System

Abstract

A system is provided.SOLUTION: A system, comprising: means for collecting energy consumption information and CO2 emission information in real time through IoT terminals; means for storing and pre-processing the received information in cloud servers; means for analyzing the pre-processed information according to a generation AI and generating a proposed investment in renewable energies; and means for generating and presenting a proposed investment report to users.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In recent years, the importance of sustainable energy use and CO2 emission reduction has increased. However, many companies lack the knowledge and resources to develop specific energy consumption reduction methods and renewable energy investment strategies. Therefore, in order for these companies to achieve efficient and sustainable energy management, they need a system that provides optimal investment proposals based on real-time data collection and analysis. [Means for solving the problem]

[0005] This system includes an IoT device that collects a company's energy consumption and CO2 emissions data in real time, a means for storing and preprocessing the received data on a cloud server, a means for analyzing the data using AI and generating optimal renewable energy investment proposals, and a means for presenting investment proposal reports to users. This enables companies to streamline their energy consumption and achieve sustainable energy management. Furthermore, the system also includes a means for verifying the consistency of energy consumption and CO2 emissions data and correcting outliers, as well as a means for monitoring performance and providing feedback after the introduction of renewable energy systems. This shortens the payback period for initial investments, improves energy efficiency, and reduces future costs, supporting the sustainable growth of companies.

[0006] An "IoT terminal" is a device for collecting energy consumption data and CO2 emissions data in real time.

[0007] "Cloud Server" means an online database and computer network that stores, pre-processes, and analyzes data.

[0008] "Generative AI" is an artificial intelligence that uses machine learning algorithms to analyze energy consumption data and CO2 emissions data to generate optimal renewable energy investment proposals.

[0009] "Data storage" refers to the process of storing energy consumption data and CO2 emission data sent from IoT devices in a database on a cloud server.

[0010] "Data preprocessing" refers to the step of detecting outliers in the stored data, filling in missing data, and organizing the data in chronological order.

[0011] "Data analysis" is the process by which the AI ​​generator identifies energy consumption patterns and CO2 emission trends based on pre-processed data.

[0012] A "renewable energy investment proposal" is a specific proposal for investing in renewable energy sources such as solar power and wind power.

[0013] "Report generation" is the process of creating a report to explain the investment proposals generated by the generation AI in an easy-to-understand manner to the user.

[0014] "Consistency check" is a process of verifying the consistency of stored data and checking for inconsistencies.

[0015] "Outlier correction" is the process of correcting extreme or missing values ​​in data to more appropriate values.

[0016] "Performance monitoring" is the step of continuously monitoring the performance of a renewable energy system after its installation and analyzing the results.

[0017] "Providing feedback" is the act of providing users with suggestions for improving or optimizing the system based on the monitoring results. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

[0026] [First embodiment]

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

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

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

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

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

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

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

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

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

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

[0037] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

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

[0039] The present invention is a system that analyzes a company's energy consumption and CO2 emissions in real time and proposes optimal renewable energy investments. Specific embodiments of this system will be described below.

[0040] Overall system overview

[0041] The system of the present invention consists of the following main components:

[0042] 1. IoT devices that collect energy consumption data and CO2 emissions data in real time.

[0043] 2. A cloud server that stores and preprocesses the received data.

[0044] 3. A means of analyzing data and generating renewable energy investment proposals using generative AI.

[0045] 4. A means for generating and presenting investment proposal reports to the user.

[0046] Specific examples of program execution

[0047] IoT device operation (data collection)

[0048] The terminals are installed in the company's factories and monitor electricity consumption, gas usage, and CO2 emissions every minute. The monitored data is then sent to a cloud server using a stable communication protocol.

[0049] Cloud server operation (data storage and preprocessing)

[0050] The server stores the received data in the cloud. The stored data is then processed as follows:

[0051] Integrity check: Checks the consistency and accuracy of received data and detects invalid data.

[0052] Outlier correction: Correcting extreme values ​​in the data to more appropriate values ​​and imputing missing data.

[0053] Generative AI operation (data analysis and investment proposal generation)

[0054] The generative AI, housed on the server, analyzes the pre-processed data. The generative AI does the following:

[0055] Pattern Recognition: Identify trends in energy consumption patterns and CO2 emissions.

[0056] Trend analysis: Identify optimal investment opportunities based on the latest renewable energy trends.

[0057] Investment proposal generation: Generate specific investment proposals, including plans for the introduction of renewable energy systems (e.g., solar panels or wind turbines).

[0058] Present investment proposals (report generation and user presentation)

[0059] The server generates a report of the investment proposals calculated by the AI, which includes:

[0060] Specific specifications of the renewable energy equipment to be introduced

[0061] The initial investment required and the expected payback period

[0062] Energy efficiency improvement effect and CO2 emission reduction amount

[0063] The reports are provided to the user, who can view the contents through a web interface or dashboard.

[0064] Specific examples

[0065] Here is an example of a manufacturing company that implemented this system. Here is a specific scenario:

[0066] 1. Terminals are installed in each area of ​​the factory to collect electricity and gas consumption data and CO2 emissions in real time.

[0067] 2. The data is sent to a server on the cloud at regular intervals.

[0068] 3. The server receives the data, stores it, checks its integrity, and corrects any outliers.

[0069] 4. The server's generation AI analyzes the data and generates optimal investment proposals based on the latest renewable energy trends.

[0070] 5. The server creates a report of the generated investment proposal and presents it to the user.

[0071] 6. The user reviews the report and decides to install a renewable energy system based on the information provided.

[0072] This system will enable companies to improve their energy efficiency and reduce CO2 emissions, and provide concrete guidelines for sustainable energy management.

[0073] The processing flow will be explained below.

[0074] Step 1:

[0075] The terminal collects a company's energy consumption and CO2 emissions in real time. Data is acquired from various sensor locations and sent to a cloud server at regular intervals. The data includes electricity consumption, gas consumption, CO2 emissions, etc.

[0076] Step 2:

[0077] The server receives the data sent from the device and stores it in a cloud database. The received data is not stored as is, but is encapsulated and tagged. At this point, data integrity checks and initial filtering can also be performed.

[0078] Step 3:

[0079] The server performs pre-processing on the stored data, which includes the following steps:

[0080] Integrity check: Validate data consistency and check for inconsistencies.

[0081] Outlier correction: Correcting abnormal or missing values ​​in the data. For example, imputing abnormally high electricity usage data with the average value.

[0082] Step 4:

[0083] The server's generation AI analyzes the preprocessed data. The generation AI performs the following tasks:

[0084] Recognizing energy consumption patterns: Analyzing time series data to identify cyclical patterns and sudden trends in consumption.

[0085] Trend Analysis: Capture renewable energy trends and market data and generate optimal energy investment plans based on this.

[0086] Step 5:

[0087] The server generates a report based on the AI ​​analysis results, summarizing optimal renewable energy investment proposals. The report includes:

[0088] Specific Equipment: Recommended renewable energy systems (e.g., specific models of solar panels or wind turbines).

[0089] Initial investment: the required implementation costs and expected payback period.

[0090] Expected effects: Improved energy efficiency and reduced CO2 emissions are expected.

[0091] Step 6:

[0092] The server generates reports and displays them on a dashboard for users to access and review. Users can view detailed reports and simulation results through the dashboard.

[0093] Step 7:

[0094] We create a specific implementation plan based on the investment proposal provided by the user, determine the equipment and measures to be introduced, and proceed with the introduction of the renewable energy system.

[0095] Step 8:

[0096] The server continuously monitors the performance of the installed renewable energy system, collecting and analyzing operational data after installation and providing regular feedback for optimization and improvement.

[0097] Through these steps, this system improves a company's energy consumption efficiency and reduces CO2 emissions.

[0098] Example 1

[0099] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0100] Currently, many companies are aiming to improve energy efficiency and reduce greenhouse gas emissions, but they lack the tools and systems to realistically achieve this. Furthermore, there are limited means to optimize investments in renewable energy. There is a need for a system that allows companies to understand energy consumption patterns and CO2 emissions in real time and use that data to derive optimal renewable energy investment plans.

[0101] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0102] In this invention, the server includes a data analysis means using AI to identify energy consumption patterns and CO2 emission trends, a means for generating renewable energy investment proposals, and a means for generating and presenting investment proposal reports to users, allowing companies to analyze energy consumption data and CO2 emission data in real time and quickly obtain optimal renewable energy investment proposals.

[0103] "Energy consumption data" refers to data on the amount of electricity, gas, and other energy consumed by a business or facility.

[0104] "CO2 emissions data" refers to data on the amount of carbon dioxide emitted by companies and facilities.

[0105] "Real-time collection means" is a general term for devices and sensors that can acquire and record data in real time, in the present.

[0106] "Means for storing and pre-processing received data" refers to devices or systems that store collected data in an appropriate format and perform procedures to correct or supplement inconsistencies or outliers prior to data analysis.

[0107] "Generative AI" refers to artificial intelligence technologies that have the ability to generate and analyze for specific tasks.

[0108] "Data Analysis Tools" means tools that use techniques and algorithms to analyze collected and pre-processed data and identify trends and patterns.

[0109] The "means for generating renewable energy investment proposals" refers to a method or system for formulating an investment plan for optimal renewable energy technologies based on the results of data analysis.

[0110] The "means for generating an investment proposal report and presenting it to the user" refers to an interface and technology for organizing the generated investment proposals in a report format and presenting them visually and informationally to the user.

[0111] "Integrity checking" is the process of checking that data is consistent and free of inconsistencies and errors.

[0112] "Outlier correction" is a method for detecting extreme or unnatural values ​​in data and correcting them to an appropriate range.

[0113] A "renewable energy system" is a device or facility that utilizes sustainable energy sources such as solar, wind, hydroelectric, and biomass.

[0114] An "investment proposal" is a proposal that outlines a planned investment in a specific project or technology.

[0115] "Performance monitoring" is the process of continuously monitoring installed systems and equipment to ensure they are performing as expected and efficiently.

[0116] "Providing feedback" is the activity of reporting the results of performance monitoring to users and providing information for necessary improvements and optimization.

[0117] This system analyzes a company's energy consumption and CO2 emissions in real time and proposes optimal renewable energy investments based on that data. The entire system consists of the following main components:

[0118] 1. IoT devices that collect energy consumption and CO2 emissions data in real time

[0119] Terminals are installed in factories and offices to monitor electricity consumption, gas usage, and CO2 emissions every minute. These data are collected using smart meters and IoT sensors. For example, minute-by-minute electricity consumption data obtained by a smart meter and emission data from a CO2 sensor can be considered. This data is then sent to a cloud server using a reliable communication protocol (e.g., MQTT, HTTP).

[0120] 2. Cloud server that stores and preprocesses received data

[0121] The data received by the server is stored in a cloud database (e.g., Amazon RDS, Google (registered trademark) BigQuery). The integrity of the received data is checked and outliers are corrected. When checking the integrity of the data, consistency is checked using SQL queries to detect whether there are any outliers. If an outlier is detected, it is corrected using statistical methods and missing data is filled in.

[0122] 3. Data analysis using generative AI

[0123] The server's built-in generative AI (e.g., OpenAI® GPT-4®) analyzes the pre-processed data. The generative AI identifies energy consumption patterns and CO2 emission trends, and identifies optimal investment targets based on the latest renewable energy trend information. Time series analysis and trend analysis techniques are used in the analysis to identify optimal investment targets.

[0124] 4. A means of generating renewable energy investment proposals

[0125] Based on the results of the analysis by the AI, the server generates a specific renewable energy system implementation plan. Investment proposals include plans for installing solar panels and wind turbines. The required initial investment amount, payback period, energy efficiency improvement effect, and CO2 emission reduction amount are calculated.

[0126] 5. Means for generating and presenting investment proposal reports to users

[0127] The server generates a report based on the AI-generated investment proposal. This report includes detailed specifications of the renewable energy equipment to be installed, the required initial investment amount, payback period, energy efficiency improvement effects, and CO2 emission reduction amounts. The generated report can be accessed by the user via a web interface (e.g., a React-based dashboard) or a dedicated app.

[0128] Specific examples

[0129] A specific example will be described in which a manufacturing company has introduced this system.

[0130] 1. Terminals are installed in each area of ​​the factory to collect electricity consumption data, gas usage, and CO2 emissions in real time.

[0131] 2. The data is sent to a server on the cloud at regular intervals.

[0132] 3. The server stores the received data, checks its integrity, and corrects outliers.

[0133] 4. Server generation AI analyzes the data and generates optimal investment proposals based on the latest renewable energy trends.

[0134] 5. The server creates a report of the investment proposals and presents it to the user.

[0135] 6. The user reviews the report and decides on the optimal renewable energy system implementation based on the information provided.

[0136] Prompt Sentence Examples

[0137] Here is an example of a prompt to input to a generative AI model:

[0138] "Generate optimal renewable energy investment proposals based on Company X's energy consumption and CO2 emissions data. Average monthly electricity consumption is 100,000 kWh, average monthly gas consumption is 20,000 m3, and CO2 emissions total 2,400 tons per year. Please refer to the latest renewable energy trends."

[0139] Using these prompts, the generative AI can suggest optimal investment ideas.

[0140] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0141] Step 1: Data collection

[0142] The terminal collects electricity consumption, gas usage, and CO2 emissions in real time from sensors and meters installed in factories and businesses. The input is data from various sensors, and the output is that this data is temporarily stored in the terminal. Specifically, smart meters and CO2 sensors measure data every minute and store it in the terminal's memory.

[0143] Step 2: Send data

[0144] The data collected by the terminal is sent to the cloud server using a stable communication protocol (e.g., MQTT, HTTP). The input is the data collected in step 1, and the output is the data sent to the cloud server. Specifically, the terminal aggregates the data into packets at regular intervals and sends them to the cloud server. If a communication error occurs, it attempts to resend the data.

[0145] Step 3: Save Data

[0146] The server stores the data it receives in a cloud database (e.g., Amazon RDS, Google BigQuery). The input is data sent from the device, and the output is data stored in the cloud database. Specifically, the server executes an insert query in the database, saves the data, and performs automatic backups.

[0147] Step 4: Data Preprocessing

[0148] The server checks the consistency of the incoming data and corrects outliers. The input is data stored in a cloud database, and the output is preprocessed data. Specifically, the server checks consistency using SQL queries and corrects any outliers detected using statistical methods. It also runs algorithms to impute missing data.

[0149] Step 5: Data analysis

[0150] The generative AI built into the server analyzes the preprocessed data. The input is the preprocessed data, and the output is energy consumption patterns, CO2 emission trends, and optimal renewable energy trend information. The generative AI performs time series analysis and trend analysis to identify optimal investment targets. Specifically, the AI ​​model processes large amounts of data and extracts specific patterns and trends.

[0151] Step 6: Investment proposal generation

[0152] The server generates a specific renewable energy system implementation plan based on the analysis results of the AI. The input is the results of the data analysis, and the output is a specific investment proposal. Specifically, the server runs an implementation plan simulation and calculates the required initial investment amount, payback period, energy efficiency improvement effect, and CO2 emission reduction amount.

[0153] Step 7: Generate reports

[0154] The server compiles the generated investment proposals into a report format. The input is the investment proposal data, and the output is a document in report format. Specifically, the server embeds the data into a report template, generates visual elements such as graphs and tables, and exports them in PDF format.

[0155] Step 8: User Presentation

[0156] The user checks the generated report through a web interface or a dedicated app. The input is the report provided by the server, and the output is the information checked by the user. Specifically, the user accesses the dashboard in a web browser, downloads and views the report, and, if necessary, shares the report with other decision makers.

[0157] This allows the processing of the entire system to be realized.

[0158] (Application example 1)

[0159] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0160] In modern manufacturing and large-scale facilities, managing energy consumption and CO2 emissions is important, but there is no effective system for analyzing this data in real time and determining optimal renewable energy investments. Furthermore, there is a lack of support tools that allow managers to instantly grasp the situation and make quick decisions. In such environments, there are issues with reduced energy efficiency and unnecessary costs.

[0161] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0162] In this invention, the server includes means for collecting energy consumption data and CO2 emission data in real time from IoT devices, means for storing and preprocessing the received data in a cloud computing platform, means for analyzing the preprocessed data using a generative AI model and generating a renewable energy investment proposal, and means for presenting the generated investment proposal report to a user using a head-mounted display, thereby enabling real-time monitoring of energy consumption and CO2 emissions and proposals for optimal renewable energy investments.

[0163] An "IoT terminal" is a device that is connected to the Internet and is capable of collecting and transmitting data.

[0164] "Energy consumption data" refers to data that indicates the amount of energy consumed, such as electricity and gas, by factories and facilities.

[0165] "CO2 emission data" is data that indicates the amount of carbon dioxide emitted as a result of energy consumption.

[0166] A "cloud computing platform" is a service infrastructure that allows data to be stored, processed, and analyzed via the Internet.

[0167] A "generative AI model" is an artificial intelligence algorithm or model that analyzes large amounts of data and extracts specific patterns and trends.

[0168] "Preprocessing" refers to processing carried out prior to analysis, such as checking the consistency of collected data and correcting outliers.

[0169] "Analysis" is the act of examining data in detail to find specific patterns and trends.

[0170] A "renewable energy investment plan" is a specific proposal for introducing renewable energy sources such as solar power and wind power.

[0171] A "head-mounted display" is a display device that is worn on the head and provides visual information.

[0172] The "Investment Proposal Report" is a report summarizing the specific details of renewable energy investments proposed by the generative AI model.

[0173] "Users" are companies and individuals who use the system.

[0174] The present invention relates to a system that analyzes a company's energy consumption and CO2 emissions in real time and proposes optimal renewable energy investments. Specific embodiments of this system will be described below.

[0175] Overall system overview

[0176] The system of the present invention consists of the following main components:

[0177] 1. IoT devices

[0178] 2. Cloud Computing Platform

[0179] 3. Generative AI Models

[0180] 4. Head-Mounted Display (HMD)

[0181] IoT device operation

[0182] IoT devices are installed in various areas of the factory to monitor electricity consumption, gas usage, and CO2 emissions every minute, and the monitored data is sent to a cloud computing platform via internet communication.

[0183] Cloud Computing Platform Operation

[0184] A cloud computing platform provides the following capabilities:

[0185] Data storage: The received data is stored in the cloud. This process uses a data storage service.

[0186] Data preprocessing: Consistency checks and outlier corrections are performed to generate reliable data.

[0187] How generative AI models work

[0188] A generative AI model analyzes the preprocessed data, specifically:

[0189] Pattern Recognition: Identify trends in energy consumption patterns and CO2 emissions.

[0190] Trend analysis: Identify optimal investment opportunities based on the latest renewable energy trends.

[0191] Investment proposal generation: Generate specific investment proposals, including plans for the introduction of renewable energy systems (e.g., solar panels and wind turbines).

[0192] Head-mounted display operation

[0193] The head-mounted display (HMD) is a device that allows factory managers to check energy consumption data and CO2 emissions in real time and view generated investment proposal reports. The HMD provides the following functions:

[0194] Real-time monitoring: Data obtained from IoT devices installed in each area of ​​the factory is displayed in real time.

[0195] Abnormality alert: A function that automatically notifies you if energy consumption or CO2 emissions show abnormal values.

[0196] Investment proposal presentation: The generation AI analyzes and displays proposed investment proposals in report format.

[0197] Voice command function: Factory managers can access and review data and suggestions using voice commands.

[0198] Collaboration function: The ability to share data and proposals with other managers in real time and work together to come up with solutions.

[0199] Specific examples

[0200] Here is a scenario in which a manufacturing company implemented this system:

[0201] 1. IoT terminals are installed in each area of ​​the factory to collect electricity and gas consumption data and CO2 emissions in real time.

[0202] 2. The data is transmitted to the cloud computing platform via internet communication.

[0203] 3. The cloud computing platform stores the received data and performs preprocessing, specifically integrity checking and outlier correction.

[0204] 4. The generative AI model analyzes the pre-processed data to identify trends in energy consumption patterns and CO2 emissions, and generates renewable energy investment recommendations based on the latest trend analysis.

[0205] 5. The head-mounted display presents the generated investment proposal report to the factory manager, providing real-time monitoring and alerting in case of abnormalities.

[0206] An example prompt is, "Analyze the factory's energy consumption data and CO2 emissions trends to generate optimal renewable energy investment proposals."

[0207] This system will achieve both energy cost reduction and environmental protection by significantly improving the efficiency of factory energy management.

[0208] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0209] Step 1:

[0210] Terminals are installed in each area of ​​the factory to monitor power consumption, gas usage, and CO2 emissions in real time. These data are collected by the IoT terminals. The input is energy data and CO2 emission data from each sensor, and the output is data sent to a cloud computing platform via the internet.

[0211] Step 2:

[0212] The server saves the received data on the cloud computing platform. The saved data is temporarily stored on the data storage for preprocessing. The input is the energy data and CO2 emission data sent in the previous step, and the output is the data saved in the cloud.

[0213] Step 3:

[0214] The server checks the integrity of the received data and corrects outliers. Specifically, it uses a data cleaning algorithm to check the consistency of the data and appropriately corrects extreme values ​​and missing values. The input is raw data stored in the cloud, and the output is preprocessed data.

[0215] Step 4:

[0216] The generative AI model analyzes the pre-processed data. First, it performs pattern recognition to identify trends in energy consumption patterns and CO2 emissions. Second, it performs trend analysis based on renewable energy trend information to identify optimal investment targets. Finally, it generates specific investment proposals, including plans for the introduction of renewable energy systems (e.g., solar panels and wind turbines). The input is the pre-processed data, and the output is the optimal renewable energy investment proposal.

[0217] Step 5:

[0218] The server creates a report of the generated investment proposal, which includes the specific specifications of the renewable energy equipment to be introduced, the required initial investment amount and expected payback period, the effect of improving energy efficiency, and the amount of CO2 emissions reduced. The input is the investment proposal output by the generative AI model, and the output is the completed investment proposal report.

[0219] Step 6:

[0220] A head-mounted display (HMD) presents the generated investment proposal report to the user in real time. Factory managers can check energy consumption data and CO2 emissions in real time through the HMD and receive instant notifications if an abnormality occurs. Voice commands can also be used to easily call up and check data and proposals. The input is the investment proposal report and real-time data sent from the server, and the output is the information presented to the user.

[0221] In this way, factory managers can make quick and accurate decisions to increase energy efficiency and reduce CO2 emissions.

[0222] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0223] The present invention is a system that analyzes a company's energy consumption and CO2 emissions in real time and proposes optimal renewable energy investments by combining an emotion engine. Specific embodiments of this system are described below.

[0224] Overall system overview

[0225] The system of the present invention consists of the following main components:

[0226] 1. IoT devices that collect energy consumption data and CO2 emissions data in real time.

[0227] 2. A cloud server that stores and preprocesses the received data.

[0228] 3. A means of analyzing data and generating renewable energy investment proposals using generative AI.

[0229] 4. A means for generating and presenting investment proposal reports to the user.

[0230] 5. An emotion engine that recognizes and analyzes user emotions.

[0231] Specific examples of program execution

[0232] IoT device operation (data collection)

[0233] The terminals are installed in the company's factories and monitor electricity consumption, gas consumption, and CO2 emissions every minute. The monitored data is sent to a cloud server using a stable communication protocol.

[0234] Cloud server operation (data storage and preprocessing)

[0235] The server receives the data sent from the device and stores it in a cloud database. The received data is not stored as is, but is encapsulated and tagged. At this point, data integrity checks and initial filtering are also performed.

[0236] Generative AI operation (data analysis and investment proposal generation)

[0237] The generative AI built into the server analyzes the pre-processed data and performs the following tasks:

[0238] Recognizing energy consumption patterns: Analyzing time series data to identify cyclical patterns and sudden trends in consumption.

[0239] Trend Analysis: Capture the latest renewable energy trends and market data to generate optimal energy investment plans.

[0240] Investment proposal generation: Generate specific investment proposals, including plans for the installation of renewable energy systems (e.g., specific models of solar panels or wind turbines).

[0241] Emotion engine operation (emotion recognition and analysis)

[0242] The server collects and analyzes the user's emotional data. Emotion data is collected from facial expressions, tone of voice, and input history when the user views an investment report. The emotion engine performs the following:

[0243] Emotion recognition: Detecting emotions from a user's facial expressions, voice, and text data.

[0244] Sentiment Analysis: Analyze how the detected sentiment tends to react to the investment proposal.

[0245] Present investment proposals (report generation and user presentation)

[0246] The server generates a report based on the results of the AI ​​analysis and the emotion engine analysis, summarizing optimal renewable energy investment proposals. The report includes:

[0247] Specific equipment to be installed: Recommended renewable energy systems.

[0248] Initial investment: the required implementation costs and expected payback period.

[0249] Expected effects: Improved energy efficiency and reduced CO2 emissions are expected.

[0250] Customization based on user emotions: Presentation content and presentation methods are optimized according to the user's emotions.

[0251] Reports are provided to users, who can view the contents through a web interface or dashboard.

[0252] Specific examples

[0253] Here is an example of a manufacturing company that implemented this system. Here is a specific scenario:

[0254] 1. Terminals are installed in each area of ​​the factory to collect electricity and gas consumption data and CO2 emissions in real time.

[0255] 2. The data is sent to a server on the cloud at regular intervals.

[0256] 3. The server receives the data, stores it, checks its integrity, and corrects any outliers.

[0257] 4. The server's generation AI analyzes the data and generates optimal investment proposals based on the latest renewable energy trends.

[0258] 5. The server uses an emotion engine to collect user emotion data and optimize the format and content of the investment proposal report.

[0259] 6. The server presents the generated investment proposal report to the user and re-analyzes the user's feedback if necessary.

[0260] 7. The user reviews the report and decides to install a renewable energy system based on the information provided.

[0261] This system will enable companies to improve their energy efficiency and reduce CO2 emissions, and will also maximize the effectiveness of its proposals by taking into account the user's emotions.

[0262] The processing flow will be explained below.

[0263] Step 1:

[0264] The terminal collects a company's energy consumption and CO2 emissions in real time. Data is acquired from various sensor locations and sent to a cloud server at regular intervals. The data includes electricity consumption, gas consumption, CO2 emissions, etc.

[0265] Step 2:

[0266] The server receives the data sent from the device and stores it in a cloud database. The received data is not stored as is, but is encapsulated and tagged. At this point, data integrity checks and initial filtering are also performed.

[0267] Step 3:

[0268] The server performs pre-processing on the stored data, which includes the following steps:

[0269] Integrity check: Validate data consistency and check for inconsistencies.

[0270] Outlier correction: Correcting abnormal or missing values ​​in the data. For example, imputing abnormally high electricity usage data with the average value.

[0271] Step 4:

[0272] The server's generation AI analyzes the preprocessed data. The generation AI performs the following tasks:

[0273] Recognizing energy consumption patterns: Analyzing time series data to identify cyclical patterns and sudden trends in consumption.

[0274] Trend Analysis: Capture the latest renewable energy trends and market data to generate optimal energy investment plans.

[0275] Investment proposal generation: Generate specific investment proposals, including plans for the installation of renewable energy systems (e.g., specific models of solar panels or wind turbines).

[0276] Step 5:

[0277] The server generates a report based on the AI ​​analysis results, summarizing optimal renewable energy investment proposals. The report includes:

[0278] Specific Equipment: Recommended renewable energy systems (e.g., specific models of solar panels or wind turbines).

[0279] Initial investment: the required implementation costs and expected payback period.

[0280] Expected effects: Improved energy efficiency and reduced CO2 emissions are expected.

[0281] Step 6:

[0282] The server collects and analyzes user emotion data using an emotion engine. The emotion engine performs the following tasks:

[0283] Emotion recognition: Detecting emotions from a user's facial expressions, voice, and text data.

[0284] Sentiment analysis: Analyze the detected sentiment to see how it tends to affect the investment proposal. Based on the results of the sentiment analysis, optimize the presentation and content of the investment proposal report.

[0285] Step 7:

[0286] The server displays the report on a dashboard for users to access and review. Users can view detailed reports and simulation results through the dashboard. Based on the results of sentiment analysis, customized content is presented that is tailored to the user's interests.

[0287] Step 8:

[0288] We create a specific implementation plan based on the investment proposal provided by the user, determine the equipment and measures to be introduced, and proceed with the introduction of the renewable energy system.

[0289] Step 9:

[0290] The server continuously monitors the performance of the installed renewable energy system, collecting and analyzing operational data after installation and providing regular feedback for optimization and improvement.

[0291] Through these steps, this system improves the energy consumption efficiency of companies and reduces CO2 emissions. Furthermore, by taking into account the user's emotions, it maximizes the effectiveness of the suggestions.

[0292] Example 2

[0293] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0294] Reducing energy consumption and CO2 emissions in modern companies is extremely important from the perspective of reducing environmental impact and cutting costs. However, systems for efficiently collecting and analyzing energy consumption and CO2 emission data and generating optimal renewable energy investment proposals are inadequate. Furthermore, there are no established methods for verifying the consistency of collected data, correcting outliers, or customizing proposals based on user sentiment. As a result, companies have difficulty obtaining actionable information for making appropriate renewable energy investments.

[0295] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for collecting energy consumption data and CO2 emission data in real time using IoT terminals, means for storing and preprocessing the received data in a cloud device, means for analyzing the preprocessed data using a generation AI and generating a renewable energy investment proposal, means for generating an investment proposal report and presenting it to the user, and means for collecting and analyzing user emotion data. This enables accurate analysis of the data collected in real time and makes it possible to provide an optimal renewable energy investment proposal based on the user's emotions.

[0296] An "IoT device" is a device that is connected to the Internet and collects data from the physical environment and transmits it to the cloud or other devices.

[0297] "Energy consumption data" is data relating to the amount of energy consumed within a certain period of time.

[0298] "CO2 emissions data" refers to data on the amount of carbon dioxide emitted within a certain period of time.

[0299] "Cloud device" refers to a remote server or data center used to store, process, and manage data over the Internet.

[0300] "Generative AI" is a system that uses machine learning and artificial intelligence techniques to analyze data and generate new information and suggestions.

[0301] "Preprocessing" refers to processes such as data formatting, encapsulation, tagging, consistency checking, and outlier correction that are performed before data analysis.

[0302] An "investment proposal report" is a document that summarizes specific proposals for the introduction of renewable energy systems.

[0303] "User emotion data" refers to data related to emotions acquired from facial expressions, tone of voice, and input history when a user uses the system interface.

[0304] "Renewable energy investment proposal" refers to a specific plan or proposal for the introduction of a renewable energy system.

[0305] The present invention is a system that analyzes a company's energy consumption and CO2 emissions in real time and proposes optimal renewable energy investments by combining an emotion engine. Specific embodiments of this system are described below.

[0306] Overall system overview

[0307] The system of the present invention consists of the following main components:

[0308] 1. IoT devices that collect energy consumption data and CO2 emissions data in real time.

[0309] 2. A cloud server that stores and preprocesses the received data.

[0310] 3. A means of analyzing data and generating renewable energy investment proposals using generative AI.

[0311] 4. A means for generating and presenting investment proposal reports to the user.

[0312] 5. An emotion engine that recognizes and analyzes user emotions.

[0313] IoT device operation (data collection)

[0314] Terminals are installed in companies' factories and offices. These terminals collect electricity consumption, gas consumption, and CO2 emissions in real time. The monitored data is sent to a cloud server using a stable communication protocol (e.g., MQTT).

[0315] Cloud server operation (data storage and preprocessing)

[0316] The server receives the data sent from the device and stores it in a cloud database (e.g., Amazon RDS). The stored data is encapsulated and tagged, and data integrity checks and initial filtering are performed to eliminate outliers and missing data.

[0317] Generative AI operation (data analysis and investment proposal generation)

[0318] A generative AI (e.g., OpenAI's GPT model) built into the server analyzes the preprocessed data. Specific tasks include:

[0319] Recognizing energy consumption patterns: Analyzing time series data to identify cyclical patterns and sudden trends in consumption.

[0320] Trend Analysis: Capture the latest renewable energy trends and market data to generate optimal energy investment plans.

[0321] Investment proposal generation: Generate an investment proposal that includes a plan to introduce a specific renewable energy system (e.g., a specific model of solar panel).

[0322] Emotion engine operation (emotion recognition and analysis)

[0323] The server collects and analyzes the user's emotional data. The data includes facial expressions, tone of voice, and input history when the user views an investment report. The emotion engine (e.g., Microsoft® Azure® Emotion API) performs the following:

[0324] Emotion recognition: Detecting emotions from a user's facial expressions, voice, and text data.

[0325] Sentiment analysis: Analyze how the detected sentiment tends to react to the investment proposal.

[0326] Generate and present investment proposal reports (server operation)

[0327] The server generates a report based on the results of the AI ​​analysis and the emotion engine analysis, summarizing optimal renewable energy investment proposals. The report includes:

[0328] Specific equipment to be installed: Recommended renewable energy systems.

[0329] Initial investment: the required implementation costs and expected payback period.

[0330] Expected effects: Improved energy efficiency and reduced CO2 emissions are expected.

[0331] Customization based on user emotions: Presentation content and presentation methods are optimized according to the user's emotions.

[0332] The reports are provided to the user, who can view the contents through a web interface or dashboard.

[0333] Specific examples

[0334] Here is a specific example of how a manufacturing company implemented this system:

[0335] 1. Terminals are installed in each area of ​​the factory to collect electricity consumption, gas consumption, and CO2 emissions in real time. For example, a terminal is installed on a specific production line to collect energy consumption data for that line every minute.

[0336] 2. The data is sent to a server on the cloud at regular intervals.

[0337] 3. The server receives the data, stores it, checks its integrity, and corrects outliers. For example, if abnormally high consumption data is detected, it will be corrected and recorded.

[0338] 4. Server generation AI analyzes the data and generates optimal investment proposals based on the latest renewable energy trends. For example, it creates a plan to introduce the latest solar panels.

[0339] 5. The server uses an emotion engine to collect user emotion data (facial expressions and tone of voice) and optimize the format and content of the investment proposal report. For example, if the user expresses surprise, the server will provide more detailed explanations on that part.

[0340] 6. The server generates a report of the proposed investment and presents it to the user, who can then view it in a dashboard, for example, a web interface that provides a detailed cost analysis and impact forecast.

[0341] 7. The user decides to install a renewable energy system based on the information provided.

[0342] Prompt Sentence Examples

[0343] Generate optimal investment proposals to maximize cost savings from installing commercial solar power systems. Also, how can you customize the investment proposals by taking user sentiment data into account?

[0344] This system enables companies to improve energy efficiency and reduce CO2 emissions, and by taking user emotions into consideration, it also maximizes the effectiveness of its proposals.

[0345] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0346] Step 1: Data collection (device operation)

[0347] Terminals are installed in companies' factories and offices to monitor electricity consumption, gas usage, and CO2 emissions in real time. When this data is collected, the terminals use a communication protocol (e.g., MQTT) to send the data to a cloud server. The input is data from various sensors, and the output is raw data sent to the cloud server. Specifically, the terminals collect data from sensors installed in each location at regular intervals and send it to the cloud server in real time.

[0348] Step 2: Data storage and preprocessing (server operation)

[0349] The server receives data sent from the terminal. The input is the raw data sent from the terminal, and the output is the preprocessed data. Specifically, the server stores the received data in a cloud database (e.g., Amazon RDS), encapsulates and tags the data, and performs data integrity checks and outlier corrections, thereby creating a reliable dataset.

[0350] Step 3: Data analysis and investment proposal generation (operation of the generative AI model)

[0351] The server's generation AI analyzes the preprocessed data. The input is the preprocessed data, and the output is a renewable energy investment proposal. Specifically, the generation AI (e.g., OpenAI's model) analyzes time-series data and recognizes energy consumption patterns. It also incorporates the latest renewable energy trends and generates an optimal energy investment plan. This provides a specific investment proposal, including plans for the introduction of specific models of solar panels, wind turbines, etc.

[0352] Step 4: Emotion Recognition and Analysis (How the Emotion Engine Works)

[0353] The server collects and analyzes the user's emotional data. The input is the user's facial expression, tone of voice, and input history, and the output is the result of emotion recognition. Specifically, an emotion engine (e.g., Microsoft Azure's Emotion API) analyzes the user's facial expression and tone of voice in real time as they view reports to detect their emotions. Based on this detected data, the system analyzes their reaction to the investment proposal.

[0354] Step 5: Generate and present the investment proposal report (server operation)

[0355] The server compiles optimal renewable energy investment proposals in the form of a report based on the analysis results of the generated AI and the emotion engine. The inputs are the analysis results and emotion recognition results, and the output is a customized investment proposal report. Specifically, the report includes recommended renewable energy systems, initial investment amounts, predicted benefits, and customization elements based on the user's emotions. The server presents this to the user via a dashboard or web interface.

[0356] Step 6: Collecting user feedback and re-analyzing (server actions)

[0357] The server collects feedback from users and reanalyzes it. The input is the user's feedback data, and the output is an updated investment proposal report. Specifically, based on the feedback provided by the user (e.g., questions or requests for revisions), the generative AI and emotion engine reanalyze and update the investment proposals. This makes it possible to provide more precise information to the user.

[0358] (Application example 2)

[0359] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0360] Optimizing energy consumption and CO2 emissions in companies is an important issue, but currently it is difficult to collect and analyze data in real time, and there are also challenges in proposing renewable energy investment plans that take user emotions into account. The purpose of this invention is to solve these challenges and achieve improved energy efficiency and reduced CO2 emissions in companies.

[0361] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0362] In this invention, the server includes a means for collecting energy consumption data and CO2 emission data in real time, a means for storing and preprocessing the data, a means for analyzing the preprocessed data and generating renewable energy investment proposals, a means for recognizing and analyzing user emotion data, and a means for optimizing the format and content of the investment proposal report based on the user emotion, thereby enabling real-time analysis of a company's energy consumption and CO2 emission, and further enabling the proposal of an optimal renewable energy investment proposal taking into account the user emotion.

[0363] An "IoT terminal" is a terminal for collecting and transmitting data via the Internet.

[0364] "Energy consumption data" means data relating to the energy usage of a particular system or facility.

[0365] "CO2 emissions data" means data relating to the amount of carbon dioxide emitted by a particular system or facility.

[0366] A "cloud server" is a server that stores and processes data over the Internet.

[0367] "Preprocessing" refers to processes such as checking the consistency of data and correcting outliers that are carried out before analyzing collected data.

[0368] "Generative AI" is a system that uses artificial intelligence to analyze data and generate specific outputs.

[0369] A "renewable energy investment proposal" is a plan or proposal for the introduction of a renewable energy system.

[0370] An "investment proposal report" is a report that details the contents of a renewable energy investment proposal.

[0371] An "emotion engine" is a system for recognizing and analyzing a user's emotional data.

[0372] "User emotion data" is data that indicates the user's emotion, and is obtained from facial expressions, voice, text, and the like.

[0373] "Optimization" is the process of adjusting a system or process to be most efficient in order to achieve a specific purpose.

[0374] The present invention is a system that uses a robot installed in a factory to analyze a company's energy consumption and CO2 emissions in real time and provides optimal renewable energy investment proposals. Specific embodiments of the present invention are described below.

[0375] System configuration

[0376] The system consists of the following main components:

[0377] 1. IoT devices:

[0378] The IoT terminal is built into the robot and collects energy consumption and CO2 emission data in real time within the factory, and transmits the data to a cloud server via Wi-Fi.

[0379] 2. Cloud Server:

[0380] Data storage and pre-processing:

[0381] The cloud server receives and stores data sent from IoT devices, and performs preprocessing such as checking data consistency and correcting outliers.

[0382] Generation AI:

[0383] The pre-processed data is analyzed to recognize energy consumption patterns and generate renewable energy investment proposals.

[0384] Emotion Engine:

[0385] Recognize and analyze user emotion data. Collect facial expressions, voice, and text data when users view investment proposal reports to detect emotions.

[0386] 3. Generate and present investment proposal reports:

[0387] Based on the analysis results of the generative AI and the emotion engine, optimal renewable energy investment proposals are compiled in report format.

[0388] The report includes recommended renewable energy systems, initial investment amounts, expected payback periods, and potential energy efficiency gains and CO2 emission reductions.

[0389] The report optimizes its content and presentation based on user sentiment.

[0390] Hardware and software used

[0391] IoT terminal: Sensors and Wi-Fi modules built into the robot

[0392] Cloud server: Cloud infrastructure (e.g., AWS (registered trademark), Google Cloud) can be used for data storage and processing, and Tensorflow (registered trademark) or PyTorch can be used for generative AI models.

[0393] Emotion engine: Emotion recognition using OpenCV and Emotion API

[0394] Example of a system

[0395] For example, if an automobile manufacturing plant were to implement this system, it would work as follows:

[0396] 1. Installation of robots: Robots installed in each section will collect energy consumption data and CO2 emissions data in real time.

[0397] 2. Data transmission and storage: The collected data is transmitted to a cloud server where it is stored and pre-processed.

[0398] 3. Data analysis: A generative AI model analyzes the pre-processed data, identifies energy consumption patterns, and generates renewable energy investment proposals.

[0399] 4. Emotion analysis: The factory manager views the investment proposal report, and the facial expressions and voice data are analyzed by the emotion engine.

[0400] 5. Optimization of investment proposals: Based on the results of user sentiment analysis, investment proposal reports are provided in the most appropriate format and content.

[0401] Example prompts for generative AI models

[0402] "Analyze the user's emotions while viewing the investment proposal report presented to them. Based on the following input data, classify the user's emotions as either 'positive', 'neutral' or 'negative'."

[0403] Input data:

[0404] Facial expression image file: user_expression.png

[0405] Audio file: user_voice.wav

[0406] Text log: "This investment proposal is attractive. I would like to consider implementing it."

[0407] Output formats:

[0408] {

[0409] "emotion": "positive"

[0410] }"

[0411] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0412] Step 1:

[0413] The terminal uses sensors on robots installed within the factory to collect energy consumption data and CO2 emissions data in real time.

[0414] Input: Energy consumption, gas consumption, and CO2 emissions captured by each sensor

[0415] Output: Collected data is temporarily saved on the device.

[0416] Specific operation: The robot's built-in sensors collect data at specified intervals and prepare to send it to the cloud server via the Wi-Fi module.

[0417] Step 2:

[0418] The device sends the collected data to a cloud server via a Wi-Fi network.

[0419] Input: Data acquired by the sensor and the address of the destination cloud server

[0420] Output: Data sent to the cloud server

[0421] Specific operation: The device's communication module is used to send data to a cloud server via Wi-Fi.

[0422] Step 3:

[0423] The server stores the received data and performs preprocessing of the data.

[0424] Input: Energy consumption data and CO2 emissions data sent from the device

[0425] Output: Preprocessed data, outliers corrected, and data integrity checked

[0426] Specific operation: The server stores the received data in the database, performs data encapsulation, tagging, and outlier correction, and verifies any inconsistencies through consistency checks.

[0427] Step 4:

[0428] The server inputs the preprocessed data into the generative AI model and performs data analysis.

[0429] Input: Preprocessed energy consumption data and CO2 emissions data

[0430] Output: Energy consumption patterns, renewable energy investment proposals

[0431] How it works: Analyzes data using a generative AI model (e.g., using TensorFlow or PyTorch) and generates investment recommendations that incorporate time-series pattern recognition and emerging trends.

[0432] Step 5:

[0433] The server collects the user's emotional data and analyzes it using an emotion engine.

[0434] Input: User facial expression images, voice data, text input

[0435] Output: Sentiment analysis result (positive, neutral, negative, etc.)

[0436] How it works: Using emotion recognition technologies such as OpenCV and Emotion API, the system analyzes users' emotions in real time. The analysis results are recorded and reflected in the presentation of investment proposal reports.

[0437] Step 6:

[0438] The server generates an investment proposal report based on the analysis results of the generated AI and the results of the emotion engine.

[0439] Input: Renewable energy investment proposals generated by generative AI, emotion analysis results from the emotion engine

[0440] Output: Optimized investment proposal report

[0441] Specific operation: The generated investment proposals are compiled into a document and customized according to the user's emotions. For example, if the emotion is "positive," the expressions become more optimistic. If the emotion is "negative," the expressions are changed to give a sense of security.

[0442] Step 7:

[0443] The server presents the generated investment proposal report to the user.

[0444] Input: Optimized Investment Proposal Report

[0445] Output: A user-viewable investment proposal report

[0446] Specific operation: The reports generated on the cloud server are displayed on the user dashboard, and feedback is collected from the user as needed, helping the user make decisions based on the investment proposals.

[0447] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0448] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0449] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0450] [Second embodiment]

[0451] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0452] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0453] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0454] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0455] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0456] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0457] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0458] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0459] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0460] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0461] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0462] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0463] The present invention is a system that analyzes a company's energy consumption and CO2 emissions in real time and proposes optimal renewable energy investments. Specific embodiments of this system will be described below.

[0464] Overall system overview

[0465] The system of the present invention consists of the following main components:

[0466] 1. IoT devices that collect energy consumption data and CO2 emissions data in real time.

[0467] 2. A cloud server that stores and preprocesses the received data.

[0468] 3. A means of analyzing data and generating renewable energy investment proposals using generative AI.

[0469] 4. A means for generating and presenting investment proposal reports to the user.

[0470] Specific examples of program execution

[0471] IoT device operation (data collection)

[0472] The terminals are installed in the company's factories and monitor electricity consumption, gas usage, and CO2 emissions every minute. The monitored data is then sent to a cloud server using a stable communication protocol.

[0473] Cloud server operation (data storage and preprocessing)

[0474] The server stores the received data in the cloud. The stored data is then processed as follows:

[0475] Integrity check: Checks the consistency and accuracy of received data and detects invalid data.

[0476] Outlier correction: Correcting extreme values ​​in the data to more appropriate values ​​and imputing missing data.

[0477] Generative AI operation (data analysis and investment proposal generation)

[0478] The generative AI, housed on the server, analyzes the pre-processed data. The generative AI does the following:

[0479] Pattern Recognition: Identify trends in energy consumption patterns and CO2 emissions.

[0480] Trend analysis: Identify optimal investment opportunities based on the latest renewable energy trends.

[0481] Investment proposal generation: Generate specific investment proposals, including plans for the introduction of renewable energy systems (e.g., solar panels or wind turbines).

[0482] Present investment proposals (report generation and user presentation)

[0483] The server generates a report of the investment proposals calculated by the AI, which includes:

[0484] Specific specifications of the renewable energy equipment to be introduced

[0485] The initial investment required and the expected payback period

[0486] Energy efficiency improvement effect and CO2 emission reduction amount

[0487] The reports are provided to the user, who can view the contents through a web interface or dashboard.

[0488] Specific examples

[0489] Here is an example of a manufacturing company that implemented this system. Here is a specific scenario:

[0490] 1. Terminals are installed in each area of ​​the factory to collect electricity and gas consumption data and CO2 emissions in real time.

[0491] 2. The data is sent to a server on the cloud at regular intervals.

[0492] 3. The server receives the data, stores it, checks its integrity, and corrects any outliers.

[0493] 4. The server's generation AI analyzes the data and generates optimal investment proposals based on the latest renewable energy trends.

[0494] 5. The server creates a report of the generated investment proposal and presents it to the user.

[0495] 6. The user reviews the report and decides to install a renewable energy system based on the information provided.

[0496] This system will enable companies to improve their energy efficiency and reduce CO2 emissions, and provide concrete guidelines for sustainable energy management.

[0497] The processing flow will be explained below.

[0498] Step 1:

[0499] The terminal collects a company's energy consumption and CO2 emissions in real time. Data is acquired from various sensor locations and sent to a cloud server at regular intervals. The data includes electricity consumption, gas consumption, CO2 emissions, etc.

[0500] Step 2:

[0501] The server receives the data sent from the device and stores it in a cloud database. The received data is not stored as is, but is encapsulated and tagged. At this point, data integrity checks and initial filtering can also be performed.

[0502] Step 3:

[0503] The server performs pre-processing on the stored data, which includes the following steps:

[0504] Integrity check: Validate data consistency and check for inconsistencies.

[0505] Outlier correction: Correcting abnormal or missing values ​​in the data. For example, imputing abnormally high electricity usage data with the average value.

[0506] Step 4:

[0507] The server's generation AI analyzes the preprocessed data. The generation AI performs the following tasks:

[0508] Recognizing energy consumption patterns: Analyzing time series data to identify cyclical patterns and sudden trends in consumption.

[0509] Trend Analysis: Capture renewable energy trends and market data and generate optimal energy investment plans based on this.

[0510] Step 5:

[0511] The server generates a report based on the AI ​​analysis results, summarizing optimal renewable energy investment proposals. The report includes:

[0512] Specific Equipment: Recommended renewable energy systems (e.g., specific models of solar panels or wind turbines).

[0513] Initial investment: the required implementation costs and expected payback period.

[0514] Expected effects: Improved energy efficiency and reduced CO2 emissions are expected.

[0515] Step 6:

[0516] The server generates reports and displays them on a dashboard for users to access and review. Users can view detailed reports and simulation results through the dashboard.

[0517] Step 7:

[0518] We create a specific implementation plan based on the investment proposal provided by the user, determine the equipment and measures to be introduced, and proceed with the introduction of the renewable energy system.

[0519] Step 8:

[0520] The server continuously monitors the performance of the installed renewable energy system, collecting and analyzing operational data after installation and providing regular feedback for optimization and improvement.

[0521] Through these steps, this system improves a company's energy consumption efficiency and reduces CO2 emissions.

[0522] Example 1

[0523] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0524] Currently, many companies are aiming to improve energy efficiency and reduce greenhouse gas emissions, but they lack the tools and systems to realistically achieve this. Furthermore, there are limited means to optimize investments in renewable energy. There is a need for a system that allows companies to understand energy consumption patterns and CO2 emissions in real time and use that data to derive optimal renewable energy investment plans.

[0525] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0526] In this invention, the server includes a data analysis means using AI to identify energy consumption patterns and CO2 emission trends, a means for generating renewable energy investment proposals, and a means for generating and presenting investment proposal reports to users, allowing companies to analyze energy consumption data and CO2 emission data in real time and quickly obtain optimal renewable energy investment proposals.

[0527] "Energy consumption data" refers to data on the amount of electricity, gas, and other energy consumed by a business or facility.

[0528] "CO2 emissions data" refers to data on the amount of carbon dioxide emitted by companies and facilities.

[0529] "Real-time collection means" is a general term for devices and sensors that can acquire and record data in real time, in the present.

[0530] "Means for storing and pre-processing received data" refers to devices or systems that store collected data in an appropriate format and perform procedures to correct or supplement inconsistencies or outliers prior to data analysis.

[0531] "Generative AI" refers to artificial intelligence technologies that have the ability to generate and analyze for specific tasks.

[0532] "Data Analysis Tools" means tools that use techniques and algorithms to analyze collected and pre-processed data and identify trends and patterns.

[0533] The "means for generating renewable energy investment proposals" refers to a method or system for formulating an investment plan for optimal renewable energy technologies based on the results of data analysis.

[0534] The "means for generating an investment proposal report and presenting it to the user" refers to an interface and technology for organizing the generated investment proposals in a report format and presenting them visually and informationally to the user.

[0535] "Integrity checking" is the process of checking that data is consistent and free of inconsistencies and errors.

[0536] "Outlier correction" is a method for detecting extreme or unnatural values ​​in data and correcting them to an appropriate range.

[0537] A "renewable energy system" is a device or facility that utilizes sustainable energy sources such as solar, wind, hydroelectric, and biomass.

[0538] An "investment proposal" is a proposal that outlines a planned investment in a specific project or technology.

[0539] "Performance monitoring" is the process of continuously monitoring installed systems and equipment to ensure they are performing as expected and efficiently.

[0540] "Providing feedback" is the activity of reporting the results of performance monitoring to users and providing information for necessary improvements and optimization.

[0541] This system analyzes a company's energy consumption and CO2 emissions in real time and proposes optimal renewable energy investments based on that data. The entire system consists of the following main components:

[0542] 1. IoT devices that collect energy consumption and CO2 emissions data in real time

[0543] Terminals are installed in factories and offices to monitor electricity consumption, gas usage, and CO2 emissions every minute. These data are collected using smart meters and IoT sensors. For example, minute-by-minute electricity consumption data obtained by a smart meter and emission data from a CO2 sensor can be considered. This data is then sent to a cloud server using a reliable communication protocol (e.g., MQTT, HTTP).

[0544] 2. Cloud server that stores and preprocesses received data

[0545] The data received by the server is stored in a cloud database (e.g., Amazon RDS, Google BigQuery). The integrity of the received data is checked and outliers are corrected. When checking data integrity, consistency is checked using SQL queries to detect whether there are any outliers. If an outlier is detected, it is corrected using statistical methods and missing data is filled in.

[0546] 3. Data analysis using generative AI

[0547] The server's built-in generative AI (e.g., OpenAI GPT-4) analyzes the pre-processed data. The generative AI identifies energy consumption patterns and CO2 emission trends, and identifies optimal investment targets based on the latest renewable energy trends. Time series analysis and trend analysis techniques are used in the analysis to identify optimal investment targets.

[0548] 4. A means of generating renewable energy investment proposals

[0549] Based on the results of the analysis by the AI, the server generates a specific renewable energy system implementation plan. Investment proposals include plans for installing solar panels and wind turbines. The required initial investment amount, payback period, energy efficiency improvement effect, and CO2 emission reduction amount are calculated.

[0550] 5. Means for generating and presenting investment proposal reports to users

[0551] The server generates a report based on the AI-generated investment proposal. This report includes detailed specifications of the renewable energy equipment to be installed, the required initial investment amount, payback period, energy efficiency improvement effects, and CO2 emission reduction amounts. The generated report can be accessed by the user via a web interface (e.g., a React-based dashboard) or a dedicated app.

[0552] Specific examples

[0553] A specific example will be described in which a manufacturing company has introduced this system.

[0554] 1. Terminals are installed in each area of ​​the factory to collect electricity consumption data, gas usage, and CO2 emissions in real time.

[0555] 2. The data is sent to a server on the cloud at regular intervals.

[0556] 3. The server stores the received data, checks its integrity, and corrects outliers.

[0557] 4. Server generation AI analyzes the data and generates optimal investment proposals based on the latest renewable energy trends.

[0558] 5. The server creates a report of the investment proposals and presents it to the user.

[0559] 6. The user reviews the report and decides on the optimal renewable energy system implementation based on the information provided.

[0560] Prompt Sentence Examples

[0561] Here is an example of a prompt to input to a generative AI model:

[0562] "Generate optimal renewable energy investment proposals based on Company X's energy consumption and CO2 emissions data. Average monthly electricity consumption is 100,000 kWh, average monthly gas consumption is 20,000 m3, and CO2 emissions total 2,400 tons per year. Please refer to the latest renewable energy trends."

[0563] Using these prompts, the generative AI can suggest optimal investment ideas.

[0564] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0565] Step 1: Data collection

[0566] The terminal collects electricity consumption, gas usage, and CO2 emissions in real time from sensors and meters installed in factories and businesses. The input is data from various sensors, and the output is that this data is temporarily stored in the terminal. Specifically, smart meters and CO2 sensors measure data every minute and store it in the terminal's memory.

[0567] Step 2: Send data

[0568] The data collected by the terminal is sent to the cloud server using a stable communication protocol (e.g., MQTT, HTTP). The input is the data collected in step 1, and the output is the data sent to the cloud server. Specifically, the terminal aggregates the data into packets at regular intervals and sends them to the cloud server. If a communication error occurs, it attempts to resend the data.

[0569] Step 3: Save Data

[0570] The server stores the data it receives in a cloud database (e.g., Amazon RDS, Google BigQuery). The input is data sent from the device, and the output is data stored in the cloud database. Specifically, the server executes an insert query in the database, saves the data, and performs automatic backups.

[0571] Step 4: Data Preprocessing

[0572] The server checks the consistency of the incoming data and corrects outliers. The input is data stored in a cloud database, and the output is preprocessed data. Specifically, the server checks consistency using SQL queries and corrects any outliers detected using statistical methods. It also runs algorithms to impute missing data.

[0573] Step 5: Data analysis

[0574] The generative AI built into the server analyzes the preprocessed data. The input is the preprocessed data, and the output is energy consumption patterns, CO2 emission trends, and optimal renewable energy trend information. The generative AI performs time series analysis and trend analysis to identify optimal investment targets. Specifically, the AI ​​model processes large amounts of data and extracts specific patterns and trends.

[0575] Step 6: Investment proposal generation

[0576] The server generates a specific renewable energy system implementation plan based on the analysis results of the AI. The input is the results of the data analysis, and the output is a specific investment proposal. Specifically, the server runs an implementation plan simulation and calculates the required initial investment amount, payback period, energy efficiency improvement effect, and CO2 emission reduction amount.

[0577] Step 7: Generate reports

[0578] The server compiles the generated investment proposals into a report format. The input is the investment proposal data, and the output is a document in report format. Specifically, the server embeds the data into a report template, generates visual elements such as graphs and tables, and exports them in PDF format.

[0579] Step 8: User Presentation

[0580] The user checks the generated report through a web interface or a dedicated app. The input is the report provided by the server, and the output is the information checked by the user. Specifically, the user accesses the dashboard in a web browser, downloads and views the report, and, if necessary, shares the report with other decision makers.

[0581] This allows the processing of the entire system to be realized.

[0582] (Application example 1)

[0583] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0584] In modern manufacturing and large-scale facilities, managing energy consumption and CO2 emissions is important, but there is no effective system for analyzing this data in real time and determining optimal renewable energy investments. Furthermore, there is a lack of support tools that allow managers to instantly grasp the situation and make quick decisions. In such environments, there are issues with reduced energy efficiency and unnecessary costs.

[0585] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0586] In this invention, the server includes means for collecting energy consumption data and CO2 emission data in real time from IoT devices, means for storing and preprocessing the received data in a cloud computing platform, means for analyzing the preprocessed data using a generative AI model and generating a renewable energy investment proposal, and means for presenting the generated investment proposal report to a user using a head-mounted display, thereby enabling real-time monitoring of energy consumption and CO2 emissions and proposals for optimal renewable energy investments.

[0587] An "IoT terminal" is a device that is connected to the Internet and is capable of collecting and transmitting data.

[0588] "Energy consumption data" refers to data that indicates the amount of energy consumed, such as electricity and gas, by factories and facilities.

[0589] "CO2 emission data" is data that indicates the amount of carbon dioxide emitted as a result of energy consumption.

[0590] A "cloud computing platform" is a service infrastructure that allows data to be stored, processed, and analyzed via the Internet.

[0591] A "generative AI model" is an artificial intelligence algorithm or model that analyzes large amounts of data and extracts specific patterns and trends.

[0592] "Preprocessing" refers to processing carried out prior to analysis, such as checking the consistency of collected data and correcting outliers.

[0593] "Analysis" is the act of examining data in detail to find specific patterns and trends.

[0594] A "renewable energy investment plan" is a specific proposal for introducing renewable energy sources such as solar power and wind power.

[0595] A "head-mounted display" is a display device that is worn on the head and provides visual information.

[0596] The "Investment Proposal Report" is a report summarizing the specific details of renewable energy investments proposed by the generative AI model.

[0597] "Users" are companies and individuals who use the system.

[0598] The present invention relates to a system that analyzes a company's energy consumption and CO2 emissions in real time and proposes optimal renewable energy investments. Specific embodiments of this system will be described below.

[0599] Overall system overview

[0600] The system of the present invention consists of the following main components:

[0601] 1. IoT devices

[0602] 2. Cloud Computing Platform

[0603] 3. Generative AI Models

[0604] 4. Head-Mounted Display (HMD)

[0605] IoT device operation

[0606] IoT devices are installed in various areas of the factory to monitor electricity consumption, gas usage, and CO2 emissions every minute, and the monitored data is sent to a cloud computing platform via internet communication.

[0607] Cloud Computing Platform Operation

[0608] A cloud computing platform provides the following capabilities:

[0609] Data storage: The received data is stored in the cloud. This process uses a data storage service.

[0610] Data preprocessing: Consistency checks and outlier corrections are performed to generate reliable data.

[0611] How generative AI models work

[0612] A generative AI model analyzes the preprocessed data, specifically:

[0613] Pattern Recognition: Identify trends in energy consumption patterns and CO2 emissions.

[0614] Trend analysis: Identify optimal investment opportunities based on the latest renewable energy trends.

[0615] Investment proposal generation: Generate specific investment proposals, including plans for the introduction of renewable energy systems (e.g., solar panels and wind turbines).

[0616] Head-mounted display operation

[0617] The head-mounted display (HMD) is a device that allows factory managers to check energy consumption data and CO2 emissions in real time and view generated investment proposal reports. The HMD provides the following functions:

[0618] Real-time monitoring: Data obtained from IoT devices installed in each area of ​​the factory is displayed in real time.

[0619] Abnormality alert: A function that automatically notifies you if energy consumption or CO2 emissions show abnormal values.

[0620] Investment proposal presentation: The generation AI analyzes and displays proposed investment proposals in report format.

[0621] Voice command function: Factory managers can access and review data and suggestions using voice commands.

[0622] Collaboration function: The ability to share data and proposals with other managers in real time and work together to come up with solutions.

[0623] Specific examples

[0624] Here is a scenario in which a manufacturing company implemented this system:

[0625] 1. IoT terminals are installed in each area of ​​the factory to collect electricity and gas consumption data and CO2 emissions in real time.

[0626] 2. The data is transmitted to the cloud computing platform via internet communication.

[0627] 3. The cloud computing platform stores the received data and performs preprocessing, specifically integrity checking and outlier correction.

[0628] 4. The generative AI model analyzes the pre-processed data to identify trends in energy consumption patterns and CO2 emissions, and generates renewable energy investment recommendations based on the latest trend analysis.

[0629] 5. The head-mounted display presents the generated investment proposal report to the factory manager, providing real-time monitoring and alerting in case of abnormalities.

[0630] An example prompt is, "Analyze the factory's energy consumption data and CO2 emissions trends to generate optimal renewable energy investment proposals."

[0631] This system will achieve both energy cost reduction and environmental protection by significantly improving the efficiency of factory energy management.

[0632] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0633] Step 1:

[0634] Terminals are installed in each area of ​​the factory to monitor power consumption, gas usage, and CO2 emissions in real time. These data are collected by the IoT terminals. The input is energy data and CO2 emission data from each sensor, and the output is data sent to a cloud computing platform via the internet.

[0635] Step 2:

[0636] The server saves the received data on the cloud computing platform. The saved data is temporarily stored on the data storage for preprocessing. The input is the energy data and CO2 emission data sent in the previous step, and the output is the data saved in the cloud.

[0637] Step 3:

[0638] The server checks the integrity of the received data and corrects outliers. Specifically, it uses a data cleaning algorithm to check the consistency of the data and appropriately corrects extreme values ​​and missing values. The input is raw data stored in the cloud, and the output is preprocessed data.

[0639] Step 4:

[0640] The generative AI model analyzes the pre-processed data. First, it performs pattern recognition to identify trends in energy consumption patterns and CO2 emissions. Second, it performs trend analysis based on renewable energy trend information to identify optimal investment targets. Finally, it generates specific investment proposals, including plans for the introduction of renewable energy systems (e.g., solar panels and wind turbines). The input is the pre-processed data, and the output is the optimal renewable energy investment proposal.

[0641] Step 5:

[0642] The server creates a report of the generated investment proposal, which includes the specific specifications of the renewable energy equipment to be introduced, the required initial investment amount and expected payback period, the effect of improving energy efficiency, and the amount of CO2 emissions reduced. The input is the investment proposal output by the generative AI model, and the output is the completed investment proposal report.

[0643] Step 6:

[0644] A head-mounted display (HMD) presents the generated investment proposal report to the user in real time. Factory managers can check energy consumption data and CO2 emissions in real time through the HMD and receive instant notifications if an abnormality occurs. Voice commands can also be used to easily call up and check data and proposals. The input is the investment proposal report and real-time data sent from the server, and the output is the information presented to the user.

[0645] In this way, factory managers can make quick and accurate decisions to increase energy efficiency and reduce CO2 emissions.

[0646] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0647] The present invention is a system that analyzes a company's energy consumption and CO2 emissions in real time and proposes optimal renewable energy investments by combining an emotion engine. Specific embodiments of this system are described below.

[0648] Overall system overview

[0649] The system of the present invention consists of the following main components:

[0650] 1. IoT devices that collect energy consumption data and CO2 emissions data in real time.

[0651] 2. A cloud server that stores and preprocesses the received data.

[0652] 3. A means of analyzing data and generating renewable energy investment proposals using generative AI.

[0653] 4. A means for generating and presenting investment proposal reports to the user.

[0654] 5. An emotion engine that recognizes and analyzes user emotions.

[0655] Specific examples of program execution

[0656] IoT device operation (data collection)

[0657] The terminals are installed in the company's factories and monitor electricity consumption, gas consumption, and CO2 emissions every minute. The monitored data is sent to a cloud server using a stable communication protocol.

[0658] Cloud server operation (data storage and preprocessing)

[0659] The server receives the data sent from the device and stores it in a cloud database. The received data is not stored as is, but is encapsulated and tagged. At this point, data integrity checks and initial filtering are also performed.

[0660] Generative AI operation (data analysis and investment proposal generation)

[0661] The generative AI built into the server analyzes the pre-processed data and performs the following tasks:

[0662] Recognizing energy consumption patterns: Analyzing time series data to identify cyclical patterns and sudden trends in consumption.

[0663] Trend Analysis: Capture the latest renewable energy trends and market data to generate optimal energy investment plans.

[0664] Investment proposal generation: Generate specific investment proposals, including plans for the installation of renewable energy systems (e.g., specific models of solar panels or wind turbines).

[0665] Emotion engine operation (emotion recognition and analysis)

[0666] The server collects and analyzes the user's emotional data. Emotion data is collected from facial expressions, tone of voice, and input history when the user views an investment report. The emotion engine performs the following:

[0667] Emotion recognition: Detecting emotions from a user's facial expressions, voice, and text data.

[0668] Sentiment Analysis: Analyze how the detected sentiment tends to react to the investment proposal.

[0669] Present investment proposals (report generation and user presentation)

[0670] The server generates a report based on the results of the AI ​​analysis and the emotion engine analysis, summarizing optimal renewable energy investment proposals. The report includes:

[0671] Specific equipment to be installed: Recommended renewable energy systems.

[0672] Initial investment: the required implementation costs and expected payback period.

[0673] Expected effects: Improved energy efficiency and reduced CO2 emissions are expected.

[0674] Customization based on user emotions: Presentation content and presentation methods are optimized according to the user's emotions.

[0675] Reports are provided to users, who can view the contents through a web interface or dashboard.

[0676] Specific examples

[0677] Here is an example of a manufacturing company that implemented this system. Here is a specific scenario:

[0678] 1. Terminals are installed in each area of ​​the factory to collect electricity and gas consumption data and CO2 emissions in real time.

[0679] 2. The data is sent to a server on the cloud at regular intervals.

[0680] 3. The server receives the data, stores it, checks its integrity, and corrects any outliers.

[0681] 4. The server's generation AI analyzes the data and generates optimal investment proposals based on the latest renewable energy trends.

[0682] 5. The server uses an emotion engine to collect user emotion data and optimize the format and content of the investment proposal report.

[0683] 6. The server presents the generated investment proposal report to the user and re-analyzes the user's feedback if necessary.

[0684] 7. The user reviews the report and decides to install a renewable energy system based on the information provided.

[0685] This system will enable companies to improve their energy efficiency and reduce CO2 emissions, and will also maximize the effectiveness of its proposals by taking into account the user's emotions.

[0686] The processing flow will be explained below.

[0687] Step 1:

[0688] The terminal collects a company's energy consumption and CO2 emissions in real time. Data is acquired from various sensor locations and sent to a cloud server at regular intervals. The data includes electricity consumption, gas consumption, CO2 emissions, etc.

[0689] Step 2:

[0690] The server receives the data sent from the device and stores it in a cloud database. The received data is not stored as is, but is encapsulated and tagged. At this point, data integrity checks and initial filtering are also performed.

[0691] Step 3:

[0692] The server performs pre-processing on the stored data, which includes the following steps:

[0693] Integrity check: Validate data consistency and check for inconsistencies.

[0694] Outlier correction: Correcting abnormal or missing values ​​in the data. For example, imputing abnormally high electricity usage data with the average value.

[0695] Step 4:

[0696] The server's generation AI analyzes the preprocessed data. The generation AI performs the following tasks:

[0697] Recognizing energy consumption patterns: Analyzing time series data to identify cyclical patterns and sudden trends in consumption.

[0698] Trend Analysis: Capture the latest renewable energy trends and market data to generate optimal energy investment plans.

[0699] Investment proposal generation: Generate specific investment proposals, including plans for the installation of renewable energy systems (e.g., specific models of solar panels or wind turbines).

[0700] Step 5:

[0701] The server generates a report based on the AI ​​analysis results, summarizing optimal renewable energy investment proposals. The report includes:

[0702] Specific Equipment: Recommended renewable energy systems (e.g., specific models of solar panels or wind turbines).

[0703] Initial investment: the required implementation costs and expected payback period.

[0704] Expected effects: Improved energy efficiency and reduced CO2 emissions are expected.

[0705] Step 6:

[0706] The server collects and analyzes user emotion data using an emotion engine. The emotion engine performs the following tasks:

[0707] Emotion recognition: Detecting emotions from a user's facial expressions, voice, and text data.

[0708] Sentiment analysis: Analyze the detected sentiment to see how it tends to affect the investment proposal. Based on the results of the sentiment analysis, optimize the presentation and content of the investment proposal report.

[0709] Step 7:

[0710] The server displays the report on a dashboard for users to access and review. Users can view detailed reports and simulation results through the dashboard. Based on the results of sentiment analysis, customized content is presented that is tailored to the user's interests.

[0711] Step 8:

[0712] We create a specific implementation plan based on the investment proposal provided by the user, determine the equipment and measures to be introduced, and proceed with the introduction of the renewable energy system.

[0713] Step 9:

[0714] The server continuously monitors the performance of the installed renewable energy system, collecting and analyzing operational data after installation and providing regular feedback for optimization and improvement.

[0715] Through these steps, this system improves the energy consumption efficiency of companies and reduces CO2 emissions. Furthermore, by taking into account the user's emotions, it maximizes the effectiveness of the suggestions.

[0716] Example 2

[0717] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0718] Reducing energy consumption and CO2 emissions in modern companies is extremely important from the perspective of reducing environmental impact and cutting costs. However, systems for efficiently collecting and analyzing energy consumption and CO2 emission data and generating optimal renewable energy investment proposals are inadequate. Furthermore, there are no established methods for verifying the consistency of collected data, correcting outliers, or customizing proposals based on user sentiment. As a result, companies have difficulty obtaining actionable information for making appropriate renewable energy investments.

[0719] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for collecting energy consumption data and CO2 emission data in real time using IoT terminals, means for storing and preprocessing the received data in a cloud device, means for analyzing the preprocessed data using a generation AI and generating a renewable energy investment proposal, means for generating an investment proposal report and presenting it to the user, and means for collecting and analyzing user emotion data. This enables accurate analysis of the data collected in real time and makes it possible to provide an optimal renewable energy investment proposal based on the user's emotions.

[0720] An "IoT device" is a device that is connected to the Internet and collects data from the physical environment and transmits it to the cloud or other devices.

[0721] "Energy consumption data" is data relating to the amount of energy consumed within a certain period of time.

[0722] "CO2 emissions data" refers to data on the amount of carbon dioxide emitted within a certain period of time.

[0723] "Cloud device" refers to a remote server or data center used to store, process, and manage data over the Internet.

[0724] "Generative AI" is a system that uses machine learning and artificial intelligence techniques to analyze data and generate new information and suggestions.

[0725] "Preprocessing" refers to processes such as data formatting, encapsulation, tagging, consistency checking, and outlier correction that are performed before data analysis.

[0726] An "investment proposal report" is a document that summarizes specific proposals for the introduction of renewable energy systems.

[0727] "User emotion data" refers to data related to emotions acquired from facial expressions, tone of voice, and input history when a user uses the system interface.

[0728] "Renewable energy investment proposal" refers to a specific plan or proposal for the introduction of a renewable energy system.

[0729] The present invention is a system that analyzes a company's energy consumption and CO2 emissions in real time and proposes optimal renewable energy investments by combining an emotion engine. Specific embodiments of this system are described below.

[0730] Overall system overview

[0731] The system of the present invention consists of the following main components:

[0732] 1. IoT devices that collect energy consumption data and CO2 emissions data in real time.

[0733] 2. A cloud server that stores and preprocesses the received data.

[0734] 3. A means of analyzing data and generating renewable energy investment proposals using generative AI.

[0735] 4. A means for generating and presenting investment proposal reports to the user.

[0736] 5. An emotion engine that recognizes and analyzes user emotions.

[0737] IoT device operation (data collection)

[0738] Terminals are installed in companies' factories and offices. These terminals collect electricity consumption, gas consumption, and CO2 emissions in real time. The monitored data is sent to a cloud server using a stable communication protocol (e.g., MQTT).

[0739] Cloud server operation (data storage and preprocessing)

[0740] The server receives the data sent from the device and stores it in a cloud database (e.g., Amazon RDS). The stored data is encapsulated and tagged, and data integrity checks and initial filtering are performed to eliminate outliers and missing data.

[0741] Generative AI operation (data analysis and investment proposal generation)

[0742] A generative AI (e.g., OpenAI's GPT model) built into the server analyzes the preprocessed data. Specific tasks include:

[0743] Recognizing energy consumption patterns: Analyzing time series data to identify cyclical patterns and sudden trends in consumption.

[0744] Trend Analysis: Capture the latest renewable energy trends and market data to generate optimal energy investment plans.

[0745] Investment proposal generation: Generate an investment proposal that includes a plan to introduce a specific renewable energy system (e.g., a specific model of solar panel).

[0746] Emotion engine operation (emotion recognition and analysis)

[0747] The server collects and analyzes the user's emotional data. The data includes facial expressions, tone of voice, and input history when the user views an investment report. The emotion engine (e.g., Microsoft Azure's Emotion API) performs the following:

[0748] Emotion recognition: Detecting emotions from a user's facial expressions, voice, and text data.

[0749] Sentiment analysis: Analyze how the detected sentiment tends to react to the investment proposal.

[0750] Generate and present investment proposal reports (server operation)

[0751] The server generates a report based on the results of the AI ​​analysis and the emotion engine analysis, summarizing optimal renewable energy investment proposals. The report includes:

[0752] Specific equipment to be installed: Recommended renewable energy systems.

[0753] Initial investment: the required implementation costs and expected payback period.

[0754] Expected effects: Improved energy efficiency and reduced CO2 emissions are expected.

[0755] Customization based on user emotions: Presentation content and presentation methods are optimized according to the user's emotions.

[0756] The reports are provided to the user, who can view the contents through a web interface or dashboard.

[0757] Specific examples

[0758] Here is a specific example of how a manufacturing company implemented this system:

[0759] 1. Terminals are installed in each area of ​​the factory to collect electricity consumption, gas consumption, and CO2 emissions in real time. For example, a terminal is installed on a specific production line to collect energy consumption data for that line every minute.

[0760] 2. The data is sent to a server on the cloud at regular intervals.

[0761] 3. The server receives the data, stores it, checks its integrity, and corrects outliers. For example, if abnormally high consumption data is detected, it will be corrected and recorded.

[0762] 4. Server generation AI analyzes the data and generates optimal investment proposals based on the latest renewable energy trends. For example, it creates a plan to introduce the latest solar panels.

[0763] 5. The server uses an emotion engine to collect user emotion data (facial expressions and tone of voice) and optimize the format and content of the investment proposal report. For example, if the user expresses surprise, the server will provide more detailed explanations on that part.

[0764] 6. The server generates a report of the proposed investment and presents it to the user, who can then view it in a dashboard, for example, a web interface that provides a detailed cost analysis and impact forecast.

[0765] 7. The user decides to install a renewable energy system based on the information provided.

[0766] Prompt Sentence Examples

[0767] Generate optimal investment proposals to maximize cost savings from installing commercial solar power systems. Also, how can you customize the investment proposals by taking user sentiment data into account?

[0768] This system enables companies to improve energy efficiency and reduce CO2 emissions, and by taking user emotions into consideration, it also maximizes the effectiveness of its proposals.

[0769] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0770] Step 1: Data collection (device operation)

[0771] Terminals are installed in companies' factories and offices to monitor electricity consumption, gas usage, and CO2 emissions in real time. When this data is collected, the terminals use a communication protocol (e.g., MQTT) to send the data to a cloud server. The input is data from various sensors, and the output is raw data sent to the cloud server. Specifically, the terminals collect data from sensors installed in each location at regular intervals and send it to the cloud server in real time.

[0772] Step 2: Data storage and preprocessing (server operation)

[0773] The server receives data sent from the terminal. The input is the raw data sent from the terminal, and the output is the preprocessed data. Specifically, the server stores the received data in a cloud database (e.g., Amazon RDS), encapsulates and tags the data, and performs data integrity checks and outlier corrections, thereby creating a reliable dataset.

[0774] Step 3: Data analysis and investment proposal generation (operation of the generative AI model)

[0775] The server's generation AI analyzes the preprocessed data. The input is the preprocessed data, and the output is a renewable energy investment proposal. Specifically, the generation AI (e.g., OpenAI's model) analyzes time-series data and recognizes energy consumption patterns. It also incorporates the latest renewable energy trends and generates an optimal energy investment plan. This provides a specific investment proposal, including plans for the introduction of specific models of solar panels, wind turbines, etc.

[0776] Step 4: Emotion Recognition and Analysis (How the Emotion Engine Works)

[0777] The server collects and analyzes the user's emotional data. The input is the user's facial expression, tone of voice, and input history, and the output is the result of emotion recognition. Specifically, an emotion engine (e.g., Microsoft Azure's Emotion API) analyzes the user's facial expression and tone of voice in real time as they view reports to detect their emotions. Based on this detected data, the system analyzes their reaction to the investment proposal.

[0778] Step 5: Generate and present the investment proposal report (server operation)

[0779] The server compiles optimal renewable energy investment proposals in the form of a report based on the analysis results of the generated AI and the emotion engine. The inputs are the analysis results and emotion recognition results, and the output is a customized investment proposal report. Specifically, the report includes recommended renewable energy systems, initial investment amounts, predicted benefits, and customization elements based on the user's emotions. The server presents this to the user via a dashboard or web interface.

[0780] Step 6: Collecting user feedback and re-analyzing (server actions)

[0781] The server collects feedback from users and reanalyzes it. The input is the user's feedback data, and the output is an updated investment proposal report. Specifically, based on the feedback provided by the user (e.g., questions or requests for revisions), the generative AI and emotion engine reanalyze and update the investment proposals. This makes it possible to provide more precise information to the user.

[0782] (Application example 2)

[0783] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0784] Optimizing energy consumption and CO2 emissions in companies is an important issue, but currently it is difficult to collect and analyze data in real time, and there are also challenges in proposing renewable energy investment plans that take user emotions into account. The purpose of this invention is to solve these challenges and achieve improved energy efficiency and reduced CO2 emissions in companies.

[0785] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0786] In this invention, the server includes a means for collecting energy consumption data and CO2 emission data in real time, a means for storing and preprocessing the data, a means for analyzing the preprocessed data and generating renewable energy investment proposals, a means for recognizing and analyzing user emotion data, and a means for optimizing the format and content of the investment proposal report based on the user emotion, thereby enabling real-time analysis of a company's energy consumption and CO2 emission, and further enabling the proposal of an optimal renewable energy investment proposal taking into account the user emotion.

[0787] An "IoT terminal" is a terminal for collecting and transmitting data via the Internet.

[0788] "Energy consumption data" means data relating to the energy usage of a particular system or facility.

[0789] "CO2 emissions data" means data relating to the amount of carbon dioxide emitted by a particular system or facility.

[0790] A "cloud server" is a server that stores and processes data over the Internet.

[0791] "Preprocessing" refers to processes such as checking the consistency of data and correcting outliers that are carried out before analyzing collected data.

[0792] "Generative AI" is a system that uses artificial intelligence to analyze data and generate specific outputs.

[0793] A "renewable energy investment proposal" is a plan or proposal for the introduction of a renewable energy system.

[0794] An "investment proposal report" is a report that details the contents of a renewable energy investment proposal.

[0795] An "emotion engine" is a system for recognizing and analyzing a user's emotional data.

[0796] "User emotion data" is data that indicates the user's emotion, and is obtained from facial expressions, voice, text, and the like.

[0797] "Optimization" is the process of adjusting a system or process to be most efficient in order to achieve a specific purpose.

[0798] The present invention is a system that uses a robot installed in a factory to analyze a company's energy consumption and CO2 emissions in real time and provides optimal renewable energy investment proposals. Specific embodiments of the present invention are described below.

[0799] System configuration

[0800] The system consists of the following main components:

[0801] 1. IoT devices:

[0802] The IoT terminal is built into the robot and collects energy consumption and CO2 emission data in real time within the factory, and transmits the data to a cloud server via Wi-Fi.

[0803] 2. Cloud Server:

[0804] Data storage and pre-processing:

[0805] The cloud server receives and stores data sent from IoT devices, and performs preprocessing such as checking data consistency and correcting outliers.

[0806] Generation AI:

[0807] The pre-processed data is analyzed to recognize energy consumption patterns and generate renewable energy investment proposals.

[0808] Emotion Engine:

[0809] Recognize and analyze user emotion data. Collect facial expressions, voice, and text data when users view investment proposal reports to detect emotions.

[0810] 3. Generate and present investment proposal reports:

[0811] Based on the analysis results of the generative AI and the emotion engine, optimal renewable energy investment proposals are compiled in report format.

[0812] The report includes recommended renewable energy systems, initial investment amounts, expected payback periods, and potential energy efficiency gains and CO2 emission reductions.

[0813] The report optimizes its content and presentation based on user sentiment.

[0814] Hardware and software used

[0815] IoT terminal: Sensors and Wi-Fi modules built into the robot

[0816] Cloud servers: Cloud infrastructure (e.g., AWS, Google Cloud) can be used for data storage and processing, and TensorFlow or PyTorch can be used for generative AI models.

[0817] Emotion engine: Emotion recognition using OpenCV and Emotion API

[0818] Example of a system

[0819] For example, if an automobile manufacturing plant were to implement this system, it would work as follows:

[0820] 1. Installation of robots: Robots installed in each section will collect energy consumption data and CO2 emissions data in real time.

[0821] 2. Data transmission and storage: The collected data is transmitted to a cloud server where it is stored and pre-processed.

[0822] 3. Data analysis: A generative AI model analyzes the pre-processed data, identifies energy consumption patterns, and generates renewable energy investment proposals.

[0823] 4. Emotion analysis: The factory manager views the investment proposal report, and the facial expressions and voice data are analyzed by the emotion engine.

[0824] 5. Optimization of investment proposals: Based on the results of user sentiment analysis, investment proposal reports are provided in the most appropriate format and content.

[0825] Example prompts for generative AI models

[0826] "Analyze the user's emotions while viewing the investment proposal report presented to them. Based on the following input data, classify the user's emotions as either 'positive', 'neutral' or 'negative'."

[0827] Input data:

[0828] Facial expression image file: user_expression.png

[0829] Audio file: user_voice.wav

[0830] Text log: "This investment proposal is attractive. I would like to consider implementing it."

[0831] Output formats:

[0832] {

[0833] "emotion": "positive"

[0834] }"

[0835] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0836] Step 1:

[0837] The terminal uses sensors on robots installed within the factory to collect energy consumption data and CO2 emissions data in real time.

[0838] Input: Energy consumption, gas consumption, and CO2 emissions captured by each sensor

[0839] Output: Collected data is temporarily saved on the device.

[0840] Specific operation: The robot's built-in sensors collect data at specified intervals and prepare to send it to the cloud server via the Wi-Fi module.

[0841] Step 2:

[0842] The device sends the collected data to a cloud server via a Wi-Fi network.

[0843] Input: Data acquired by the sensor and the address of the destination cloud server

[0844] Output: Data sent to the cloud server

[0845] Specific operation: The device's communication module is used to send data to a cloud server via Wi-Fi.

[0846] Step 3:

[0847] The server stores the received data and performs preprocessing of the data.

[0848] Input: Energy consumption data and CO2 emissions data sent from the device

[0849] Output: Preprocessed data, outliers corrected, and data integrity checked

[0850] Specific operation: The server stores the received data in the database, performs data encapsulation, tagging, and outlier correction, and verifies any inconsistencies through consistency checks.

[0851] Step 4:

[0852] The server inputs the preprocessed data into the generative AI model and performs data analysis.

[0853] Input: Preprocessed energy consumption data and CO2 emissions data

[0854] Output: Energy consumption patterns, renewable energy investment proposals

[0855] How it works: Analyzes data using a generative AI model (e.g., using TensorFlow or PyTorch) and generates investment recommendations that incorporate time-series pattern recognition and emerging trends.

[0856] Step 5:

[0857] The server collects the user's emotional data and analyzes it using an emotion engine.

[0858] Input: User facial expression images, voice data, text input

[0859] Output: Sentiment analysis result (positive, neutral, negative, etc.)

[0860] How it works: Using emotion recognition technologies such as OpenCV and Emotion API, the system analyzes users' emotions in real time. The analysis results are recorded and reflected in the presentation of investment proposal reports.

[0861] Step 6:

[0862] The server generates an investment proposal report based on the analysis results of the generated AI and the results of the emotion engine.

[0863] Input: Renewable energy investment proposals generated by generative AI, emotion analysis results from the emotion engine

[0864] Output: Optimized investment proposal report

[0865] Specific operation: The generated investment proposals are compiled into a document and customized according to the user's emotions. For example, if the emotion is "positive," the expressions become more optimistic. If the emotion is "negative," the expressions are changed to give a sense of security.

[0866] Step 7:

[0867] The server presents the generated investment proposal report to the user.

[0868] Input: Optimized Investment Proposal Report

[0869] Output: A user-viewable investment proposal report

[0870] Specific operation: The reports generated on the cloud server are displayed on the user dashboard, and feedback is collected from the user as needed, helping the user make decisions based on the investment proposals.

[0871] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0872] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0873] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0874] [Third embodiment]

[0875] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0876] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0877] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0878] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0879] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0880] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0881] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0882] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0883] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0884] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0885] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0886] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0887] The present invention is a system that analyzes a company's energy consumption and CO2 emissions in real time and proposes optimal renewable energy investments. Specific embodiments of this system will be described below.

[0888] Overall system overview

[0889] The system of the present invention consists of the following main components:

[0890] 1. IoT devices that collect energy consumption data and CO2 emissions data in real time.

[0891] 2. A cloud server that stores and preprocesses the received data.

[0892] 3. A means of analyzing data and generating renewable energy investment proposals using generative AI.

[0893] 4. A means for generating and presenting investment proposal reports to the user.

[0894] Specific examples of program execution

[0895] IoT device operation (data collection)

[0896] The terminals are installed in the company's factories and monitor electricity consumption, gas usage, and CO2 emissions every minute. The monitored data is then sent to a cloud server using a stable communication protocol.

[0897] Cloud server operation (data storage and preprocessing)

[0898] The server stores the received data in the cloud. The stored data is then processed as follows:

[0899] Integrity check: Checks the consistency and accuracy of received data and detects invalid data.

[0900] Outlier correction: Correcting extreme values ​​in the data to more appropriate values ​​and imputing missing data.

[0901] Generative AI operation (data analysis and investment proposal generation)

[0902] The generative AI, housed on the server, analyzes the pre-processed data. The generative AI does the following:

[0903] Pattern Recognition: Identify trends in energy consumption patterns and CO2 emissions.

[0904] Trend analysis: Identify optimal investment opportunities based on the latest renewable energy trends.

[0905] Investment proposal generation: Generate specific investment proposals, including plans for the introduction of renewable energy systems (e.g., solar panels or wind turbines).

[0906] Present investment proposals (report generation and user presentation)

[0907] The server generates a report of the investment proposals calculated by the AI, which includes:

[0908] Specific specifications of the renewable energy equipment to be introduced

[0909] The initial investment required and the expected payback period

[0910] Energy efficiency improvement effect and CO2 emission reduction amount

[0911] The reports are provided to the user, who can view the contents through a web interface or dashboard.

[0912] Specific examples

[0913] Here is an example of a manufacturing company that implemented this system. Here is a specific scenario:

[0914] 1. Terminals are installed in each area of ​​the factory to collect electricity and gas consumption data and CO2 emissions in real time.

[0915] 2. The data is sent to a server on the cloud at regular intervals.

[0916] 3. The server receives the data, stores it, checks its integrity, and corrects any outliers.

[0917] 4. The server's generation AI analyzes the data and generates optimal investment proposals based on the latest renewable energy trends.

[0918] 5. The server creates a report of the generated investment proposal and presents it to the user.

[0919] 6. The user reviews the report and decides to install a renewable energy system based on the information provided.

[0920] This system will enable companies to improve their energy efficiency and reduce CO2 emissions, and provide concrete guidelines for sustainable energy management.

[0921] The processing flow will be explained below.

[0922] Step 1:

[0923] The terminal collects a company's energy consumption and CO2 emissions in real time. Data is acquired from various sensor locations and sent to a cloud server at regular intervals. The data includes electricity consumption, gas consumption, CO2 emissions, etc.

[0924] Step 2:

[0925] The server receives the data sent from the device and stores it in a cloud database. The received data is not stored as is, but is encapsulated and tagged. At this point, data integrity checks and initial filtering can also be performed.

[0926] Step 3:

[0927] The server performs pre-processing on the stored data, which includes the following steps:

[0928] Integrity check: Validate data consistency and check for inconsistencies.

[0929] Outlier correction: Correcting abnormal or missing values ​​in the data. For example, imputing abnormally high electricity usage data with the average value.

[0930] Step 4:

[0931] The server's generation AI analyzes the preprocessed data. The generation AI performs the following tasks:

[0932] Recognizing energy consumption patterns: Analyzing time series data to identify cyclical patterns and sudden trends in consumption.

[0933] Trend Analysis: Capture renewable energy trends and market data and generate optimal energy investment plans based on this.

[0934] Step 5:

[0935] The server generates a report based on the AI ​​analysis results, summarizing optimal renewable energy investment proposals. The report includes:

[0936] Specific Equipment: Recommended renewable energy systems (e.g., specific models of solar panels or wind turbines).

[0937] Initial investment: the required implementation costs and expected payback period.

[0938] Expected effects: Improved energy efficiency and reduced CO2 emissions are expected.

[0939] Step 6:

[0940] The server generates reports and displays them on a dashboard for users to access and review. Users can view detailed reports and simulation results through the dashboard.

[0941] Step 7:

[0942] We create a specific implementation plan based on the investment proposal provided by the user, determine the equipment and measures to be introduced, and proceed with the introduction of the renewable energy system.

[0943] Step 8:

[0944] The server continuously monitors the performance of the installed renewable energy system, collecting and analyzing operational data after installation and providing regular feedback for optimization and improvement.

[0945] Through these steps, this system improves a company's energy consumption efficiency and reduces CO2 emissions.

[0946] Example 1

[0947] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0948] Currently, many companies are aiming to improve energy efficiency and reduce greenhouse gas emissions, but they lack the tools and systems to realistically achieve this. Furthermore, there are limited means to optimize investments in renewable energy. There is a need for a system that allows companies to understand energy consumption patterns and CO2 emissions in real time and use that data to derive optimal renewable energy investment plans.

[0949] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0950] In this invention, the server includes a data analysis means using AI to identify energy consumption patterns and CO2 emission trends, a means for generating renewable energy investment proposals, and a means for generating and presenting investment proposal reports to users, allowing companies to analyze energy consumption data and CO2 emission data in real time and quickly obtain optimal renewable energy investment proposals.

[0951] "Energy consumption data" refers to data on the amount of electricity, gas, and other energy consumed by a business or facility.

[0952] "CO2 emissions data" refers to data on the amount of carbon dioxide emitted by companies and facilities.

[0953] "Real-time collection means" is a general term for devices and sensors that can acquire and record data in real time, in the present.

[0954] "Means for storing and pre-processing received data" refers to devices or systems that store collected data in an appropriate format and perform procedures to correct or supplement inconsistencies or outliers prior to data analysis.

[0955] "Generative AI" refers to artificial intelligence technologies that have the ability to generate and analyze for specific tasks.

[0956] "Data Analysis Tools" means tools that use techniques and algorithms to analyze collected and pre-processed data and identify trends and patterns.

[0957] The "means for generating renewable energy investment proposals" refers to a method or system for formulating an investment plan for optimal renewable energy technologies based on the results of data analysis.

[0958] The "means for generating an investment proposal report and presenting it to the user" refers to an interface and technology for organizing the generated investment proposals in a report format and presenting them visually and informationally to the user.

[0959] "Integrity checking" is the process of checking that data is consistent and free of inconsistencies and errors.

[0960] "Outlier correction" is a method for detecting extreme or unnatural values ​​in data and correcting them to an appropriate range.

[0961] A "renewable energy system" is a device or facility that utilizes sustainable energy sources such as solar, wind, hydroelectric, and biomass.

[0962] An "investment proposal" is a proposal that outlines a planned investment in a specific project or technology.

[0963] "Performance monitoring" is the process of continuously monitoring installed systems and equipment to ensure they are performing as expected and efficiently.

[0964] "Providing feedback" is the activity of reporting the results of performance monitoring to users and providing information for necessary improvements and optimization.

[0965] This system analyzes a company's energy consumption and CO2 emissions in real time and proposes optimal renewable energy investments based on that data. The entire system consists of the following main components:

[0966] 1. IoT devices that collect energy consumption and CO2 emissions data in real time

[0967] Terminals are installed in factories and offices to monitor electricity consumption, gas usage, and CO2 emissions every minute. These data are collected using smart meters and IoT sensors. For example, minute-by-minute electricity consumption data obtained by a smart meter and emission data from a CO2 sensor can be considered. This data is then sent to a cloud server using a reliable communication protocol (e.g., MQTT, HTTP).

[0968] 2. Cloud server that stores and preprocesses received data

[0969] The data received by the server is stored in a cloud database (e.g., Amazon RDS, Google BigQuery). The integrity of the received data is checked and outliers are corrected. When checking data integrity, consistency is checked using SQL queries to detect whether there are any outliers. If an outlier is detected, it is corrected using statistical methods and missing data is filled in.

[0970] 3. Data analysis using generative AI

[0971] The server's built-in generative AI (e.g., OpenAI GPT-4) analyzes the pre-processed data. The generative AI identifies energy consumption patterns and CO2 emission trends, and identifies optimal investment targets based on the latest renewable energy trends. Time series analysis and trend analysis techniques are used in the analysis to identify optimal investment targets.

[0972] 4. A means of generating renewable energy investment proposals

[0973] Based on the results of the analysis by the AI, the server generates a specific renewable energy system implementation plan. Investment proposals include plans for installing solar panels and wind turbines. The required initial investment amount, payback period, energy efficiency improvement effect, and CO2 emission reduction amount are calculated.

[0974] 5. Means for generating and presenting investment proposal reports to users

[0975] The server generates a report based on the AI-generated investment proposal. This report includes detailed specifications of the renewable energy equipment to be installed, the required initial investment amount, payback period, energy efficiency improvement effects, and CO2 emission reduction amounts. The generated report can be accessed by the user via a web interface (e.g., a React-based dashboard) or a dedicated app.

[0976] Specific examples

[0977] A specific example will be described in which a manufacturing company has introduced this system.

[0978] 1. Terminals are installed in each area of ​​the factory to collect electricity consumption data, gas usage, and CO2 emissions in real time.

[0979] 2. The data is sent to a server on the cloud at regular intervals.

[0980] 3. The server stores the received data, checks its integrity, and corrects outliers.

[0981] 4. Server generation AI analyzes the data and generates optimal investment proposals based on the latest renewable energy trends.

[0982] 5. The server creates a report of the investment proposals and presents it to the user.

[0983] 6. The user reviews the report and decides on the optimal renewable energy system implementation based on the information provided.

[0984] Prompt Sentence Examples

[0985] Here is an example of a prompt to input to a generative AI model:

[0986] "Generate optimal renewable energy investment proposals based on Company X's energy consumption and CO2 emissions data. Average monthly electricity consumption is 100,000 kWh, average monthly gas consumption is 20,000 m3, and CO2 emissions total 2,400 tons per year. Please refer to the latest renewable energy trends."

[0987] Using these prompts, the generative AI can suggest optimal investment ideas.

[0988] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0989] Step 1: Data collection

[0990] The terminal collects electricity consumption, gas usage, and CO2 emissions in real time from sensors and meters installed in factories and businesses. The input is data from various sensors, and the output is that this data is temporarily stored in the terminal. Specifically, smart meters and CO2 sensors measure data every minute and store it in the terminal's memory.

[0991] Step 2: Send data

[0992] The data collected by the terminal is sent to the cloud server using a stable communication protocol (e.g., MQTT, HTTP). The input is the data collected in step 1, and the output is the data sent to the cloud server. Specifically, the terminal aggregates the data into packets at regular intervals and sends them to the cloud server. If a communication error occurs, it attempts to resend the data.

[0993] Step 3: Save Data

[0994] The server stores the data it receives in a cloud database (e.g., Amazon RDS, Google BigQuery). The input is data sent from the device, and the output is data stored in the cloud database. Specifically, the server executes an insert query in the database, saves the data, and performs automatic backups.

[0995] Step 4: Data Preprocessing

[0996] The server checks the consistency of the incoming data and corrects outliers. The input is data stored in a cloud database, and the output is preprocessed data. Specifically, the server checks consistency using SQL queries and corrects any outliers detected using statistical methods. It also runs algorithms to impute missing data.

[0997] Step 5: Data analysis

[0998] The generative AI built into the server analyzes the preprocessed data. The input is the preprocessed data, and the output is energy consumption patterns, CO2 emission trends, and optimal renewable energy trend information. The generative AI performs time series analysis and trend analysis to identify optimal investment targets. Specifically, the AI ​​model processes large amounts of data and extracts specific patterns and trends.

[0999] Step 6: Investment proposal generation

[1000] The server generates a specific renewable energy system implementation plan based on the analysis results of the AI. The input is the results of the data analysis, and the output is a specific investment proposal. Specifically, the server runs an implementation plan simulation and calculates the required initial investment amount, payback period, energy efficiency improvement effect, and CO2 emission reduction amount.

[1001] Step 7: Generate reports

[1002] The server compiles the generated investment proposals into a report format. The input is the investment proposal data, and the output is a document in report format. Specifically, the server embeds the data into a report template, generates visual elements such as graphs and tables, and exports them in PDF format.

[1003] Step 8: User Presentation

[1004] The user checks the generated report through a web interface or a dedicated app. The input is the report provided by the server, and the output is the information checked by the user. Specifically, the user accesses the dashboard in a web browser, downloads and views the report, and, if necessary, shares the report with other decision makers.

[1005] This allows the processing of the entire system to be realized.

[1006] (Application example 1)

[1007] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1008] In modern manufacturing and large-scale facilities, managing energy consumption and CO2 emissions is important, but there is no effective system for analyzing this data in real time and determining optimal renewable energy investments. Furthermore, there is a lack of support tools that allow managers to instantly grasp the situation and make quick decisions. In such environments, there are issues with reduced energy efficiency and unnecessary costs.

[1009] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1010] In this invention, the server includes means for collecting energy consumption data and CO2 emission data in real time from IoT devices, means for storing and preprocessing the received data in a cloud computing platform, means for analyzing the preprocessed data using a generative AI model and generating a renewable energy investment proposal, and means for presenting the generated investment proposal report to a user using a head-mounted display, thereby enabling real-time monitoring of energy consumption and CO2 emissions and proposals for optimal renewable energy investments.

[1011] An "IoT terminal" is a device that is connected to the Internet and is capable of collecting and transmitting data.

[1012] "Energy consumption data" refers to data that indicates the amount of energy consumed, such as electricity and gas, by factories and facilities.

[1013] "CO2 emission data" is data that indicates the amount of carbon dioxide emitted as a result of energy consumption.

[1014] A "cloud computing platform" is a service infrastructure that allows data to be stored, processed, and analyzed via the Internet.

[1015] A "generative AI model" is an artificial intelligence algorithm or model that analyzes large amounts of data and extracts specific patterns and trends.

[1016] "Preprocessing" refers to processing carried out prior to analysis, such as checking the consistency of collected data and correcting outliers.

[1017] "Analysis" is the act of examining data in detail to find specific patterns and trends.

[1018] A "renewable energy investment plan" is a specific proposal for introducing renewable energy sources such as solar power and wind power.

[1019] A "head-mounted display" is a display device that is worn on the head and provides visual information.

[1020] The "Investment Proposal Report" is a report summarizing the specific details of renewable energy investments proposed by the generative AI model.

[1021] "Users" are companies and individuals who use the system.

[1022] The present invention relates to a system that analyzes a company's energy consumption and CO2 emissions in real time and proposes optimal renewable energy investments. Specific embodiments of this system will be described below.

[1023] Overall system overview

[1024] The system of the present invention consists of the following main components:

[1025] 1. IoT devices

[1026] 2. Cloud Computing Platform

[1027] 3. Generative AI Models

[1028] 4. Head-Mounted Display (HMD)

[1029] IoT device operation

[1030] IoT devices are installed in various areas of the factory to monitor electricity consumption, gas usage, and CO2 emissions every minute, and the monitored data is sent to a cloud computing platform via internet communication.

[1031] Cloud Computing Platform Operation

[1032] A cloud computing platform provides the following capabilities:

[1033] Data storage: The received data is stored in the cloud. This process uses a data storage service.

[1034] Data preprocessing: Consistency checks and outlier corrections are performed to generate reliable data.

[1035] How generative AI models work

[1036] A generative AI model analyzes the preprocessed data, specifically:

[1037] Pattern Recognition: Identify trends in energy consumption patterns and CO2 emissions.

[1038] Trend analysis: Identify optimal investment opportunities based on the latest renewable energy trends.

[1039] Investment proposal generation: Generate specific investment proposals, including plans for the introduction of renewable energy systems (e.g., solar panels and wind turbines).

[1040] Head-mounted display operation

[1041] The head-mounted display (HMD) is a device that allows factory managers to check energy consumption data and CO2 emissions in real time and view generated investment proposal reports. The HMD provides the following functions:

[1042] Real-time monitoring: Data obtained from IoT devices installed in each area of ​​the factory is displayed in real time.

[1043] Abnormality alert: A function that automatically notifies you if energy consumption or CO2 emissions show abnormal values.

[1044] Investment proposal presentation: The generation AI analyzes and displays proposed investment proposals in report format.

[1045] Voice command function: Factory managers can access and review data and suggestions using voice commands.

[1046] Collaboration function: The ability to share data and proposals with other managers in real time and work together to come up with solutions.

[1047] Specific examples

[1048] Here is a scenario in which a manufacturing company implemented this system:

[1049] 1. IoT terminals are installed in each area of ​​the factory to collect electricity and gas consumption data and CO2 emissions in real time.

[1050] 2. The data is transmitted to the cloud computing platform via internet communication.

[1051] 3. The cloud computing platform stores the received data and performs preprocessing, specifically integrity checking and outlier correction.

[1052] 4. The generative AI model analyzes the pre-processed data to identify trends in energy consumption patterns and CO2 emissions, and generates renewable energy investment recommendations based on the latest trend analysis.

[1053] 5. The head-mounted display presents the generated investment proposal report to the factory manager, providing real-time monitoring and alerting in case of abnormalities.

[1054] An example prompt is, "Analyze the factory's energy consumption data and CO2 emissions trends to generate optimal renewable energy investment proposals."

[1055] This system will achieve both energy cost reduction and environmental protection by significantly improving the efficiency of factory energy management.

[1056] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1057] Step 1:

[1058] Terminals are installed in each area of ​​the factory to monitor power consumption, gas usage, and CO2 emissions in real time. These data are collected by the IoT terminals. The input is energy data and CO2 emission data from each sensor, and the output is data sent to a cloud computing platform via the internet.

[1059] Step 2:

[1060] The server saves the received data on the cloud computing platform. The saved data is temporarily stored on the data storage for preprocessing. The input is the energy data and CO2 emission data sent in the previous step, and the output is the data saved in the cloud.

[1061] Step 3:

[1062] The server checks the integrity of the received data and corrects outliers. Specifically, it uses a data cleaning algorithm to check the consistency of the data and appropriately corrects extreme values ​​and missing values. The input is raw data stored in the cloud, and the output is preprocessed data.

[1063] Step 4:

[1064] The generative AI model analyzes the pre-processed data. First, it performs pattern recognition to identify trends in energy consumption patterns and CO2 emissions. Second, it performs trend analysis based on renewable energy trend information to identify optimal investment targets. Finally, it generates specific investment proposals, including plans for the introduction of renewable energy systems (e.g., solar panels and wind turbines). The input is the pre-processed data, and the output is the optimal renewable energy investment proposal.

[1065] Step 5:

[1066] The server creates a report of the generated investment proposal, which includes the specific specifications of the renewable energy equipment to be introduced, the required initial investment amount and expected payback period, the effect of improving energy efficiency, and the amount of CO2 emissions reduced. The input is the investment proposal output by the generative AI model, and the output is the completed investment proposal report.

[1067] Step 6:

[1068] A head-mounted display (HMD) presents the generated investment proposal report to the user in real time. Factory managers can check energy consumption data and CO2 emissions in real time through the HMD and receive instant notifications if an abnormality occurs. Voice commands can also be used to easily call up and check data and proposals. The input is the investment proposal report and real-time data sent from the server, and the output is the information presented to the user.

[1069] In this way, factory managers can make quick and accurate decisions to increase energy efficiency and reduce CO2 emissions.

[1070] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1071] The present invention is a system that analyzes a company's energy consumption and CO2 emissions in real time and proposes optimal renewable energy investments by combining an emotion engine. Specific embodiments of this system are described below.

[1072] Overall system overview

[1073] The system of the present invention consists of the following main components:

[1074] 1. IoT devices that collect energy consumption data and CO2 emissions data in real time.

[1075] 2. A cloud server that stores and preprocesses the received data.

[1076] 3. A means of analyzing data and generating renewable energy investment proposals using generative AI.

[1077] 4. A means for generating and presenting investment proposal reports to the user.

[1078] 5. An emotion engine that recognizes and analyzes user emotions.

[1079] Specific examples of program execution

[1080] IoT device operation (data collection)

[1081] The terminals are installed in the company's factories and monitor electricity consumption, gas consumption, and CO2 emissions every minute. The monitored data is sent to a cloud server using a stable communication protocol.

[1082] Cloud server operation (data storage and preprocessing)

[1083] The server receives the data sent from the device and stores it in a cloud database. The received data is not stored as is, but is encapsulated and tagged. At this point, data integrity checks and initial filtering are also performed.

[1084] Generative AI operation (data analysis and investment proposal generation)

[1085] The generative AI built into the server analyzes the pre-processed data and performs the following tasks:

[1086] Recognizing energy consumption patterns: Analyzing time series data to identify cyclical patterns and sudden trends in consumption.

[1087] Trend Analysis: Capture the latest renewable energy trends and market data to generate optimal energy investment plans.

[1088] Investment proposal generation: Generate specific investment proposals, including plans for the installation of renewable energy systems (e.g., specific models of solar panels or wind turbines).

[1089] Emotion engine operation (emotion recognition and analysis)

[1090] The server collects and analyzes the user's emotional data. Emotion data is collected from facial expressions, tone of voice, and input history when the user views an investment report. The emotion engine performs the following:

[1091] Emotion recognition: Detecting emotions from a user's facial expressions, voice, and text data.

[1092] Sentiment Analysis: Analyze how the detected sentiment tends to react to the investment proposal.

[1093] Present investment proposals (report generation and user presentation)

[1094] The server generates a report based on the results of the AI ​​analysis and the emotion engine analysis, summarizing optimal renewable energy investment proposals. The report includes:

[1095] Specific equipment to be installed: Recommended renewable energy systems.

[1096] Initial investment: the required implementation costs and expected payback period.

[1097] Expected effects: Improved energy efficiency and reduced CO2 emissions are expected.

[1098] Customization based on user emotions: Presentation content and presentation methods are optimized according to the user's emotions.

[1099] Reports are provided to users, who can view the contents through a web interface or dashboard.

[1100] Specific examples

[1101] Here is an example of a manufacturing company that implemented this system. Here is a specific scenario:

[1102] 1. Terminals are installed in each area of ​​the factory to collect electricity and gas consumption data and CO2 emissions in real time.

[1103] 2. The data is sent to a server on the cloud at regular intervals.

[1104] 3. The server receives the data, stores it, checks its integrity, and corrects any outliers.

[1105] 4. The server's generation AI analyzes the data and generates optimal investment proposals based on the latest renewable energy trends.

[1106] 5. The server uses an emotion engine to collect user emotion data and optimize the format and content of the investment proposal report.

[1107] 6. The server presents the generated investment proposal report to the user and re-analyzes the user's feedback if necessary.

[1108] 7. The user reviews the report and decides to install a renewable energy system based on the information provided.

[1109] This system will enable companies to improve their energy efficiency and reduce CO2 emissions, and will also maximize the effectiveness of its proposals by taking into account the user's emotions.

[1110] The processing flow will be explained below.

[1111] Step 1:

[1112] The terminal collects a company's energy consumption and CO2 emissions in real time. Data is acquired from various sensor locations and sent to a cloud server at regular intervals. The data includes electricity consumption, gas consumption, CO2 emissions, etc.

[1113] Step 2:

[1114] The server receives the data sent from the device and stores it in a cloud database. The received data is not stored as is, but is encapsulated and tagged. At this point, data integrity checks and initial filtering are also performed.

[1115] Step 3:

[1116] The server performs pre-processing on the stored data, which includes the following steps:

[1117] Integrity check: Validate data consistency and check for inconsistencies.

[1118] Outlier correction: Correcting abnormal or missing values ​​in the data. For example, imputing abnormally high electricity usage data with the average value.

[1119] Step 4:

[1120] The server's generation AI analyzes the preprocessed data. The generation AI performs the following tasks:

[1121] Recognizing energy consumption patterns: Analyzing time series data to identify cyclical patterns and sudden trends in consumption.

[1122] Trend Analysis: Capture the latest renewable energy trends and market data to generate optimal energy investment plans.

[1123] Investment proposal generation: Generate specific investment proposals, including plans for the installation of renewable energy systems (e.g., specific models of solar panels or wind turbines).

[1124] Step 5:

[1125] The server generates a report based on the AI ​​analysis results, summarizing optimal renewable energy investment proposals. The report includes:

[1126] Specific Equipment: Recommended renewable energy systems (e.g., specific models of solar panels or wind turbines).

[1127] Initial investment: the required implementation costs and expected payback period.

[1128] Expected effects: Improved energy efficiency and reduced CO2 emissions are expected.

[1129] Step 6:

[1130] The server collects and analyzes user emotion data using an emotion engine. The emotion engine performs the following tasks:

[1131] Emotion recognition: Detecting emotions from a user's facial expressions, voice, and text data.

[1132] Sentiment analysis: Analyze the detected sentiment to see how it tends to affect the investment proposal. Based on the results of the sentiment analysis, optimize the presentation and content of the investment proposal report.

[1133] Step 7:

[1134] The server displays the report on a dashboard for users to access and review. Users can view detailed reports and simulation results through the dashboard. Based on the results of sentiment analysis, customized content is presented that is tailored to the user's interests.

[1135] Step 8:

[1136] We create a specific implementation plan based on the investment proposal provided by the user, determine the equipment and measures to be introduced, and proceed with the introduction of the renewable energy system.

[1137] Step 9:

[1138] The server continuously monitors the performance of the installed renewable energy system, collecting and analyzing operational data after installation and providing regular feedback for optimization and improvement.

[1139] Through these steps, this system improves the energy consumption efficiency of companies and reduces CO2 emissions. Furthermore, by taking into account the user's emotions, it maximizes the effectiveness of the suggestions.

[1140] Example 2

[1141] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1142] Reducing energy consumption and CO2 emissions in modern companies is extremely important from the perspective of reducing environmental impact and cutting costs. However, systems for efficiently collecting and analyzing energy consumption and CO2 emission data and generating optimal renewable energy investment proposals are inadequate. Furthermore, there are no established methods for verifying the consistency of collected data, correcting outliers, or customizing proposals based on user sentiment. As a result, companies have difficulty obtaining actionable information for making appropriate renewable energy investments.

[1143] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for collecting energy consumption data and CO2 emission data in real time using IoT terminals, means for storing and preprocessing the received data in a cloud device, means for analyzing the preprocessed data using a generation AI and generating a renewable energy investment proposal, means for generating an investment proposal report and presenting it to the user, and means for collecting and analyzing user emotion data. This enables accurate analysis of the data collected in real time and makes it possible to provide an optimal renewable energy investment proposal based on the user's emotions.

[1144] An "IoT device" is a device that is connected to the Internet and collects data from the physical environment and transmits it to the cloud or other devices.

[1145] "Energy consumption data" is data relating to the amount of energy consumed within a certain period of time.

[1146] "CO2 emissions data" refers to data on the amount of carbon dioxide emitted within a certain period of time.

[1147] "Cloud device" refers to a remote server or data center used to store, process, and manage data over the Internet.

[1148] "Generative AI" is a system that uses machine learning and artificial intelligence techniques to analyze data and generate new information and suggestions.

[1149] "Preprocessing" refers to processes such as data formatting, encapsulation, tagging, consistency checking, and outlier correction that are performed before data analysis.

[1150] An "investment proposal report" is a document that summarizes specific proposals for the introduction of renewable energy systems.

[1151] "User emotion data" refers to data related to emotions acquired from facial expressions, tone of voice, and input history when a user uses the system interface.

[1152] "Renewable energy investment proposal" refers to a specific plan or proposal for the introduction of a renewable energy system.

[1153] The present invention is a system that analyzes a company's energy consumption and CO2 emissions in real time and proposes optimal renewable energy investments by combining an emotion engine. Specific embodiments of this system are described below.

[1154] Overall system overview

[1155] The system of the present invention consists of the following main components:

[1156] 1. IoT devices that collect energy consumption data and CO2 emissions data in real time.

[1157] 2. A cloud server that stores and preprocesses the received data.

[1158] 3. A means of analyzing data and generating renewable energy investment proposals using generative AI.

[1159] 4. A means for generating and presenting investment proposal reports to the user.

[1160] 5. An emotion engine that recognizes and analyzes user emotions.

[1161] IoT device operation (data collection)

[1162] Terminals are installed in companies' factories and offices. These terminals collect electricity consumption, gas consumption, and CO2 emissions in real time. The monitored data is sent to a cloud server using a stable communication protocol (e.g., MQTT).

[1163] Cloud server operation (data storage and preprocessing)

[1164] The server receives the data sent from the device and stores it in a cloud database (e.g., Amazon RDS). The stored data is encapsulated and tagged, and data integrity checks and initial filtering are performed to eliminate outliers and missing data.

[1165] Generative AI operation (data analysis and investment proposal generation)

[1166] A generative AI (e.g., OpenAI's GPT model) built into the server analyzes the preprocessed data. Specific tasks include:

[1167] Recognizing energy consumption patterns: Analyzing time series data to identify cyclical patterns and sudden trends in consumption.

[1168] Trend Analysis: Capture the latest renewable energy trends and market data to generate optimal energy investment plans.

[1169] Investment proposal generation: Generate an investment proposal that includes a plan to introduce a specific renewable energy system (e.g., a specific model of solar panel).

[1170] Emotion engine operation (emotion recognition and analysis)

[1171] The server collects and analyzes the user's emotional data. The data includes facial expressions, tone of voice, and input history when the user views an investment report. The emotion engine (e.g., Microsoft Azure's Emotion API) performs the following:

[1172] Emotion recognition: Detecting emotions from a user's facial expressions, voice, and text data.

[1173] Sentiment analysis: Analyze how the detected sentiment tends to react to the investment proposal.

[1174] Generate and present investment proposal reports (server operation)

[1175] The server generates a report based on the results of the AI ​​analysis and the emotion engine analysis, summarizing optimal renewable energy investment proposals. The report includes:

[1176] Specific equipment to be installed: Recommended renewable energy systems.

[1177] Initial investment: the required implementation costs and expected payback period.

[1178] Expected effects: Improved energy efficiency and reduced CO2 emissions are expected.

[1179] Customization based on user emotions: Presentation content and presentation methods are optimized according to the user's emotions.

[1180] The reports are provided to the user, who can view the contents through a web interface or dashboard.

[1181] Specific examples

[1182] Here is a specific example of how a manufacturing company implemented this system:

[1183] 1. Terminals are installed in each area of ​​the factory to collect electricity consumption, gas consumption, and CO2 emissions in real time. For example, a terminal is installed on a specific production line to collect energy consumption data for that line every minute.

[1184] 2. The data is sent to a server on the cloud at regular intervals.

[1185] 3. The server receives the data, stores it, checks its integrity, and corrects outliers. For example, if abnormally high consumption data is detected, it will be corrected and recorded.

[1186] 4. Server generation AI analyzes the data and generates optimal investment proposals based on the latest renewable energy trends. For example, it creates a plan to introduce the latest solar panels.

[1187] 5. The server uses an emotion engine to collect user emotion data (facial expressions and tone of voice) and optimize the format and content of the investment proposal report. For example, if the user expresses surprise, the server will provide more detailed explanations on that part.

[1188] 6. The server generates a report of the proposed investment and presents it to the user, who can then view it in a dashboard, for example, a web interface that provides a detailed cost analysis and impact forecast.

[1189] 7. The user decides to install a renewable energy system based on the information provided.

[1190] Prompt Sentence Examples

[1191] Generate optimal investment proposals to maximize cost savings from installing commercial solar power systems. Also, how can you customize the investment proposals by taking user sentiment data into account?

[1192] This system enables companies to improve energy efficiency and reduce CO2 emissions, and by taking user emotions into consideration, it also maximizes the effectiveness of its proposals.

[1193] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1194] Step 1: Data collection (device operation)

[1195] Terminals are installed in companies' factories and offices to monitor electricity consumption, gas usage, and CO2 emissions in real time. When this data is collected, the terminals use a communication protocol (e.g., MQTT) to send the data to a cloud server. The input is data from various sensors, and the output is raw data sent to the cloud server. Specifically, the terminals collect data from sensors installed in each location at regular intervals and send it to the cloud server in real time.

[1196] Step 2: Data storage and preprocessing (server operation)

[1197] The server receives data sent from the terminal. The input is the raw data sent from the terminal, and the output is the preprocessed data. Specifically, the server stores the received data in a cloud database (e.g., Amazon RDS), encapsulates and tags the data, and performs data integrity checks and outlier corrections, thereby creating a reliable dataset.

[1198] Step 3: Data analysis and investment proposal generation (operation of the generative AI model)

[1199] The server's generation AI analyzes the preprocessed data. The input is the preprocessed data, and the output is a renewable energy investment proposal. Specifically, the generation AI (e.g., OpenAI's model) analyzes time-series data and recognizes energy consumption patterns. It also incorporates the latest renewable energy trends and generates an optimal energy investment plan. This provides a specific investment proposal, including plans for the introduction of specific models of solar panels, wind turbines, etc.

[1200] Step 4: Emotion Recognition and Analysis (How the Emotion Engine Works)

[1201] The server collects and analyzes the user's emotional data. The input is the user's facial expression, tone of voice, and input history, and the output is the result of emotion recognition. Specifically, an emotion engine (e.g., Microsoft Azure's Emotion API) analyzes the user's facial expression and tone of voice in real time as they view reports to detect their emotions. Based on this detected data, the system analyzes their reaction to the investment proposal.

[1202] Step 5: Generate and present the investment proposal report (server operation)

[1203] The server compiles optimal renewable energy investment proposals in the form of a report based on the analysis results of the generated AI and the emotion engine. The inputs are the analysis results and emotion recognition results, and the output is a customized investment proposal report. Specifically, the report includes recommended renewable energy systems, initial investment amounts, predicted benefits, and customization elements based on the user's emotions. The server presents this to the user via a dashboard or web interface.

[1204] Step 6: Collecting user feedback and re-analyzing (server actions)

[1205] The server collects feedback from users and reanalyzes it. The input is the user's feedback data, and the output is an updated investment proposal report. Specifically, based on the feedback provided by the user (e.g., questions or requests for revisions), the generative AI and emotion engine reanalyze and update the investment proposals. This makes it possible to provide more precise information to the user.

[1206] (Application example 2)

[1207] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1208] Optimizing energy consumption and CO2 emissions in companies is an important issue, but currently it is difficult to collect and analyze data in real time, and there are also challenges in proposing renewable energy investment plans that take user emotions into account. The purpose of this invention is to solve these challenges and achieve improved energy efficiency and reduced CO2 emissions in companies.

[1209] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1210] In this invention, the server includes a means for collecting energy consumption data and CO2 emission data in real time, a means for storing and preprocessing the data, a means for analyzing the preprocessed data and generating renewable energy investment proposals, a means for recognizing and analyzing user emotion data, and a means for optimizing the format and content of the investment proposal report based on the user emotion, thereby enabling real-time analysis of a company's energy consumption and CO2 emission, and further enabling the proposal of an optimal renewable energy investment proposal taking into account the user emotion.

[1211] An "IoT terminal" is a terminal for collecting and transmitting data via the Internet.

[1212] "Energy consumption data" means data relating to the energy usage of a particular system or facility.

[1213] "CO2 emissions data" means data relating to the amount of carbon dioxide emitted by a particular system or facility.

[1214] A "cloud server" is a server that stores and processes data over the Internet.

[1215] "Preprocessing" refers to processes such as checking the consistency of data and correcting outliers that are carried out before analyzing collected data.

[1216] "Generative AI" is a system that uses artificial intelligence to analyze data and generate specific outputs.

[1217] A "renewable energy investment proposal" is a plan or proposal for the introduction of a renewable energy system.

[1218] An "investment proposal report" is a report that details the contents of a renewable energy investment proposal.

[1219] An "emotion engine" is a system for recognizing and analyzing a user's emotional data.

[1220] "User emotion data" is data that indicates the user's emotion, and is obtained from facial expressions, voice, text, and the like.

[1221] "Optimization" is the process of adjusting a system or process to be most efficient in order to achieve a specific purpose.

[1222] The present invention is a system that uses a robot installed in a factory to analyze a company's energy consumption and CO2 emissions in real time and provides optimal renewable energy investment proposals. Specific embodiments of the present invention are described below.

[1223] System configuration

[1224] The system consists of the following main components:

[1225] 1. IoT devices:

[1226] The IoT terminal is built into the robot and collects energy consumption and CO2 emission data in real time within the factory, and transmits the data to a cloud server via Wi-Fi.

[1227] 2. Cloud Server:

[1228] Data storage and pre-processing:

[1229] The cloud server receives and stores data sent from IoT devices, and performs preprocessing such as checking data consistency and correcting outliers.

[1230] Generation AI:

[1231] The pre-processed data is analyzed to recognize energy consumption patterns and generate renewable energy investment proposals.

[1232] Emotion Engine:

[1233] Recognize and analyze user emotion data. Collect facial expressions, voice, and text data when users view investment proposal reports to detect emotions.

[1234] 3. Generate and present investment proposal reports:

[1235] Based on the analysis results of the generative AI and the emotion engine, optimal renewable energy investment proposals are compiled in report format.

[1236] The report includes recommended renewable energy systems, initial investment amounts, expected payback periods, and potential energy efficiency gains and CO2 emission reductions.

[1237] The report optimizes its content and presentation based on user sentiment.

[1238] Hardware and software used

[1239] IoT terminal: Sensors and Wi-Fi modules built into the robot

[1240] Cloud servers: Cloud infrastructure (e.g., AWS, Google Cloud) can be used for data storage and processing, and TensorFlow or PyTorch can be used for generative AI models.

[1241] Emotion engine: Emotion recognition using OpenCV and Emotion API

[1242] Example of a system

[1243] For example, if an automobile manufacturing plant were to implement this system, it would work as follows:

[1244] 1. Installation of robots: Robots installed in each section will collect energy consumption data and CO2 emissions data in real time.

[1245] 2. Data transmission and storage: The collected data is transmitted to a cloud server where it is stored and pre-processed.

[1246] 3. Data analysis: A generative AI model analyzes the pre-processed data, identifies energy consumption patterns, and generates renewable energy investment proposals.

[1247] 4. Emotion analysis: The factory manager views the investment proposal report, and the facial expressions and voice data are analyzed by the emotion engine.

[1248] 5. Optimization of investment proposals: Based on the results of user sentiment analysis, investment proposal reports are provided in the most appropriate format and content.

[1249] Example prompts for generative AI models

[1250] "Analyze the user's emotions while viewing the investment proposal report presented to them. Based on the following input data, classify the user's emotions as either 'positive', 'neutral' or 'negative'."

[1251] Input data:

[1252] Facial expression image file: user_expression.png

[1253] Audio file: user_voice.wav

[1254] Text log: "This investment proposal is attractive. I would like to consider implementing it."

[1255] Output formats:

[1256] {

[1257] "emotion": "positive"

[1258] }"

[1259] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1260] Step 1:

[1261] The terminal uses sensors on robots installed within the factory to collect energy consumption data and CO2 emissions data in real time.

[1262] Input: Energy consumption, gas consumption, and CO2 emissions captured by each sensor

[1263] Output: Collected data is temporarily saved on the device.

[1264] Specific operation: The robot's built-in sensors collect data at specified intervals and prepare to send it to the cloud server via the Wi-Fi module.

[1265] Step 2:

[1266] The device sends the collected data to a cloud server via a Wi-Fi network.

[1267] Input: Data acquired by the sensor and the address of the destination cloud server

[1268] Output: Data sent to the cloud server

[1269] Specific operation: The device's communication module is used to send data to a cloud server via Wi-Fi.

[1270] Step 3:

[1271] The server stores the received data and performs preprocessing of the data.

[1272] Input: Energy consumption data and CO2 emissions data sent from the device

[1273] Output: Preprocessed data, outliers corrected, and data integrity checked

[1274] Specific operation: The server stores the received data in the database, performs data encapsulation, tagging, and outlier correction, and verifies any inconsistencies through consistency checks.

[1275] Step 4:

[1276] The server inputs the preprocessed data into the generative AI model and performs data analysis.

[1277] Input: Preprocessed energy consumption data and CO2 emissions data

[1278] Output: Energy consumption patterns, renewable energy investment proposals

[1279] How it works: Analyzes data using a generative AI model (e.g., using TensorFlow or PyTorch) and generates investment recommendations that incorporate time-series pattern recognition and emerging trends.

[1280] Step 5:

[1281] The server collects the user's emotional data and analyzes it using an emotion engine.

[1282] Input: User facial expression images, voice data, text input

[1283] Output: Sentiment analysis result (positive, neutral, negative, etc.)

[1284] How it works: Using emotion recognition technologies such as OpenCV and Emotion API, the system analyzes users' emotions in real time. The analysis results are recorded and reflected in the presentation of investment proposal reports.

[1285] Step 6:

[1286] The server generates an investment proposal report based on the analysis results of the generated AI and the results of the emotion engine.

[1287] Input: Renewable energy investment proposals generated by generative AI, emotion analysis results from the emotion engine

[1288] Output: Optimized investment proposal report

[1289] Specific operation: The generated investment proposals are compiled into a document and customized according to the user's emotions. For example, if the emotion is "positive," the expressions become more optimistic. If the emotion is "negative," the expressions are changed to give a sense of security.

[1290] Step 7:

[1291] The server presents the generated investment proposal report to the user.

[1292] Input: Optimized Investment Proposal Report

[1293] Output: A user-viewable investment proposal report

[1294] Specific operation: The reports generated on the cloud server are displayed on the user dashboard, and feedback is collected from the user as needed, helping the user make decisions based on the investment proposals.

[1295] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1296] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1297] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1298] [Fourth embodiment]

[1299] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1300] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1301] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1302] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1303] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1304] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1305] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1306] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1307] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1308] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1309] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1310] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1311] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1312] The present invention is a system that analyzes a company's energy consumption and CO2 emissions in real time and proposes optimal renewable energy investments. Specific embodiments of this system will be described below.

[1313] Overall system overview

[1314] The system of the present invention consists of the following main components:

[1315] 1. IoT devices that collect energy consumption data and CO2 emissions data in real time.

[1316] 2. A cloud server that stores and preprocesses the received data.

[1317] 3. A means of analyzing data and generating renewable energy investment proposals using generative AI.

[1318] 4. A means for generating and presenting investment proposal reports to the user.

[1319] Specific examples of program execution

[1320] IoT device operation (data collection)

[1321] The terminals are installed in the company's factories and monitor electricity consumption, gas usage, and CO2 emissions every minute. The monitored data is then sent to a cloud server using a stable communication protocol.

[1322] Cloud server operation (data storage and preprocessing)

[1323] The server stores the received data in the cloud. The stored data is then processed as follows:

[1324] Integrity check: Checks the consistency and accuracy of received data and detects invalid data.

[1325] Outlier correction: Correcting extreme values ​​in the data to more appropriate values ​​and imputing missing data.

[1326] Generative AI operation (data analysis and investment proposal generation)

[1327] The generative AI, housed on the server, analyzes the pre-processed data. The generative AI does the following:

[1328] Pattern Recognition: Identify trends in energy consumption patterns and CO2 emissions.

[1329] Trend analysis: Identify optimal investment opportunities based on the latest renewable energy trends.

[1330] Investment proposal generation: Generate specific investment proposals, including plans for the introduction of renewable energy systems (e.g., solar panels or wind turbines).

[1331] Present investment proposals (report generation and user presentation)

[1332] The server generates a report of the investment proposals calculated by the AI, which includes:

[1333] Specific specifications of the renewable energy equipment to be introduced

[1334] The initial investment required and the expected payback period

[1335] Energy efficiency improvement effect and CO2 emission reduction amount

[1336] The reports are provided to the user, who can view the contents through a web interface or dashboard.

[1337] Specific examples

[1338] Here is an example of a manufacturing company that implemented this system. Here is a specific scenario:

[1339] 1. Terminals are installed in each area of ​​the factory to collect electricity and gas consumption data and CO2 emissions in real time.

[1340] 2. The data is sent to a server on the cloud at regular intervals.

[1341] 3. The server receives the data, stores it, checks its integrity, and corrects any outliers.

[1342] 4. The server's generation AI analyzes the data and generates optimal investment proposals based on the latest renewable energy trends.

[1343] 5. The server creates a report of the generated investment proposal and presents it to the user.

[1344] 6. The user reviews the report and decides to install a renewable energy system based on the information provided.

[1345] This system will enable companies to improve their energy efficiency and reduce CO2 emissions, and provide concrete guidelines for sustainable energy management.

[1346] The processing flow will be explained below.

[1347] Step 1:

[1348] The terminal collects a company's energy consumption and CO2 emissions in real time. Data is acquired from various sensor locations and sent to a cloud server at regular intervals. The data includes electricity consumption, gas consumption, CO2 emissions, etc.

[1349] Step 2:

[1350] The server receives the data sent from the device and stores it in a cloud database. The received data is not stored as is, but is encapsulated and tagged. At this point, data integrity checks and initial filtering can also be performed.

[1351] Step 3:

[1352] The server performs pre-processing on the stored data, which includes the following steps:

[1353] Integrity check: Validate data consistency and check for inconsistencies.

[1354] Outlier correction: Correcting abnormal or missing values ​​in the data. For example, imputing abnormally high electricity usage data with the average value.

[1355] Step 4:

[1356] The server's generation AI analyzes the preprocessed data. The generation AI performs the following tasks:

[1357] Recognizing energy consumption patterns: Analyzing time series data to identify cyclical patterns and sudden trends in consumption.

[1358] Trend Analysis: Capture renewable energy trends and market data and generate optimal energy investment plans based on this.

[1359] Step 5:

[1360] The server generates a report based on the AI ​​analysis results, summarizing optimal renewable energy investment proposals. The report includes:

[1361] Specific Equipment: Recommended renewable energy systems (e.g., specific models of solar panels or wind turbines).

[1362] Initial investment: the required implementation costs and expected payback period.

[1363] Expected effects: Improved energy efficiency and reduced CO2 emissions are expected.

[1364] Step 6:

[1365] The server generates reports and displays them on a dashboard for users to access and review. Users can view detailed reports and simulation results through the dashboard.

[1366] Step 7:

[1367] We create a specific implementation plan based on the investment proposal provided by the user, determine the equipment and measures to be introduced, and proceed with the introduction of the renewable energy system.

[1368] Step 8:

[1369] The server continuously monitors the performance of the installed renewable energy system, collecting and analyzing operational data after installation and providing regular feedback for optimization and improvement.

[1370] Through these steps, this system improves a company's energy consumption efficiency and reduces CO2 emissions.

[1371] Example 1

[1372] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1373] Currently, many companies are aiming to improve energy efficiency and reduce greenhouse gas emissions, but they lack the tools and systems to realistically achieve this. Furthermore, there are limited means to optimize investments in renewable energy. There is a need for a system that allows companies to understand energy consumption patterns and CO2 emissions in real time and use that data to derive optimal renewable energy investment plans.

[1374] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1375] In this invention, the server includes a data analysis means using AI to identify energy consumption patterns and CO2 emission trends, a means for generating renewable energy investment proposals, and a means for generating and presenting investment proposal reports to users, allowing companies to analyze energy consumption data and CO2 emission data in real time and quickly obtain optimal renewable energy investment proposals.

[1376] "Energy consumption data" refers to data on the amount of electricity, gas, and other energy consumed by a business or facility.

[1377] "CO2 emissions data" refers to data on the amount of carbon dioxide emitted by companies and facilities.

[1378] "Real-time collection means" is a general term for devices and sensors that can acquire and record data in real time, in the present.

[1379] "Means for storing and pre-processing received data" refers to devices or systems that store collected data in an appropriate format and perform procedures to correct or supplement inconsistencies or outliers prior to data analysis.

[1380] "Generative AI" refers to artificial intelligence technologies that have the ability to generate and analyze for specific tasks.

[1381] "Data Analysis Tools" means tools that use techniques and algorithms to analyze collected and pre-processed data and identify trends and patterns.

[1382] The "means for generating renewable energy investment proposals" refers to a method or system for formulating an investment plan for optimal renewable energy technologies based on the results of data analysis.

[1383] The "means for generating an investment proposal report and presenting it to the user" refers to an interface and technology for organizing the generated investment proposals in a report format and presenting them visually and informationally to the user.

[1384] "Integrity checking" is the process of checking that data is consistent and free of inconsistencies and errors.

[1385] "Outlier correction" is a method for detecting extreme or unnatural values ​​in data and correcting them to an appropriate range.

[1386] A "renewable energy system" is a device or facility that utilizes sustainable energy sources such as solar, wind, hydroelectric, and biomass.

[1387] An "investment proposal" is a proposal that outlines a planned investment in a specific project or technology.

[1388] "Performance monitoring" is the process of continuously monitoring installed systems and equipment to ensure they are performing as expected and efficiently.

[1389] "Providing feedback" is the activity of reporting the results of performance monitoring to users and providing information for necessary improvements and optimization.

[1390] This system analyzes a company's energy consumption and CO2 emissions in real time and proposes optimal renewable energy investments based on that data. The entire system consists of the following main components:

[1391] 1. IoT devices that collect energy consumption and CO2 emissions data in real time

[1392] Terminals are installed in factories and offices to monitor electricity consumption, gas usage, and CO2 emissions every minute. These data are collected using smart meters and IoT sensors. For example, minute-by-minute electricity consumption data obtained by a smart meter and emission data from a CO2 sensor can be considered. This data is then sent to a cloud server using a reliable communication protocol (e.g., MQTT, HTTP).

[1393] 2. Cloud server that stores and preprocesses received data

[1394] The data received by the server is stored in a cloud database (e.g., Amazon RDS, Google BigQuery). The integrity of the received data is checked and outliers are corrected. When checking data integrity, consistency is checked using SQL queries to detect whether there are any outliers. If an outlier is detected, it is corrected using statistical methods and missing data is filled in.

[1395] 3. Data analysis using generative AI

[1396] The server's built-in generative AI (e.g., OpenAI GPT-4) analyzes the pre-processed data. The generative AI identifies energy consumption patterns and CO2 emission trends, and identifies optimal investment targets based on the latest renewable energy trends. Time series analysis and trend analysis techniques are used in the analysis to identify optimal investment targets.

[1397] 4. A means of generating renewable energy investment proposals

[1398] Based on the results of the analysis by the AI, the server generates a specific renewable energy system implementation plan. Investment proposals include plans for installing solar panels and wind turbines. The required initial investment amount, payback period, energy efficiency improvement effect, and CO2 emission reduction amount are calculated.

[1399] 5. Means for generating and presenting investment proposal reports to users

[1400] The server generates a report based on the AI-generated investment proposal. This report includes detailed specifications of the renewable energy equipment to be installed, the required initial investment amount, payback period, energy efficiency improvement effects, and CO2 emission reduction amounts. The generated report can be accessed by the user via a web interface (e.g., a React-based dashboard) or a dedicated app.

[1401] Specific examples

[1402] A specific example will be described in which a manufacturing company has introduced this system.

[1403] 1. Terminals are installed in each area of ​​the factory to collect electricity consumption data, gas usage, and CO2 emissions in real time.

[1404] 2. The data is sent to a server on the cloud at regular intervals.

[1405] 3. The server stores the received data, checks its integrity, and corrects outliers.

[1406] 4. Server generation AI analyzes the data and generates optimal investment proposals based on the latest renewable energy trends.

[1407] 5. The server creates a report of the investment proposals and presents it to the user.

[1408] 6. The user reviews the report and decides on the optimal renewable energy system implementation based on the information provided.

[1409] Prompt Sentence Examples

[1410] Here is an example of a prompt to input to a generative AI model:

[1411] "Generate optimal renewable energy investment proposals based on Company X's energy consumption and CO2 emissions data. Average monthly electricity consumption is 100,000 kWh, average monthly gas consumption is 20,000 m3, and CO2 emissions total 2,400 tons per year. Please refer to the latest renewable energy trends."

[1412] Using these prompts, the generative AI can suggest optimal investment ideas.

[1413] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1414] Step 1: Data collection

[1415] The terminal collects electricity consumption, gas usage, and CO2 emissions in real time from sensors and meters installed in factories and businesses. The input is data from various sensors, and the output is that this data is temporarily stored in the terminal. Specifically, smart meters and CO2 sensors measure data every minute and store it in the terminal's memory.

[1416] Step 2: Send data

[1417] The data collected by the terminal is sent to the cloud server using a stable communication protocol (e.g., MQTT, HTTP). The input is the data collected in step 1, and the output is the data sent to the cloud server. Specifically, the terminal aggregates the data into packets at regular intervals and sends them to the cloud server. If a communication error occurs, it attempts to resend the data.

[1418] Step 3: Save Data

[1419] The server stores the data it receives in a cloud database (e.g., Amazon RDS, Google BigQuery). The input is data sent from the device, and the output is data stored in the cloud database. Specifically, the server executes an insert query in the database, saves the data, and performs automatic backups.

[1420] Step 4: Data Preprocessing

[1421] The server checks the consistency of the incoming data and corrects outliers. The input is data stored in a cloud database, and the output is preprocessed data. Specifically, the server checks consistency using SQL queries and corrects any outliers detected using statistical methods. It also runs algorithms to impute missing data.

[1422] Step 5: Data analysis

[1423] The generative AI built into the server analyzes the preprocessed data. The input is the preprocessed data, and the output is energy consumption patterns, CO2 emission trends, and optimal renewable energy trend information. The generative AI performs time series analysis and trend analysis to identify optimal investment targets. Specifically, the AI ​​model processes large amounts of data and extracts specific patterns and trends.

[1424] Step 6: Investment proposal generation

[1425] The server generates a specific renewable energy system implementation plan based on the analysis results of the AI. The input is the results of the data analysis, and the output is a specific investment proposal. Specifically, the server runs an implementation plan simulation and calculates the required initial investment amount, payback period, energy efficiency improvement effect, and CO2 emission reduction amount.

[1426] Step 7: Generate reports

[1427] The server compiles the generated investment proposals into a report format. The input is the investment proposal data, and the output is a document in report format. Specifically, the server embeds the data into a report template, generates visual elements such as graphs and tables, and exports them in PDF format.

[1428] Step 8: User Presentation

[1429] The user checks the generated report through a web interface or a dedicated app. The input is the report provided by the server, and the output is the information checked by the user. Specifically, the user accesses the dashboard in a web browser, downloads and views the report, and, if necessary, shares the report with other decision makers.

[1430] This allows the processing of the entire system to be realized.

[1431] (Application example 1)

[1432] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1433] In modern manufacturing and large-scale facilities, managing energy consumption and CO2 emissions is important, but there is no effective system for analyzing this data in real time and determining optimal renewable energy investments. Furthermore, there is a lack of support tools that allow managers to instantly grasp the situation and make quick decisions. In such environments, there are issues with reduced energy efficiency and unnecessary costs.

[1434] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1435] In this invention, the server includes means for collecting energy consumption data and CO2 emission data in real time from IoT devices, means for storing and preprocessing the received data in a cloud computing platform, means for analyzing the preprocessed data using a generative AI model and generating a renewable energy investment proposal, and means for presenting the generated investment proposal report to a user using a head-mounted display, thereby enabling real-time monitoring of energy consumption and CO2 emissions and proposals for optimal renewable energy investments.

[1436] An "IoT terminal" is a device that is connected to the Internet and is capable of collecting and transmitting data.

[1437] "Energy consumption data" refers to data that indicates the amount of energy consumed, such as electricity and gas, by factories and facilities.

[1438] "CO2 emission data" is data that indicates the amount of carbon dioxide emitted as a result of energy consumption.

[1439] A "cloud computing platform" is a service infrastructure that allows data to be stored, processed, and analyzed via the Internet.

[1440] A "generative AI model" is an artificial intelligence algorithm or model that analyzes large amounts of data and extracts specific patterns and trends.

[1441] "Preprocessing" refers to processing carried out prior to analysis, such as checking the consistency of collected data and correcting outliers.

[1442] "Analysis" is the act of examining data in detail to find specific patterns and trends.

[1443] A "renewable energy investment plan" is a specific proposal for introducing renewable energy sources such as solar power and wind power.

[1444] A "head-mounted display" is a display device that is worn on the head and provides visual information.

[1445] The "Investment Proposal Report" is a report summarizing the specific details of renewable energy investments proposed by the generative AI model.

[1446] "Users" are companies and individuals who use the system.

[1447] The present invention relates to a system that analyzes a company's energy consumption and CO2 emissions in real time and proposes optimal renewable energy investments. Specific embodiments of this system will be described below.

[1448] Overall system overview

[1449] The system of the present invention consists of the following main components:

[1450] 1. IoT devices

[1451] 2. Cloud Computing Platform

[1452] 3. Generative AI Models

[1453] 4. Head-Mounted Display (HMD)

[1454] IoT device operation

[1455] IoT devices are installed in various areas of the factory to monitor electricity consumption, gas usage, and CO2 emissions every minute, and the monitored data is sent to a cloud computing platform via internet communication.

[1456] Cloud Computing Platform Operation

[1457] A cloud computing platform provides the following capabilities:

[1458] Data storage: The received data is stored in the cloud. This process uses a data storage service.

[1459] Data preprocessing: Consistency checks and outlier corrections are performed to generate reliable data.

[1460] How generative AI models work

[1461] A generative AI model analyzes the preprocessed data, specifically:

[1462] Pattern Recognition: Identify trends in energy consumption patterns and CO2 emissions.

[1463] Trend analysis: Identify optimal investment opportunities based on the latest renewable energy trends.

[1464] Investment proposal generation: Generate specific investment proposals, including plans for the introduction of renewable energy systems (e.g., solar panels and wind turbines).

[1465] Head-mounted display operation

[1466] The head-mounted display (HMD) is a device that allows factory managers to check energy consumption data and CO2 emissions in real time and view generated investment proposal reports. The HMD provides the following functions:

[1467] Real-time monitoring: Data obtained from IoT devices installed in each area of ​​the factory is displayed in real time.

[1468] Abnormality alert: A function that automatically notifies you if energy consumption or CO2 emissions show abnormal values.

[1469] Investment proposal presentation: The generation AI analyzes and displays proposed investment proposals in report format.

[1470] Voice command function: Factory managers can access and review data and suggestions using voice commands.

[1471] Collaboration function: The ability to share data and proposals with other managers in real time and work together to come up with solutions.

[1472] Specific examples

[1473] Here is a scenario in which a manufacturing company implemented this system:

[1474] 1. IoT terminals are installed in each area of ​​the factory to collect electricity and gas consumption data and CO2 emissions in real time.

[1475] 2. The data is transmitted to the cloud computing platform via internet communication.

[1476] 3. The cloud computing platform stores the received data and performs preprocessing, specifically integrity checking and outlier correction.

[1477] 4. The generative AI model analyzes the pre-processed data to identify trends in energy consumption patterns and CO2 emissions, and generates renewable energy investment recommendations based on the latest trend analysis.

[1478] 5. The head-mounted display presents the generated investment proposal report to the factory manager, providing real-time monitoring and alerting in case of abnormalities.

[1479] An example prompt is, "Analyze the factory's energy consumption data and CO2 emissions trends to generate optimal renewable energy investment proposals."

[1480] This system will achieve both energy cost reduction and environmental protection by significantly improving the efficiency of factory energy management.

[1481] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1482] Step 1:

[1483] Terminals are installed in each area of ​​the factory to monitor power consumption, gas usage, and CO2 emissions in real time. These data are collected by the IoT terminals. The input is energy data and CO2 emission data from each sensor, and the output is data sent to a cloud computing platform via the internet.

[1484] Step 2:

[1485] The server saves the received data on the cloud computing platform. The saved data is temporarily stored on the data storage for preprocessing. The input is the energy data and CO2 emission data sent in the previous step, and the output is the data saved in the cloud.

[1486] Step 3:

[1487] The server checks the integrity of the received data and corrects outliers. Specifically, it uses a data cleaning algorithm to check the consistency of the data and appropriately corrects extreme values ​​and missing values. The input is raw data stored in the cloud, and the output is preprocessed data.

[1488] Step 4:

[1489] The generative AI model analyzes the pre-processed data. First, it performs pattern recognition to identify trends in energy consumption patterns and CO2 emissions. Second, it performs trend analysis based on renewable energy trend information to identify optimal investment targets. Finally, it generates specific investment proposals, including plans for the introduction of renewable energy systems (e.g., solar panels and wind turbines). The input is the pre-processed data, and the output is the optimal renewable energy investment proposal.

[1490] Step 5:

[1491] The server creates a report of the generated investment proposal, which includes the specific specifications of the renewable energy equipment to be introduced, the required initial investment amount and expected payback period, the effect of improving energy efficiency, and the amount of CO2 emissions reduced. The input is the investment proposal output by the generative AI model, and the output is the completed investment proposal report.

[1492] Step 6:

[1493] A head-mounted display (HMD) presents the generated investment proposal report to the user in real time. Factory managers can check energy consumption data and CO2 emissions in real time through the HMD and receive instant notifications if an abnormality occurs. Voice commands can also be used to easily call up and check data and proposals. The input is the investment proposal report and real-time data sent from the server, and the output is the information presented to the user.

[1494] In this way, factory managers can make quick and accurate decisions to increase energy efficiency and reduce CO2 emissions.

[1495] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1496] The present invention is a system that analyzes a company's energy consumption and CO2 emissions in real time and proposes optimal renewable energy investments by combining an emotion engine. Specific embodiments of this system are described below.

[1497] Overall system overview

[1498] The system of the present invention consists of the following main components:

[1499] 1. IoT devices that collect energy consumption data and CO2 emissions data in real time.

[1500] 2. A cloud server that stores and preprocesses the received data.

[1501] 3. A means of analyzing data and generating renewable energy investment proposals using generative AI.

[1502] 4. A means for generating and presenting investment proposal reports to the user.

[1503] 5. An emotion engine that recognizes and analyzes user emotions.

[1504] Specific examples of program execution

[1505] IoT device operation (data collection)

[1506] The terminals are installed in the company's factories and monitor electricity consumption, gas consumption, and CO2 emissions every minute. The monitored data is sent to a cloud server using a stable communication protocol.

[1507] Cloud server operation (data storage and preprocessing)

[1508] The server receives the data sent from the device and stores it in a cloud database. The received data is not stored as is, but is encapsulated and tagged. At this point, data integrity checks and initial filtering are also performed.

[1509] Generative AI operation (data analysis and investment proposal generation)

[1510] The generative AI built into the server analyzes the pre-processed data and performs the following tasks:

[1511] Recognizing energy consumption patterns: Analyzing time series data to identify cyclical patterns and sudden trends in consumption.

[1512] Trend Analysis: Capture the latest renewable energy trends and market data to generate optimal energy investment plans.

[1513] Investment proposal generation: Generate specific investment proposals, including plans for the installation of renewable energy systems (e.g., specific models of solar panels or wind turbines).

[1514] Emotion engine operation (emotion recognition and analysis)

[1515] The server collects and analyzes the user's emotional data. Emotion data is collected from facial expressions, tone of voice, and input history when the user views an investment report. The emotion engine performs the following:

[1516] Emotion recognition: Detecting emotions from a user's facial expressions, voice, and text data.

[1517] Sentiment Analysis: Analyze how the detected sentiment tends to react to the investment proposal.

[1518] Present investment proposals (report generation and user presentation)

[1519] The server generates a report based on the results of the AI ​​analysis and the emotion engine analysis, summarizing optimal renewable energy investment proposals. The report includes:

[1520] Specific equipment to be installed: Recommended renewable energy systems.

[1521] Initial investment: the required implementation costs and expected payback period.

[1522] Expected effects: Improved energy efficiency and reduced CO2 emissions are expected.

[1523] Customization based on user emotions: Presentation content and presentation methods are optimized according to the user's emotions.

[1524] Reports are provided to users, who can view the contents through a web interface or dashboard.

[1525] Specific examples

[1526] Here is an example of a manufacturing company that implemented this system. Here is a specific scenario:

[1527] 1. Terminals are installed in each area of ​​the factory to collect electricity and gas consumption data and CO2 emissions in real time.

[1528] 2. The data is sent to a server on the cloud at regular intervals.

[1529] 3. The server receives the data, stores it, checks its integrity, and corrects any outliers.

[1530] 4. The server's generation AI analyzes the data and generates optimal investment proposals based on the latest renewable energy trends.

[1531] 5. The server uses an emotion engine to collect user emotion data and optimize the format and content of the investment proposal report.

[1532] 6. The server presents the generated investment proposal report to the user and re-analyzes the user's feedback if necessary.

[1533] 7. The user reviews the report and decides to install a renewable energy system based on the information provided.

[1534] This system will enable companies to improve their energy efficiency and reduce CO2 emissions, and will also maximize the effectiveness of its proposals by taking into account the user's emotions.

[1535] The processing flow will be explained below.

[1536] Step 1:

[1537] The terminal collects a company's energy consumption and CO2 emissions in real time. Data is acquired from various sensor locations and sent to a cloud server at regular intervals. The data includes electricity consumption, gas consumption, CO2 emissions, etc.

[1538] Step 2:

[1539] The server receives the data sent from the device and stores it in a cloud database. The received data is not stored as is, but is encapsulated and tagged. At this point, data integrity checks and initial filtering are also performed.

[1540] Step 3:

[1541] The server performs pre-processing on the stored data, which includes the following steps:

[1542] Integrity check: Validate data consistency and check for inconsistencies.

[1543] Outlier correction: Correcting abnormal or missing values ​​in the data. For example, imputing abnormally high electricity usage data with the average value.

[1544] Step 4:

[1545] The server's generation AI analyzes the preprocessed data. The generation AI performs the following tasks:

[1546] Recognizing energy consumption patterns: Analyzing time series data to identify cyclical patterns and sudden trends in consumption.

[1547] Trend Analysis: Capture the latest renewable energy trends and market data to generate optimal energy investment plans.

[1548] Investment proposal generation: Generate specific investment proposals, including plans for the installation of renewable energy systems (e.g., specific models of solar panels or wind turbines).

[1549] Step 5:

[1550] The server generates a report based on the AI ​​analysis results, summarizing optimal renewable energy investment proposals. The report includes:

[1551] Specific Equipment: Recommended renewable energy systems (e.g., specific models of solar panels or wind turbines).

[1552] Initial investment: the required implementation costs and expected payback period.

[1553] Expected effects: Improved energy efficiency and reduced CO2 emissions are expected.

[1554] Step 6:

[1555] The server collects and analyzes user emotion data using an emotion engine. The emotion engine performs the following tasks:

[1556] Emotion recognition: Detecting emotions from a user's facial expressions, voice, and text data.

[1557] Sentiment analysis: Analyze the detected sentiment to see how it tends to affect the investment proposal. Based on the results of the sentiment analysis, optimize the presentation and content of the investment proposal report.

[1558] Step 7:

[1559] The server displays the report on a dashboard for users to access and review. Users can view detailed reports and simulation results through the dashboard. Based on the results of sentiment analysis, customized content is presented that is tailored to the user's interests.

[1560] Step 8:

[1561] We create a specific implementation plan based on the investment proposal provided by the user, determine the equipment and measures to be introduced, and proceed with the introduction of the renewable energy system.

[1562] Step 9:

[1563] The server continuously monitors the performance of the installed renewable energy system, collecting and analyzing operational data after installation and providing regular feedback for optimization and improvement.

[1564] Through these steps, this system improves the energy consumption efficiency of companies and reduces CO2 emissions. Furthermore, by taking into account the user's emotions, it maximizes the effectiveness of the suggestions.

[1565] Example 2

[1566] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1567] Reducing energy consumption and CO2 emissions in modern companies is extremely important from the perspective of reducing environmental impact and cutting costs. However, systems for efficiently collecting and analyzing energy consumption and CO2 emission data and generating optimal renewable energy investment proposals are inadequate. Furthermore, there are no established methods for verifying the consistency of collected data, correcting outliers, or customizing proposals based on user sentiment. As a result, companies have difficulty obtaining actionable information for making appropriate renewable energy investments.

[1568] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for collecting energy consumption data and CO2 emission data in real time using IoT terminals, means for storing and preprocessing the received data in a cloud device, means for analyzing the preprocessed data using a generation AI and generating a renewable energy investment proposal, means for generating an investment proposal report and presenting it to the user, and means for collecting and analyzing user emotion data. This enables accurate analysis of the data collected in real time and makes it possible to provide an optimal renewable energy investment proposal based on the user's emotions.

[1569] An "IoT device" is a device that is connected to the Internet and collects data from the physical environment and transmits it to the cloud or other devices.

[1570] "Energy consumption data" is data relating to the amount of energy consumed within a certain period of time.

[1571] "CO2 emissions data" refers to data on the amount of carbon dioxide emitted within a certain period of time.

[1572] "Cloud device" refers to a remote server or data center used to store, process, and manage data over the Internet.

[1573] "Generative AI" is a system that uses machine learning and artificial intelligence techniques to analyze data and generate new information and suggestions.

[1574] "Preprocessing" refers to processes such as data formatting, encapsulation, tagging, consistency checking, and outlier correction that are performed before data analysis.

[1575] An "investment proposal report" is a document that summarizes specific proposals for the introduction of renewable energy systems.

[1576] "User emotion data" refers to data related to emotions acquired from facial expressions, tone of voice, and input history when a user uses the system interface.

[1577] "Renewable energy investment proposal" refers to a specific plan or proposal for the introduction of a renewable energy system.

[1578] The present invention is a system that analyzes a company's energy consumption and CO2 emissions in real time and proposes optimal renewable energy investments by combining an emotion engine. Specific embodiments of this system are described below.

[1579] Overall system overview

[1580] The system of the present invention consists of the following main components:

[1581] 1. IoT devices that collect energy consumption data and CO2 emissions data in real time.

[1582] 2. A cloud server that stores and preprocesses the received data.

[1583] 3. A means of analyzing data and generating renewable energy investment proposals using generative AI.

[1584] 4. A means for generating and presenting investment proposal reports to the user.

[1585] 5. An emotion engine that recognizes and analyzes user emotions.

[1586] IoT device operation (data collection)

[1587] Terminals are installed in companies' factories and offices. These terminals collect electricity consumption, gas consumption, and CO2 emissions in real time. The monitored data is sent to a cloud server using a stable communication protocol (e.g., MQTT).

[1588] Cloud server operation (data storage and preprocessing)

[1589] The server receives the data sent from the device and stores it in a cloud database (e.g., Amazon RDS). The stored data is encapsulated and tagged, and data integrity checks and initial filtering are performed to eliminate outliers and missing data.

[1590] Generative AI operation (data analysis and investment proposal generation)

[1591] A generative AI (e.g., OpenAI's GPT model) built into the server analyzes the preprocessed data. Specific tasks include:

[1592] Recognizing energy consumption patterns: Analyzing time series data to identify cyclical patterns and sudden trends in consumption.

[1593] Trend Analysis: Capture the latest renewable energy trends and market data to generate optimal energy investment plans.

[1594] Investment proposal generation: Generate an investment proposal that includes a plan to introduce a specific renewable energy system (e.g., a specific model of solar panel).

[1595] Emotion engine operation (emotion recognition and analysis)

[1596] The server collects and analyzes the user's emotional data. The data includes facial expressions, tone of voice, and input history when the user views an investment report. The emotion engine (e.g., Microsoft Azure's Emotion API) performs the following:

[1597] Emotion recognition: Detecting emotions from a user's facial expressions, voice, and text data.

[1598] Sentiment analysis: Analyze how the detected sentiment tends to react to the investment proposal.

[1599] Generate and present investment proposal reports (server operation)

[1600] The server generates a report based on the results of the AI ​​analysis and the emotion engine analysis, summarizing optimal renewable energy investment proposals. The report includes:

[1601] Specific equipment to be installed: Recommended renewable energy systems.

[1602] Initial investment: the required implementation costs and expected payback period.

[1603] Expected effects: Improved energy efficiency and reduced CO2 emissions are expected.

[1604] Customization based on user emotions: Presentation content and presentation methods are optimized according to the user's emotions.

[1605] The reports are provided to the user, who can view the contents through a web interface or dashboard.

[1606] Specific examples

[1607] Here is a specific example of how a manufacturing company implemented this system:

[1608] 1. Terminals are installed in each area of ​​the factory to collect electricity consumption, gas consumption, and CO2 emissions in real time. For example, a terminal is installed on a specific production line to collect energy consumption data for that line every minute.

[1609] 2. The data is sent to a server on the cloud at regular intervals.

[1610] 3. The server receives the data, stores it, checks its integrity, and corrects outliers. For example, if abnormally high consumption data is detected, it will be corrected and recorded.

[1611] 4. Server generation AI analyzes the data and generates optimal investment proposals based on the latest renewable energy trends. For example, it creates a plan to introduce the latest solar panels.

[1612] 5. The server uses an emotion engine to collect user emotion data (facial expressions and tone of voice) and optimize the format and content of the investment proposal report. For example, if the user expresses surprise, the server will provide more detailed explanations on that part.

[1613] 6. The server generates a report of the proposed investment and presents it to the user, who can then view it in a dashboard, for example, a web interface that provides a detailed cost analysis and impact forecast.

[1614] 7. The user decides to install a renewable energy system based on the information provided.

[1615] Prompt Sentence Examples

[1616] Generate optimal investment proposals to maximize cost savings from installing commercial solar power systems. Also, how can you customize the investment proposals by taking user sentiment data into account?

[1617] This system enables companies to improve energy efficiency and reduce CO2 emissions, and by taking user emotions into consideration, it also maximizes the effectiveness of its proposals.

[1618] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1619] Step 1: Data collection (device operation)

[1620] Terminals are installed in companies' factories and offices to monitor electricity consumption, gas usage, and CO2 emissions in real time. When this data is collected, the terminals use a communication protocol (e.g., MQTT) to send the data to a cloud server. The input is data from various sensors, and the output is raw data sent to the cloud server. Specifically, the terminals collect data from sensors installed in each location at regular intervals and send it to the cloud server in real time.

[1621] Step 2: Data storage and preprocessing (server operation)

[1622] The server receives data sent from the terminal. The input is the raw data sent from the terminal, and the output is the preprocessed data. Specifically, the server stores the received data in a cloud database (e.g., Amazon RDS), encapsulates and tags the data, and performs data integrity checks and outlier corrections, thereby creating a reliable dataset.

[1623] Step 3: Data analysis and investment proposal generation (operation of the generative AI model)

[1624] The server's generation AI analyzes the preprocessed data. The input is the preprocessed data, and the output is a renewable energy investment proposal. Specifically, the generation AI (e.g., OpenAI's model) analyzes time-series data and recognizes energy consumption patterns. It also incorporates the latest renewable energy trends and generates an optimal energy investment plan. This provides a specific investment proposal, including plans for the introduction of specific models of solar panels, wind turbines, etc.

[1625] Step 4: Emotion Recognition and Analysis (How the Emotion Engine Works)

[1626] The server collects and analyzes the user's emotional data. The input is the user's facial expression, tone of voice, and input history, and the output is the result of emotion recognition. Specifically, an emotion engine (e.g., Microsoft Azure's Emotion API) analyzes the user's facial expression and tone of voice in real time as they view reports to detect their emotions. Based on this detected data, the system analyzes their reaction to the investment proposal.

[1627] Step 5: Generate and present the investment proposal report (server operation)

[1628] The server compiles optimal renewable energy investment proposals in the form of a report based on the analysis results of the generated AI and the emotion engine. The inputs are the analysis results and emotion recognition results, and the output is a customized investment proposal report. Specifically, the report includes recommended renewable energy systems, initial investment amounts, predicted benefits, and customization elements based on the user's emotions. The server presents this to the user via a dashboard or web interface.

[1629] Step 6: Collecting user feedback and re-analyzing (server actions)

[1630] The server collects feedback from users and reanalyzes it. The input is the user's feedback data, and the output is an updated investment proposal report. Specifically, based on the feedback provided by the user (e.g., questions or requests for revisions), the generative AI and emotion engine reanalyze and update the investment proposals. This makes it possible to provide more precise information to the user.

[1631] (Application example 2)

[1632] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1633] Optimizing energy consumption and CO2 emissions in companies is an important issue, but currently it is difficult to collect and analyze data in real time, and there are also challenges in proposing renewable energy investment plans that take user emotions into account. The purpose of this invention is to solve these challenges and achieve improved energy efficiency and reduced CO2 emissions in companies.

[1634] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1635] In this invention, the server includes a means for collecting energy consumption data and CO2 emission data in real time, a means for storing and preprocessing the data, a means for analyzing the preprocessed data and generating renewable energy investment proposals, a means for recognizing and analyzing user emotion data, and a means for optimizing the format and content of the investment proposal report based on the user emotion, thereby enabling real-time analysis of a company's energy consumption and CO2 emission, and further enabling the proposal of an optimal renewable energy investment proposal taking into account the user emotion.

[1636] An "IoT terminal" is a terminal for collecting and transmitting data via the Internet.

[1637] "Energy consumption data" means data relating to the energy usage of a particular system or facility.

[1638] "CO2 emissions data" means data relating to the amount of carbon dioxide emitted by a particular system or facility.

[1639] A "cloud server" is a server that stores and processes data over the Internet.

[1640] "Preprocessing" refers to processes such as checking the consistency of data and correcting outliers that are carried out before analyzing collected data.

[1641] "Generative AI" is a system that uses artificial intelligence to analyze data and generate specific outputs.

[1642] A "renewable energy investment proposal" is a plan or proposal for the introduction of a renewable energy system.

[1643] An "investment proposal report" is a report that details the contents of a renewable energy investment proposal.

[1644] An "emotion engine" is a system for recognizing and analyzing a user's emotional data.

[1645] "User emotion data" is data that indicates the user's emotion, and is obtained from facial expressions, voice, text, and the like.

[1646] "Optimization" is the process of adjusting a system or process to be most efficient in order to achieve a specific purpose.

[1647] The present invention is a system that uses a robot installed in a factory to analyze a company's energy consumption and CO2 emissions in real time and provides optimal renewable energy investment proposals. Specific embodiments of the present invention are described below.

[1648] System configuration

[1649] The system consists of the following main components:

[1650] 1. IoT devices:

[1651] The IoT terminal is built into the robot and collects energy consumption and CO2 emission data in real time within the factory, and transmits the data to a cloud server via Wi-Fi.

[1652] 2. Cloud Server:

[1653] Data storage and pre-processing:

[1654] The cloud server receives and stores data sent from IoT devices, and performs preprocessing such as checking data consistency and correcting outliers.

[1655] Generation AI:

[1656] The pre-processed data is analyzed to recognize energy consumption patterns and generate renewable energy investment proposals.

[1657] Emotion Engine:

[1658] Recognize and analyze user emotion data. Collect facial expressions, voice, and text data when users view investment proposal reports to detect emotions.

[1659] 3. Generate and present investment proposal reports:

[1660] Based on the analysis results of the generative AI and the emotion engine, optimal renewable energy investment proposals are compiled in report format.

[1661] The report includes recommended renewable energy systems, initial investment amounts, expected payback periods, and potential energy efficiency gains and CO2 emission reductions.

[1662] The report optimizes its content and presentation based on user sentiment.

[1663] Hardware and software used

[1664] IoT terminal: Sensors and Wi-Fi modules built into the robot

[1665] Cloud servers: Cloud infrastructure (e.g., AWS, Google Cloud) can be used for data storage and processing, and TensorFlow or PyTorch can be used for generative AI models.

[1666] Emotion engine: Emotion recognition using OpenCV and Emotion API

[1667] Example of a system

[1668] For example, if an automobile manufacturing plant were to implement this system, it would work as follows:

[1669] 1. Installation of robots: Robots installed in each section will collect energy consumption data and CO2 emissions data in real time.

[1670] 2. Data transmission and storage: The collected data is transmitted to a cloud server where it is stored and pre-processed.

[1671] 3. Data analysis: A generative AI model analyzes the pre-processed data, identifies energy consumption patterns, and generates renewable energy investment proposals.

[1672] 4. Emotion analysis: The factory manager views the investment proposal report, and the facial expressions and voice data are analyzed by the emotion engine.

[1673] 5. Optimization of investment proposals: Based on the results of user sentiment analysis, investment proposal reports are provided in the most appropriate format and content.

[1674] Example prompts for generative AI models

[1675] "Analyze the user's emotions while viewing the investment proposal report presented to them. Based on the following input data, classify the user's emotions as either 'positive', 'neutral' or 'negative'."

[1676] Input data:

[1677] Facial expression image file: user_expression.png

[1678] Audio file: user_voice.wav

[1679] Text log: "This investment proposal is attractive. I would like to consider implementing it."

[1680] Output formats:

[1681] {

[1682] "emotion": "positive"

[1683] }"

[1684] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1685] Step 1:

[1686] The terminal uses sensors on robots installed within the factory to collect energy consumption data and CO2 emissions data in real time.

[1687] Input: Energy consumption, gas consumption, and CO2 emissions captured by each sensor

[1688] Output: Collected data is temporarily saved on the device.

[1689] Specific operation: The robot's built-in sensors collect data at specified intervals and prepare to send it to the cloud server via the Wi-Fi module.

[1690] Step 2:

[1691] The device sends the collected data to a cloud server via a Wi-Fi network.

[1692] Input: Data acquired by the sensor and the address of the destination cloud server

[1693] Output: Data sent to the cloud server

[1694] Specific operation: The device's communication module is used to send data to a cloud server via Wi-Fi.

[1695] Step 3:

[1696] The server stores the received data and performs preprocessing of the data.

[1697] Input: Energy consumption data and CO2 emissions data sent from the device

[1698] Output: Preprocessed data, outliers corrected, and data integrity checked

[1699] Specific operation: The server stores the received data in the database, performs data encapsulation, tagging, and outlier correction, and verifies any inconsistencies through consistency checks.

[1700] Step 4:

[1701] The server inputs the preprocessed data into the generative AI model and performs data analysis.

[1702] Input: Preprocessed energy consumption data and CO2 emissions data

[1703] Output: Energy consumption patterns, renewable energy investment proposals

[1704] How it works: Analyzes data using a generative AI model (e.g., using TensorFlow or PyTorch) and generates investment recommendations that incorporate time-series pattern recognition and emerging trends.

[1705] Step 5:

[1706] The server collects the user's emotional data and analyzes it using an emotion engine.

[1707] Input: User facial expression images, voice data, text input

[1708] Output: Sentiment analysis result (positive, neutral, negative, etc.)

[1709] How it works: Using emotion recognition technologies such as OpenCV and Emotion API, the system analyzes users' emotions in real time. The analysis results are recorded and reflected in the presentation of investment proposal reports.

[1710] Step 6:

[1711] The server generates an investment proposal report based on the analysis results of the generated AI and the results of the emotion engine.

[1712] Input: Renewable energy investment proposals generated by generative AI, emotion analysis results from the emotion engine

[1713] Output: Optimized investment proposal report

[1714] Specific operation: The generated investment proposals are compiled into a document and customized according to the user's emotions. For example, if the emotion is "positive," the expressions become more optimistic. If the emotion is "negative," the expressions are changed to give a sense of security.

[1715] Step 7:

[1716] The server presents the generated investment proposal report to the user.

[1717] Input: Optimized Investment Proposal Report

[1718] Output: A user-viewable investment proposal report

[1719] Specific operation: The reports generated on the cloud server are displayed on the user dashboard, and feedback is collected from the user as needed, helping the user make decisions based on the investment proposals.

[1720] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1721] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1722] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1723] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1724] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1725] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1726] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1727] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1728] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1729] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1730] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1731] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1732] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1733] 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.

[1734] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1735] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1736] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1737] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1738] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1739] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1740] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1741] The following is further disclosed regarding the above embodiment.

[1742] (Claim 1)

[1743] A means for collecting energy consumption data and CO2 emission data in real time using IoT devices;

[1744] A means for storing and preprocessing the received data in a cloud server;

[1745] A means for analyzing the pre-processed data by a generation AI and generating renewable energy investment proposals;

[1746] means for generating and presenting to a user an investment proposal report;

[1747] A system including:

[1748] (Claim 2)

[1749] 10. The system of claim 1, further comprising means for performing consistency checking and outlier correction of the energy consumption data and the CO2 emission data.

[1750] (Claim 3)

[1751] 10. The system of claim 1, further comprising means for monitoring performance and providing feedback after deployment of the renewable energy system.

[1752] "Example 1"

[1753] (Claim 1)

[1754] A means for collecting energy consumption data and CO2 emission data in real time;

[1755] means for storing and pre-processing received data;

[1756] A generative AI-based data analysis method to identify energy consumption patterns and CO2 emissions trends;

[1757] a means for generating renewable energy investment proposals;

[1758] means for generating and presenting to a user an investment proposal report;

[1759] A system including:

[1760] (Claim 2)

[1761] 10. The system of claim 1, further comprising means for performing data consistency checking and outlier correction.

[1762] (Claim 3)

[1763] The system of claim 1, further comprising means for calculating the required initial investment amount, expected payback period, energy efficiency improvement effect, and CO2 emission reduction amount based on the renewable energy investment proposal, and providing the results to the user in the form of a report.

[1764] "Application Example 1"

[1765] (Claim 1)

[1766] A means for collecting energy consumption data and CO2 emission data in real time using IoT devices;

[1767] means for storing and pre-processing the received data in a cloud computing platform;

[1768] A means for analyzing the pre-processed data using a generative AI model and generating renewable energy investment proposals;

[1769] means for presenting the generated investment proposal report to a user using a head-mounted display;

[1770] A system including:

[1771] (Claim 2)

[1772] 10. The system of claim 1, further comprising means for performing consistency checking and outlier correction of the energy consumption data and the CO2 emission data.

[1773] (Claim 3)

[1774] 10. The system of claim 1, further comprising means for monitoring performance and providing feedback after deployment of the renewable energy system.

[1775] "Example 2: Combining Emotion Engines"

[1776] (Claim 1)

[1777] A means for collecting energy consumption data and CO2 emissions data in real time using IoT devices;

[1778] means for storing and pre-processing the received data in the cloud device;

[1779] A means for analyzing the pre-processed data by a generative AI and generating renewable energy investment proposals;

[1780] means for generating and presenting to a user an investment proposal report;

[1781] means for collecting and analyzing user emotion data;

[1782] A system including:

[1783] (Claim 2)

[1784] 10. The system of claim 1, further comprising means for performing consistency checking and outlier correction of the energy consumption data and the CO2 emission data.

[1785] (Claim 3)

[1786] 10. The system of claim 1, further comprising means for post-deployment performance monitoring and feedback of the renewable energy system.

[1787] "Application example 2 when combining emotion engines"

[1788] (Claim 1)

[1789] A means for collecting energy consumption data and CO2 emission data in real time using IoT devices;

[1790] A means for storing and preprocessing the received data in a cloud server;

[1791] A means for analyzing the pre-processed data by a generation AI and generating renewable energy investment proposals;

[1792] means for generating and presenting to a user an investment proposal report;

[1793] means for recognizing and analyzing user emotion data using an emotion engine;

[1794] A means for optimizing the format and content of the investment proposal report based on user sentiment;

[1795] A system including:

[1796] (Claim 2)

[1797] 10. The system of claim 1, further comprising means for performing consistency checking and outlier correction of the energy consumption data and the CO2 emission data.

[1798] (Claim 3)

[1799] 10. The system of claim 1, further comprising means for monitoring performance and providing feedback after deployment of the renewable energy system. [Explanation of symbols]

[1800] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for collecting energy consumption data and CO2 emission data in real time using IoT devices; A means for storing and preprocessing the received data in a cloud server; A means for analyzing the pre-processed data by a generation AI and generating renewable energy investment proposals; means for generating and presenting to a user an investment proposal report; A system including:

2. The system of claim 1 , further comprising means for performing consistency checking and outlier correction of the energy consumption data and the CO 2 emission data.

3. The system of claim 1 , further comprising means for monitoring performance and providing feedback after deployment of the renewable energy system.

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