terminal includes smartphones and smart devices, and provides information through a user interface.

[0615] Users can choose whether or not to accept the suggestions provided and act accordingly. For example, if a user is feeling tired, the server might suggest, "We have detected your current stress level. We recommend listening to music and dimming the lights slightly to help you relax."

[0616] An example of a prompt to input into the generation AI model would be: "Analyze the user's emotional state and generate optimal energy consumption suggestions. Please also include specific suggestions for when the user is tired." This prompt will automatically generate appropriate suggestions that take the user's emotions into consideration.

[0617] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0618] Step 1:

[0619] The server collects user power usage data and weather information. Input data comes from power meters and weather sensors, which are stored in a database. This provides the foundational data necessary for subsequent analysis.

[0620] Step 2:

[0621] The server uses an AI model based on the collected data to predict power consumption patterns. The input consists of power usage data and weather information obtained in the previous step, which are then fed into an AI model built with TensorFlow or PyTorch. The output is predicted data showing future consumption patterns.

[0622] Step 3:

[0623] The server analyzes user voice and facial expression data obtained from the camera and microphone built into the terminal. The input consists of voice and image data transmitted from the terminal. Facial expressions are analyzed using OpenCV, and voice is analyzed using the SpeechRecognition library. The output is data indicating the user's emotional state.

[0624] Step 4:

[0625] The server generates energy consumption optimization suggestions based on predicted consumption patterns and emotional state data. The input consists of predicted consumption pattern data and emotional state data, which are used to generate suggestions using a generative AI model. The output is an optimization suggestion tailored to the user's situation.

[0626] Step 5:

[0627] The terminal notifies the user of optimization suggestions received from the server. The input is the suggestion data generated by the server, which is presented to the user visually or audibly via the user interface. The output is the energy consumption optimization notification received by the user.

[0628] Step 6:

[0629] The user evaluates suggestions from the device and takes appropriate action. The input is a notification from the device, which the user uses to adjust the usage time and settings of home appliances. The output is the actual adjusted energy consumption behavior.

[0630] 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.

[0631] 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.

[0632] 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.

[0633] [Fourth Embodiment]

[0634] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0635] 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.

[0636] 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).

[0637] 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.

[0638] 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.

[0639] 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).

[0640] 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.

[0641] 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.

[0642] 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.

[0643] 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.

[0644] 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.

[0645] 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.

[0646] 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".

[0647] This invention relates to a power management system that predicts power consumption using power usage data and weather information, and optimizes the supply of power generators. This system consists of a server, terminals, and users, and each function works together to achieve efficient power supply.

[0648] System Configuration

[0649] 1. Server

[0650] The server is responsible for collecting and analyzing power usage data and weather information. Using AI agents, it analyzes past consumption data and weather information to predict future consumption patterns. This predicted data is used to efficiently adjust the operation of power generators. It also utilizes a multi-agent system to issue power supply instructions to each power plant.

[0651] 2. Terminal

[0652] The terminal, acting as a user device, notifies the user of energy allocation optimization suggestions from the server. The terminal displays energy-saving suggestions from the AI ​​agent, allowing the user to optimize their actions based on these suggestions.

[0653] 3. User

[0654] Users receive notifications about energy usage through their devices and can follow optimization suggestions to achieve efficient use of electricity. During peak consumption periods, users are given suggestions to conserve energy, thereby leveling out electricity consumption.

[0655] Example of operation

[0656] As a concrete example, let's explain the daytime electricity management process for a household. Consider a case where a household receives a proposal to reduce electricity consumption during peak hours.

[0657] 1. In the morning, the server predicts that heater usage will increase due to a drop in temperature during the night, based on the day's weather forecast and past power usage data.

[0658] 2. The AI ​​agent analyzes this forecast data and determines that the peak in electricity demand will occur between 6:00 PM and 9:00 PM.

[0659] 3. The server adjusts the output of each generator based on its predictions and prepares backup power generation capacity if necessary.

[0660] 4. The device notifies the user based on the forecast results. A message will be displayed stating, "Peak electricity demand is expected between 18:00 and 21:00. By reducing electricity usage during this time, you can minimize energy costs."

[0661] 5. Based on this notification, users can take action such as adjusting their home heating timers or turning off unused lights, thereby avoiding peak electricity usage.

[0662] In this way, servers, terminals, and users work together to achieve a stable power supply and reduce energy costs.

[0663] The following describes the processing flow.

[0664] Step 1:

[0665] The server collects real-time electricity usage data from individual homes and businesses. Simultaneously, it obtains weather information such as temperature and humidity from external data providers and stores this information in a centrally managed database.

[0666] Step 2:

[0667] An AI agent on the server analyzes collected electricity usage data and weather information. It uses machine learning algorithms to predict future electricity demand, evaluating the relationship between past consumption patterns and weather. In particular, it precisely models demand fluctuations by time of day.

[0668] Step 3:

[0669] The server adjusts the power supply from each generator based on consumption pattern predictions obtained from AI agents. Using a multi-agent system, it provides appropriate output instructions to each power plant. In doing so, it also considers the availability of renewable energy to ensure a balanced distribution.

[0670] Step 4:

[0671] The server uses quantum communication technology to exchange data between generators and consumers. This ensures the secure delivery of proposed energy allocation instructions and change information.

[0672] Step 5:

[0673] The device notifies the user of energy-saving suggestions received from the server. Specifically, it displays information such as, "18:00 to 21:00 is peak time. Reducing your electricity usage will save you money on your electricity bill."

[0674] Step 6:

[0675] Users receive notifications from their devices and take actions to reduce power consumption. For example, they might turn off unnecessary lights or shift the use of appliances to off-peak hours to avoid peak electricity demand.

[0676] Step 7:

[0677] The server collects actual consumption data based on user behavior and feeds it back to the AI ​​agent. This improves the accuracy of demand forecasts for future use, enabling more efficient energy management.

[0678] (Example 1)

[0679] 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".

[0680] In modern society, effectively managing peak electricity demand periods is becoming increasingly important. However, conventional power management systems have insufficient demand forecasting accuracy, making it difficult to maximize the operating efficiency of power equipment. Furthermore, there are many limitations in adjusting supply levels and providing efficient information to users, resulting in insufficient optimization.

[0681] 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.

[0682] In this invention, the server includes means for analyzing usage data and weather information to predict future consumption patterns, means for dynamically managing the operation of power equipment based on the predicted demand, and means for realizing the coordinated operation of multiple devices using an agent model. This makes it possible to maximize the efficiency of power supply and optimize the operation of power equipment.

[0683] "Usage data" refers to data that shows how energy consumers are using electricity.

[0684] "Weather information" refers to information about meteorological conditions such as weather, temperature, and humidity.

[0685] "Consumption patterns" refer to trends and fluctuations in electricity usage over a certain period.

[0686] A "power device" is a mechanical device that is responsible for generating or supplying electricity.

[0687] "Communication technology" refers to a set of technologies used to safely and quickly transmit data from a sender to a receiver.

[0688] The "agent model" is a technology that enables multiple program agents to work together in a coordinated manner.

[0689] A "notification device" is a communication device or equipment used to provide information or suggestions to users.

[0690] "Means of optimizing energy distribution" refer to methods and techniques for maximizing efficiency by adjusting the amount and timing of energy supplied.

[0691] In this invention, a server, terminals, and users work together to build a power management system.

[0692] The server's primary role is to collect and analyze usage data and weather information. Usage data includes real-time acquisition of each consumer's power usage through sensor devices. Weather information is obtained using APIs from weather information providers. On the server, machine learning frameworks such as TensorFlow and PyTorch are used to model consumption patterns based on this data and predict future demand.

[0693] The server dynamically manages the operation of power units based on predicted demand information. This management uses an agent model to ensure that multiple power units operate efficiently in coordination. Secure communication technology is also used for transmitting data to each power unit.

[0694] A device is a medium for providing information and notifications to users. Users can receive notifications displayed on the device and optimize their energy usage. Devices include smartphones and smart devices in the home, and notifications are delivered as push messages.

[0695] For example, a server uses weather data to predict the peak hours of electricity demand in the evening and sends a message to the terminal saying, "Electricity demand will be high between 6:00 PM and 9:00 PM. Please reduce your usage." Based on this notification, users can adjust their electricity usage, thereby reducing peak demand.

[0696] An example of a prompt using the generated AI model would be, "Based on power consumption data and weather forecasts, please predict the peak power demand for October 21, 2023." In this way, this invention achieves efficient power management and resource optimization through the cooperation of servers, terminals, and users.

[0697] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0698] Step 1:

[0699] The server collects usage data from each consumer in real time. Input data from sensor devices includes hourly records of power consumption. This data is analyzed and cleaned, anomalies are removed, and then stored in a database. The output is a formatted set of usage data.

[0700] Step 2:

[0701] The server obtains weather information from external sources via a weather information API. Inputs include weather conditions, temperature, humidity, etc., which are used to form a weather dataset. The output is a dataset integrating this weather data.

[0702] Step 3:

[0703] The server inputs usage data and weather data into a machine learning platform such as TensorFlow to predict future consumption patterns. Data processing involves fitting this data to a time-series model for training. The output is the predicted consumption pattern, representing future electricity demand numerically.

[0704] Step 4:

[0705] The server develops an operating plan for the power units based on predictive data. Specifically, it uses an agent model to determine the optimal output level for each power unit and sends operating instructions to each unit. The output is operating instruction information for each unit.

[0706] Step 5:

[0707] The terminal notifies the user of forecast information and energy usage suggestions sent from the server. The input is notification data from the server, which is displayed on the user's screen. For example, it sends a push notification such as, "Electricity demand will be high between 18:00 and 21:00." The output is a message notification to the user.

[0708] Step 6:

[0709] Users adjust their actions based on notifications received from their devices. Specifically, they might change heating timers to match the notified peak times or turn off unnecessary electrical appliances. The input is notification information from the device, and the output is the user's change in power usage patterns.

[0710] (Application Example 1)

[0711] 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".

[0712] Managing electricity in modern cities remains a major challenge, given the increasing demand for volatile demand and efficient use of energy resources. In particular, while appropriate adjustments to electricity supply in response to rapid changes in demand are required, optimizing consumption through the actions of individual residents is not sufficiently achieved in many cities. Therefore, improving energy efficiency across the entire community is necessary.

[0713] 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.

[0714] In this invention, the server includes means for collecting data for predicting power consumption, means for analyzing the data and weather information to predict demand, and means for adjusting the supply of power generation equipment. This enables efficient energy distribution and the provision of information to optimize consumption for residents.

[0715] "Data collection methods" refer to technical techniques for obtaining information about electricity consumption from multiple sources.

[0716] "Analysis means" refers to processing technology used to derive future demand patterns based on collected electricity usage data and weather information.

[0717] A "supply adjustment mechanism" is a function for optimizing the output of power generation equipment based on demand forecasts.

[0718] "Communication technology" is the infrastructure for securely and quickly exchanging necessary data between energy supply facilities and electricity consumers.

[0719] An "energy distribution optimization method" is a method for allocating energy resources most effectively based on predicted consumption patterns.

[0720] "Information provision means" refers to technologies for notifying residents of suggestions regarding optimizing their consumption.

[0721] This invention is a power management system for smart cities, realized through the interaction of servers, terminals, and residents. The server has the function of collecting power consumption data and weather information, and performing data analysis, thereby predicting future power demand.

[0722] The server runs on cloud infrastructure such as AWS and analyzes consumption patterns using data analysis libraries such as Python, Pandas, and Scikit-learn. The server's role is to train an AI model based on the collected data and predict future electricity demand with high accuracy. Based on this prediction, the server adjusts the output of power generation equipment to meet demand.

[0723] The terminal operates on smart devices owned by residents. It has the functionality to provide residents with real-time information on optimizing their consumption through an application developed with React Native. The application displays suggestions based on consumption forecasts sent from the server, prompting consumer action. For example, it might notify users with a message such as, "You can save 20% energy by refraining from using air conditioning around 6 PM. Try to dress in cooler clothing."

[0724] Based on information received via their devices, users optimize their individual consumption behavior, contributing to overall energy efficiency improvements. They are expected to receive suggestions indicating specific actions they should take and adjust their energy consumption at appropriate times.

[0725] As a specific example, the server predicts that when high summer temperatures are forecast for a weekday afternoon, the peak demand for air conditioning will be between 6:00 PM and 9:00 PM, and adjusts energy consumption accordingly. Based on this prediction, the terminal sends the user specific energy-saving instructions.

[0726] Examples of prompts for a generative AI model include the following:

[0727] "According to weather forecasts and historical consumption data, electricity demand is expected to peak tonight between 6:00 PM and 9:00 PM. What energy-saving actions would you suggest?"

[0728] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0729] Step 1:

[0730] The server collects electricity usage data and weather information from multiple data providers on the internet. Specifically, it periodically retrieves data using APIs and stores it in a database. The input for this step is electricity usage history and weather forecast information, and the output is an analyzable dataset.

[0731] Step 2:

[0732] The server analyzes consumption patterns using the collected data and predicts future electricity demand. Here, Python and machine learning libraries (e.g., Scikit-learn) are used. The input is the dataset generated in the previous step, and the AI ​​model calculates the supply and demand forecast. The output is the future electricity demand forecast.

[0733] Step 3:

[0734] The server optimally adjusts the supply of power generation equipment based on the prediction results. This process involves sending instructions to power plants to establish an efficient energy distribution. The input is future prediction data, and the output is the adjusted supply plan.

[0735] Step 4:

[0736] The terminal notifies residents of supply and demand forecasts and energy-saving suggestions sent from the server. These notifications include specific energy-saving actions and are received by the user via a smartphone app. The input is energy-saving suggestions from the server, and the output is the notification message to the user.

[0737] Step 5:

[0738] Users adjust their daily energy usage based on information from their devices. They use the app to create concrete action plans for energy saving and optimize the use of home appliances. The input is suggestive information from the device, and the output is improved energy efficiency.

[0739] 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.

[0740] This invention combines an emotion engine with a power management system to achieve energy management that takes into account the user's emotional state. This system consists of a server, terminals, users, and an emotion engine, and provides efficient power supply while maintaining user comfort.

[0741] System Configuration

[0742] 1. Server

[0743] The server collects power usage data and weather information, and analyzes it with AI agents. It predicts consumption patterns and issues supply instructions to each generator. A multi-agent system is utilized here to ensure efficient energy distribution. It also receives input from an emotion engine and adjusts suggestions based on the user's emotional state.

[0744] 2. Terminal

[0745] The device notifies the user of energy allocation optimization suggestions from the server. Based on feedback analyzed by the emotion engine, the suggestions are automatically adjusted. For example, if the user is stressed, a gentler suggestion will be provided.

[0746] 3. User

[0747] Users receive suggestions via their devices and optimize their actions. An emotion engine operates in the background, analyzing the user's voice and facial expressions to understand their emotional state in real time.

[0748] 4. Emotional Engine

[0749] The emotion engine includes sensors that estimate the user's emotions using speech recognition and facial recognition technologies. This engine continuously learns from the user's responses in everyday life and improves the accuracy of its suggestions.

[0750] Example of operation

[0751] As a concrete example, let's explain the power management process in a user's daily life. Consider a case where a user has returned home from work and wants to relax.

[0752] 1. The server predicts that electricity demand will increase from evening to night based on past data from when the user returned home and weather information. However, the emotion engine recognizes the user's fatigue level from their voice.

[0753] 2. Based on the prediction results, energy-saving suggestions are prepared, but because the user is tired, the notification content is adjusted to "You must be tired. To relax, we recommend using appliances after peak consumption hours."

[0754] 3. The device displays these adjusted suggestions to the user. Based on this, the user can adjust the usage time of heating and lighting appliances.

[0755] 4. Users should follow the notification and use the heating slightly later to avoid peak consumption while maintaining comfort.

[0756] In this way, the server, terminal, and emotion engine work together to achieve power management that takes user emotions into consideration.

[0757] The following describes the processing flow.

[0758] Step 1:

[0759] The server collects electricity usage data from individual homes and businesses. It also obtains weather information such as temperature and humidity from external data services and stores this information in a database. Furthermore, it prepares to receive user emotion data from an emotion engine.

[0760] Step 2:

[0761] The emotion engine analyzes the user's voice and facial expressions in real time to recognize their emotions at that moment. It detects specific emotional patterns, such as stress or relaxation, and sends the results to the server.

[0762] Step 3:

[0763] An AI agent on the server analyzes collected power usage data, weather information, and sentiment data. It evaluates the relationship between past usage patterns, current sentiment states, and weather to predict future power demand.

[0764] Step 4:

[0765] The server adjusts the power supply from each generator based on consumption pattern predictions obtained from AI agents and emotion data from the emotion engine. It uses a multi-agent system to send appropriate output instructions to each power plant.

[0766] Step 5:

[0767] The server uses quantum communication technology to securely and quickly exchange data between the generator and the consumer. This data includes adjusted energy distribution and suggestions tailored to the user's emotional state.

[0768] Step 6:

[0769] The device receives information sent from the server and notifies the user of energy-saving suggestions that have been adjusted based on input from the emotion engine. For example, it might display a message such as, "Today is a day to relax. Enjoy your day comfortably while avoiding peak consumption."

[0770] Step 7:

[0771] Users receive notifications from their devices and adjust their lifestyle in a way that suits their emotional state. For example, on days when they feel stressed, they can choose a manageable energy-saving method to stabilize their emotions while simultaneously optimizing their power usage.

[0772] Step 8:

[0773] The server continuously collects user behavior data and feeds it back to the emotion engine and AI agent. This further improves the accuracy of future suggestions and enables more personalized energy management.

[0774] (Example 2)

[0775] 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".

[0776] Conventional power management systems struggled to efficiently allocate energy without considering the emotional state of users. In particular, energy recommendations when users were stressed or fatigued could worsen their condition. This resulted in a challenge in providing electricity that would improve user satisfaction and quality of life.

[0777] 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.

[0778] In this invention, the server includes means for collecting power data, means for analyzing the power data and weather data to predict future consumption patterns, and means for analyzing the user's emotional state and generating energy suggestions based on the analysis results. This enables efficient and satisfying energy management that takes the user's emotional state into consideration.

[0779] "Power data" refers to information about electricity usage, usage time, and location.

[0780] "Weather data" refers to information about weather conditions such as temperature, humidity, and precipitation.

[0781] "Consumption patterns" refer to trends and fluctuations in electricity usage derived from past electricity usage data.

[0782] "Power generation equipment" refers to devices and systems for generating electricity, and specifically includes thermal power plants and solar power generation systems.

[0783] "Communication technology" refers to methods and techniques for sending and receiving information, and specifically includes wireless communication, optical communication, and the like.

[0784] "Energy distribution" refers to the appropriate distribution of electricity supplied from multiple power sources.

[0785] "User's emotional state" refers to the emotional reactions and situations experienced by the user, and generally includes stress, fatigue, and feelings of well-being.

[0786] "Energy proposals" refer to recommendations and advice on energy use provided to users.

[0787] This invention is a system that manages energy based on the user's emotional state. A server, terminal, and emotion analysis technology work together to efficiently manage power supply from a power generator and provide energy recommendations that take the user's emotions into consideration.

[0788] The server collects real-time electricity and weather data using smart meters and weather APIs. Based on this data, it uses a generative AI model to predict future consumption patterns. This prediction makes it possible to balance energy supply and demand.

[0789] The device uses emotion analysis technology to analyze the user's voice and facial expressions. Specifically, it incorporates voice recognition and facial recognition technologies using a microphone and camera to grasp the user's current emotional state in real time. This data is sent to a server and used to provide energy recommendations tailored to the user's situation.

[0790] Users receive energy suggestions displayed on their devices to optimize their daily electricity usage. These suggestions are tailored to their emotional state; for example, a stressed user might receive suggestions to promote relaxation. This ensures user comfort while achieving efficient energy management.

[0791] As a concrete example, consider a user who has returned home from work and wants to relax. Based on past return-home data and weather conditions, the server suggests energy usage to avoid peak consumption. Furthermore, if the emotional analysis determines that the user is tired, the device will notify the user with a message such as, "You must be tired. To help you relax, we recommend using your home appliances after the peak consumption time."

[0792] As an example of a prompt, the input to the generating AI model might be text such as, "Please provide a prompt for an AI model that generates appropriate energy usage suggestions when a user is feeling tired but wants to relax." This allows the model to generate specific and appropriate energy suggestions.

[0793] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0794] Step 1:

[0795] The server collects power and weather data from smart meters and weather APIs. It receives power consumption data from homes and facilities, along with external weather information, as input. By storing this data with timestamps, it generates a time-series dataset. Specifically, it executes API calls and records real-time data streams into the database.

[0796] Step 2:

[0797] The server uses a generative AI model to predict future consumption patterns based on collected power and weather data. This process involves inputting historical consumption data into the model and applying a prediction algorithm to estimate future demand. The output is time-of-day consumption forecast data. Specifically, it calculates predicted values ​​based on the model's training results and saves them in a visualized format.

[0798] Step 3:

[0799] The device uses its built-in microphone and camera to capture the user's voice and facial expressions in real time. This input data is sent to an emotion analysis engine. The engine performs voice tone analysis and facial expression recognition, and outputs the user's stress level and emotional state. Specific operations include acquiring data from sensors and applying analysis algorithms.

[0800] Step 4:

[0801] The server integrates predictions of consumption patterns and analysis results of emotional states, and generates optimal energy recommendations using a generative AI model. This process takes integrated data as input in the form of prompts and generates adjusted energy recommendations as output. Specifically, it involves data integration and the execution of a generative algorithm.

[0802] Step 5:

[0803] The terminal notifies the user of energy recommendations received from the server. The input is the generated recommendation data, and the output is a notification via screen display or voice message. Specifically, the screen displays "Thank you for your hard work. To help you relax, we recommend using your home appliances after peak consumption hours."

[0804] Step 6:

[0805] Users adjust their electricity usage based on energy suggestions provided by their devices. They receive the suggestions as input and modify their energy consumption behavior as output. Specifically, they adjust heating and lighting usage times to optimize energy costs while maintaining comfort.

[0806] (Application Example 2)

[0807] 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".

[0808] In recent years, there has been a growing demand for both efficient power supply and user comfort. However, conventional power management systems have struggled to implement energy management that takes into account the emotional state of users, and proposals for optimizing energy consumption sometimes do not match the user's current situation. Therefore, achieving both efficient power consumption and user comfort that takes their emotions into consideration remains a challenge.

[0809] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting power usage data, means for voice and facial expression analysis for estimating the user's emotional state, and means for adjusting energy consumption optimization suggestions based on the estimated emotional state. This makes it possible to propose flexible energy consumption optimizations according to the user's emotional state.

[0810] "Electricity usage data" is a collection of information about the amount of electricity used by energy consumers, and is collected in order to understand energy consumption patterns.

[0811] "Weather information" refers to information related to the natural environment, such as weather, temperature, and humidity, and is a factor that affects electricity demand.

[0812] A "power generation device" refers to a machine or equipment used to generate electricity, and it constitutes part of the power supply system.

[0813] "Communication technology" is a general term for technical means of exchanging data between different devices, enabling safe and efficient information transmission.

[0814] "Emotional state" refers to the psychological and emotional conditions a user experiences at a particular time, and can be estimated from voice and facial expressions.

[0815] "Voice and facial expression analysis means" refers to technology that analyzes voice data and facial expression data in order to understand the user's emotional state.

[0816] "Means for adjusting energy consumption optimization suggestions" refers to a technology that modifies energy consumption suggestions based on the estimated emotional state of the user, taking into consideration the user's comfort.

[0817] This invention relates to a system aimed at flexibly optimizing energy consumption based on the user's emotional state. This system consists of a server, a terminal, a user, and an emotion analysis engine.

[0818] The server collects power usage data and weather information, and uses an AI model to predict future consumption patterns. Machine learning libraries such as TensorFlow and PyTorch are utilized for data analysis. Furthermore, by combining voice and facial expression analysis techniques, the system estimates the user's emotional state in real time. This estimation process involves processing image data using libraries such as OpenCV and analyzing voice data using the SpeechRecognition library.

[0819] The terminal notifies the user of energy consumption optimization suggestions provided by the server. The terminal includes smartphones and smart devices, and provides information through a user interface.

[0820] Users can choose whether or not to accept the suggestions provided and act accordingly. For example, if a user is feeling tired, the server might suggest, "We have detected your current stress level. We recommend listening to music and dimming the lights slightly to help you relax."

[0821] An example of a prompt to input into the generation AI model would be: "Analyze the user's emotional state and generate optimal energy consumption suggestions. Please also include specific suggestions for when the user is tired." This prompt will automatically generate appropriate suggestions that take the user's emotions into consideration.

[0822] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0823] Step 1:

[0824] The server collects user power usage data and weather information. Input data comes from power meters and weather sensors, which are stored in a database. This provides the foundational data necessary for subsequent analysis.

[0825] Step 2:

[0826] The server uses an AI model based on the collected data to predict power consumption patterns. The input consists of power usage data and weather information obtained in the previous step, which are then fed into an AI model built with TensorFlow or PyTorch. The output is predicted data showing future consumption patterns.

[0827] Step 3:

[0828] The server analyzes user voice and facial expression data obtained from the camera and microphone built into the terminal. The input consists of voice and image data transmitted from the terminal. Facial expressions are analyzed using OpenCV, and voice is analyzed using the SpeechRecognition library. The output is data indicating the user's emotional state.

[0829] Step 4:

[0830] The server generates energy consumption optimization suggestions based on predicted consumption patterns and emotional state data. The input consists of predicted consumption pattern data and emotional state data, which are used to generate suggestions using a generative AI model. The output is an optimization suggestion tailored to the user's situation.

[0831] Step 5:

[0832] The terminal notifies the user of optimization suggestions received from the server. The input is the suggestion data generated by the server, which is presented to the user visually or audibly via the user interface. The output is the energy consumption optimization notification received by the user.

[0833] Step 6:

[0834] The user evaluates suggestions from the device and takes appropriate action. The input is a notification from the device, which the user uses to adjust the usage time and settings of home appliances. The output is the actual adjusted energy consumption behavior.

[0835] 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.

[0836] 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.

[0837] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0838] 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.

[0839] 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.

[0840] 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.

[0841] 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.

[0842] 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.

[0843] 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."

[0844] 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.

[0845] 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.

[0846] 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.

[0847] 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.

[0848] 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.

[0849] 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.

[0850] 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.

[0851] 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.

[0852] 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.

[0853] 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.

[0854] 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.

[0855] 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.

[0856] The following is further disclosed regarding the embodiments described above.

[0857] (Claim 1)

[0858] Means for collecting electricity usage data,

[0859] A means for analyzing the aforementioned power usage data and weather information to predict future consumption patterns,

[0860] A means for adjusting the amount of power supplied from each generator,

[0861] A means of securely and quickly exchanging data between a generator and a consumer using quantum communication technology,

[0862] A means for optimizing energy distribution based on consumption patterns,

[0863] A power management system including

[0864] (Claim 2)

[0865] The power management system according to claim 1, which uses a multi-agent system to adjust the amount of power supplied.

[0866] (Claim 3)

[0867] The power management system according to claim 1, further comprising means for notifying a user terminal of suggestions for optimizing power consumption.

[0868] "Example 1"

[0869] (Claim 1)

[0870] Means for collecting usage data,

[0871] A means for analyzing the aforementioned usage data and weather information to predict future consumption patterns,

[0872] Means for adjusting the supply amount from each power unit,

[0873] A means of securely and quickly exchanging data between power equipment and consumers using communication technology,

[0874] A means for optimizing energy distribution based on consumption patterns,

[0875] A means for dynamically managing the operation of power equipment based on predicted demand,

[0876] A means of achieving coordinated operation of multiple devices using an agent model,

[0877] A means of suggesting energy efficiency improvements to users through a notification device,

[0878] A system that includes this.

[0879] (Claim 2)

[0880] The system according to claim 1, which uses an agent system to adjust the supply amount.

[0881] (Claim 3)

[0882] The system according to claim 1, further comprising means for notifying the user device of suggestions for improving usage efficiency.

[0883] "Application Example 1"

[0884] (Claim 1)

[0885] Means for collecting data to predict power consumption,

[0886] A means for analyzing the aforementioned data and weather information to predict future electricity demand,

[0887] Means for adjusting the power supply from power generation facilities,

[0888] A means of exchanging data between energy supply facilities and consumers using communication technology,

[0889] A means for optimizing energy distribution based on consumption forecasts,

[0890] A means of providing residents with suggestions for optimizing consumption,

[0891] A system that includes this.

[0892] (Claim 2)

[0893] The system according to claim 1, which uses a multi-agent system to adjust the power supply.

[0894] (Claim 3)

[0895] The system according to claim 1, comprising means for notifying of consumption optimization through an application running on an energy device.

[0896] "Example 2 of combining an emotion engine"

[0897] (Claim 1)

[0898] Means for collecting power data,

[0899] A means for analyzing the aforementioned power data and weather data to predict future consumption patterns,

[0900] A means for adjusting the power supply from each power generation device,

[0901] A means of exchanging data between a power generation device and a user using communication technology,

[0902] A means for optimizing energy distribution based on consumption patterns,

[0903] A means for analyzing the user's emotional state and generating energy suggestions based on the analysis results,

[0904] A system that includes this.

[0905] (Claim 2)

[0906] The system according to claim 1, which uses a multi-agent system to adjust the power supply.

[0907] (Claim 3)

[0908] The system according to claim 1, further comprising means for notifying the user terminal of suggestions for optimizing consumption and adjusting the content of the suggestions based on the emotional state.

[0909] "Application example 2 when combining with an emotional engine"

[0910] (Claim 1)

[0911] Means for collecting electricity usage data,

[0912] A means for analyzing the aforementioned power usage data and weather information to predict future consumption patterns,

[0913] Means for adjusting the amount of power supplied from each power generation device,

[0914] A means of securely and quickly exchanging information between a power generation device and its user using communication technology,

[0915] A means for optimizing energy distribution based on consumption patterns,

[0916] A means for analyzing voice and facial expressions to estimate the user's emotional state,

[0917] A means of adjusting energy consumption optimization proposals based on estimated emotional states,

[0918] A system that includes this.

[0919] (Claim 2)

[0920] The system according to claim 1, which uses an agent system to adjust the amount of power supplied.

[0921] (Claim 3)

[0922] The system according to claim 1, further comprising means for notifying a user device of suggestions for optimizing consumption. [Explanation of symbols]

[0923] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. Means for collecting electricity usage data, A means for analyzing the aforementioned power usage data and weather information to predict future consumption patterns, A means for adjusting the amount of power supplied from each generator, A means of securely and quickly exchanging data between a generator and a consumer using quantum communication technology, A means for optimizing energy distribution based on consumption patterns, A power management system including

2. The power management system according to claim 1, which uses a multi-agent system to adjust the amount of power supplied.

3. The power management system according to claim 1, further comprising means for notifying a user terminal of a suggestion for optimizing power consumption.

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