system

Abstract

We provide the system. [Solution] An information collection means for collecting energy usage data and storing it in a database, A data analysis means for analyzing the aforementioned energy usage data and identifying energy consumption patterns, A strategic proposal means for formulating a strategy to optimize the use of renewable energy based on the aforementioned analysis results, An interface means for visually displaying the aforementioned strategy and receiving feedback from users, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] With the soaring energy prices and strengthened environmental regulations, there is a demand for improving the efficiency of energy management in enterprises and local governments. However, since it usually requires advanced expertise and time to analyze energy data precisely and quickly and formulate an effective energy strategy, there is a problem that it is difficult to obtain an immediately executable strategy.

Means for Solving the Problems

[0005] To solve this problem, the present invention employs an information gathering means for collecting energy usage data and storing it in a database. Furthermore, it includes a data analysis means for identifying energy consumption patterns based on the collected data, and provides a strategy proposal means for formulating a strategy to optimize the use of renewable energy based on the analysis results. This makes it possible to quickly propose an effective energy strategy. In addition, it has an interface means for visually displaying the strategy and receiving feedback from the user, which helps the user understand and allows them to experience the effects of energy management.

[0006] "Energy utilization data" refers to information regarding the amount of energy consumed in the equipment and facilities of companies and local governments.

[0007] "Information gathering means" refers to devices and systems that have the function of acquiring energy usage data from various equipment and sensors and storing it in a database.

[0008] "Data analysis means" refers to algorithms and processing devices that analyze consumption patterns based on collected energy usage data and detect trends and anomalies.

[0009] A "strategic proposal tool" refers to a system that, based on analyzed data, has the function of formulating strategies for the optimal use of renewable energy and for improving energy efficiency.

[0010] "Interface means" refers to a device or software equipped with a UI (user interface) for visually displaying a proposed energy strategy to the user and receiving feedback from the user.

[0011] "Peak shifting means" refers to a method or device that improves energy efficiency by predicting peak energy usage times and shifting those peaks to other time periods.

[0012] A "machine learning algorithm" refers to a computational method that learns patterns from energy consumption data and performs predictions and classifications based on new data. [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]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying Out the Invention

[0014] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

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

[0016] [[ID=十六]]In the following embodiments, a labeled 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] [[ID=二十]]In the following embodiments, a labeled 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, a labeled 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] It should be noted that in the translation of the text in item , the original text's expression "符号付きのプロセッサ" is translated as "labeled processor", which may not be a very common or accurate technical term. It is recommended to check if there is a more appropriate and standard translation in the relevant technical field. Also, in item , "符号付きのRAM" is translated as "labeled RAM", and in item , "符号付きのストレージ" is translated as "labeled storage", which may also need to be further verified according to the specific context and technical norms.In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[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 provides a system for streamlining energy management and optimizing the use of renewable energy. Specifically, energy usage data is collected by a server, and based on this data, energy consumption patterns are analyzed to propose an optimal energy strategy.

[0035] The server periodically receives energy usage data from each piece of equipment and sensor. It also stores this data in a database and analyzes consumption patterns in real time. Machine learning algorithms are used for data analysis, and by combining this data with external weather data, the system can more accurately predict consumption trends and anomalies.

[0036] Based on the analyzed data, the server identifies peak hours when power consumption tends to be high and develops strategies for peak shifting. Specifically, it develops plans for utilizing renewable energy sources and aims for efficient energy use. This is expected to reduce energy costs and environmental impact.

[0037] The terminal visually displays the energy strategy provided by the server, supporting user understanding. Users can check their energy consumption status and the proposed strategy on the terminal and make adjustments as needed. Furthermore, the server incorporates user input and feedback into improvements to the proposed strategy through this interface.

[0038] For example, in an office building, a server collects power consumption data from each floor and generates a predictive model based on the day's weather data. The analysis results are displayed on a terminal, and the system suggests to the user how to optimize the air conditioning system and how to utilize solar power. Based on this information, the user can adjust the air conditioning operating schedule and shift peak power consumption to other times. In this way, the system achieves improved energy efficiency.

[0039] The following describes the processing flow.

[0040] Step 1:

[0041] The server receives energy usage data in real time from each piece of equipment and sensor. This includes information such as power consumption, operating time, and operating mode. The server records and manages this data in a database.

[0042] Step 2:

[0043] The server cleanses the collected data. Specifically, it imputes missing values, removes outliers, and identifies invalid data, preparing the data for improved analysis accuracy.

[0044] Step 3:

[0045] The server applies machine learning algorithms based on past consumption data to analyze consumption patterns. This analysis identifies energy consumption trends and peak times.

[0046] Step 4:

[0047] The server combines weather data and calendar information to generate a model that predicts future energy demand. This allows it to assess the possibility of peak shifting.

[0048] Step 5:

[0049] Based on the analysis results and predictive models, the server develops renewable energy introduction plans and energy utilization optimization strategies. Specifically, it proposes the use of solar and wind power generation.

[0050] Step 6:

[0051] The terminal displays the energy strategy obtained from the server on a dashboard. Users review this visualized information and make decisions tailored to their specific tasks.

[0052] Step 7:

[0053] Users can provide feedback on the server's suggestions and enter questions through their device. The device then sends this feedback to the server.

[0054] Step 8:

[0055] The server uses user feedback to make adjustments that improve the accuracy of future energy strategy proposals.

[0056] (Example 1)

[0057] 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."

[0058] In modern times, efficient energy management and optimal use of renewable energy are essential for reducing environmental impact and lowering costs. However, optimizing energy use while fully considering fluctuations in energy demand and external influences is difficult. This invention aims to solve these problems and realize efficient and flexible energy management.

[0059] 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.

[0060] In this invention, the server includes information acquisition means for collecting energy utilization indicators and storing them in a storage device, data analysis means for analyzing the energy utilization indicators and identifying trends in energy consumption, and strategy proposal means for formulating strategies to optimize the use of renewable energy based on the analysis results. This enables improved energy efficiency and maximum utilization of renewable energy sources.

[0061] "Energy utilization indicators" refer to data that quantitatively shows the state of energy consumption, including information on the amount and trends of use of electricity, gas, and other energy sources.

[0062] "Means for acquiring information to be stored in a storage device" refers to hardware or software components for temporarily storing or permanently recording energy-related data collected by various sensors and devices.

[0063] "Data analysis means" refers to algorithms and programs that use collected energy-related data to analyze consumption patterns, predict trends, and detect anomalies.

[0064] A "policy proposal mechanism" refers to a system for formulating action plans and strategies to promote the efficient use of energy and the utilization of renewable energy, based on the results of data analysis.

[0065] "Display means" refers to interfaces or devices that visually present analyzed data and proposed strategies in a way that is easy for users to understand.

[0066] "Improvement methods" refer to methods and processes for accepting user feedback and using that feedback to consider and adjust proposed strategies and the overall effectiveness of the system.

[0067] The system in this invention mainly consists of three components: a server, a terminal, and a user. Specific embodiments of each component are shown below.

[0068] server:

[0069] The server's role is to collect and store energy usage indicators in storage. Specifically, it acquires data from various sensors and devices installed within the building, including electricity meters, temperature sensors, and illuminance sensors. General-purpose data management systems such as MySQL® or MongoDB are used as databases. This allows for the accumulation of data to identify trends in energy usage.

[0070] Next, the server analyzes the collected data using data analysis tools. For this analysis, machine learning algorithms are implemented using libraries such as Python's scikit-learn to detect trends and anomalies in energy consumption. Furthermore, external weather data is obtained via API and combined with the consumption data to generate a more accurate prediction model. By utilizing this generated AI model, more advanced analysis becomes possible.

[0071] Based on the analysis results, the server proposes strategies to maximize the efficient use of energy. This strategy proposal mechanism develops optimized strategies centered on the utilization of renewable energy. For example, it might create a plan to optimize the usage time of solar power generation.

[0072] Terminal:

[0073] The terminal visualizes the strategies received from the server and presents them clearly to the user. Charts and graphs are used as display methods to allow the user to intuitively understand the proposed strategies. This enables the user to visually grasp energy consumption trends and optimization strategies.

[0074] User:

[0075] Users can check their energy usage and suggested measures via their terminals and adjust their energy consumption behavior based on that information. For example, in an office building, a server can display a predictive model generated based on weather data and power consumption data on the terminal, allowing users to appropriately adjust the air conditioning operating schedule.

[0076] As an example of a prompt, entering "Based on this week's weather data and power consumption patterns, please propose an optimization strategy for the office building's air conditioning system. This proposal should include specific peak shift times and ways to utilize renewable energy" will allow you to obtain a specific proposal using a generated AI model.

[0077] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0078] Step 1:

[0079] The server collects energy usage indicators from multiple sensors. It uses data from building energy meters, temperature sensors, and illuminance sensors as input. This data is sent to the server and temporarily stored in memory. Specifically, the server requests data from the sensors at regular intervals and records the acquired data.

[0080] Step 2:

[0081] The server stores the data collected in step 1 into a storage device. It uses temporarily stored sensor data as input. A data management system such as MySQL or MongoDB is used for the database, and the data is stored persistently while being organized with timestamps. Specifically, the server performs duplicate data checks and detects outliers when storing the data.

[0082] Step 3:

[0083] The server executes data analysis measures to analyze the data stored in step 2. It uses historical energy consumption patterns and the latest sensor data stored in the database as input. This data is analyzed using the scikit-learn library in Python to generate consumption trends and predictive models. Specifically, the server applies anomaly detection algorithms to identify consumption patterns.

[0084] Step 4:

[0085] The server acquires external weather data and combines it with the data analysis results obtained in step 3. The acquired weather data and the analyzed consumption pattern data are used as input. This enables advanced predictions utilizing a generative AI model, allowing for more accurate analysis of energy consumption trends and anomalies. Specifically, the server accesses an external API to acquire the necessary weather data and integrate it into the database.

[0086] Step 5:

[0087] The server devises strategies for energy efficiency based on the analysis results. It uses consumption pattern analysis and weather data forecasts as input. Through its strategy proposal mechanism, it develops a concrete action plan to maximize the use of renewable energy. Specifically, the server formulates an optimization strategy that includes possibilities such as peak shifting.

[0088] Step 6:

[0089] The terminal presents the policy received from the server to the user. It uses policy data sent from the server as input. This data is displayed on the UI in the form of graphs and charts to make it easy for the user to understand. Specifically, the terminal updates the graphical display in real time and accepts feedback through the user interface.

[0090] Step 7:

[0091] Users review energy efficiency strategies presented via the terminal and adjust their energy consumption behavior as needed. Inputs include referring to graphs and charts displayed on the terminal. Outputs include planning and executing specific actions such as adjusting air conditioning schedules. In terms of specific operations, users input feedback using the terminal's interface, which is received by the server and used to improve the strategies.

[0092] (Application Example 1)

[0093] 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."

[0094] In modern cities, improving energy efficiency and optimizing the use of renewable energy are crucial challenges. However, there is a lack of mechanisms to monitor city-wide energy usage in real time and promote efficient use. As a result, energy waste and increased peak loads are becoming problems.

[0095] 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.

[0096] In this invention, the server includes means for collecting energy usage data and storing it in an information storage device, means for analyzing the energy usage data and identifying energy consumption patterns, and means for formulating a strategy to optimize the use of renewable energy based on the analysis results. This enables real-time monitoring of energy consumption across the entire city and allows users to utilize energy efficiently.

[0097] "Energy usage data" refers to a collection of information regarding energy consumption and usage patterns.

[0098] An "information storage device" is a device or system for storing data.

[0099] A "data analysis tool" is a system that analyzes collected data and extracts valuable information.

[0100] An "energy consumption pattern" refers to the tendencies and regularities regarding when and how much energy is consumed.

[0101] A "strategic proposal method" is a process for providing an optimal action plan based on analyzed data.

[0102] An "interface means" is a medium or mechanism for a user to interact with a system.

[0103] "City-wide energy consumption" refers to the overall picture of the state and changes in energy consumption within a specific urban area.

[0104] "Real-time display" means processing data instantly and showing it to the user immediately.

[0105] "Efficient energy utilization" means using the necessary energy optimally while eliminating waste.

[0106] The system that realizes this invention aims to maximize the energy efficiency of the entire city and consists of the interaction of a server, terminals, and users. The server communicates with various sensors to collect energy usage data in an information storage device in order to monitor energy usage in detail. It also uses software such as Python and TENSORFLOW® / Keras to build machine learning models and analyze energy consumption patterns based on the collected data.

[0107] Once the analysis is complete, the server uses strategic proposal tools to develop a strategy to improve the city's energy efficiency. This strategy is visually displayed through a terminal interface developed using React Native. The terminal allows users to monitor energy consumption in real time and adjust energy use based on the proposed solutions.

[0108] Through the application, users can receive suggestions for energy-saving strategies in response to the increased power consumption predicted by the server, for example, when a large-scale event is held in the city on a particular weekend. In this way, users can use the application to improve the efficiency of their energy use and appropriately shift consumption away from peak times.

[0109] An example of a prompt might be, "Please suggest the most effective energy-saving strategy for the upcoming weekend event." This prompt would then prompt the system to generate customized energy management suggestions for the user.

[0110] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0111] Step 1:

[0112] The server collects energy usage data from multiple energy sensors within the city. The data acquired from the sensors is received in JSON format and stored in a data storage device. The input is raw data from each sensor, and the output is processed energy usage data. Because this data is used for subsequent analysis, the server stores it while maintaining data integrity.

[0113] Step 2:

[0114] The server analyzes energy usage data stored in an information storage device. Using Python, it preprocesses the data with the Pandas library and identifies energy consumption patterns using machine learning algorithms (TensorFlow / Keras). The input is formatted data, and the output is a model of consumption patterns. Specifically, an LSTM model analyzes past consumption data and predicts future consumption trends.

[0115] Step 3:

[0116] The server develops an optimization strategy for renewable energy based on the analyzed pattern data. The input is the predicted consumption pattern, and the output is the proposed strategy. In this process, external weather data is obtained via an API, and the proposed strategy is enhanced by a generative AI model. For example, a prompt message set by the user in the application, such as "Propose an effective strategy for this event," is used.

[0117] Step 4:

[0118] The device visually presents strategic suggestions to the user. The strategies are displayed in a UI built with React Native and are updated in real time. Input is strategic suggestions from the server, and output is visually organized strategic information. The user receives this information and can take actionable steps, such as adjusting air conditioner settings.

[0119] Step 5:

[0120] Users can provide feedback on the presented strategies. They send responses to the server via their terminal, conveying their opinions on the strategy's effectiveness and usability. The input is the user's feedback, and the output is feedback data stored on the server. The server can further analyze this data to improve the strategy proposals.

[0121] 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.

[0122] This invention combines an emotion engine with an energy management system to create a system that provides an optimal energy strategy that takes into account the user's emotional state. Specifically, the server collects energy usage data from each piece of equipment and analyzes consumption patterns based on this data. Furthermore, it formulates proposals for optimizing the use of renewable energy.

[0123] The newly integrated emotion engine uses the device's camera and microphone to recognize emotions from the user's facial expressions and voice. The emotion engine analyzes this data to identify the emotions the user expresses in response to energy suggestions. This information is then reflected in the strategic suggestion system, enabling the generation of suggestions tailored to the user's state.

[0124] For example, if the server's energy consumption is higher than expected, it might suggest using some renewable energy. However, if the emotion engine detects stress from the user's facial expressions, the server might adjust the tone of its suggestion, making it more concise or emphasizing only what is necessary.

[0125] Users can view displayed suggestions via their devices and provide feedback that aligns with their emotions. The server receives this feedback and uses it to improve the accuracy of the suggestions. Furthermore, the emotion engine can accumulate and analyze users' long-term emotional data to provide more personalized energy management.

[0126] In this way, the present invention enables interactive energy management that takes user emotions into consideration, thereby improving user satisfaction.

[0127] The following describes the processing flow.

[0128] Step 1:

[0129] The server periodically receives energy usage data from the equipment's sensors. This includes detailed information such as electricity consumption, equipment operating status, and usage frequency. The server stores this data in a database.

[0130] Step 2:

[0131] The server cleanses and prepares the stored data for analysis. It performs preprocessing to remove missing values ​​and noise data, thereby improving the accuracy of the analysis.

[0132] Step 3:

[0133] The server uses machine learning algorithms to analyze data and identify energy consumption patterns. This analysis clarifies energy usage trends, peak times, and potential energy-saving points.

[0134] Step 4:

[0135] Using the device's camera and microphone, the emotion engine captures the user's facial expressions and voice in real time. It then analyzes the user's emotions to identify states such as stress, frustration, and excitement.

[0136] Step 5:

[0137] The server combines analyzed energy consumption patterns with the user's emotional state to develop a strategy for optimizing renewable energy use. It adjusts the tone and content of suggestions based on the user's emotions.

[0138] Step 6:

[0139] The device visually displays the generated energy strategy on a dashboard. Users review the suggestions and evaluate whether they align with their own feelings.

[0140] Step 7:

[0141] Users provide feedback on the proposals via their devices. This feedback can include agreement, rejection, or requests for improvement.

[0142] Step 8:

[0143] The server receives user feedback along with sentiment analysis and uses it to improve the accuracy of future suggestions. Long-term user sentiment data is also accumulated and used for analysis.

[0144] (Example 2)

[0145] 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".

[0146] Existing energy management systems focus on optimizing energy consumption, but they lack the ability to propose strategies that consider the user's emotional state, resulting in a lack of user satisfaction and acceptance. Furthermore, there is a need for methods to more accurately model energy consumption patterns and optimize the use of renewable energy.

[0147] 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.

[0148] In this invention, the server includes data collection means for collecting information on energy use and storing it in a storage device; data analysis means for analyzing the information on energy use and identifying trends in energy consumption; and emotion recognition means for analyzing data acquired by a device for recognizing human emotions and determining the user's emotional state. This makes it possible to propose a personalized energy strategy that corresponds to the user's emotional state.

[0149] "Information on energy use" refers to data showing the usage of electricity, gas, and renewable energy in facilities such as homes and businesses.

[0150] A "storage device" is a storage medium or database used to permanently store data and make it accessible as needed.

[0151] "Data collection means" refers to functions for acquiring information about energy use through hardware such as sensors and smart meters.

[0152] "Data analysis means" refers to devices and software used to process collected information on energy use and identify consumption trends and patterns.

[0153] A "trend" refers to the general behavior or movement of energy consumption observed over time.

[0154] "Strategic planning tools" refer to the function of creating plans and proposals to promote efficient energy use based on analyzed data.

[0155] "Emotion recognition means" refers to technologies and systems that analyze a user's facial expressions and voice to determine their emotional state.

[0156] The "proposal adjustment mechanism" is a function that, based on the results of emotion recognition, rewrites the energy usage strategies and advice presented to the user into appropriate expressions.

[0157] "Display means" refers to interfaces or devices used to visualize strategic proposals and data analysis results and provide them to users.

[0158] This invention aims to improve energy management systems. It provides energy usage suggestions that take into account the user's emotional state via a server and terminal, thereby achieving efficient and user-friendly energy management. Specific embodiments are described below.

[0159] The server collects data from smart meters and sensors installed within the building to gather information about energy usage. MQTT and HTTP are used as communication protocols for data collection, and the collected data is stored in a database (e.g., MySQL or PostgreSQL).

[0160] The collected data is processed by the server using data analysis tools. The server uses data analysis software such as Python or R to perform the analysis and employs time series analysis models (e.g., the ARIMA model) to clarify energy consumption trends. Based on the results of this analysis, the server formulates strategies to optimize the use of renewable energy.

[0161] Meanwhile, the device uses emotion recognition to understand the user's emotional state. The device captures the user's facial expressions through its camera and records their voice using its microphone. This data is analyzed using AI libraries such as OpenCV and TensorFlow. The analysis results are sent to a server and incorporated into strategic proposals.

[0162] In strategic proposals, generative AI models are used. Proposals, created based on sentiment data, are presented to the user in an appropriate manner. For example, if the server determines that the user is experiencing stress, it simplifies the proposal and adjusts the language to be easily understood.

[0163] Users can view suggestions from the server via their devices. Users can provide feedback on the suggestions, including their opinions and impressions, and this feedback is also stored in the server's database. This helps to improve the accuracy of future strategic suggestions.

[0164] For example, if the server determines that a household has high energy consumption, it might generate a suggestion such as, "Prioritize the use of electricity generated by solar power between 2 PM and 4 PM." If the user is concerned, this suggestion might be adjusted to reassure them by saying something like, "By prioritizing the use of solar power during the day, you can reduce your monthly costs."

[0165] Example prompt: "Please suggest how renewable energy should be used to optimize household electricity consumption. Also, please adjust your suggestions considering the user's emotional state."

[0166] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0167] Step 1:

[0168] The server collects energy usage data from each piece of equipment. Specifically, it acquires data in real time from smart meters and sensors and receives it using a secure communication protocol (e.g., MQTT or HTTP). The input is energy usage data from each piece of equipment, and the output is completed when this data is stored in a database (e.g., MySQL).

[0169] Step 2:

[0170] The server uses data analysis tools to analyze the collected data. Using data analysis tools such as Python or R, the server employs time-series analysis models (e.g., the ARIMA model) to identify consumption patterns. The input is energy usage data stored in a database, and the output is the analysis result of consumption patterns. This analysis forms the basis for the next strategic proposal.

[0171] Step 3:

[0172] The server develops a strategy to optimize the use of renewable energy based on the analysis results. It uses a generative AI model to generate specific proposals in natural language. The input is the analysis results of consumption patterns, and the output is a specific energy use proposal such as "prioritize solar power generation from 2pm to 4pm."

[0173] Step 4:

[0174] The device uses emotion recognition to understand the user's emotions. It captures the user's facial expressions with its built-in camera and records their voice with its microphone. It analyzes the emotional state using AI libraries such as OpenCV and TensorFlow. The input is the user's facial expressions and voice, and the output is the emotional state the user is experiencing.

[0175] Step 5:

[0176] The server adjusts the wording of the strategy based on sentiment data. A generative AI model generates natural-sounding suggestions that respond to the user's emotions. The input is the analyzed sentiment state, and the output is a positive suggestion such as, "By prioritizing the use of solar power during the day, you can reduce your monthly costs."

[0177] Step 6:

[0178] Users review energy usage suggestions presented by the server via their terminals and provide feedback, including their thoughts and opinions. The input is the suggestions received by the user, and the output is the feedback information sent to the server. This information will be used to improve the system in the future.

[0179] (Application Example 2)

[0180] 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".

[0181] In conventional energy management systems, energy recommendations were made without considering the user's emotional state, making it difficult to provide the optimal energy utilization strategy for the user. Therefore, interactive energy management that responds to the user's psychological state is required.

[0182] 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.

[0183] In this invention, the server includes emotion recognition means for acquiring emotion information and transmitting it to data analysis means, information collection means for collecting energy usage data and storing it in a database, and data analysis means for analyzing the energy usage data and emotion information and identifying patterns of energy consumption. This makes it possible to optimize energy usage strategies that take into account the user's emotional state.

[0184] An "emotion recognition method" is a function that uses data such as the user's facial expressions and voice to identify the user's emotional state.

[0185] "Information gathering means" refers to a function for acquiring data on energy use and storing it in a database.

[0186] "Data analysis means" refers to a function that analyzes collected energy usage data and emotional information to identify patterns in energy consumption.

[0187] The "strategic proposal tool" is a function that formulates strategies to optimize the use of renewable energy based on the analysis results.

[0188] An "interface means" is a function that visually displays the devised strategy and receives feedback from users.

[0189] The system for implementing this invention mainly consists of three elements: a server, a terminal, and a user. The server has energy management and emotion recognition functions, analyzes the user's emotional state, and generates an optimal energy utilization strategy based on energy consumption patterns. The operation of the system is described in detail below.

[0190] The server uses the device's camera and microphone to acquire emotional information. Specifically, it utilizes Microsoft® Azure®'s "Emotion Recognition API" to recognize emotions based on the user's facial expressions and voice data.

[0191] Furthermore, the server uses cloud services such as AWS® Greengrass and Google® Cloud Platform to collect energy usage data. This data is stored in a database.

[0192] The server uses machine learning algorithms to identify energy consumption patterns based on collected energy usage data and sentiment information. This analysis particularly utilizes machine learning libraries such as Python's Scikit-learn.

[0193] Based on the analysis results, the server develops a strategy for optimally utilizing renewable energy. For example, if a user's stress levels are high, it might suggest adjusting the lighting to promote relaxation.

[0194] Users can view and select a visually displayed energy strategy through the device's interface. This process involves designing the interface using Figma, a user experience design tool.

[0195] As a concrete example, consider a user who uses their smartphone at the end of the day. If the user's face is recognized as showing signs of stress, an energy strategy such as, "To help you relax today, we suggest changing the lighting to a warmer color," might be presented.

[0196] Examples of prompts for a generative AI model are as follows:

[0197] "Please create suggestions for energy management that can alleviate the stress users are experiencing."

[0198] Current emotional state: {User's emotion}.

[0199] Example of household situation: {Current energy consumption situation}.

[0200] Example of a proposed solution: {Suggestions for adjusting lighting and saving energy}.

[0201] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0202] Step 1:

[0203] The device's camera and microphone are used to capture the user's face and voice. The input consists of facial video data and audio data. Based on this, the server processes the data using Microsoft Azure's "Emotion Recognition API" to identify the user's emotions. Emotional state data is generated as output.

[0204] Step 2:

[0205] The server collects energy usage data through cloud services (e.g., AWS Greengrass or Google Cloud Platform). This data collection is performed by receiving data streams from smart electricity meters and IoT devices. Inputs include instantaneous power consumption and historical consumption history. By storing this in a database, time-series data of energy usage is obtained as output.

[0206] Step 3:

[0207] The server uses collected emotional state data and energy usage data to identify energy consumption patterns using machine learning algorithms. It performs data analysis based on the input data, utilizing libraries such as Scikit-learn. The output is a model relating to the identified consumption patterns.

[0208] Step 4:

[0209] The server uses a strategic proposal mechanism to formulate a strategy to optimize the use of renewable energy based on analysis results and emotional information. The strategy includes specific actions such as "adjusting lighting" or "proposing an energy-saving mode." The inputs are identified consumption patterns and emotional states, and the output generates data for the optimal strategy.

[0210] Step 5:

[0211] The proposed energy strategy is visually displayed to the user via the terminal interface. The user reviews and selects a strategy through a UI designed in Figma. The input is data on the optimal strategy, and the output receives the user's selection.

[0212] Step 6:

[0213] The server receives user feedback and uses this feedback data to analyze and train the system, thereby improving the accuracy of its suggestions. The input is user feedback, and the final output is a performance evaluation of the improved strategic suggestions.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] [Second Embodiment]

[0218] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0219] 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.

[0220] 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).

[0221] 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.

[0222] 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.

[0223] 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).

[0224] 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.

[0225] 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.

[0226] 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.

[0227] 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.

[0228] 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.

[0229] 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".

[0230] This invention provides a system for streamlining energy management and optimizing the use of renewable energy. Specifically, energy usage data is collected by a server, and based on this data, energy consumption patterns are analyzed to propose an optimal energy strategy.

[0231] The server periodically receives energy usage data from each piece of equipment and sensor. It also stores this data in a database and analyzes consumption patterns in real time. Machine learning algorithms are used for data analysis, and by combining this data with external weather data, the system can more accurately predict consumption trends and anomalies.

[0232] Based on the analyzed data, the server identifies peak hours when power consumption tends to be high and develops strategies for peak shifting. Specifically, it develops plans for utilizing renewable energy sources and aims for efficient energy use. This is expected to reduce energy costs and environmental impact.

[0233] The terminal visually displays the energy strategy provided by the server, supporting user understanding. Users can check their energy consumption status and the proposed strategy on the terminal and make adjustments as needed. Furthermore, the server incorporates user input and feedback into improvements to the proposed strategy through this interface.

[0234] For example, in an office building, a server collects power consumption data from each floor and generates a predictive model based on the day's weather data. The analysis results are displayed on a terminal, and the system suggests to the user how to optimize the air conditioning system and how to utilize solar power. Based on this information, the user can adjust the air conditioning operating schedule and shift peak power consumption to other times. In this way, the system achieves improved energy efficiency.

[0235] The following describes the processing flow.

[0236] Step 1:

[0237] The server receives energy usage data in real time from each piece of equipment and sensor. This includes information such as power consumption, operating time, and operating mode. The server records and manages this data in a database.

[0238] Step 2:

[0239] The server cleanses the collected data. Specifically, it imputes missing values, removes outliers, and identifies invalid data, preparing the data for improved analysis accuracy.

[0240] Step 3:

[0241] The server applies machine learning algorithms based on past consumption data to analyze consumption patterns. This analysis identifies energy consumption trends and peak times.

[0242] Step 4:

[0243] The server combines weather data and calendar information to generate a model that predicts future energy demand. This allows it to assess the possibility of peak shifting.

[0244] Step 5:

[0245] Based on the analysis results and predictive models, the server develops renewable energy introduction plans and energy utilization optimization strategies. Specifically, it proposes the use of solar and wind power generation.

[0246] Step 6:

[0247] The terminal displays the energy strategy obtained from the server on a dashboard. Users review this visualized information and make decisions tailored to their specific tasks.

[0248] Step 7:

[0249] Users can provide feedback on the server's suggestions and enter questions through their device. The device then sends this feedback to the server.

[0250] Step 8:

[0251] The server uses user feedback to make adjustments that improve the accuracy of future energy strategy proposals.

[0252] (Example 1)

[0253] 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."

[0254] In modern times, efficient energy management and optimal use of renewable energy are essential for reducing environmental impact and lowering costs. However, optimizing energy use while fully considering fluctuations in energy demand and external influences is difficult. This invention aims to solve these problems and realize efficient and flexible energy management.

[0255] 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.

[0256] In this invention, the server includes information acquisition means for collecting energy utilization indicators and storing them in a storage device, data analysis means for analyzing the energy utilization indicators and identifying trends in energy consumption, and strategy proposal means for formulating strategies to optimize the use of renewable energy based on the analysis results. This enables improved energy efficiency and maximum utilization of renewable energy sources.

[0257] "Energy utilization indicators" refer to data that quantitatively shows the state of energy consumption, including information on the amount and trends of use of electricity, gas, and other energy sources.

[0258] "Means for acquiring information to be stored in a storage device" refers to hardware or software components for temporarily storing or permanently recording energy-related data collected by various sensors and devices.

[0259] "Data analysis means" refers to algorithms and programs that use collected energy-related data to analyze consumption patterns, predict trends, and detect anomalies.

[0260] A "policy proposal mechanism" refers to a system for formulating action plans and strategies to promote the efficient use of energy and the utilization of renewable energy, based on the results of data analysis.

[0261] "Display means" refers to interfaces or devices that visually present analyzed data and proposed strategies in a way that is easy for users to understand.

[0262] "Improvement methods" refer to methods and processes for accepting user feedback and using that feedback to consider and adjust proposed strategies and the overall effectiveness of the system.

[0263] The system in this invention mainly consists of three components: a server, a terminal, and a user. Specific embodiments of each component are shown below.

[0264] server:

[0265] The server's role is to collect and store energy usage indicators in storage. Specifically, it acquires data from various sensors and devices installed within the building, including electricity meters, temperature sensors, and illuminance sensors. A general-purpose data management system such as MySQL or MongoDB is used as the database. This allows for the accumulation of data to identify trends in energy usage.

[0266] Next, the server analyzes the collected data using data analysis tools. For this analysis, machine learning algorithms are implemented using libraries such as Python's scikit-learn to detect trends and anomalies in energy consumption. Furthermore, external weather data is obtained via API and combined with the consumption data to generate a more accurate prediction model. By utilizing this generated AI model, more advanced analysis becomes possible.

[0267] Based on the analysis results, the server proposes strategies to maximize the efficient use of energy. This strategy proposal mechanism develops optimized strategies centered on the utilization of renewable energy. For example, it might create a plan to optimize the usage time of solar power generation.

[0268] Terminal:

[0269] The terminal visualizes the strategies received from the server and presents them clearly to the user. Charts and graphs are used as display methods to allow the user to intuitively understand the proposed strategies. This enables the user to visually grasp energy consumption trends and optimization strategies.

[0270] User:

[0271] Users can check their energy usage and suggested measures via their terminals and adjust their energy consumption behavior based on that information. For example, in an office building, a server can display a predictive model generated based on weather data and power consumption data on the terminal, allowing users to appropriately adjust the air conditioning operating schedule.

[0272] As an example of a prompt, entering "Based on this week's weather data and power consumption patterns, please propose an optimization strategy for the office building's air conditioning system. This proposal should include specific peak shift times and ways to utilize renewable energy" will allow you to obtain a specific proposal using a generated AI model.

[0273] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0274] Step 1:

[0275] The server collects energy usage indicators from multiple sensors. It uses data from building energy meters, temperature sensors, and illuminance sensors as input. This data is sent to the server and temporarily stored in memory. Specifically, the server requests data from the sensors at regular intervals and records the acquired data.

[0276] Step 2:

[0277] The server stores the data collected in step 1 into a storage device. It uses temporarily stored sensor data as input. A data management system such as MySQL or MongoDB is used for the database, and the data is stored persistently while being organized with timestamps. Specifically, the server performs duplicate data checks and detects outliers when storing the data.

[0278] Step 3:

[0279] The server executes data analysis measures to analyze the data stored in step 2. It uses historical energy consumption patterns and the latest sensor data stored in the database as input. This data is analyzed using the scikit-learn library in Python to generate consumption trends and predictive models. Specifically, the server applies anomaly detection algorithms to identify consumption patterns.

[0280] Step 4:

[0281] The server acquires external weather data and combines it with the data analysis results obtained in step 3. The acquired weather data and the analyzed consumption pattern data are used as input. This enables advanced predictions utilizing a generative AI model, allowing for more accurate analysis of energy consumption trends and anomalies. Specifically, the server accesses an external API to acquire the necessary weather data and integrate it into the database.

[0282] Step 5:

[0283] The server formulates strategies for energy efficiency improvement based on the analysis results. As input, the results of consumption pattern analysis and weather data prediction are used. By the strategy proposal means, a specific action plan is established to maximize the utilization of renewable energy. As a specific operation, the server formulates an optimization strategy including the possibility of peak shifting, etc.

[0284] Step 6:

[0285] The terminal presents the strategy received from the server to the user. As input, the strategy data transmitted from the server is used. It is displayed in the form of graphs and charts on the UI so that the user can easily understand it. As a specific operation, the terminal updates the graphical display in real time and receives feedback through the user interface.

[0286] Step 7:

[0287] The user checks the energy efficiency improvement strategies presented via the terminal and adjusts their energy consumption behavior as needed. As input, the graphs and charts displayed on the terminal are referred to. As output, specific actions such as adjusting the operation schedule of the air conditioner are planned and executed. As a specific operation, the user inputs feedback using the interface of the terminal, and the server receives it and utilizes it for improving the strategy.

[0288] (Application Example 1)

[0289] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0290] In modern cities, improving energy efficiency and optimizing the use of renewable energy are crucial challenges. However, there is a lack of mechanisms to monitor city-wide energy usage in real time and promote efficient use. As a result, energy waste and increased peak loads are becoming problems.

[0291] 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.

[0292] In this invention, the server includes means for collecting energy usage data and storing it in an information storage device, means for analyzing the energy usage data and identifying energy consumption patterns, and means for formulating a strategy to optimize the use of renewable energy based on the analysis results. This enables real-time monitoring of energy consumption across the entire city and allows users to utilize energy efficiently.

[0293] "Energy usage data" refers to a collection of information regarding energy consumption and usage patterns.

[0294] An "information storage device" is a device or system for storing data.

[0295] A "data analysis tool" is a system that analyzes collected data and extracts valuable information.

[0296] An "energy consumption pattern" refers to the tendencies and regularities regarding when and how much energy is consumed.

[0297] A "strategic proposal method" is a process for providing an optimal action plan based on analyzed data.

[0298] An "interface means" is a medium or mechanism for a user to interact with a system.

[0299] "City-wide energy consumption" refers to the overall picture of the state and changes in energy consumption within a specific urban area.

[0300] "Real-time display" means processing data instantly and showing it to the user immediately.

[0301] "Efficient energy utilization" means using the necessary energy optimally while eliminating waste.

[0302] The system that realizes this invention aims to maximize the energy efficiency of the entire city and consists of the interaction of a server, terminals, and users. The server communicates with various sensors to collect energy usage data in an information storage device in order to monitor energy usage in detail. It also uses software such as Python and TensorFlow / Keras to build machine learning models and analyze energy consumption patterns based on the collected data.

[0303] Once the analysis is complete, the server uses strategic proposal tools to develop a strategy to improve the city's energy efficiency. This strategy is visually displayed through a terminal interface developed using React Native. The terminal allows users to monitor energy consumption in real time and adjust energy use based on the proposed solutions.

[0304] Through the application, users can receive suggestions for energy-saving strategies in response to the increased power consumption predicted by the server, for example, when a large-scale event is held in the city on a particular weekend. In this way, users can use the application to improve the efficiency of their energy use and appropriately shift consumption away from peak times.

[0305] Examples of prompt texts include "Please propose the most effective energy-saving strategies for the event next weekend." With this prompt, the system generates customized energy management proposals for the user.

[0306] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0307] Step 1:

[0308] The server collects energy usage data from multiple energy sensors within the city. At this time, the data obtained from the sensors is received in JSON format and stored in the information storage device. The input is the raw data from each sensor, and the output is the processed energy usage data. Since this data is used for subsequent analysis processing, the server stores it while maintaining data consistency.

[0309] Step 2:

[0310] The server analyzes the energy usage data stored in the information storage device. Using Python, the data is preprocessed with the Pandas library, and the pattern of energy consumption is identified using a machine learning algorithm (using TensorFlow / Keras). The input is the formatted data, and the output is the model of the consumption pattern. Specifically, the LSTM model analyzes past consumption data and predicts future consumption trends.

[0311] Step 3:

[0312] Based on the analyzed pattern data, the server formulates an optimization strategy for renewable energy. The input is the predicted consumption pattern, and the output is the strategy proposal. In this process, external weather data is obtained from the API, and the proposed content is enhanced by the generation AI model. As a specific example, a prompt text such as "Propose an effective strategy during the event" set by the user in the application is used.

[0313] Step 4:

[0314] The device visually presents strategic suggestions to the user. The strategies are displayed in a UI built with React Native and are updated in real time. Input is strategic suggestions from the server, and output is visually organized strategic information. The user receives this information and can take actionable steps, such as adjusting air conditioner settings.

[0315] Step 5:

[0316] Users can provide feedback on the presented strategies. They send responses to the server via their terminal, conveying their opinions on the strategy's effectiveness and usability. The input is the user's feedback, and the output is feedback data stored on the server. The server can further analyze this data to improve the strategy proposals.

[0317] 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.

[0318] This invention combines an emotion engine with an energy management system to create a system that provides an optimal energy strategy that takes into account the user's emotional state. Specifically, the server collects energy usage data from each piece of equipment and analyzes consumption patterns based on this data. Furthermore, it formulates proposals for optimizing the use of renewable energy.

[0319] The newly integrated emotion engine uses the device's camera and microphone to recognize emotions from the user's facial expressions and voice. The emotion engine analyzes this data to identify the emotions the user expresses in response to energy suggestions. This information is then reflected in the strategic suggestion system, enabling the generation of suggestions tailored to the user's state.

[0320] For example, if the server's energy consumption is higher than expected, it might suggest using some renewable energy. However, if the emotion engine detects stress from the user's facial expressions, the server might adjust the tone of its suggestion, making it more concise or emphasizing only what is necessary.

[0321] Users can view displayed suggestions via their devices and provide feedback that aligns with their emotions. The server receives this feedback and uses it to improve the accuracy of the suggestions. Furthermore, the emotion engine can accumulate and analyze users' long-term emotional data to provide more personalized energy management.

[0322] In this way, the present invention enables interactive energy management that takes user emotions into consideration, thereby improving user satisfaction.

[0323] The following describes the processing flow.

[0324] Step 1:

[0325] The server periodically receives energy usage data from the equipment's sensors. This includes detailed information such as electricity consumption, equipment operating status, and usage frequency. The server stores this data in a database.

[0326] Step 2:

[0327] The server cleanses and prepares the stored data for analysis. It performs preprocessing to remove missing values ​​and noise data, thereby improving the accuracy of the analysis.

[0328] Step 3:

[0329] The server uses machine learning algorithms to analyze data and identify energy consumption patterns. This analysis clarifies energy usage trends, peak times, and potential energy-saving points.

[0330] Step 4:

[0331] Using the device's camera and microphone, the emotion engine captures the user's facial expressions and voice in real time. It then analyzes the user's emotions to identify states such as stress, frustration, and excitement.

[0332] Step 5:

[0333] The server combines analyzed energy consumption patterns with the user's emotional state to develop a strategy for optimizing renewable energy use. It adjusts the tone and content of suggestions based on the user's emotions.

[0334] Step 6:

[0335] The device visually displays the generated energy strategy on a dashboard. Users review the suggestions and evaluate whether they align with their own feelings.

[0336] Step 7:

[0337] Users provide feedback on the proposals via their devices. This feedback can include agreement, rejection, or requests for improvement.

[0338] Step 8:

[0339] The server receives user feedback along with sentiment analysis and uses it to improve the accuracy of future suggestions. Long-term user sentiment data is also accumulated and used for analysis.

[0340] (Example 2)

[0341] 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".

[0342] Existing energy management systems focus on optimizing energy consumption, but they lack the ability to propose strategies that consider the user's emotional state, resulting in a lack of user satisfaction and acceptance. Furthermore, there is a need for methods to more accurately model energy consumption patterns and optimize the use of renewable energy.

[0343] 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.

[0344] In this invention, the server includes data collection means for collecting information on energy use and storing it in a storage device; data analysis means for analyzing the information on energy use and identifying trends in energy consumption; and emotion recognition means for analyzing data acquired by a device for recognizing human emotions and determining the user's emotional state. This makes it possible to propose a personalized energy strategy that corresponds to the user's emotional state.

[0345] "Information on energy use" refers to data showing the usage of electricity, gas, and renewable energy in facilities such as homes and businesses.

[0346] A "storage device" is a storage medium or database used to permanently store data and make it accessible as needed.

[0347] "Data collection means" refers to functions for acquiring information about energy use through hardware such as sensors and smart meters.

[0348] "Data analysis means" refers to devices and software used to process collected information on energy use and identify consumption trends and patterns.

[0349] A "trend" refers to the general behavior or movement of energy consumption observed over time.

[0350] "Strategic planning tools" refer to the function of creating plans and proposals to promote efficient energy use based on analyzed data.

[0351] "Emotion recognition means" refers to technologies and systems that analyze a user's facial expressions and voice to determine their emotional state.

[0352] The "proposal adjustment mechanism" is a function that, based on the results of emotion recognition, rewrites the energy usage strategies and advice presented to the user into appropriate expressions.

[0353] "Display means" refers to interfaces or devices used to visualize strategic proposals and data analysis results and provide them to users.

[0354] This invention aims to improve energy management systems. It provides energy usage suggestions that take into account the user's emotional state via a server and terminal, thereby achieving efficient and user-friendly energy management. Specific embodiments are described below.

[0355] The server collects data from smart meters and sensors installed within the building to gather information about energy usage. MQTT and HTTP are used as communication protocols for data collection, and the collected data is stored in a database (e.g., MySQL or PostgreSQL).

[0356] The collected data is processed by the server using data analysis tools. The server uses data analysis software such as Python or R to perform the analysis and employs time series analysis models (e.g., the ARIMA model) to clarify energy consumption trends. Based on the results of this analysis, the server formulates strategies to optimize the use of renewable energy.

[0357] Meanwhile, the device uses emotion recognition to understand the user's emotional state. The device captures the user's facial expressions through its camera and records their voice using its microphone. This data is analyzed using AI libraries such as OpenCV and TensorFlow. The analysis results are sent to a server and incorporated into strategic proposals.

[0358] In strategic proposals, generative AI models are used. Proposals, created based on sentiment data, are presented to the user in an appropriate manner. For example, if the server determines that the user is experiencing stress, it simplifies the proposal and adjusts the language to be easily understood.

[0359] Users can view suggestions from the server via their devices. Users can provide feedback on the suggestions, including their opinions and impressions, and this feedback is also stored in the server's database. This helps to improve the accuracy of future strategic suggestions.

[0360] For example, if the server determines that a household has high energy consumption, it might generate a suggestion such as, "Prioritize the use of electricity generated by solar power between 2 PM and 4 PM." If the user is concerned, this suggestion might be adjusted to reassure them by saying something like, "By prioritizing the use of solar power during the day, you can reduce your monthly costs."

[0361] Example prompt: "Please suggest how renewable energy should be used to optimize household electricity consumption. Also, please adjust your suggestions considering the user's emotional state."

[0362] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0363] Step 1:

[0364] The server collects energy usage data from each piece of equipment. Specifically, it acquires data in real time from smart meters and sensors and receives it using a secure communication protocol (e.g., MQTT or HTTP). The input is energy usage data from each piece of equipment, and the output is completed when this data is stored in a database (e.g., MySQL).

[0365] Step 2:

[0366] The server uses data analysis tools to analyze the collected data. Using data analysis tools such as Python or R, the server employs time-series analysis models (e.g., the ARIMA model) to identify consumption patterns. The input is energy usage data stored in a database, and the output is the analysis result of consumption patterns. This analysis forms the basis for the next strategic proposal.

[0367] Step 3:

[0368] The server develops a strategy to optimize the use of renewable energy based on the analysis results. It uses a generative AI model to generate specific proposals in natural language. The input is the analysis results of consumption patterns, and the output is a specific energy use proposal such as "prioritize solar power generation from 2pm to 4pm."

[0369] Step 4:

[0370] The device uses emotion recognition to understand the user's emotions. It captures the user's facial expressions with its built-in camera and records their voice with its microphone. It analyzes the emotional state using AI libraries such as OpenCV and TensorFlow. The input is the user's facial expressions and voice, and the output is the emotional state the user is experiencing.

[0371] Step 5:

[0372] The server adjusts the wording of the strategy based on sentiment data. A generative AI model generates natural-sounding suggestions that respond to the user's emotions. The input is the analyzed sentiment state, and the output is a positive suggestion such as, "By prioritizing the use of solar power during the day, you can reduce your monthly costs."

[0373] Step 6:

[0374] Users review energy usage suggestions presented by the server via their terminals and provide feedback, including their thoughts and opinions. The input is the suggestions received by the user, and the output is the feedback information sent to the server. This information will be used to improve the system in the future.

[0375] (Application Example 2)

[0376] 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."

[0377] In conventional energy management systems, energy recommendations were made without considering the user's emotional state, making it difficult to provide the optimal energy utilization strategy for the user. Therefore, interactive energy management that responds to the user's psychological state is required.

[0378] 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.

[0379] In this invention, the server includes emotion recognition means for acquiring emotion information and transmitting it to data analysis means, information collection means for collecting energy usage data and storing it in a database, and data analysis means for analyzing the energy usage data and emotion information and identifying patterns of energy consumption. This makes it possible to optimize energy usage strategies that take into account the user's emotional state.

[0380] An "emotion recognition method" is a function that uses data such as the user's facial expressions and voice to identify the user's emotional state.

[0381] "Information gathering means" refers to a function for acquiring data on energy use and storing it in a database.

[0382] "Data analysis means" refers to a function that analyzes collected energy usage data and emotional information to identify patterns in energy consumption.

[0383] The "strategic proposal tool" is a function that formulates strategies to optimize the use of renewable energy based on the analysis results.

[0384] An "interface means" is a function that visually displays the devised strategy and receives feedback from users.

[0385] The system for implementing this invention mainly consists of three elements: a server, a terminal, and a user. The server has energy management and emotion recognition functions, analyzes the user's emotional state, and generates an optimal energy utilization strategy based on energy consumption patterns. The operation of the system is described in detail below.

[0386] The server uses the device's camera and microphone to acquire emotional information. Specifically, it utilizes Microsoft Azure's "Emotion Recognition API" to recognize emotions based on the user's facial expressions and voice data.

[0387] Furthermore, the servers use cloud services such as AWS Greengrass and Google Cloud Platform to collect energy usage data. This data is stored in a database.

[0388] The server uses machine learning algorithms to identify energy consumption patterns based on collected energy usage data and sentiment information. This analysis particularly utilizes machine learning libraries such as Python's Scikit-learn.

[0389] Based on the analysis results, the server develops a strategy for optimally utilizing renewable energy. For example, if a user's stress levels are high, it might suggest adjusting the lighting to promote relaxation.

[0390] Users can view and select a visually displayed energy strategy through the device's interface. This process involves designing the interface using Figma, a user experience design tool.

[0391] As a concrete example, consider a user who uses their smartphone at the end of the day. If the user's face is recognized as showing signs of stress, an energy strategy such as, "To help you relax today, we suggest changing the lighting to a warmer color," might be presented.

[0392] Examples of prompts for a generative AI model are as follows:

[0393] "Please create suggestions for energy management that can alleviate the stress users are experiencing."

[0394] Current emotional state: {User's emotion}.

[0395] Example of household situation: {Current energy consumption situation}.

[0396] Example of a proposed solution: {Suggestions for adjusting lighting and saving energy}.

[0397] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0398] Step 1:

[0399] The device's camera and microphone are used to capture the user's face and voice. The input consists of facial video data and audio data. Based on this, the server processes the data using Microsoft Azure's "Emotion Recognition API" to identify the user's emotions. Emotional state data is generated as output.

[0400] Step 2:

[0401] The server collects energy usage data through cloud services (e.g., AWS Greengrass or Google Cloud Platform). This data collection is performed by receiving data streams from smart electricity meters and IoT devices. Inputs include instantaneous power consumption and historical consumption history. By storing this in a database, time-series data of energy usage is obtained as output.

[0402] Step 3:

[0403] The server uses collected emotional state data and energy usage data to identify energy consumption patterns using machine learning algorithms. It performs data analysis based on the input data, utilizing libraries such as Scikit-learn. The output is a model relating to the identified consumption patterns.

[0404] Step 4:

[0405] The server uses a strategic proposal mechanism to formulate a strategy to optimize the use of renewable energy based on analysis results and emotional information. The strategy includes specific actions such as "adjusting lighting" or "proposing an energy-saving mode." The inputs are identified consumption patterns and emotional states, and the output generates data for the optimal strategy.

[0406] Step 5:

[0407] The proposed energy strategy is visually displayed to the user via the terminal interface. The user reviews and selects a strategy through a UI designed in Figma. The input is data on the optimal strategy, and the output receives the user's selection.

[0408] Step 6:

[0409] The server receives user feedback and uses this feedback data to analyze and train the system, thereby improving the accuracy of its suggestions. The input is user feedback, and the final output is a performance evaluation of the improved strategic suggestions.

[0410] 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.

[0411] 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.

[0412] 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.

[0413] [Third Embodiment]

[0414] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0415] 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.

[0416] 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).

[0417] 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.

[0418] 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.

[0419] 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).

[0420] 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.

[0421] 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.

[0422] 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.

[0423] 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.

[0424] 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.

[0425] 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".

[0426] This invention provides a system for streamlining energy management and optimizing the use of renewable energy. Specifically, energy usage data is collected by a server, and based on this data, energy consumption patterns are analyzed to propose an optimal energy strategy.

[0427] The server periodically receives energy usage data from each piece of equipment and sensor. It also stores this data in a database and analyzes consumption patterns in real time. Machine learning algorithms are used for data analysis, and by combining this data with external weather data, the system can more accurately predict consumption trends and anomalies.

[0428] Based on the analyzed data, the server identifies peak hours when power consumption tends to be high and develops strategies for peak shifting. Specifically, it develops plans for utilizing renewable energy sources and aims for efficient energy use. This is expected to reduce energy costs and environmental impact.

[0429] The terminal visually displays the energy strategy provided by the server, supporting user understanding. Users can check their energy consumption status and the proposed strategy on the terminal and make adjustments as needed. Furthermore, the server incorporates user input and feedback into improvements to the proposed strategy through this interface.

[0430] For example, in an office building, a server collects power consumption data from each floor and generates a predictive model based on the day's weather data. The analysis results are displayed on a terminal, and the system suggests to the user how to optimize the air conditioning system and how to utilize solar power. Based on this information, the user can adjust the air conditioning operating schedule and shift peak power consumption to other times. In this way, the system achieves improved energy efficiency.

[0431] The following describes the processing flow.

[0432] Step 1:

[0433] The server receives energy usage data in real time from each piece of equipment and sensor. This includes information such as power consumption, operating time, and operating mode. The server records and manages this data in a database.

[0434] Step 2:

[0435] The server cleanses the collected data. Specifically, it imputes missing values, removes outliers, and identifies invalid data, preparing the data for improved analysis accuracy.

[0436] Step 3:

[0437] The server applies machine learning algorithms based on past consumption data to analyze consumption patterns. This analysis identifies energy consumption trends and peak times.

[0438] Step 4:

[0439] The server combines weather data and calendar information to generate a model that predicts future energy demand. This allows it to assess the possibility of peak shifting.

[0440] Step 5:

[0441] Based on the analysis results and predictive models, the server develops renewable energy introduction plans and energy utilization optimization strategies. Specifically, it proposes the use of solar and wind power generation.

[0442] Step 6:

[0443] The terminal displays the energy strategy obtained from the server on a dashboard. Users review this visualized information and make decisions tailored to their specific tasks.

[0444] Step 7:

[0445] Users can provide feedback on the server's suggestions and enter questions through their device. The device then sends this feedback to the server.

[0446] Step 8:

[0447] The server uses user feedback to make adjustments that improve the accuracy of future energy strategy proposals.

[0448] (Example 1)

[0449] 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."

[0450] In modern times, efficient energy management and optimal use of renewable energy are essential for reducing environmental impact and lowering costs. However, optimizing energy use while fully considering fluctuations in energy demand and external influences is difficult. This invention aims to solve these problems and realize efficient and flexible energy management.

[0451] 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.

[0452] In this invention, the server includes information acquisition means for collecting energy utilization indicators and storing them in a storage device, data analysis means for analyzing the energy utilization indicators and identifying trends in energy consumption, and strategy proposal means for formulating strategies to optimize the use of renewable energy based on the analysis results. This enables improved energy efficiency and maximum utilization of renewable energy sources.

[0453] "Energy utilization indicators" refer to data that quantitatively shows the state of energy consumption, including information on the amount and trends of use of electricity, gas, and other energy sources.

[0454] "Means for acquiring information to be stored in a storage device" refers to hardware or software components for temporarily storing or permanently recording energy-related data collected by various sensors and devices.

[0455] "Data analysis means" refers to algorithms and programs that use collected energy-related data to analyze consumption patterns, predict trends, and detect anomalies.

[0456] A "policy proposal mechanism" refers to a system for formulating action plans and strategies to promote the efficient use of energy and the utilization of renewable energy, based on the results of data analysis.

[0457] "Display means" refers to interfaces or devices that visually present analyzed data and proposed strategies in a way that is easy for users to understand.

[0458] "Improvement methods" refer to methods and processes for accepting user feedback and using that feedback to consider and adjust proposed strategies and the overall effectiveness of the system.

[0459] The system in this invention mainly consists of three components: a server, a terminal, and a user. Specific embodiments of each component are shown below.

[0460] server:

[0461] The server's role is to collect and store energy usage indicators in storage. Specifically, it acquires data from various sensors and devices installed within the building, including electricity meters, temperature sensors, and illuminance sensors. A general-purpose data management system such as MySQL or MongoDB is used as the database. This allows for the accumulation of data to identify trends in energy usage.

[0462] Next, the server analyzes the collected data using data analysis tools. For this analysis, machine learning algorithms are implemented using libraries such as Python's scikit-learn to detect trends and anomalies in energy consumption. Furthermore, external weather data is obtained via API and combined with the consumption data to generate a more accurate prediction model. By utilizing this generated AI model, more advanced analysis becomes possible.

[0463] Based on the analysis results, the server proposes strategies to maximize the efficient use of energy. This strategy proposal mechanism develops optimized strategies centered on the utilization of renewable energy. For example, it might create a plan to optimize the usage time of solar power generation.

[0464] Terminal:

[0465] The terminal visualizes the strategies received from the server and presents them clearly to the user. Charts and graphs are used as display methods to allow the user to intuitively understand the proposed strategies. This enables the user to visually grasp energy consumption trends and optimization strategies.

[0466] User:

[0467] Users can check their energy usage and suggested measures via their terminals and adjust their energy consumption behavior based on that information. For example, in an office building, a server can display a predictive model generated based on weather data and power consumption data on the terminal, allowing users to appropriately adjust the air conditioning operating schedule.

[0468] As an example of a prompt, entering "Based on this week's weather data and power consumption patterns, please propose an optimization strategy for the office building's air conditioning system. This proposal should include specific peak shift times and ways to utilize renewable energy" will allow you to obtain a specific proposal using a generated AI model.

[0469] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0470] Step 1:

[0471] The server collects energy usage indicators from multiple sensors. It uses data from building energy meters, temperature sensors, and illuminance sensors as input. This data is sent to the server and temporarily stored in memory. Specifically, the server requests data from the sensors at regular intervals and records the acquired data.

[0472] Step 2:

[0473] The server stores the data collected in step 1 into a storage device. It uses temporarily stored sensor data as input. A data management system such as MySQL or MongoDB is used for the database, and the data is stored persistently while being organized with timestamps. Specifically, the server performs duplicate data checks and detects outliers when storing the data.

[0474] Step 3:

[0475] The server executes data analysis measures to analyze the data stored in step 2. It uses historical energy consumption patterns and the latest sensor data stored in the database as input. This data is analyzed using the scikit-learn library in Python to generate consumption trends and predictive models. Specifically, the server applies anomaly detection algorithms to identify consumption patterns.

[0476] Step 4:

[0477] The server acquires external weather data and combines it with the data analysis results obtained in step 3. The acquired weather data and the analyzed consumption pattern data are used as input. This enables advanced predictions utilizing a generative AI model, allowing for more accurate analysis of energy consumption trends and anomalies. Specifically, the server accesses an external API to acquire the necessary weather data and integrate it into the database.

[0478] Step 5:

[0479] The server devises strategies for energy efficiency based on the analysis results. It uses consumption pattern analysis and weather data forecasts as input. Through its strategy proposal mechanism, it develops a concrete action plan to maximize the use of renewable energy. Specifically, the server formulates an optimization strategy that includes possibilities such as peak shifting.

[0480] Step 6:

[0481] The terminal presents the policy received from the server to the user. It uses policy data sent from the server as input. This data is displayed on the UI in the form of graphs and charts to make it easy for the user to understand. Specifically, the terminal updates the graphical display in real time and accepts feedback through the user interface.

[0482] Step 7:

[0483] Users review energy efficiency strategies presented via the terminal and adjust their energy consumption behavior as needed. Inputs include referring to graphs and charts displayed on the terminal. Outputs include planning and executing specific actions such as adjusting air conditioning schedules. In terms of specific operations, users input feedback using the terminal's interface, which is received by the server and used to improve the strategies.

[0484] (Application Example 1)

[0485] 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."

[0486] In modern cities, improving energy efficiency and optimizing the use of renewable energy are crucial challenges. However, there is a lack of mechanisms to monitor city-wide energy usage in real time and promote efficient use. As a result, energy waste and increased peak loads are becoming problems.

[0487] 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.

[0488] In this invention, the server includes means for collecting energy usage data and storing it in an information storage device, means for analyzing the energy usage data and identifying energy consumption patterns, and means for formulating a strategy to optimize the use of renewable energy based on the analysis results. This enables real-time monitoring of energy consumption across the entire city and allows users to utilize energy efficiently.

[0489] "Energy usage data" refers to a collection of information regarding energy consumption and usage patterns.

[0490] An "information storage device" is a device or system for storing data.

[0491] A "data analysis tool" is a system that analyzes collected data and extracts valuable information.

[0492] An "energy consumption pattern" refers to the tendencies and regularities regarding when and how much energy is consumed.

[0493] A "strategic proposal method" is a process for providing an optimal action plan based on analyzed data.

[0494] An "interface means" is a medium or mechanism for a user to interact with a system.

[0495] "City-wide energy consumption" refers to the overall picture of the state and changes in energy consumption within a specific urban area.

[0496] "Real-time display" means processing data instantly and showing it to the user immediately.

[0497] "Efficient energy utilization" means using the necessary energy optimally while eliminating waste.

[0498] The system that realizes this invention aims to maximize the energy efficiency of the entire city and consists of the interaction of a server, terminals, and users. The server communicates with various sensors to collect energy usage data in an information storage device in order to monitor energy usage in detail. It also uses software such as Python and TensorFlow / Keras to build machine learning models and analyze energy consumption patterns based on the collected data.

[0499] Once the analysis is complete, the server uses strategic proposal tools to develop a strategy to improve the city's energy efficiency. This strategy is visually displayed through a terminal interface developed using React Native. The terminal allows users to monitor energy consumption in real time and adjust energy use based on the proposed solutions.

[0500] Through the application, users can receive suggestions for energy-saving strategies in response to the increased power consumption predicted by the server, for example, when a large-scale event is held in the city on a particular weekend. In this way, users can use the application to improve the efficiency of their energy use and appropriately shift consumption away from peak times.

[0501] An example of a prompt might be, "Please suggest the most effective energy-saving strategy for the upcoming weekend event." This prompt would then prompt the system to generate customized energy management suggestions for the user.

[0502] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0503] Step 1:

[0504] The server collects energy usage data from multiple energy sensors within the city. The data acquired from the sensors is received in JSON format and stored in a data storage device. The input is raw data from each sensor, and the output is processed energy usage data. Because this data is used for subsequent analysis, the server stores it while maintaining data integrity.

[0505] Step 2:

[0506] The server analyzes energy usage data stored in an information storage device. Using Python, it preprocesses the data with the Pandas library and identifies energy consumption patterns using machine learning algorithms (TensorFlow / Keras). The input is formatted data, and the output is a model of consumption patterns. Specifically, an LSTM model analyzes past consumption data and predicts future consumption trends.

[0507] Step 3:

[0508] The server develops an optimization strategy for renewable energy based on the analyzed pattern data. The input is the predicted consumption pattern, and the output is the proposed strategy. In this process, external weather data is obtained via an API, and the proposed strategy is enhanced by a generative AI model. For example, a prompt message set by the user in the application, such as "Propose an effective strategy for this event," is used.

[0509] Step 4:

[0510] The device visually presents strategic suggestions to the user. The strategies are displayed in a UI built with React Native and are updated in real time. Input is strategic suggestions from the server, and output is visually organized strategic information. The user receives this information and can take actionable steps, such as adjusting air conditioner settings.

[0511] Step 5:

[0512] Users can provide feedback on the presented strategies. They send responses to the server via their terminal, conveying their opinions on the strategy's effectiveness and usability. The input is the user's feedback, and the output is feedback data stored on the server. The server can further analyze this data to improve the strategy proposals.

[0513] 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.

[0514] This invention combines an emotion engine with an energy management system to create a system that provides an optimal energy strategy that takes into account the user's emotional state. Specifically, the server collects energy usage data from each piece of equipment and analyzes consumption patterns based on this data. Furthermore, it formulates proposals for optimizing the use of renewable energy.

[0515] The newly integrated emotion engine uses the device's camera and microphone to recognize emotions from the user's facial expressions and voice. The emotion engine analyzes this data to identify the emotions the user expresses in response to energy suggestions. This information is then reflected in the strategic suggestion system, enabling the generation of suggestions tailored to the user's state.

[0516] For example, if the server's energy consumption is higher than expected, it might suggest using some renewable energy. However, if the emotion engine detects stress from the user's facial expressions, the server might adjust the tone of its suggestion, making it more concise or emphasizing only what is necessary.

[0517] Users can view displayed suggestions via their devices and provide feedback that aligns with their emotions. The server receives this feedback and uses it to improve the accuracy of the suggestions. Furthermore, the emotion engine can accumulate and analyze users' long-term emotional data to provide more personalized energy management.

[0518] In this way, the present invention enables interactive energy management that takes user emotions into consideration, thereby improving user satisfaction.

[0519] The following describes the processing flow.

[0520] Step 1:

[0521] The server periodically receives energy usage data from the equipment's sensors. This includes detailed information such as electricity consumption, equipment operating status, and usage frequency. The server stores this data in a database.

[0522] Step 2:

[0523] The server cleanses and prepares the stored data for analysis. It performs preprocessing to remove missing values ​​and noise data, thereby improving the accuracy of the analysis.

[0524] Step 3:

[0525] The server uses machine learning algorithms to analyze data and identify energy consumption patterns. This analysis clarifies energy usage trends, peak times, and potential energy-saving points.

[0526] Step 4:

[0527] Using the device's camera and microphone, the emotion engine captures the user's facial expressions and voice in real time. It then analyzes the user's emotions to identify states such as stress, frustration, and excitement.

[0528] Step 5:

[0529] The server combines analyzed energy consumption patterns with the user's emotional state to develop a strategy for optimizing renewable energy use. It adjusts the tone and content of suggestions based on the user's emotions.

[0530] Step 6:

[0531] The device visually displays the generated energy strategy on a dashboard. Users review the suggestions and evaluate whether they align with their own feelings.

[0532] Step 7:

[0533] Users provide feedback on the proposals via their devices. This feedback can include agreement, rejection, or requests for improvement.

[0534] Step 8:

[0535] The server receives user feedback along with sentiment analysis and uses it to improve the accuracy of future suggestions. Long-term user sentiment data is also accumulated and used for analysis.

[0536] (Example 2)

[0537] 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."

[0538] Existing energy management systems focus on optimizing energy consumption, but they lack the ability to propose strategies that consider the user's emotional state, resulting in a lack of user satisfaction and acceptance. Furthermore, there is a need for methods to more accurately model energy consumption patterns and optimize the use of renewable energy.

[0539] 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.

[0540] In this invention, the server includes data collection means for collecting information on energy use and storing it in a storage device; data analysis means for analyzing the information on energy use and identifying trends in energy consumption; and emotion recognition means for analyzing data acquired by a device for recognizing human emotions and determining the user's emotional state. This makes it possible to propose a personalized energy strategy that corresponds to the user's emotional state.

[0541] "Information on energy use" refers to data showing the usage of electricity, gas, and renewable energy in facilities such as homes and businesses.

[0542] A "storage device" is a storage medium or database used to permanently store data and make it accessible as needed.

[0543] "Data collection means" refers to functions for acquiring information about energy use through hardware such as sensors and smart meters.

[0544] "Data analysis means" refers to devices and software used to process collected information on energy use and identify consumption trends and patterns.

[0545] A "trend" refers to the general behavior or movement of energy consumption observed over time.

[0546] "Strategic planning tools" refer to the function of creating plans and proposals to promote efficient energy use based on analyzed data.

[0547] "Emotion recognition means" refers to technologies and systems that analyze a user's facial expressions and voice to determine their emotional state.

[0548] The "proposal adjustment mechanism" is a function that, based on the results of emotion recognition, rewrites the energy usage strategies and advice presented to the user into appropriate expressions.

[0549] "Display means" refers to interfaces or devices used to visualize strategic proposals and data analysis results and provide them to users.

[0550] This invention aims to improve energy management systems. It provides energy usage suggestions that take into account the user's emotional state via a server and terminal, thereby achieving efficient and user-friendly energy management. Specific embodiments are described below.

[0551] The server collects data from smart meters and sensors installed within the building to gather information about energy usage. MQTT and HTTP are used as communication protocols for data collection, and the collected data is stored in a database (e.g., MySQL or PostgreSQL).

[0552] The collected data is processed by the server using data analysis tools. The server uses data analysis software such as Python or R to perform the analysis and employs time series analysis models (e.g., the ARIMA model) to clarify energy consumption trends. Based on the results of this analysis, the server formulates strategies to optimize the use of renewable energy.

[0553] Meanwhile, the device uses emotion recognition to understand the user's emotional state. The device captures the user's facial expressions through its camera and records their voice using its microphone. This data is analyzed using AI libraries such as OpenCV and TensorFlow. The analysis results are sent to a server and incorporated into strategic proposals.

[0554] In strategic proposals, generative AI models are used. Proposals, created based on sentiment data, are presented to the user in an appropriate manner. For example, if the server determines that the user is experiencing stress, it simplifies the proposal and adjusts the language to be easily understood.

[0555] Users can view suggestions from the server via their devices. Users can provide feedback on the suggestions, including their opinions and impressions, and this feedback is also stored in the server's database. This helps to improve the accuracy of future strategic suggestions.

[0556] For example, if the server determines that a household has high energy consumption, it might generate a suggestion such as, "Prioritize the use of electricity generated by solar power between 2 PM and 4 PM." If the user is concerned, this suggestion might be adjusted to reassure them by saying something like, "By prioritizing the use of solar power during the day, you can reduce your monthly costs."

[0557] Example prompt: "Please suggest how renewable energy should be used to optimize household electricity consumption. Also, please adjust your suggestions considering the user's emotional state."

[0558] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0559] Step 1:

[0560] The server collects energy usage data from each piece of equipment. Specifically, it acquires data in real time from smart meters and sensors and receives it using a secure communication protocol (e.g., MQTT or HTTP). The input is energy usage data from each piece of equipment, and the output is completed when this data is stored in a database (e.g., MySQL).

[0561] Step 2:

[0562] The server uses data analysis tools to analyze the collected data. Using data analysis tools such as Python or R, the server employs time-series analysis models (e.g., the ARIMA model) to identify consumption patterns. The input is energy usage data stored in a database, and the output is the analysis result of consumption patterns. This analysis forms the basis for the next strategic proposal.

[0563] Step 3:

[0564] The server develops a strategy to optimize the use of renewable energy based on the analysis results. It uses a generative AI model to generate specific proposals in natural language. The input is the analysis results of consumption patterns, and the output is a specific energy use proposal such as "prioritize solar power generation from 2pm to 4pm."

[0565] Step 4:

[0566] The device uses emotion recognition to understand the user's emotions. It captures the user's facial expressions with its built-in camera and records their voice with its microphone. It analyzes the emotional state using AI libraries such as OpenCV and TensorFlow. The input is the user's facial expressions and voice, and the output is the emotional state the user is experiencing.

[0567] Step 5:

[0568] The server adjusts the wording of the strategy based on sentiment data. A generative AI model generates natural-sounding suggestions that respond to the user's emotions. The input is the analyzed sentiment state, and the output is a positive suggestion such as, "By prioritizing the use of solar power during the day, you can reduce your monthly costs."

[0569] Step 6:

[0570] Users review energy usage suggestions presented by the server via their terminals and provide feedback, including their thoughts and opinions. The input is the suggestions received by the user, and the output is the feedback information sent to the server. This information will be used to improve the system in the future.

[0571] (Application Example 2)

[0572] 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."

[0573] In conventional energy management systems, energy recommendations were made without considering the user's emotional state, making it difficult to provide the optimal energy utilization strategy for the user. Therefore, interactive energy management that responds to the user's psychological state is required.

[0574] 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.

[0575] In this invention, the server includes emotion recognition means for acquiring emotion information and transmitting it to data analysis means, information collection means for collecting energy usage data and storing it in a database, and data analysis means for analyzing the energy usage data and emotion information and identifying patterns of energy consumption. This makes it possible to optimize energy usage strategies that take into account the user's emotional state.

[0576] An "emotion recognition method" is a function that uses data such as the user's facial expressions and voice to identify the user's emotional state.

[0577] "Information gathering means" refers to a function for acquiring data on energy use and storing it in a database.

[0578] "Data analysis means" refers to a function that analyzes collected energy usage data and emotional information to identify patterns in energy consumption.

[0579] The "strategic proposal tool" is a function that formulates strategies to optimize the use of renewable energy based on the analysis results.

[0580] An "interface means" is a function that visually displays the devised strategy and receives feedback from users.

[0581] The system for implementing this invention mainly consists of three elements: a server, a terminal, and a user. The server has energy management and emotion recognition functions, analyzes the user's emotional state, and generates an optimal energy utilization strategy based on energy consumption patterns. The operation of the system is described in detail below.

[0582] The server uses the device's camera and microphone to acquire emotional information. Specifically, it utilizes Microsoft Azure's "Emotion Recognition API" to recognize emotions based on the user's facial expressions and voice data.

[0583] Furthermore, the servers use cloud services such as AWS Greengrass and Google Cloud Platform to collect energy usage data. This data is stored in a database.

[0584] The server uses machine learning algorithms to identify energy consumption patterns based on collected energy usage data and sentiment information. This analysis particularly utilizes machine learning libraries such as Python's Scikit-learn.

[0585] Based on the analysis results, the server develops a strategy for optimally utilizing renewable energy. For example, if a user's stress levels are high, it might suggest adjusting the lighting to promote relaxation.

[0586] Users can view and select a visually displayed energy strategy through the device's interface. This process involves designing the interface using Figma, a user experience design tool.

[0587] As a concrete example, consider a user who uses their smartphone at the end of the day. If the user's face is recognized as showing signs of stress, an energy strategy such as, "To help you relax today, we suggest changing the lighting to a warmer color," might be presented.

[0588] Examples of prompts for a generative AI model are as follows:

[0589] "Please create suggestions for energy management that can alleviate the stress users are experiencing."

[0590] Current emotional state: {User's emotion}.

[0591] Example of household situation: {Current energy consumption situation}.

[0592] Example of a proposed solution: {Suggestions for adjusting lighting and saving energy}.

[0593] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0594] Step 1:

[0595] The device's camera and microphone are used to capture the user's face and voice. The input consists of facial video data and audio data. Based on this, the server processes the data using Microsoft Azure's "Emotion Recognition API" to identify the user's emotions. Emotional state data is generated as output.

[0596] Step 2:

[0597] The server collects energy usage data through cloud services (e.g., AWS Greengrass or Google Cloud Platform). This data collection is performed by receiving data streams from smart electricity meters and IoT devices. Inputs include instantaneous power consumption and historical consumption history. By storing this in a database, time-series data of energy usage is obtained as output.

[0598] Step 3:

[0599] The server uses collected emotional state data and energy usage data to identify energy consumption patterns using machine learning algorithms. It performs data analysis based on the input data, utilizing libraries such as Scikit-learn. The output is a model relating to the identified consumption patterns.

[0600] Step 4:

[0601] The server uses a strategic proposal mechanism to formulate a strategy to optimize the use of renewable energy based on analysis results and emotional information. The strategy includes specific actions such as "adjusting lighting" or "proposing an energy-saving mode." The inputs are identified consumption patterns and emotional states, and the output generates data for the optimal strategy.

[0602] Step 5:

[0603] The proposed energy strategy is visually displayed to the user via the terminal interface. The user reviews and selects a strategy through a UI designed in Figma. The input is data on the optimal strategy, and the output receives the user's selection.

[0604] Step 6:

[0605] The server receives user feedback and uses this feedback data to analyze and train the system, thereby improving the accuracy of its suggestions. The input is user feedback, and the final output is a performance evaluation of the improved strategic suggestions.

[0606] 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.

[0607] 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.

[0608] 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.

[0609] [Fourth Embodiment]

[0610] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0611] 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.

[0612] 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).

[0613] 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.

[0614] 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.

[0615] 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).

[0616] 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.

[0617] 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.

[0618] 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.

[0619] 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.

[0620] 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.

[0621] 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.

[0622] 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".

[0623] This invention provides a system for streamlining energy management and optimizing the use of renewable energy. Specifically, energy usage data is collected by a server, and based on this data, energy consumption patterns are analyzed to propose an optimal energy strategy.

[0624] The server periodically receives energy usage data from each piece of equipment and sensor. It also stores this data in a database and analyzes consumption patterns in real time. Machine learning algorithms are used for data analysis, and by combining this data with external weather data, the system can more accurately predict consumption trends and anomalies.

[0625] Based on the analyzed data, the server identifies peak hours when power consumption tends to be high and develops strategies for peak shifting. Specifically, it develops plans for utilizing renewable energy sources and aims for efficient energy use. This is expected to reduce energy costs and environmental impact.

[0626] The terminal visually displays the energy strategy provided by the server, supporting user understanding. Users can check their energy consumption status and the proposed strategy on the terminal and make adjustments as needed. Furthermore, the server incorporates user input and feedback into improvements to the proposed strategy through this interface.

[0627] For example, in an office building, a server collects power consumption data from each floor and generates a predictive model based on the day's weather data. The analysis results are displayed on a terminal, and the system suggests to the user how to optimize the air conditioning system and how to utilize solar power. Based on this information, the user can adjust the air conditioning operating schedule and shift peak power consumption to other times. In this way, the system achieves improved energy efficiency.

[0628] The following describes the processing flow.

[0629] Step 1:

[0630] The server receives energy usage data in real time from each piece of equipment and sensor. This includes information such as power consumption, operating time, and operating mode. The server records and manages this data in a database.

[0631] Step 2:

[0632] The server cleanses the collected data. Specifically, it imputes missing values, removes outliers, and identifies invalid data, preparing the data for improved analysis accuracy.

[0633] Step 3:

[0634] The server applies machine learning algorithms based on past consumption data to analyze consumption patterns. This analysis identifies energy consumption trends and peak times.

[0635] Step 4:

[0636] The server combines weather data and calendar information to generate a model that predicts future energy demand. This allows it to assess the possibility of peak shifting.

[0637] Step 5:

[0638] Based on the analysis results and predictive models, the server develops renewable energy introduction plans and energy utilization optimization strategies. Specifically, it proposes the use of solar and wind power generation.

[0639] Step 6:

[0640] The terminal displays the energy strategy obtained from the server on a dashboard. Users review this visualized information and make decisions tailored to their specific tasks.

[0641] Step 7:

[0642] Users can provide feedback on the server's suggestions and enter questions through their device. The device then sends this feedback to the server.

[0643] Step 8:

[0644] The server uses user feedback to make adjustments that improve the accuracy of future energy strategy proposals.

[0645] (Example 1)

[0646] 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".

[0647] In modern times, efficient energy management and optimal use of renewable energy are essential for reducing environmental impact and lowering costs. However, optimizing energy use while fully considering fluctuations in energy demand and external influences is difficult. This invention aims to solve these problems and realize efficient and flexible energy management.

[0648] 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.

[0649] In this invention, the server includes information acquisition means for collecting energy utilization indicators and storing them in a storage device, data analysis means for analyzing the energy utilization indicators and identifying trends in energy consumption, and strategy proposal means for formulating strategies to optimize the use of renewable energy based on the analysis results. This enables improved energy efficiency and maximum utilization of renewable energy sources.

[0650] "Energy utilization indicators" refer to data that quantitatively shows the state of energy consumption, including information on the amount and trends of use of electricity, gas, and other energy sources.

[0651] "Means for acquiring information to be stored in a storage device" refers to hardware or software components for temporarily storing or permanently recording energy-related data collected by various sensors and devices.

[0652] "Data analysis means" refers to algorithms and programs that use collected energy-related data to analyze consumption patterns, predict trends, and detect anomalies.

[0653] A "policy proposal mechanism" refers to a system for formulating action plans and strategies to promote the efficient use of energy and the utilization of renewable energy, based on the results of data analysis.

[0654] "Display means" refers to interfaces or devices that visually present analyzed data and proposed strategies in a way that is easy for users to understand.

[0655] "Improvement methods" refer to methods and processes for accepting user feedback and using that feedback to consider and adjust proposed strategies and the overall effectiveness of the system.

[0656] The system in this invention mainly consists of three components: a server, a terminal, and a user. Specific embodiments of each component are shown below.

[0657] server:

[0658] The server's role is to collect and store energy usage indicators in storage. Specifically, it acquires data from various sensors and devices installed within the building, including electricity meters, temperature sensors, and illuminance sensors. A general-purpose data management system such as MySQL or MongoDB is used as the database. This allows for the accumulation of data to identify trends in energy usage.

[0659] Next, the server analyzes the collected data using data analysis tools. For this analysis, machine learning algorithms are implemented using libraries such as Python's scikit-learn to detect trends and anomalies in energy consumption. Furthermore, external weather data is obtained via API and combined with the consumption data to generate a more accurate prediction model. By utilizing this generated AI model, more advanced analysis becomes possible.

[0660] Based on the analysis results, the server proposes strategies to maximize the efficient use of energy. This strategy proposal mechanism develops optimized strategies centered on the utilization of renewable energy. For example, it might create a plan to optimize the usage time of solar power generation.

[0661] Terminal:

[0662] The terminal visualizes the strategies received from the server and presents them clearly to the user. Charts and graphs are used as display methods to allow the user to intuitively understand the proposed strategies. This enables the user to visually grasp energy consumption trends and optimization strategies.

[0663] User:

[0664] Users can check their energy usage and suggested measures via their terminals and adjust their energy consumption behavior based on that information. For example, in an office building, a server can display a predictive model generated based on weather data and power consumption data on the terminal, allowing users to appropriately adjust the air conditioning operating schedule.

[0665] As an example of a prompt, entering "Based on this week's weather data and power consumption patterns, please propose an optimization strategy for the office building's air conditioning system. This proposal should include specific peak shift times and ways to utilize renewable energy" will allow you to obtain a specific proposal using a generated AI model.

[0666] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0667] Step 1:

[0668] The server collects energy usage indicators from multiple sensors. It uses data from building energy meters, temperature sensors, and illuminance sensors as input. This data is sent to the server and temporarily stored in memory. Specifically, the server requests data from the sensors at regular intervals and records the acquired data.

[0669] Step 2:

[0670] The server stores the data collected in step 1 into a storage device. It uses temporarily stored sensor data as input. A data management system such as MySQL or MongoDB is used for the database, and the data is stored persistently while being organized with timestamps. Specifically, the server performs duplicate data checks and detects outliers when storing the data.

[0671] Step 3:

[0672] The server executes data analysis measures to analyze the data stored in step 2. It uses historical energy consumption patterns and the latest sensor data stored in the database as input. This data is analyzed using the scikit-learn library in Python to generate consumption trends and predictive models. Specifically, the server applies anomaly detection algorithms to identify consumption patterns.

[0673] Step 4:

[0674] The server acquires external weather data and combines it with the data analysis results obtained in step 3. The acquired weather data and the analyzed consumption pattern data are used as input. This enables advanced predictions utilizing a generative AI model, allowing for more accurate analysis of energy consumption trends and anomalies. Specifically, the server accesses an external API to acquire the necessary weather data and integrate it into the database.

[0675] Step 5:

[0676] The server devises strategies for energy efficiency based on the analysis results. It uses consumption pattern analysis and weather data forecasts as input. Through its strategy proposal mechanism, it develops a concrete action plan to maximize the use of renewable energy. Specifically, the server formulates an optimization strategy that includes possibilities such as peak shifting.

[0677] Step 6:

[0678] The terminal presents the policy received from the server to the user. It uses policy data sent from the server as input. This data is displayed on the UI in the form of graphs and charts to make it easy for the user to understand. Specifically, the terminal updates the graphical display in real time and accepts feedback through the user interface.

[0679] Step 7:

[0680] Users review energy efficiency strategies presented via the terminal and adjust their energy consumption behavior as needed. Inputs include referring to graphs and charts displayed on the terminal. Outputs include planning and executing specific actions such as adjusting air conditioning schedules. In terms of specific operations, users input feedback using the terminal's interface, which is received by the server and used to improve the strategies.

[0681] (Application Example 1)

[0682] 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".

[0683] In modern cities, improving energy efficiency and optimizing the use of renewable energy are crucial challenges. However, there is a lack of mechanisms to monitor city-wide energy usage in real time and promote efficient use. As a result, energy waste and increased peak loads are becoming problems.

[0684] 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.

[0685] In this invention, the server includes means for collecting energy usage data and storing it in an information storage device, means for analyzing the energy usage data and identifying energy consumption patterns, and means for formulating a strategy to optimize the use of renewable energy based on the analysis results. This enables real-time monitoring of energy consumption across the entire city and allows users to utilize energy efficiently.

[0686] "Energy usage data" refers to a collection of information regarding energy consumption and usage patterns.

[0687] An "information storage device" is a device or system for storing data.

[0688] A "data analysis tool" is a system that analyzes collected data and extracts valuable information.

[0689] An "energy consumption pattern" refers to the tendencies and regularities regarding when and how much energy is consumed.

[0690] A "strategic proposal method" is a process for providing an optimal action plan based on analyzed data.

[0691] An "interface means" is a medium or mechanism for a user to interact with a system.

[0692] "City-wide energy consumption" refers to the overall picture of the state and changes in energy consumption within a specific urban area.

[0693] "Real-time display" means processing data instantly and showing it to the user immediately.

[0694] "Efficient energy utilization" means using the necessary energy optimally while eliminating waste.

[0695] The system that realizes this invention aims to maximize the energy efficiency of the entire city and consists of the interaction of a server, terminals, and users. The server communicates with various sensors to collect energy usage data in an information storage device in order to monitor energy usage in detail. It also uses software such as Python and TensorFlow / Keras to build machine learning models and analyze energy consumption patterns based on the collected data.

[0696] Once the analysis is complete, the server uses strategic proposal tools to develop a strategy to improve the city's energy efficiency. This strategy is visually displayed through a terminal interface developed using React Native. The terminal allows users to monitor energy consumption in real time and adjust energy use based on the proposed solutions.

[0697] Through the application, users can receive suggestions for energy-saving strategies in response to the increased power consumption predicted by the server, for example, when a large-scale event is held in the city on a particular weekend. In this way, users can use the application to improve the efficiency of their energy use and appropriately shift consumption away from peak times.

[0698] An example of a prompt might be, "Please suggest the most effective energy-saving strategy for the upcoming weekend event." This prompt would then prompt the system to generate customized energy management suggestions for the user.

[0699] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0700] Step 1:

[0701] The server collects energy usage data from multiple energy sensors within the city. The data acquired from the sensors is received in JSON format and stored in a data storage device. The input is raw data from each sensor, and the output is processed energy usage data. Because this data is used for subsequent analysis, the server stores it while maintaining data integrity.

[0702] Step 2:

[0703] The server analyzes energy usage data stored in an information storage device. Using Python, it preprocesses the data with the Pandas library and identifies energy consumption patterns using machine learning algorithms (TensorFlow / Keras). The input is formatted data, and the output is a model of consumption patterns. Specifically, an LSTM model analyzes past consumption data and predicts future consumption trends.

[0704] Step 3:

[0705] The server develops an optimization strategy for renewable energy based on the analyzed pattern data. The input is the predicted consumption pattern, and the output is the proposed strategy. In this process, external weather data is obtained via an API, and the proposed strategy is enhanced by a generative AI model. For example, a prompt message set by the user in the application, such as "Propose an effective strategy for this event," is used.

[0706] Step 4:

[0707] The device visually presents strategic suggestions to the user. The strategies are displayed in a UI built with React Native and are updated in real time. Input is strategic suggestions from the server, and output is visually organized strategic information. The user receives this information and can take actionable steps, such as adjusting air conditioner settings.

[0708] Step 5:

[0709] Users can provide feedback on the presented strategies. They send responses to the server via their terminal, conveying their opinions on the strategy's effectiveness and usability. The input is the user's feedback, and the output is feedback data stored on the server. The server can further analyze this data to improve the strategy proposals.

[0710] 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.

[0711] This invention combines an emotion engine with an energy management system to create a system that provides an optimal energy strategy that takes into account the user's emotional state. Specifically, the server collects energy usage data from each piece of equipment and analyzes consumption patterns based on this data. Furthermore, it formulates proposals for optimizing the use of renewable energy.

[0712] The newly integrated emotion engine uses the device's camera and microphone to recognize emotions from the user's facial expressions and voice. The emotion engine analyzes this data to identify the emotions the user expresses in response to energy suggestions. This information is then reflected in the strategic suggestion system, enabling the generation of suggestions tailored to the user's state.

[0713] For example, if the server's energy consumption is higher than expected, it might suggest using some renewable energy. However, if the emotion engine detects stress from the user's facial expressions, the server might adjust the tone of its suggestion, making it more concise or emphasizing only what is necessary.

[0714] Users can view displayed suggestions via their devices and provide feedback that aligns with their emotions. The server receives this feedback and uses it to improve the accuracy of the suggestions. Furthermore, the emotion engine can accumulate and analyze users' long-term emotional data to provide more personalized energy management.

[0715] In this way, the present invention enables interactive energy management that takes user emotions into consideration, thereby improving user satisfaction.

[0716] The following describes the processing flow.

[0717] Step 1:

[0718] The server periodically receives energy usage data from the equipment's sensors. This includes detailed information such as electricity consumption, equipment operating status, and usage frequency. The server stores this data in a database.

[0719] Step 2:

[0720] The server cleanses and prepares the stored data for analysis. It performs preprocessing to remove missing values ​​and noise data, thereby improving the accuracy of the analysis.

[0721] Step 3:

[0722] The server uses machine learning algorithms to analyze data and identify energy consumption patterns. This analysis clarifies energy usage trends, peak times, and potential energy-saving points.

[0723] Step 4:

[0724] Using the device's camera and microphone, the emotion engine captures the user's facial expressions and voice in real time. It then analyzes the user's emotions to identify states such as stress, frustration, and excitement.

[0725] Step 5:

[0726] The server combines analyzed energy consumption patterns with the user's emotional state to develop a strategy for optimizing renewable energy use. It adjusts the tone and content of suggestions based on the user's emotions.

[0727] Step 6:

[0728] The device visually displays the generated energy strategy on a dashboard. Users review the suggestions and evaluate whether they align with their own feelings.

[0729] Step 7:

[0730] Users provide feedback on the proposals via their devices. This feedback can include agreement, rejection, or requests for improvement.

[0731] Step 8:

[0732] The server receives user feedback along with sentiment analysis and uses it to improve the accuracy of future suggestions. Long-term user sentiment data is also accumulated and used for analysis.

[0733] (Example 2)

[0734] 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".

[0735] Existing energy management systems focus on optimizing energy consumption, but they lack the ability to propose strategies that consider the user's emotional state, resulting in a lack of user satisfaction and acceptance. Furthermore, there is a need for methods to more accurately model energy consumption patterns and optimize the use of renewable energy.

[0736] 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.

[0737] In this invention, the server includes data collection means for collecting information on energy use and storing it in a storage device; data analysis means for analyzing the information on energy use and identifying trends in energy consumption; and emotion recognition means for analyzing data acquired by a device for recognizing human emotions and determining the user's emotional state. This makes it possible to propose a personalized energy strategy that corresponds to the user's emotional state.

[0738] "Information on energy use" refers to data showing the usage of electricity, gas, and renewable energy in facilities such as homes and businesses.

[0739] A "storage device" is a storage medium or database used to permanently store data and make it accessible as needed.

[0740] "Data collection means" refers to functions for acquiring information about energy use through hardware such as sensors and smart meters.

[0741] "Data analysis means" refers to devices and software used to process collected information on energy use and identify consumption trends and patterns.

[0742] A "trend" refers to the general behavior or movement of energy consumption observed over time.

[0743] "Strategic planning tools" refer to the function of creating plans and proposals to promote efficient energy use based on analyzed data.

[0744] "Emotion recognition means" refers to technologies and systems that analyze a user's facial expressions and voice to determine their emotional state.

[0745] The "proposal adjustment mechanism" is a function that, based on the results of emotion recognition, rewrites the energy usage strategies and advice presented to the user into appropriate expressions.

[0746] "Display means" refers to interfaces or devices used to visualize strategic proposals and data analysis results and provide them to users.

[0747] This invention aims to improve energy management systems. It provides energy usage suggestions that take into account the user's emotional state via a server and terminal, thereby achieving efficient and user-friendly energy management. Specific embodiments are described below.

[0748] The server collects data from smart meters and sensors installed within the building to gather information about energy usage. MQTT and HTTP are used as communication protocols for data collection, and the collected data is stored in a database (e.g., MySQL or PostgreSQL).

[0749] The collected data is processed by the server using data analysis tools. The server uses data analysis software such as Python or R to perform the analysis and employs time series analysis models (e.g., the ARIMA model) to clarify energy consumption trends. Based on the results of this analysis, the server formulates strategies to optimize the use of renewable energy.

[0750] Meanwhile, the device uses emotion recognition to understand the user's emotional state. The device captures the user's facial expressions through its camera and records their voice using its microphone. This data is analyzed using AI libraries such as OpenCV and TensorFlow. The analysis results are sent to a server and incorporated into strategic proposals.

[0751] In strategic proposals, generative AI models are used. Proposals, created based on sentiment data, are presented to the user in an appropriate manner. For example, if the server determines that the user is experiencing stress, it simplifies the proposal and adjusts the language to be easily understood.

[0752] Users can view suggestions from the server via their devices. Users can provide feedback on the suggestions, including their opinions and impressions, and this feedback is also stored in the server's database. This helps to improve the accuracy of future strategic suggestions.

[0753] For example, if the server determines that a household has high energy consumption, it might generate a suggestion such as, "Prioritize the use of electricity generated by solar power between 2 PM and 4 PM." If the user is concerned, this suggestion might be adjusted to reassure them by saying something like, "By prioritizing the use of solar power during the day, you can reduce your monthly costs."

[0754] Example prompt: "Please suggest how renewable energy should be used to optimize household electricity consumption. Also, please adjust your suggestions considering the user's emotional state."

[0755] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0756] Step 1:

[0757] The server collects energy usage data from each piece of equipment. Specifically, it acquires data in real time from smart meters and sensors and receives it using a secure communication protocol (e.g., MQTT or HTTP). The input is energy usage data from each piece of equipment, and the output is completed when this data is stored in a database (e.g., MySQL).

[0758] Step 2:

[0759] The server uses data analysis tools to analyze the collected data. Using data analysis tools such as Python or R, the server employs time-series analysis models (e.g., the ARIMA model) to identify consumption patterns. The input is energy usage data stored in a database, and the output is the analysis result of consumption patterns. This analysis forms the basis for the next strategic proposal.

[0760] Step 3:

[0761] The server develops a strategy to optimize the use of renewable energy based on the analysis results. It uses a generative AI model to generate specific proposals in natural language. The input is the analysis results of consumption patterns, and the output is a specific energy use proposal such as "prioritize solar power generation from 2pm to 4pm."

[0762] Step 4:

[0763] The device uses emotion recognition to understand the user's emotions. It captures the user's facial expressions with its built-in camera and records their voice with its microphone. It analyzes the emotional state using AI libraries such as OpenCV and TensorFlow. The input is the user's facial expressions and voice, and the output is the emotional state the user is experiencing.

[0764] Step 5:

[0765] The server adjusts the wording of the strategy based on sentiment data. A generative AI model generates natural-sounding suggestions that respond to the user's emotions. The input is the analyzed sentiment state, and the output is a positive suggestion such as, "By prioritizing the use of solar power during the day, you can reduce your monthly costs."

[0766] Step 6:

[0767] Users review energy usage suggestions presented by the server via their terminals and provide feedback, including their thoughts and opinions. The input is the suggestions received by the user, and the output is the feedback information sent to the server. This information will be used to improve the system in the future.

[0768] (Application Example 2)

[0769] 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".

[0770] In conventional energy management systems, energy recommendations were made without considering the user's emotional state, making it difficult to provide the optimal energy utilization strategy for the user. Therefore, interactive energy management that responds to the user's psychological state is required.

[0771] 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.

[0772] In this invention, the server includes emotion recognition means for acquiring emotion information and transmitting it to data analysis means, information collection means for collecting energy usage data and storing it in a database, and data analysis means for analyzing the energy usage data and emotion information and identifying patterns of energy consumption. This makes it possible to optimize energy usage strategies that take into account the user's emotional state.

[0773] An "emotion recognition method" is a function that uses data such as the user's facial expressions and voice to identify the user's emotional state.

[0774] "Information gathering means" refers to a function for acquiring data on energy use and storing it in a database.

[0775] "Data analysis means" refers to a function that analyzes collected energy usage data and emotional information to identify patterns in energy consumption.

[0776] The "strategic proposal tool" is a function that formulates strategies to optimize the use of renewable energy based on the analysis results.

[0777] An "interface means" is a function that visually displays the devised strategy and receives feedback from users.

[0778] The system for implementing this invention mainly consists of three elements: a server, a terminal, and a user. The server has energy management and emotion recognition functions, analyzes the user's emotional state, and generates an optimal energy utilization strategy based on energy consumption patterns. The operation of the system is described in detail below.

[0779] The server uses the device's camera and microphone to acquire emotional information. Specifically, it utilizes Microsoft Azure's "Emotion Recognition API" to recognize emotions based on the user's facial expressions and voice data.

[0780] Furthermore, the servers use cloud services such as AWS Greengrass and Google Cloud Platform to collect energy usage data. This data is stored in a database.

[0781] The server uses machine learning algorithms to identify energy consumption patterns based on collected energy usage data and sentiment information. This analysis particularly utilizes machine learning libraries such as Python's Scikit-learn.

[0782] Based on the analysis results, the server develops a strategy for optimally utilizing renewable energy. For example, if a user's stress levels are high, it might suggest adjusting the lighting to promote relaxation.

[0783] Users can view and select a visually displayed energy strategy through the device's interface. This process involves designing the interface using Figma, a user experience design tool.

[0784] As a concrete example, consider a user who uses their smartphone at the end of the day. If the user's face is recognized as showing signs of stress, an energy strategy such as, "To help you relax today, we suggest changing the lighting to a warmer color," might be presented.

[0785] Examples of prompts for a generative AI model are as follows:

[0786] "Please create suggestions for energy management that can alleviate the stress users are experiencing."

[0787] Current emotional state: {User's emotion}.

[0788] Example of household situation: {Current energy consumption situation}.

[0789] Example of a proposed solution: {Suggestions for adjusting lighting and saving energy}.

[0790] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0791] Step 1:

[0792] The device's camera and microphone are used to capture the user's face and voice. The input consists of facial video data and audio data. Based on this, the server processes the data using Microsoft Azure's "Emotion Recognition API" to identify the user's emotions. Emotional state data is generated as output.

[0793] Step 2:

[0794] The server collects energy usage data through cloud services (e.g., AWS Greengrass or Google Cloud Platform). This data collection is performed by receiving data streams from smart electricity meters and IoT devices. Inputs include instantaneous power consumption and historical consumption history. By storing this in a database, time-series data of energy usage is obtained as output.

[0795] Step 3:

[0796] The server uses collected emotional state data and energy usage data to identify energy consumption patterns using machine learning algorithms. It performs data analysis based on the input data, utilizing libraries such as Scikit-learn. The output is a model relating to the identified consumption patterns.

[0797] Step 4:

[0798] The server uses a strategic proposal mechanism to formulate a strategy to optimize the use of renewable energy based on analysis results and emotional information. The strategy includes specific actions such as "adjusting lighting" or "proposing an energy-saving mode." The inputs are identified consumption patterns and emotional states, and the output generates data for the optimal strategy.

[0799] Step 5:

[0800] The proposed energy strategy is visually displayed to the user via the terminal interface. The user reviews and selects a strategy through a UI designed in Figma. The input is data on the optimal strategy, and the output receives the user's selection.

[0801] Step 6:

[0802] The server receives user feedback and uses this feedback data to analyze and train the system, thereby improving the accuracy of its suggestions. The input is user feedback, and the final output is a performance evaluation of the improved strategic suggestions.

[0803] 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.

[0804] 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.

[0805] 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.

[0806] 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.

[0807] 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.

[0808] 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.

[0809] 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.

[0810] 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.

[0811] 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."

[0812] 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.

[0813] 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.

[0814] 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.

[0815] 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.

[0816] 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.

[0817] 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.

[0818] 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.

[0819] 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.

[0820] 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.

[0821] 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.

[0822] 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.

[0823] 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.

[0824] The following is further disclosed regarding the embodiments described above.

[0825] (Claim 1)

[0826] An information collection means for collecting energy usage data and storing it in a database,

[0827] A data analysis means for analyzing the aforementioned energy usage data and identifying energy consumption patterns,

[0828] A strategic proposal means for formulating a strategy to optimize the use of renewable energy based on the aforementioned analysis results,

[0829] An interface means for visually displaying the aforementioned strategy and receiving feedback from users,

[0830] A system that includes this.

[0831] (Claim 2)

[0832] The system according to claim 1, further comprising a peak shifting means for predicting the energy consumption pattern using external data and performing peak shifting.

[0833] (Claim 3)

[0834] The system according to claim 1, wherein the data analysis means generates an energy consumption model using a machine learning algorithm.

[0835] "Example 1"

[0836] (Claim 1)

[0837] An information acquisition means for collecting indicators related to energy use and storing them in a storage device,

[0838] A data analysis means for analyzing the aforementioned energy utilization indicators and identifying trends in energy consumption,

[0839] A means for proposing strategies to optimize the use of renewable energy based on the aforementioned analysis results,

[0840] A display means for visually displaying the aforementioned measures and receiving feedback from users,

[0841] An improvement means for improving the aforementioned measures based on the opinions of the aforementioned users,

[0842] A system that includes this.

[0843] (Claim 2)

[0844] The system according to claim 1, further comprising a demand shifting means for predicting the aforementioned energy consumption trend using external information and shifting demand.

[0845] (Claim 3)

[0846] The system according to claim 1, wherein the data analysis means generates an energy consumption prediction model using a generated AI model.

[0847] "Application Example 1"

[0848] (Claim 1)

[0849] An information collection means for collecting energy usage data and storing it in an information storage device,

[0850] A data analysis means for analyzing the aforementioned energy usage data and identifying energy consumption patterns,

[0851] A strategic proposal means for formulating a strategy to optimize the use of renewable energy based on the aforementioned analysis results,

[0852] An interface means for visually displaying the aforementioned strategy and receiving responses from users,

[0853] A means to display the energy consumption status of the entire city in real time and to support users in using energy efficiently,

[0854] A system that includes this.

[0855] (Claim 2)

[0856] The system according to claim 1, further comprising a peak shifting means for predicting energy consumption patterns using external data and performing peak shifting.

[0857] (Claim 3)

[0858] The system according to claim 1, wherein the data analysis means generates an energy consumption model using a computational model.

[0859] "Example 2 of combining an emotion engine"

[0860] (Claim 1)

[0861] A data collection means for collecting information on energy use and storing it in a storage device,

[0862] A data analysis means for analyzing the aforementioned information on energy use and identifying trends in energy consumption,

[0863] A strategic planning means for formulating a method to optimize the use of renewable energy based on the aforementioned analysis results,

[0864] An emotion recognition means that analyzes data acquired by a device for recognizing human emotions to determine the user's emotional state,

[0865] A proposal adjustment means for adjusting the expression of the strategy based on the aforementioned emotional state,

[0866] A means for visually displaying the aforementioned strategy and receiving feedback from users,

[0867] A system that includes this.

[0868] (Claim 2)

[0869] The system according to claim 1, further comprising a function for predicting the aforementioned energy consumption trend using external information and for distributing the load.

[0870] (Claim 3)

[0871] The system according to claim 1, wherein the data analysis means generates an energy consumption model using machine learning techniques.

[0872] "Application example 2 when combining with an emotional engine"

[0873] (Claim 1)

[0874] An emotion recognition means that acquires emotional information and transmits it to a data analysis means,

[0875] An information collection means for collecting energy usage data and storing it in a database,

[0876] A data analysis means for analyzing the aforementioned energy usage data and emotional information to identify patterns in energy consumption,

[0877] A strategic proposal means for formulating a strategy to optimize the use of renewable energy based on the aforementioned analysis results and emotional information,

[0878] An interface means for visually displaying the aforementioned strategy and receiving feedback from users,

[0879] A system that includes this.

[0880] (Claim 2)

[0881] The system according to claim 1, further comprising a peak shifting means for predicting the energy consumption pattern using external data and performing peak shifting.

[0882] (Claim 3)

[0883] The system according to claim 1, wherein the data analysis means generates an energy consumption model using a machine learning algorithm. [Explanation of Symbols]

[0884] 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. An information collection means for collecting energy usage data and storing it in a database, A data analysis means for analyzing the aforementioned energy usage data and identifying energy consumption patterns, A strategic proposal means for formulating a strategy to optimize the use of renewable energy based on the aforementioned analysis results, An interface means for visually displaying the aforementioned strategy and receiving feedback from users, A system that includes this.

2. The system according to claim 1, further comprising a peak shifting means for predicting the energy consumption pattern using external data and performing peak shifting.

3. The system according to claim 1, wherein the data analysis means generates an energy consumption model using a machine learning algorithm.

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