System

Abstract

A system is provided.SOLUTION: A system, comprising: means for collecting operational information of a base station; means for obtaining weather data; means for analyzing the collected operational information and the obtained weather data to determine an optimal power consumption pattern and energy source switching timing; means for controlling power supply of the base station based on the determined power consumption pattern and energy source switching timing; means for monitoring power generation and consumption of renewable energy devices to optimize efficient energy utilization; and means for providing system status and manually adjusting settings through a user interface.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] With the spread of 5G, it has become clear that communication equipment consumes a large amount of electricity. As a result, communication carriers and network infrastructure companies are facing major challenges in achieving carbon neutrality. In particular, efficient management of the energy required to operate base stations and maximizing the use of renewable energy are urgent issues. [Means for solving the problem]

[0005] To solve the above problems, the present invention provides the following means. The system includes a means for collecting base station operation information and a means for acquiring weather data, and a means for analyzing the collected operation information and acquired weather data to determine an optimal power consumption pattern and timing for switching between energy sources. The system also includes a means for controlling the power supply to the base station based on the determined power consumption pattern and timing for switching between energy sources, and a means for monitoring the amount of power generated and consumed by a renewable energy device to optimize efficient energy use. The above problems are further solved by providing a system including a means for providing the system status through a user interface and manually adjusting settings.

[0006] A "base station" is a facility in a wireless communication system that mediates communication with terminal devices and transmits and receives data.

[0007] "Operation information" is data that indicates the level of activity of a base station, and specifically includes power consumption, number of connected terminals, data traffic volume, and the like.

[0008] "Weather data" refers to data that indicates current and future weather conditions, and specifically includes temperature, humidity, precipitation, wind speed, and sunshine hours.

[0009] "Analysis" is the process of statistically or algorithmically processing collected data to derive meaningful information.

[0010] An "electricity consumption pattern" is a set of patterns that indicate the amount and timing of electricity consumption and the type of energy source used over a certain period of time.

[0011] "Energy source switching timing" refers to the optimal time to switch to a different energy source (e.g., solar power, wind power, power grid, etc.).

[0012] "Control" refers to the action of monitoring and adjusting equipment or processes to ensure that the system operates correctly.

[0013] A "renewable energy device" is a device that generates renewable energy using natural forces, such as solar panels or wind turbines.

[0014] "Yield" refers to the total amount of electricity generated by a particular renewable energy device over a given period of time.

[0015] "Consumption" refers to the total amount of electricity used by equipment such as base stations over a certain period of time.

[0016] "User interface" refers to the screens and methods of operation that make it easy for users to interact with a system.

[0017] "System" is a collective term for multiple devices and software that include the aforementioned means and are configured to achieve a certain purpose. [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 relates to a system for optimizing energy conservation and renewable energy utilization in base stations. The system collects base station operation information, acquires weather data, analyzes the data, and determines optimal power consumption patterns and energy source switching timing. It also controls the base station's power supply and monitors the power generation and consumption of renewable energy devices to optimize efficient energy utilization. Furthermore, the system status is provided through a user interface, and settings can be manually adjusted.

[0040] Explanation of program processing

[0041] Data collection

[0042] The server collects base station operation information (power consumption, number of connected devices, data traffic volume, etc.). This includes periodic data transmission from each base station and data acquisition via API. The server also uses weather data API to acquire real-time weather information (temperature, humidity, precipitation, wind speed, sunshine hours, etc.).

[0043] Examples:

[0044] The server receives information on power consumption and the number of connected devices from base station A, and also obtains the current weather conditions through a weather data API.

[0045] Data preprocessing and analysis

[0046] The server preprocesses the collected data, fills in incomplete data, and converts data types. It then analyzes the preprocessed data to determine optimal power consumption patterns and the timing for switching energy sources. Machine learning algorithms are used for the analysis, and predictions are made based on historical data and current conditions.

[0047] Examples:

[0048] The server fills in missing data from base station A and weather data, normalizes the data, and then uses machine learning algorithms to analyze optimal power consumption patterns and determine when to switch between solar power generation and the power grid.

[0049] Control command generation and execution

[0050] Based on the analysis results, the server generates control commands for each base station to change its power consumption pattern and energy source, and sends these control commands to each base station to change its power supply.

[0051] Examples:

[0052] Based on the analysis results, the server generates a control command to switch power supply to solar power generation for base station A and sends it to base station A. Base station A receives this control command and switches the power supply source.

[0053] Renewable Energy Management

[0054] The server constantly monitors the power output of renewable energy devices (solar panels and wind turbines) and the power consumption of base stations. To ensure efficient power generation and consumption, it optimizes the use of renewable energy to maximize power generation efficiency.

[0055] Examples:

[0056] The server constantly monitors the amount of power generated by the solar panels and the power consumption of base station A, and adjusts the angle of the panels to maximize power generation efficiency.

[0057] Providing a user interface

[0058] The user can monitor the system status in real time and make necessary configuration changes using the provided user interface, which displays the analysis results and current operating status of the system.

[0059] Examples:

[0060] Users can check the power consumption and power generation status of base station A through the management panel, and if they want to manually fine-tune the settings, they can do so on the interface.

[0061] The processing flow will be explained below.

[0062] Step 1: Data collection

[0063] The server collects operational information from base stations. Specifically, it obtains information from each base station, such as power consumption, number of connected devices, and data traffic volume. This data is collected periodically using an API.

[0064] Examples:

[0065] The server obtains the power consumption of base station A through an API request.

[0066] Step 2: Obtaining weather data

[0067] The server obtains weather data in real time via an API, specifically information such as temperature, humidity, precipitation, wind speed, and sunshine hours.

[0068] Examples:

[0069] The server uses the weather data API to obtain weather data around base station A.

[0070] Step 3: Preprocessing the data

[0071] The server preprocesses the collected operational information and weather data, complementing incomplete data and converting and normalizing data types as necessary.

[0072] Examples:

[0073] If the power consumption data of base station A is missing, the server complements it based on past data.

[0074] Step 4: Analysis

[0075] The server then analyzes the preprocessed data using machine learning algorithms. Specifically, it determines the optimal power consumption pattern and timing for switching energy sources based on the collected data.

[0076] Examples:

[0077] The server provides input data to a machine learning model to infer when to switch from solar power generation to the power grid.

[0078] Step 5: Generate control commands

[0079] Based on the analysis results, the server generates power consumption patterns and control commands for switching energy sources for each base station.

[0080] Examples:

[0081] The server generates a control command including an instruction for base station A to switch from solar power generation to the power grid.

[0082] Step 6: Send and execute commands

[0083] The server transmits the generated control commands to each base station, which then executes the power supply changes accordingly.

[0084] Examples:

[0085] The server sends a control command to base station A, and based on this command, base station A switches its power source from solar power generation to the power grid.

[0086] Step 7: Renewable energy monitoring

[0087] The server constantly monitors the power generation and consumption of renewable energy devices (solar panels and wind turbines).

[0088] Examples:

[0089] The server periodically retrieves power generation data from the solar panels and compares it with consumption patterns.

[0090] Step 8: Optimize energy use

[0091] The server optimizes the settings to maximize the efficiency of renewable energy use, adjusting the angle of the panels and changing the proportion of wind power used based on weather and power generation data.

[0092] Examples:

[0093] The server sends adjustment instructions to optimize the angle of the solar panels on sunny days.

[0094] Step 9: Update the User Interface

[0095] The server updates the user interface with the analysis results and current system status accordingly.

[0096] Examples:

[0097] The server reflects the analysis results on the user interface, allowing users to view the information in real time.

[0098] Step 10: Accept manual adjustments

[0099] Users can manually adjust system settings using the provided user interface, which allows for immediate effect of setting changes under specific conditions.

[0100] Examples:

[0101] Users operate an administration panel and manually optimize the settings for a particular base station.

[0102] Example 1

[0103] 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."

[0104] In modern society, base stations consume a huge amount of power, making its optimization a critical issue. While utilizing renewable energy sources in particular contributes to the preservation of the global environment, their efficient use is difficult. Furthermore, due to fluctuations in weather conditions and daily changes in base station loads, it is necessary to optimize real-time power consumption patterns and the timing of switching between energy sources. Furthermore, systems must be able to provide users with an easy-to-understand overview of the system's status, with the ability to manually adjust it as needed. Because conventional methods are unable to adequately resolve these issues, new technologies are needed.

[0105] 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.

[0106] In this invention, the server includes means for collecting operation information of the base station, means for acquiring weather data, means for preprocessing the collected operation information and the acquired weather data to convert data types and complement incomplete data, means for analyzing the preprocessed data using a machine learning algorithm and determining an optimal power consumption pattern and timing for switching between energy sources, means for controlling the power supply of the base station based on the determined power consumption pattern and timing for switching between energy sources, means for constantly monitoring the amount of power generated and consumed by the renewable energy device and optimizing efficient energy use, and means for providing the system status through a user interface and manually adjusting settings. This makes it possible to optimize the power consumption pattern and energy source switching of the base station and achieve efficient energy use in real time.

[0107] A "base station" is a relay device for wireless communication, and is responsible for managing and controlling terminals and communications within a specific area.

[0108] "Operation information" refers to data relating to the operational status of a base station, and includes information such as power consumption, the number of connected terminals, and data traffic volume.

[0109] "Weather data" refers to information about the weather, including temperature, humidity, precipitation, wind speed, and sunshine hours.

[0110] "Preprocessing" refers to the process of completing incomplete data or converting data types before analyzing collected data.

[0111] A "machine learning algorithm" refers to a computational method that uses large amounts of data to learn patterns and rules and make predictions and classifications.

[0112] "Power consumption pattern" refers to the trend or tendency of the power consumed by a base station over a certain period of time.

[0113] "Energy source switching timing" refers to the optimal point in time to switch the power source to a different energy source, such as renewable energy or the commercial power grid.

[0114] "Control" refers to the process of giving instructions or commands to regulate and manage the behavior of a system.

[0115] A "renewable energy device" is a device that generates electricity using natural energy, including solar panels and wind turbines.

[0116] "Yield" refers to the amount of electricity generated by a renewable energy device.

[0117] "Consumption" refers to the amount of power used by the base station.

[0118] "User interface" refers to the mechanism that provides the means and environment for a user to interact with a system.

[0119] "Data analysis" refers to a series of processes for deriving new insights and patterns based on collected data.

[0120] The present invention relates to a system for optimizing energy conservation and renewable energy utilization in base stations. This system uses a server to collect base station operation information and weather data, analyzes this data, and determines optimal power consumption patterns and energy source switching timing. The server also controls the base station's power supply and monitors the power generation and consumption of renewable energy devices, optimizing efficient energy utilization. Furthermore, the system status is provided through a user interface, allowing users to manually adjust settings.

[0121] Hardware and software used

[0122] Hardware

[0123] Server: Data collection, pre-processing, analysis, and control (e.g., Dell PowerEdge R640)

[0124] Base station equipment: Provides operational status data

[0125] Renewable energy devices: solar panels, wind turbines, etc.

[0126] software

[0127] Data collection: Using APIs to obtain weather data and operational information (e.g., weather data API)

[0128] Data analysis: using machine learning algorithms (e.g., TensorFlow, scikit-learn)

[0129] User Interface: HTML5, CSS, JavaScript

[0130] Data collection

[0131] The server collects operational information from base stations, including power consumption, number of connected devices, and data traffic volume. This includes periodic data transmission from each base station and data acquisition via API. The server also obtains real-time weather information using a weather data API. This allows for optimal operation taking into account power consumption patterns and weather conditions.

[0132] Specific working example:

[0133] The server receives information on power consumption and the number of connected devices from base station A, and also obtains the current weather conditions through a weather data API.

[0134] Data preprocessing and analysis

[0135] The collected data is preprocessed, completing incomplete data and converting data types to make it suitable for analysis. The server analyzes the preprocessed data to determine optimal power consumption patterns and timing for switching energy sources. This analysis uses machine learning algorithms to make predictions based on historical data and current conditions.

[0136] Specific working example:

[0137] The server fills in missing data from base station A and weather data, normalizes the data, and then uses machine learning algorithms to analyze optimal power consumption patterns and determine when to switch between solar power generation and the power grid.

[0138] Control command generation and execution

[0139] Based on the analysis results, the server generates control commands for each base station to change the power consumption pattern and energy source, and sends the generated control commands to each base station to change the power supply.

[0140] Specific working example:

[0141] Based on the analysis results, the server generates a control command to switch power supply to solar power generation for base station A and sends it to base station A. Base station A receives this control command and switches the power supply source.

[0142] Renewable Energy Management

[0143] The server constantly monitors the amount of power generated by renewable energy devices (solar panels and wind turbines) and the power consumption of base stations, optimizing the use of renewable energy to maximize power generation efficiency.

[0144] Specific working example:

[0145] The server constantly monitors the amount of power generated by the solar panels and the power consumption of base station A, and adjusts the angle of the panels to maximize power generation efficiency.

[0146] Providing a user interface

[0147] The user can monitor the system status in real time and make necessary configuration changes using the provided user interface, which displays the system analysis results and current operating status.

[0148] Specific working example:

[0149] Users can check the power consumption and power generation status of base station A through the management panel, and if they want to manually fine-tune the settings, they can do so on the interface.

[0150] Examples of prompt statements

[0151] "Predict the optimal switching timing for base station power consumption patterns and renewable energy optimization using current weather data and past operational data."

[0152] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0153] Step 1: Data collection

[0154] Input: Operation information from base stations and weather data from weather data API

[0155] Output: Collected operational information and weather data

[0156] The server collects operational information from the base stations. This information includes power consumption, number of connected devices, data traffic volume, etc. The server periodically receives data from each base station or obtains data using an API. In addition, the server uses a weather data API to obtain real-time weather information (temperature, humidity, precipitation, wind speed, sunshine hours, etc.).

[0157] Specific working example:

[0158] The server automatically receives 24 hours' worth of power consumption data from base station A every day at midnight.

[0159] The server calls the API to obtain data on the number of connected terminals of base station B.

[0160] The server calls the weather data API at 6:00 AM to obtain the temperature and humidity data for the day in the base area.

[0161] Step 2: Preprocessing the data

[0162] Input: Collected operational information and weather data

[0163] Output: Preprocessed data (inserted data imputed and data type converted)

[0164] The server preprocesses the collected data, including imputing incomplete data and converting data types, such as by imputing missing values ​​and correcting outliers, to prepare the data in a format suitable for analysis.

[0165] Specific working example:

[0166] If there are missing values ​​in the power consumption data of base station A, the server complements them by referring to past data.

[0167] The server standardizes the units of the collected weather data and converts it into a standard format.

[0168] Step 3: Analyze the data

[0169] Input: Preprocessed data

[0170] Output: Optimal power consumption patterns and timing for switching between energy sources

[0171] The server analyzes the pre-processed data using machine learning algorithms to determine optimal power consumption patterns and timing for switching between energy sources based on historical data and current conditions.

[0172] Specific working example:

[0173] The server uses preprocessed data from base station A to predict historically most efficient power consumption patterns using machine learning algorithms.

[0174] Step 4: Generate control commands

[0175] Input: Analysis results (optimal power consumption pattern and timing for switching energy sources)

[0176] Output: Generated control commands

[0177] Based on the analysis results, the server generates power consumption patterns and control commands for switching energy sources for each base station.

[0178] Specific working example:

[0179] The server generates a control command to base station A to switch to solar power generation from 10:00 AM to 4:00 PM the next day.

[0180] Step 5: Execute control commands

[0181] Input: Generated control command

[0182] Power: Change the power supply of the base station

[0183] The base station executes the control command received from the server to change the power supply.

[0184] Specific working example:

[0185] Base station A switches to power supply from solar power generation in accordance with the control command received from the server.

[0186] Step 6: Renewable energy management

[0187] Input: Power generation amount of renewable energy device and power consumption status of base station

[0188] Output: Optimized power generation efficiency and power consumption

[0189] The server constantly monitors the amount of power generated by renewable energy devices (solar panels and wind turbines) and the power consumption of base stations, optimizing the use of renewable energy to maximize power generation efficiency.

[0190] Specific working example:

[0191] The server adjusts the angle of the solar panels to the optimal position to maximize power generation.

[0192] Step 7: Providing a User Interface

[0193] Input: System analysis results and current operating status

[0194] Output: System status and manual setting adjustments provided through the user interface

[0195] The user can monitor the system status in real time and make necessary configuration changes using the provided user interface, which displays the system analysis results and current operating status.

[0196] Specific working example:

[0197] Users can open the management panel to check base station A's power consumption data and renewable energy generation in real time, and if they want to manually fine-tune the settings, they can do so on the interface.

[0198] (Application example 1)

[0199] 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."

[0200] Conventional energy management systems for automated robots in factories face challenges in optimizing energy consumption and efficiently utilizing renewable energy. In particular, there is a need for technology that can collect robot operation information and analyze it in conjunction with weather data to determine optimal energy consumption patterns and the timing of switching between energy sources. Furthermore, there is a lack of a method for monitoring the amount of power generated and consumed by renewable energy devices in real time and optimizing efficient energy use. Additionally, a system that can be easily managed through a user interface is also needed.

[0201] 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.

[0202] In this invention, the server includes means for collecting operation information of automatic robots in a factory, means for acquiring weather data, means for analyzing the collected operation information and the acquired weather data and determining an optimal energy consumption pattern and timing for switching between energy sources, means for controlling the energy supply of the robots based on the determined energy consumption pattern and timing for switching between energy sources, means for monitoring the amount of power generated and consumed by a renewable energy device and optimizing efficient energy use, and means for providing the system status through a user interface and manually adjusting settings, thereby enabling the optimization of energy consumption of automatic robots in a factory and the efficient use of renewable energy.

[0203] An "automated factory robot" is a mechanical device that is used in the manufacturing and processing processes in a factory and can autonomously perform programmed actions.

[0204] "Operation information" is data related to the operation of the robot, and specifically includes the amount of power consumed, the operation time, and the type of task being executed.

[0205] "Weather data" refers to information about weather conditions obtained in real time, including temperature, humidity, and hours of sunshine.

[0206] "Energy consumption pattern" refers to data that indicates the state of energy consumption over a certain period of time.

[0207] "Energy source switching timing" refers to the optimal time to switch between renewable energy and conventional energy sources.

[0208] "Renewable energy devices" refer to devices that generate electricity using natural forces, such as solar power generation devices and wind power generation devices.

[0209] A "user interface" refers to the screens and input devices that allow a user to operate a system or monitor its status.

[0210] A "machine learning algorithm" is a calculation method that automatically derives optimal energy consumption patterns and timing for switching energy sources based on collected data.

[0211] This invention relates to an energy management system for automated robots in factories. This system collects and analyzes robot operation information and weather data to determine optimal energy consumption patterns and timing for switching between energy sources, thereby efficiently utilizing renewable energy.

[0212] Explanation of program processing

[0213] Data collection

[0214] The server collects operational information from the automated robots installed in the factory. This operational information includes power consumption, operating time, task type, etc. It also uses a weather data API to obtain real-time weather data, including temperature, humidity, and sunshine hours.

[0215] Data preprocessing and analysis

[0216] The server preprocesses the collected operational information and weather data, complements incomplete data, and converts data types. It then analyzes the preprocessed data and uses machine learning algorithms to make predictions to determine optimal energy consumption patterns and the timing for switching energy sources. The software used is Python and TensorFlow.

[0217] Control command generation and execution

[0218] Based on the analysis results, the server generates control commands for the robot to change its energy consumption pattern and energy source, and sends these control commands to the robot to change its energy supply.

[0219] Renewable energy equipment management

[0220] The server constantly monitors the amount of power generated and consumed by renewable energy devices (e.g., solar power generation devices, wind power generation devices) and optimizes their use to maximize power generation efficiency, thereby enabling efficient management of the robot's energy supply.

[0221] Providing a user interface

[0222] The user can monitor the system status in real time and make necessary configuration changes using the provided user interface, which displays the system analysis results and current operating status. The software used is Flask.

[0223] Specific examples

[0224] For example, you might use the following prompts to input to a generative AI model:

[0225] Prompt statement:

[0226] "Generate and send control commands to switch Robot A's power supply to solar power during sunny hours. Also provide optimal energy consumption patterns based on real-time weather data."

[0227] As described above, by building an energy management system for automated robots in factories, it is possible to optimize energy consumption and efficiently use renewable energy.

[0228] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0229] Step 1:

[0230] The server collects operational information from the automated robots installed in the factory. This operational information includes power consumption, operating time, task type, etc. The server periodically receives data from each robot and collects it via API and sensors.

[0231] Input: Operation information from the robot

[0232] Output: Collected operational information

[0233] Step 2:

[0234] The server uses a weather data API to obtain real-time weather data, including temperature, humidity, and sunshine hours. The server periodically sends requests to the API to obtain the latest weather information.

[0235] Input: API request

[0236] Output: Captured real-time weather data

[0237] Step 3:

[0238] The server preprocesses the collected operational information and acquired weather data. It performs data cleaning to fill in incomplete data and convert data types. Specifically, this includes filling in missing values, normalizing data, and correcting outliers.

[0239] Input: Collected operating information, acquired weather data

[0240] Output: Preprocessed data

[0241] Step 4:

[0242] The server analyzes the preprocessed data to determine optimal energy consumption patterns and timing for switching between energy sources. It uses machine learning algorithms to make predictions based on historical and current data. It runs the model using Python and TensorFlow.

[0243] Input: Preprocessed data

[0244] Output: Optimal energy consumption pattern, timing for switching energy sources

[0245] Step 5:

[0246] Based on the analysis results, the server generates control commands for the robot to determine its energy consumption pattern and switch its energy source. The server then sends the control commands to the robot to change its energy supply. The control commands specifically instruct the robot's behavior, including switching the power supply source.

[0247] Input: Optimal energy consumption pattern, timing to switch energy sources

[0248] Output: Control commands to the robot

[0249] Step 6:

[0250] The server constantly monitors the amount of power generated and consumed by renewable energy devices (solar power generation devices, wind power generation devices). Specifically, it periodically acquires the parameters of the power generation devices using a sensor system and sends the data to the monitoring system. The angle of the panels and turbines is automatically adjusted to maximize power generation efficiency.

[0251] Input: Sensor information of the power generation equipment

[0252] Output: Maximum efficiency power generation

[0253] Step 7:

[0254] The user uses the provided user interface to monitor the system status in real time and change the settings as needed. The interface displays the analysis results obtained from the system and the current operating status. The user can also manually adjust the settings. The interface is built using Flask.

[0255] Input: Analysis results and operational status from the system

[0256] Output: Operational status reflecting the user's configuration changes

[0257] 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.

[0258] This invention combines an emotion engine with a system for optimizing base station energy conservation and renewable energy utilization. The system collects base station operation information, acquires weather data, analyzes the data, and determines optimal power consumption patterns and energy source switching timing. It also controls the base station's power supply and monitors the power generation and consumption of renewable energy devices to optimize efficient energy utilization. Furthermore, the system status is displayed through a user interface, allowing for manual adjustment of settings. An emotion engine that recognizes user emotions has also been added to improve the user experience.

[0259] Explanation of program processing

[0260] Data collection

[0261] The server collects operational information from base stations. Specifically, it obtains information such as power consumption, number of connected devices, and data traffic volume from each base station. This data is collected periodically using an API. The server also uses a weather data API to obtain real-time weather information (temperature, humidity, precipitation, wind speed, sunshine hours, etc.).

[0262] Examples:

[0263] The server receives information on power consumption and the number of connected devices from base station A, and also obtains the current weather conditions through a weather data API.

[0264] Data preprocessing and analysis

[0265] The server preprocesses the collected data, complements incomplete data, and converts and normalizes data types as needed. The preprocessed data is then analyzed to determine optimal power consumption patterns and the timing of switching energy sources. Machine learning algorithms are used in the analysis to make predictions based on historical data and current conditions.

[0266] Examples:

[0267] The server fills in missing data from base station A and weather data, normalizes the data, and then uses machine learning algorithms to analyze optimal power consumption patterns and determine when to switch between solar power generation and the power grid.

[0268] Control command generation and execution

[0269] Based on the analysis results, the server generates control commands for each base station to change its power consumption pattern and energy source, and sends these control commands to each base station to change its power supply.

[0270] Examples:

[0271] Based on the analysis results, the server generates a control command including an instruction for base station A to switch from solar power generation to the power grid, and sends it to base station A. Base station A receives this control command and switches the power supply source.

[0272] Renewable Energy Management

[0273] The server constantly monitors the power output of renewable energy devices (solar panels and wind turbines) and the power consumption of base stations. To ensure efficient power generation and consumption, it optimizes the use of renewable energy to maximize power generation efficiency.

[0274] Examples:

[0275] The server constantly monitors the amount of power generated by the solar panels and the power consumption of base station A, and adjusts the angle of the panels to maximize power generation efficiency.

[0276] Providing a user interface

[0277] The user can monitor the system status in real time and make necessary configuration changes using the provided user interface, which displays the analysis results and current operating status of the system.

[0278] Examples:

[0279] Users can check the power consumption and power generation status of base station A through the management panel, and if they want to manually fine-tune the settings, they can do so on the interface.

[0280] Combining Emotion Engines

[0281] The server is equipped with an emotion engine that recognizes the user's emotions by analyzing the user's voice, facial expressions, text input, etc., and automatically adjusts the system settings and interface based on the analysis results.

[0282] Examples:

[0283] The server uses an emotion engine to analyze the voice tone and facial expressions of the user when operating the interface, and if it determines that the user is under high stress, it activates an assist function to make operating the interface easier.

[0284] This allows the system to maximize energy efficiency while providing a user-friendly operating environment.

[0285] The processing flow will be explained below.

[0286] Step 1: Data collection

[0287] The server collects operational information from base stations. Specifically, it obtains information from each base station, such as power consumption, number of connected devices, and data traffic volume. This data is collected periodically using an API.

[0288] Examples:

[0289] The server obtains the power consumption and number of connected terminals of base station A through an API request.

[0290] Step 2: Obtaining weather data

[0291] The server obtains weather data in real time via an API, specifically information such as temperature, humidity, precipitation, wind speed, and sunshine hours.

[0292] Examples:

[0293] The server uses the weather data API to obtain weather data around base station A.

[0294] Step 3: Preprocessing the data

[0295] The server preprocesses the collected operational information and weather data, complementing incomplete data and converting data types as necessary to normalize it.

[0296] Examples:

[0297] If there is a gap in the power consumption data of base station A, the server will supplement it based on past data.

[0298] Step 4: Analysis

[0299] The server analyzes the preprocessed data using machine learning algorithms. Specifically, it determines the optimal power consumption pattern and timing for switching energy sources based on the collected data.

[0300] Examples:

[0301] The server inputs the data into a machine learning model to infer when to switch from solar power generation to the power grid.

[0302] Step 5: Generate control commands

[0303] The server generates a power consumption pattern and a control command for switching the energy source for each base station based on the analysis result.

[0304] Examples:

[0305] The server generates a control command for base station A, which includes an instruction to switch from solar power generation to the power grid.

[0306] Step 6: Send and execute commands

[0307] The server transmits the generated control command to each base station to change the power supply.

[0308] Examples:

[0309] The server sends a control command to base station A, and base station A switches the power supply source from solar power generation to the power grid based on this command.

[0310] Step 7: Renewable energy monitoring

[0311] The server constantly monitors the power generation and consumption of renewable energy devices (solar panels and wind turbines).

[0312] Examples:

[0313] The server periodically retrieves power generation data from the solar panels and compares it with consumption patterns.

[0314] Step 8: Optimize energy use

[0315] The server optimizes the settings to maximize the efficiency of renewable energy use, adjusting the angle of the panels and changing the proportion of wind power used based on weather and power generation data.

[0316] Examples:

[0317] The server provides instructions to adjust the solar panels to the optimal angle for sunny days.

[0318] Step 9: Update the User Interface

[0319] The server updates the user interface with the analysis results and current system status accordingly.

[0320] Examples:

[0321] The server reflects the analysis results on the user interface, allowing users to view the information in real time.

[0322] Step 10: Accept manual adjustments

[0323] Users can manually adjust system settings using the provided user interface, which allows for immediate effect of setting changes under specific conditions.

[0324] Examples:

[0325] Users operate an administration panel and manually optimize the settings for a particular base station.

[0326] Step 11: Collect sentiment data

[0327] The server collects the user's emotional data, which can be obtained from the user's voice, facial expressions, text input, etc.

[0328] Examples:

[0329] The server collects voice tones and facial expressions via a camera and microphone when a user operates the management panel.

[0330] Step 12: Analyze the sentiment data

[0331] The server analyzes the collected emotion data and uses an emotion engine to determine the user's stress level and satisfaction.

[0332] Examples:

[0333] The server analyzes the user's voice data and determines that the user is feeling stressed.

[0334] Step 13: Emotion-Based System Adjustment

[0335] The server automatically adjusts the system settings based on the analysis of the emotional data, instantly changing the settings to reflect the user's emotional state.

[0336] Examples:

[0337] If the server determines that the user is feeling stressed, it activates an assist function that simplifies operation of the interface.

[0338] Through the above process, the present invention efficiently manages base stations and renewable energy, and further provides an interface that takes user emotions into consideration, thereby improving energy efficiency and maximizing user experience.

[0339] Example 2

[0340] 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."

[0341] Conventional base station energy management systems have room for improvement in optimizing power consumption and efficiently utilizing renewable energy. Furthermore, they lack effective methods for improving user experience. Therefore, there is a need to provide a more efficient and user-friendly means for base station energy management.

[0342] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0343] In this invention, the server includes means for collecting operation information of the base station, means for acquiring weather data, means for preprocessing and analyzing the collected operation information and the acquired weather data to determine an optimal power consumption pattern and timing for switching between energy sources, means for controlling the power supply of the base station based on the determined power consumption pattern and timing for switching between energy sources, means for monitoring the amount of power generated by the renewable energy device and the amount of power consumed by the base station in real time to optimize efficient energy use, means for providing the system status through a user interface and manually adjusting settings, and means for recognizing user emotions using an emotion engine and automatically adjusting the system status and interface, thereby enabling high energy efficiency and improved user satisfaction.

[0344] A "base station" is a fixed facility for conducting wireless communications that forms part of a communications network.

[0345] "Operation information" is data indicating the operational status of the base station, the amount of power consumed there, the number of connected terminals, the amount of data traffic, and so on.

[0346] "Weather data" refers to data that includes weather-related information such as temperature, humidity, precipitation, wind speed, and sunshine hours.

[0347] "Preprocessing" refers to a series of operations such as data interpolation, filtering, conversion, and normalization that are performed to prepare the collected raw data in an analyzable form.

[0348] "Analysis" is the act of using preprocessed data to extract and interpret information suitable for a specific purpose.

[0349] A "power consumption pattern" is a model that shows the tendency of how power is used and consumed under specific times and conditions.

[0350] "Energy source switching timing" refers to determining the timing of switching between different energy sources (e.g., renewable energy and the power grid) to make the most effective use of them.

[0351] A "renewable energy device" is a device that generates electricity using natural energy, such as solar panels or wind turbines.

[0352] "User interface" means the visual or operational means by which a user interacts with a system and inputs and obtains information.

[0353] The "emotion engine" is a function that analyzes the user's voice, facial expressions, text input, etc. to recognize emotions, and automatically adjusts the system settings and interface based on the results.

[0354] MODE FOR CARRYING OUT THE INVENTION

[0355] The present invention combines an emotion engine with a system for optimizing energy conservation and renewable energy utilization in base stations. The system mainly consists of the following components: a base station, a server, a renewable energy device, a user interface, and an emotion engine.

[0356] Hardware and Software Configuration

[0357] 1. Base Station: A fixed communication tower and its associated equipment that forms part of a communication network. Each base station is equipped with sensors to measure power consumption, the number of connected devices, and data traffic volume.

[0358] 2. Server: This is the central computer system that collects and analyzes data. The server has the ability to retrieve data from base stations and meteorological data services through APIs. The main software used includes a database management system, scripts for data preprocessing, and machine learning algorithms.

[0359] 3. Renewable energy devices: These are devices that generate electricity using natural energy sources such as solar panels and wind turbines.

[0360] 4. User interface: An interface that provides users with the operational status of the system in the form of an administration panel or dashboard and allows them to change settings.

[0361] 5. Emotion Engine: A software component that analyzes the user's voice, facial expressions, text input, etc. to recognize the user's emotional state, allowing the system to adaptively adjust its settings and interface.

[0362] Data collection and preprocessing

[0363] The server collects operational information from each base station, such as power consumption, number of connected devices, and data traffic volume. The server also uses a weather data API to obtain real-time weather data such as temperature, humidity, precipitation, wind speed, and sunshine hours.

[0364] Examples:

[0365] The server obtains information on power consumption and the number of connected devices from base station A via an API every five minutes.

[0366] The server uses the weather service API to obtain weather information every hour of the day.

[0367] Data preprocessing and analysis

[0368] The server preprocesses the collected data, including filling in missing values, filtering outliers, converting data types, and normalizing the data. It then performs analysis based on the preprocessed data to determine optimal power consumption patterns and the timing for switching between energy sources. Machine learning algorithms (e.g., regression analysis and time-series forecasting models) are used for the analysis.

[0369] Examples:

[0370] The server trains a machine learning model based on data from the past year and uses current data as input to predict optimal power consumption patterns.

[0371] The server takes into account the weather forecast and plans how to utilize renewable energy for the next day.

[0372] Control command generation and execution

[0373] Based on the analysis results, the server generates control commands for each base station, including details of power consumption patterns and timing for switching between energy sources. The base station then switches between power suppliers accordingly.

[0374] Examples:

[0375] The server generates a control command for base station A to prioritize power supply from the power grid during the night and solar power generation during the day.

[0376] Renewable Energy Management

[0377] The server monitors the power generation of renewable energy devices (solar panels, wind turbines, etc.) and the power consumption of base stations in real time, and adjusts consumption patterns accordingly to achieve optimal energy utilization.

[0378] Examples:

[0379] The server constantly monitors the amount of electricity generated by the solar panels and switches the power supply to renewable energy when it is more efficient.

[0380] User interface and emotion engine

[0381] The user can monitor the system status in real time through the provided user interface and make necessary setting changes. Furthermore, the server can recognize the user's emotions using an emotion engine and automatically adjust the interface and system settings based on the user's stress level and emotional state.

[0382] Examples:

[0383] Users can use the management panel to check the power consumption and power generation status of base station A in real time and fine-tune the settings as needed.

[0384] The server analyzes the user's voice tone and facial expressions using an emotion engine, and if it determines that the user is under high stress, it activates an assist function that simplifies operation.

[0385] Prompt Sentence Examples

[0386] Below are some examples of prompts that can be input to a generative AI model using this system:

[0387] "Please explain the program flow in which the server uses data collected from base station A to analyze the optimal power consumption pattern and determine the timing to switch from solar power generation to the power grid. Also, please provide a concrete example of monitoring the system status through the user interface and manually changing the settings."

[0388] In this way, the present invention can maximize the energy efficiency of the base station and provide a user-friendly operating environment.

[0389] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0390] Step 1:

[0391] The server collects operational information from each base station. This operational information includes power consumption, number of connected devices, data traffic volume, etc. The operational information received as input is obtained via API. The server also obtains temperature, humidity, precipitation, wind speed, and sunshine hours in real time from the weather data API. This information is stored in a database so that it can be used in subsequent analysis steps.

[0392] Specific behavior:

[0393] The server collects information on power consumption, number of connected terminals, and data traffic volume from base station A every five minutes.

[0394] The server retrieves weather information every hour through the weather data API.

[0395] Step 2:

[0396] The server preprocesses the collected operational information and weather data. Since the input data may contain missing or outliers, it complements the incomplete data and filters out the outliers. It also performs data type conversion and normalization to prepare the data in a format suitable for analysis. The preprocessed data is used in the next analysis step.

[0397] Specific behavior:

[0398] If there is a missing value in the data of base station A, the server complements the value based on the data before and after it.

[0399] The server filters out data points that record abnormally high power consumption.

[0400] Step 3:

[0401] The server analyzes the preprocessed data. It takes the preprocessed data as input and uses machine learning algorithms to predict optimal power consumption patterns and timing for switching between energy sources. This analysis uses regression analysis and time series prediction models. The prediction results are used in the control command generation step.

[0402] Specific behavior:

[0403] The server trains the machine learning model using operational information and weather data from the past year.

[0404] The server inputs the current data and predicts the best time to utilize solar power generation during the day the next day.

[0405] Step 4:

[0406] The server generates control commands for the base stations based on the analysis results. Using the results of the analysis step as input, the server creates control commands that include the power consumption patterns and energy source switching instructions for each base station. These commands are sent to each base station and executed.

[0407] Specific behavior:

[0408] The server generates a control command for base station A to prioritize power supply from the power grid during the night and solar power generation during the day.

[0409] The server generates commands including timing to utilize the power generation of the wind turbines when wind speeds are high.

[0410] Step 5:

[0411] The terminal (base station) executes the control command received from the server. It receives the control command from the server as input, switches the power supply source based on the command, and feeds back the execution result to the server.

[0412] Specific behavior:

[0413] Base station A switches to power supply from the power grid at night.

[0414] Base station A switches its power supply source to solar power when solar power is available during the day.

[0415] Step 6:

[0416] The server monitors the power generation of renewable energy devices and the power consumption of base stations in real time. It receives power generation data from renewable energy devices and consumption data from base stations as input, analyzes these data to ensure optimal energy usage, and makes necessary adjustments based on the results.

[0417] Specific behavior:

[0418] The server monitors the amount of electricity generated by the solar panels in real time and issues instructions to switch the power supply to renewable energy when it is more efficient.

[0419] Step 7:

[0420] The user monitors the system status in real time using the provided user interface and makes necessary configuration changes. The system receives operational information as input and adjusts the configuration based on that information.

[0421] Specific behavior:

[0422] The user can access the management panel using a tablet device and check the power consumption and power generation status of base station A.

[0423] Users can manually change settings to reduce power consumption during specific time periods.

[0424] Step 8:

[0425] The server uses an emotion engine to recognize the user's emotions, detecting and analyzing inputs such as the user's voice, facial expressions, and text input, and adjusting the system settings and interface based on the results.

[0426] Specific behavior:

[0427] The server analyzes the user's tone of voice and facial expressions, and if it determines that the user is under high stress, it activates an assist function to simplify operation.

[0428] (Application example 2)

[0429] 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."

[0430] While battery management and energy usage optimization are essential for current autonomous vehicles, there is a lack of efficient and real-time methods to do so. Furthermore, there is a lack of technology to adjust the driving environment and infotainment system according to the emotional state of the driver and passengers, creating a need for technologies to enhance user comfort.

[0431] 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.

[0432] In this invention, the server includes means for collecting operation information of the base stations, means for acquiring weather data, means for analyzing the collected operation information and the acquired weather data and determining an optimal power consumption pattern and timing for switching between energy sources, means for controlling the power supply of the base stations based on the determined power consumption pattern and timing for switching between energy sources, means for monitoring the amount of power generated and consumed by the renewable energy device and optimizing efficient energy use, means for providing the system status through a user interface and manually adjusting settings, and means for recognizing the emotions of the driver or passengers and adjusting the driving environment and infotainment system. This makes it possible to provide a comfortable driving environment that corresponds to the emotional state of the user while optimizing the energy management of the autonomous vehicle.

[0433] The "means for collecting base station operation information" refers to a means for acquiring information such as power consumption, the number of connected terminals, and data traffic volume from the base station.

[0434] "Means for acquiring weather data" refers to means for acquiring weather information such as temperature, humidity, precipitation, wind speed, and sunshine hours in real time.

[0435] The "means for analyzing the collected operation information and the acquired weather data and determining the optimal power consumption pattern and timing for switching energy sources" refers to a means for determining the optimal timing for power consumption and energy source usage based on the collected data using machine learning algorithms, etc.

[0436] The "means for controlling the power supply of the base station based on the determined power consumption pattern and energy source switching timing" refers to means for changing the power supply or switching the energy source based on the analysis results.

[0437] "Means for monitoring the amount of power generated and consumed by renewable energy devices and optimizing efficient energy use" refers to means for monitoring the amount of power generated and the usage status of renewable energy devices such as solar power generation devices and wind power generation devices, and for optimally managing energy use.

[0438] "Means for providing system status through a user interface and for manually adjusting settings" refers to means for providing a user with an interface for displaying the operating status of the system and manually changing settings as necessary.

[0439] "Means for recognizing the emotions of the driver or passenger and adjusting the driving environment or infotainment system" refers to means for analyzing the emotions of the driver or passenger from voice, facial expressions, text input, etc., and adjusting the driving environment or in-car entertainment system based on the results.

[0440] This invention provides an energy management system for autonomous vehicles that optimizes battery management and renewable energy utilization. The system collects vehicle operation information and weather data, and analyzes this data to determine optimal energy consumption patterns and timing for switching energy sources. It also includes a function that recognizes the emotions of passengers and drivers and adjusts the driving environment and infotainment system accordingly.

[0441] The server first collects information from the autonomous vehicle, including battery information, speed, mileage, interior and exterior temperatures, and the location of charging stations. It also obtains real-time weather information using a weather data API. The collected data is preprocessed to complete missing data and normalize it.

[0442] The server then uses machine learning algorithms to determine optimal power consumption patterns and timing for switching between energy sources, taking into account historical data and current conditions. Based on the analysis, the server generates control commands for the energy management system for battery management and energy source switching, which are then sent to the vehicle.

[0443] The server constantly monitors the amount of power generated and consumed by renewable energy devices, such as solar power generation devices installed in vehicles, and controls them to maximize the efficiency of energy use.

[0444] Users can monitor the system status and manually adjust settings as needed through a user interface that displays analysis results and current operating status of the system.

[0445] Additionally, the server is equipped with an emotion engine that analyzes the driver's or passenger's emotions by analyzing voice, facial expressions, text input, etc., and adjusts the driving environment and infotainment system accordingly.

[0446] For example, if a user feels "stressed," the server's emotion engine will analyze that emotion and enable the infotainment system to operate more easily while also playing appropriate music. This system will enable the creation of a comfortable driving environment while optimizing the energy management of autonomous vehicles.

[0447] Example prompts for generative AI models

[0448] "Collect battery information and speed data from vehicle ID: example_vehicle_id, and obtain weather information through API. Then, preprocess and normalize the acquired data to generate optimal battery management and energy switching control commands. Also, implement a system that simplifies system operation and plays relaxing music based on the user's input that "I'm stressed."

[0449] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0450] Step 1:

[0451] The server collects information about the autonomous vehicle's battery, speed, mileage, interior and exterior temperatures, and the location of charging stations. This data is obtained through the autonomous vehicle's API. It receives various sensor information from the vehicle as input data and stores it in an internal database.

[0452] The result is a data set that represents the current operating state of the vehicle.

[0453] Step 2:

[0454] The server uses a weather data API to obtain real-time weather information, taking weather data such as temperature, humidity, precipitation, wind speed, and sunshine hours as input, and stores it in an internal database.

[0455] The result is a real-time weather dataset representing weather conditions.

[0456] Step 3:

[0457] The server preprocesses the collected operational information and weather data. Specifically, it complements missing data, corrects outliers, normalizes data, etc. It receives operational information and weather data as input data and outputs a clean, preprocessed data set.

[0458] This will provide a dataset suitable for analysis.

[0459] Step 4:

[0460] The server uses machine learning algorithms to analyze the preprocessed data. Specifically, it utilizes predictive models to determine optimal power consumption patterns and timing for switching energy sources. It receives preprocessed operational information and weather data as input, analyzes them, and generates optimal control commands.

[0461] As a result, optimal timing for power management and energy source switching is output.

[0462] Step 5:

[0463] The server generates control commands for each autonomous vehicle based on the analysis results. Specifically, it generates commands to instruct battery management and the timing of renewable energy usage. It receives the analysis results of the machine learning algorithm as input data and outputs them as control commands.

[0464] As a result, specific control actions are sent to the vehicle as commands.

[0465] Step 6:

[0466] The server constantly monitors the amount of power generated and consumed by renewable energy devices (e.g., solar power generation devices). As input data, it receives real-time data on power generation and consumption from the renewable energy devices and makes adjustments based on that data to maximize power generation efficiency.

[0467] This allows for efficient energy utilization.

[0468] Step 7:

[0469] The server provides the current status of the system through a user interface. As input data, it receives overall operational data of the system (operation information, weather information, energy consumption patterns, etc.) and outputs it as content to be displayed on the user interface.

[0470] As a result, users can monitor the current status of their system in real time and manually adjust settings as needed.

[0471] Step 8:

[0472] The server uses an emotion engine to analyze the driver's or passenger's emotions. It receives input data such as voice, facial expressions, and text input, and the emotion engine analyzes them to output an emotional state. Based on the analysis results, it automatically adjusts the driving environment and infotainment system.

[0473] This provides a comfortable driving environment that is tailored to the user's emotional state.

[0474] 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.

[0475] 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.

[0476] 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.

[0477] [Second embodiment]

[0478] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0479] 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.

[0480] 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).

[0481] 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.

[0482] 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.

[0483] 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).

[0484] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for 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.

[0485] 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.

[0486] 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.

[0487] 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.

[0488] 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.

[0489] 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."

[0490] The present invention relates to a system for optimizing energy conservation and renewable energy utilization in base stations. The system collects base station operation information, acquires weather data, analyzes the data, and determines optimal power consumption patterns and energy source switching timing. It also controls the base station's power supply and monitors the power generation and consumption of renewable energy devices to optimize efficient energy utilization. Furthermore, the system status is provided through a user interface, and settings can be manually adjusted.

[0491] Explanation of program processing

[0492] Data collection

[0493] The server collects base station operation information (power consumption, number of connected devices, data traffic volume, etc.). This includes periodic data transmission from each base station and data acquisition via API. The server also uses weather data API to acquire real-time weather information (temperature, humidity, precipitation, wind speed, sunshine hours, etc.).

[0494] Examples:

[0495] The server receives information on power consumption and the number of connected devices from base station A, and also obtains the current weather conditions through a weather data API.

[0496] Data preprocessing and analysis

[0497] The server preprocesses the collected data, fills in incomplete data, and converts data types. It then analyzes the preprocessed data to determine optimal power consumption patterns and the timing for switching energy sources. Machine learning algorithms are used for the analysis, and predictions are made based on historical data and current conditions.

[0498] Examples:

[0499] The server fills in missing data from base station A and weather data, normalizes the data, and then uses machine learning algorithms to analyze optimal power consumption patterns and determine when to switch between solar power generation and the power grid.

[0500] Control command generation and execution

[0501] Based on the analysis results, the server generates control commands for each base station to change its power consumption pattern and energy source, and sends these control commands to each base station to change its power supply.

[0502] Examples:

[0503] Based on the analysis results, the server generates a control command to switch power supply to solar power generation for base station A and sends it to base station A. Base station A receives this control command and switches the power supply source.

[0504] Renewable Energy Management

[0505] The server constantly monitors the power output of renewable energy devices (solar panels and wind turbines) and the power consumption of base stations. To ensure efficient power generation and consumption, it optimizes the use of renewable energy to maximize power generation efficiency.

[0506] Examples:

[0507] The server constantly monitors the amount of power generated by the solar panels and the power consumption of base station A, and adjusts the angle of the panels to maximize power generation efficiency.

[0508] Providing a user interface

[0509] The user can monitor the system status in real time and make necessary configuration changes using the provided user interface, which displays the analysis results and current operating status of the system.

[0510] Examples:

[0511] Users can check the power consumption and power generation status of base station A through the management panel, and if they want to manually fine-tune the settings, they can do so on the interface.

[0512] The processing flow will be explained below.

[0513] Step 1: Data collection

[0514] The server collects operational information from base stations. Specifically, it obtains information from each base station, such as power consumption, number of connected devices, and data traffic volume. This data is collected periodically using an API.

[0515] Examples:

[0516] The server obtains the power consumption of base station A through an API request.

[0517] Step 2: Obtaining weather data

[0518] The server obtains weather data in real time via an API, specifically information such as temperature, humidity, precipitation, wind speed, and sunshine hours.

[0519] Examples:

[0520] The server uses the weather data API to obtain weather data around base station A.

[0521] Step 3: Preprocessing the data

[0522] The server preprocesses the collected operational information and weather data, complementing incomplete data and converting and normalizing data types as necessary.

[0523] Examples:

[0524] If the power consumption data of base station A is missing, the server complements it based on past data.

[0525] Step 4: Analysis

[0526] The server then analyzes the preprocessed data using machine learning algorithms. Specifically, it determines the optimal power consumption pattern and timing for switching energy sources based on the collected data.

[0527] Examples:

[0528] The server provides input data to a machine learning model to infer when to switch from solar power generation to the power grid.

[0529] Step 5: Generate control commands

[0530] Based on the analysis results, the server generates power consumption patterns and control commands for switching energy sources for each base station.

[0531] Examples:

[0532] The server generates a control command including an instruction for base station A to switch from solar power generation to the power grid.

[0533] Step 6: Send and execute commands

[0534] The server transmits the generated control commands to each base station, which then executes the power supply changes accordingly.

[0535] Examples:

[0536] The server sends a control command to base station A, and based on this command, base station A switches its power source from solar power generation to the power grid.

[0537] Step 7: Renewable energy monitoring

[0538] The server constantly monitors the power generation and consumption of renewable energy devices (solar panels and wind turbines).

[0539] Examples:

[0540] The server periodically retrieves power generation data from the solar panels and compares it with consumption patterns.

[0541] Step 8: Optimize energy use

[0542] The server optimizes the settings to maximize the efficiency of renewable energy use, adjusting the angle of the panels and changing the proportion of wind power used based on weather and power generation data.

[0543] Examples:

[0544] The server sends adjustment instructions to optimize the angle of the solar panels on sunny days.

[0545] Step 9: Update the User Interface

[0546] The server updates the user interface with the analysis results and current system status accordingly.

[0547] Examples:

[0548] The server reflects the analysis results on the user interface, allowing users to view the information in real time.

[0549] Step 10: Accept manual adjustments

[0550] Users can manually adjust system settings using the provided user interface, which allows for immediate effect of setting changes under specific conditions.

[0551] Examples:

[0552] Users operate an administration panel and manually optimize the settings for a particular base station.

[0553] Example 1

[0554] 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."

[0555] In modern society, base stations consume a huge amount of power, making its optimization a critical issue. While utilizing renewable energy sources in particular contributes to the preservation of the global environment, their efficient use is difficult. Furthermore, due to fluctuations in weather conditions and daily changes in base station loads, it is necessary to optimize real-time power consumption patterns and the timing of switching between energy sources. Furthermore, systems must be able to provide users with an easy-to-understand overview of the system's status, with the ability to manually adjust it as needed. Because conventional methods are unable to adequately resolve these issues, new technologies are needed.

[0556] 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.

[0557] In this invention, the server includes means for collecting operation information of the base station, means for acquiring weather data, means for preprocessing the collected operation information and the acquired weather data to convert data types and complement incomplete data, means for analyzing the preprocessed data using a machine learning algorithm and determining an optimal power consumption pattern and timing for switching between energy sources, means for controlling the power supply of the base station based on the determined power consumption pattern and timing for switching between energy sources, means for constantly monitoring the amount of power generated and consumed by the renewable energy device and optimizing efficient energy use, and means for providing the system status through a user interface and manually adjusting settings. This makes it possible to optimize the power consumption pattern and energy source switching of the base station and achieve efficient energy use in real time.

[0558] A "base station" is a relay device for wireless communication, and is responsible for managing and controlling terminals and communications within a specific area.

[0559] "Operation information" refers to data relating to the operational status of a base station, and includes information such as power consumption, the number of connected terminals, and data traffic volume.

[0560] "Weather data" refers to information about the weather, including temperature, humidity, precipitation, wind speed, and sunshine hours.

[0561] "Preprocessing" refers to the process of completing incomplete data or converting data types before analyzing collected data.

[0562] A "machine learning algorithm" refers to a computational method that uses large amounts of data to learn patterns and rules and make predictions and classifications.

[0563] "Power consumption pattern" refers to the trend or tendency of the power consumed by a base station over a certain period of time.

[0564] "Energy source switching timing" refers to the optimal point in time to switch the power source to a different energy source, such as renewable energy or the commercial power grid.

[0565] "Control" refers to the process of giving instructions or commands to regulate and manage the behavior of a system.

[0566] A "renewable energy device" is a device that generates electricity using natural energy, including solar panels and wind turbines.

[0567] "Yield" refers to the amount of electricity generated by a renewable energy device.

[0568] "Consumption" refers to the amount of power used by the base station.

[0569] "User interface" refers to the mechanism that provides the means and environment for a user to interact with a system.

[0570] "Data analysis" refers to a series of processes for deriving new insights and patterns based on collected data.

[0571] The present invention relates to a system for optimizing energy conservation and renewable energy utilization in base stations. This system uses a server to collect base station operation information and weather data, analyzes this data, and determines optimal power consumption patterns and energy source switching timing. The server also controls the base station's power supply and monitors the power generation and consumption of renewable energy devices, optimizing efficient energy utilization. Furthermore, the system status is provided through a user interface, allowing users to manually adjust settings.

[0572] Hardware and software used

[0573] Hardware

[0574] Server: Data collection, pre-processing, analysis, and control (e.g., Dell PowerEdge R640)

[0575] Base station equipment: Provides operational status data

[0576] Renewable energy devices: solar panels, wind turbines, etc.

[0577] software

[0578] Data collection: Using APIs to obtain weather data and operational information (e.g., weather data API)

[0579] Data analysis: using machine learning algorithms (e.g., TensorFlow, scikit-learn)

[0580] User Interface: HTML5, CSS, JavaScript

[0581] Data collection

[0582] The server collects operational information from base stations, including power consumption, number of connected devices, and data traffic volume. This includes periodic data transmission from each base station and data acquisition via API. The server also obtains real-time weather information using a weather data API. This allows for optimal operation taking into account power consumption patterns and weather conditions.

[0583] Specific working example:

[0584] The server receives information on power consumption and the number of connected devices from base station A, and also obtains the current weather conditions through a weather data API.

[0585] Data preprocessing and analysis

[0586] The collected data is preprocessed, completing incomplete data and converting data types to make it suitable for analysis. The server analyzes the preprocessed data to determine optimal power consumption patterns and timing for switching energy sources. This analysis uses machine learning algorithms to make predictions based on historical data and current conditions.

[0587] Specific working example:

[0588] The server fills in missing data from base station A and weather data, normalizes the data, and then uses machine learning algorithms to analyze optimal power consumption patterns and determine when to switch between solar power generation and the power grid.

[0589] Control command generation and execution

[0590] Based on the analysis results, the server generates control commands for each base station to change the power consumption pattern and energy source, and sends the generated control commands to each base station to change the power supply.

[0591] Specific working example:

[0592] Based on the analysis results, the server generates a control command to switch power supply to solar power generation for base station A and sends it to base station A. Base station A receives this control command and switches the power supply source.

[0593] Renewable Energy Management

[0594] The server constantly monitors the amount of power generated by renewable energy devices (solar panels and wind turbines) and the power consumption of base stations, optimizing the use of renewable energy to maximize power generation efficiency.

[0595] Specific working example:

[0596] The server constantly monitors the amount of power generated by the solar panels and the power consumption of base station A, and adjusts the angle of the panels to maximize power generation efficiency.

[0597] Providing a user interface

[0598] The user can monitor the system status in real time and make necessary configuration changes using the provided user interface, which displays the system analysis results and current operating status.

[0599] Specific working example:

[0600] Users can check the power consumption and power generation status of base station A through the management panel, and if they want to manually fine-tune the settings, they can do so on the interface.

[0601] Examples of prompt statements

[0602] "Predict the optimal switching timing for base station power consumption patterns and renewable energy optimization using current weather data and past operational data."

[0603] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0604] Step 1: Data collection

[0605] Input: Operation information from base stations and weather data from weather data API

[0606] Output: Collected operational information and weather data

[0607] The server collects operational information from the base stations. This information includes power consumption, number of connected devices, data traffic volume, etc. The server periodically receives data from each base station or obtains data using an API. In addition, the server uses a weather data API to obtain real-time weather information (temperature, humidity, precipitation, wind speed, sunshine hours, etc.).

[0608] Specific working example:

[0609] The server automatically receives 24 hours' worth of power consumption data from base station A every day at midnight.

[0610] The server calls the API to obtain data on the number of connected terminals of base station B.

[0611] The server calls the weather data API at 6:00 AM to obtain the temperature and humidity data for the day in the base area.

[0612] Step 2: Preprocessing the data

[0613] Input: Collected operational information and weather data

[0614] Output: Preprocessed data (inserted data imputed and data type converted)

[0615] The server preprocesses the collected data, including imputing incomplete data and converting data types, such as by imputing missing values ​​and correcting outliers, to prepare the data in a format suitable for analysis.

[0616] Specific working example:

[0617] If there are missing values ​​in the power consumption data of base station A, the server complements them by referring to past data.

[0618] The server standardizes the units of the collected weather data and converts it into a standard format.

[0619] Step 3: Analyze the data

[0620] Input: Preprocessed data

[0621] Output: Optimal power consumption patterns and timing for switching between energy sources

[0622] The server analyzes the pre-processed data using machine learning algorithms to determine optimal power consumption patterns and timing for switching between energy sources based on historical data and current conditions.

[0623] Specific working example:

[0624] The server uses preprocessed data from base station A to predict historically most efficient power consumption patterns using machine learning algorithms.

[0625] Step 4: Generate control commands

[0626] Input: Analysis results (optimal power consumption pattern and timing for switching energy sources)

[0627] Output: Generated control commands

[0628] Based on the analysis results, the server generates power consumption patterns and control commands for switching energy sources for each base station.

[0629] Specific working example:

[0630] The server generates a control command to base station A to switch to solar power generation from 10:00 AM to 4:00 PM the next day.

[0631] Step 5: Execute control commands

[0632] Input: Generated control command

[0633] Power: Change the power supply of the base station

[0634] The base station executes the control command received from the server to change the power supply.

[0635] Specific working example:

[0636] Base station A switches to power supply from solar power generation in accordance with the control command received from the server.

[0637] Step 6: Renewable energy management

[0638] Input: Power generation amount of renewable energy device and power consumption status of base station

[0639] Output: Optimized power generation efficiency and power consumption

[0640] The server constantly monitors the amount of power generated by renewable energy devices (solar panels and wind turbines) and the power consumption of base stations, optimizing the use of renewable energy to maximize power generation efficiency.

[0641] Specific working example:

[0642] The server adjusts the angle of the solar panels to the optimal position to maximize power generation.

[0643] Step 7: Providing a User Interface

[0644] Input: System analysis results and current operating status

[0645] Output: System status and manual setting adjustments provided through the user interface

[0646] The user can monitor the system status in real time and make necessary configuration changes using the provided user interface, which displays the system analysis results and current operating status.

[0647] Specific working example:

[0648] Users can open the management panel to check base station A's power consumption data and renewable energy generation in real time, and if they want to manually fine-tune the settings, they can do so on the interface.

[0649] (Application example 1)

[0650] 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."

[0651] Conventional energy management systems for automated robots in factories face challenges in optimizing energy consumption and efficiently utilizing renewable energy. In particular, there is a need for technology that can collect robot operation information and analyze it in conjunction with weather data to determine optimal energy consumption patterns and the timing of switching between energy sources. Furthermore, there is a lack of a method for monitoring the amount of power generated and consumed by renewable energy devices in real time and optimizing efficient energy use. Additionally, a system that can be easily managed through a user interface is also needed.

[0652] 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.

[0653] In this invention, the server includes means for collecting operation information of automatic robots in a factory, means for acquiring weather data, means for analyzing the collected operation information and the acquired weather data and determining an optimal energy consumption pattern and timing for switching between energy sources, means for controlling the energy supply of the robots based on the determined energy consumption pattern and timing for switching between energy sources, means for monitoring the amount of power generated and consumed by a renewable energy device and optimizing efficient energy use, and means for providing the system status through a user interface and manually adjusting settings, thereby enabling the optimization of energy consumption of automatic robots in a factory and the efficient use of renewable energy.

[0654] An "automated factory robot" is a mechanical device that is used in the manufacturing and processing processes in a factory and can autonomously perform programmed actions.

[0655] "Operation information" is data related to the operation of the robot, and specifically includes the amount of power consumed, the operation time, and the type of task being executed.

[0656] "Weather data" refers to information about weather conditions obtained in real time, including temperature, humidity, and hours of sunshine.

[0657] "Energy consumption pattern" refers to data that indicates the state of energy consumption over a certain period of time.

[0658] "Energy source switching timing" refers to the optimal time to switch between renewable energy and conventional energy sources.

[0659] "Renewable energy devices" refer to devices that generate electricity using natural forces, such as solar power generation devices and wind power generation devices.

[0660] A "user interface" refers to the screens and input devices that allow a user to operate a system or monitor its status.

[0661] A "machine learning algorithm" is a calculation method that automatically derives optimal energy consumption patterns and timing for switching energy sources based on collected data.

[0662] This invention relates to an energy management system for automated robots in factories. This system collects and analyzes robot operation information and weather data to determine optimal energy consumption patterns and timing for switching between energy sources, thereby efficiently utilizing renewable energy.

[0663] Explanation of program processing

[0664] Data collection

[0665] The server collects operational information from the automated robots installed in the factory. This operational information includes power consumption, operating time, task type, etc. It also uses a weather data API to obtain real-time weather data, including temperature, humidity, and sunshine hours.

[0666] Data preprocessing and analysis

[0667] The server preprocesses the collected operational information and weather data, complements incomplete data, and converts data types. It then analyzes the preprocessed data and uses machine learning algorithms to make predictions to determine optimal energy consumption patterns and the timing for switching energy sources. The software used is Python and TensorFlow.

[0668] Control command generation and execution

[0669] Based on the analysis results, the server generates control commands for the robot to change its energy consumption pattern and energy source, and sends these control commands to the robot to change its energy supply.

[0670] Renewable energy equipment management

[0671] The server constantly monitors the amount of power generated and consumed by renewable energy devices (e.g., solar power generation devices, wind power generation devices) and optimizes their use to maximize power generation efficiency, thereby enabling efficient management of the robot's energy supply.

[0672] Providing a user interface

[0673] The user can monitor the system status in real time and make necessary configuration changes using the provided user interface, which displays the system analysis results and current operating status. The software used is Flask.

[0674] Specific examples

[0675] For example, you might use the following prompts to input to a generative AI model:

[0676] Prompt statement:

[0677] "Generate and send control commands to switch Robot A's power supply to solar power during sunny hours. Also provide optimal energy consumption patterns based on real-time weather data."

[0678] As described above, by building an energy management system for automated robots in factories, it is possible to optimize energy consumption and efficiently use renewable energy.

[0679] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0680] Step 1:

[0681] The server collects operational information from the automated robots installed in the factory. This operational information includes power consumption, operating time, task type, etc. The server periodically receives data from each robot and collects it via API and sensors.

[0682] Input: Operation information from the robot

[0683] Output: Collected operational information

[0684] Step 2:

[0685] The server uses a weather data API to obtain real-time weather data, including temperature, humidity, and sunshine hours. The server periodically sends requests to the API to obtain the latest weather information.

[0686] Input: API request

[0687] Output: Captured real-time weather data

[0688] Step 3:

[0689] The server preprocesses the collected operational information and acquired weather data. It performs data cleaning to fill in incomplete data and convert data types. Specifically, this includes filling in missing values, normalizing data, and correcting outliers.

[0690] Input: Collected operating information, acquired weather data

[0691] Output: Preprocessed data

[0692] Step 4:

[0693] The server analyzes the preprocessed data to determine optimal energy consumption patterns and timing for switching between energy sources. It uses machine learning algorithms to make predictions based on historical and current data. It runs the model using Python and TensorFlow.

[0694] Input: Preprocessed data

[0695] Output: Optimal energy consumption pattern, timing for switching energy sources

[0696] Step 5:

[0697] Based on the analysis results, the server generates control commands for the robot to determine its energy consumption pattern and switch its energy source. The server then sends the control commands to the robot to change its energy supply. The control commands specifically instruct the robot's behavior, including switching the power supply source.

[0698] Input: Optimal energy consumption pattern, timing to switch energy sources

[0699] Output: Control commands to the robot

[0700] Step 6:

[0701] The server constantly monitors the amount of power generated and consumed by renewable energy devices (solar power generation devices, wind power generation devices). Specifically, it periodically acquires the parameters of the power generation devices using a sensor system and sends the data to the monitoring system. The angle of the panels and turbines is automatically adjusted to maximize power generation efficiency.

[0702] Input: Sensor information of the power generation equipment

[0703] Output: Maximum efficiency power generation

[0704] Step 7:

[0705] The user uses the provided user interface to monitor the system status in real time and change the settings as needed. The interface displays the analysis results obtained from the system and the current operating status. The user can also manually adjust the settings. The interface is built using Flask.

[0706] Input: Analysis results and operational status from the system

[0707] Output: Operational status reflecting the user's configuration changes

[0708] 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.

[0709] This invention combines an emotion engine with a system for optimizing base station energy conservation and renewable energy utilization. The system collects base station operation information, acquires weather data, analyzes the data, and determines optimal power consumption patterns and energy source switching timing. It also controls the base station's power supply and monitors the power generation and consumption of renewable energy devices to optimize efficient energy utilization. Furthermore, the system status is displayed through a user interface, allowing for manual adjustment of settings. An emotion engine that recognizes user emotions has also been added to improve the user experience.

[0710] Explanation of program processing

[0711] Data collection

[0712] The server collects operational information from base stations. Specifically, it obtains information such as power consumption, number of connected devices, and data traffic volume from each base station. This data is collected periodically using an API. The server also uses a weather data API to obtain real-time weather information (temperature, humidity, precipitation, wind speed, sunshine hours, etc.).

[0713] Examples:

[0714] The server receives information on power consumption and the number of connected devices from base station A, and also obtains the current weather conditions through a weather data API.

[0715] Data preprocessing and analysis

[0716] The server preprocesses the collected data, complements incomplete data, and converts and normalizes data types as needed. The preprocessed data is then analyzed to determine optimal power consumption patterns and the timing of switching energy sources. Machine learning algorithms are used in the analysis to make predictions based on historical data and current conditions.

[0717] Examples:

[0718] The server fills in missing data from base station A and weather data, normalizes the data, and then uses machine learning algorithms to analyze optimal power consumption patterns and determine when to switch between solar power generation and the power grid.

[0719] Control command generation and execution

[0720] Based on the analysis results, the server generates control commands for each base station to change its power consumption pattern and energy source, and sends these control commands to each base station to change its power supply.

[0721] Examples:

[0722] Based on the analysis results, the server generates a control command including an instruction for base station A to switch from solar power generation to the power grid, and sends it to base station A. Base station A receives this control command and switches the power supply source.

[0723] Renewable Energy Management

[0724] The server constantly monitors the power output of renewable energy devices (solar panels and wind turbines) and the power consumption of base stations. To ensure efficient power generation and consumption, it optimizes the use of renewable energy to maximize power generation efficiency.

[0725] Examples:

[0726] The server constantly monitors the amount of power generated by the solar panels and the power consumption of base station A, and adjusts the angle of the panels to maximize power generation efficiency.

[0727] Providing a user interface

[0728] The user can monitor the system status in real time and make necessary configuration changes using the provided user interface, which displays the analysis results and current operating status of the system.

[0729] Examples:

[0730] Users can check the power consumption and power generation status of base station A through the management panel, and if they want to manually fine-tune the settings, they can do so on the interface.

[0731] Combining Emotion Engines

[0732] The server is equipped with an emotion engine that recognizes the user's emotions by analyzing the user's voice, facial expressions, text input, etc., and automatically adjusts the system settings and interface based on the analysis results.

[0733] Examples:

[0734] The server uses an emotion engine to analyze the voice tone and facial expressions of the user when operating the interface, and if it determines that the user is under high stress, it activates an assist function to make operating the interface easier.

[0735] This allows the system to maximize energy efficiency while providing a user-friendly operating environment.

[0736] The processing flow will be explained below.

[0737] Step 1: Data collection

[0738] The server collects operational information from base stations. Specifically, it obtains information from each base station, such as power consumption, number of connected devices, and data traffic volume. This data is collected periodically using an API.

[0739] Examples:

[0740] The server obtains the power consumption and number of connected terminals of base station A through an API request.

[0741] Step 2: Obtaining weather data

[0742] The server obtains weather data in real time via an API, specifically information such as temperature, humidity, precipitation, wind speed, and sunshine hours.

[0743] Examples:

[0744] The server uses the weather data API to obtain weather data around base station A.

[0745] Step 3: Preprocessing the data

[0746] The server preprocesses the collected operational information and weather data, complementing incomplete data and converting data types as necessary to normalize it.

[0747] Examples:

[0748] If there is a gap in the power consumption data of base station A, the server will supplement it based on past data.

[0749] Step 4: Analysis

[0750] The server analyzes the preprocessed data using machine learning algorithms. Specifically, it determines the optimal power consumption pattern and timing for switching energy sources based on the collected data.

[0751] Examples:

[0752] The server inputs the data into a machine learning model to infer when to switch from solar power generation to the power grid.

[0753] Step 5: Generate control commands

[0754] The server generates a power consumption pattern and a control command for switching the energy source for each base station based on the analysis result.

[0755] Examples:

[0756] The server generates a control command for base station A, which includes an instruction to switch from solar power generation to the power grid.

[0757] Step 6: Send and execute commands

[0758] The server transmits the generated control command to each base station to change the power supply.

[0759] Examples:

[0760] The server sends a control command to base station A, and base station A switches the power supply source from solar power generation to the power grid based on this command.

[0761] Step 7: Renewable energy monitoring

[0762] The server constantly monitors the power generation and consumption of renewable energy devices (solar panels and wind turbines).

[0763] Examples:

[0764] The server periodically retrieves power generation data from the solar panels and compares it with consumption patterns.

[0765] Step 8: Optimize energy use

[0766] The server optimizes the settings to maximize the efficiency of renewable energy use, adjusting the angle of the panels and changing the proportion of wind power used based on weather and power generation data.

[0767] Examples:

[0768] The server provides instructions to adjust the solar panels to the optimal angle for sunny days.

[0769] Step 9: Update the User Interface

[0770] The server updates the user interface with the analysis results and current system status accordingly.

[0771] Examples:

[0772] The server reflects the analysis results on the user interface, allowing users to view the information in real time.

[0773] Step 10: Accept manual adjustments

[0774] Users can manually adjust system settings using the provided user interface, which allows for immediate effect of setting changes under specific conditions.

[0775] Examples:

[0776] Users operate an administration panel and manually optimize the settings for a particular base station.

[0777] Step 11: Collect sentiment data

[0778] The server collects the user's emotional data, which can be obtained from the user's voice, facial expressions, text input, etc.

[0779] Examples:

[0780] The server collects voice tones and facial expressions via a camera and microphone when a user operates the management panel.

[0781] Step 12: Analyze the sentiment data

[0782] The server analyzes the collected emotion data and uses an emotion engine to determine the user's stress level and satisfaction.

[0783] Examples:

[0784] The server analyzes the user's voice data and determines that the user is feeling stressed.

[0785] Step 13: Emotion-Based System Adjustment

[0786] The server automatically adjusts the system settings based on the analysis of the emotional data, instantly changing the settings to reflect the user's emotional state.

[0787] Examples:

[0788] If the server determines that the user is feeling stressed, it activates an assist function that simplifies operation of the interface.

[0789] Through the above process, the present invention efficiently manages base stations and renewable energy, and further provides an interface that takes user emotions into consideration, thereby improving energy efficiency and maximizing user experience.

[0790] Example 2

[0791] 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."

[0792] Conventional base station energy management systems have room for improvement in optimizing power consumption and efficiently utilizing renewable energy. Furthermore, they lack effective methods for improving user experience. Therefore, there is a need to provide a more efficient and user-friendly means for base station energy management.

[0793] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0794] In this invention, the server includes means for collecting operation information of the base station, means for acquiring weather data, means for preprocessing and analyzing the collected operation information and the acquired weather data to determine an optimal power consumption pattern and timing for switching between energy sources, means for controlling the power supply of the base station based on the determined power consumption pattern and timing for switching between energy sources, means for monitoring the amount of power generated by the renewable energy device and the amount of power consumed by the base station in real time to optimize efficient energy use, means for providing the system status through a user interface and manually adjusting settings, and means for recognizing user emotions using an emotion engine and automatically adjusting the system status and interface, thereby enabling high energy efficiency and improved user satisfaction.

[0795] A "base station" is a fixed facility for conducting wireless communications that forms part of a communications network.

[0796] "Operation information" is data indicating the operational status of the base station, the amount of power consumed there, the number of connected terminals, the amount of data traffic, and so on.

[0797] "Weather data" refers to data that includes weather-related information such as temperature, humidity, precipitation, wind speed, and sunshine hours.

[0798] "Preprocessing" refers to a series of operations such as data interpolation, filtering, conversion, and normalization that are performed to prepare the collected raw data in an analyzable form.

[0799] "Analysis" is the act of using preprocessed data to extract and interpret information suitable for a specific purpose.

[0800] A "power consumption pattern" is a model that shows the tendency of how power is used and consumed under specific times and conditions.

[0801] "Energy source switching timing" refers to determining the timing of switching between different energy sources (e.g., renewable energy and the power grid) to make the most effective use of them.

[0802] A "renewable energy device" is a device that generates electricity using natural energy, such as solar panels or wind turbines.

[0803] "User interface" means the visual or operational means by which a user interacts with a system and inputs and obtains information.

[0804] The "emotion engine" is a function that analyzes the user's voice, facial expressions, text input, etc. to recognize emotions, and automatically adjusts the system settings and interface based on the results.

[0805] MODE FOR CARRYING OUT THE INVENTION

[0806] The present invention combines an emotion engine with a system for optimizing energy conservation and renewable energy utilization in base stations. The system mainly consists of the following components: a base station, a server, a renewable energy device, a user interface, and an emotion engine.

[0807] Hardware and Software Configuration

[0808] 1. Base Station: A fixed communication tower and its associated equipment that forms part of a communication network. Each base station is equipped with sensors to measure power consumption, the number of connected devices, and data traffic volume.

[0809] 2. Server: This is the central computer system that collects and analyzes data. The server has the ability to retrieve data from base stations and meteorological data services through APIs. The main software used includes a database management system, scripts for data preprocessing, and machine learning algorithms.

[0810] 3. Renewable energy devices: These are devices that generate electricity using natural energy sources such as solar panels and wind turbines.

[0811] 4. User interface: An interface that provides users with the operational status of the system in the form of an administration panel or dashboard and allows them to change settings.

[0812] 5. Emotion Engine: A software component that analyzes the user's voice, facial expressions, text input, etc. to recognize the user's emotional state, allowing the system to adaptively adjust its settings and interface.

[0813] Data collection and preprocessing

[0814] The server collects operational information from each base station, such as power consumption, number of connected devices, and data traffic volume. The server also uses a weather data API to obtain real-time weather data such as temperature, humidity, precipitation, wind speed, and sunshine hours.

[0815] Examples:

[0816] The server obtains information on power consumption and the number of connected devices from base station A via an API every five minutes.

[0817] The server uses the weather service API to obtain weather information every hour of the day.

[0818] Data preprocessing and analysis

[0819] The server preprocesses the collected data, including filling in missing values, filtering outliers, converting data types, and normalizing the data. It then performs analysis based on the preprocessed data to determine optimal power consumption patterns and the timing for switching between energy sources. Machine learning algorithms (e.g., regression analysis and time-series forecasting models) are used for the analysis.

[0820] Examples:

[0821] The server trains a machine learning model based on data from the past year and uses current data as input to predict optimal power consumption patterns.

[0822] The server takes into account the weather forecast and plans how to utilize renewable energy for the next day.

[0823] Control command generation and execution

[0824] Based on the analysis results, the server generates control commands for each base station, including details of power consumption patterns and timing for switching between energy sources. The base station then switches between power suppliers accordingly.

[0825] Examples:

[0826] The server generates a control command for base station A to prioritize power supply from the power grid during the night and solar power generation during the day.

[0827] Renewable Energy Management

[0828] The server monitors the power generation of renewable energy devices (solar panels, wind turbines, etc.) and the power consumption of base stations in real time, and adjusts consumption patterns accordingly to achieve optimal energy utilization.

[0829] Examples:

[0830] The server constantly monitors the amount of electricity generated by the solar panels and switches the power supply to renewable energy when it is more efficient.

[0831] User interface and emotion engine

[0832] The user can monitor the system status in real time through the provided user interface and make necessary setting changes. Furthermore, the server can recognize the user's emotions using an emotion engine and automatically adjust the interface and system settings based on the user's stress level and emotional state.

[0833] Examples:

[0834] Users can use the management panel to check the power consumption and power generation status of base station A in real time and fine-tune the settings as needed.

[0835] The server analyzes the user's voice tone and facial expressions using an emotion engine, and if it determines that the user is under high stress, it activates an assist function that simplifies operation.

[0836] Prompt Sentence Examples

[0837] Below are some examples of prompts that can be input to a generative AI model using this system:

[0838] "Please explain the program flow in which the server uses data collected from base station A to analyze the optimal power consumption pattern and determine the timing to switch from solar power generation to the power grid. Also, please provide a concrete example of monitoring the system status through the user interface and manually changing the settings."

[0839] In this way, the present invention can maximize the energy efficiency of the base station and provide a user-friendly operating environment.

[0840] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0841] Step 1:

[0842] The server collects operational information from each base station. This operational information includes power consumption, number of connected devices, data traffic volume, etc. The operational information received as input is obtained via API. The server also obtains temperature, humidity, precipitation, wind speed, and sunshine hours in real time from the weather data API. This information is stored in a database so that it can be used in subsequent analysis steps.

[0843] Specific behavior:

[0844] The server collects information on power consumption, number of connected terminals, and data traffic volume from base station A every five minutes.

[0845] The server retrieves weather information every hour through the weather data API.

[0846] Step 2:

[0847] The server preprocesses the collected operational information and weather data. Since the input data may contain missing or outliers, it complements the incomplete data and filters out the outliers. It also performs data type conversion and normalization to prepare the data in a format suitable for analysis. The preprocessed data is used in the next analysis step.

[0848] Specific behavior:

[0849] If there is a missing value in the data of base station A, the server complements the value based on the data before and after it.

[0850] The server filters out data points that record abnormally high power consumption.

[0851] Step 3:

[0852] The server analyzes the preprocessed data. It takes the preprocessed data as input and uses machine learning algorithms to predict optimal power consumption patterns and timing for switching between energy sources. This analysis uses regression analysis and time series prediction models. The prediction results are used in the control command generation step.

[0853] Specific behavior:

[0854] The server trains the machine learning model using operational information and weather data from the past year.

[0855] The server inputs the current data and predicts the best time to utilize solar power generation during the day the next day.

[0856] Step 4:

[0857] The server generates control commands for the base stations based on the analysis results. Using the results of the analysis step as input, the server creates control commands that include the power consumption patterns and energy source switching instructions for each base station. These commands are sent to each base station and executed.

[0858] Specific behavior:

[0859] The server generates a control command for base station A to prioritize power supply from the power grid during the night and solar power generation during the day.

[0860] The server generates commands including timing to utilize the power generation of the wind turbines when wind speeds are high.

[0861] Step 5:

[0862] The terminal (base station) executes the control command received from the server. It receives the control command from the server as input, switches the power supply source based on the command, and feeds back the execution result to the server.

[0863] Specific behavior:

[0864] Base station A switches to power supply from the power grid at night.

[0865] Base station A switches its power supply source to solar power when solar power is available during the day.

[0866] Step 6:

[0867] The server monitors the power generation of renewable energy devices and the power consumption of base stations in real time. It receives power generation data from renewable energy devices and consumption data from base stations as input, analyzes these data to ensure optimal energy usage, and makes necessary adjustments based on the results.

[0868] Specific behavior:

[0869] The server monitors the amount of electricity generated by the solar panels in real time and issues instructions to switch the power supply to renewable energy when it is more efficient.

[0870] Step 7:

[0871] The user monitors the system status in real time using the provided user interface and makes necessary configuration changes. The system receives operational information as input and adjusts the configuration based on that information.

[0872] Specific behavior:

[0873] The user can access the management panel using a tablet device and check the power consumption and power generation status of base station A.

[0874] Users can manually change settings to reduce power consumption during specific time periods.

[0875] Step 8:

[0876] The server uses an emotion engine to recognize the user's emotions, detecting and analyzing inputs such as the user's voice, facial expressions, and text input, and adjusting the system settings and interface based on the results.

[0877] Specific behavior:

[0878] The server analyzes the user's tone of voice and facial expressions, and if it determines that the user is under high stress, it activates an assist function to simplify operation.

[0879] (Application example 2)

[0880] 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."

[0881] While battery management and energy usage optimization are essential for current autonomous vehicles, there is a lack of efficient and real-time methods to do so. Furthermore, there is a lack of technology to adjust the driving environment and infotainment system according to the emotional state of the driver and passengers, creating a need for technologies to enhance user comfort.

[0882] 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.

[0883] In this invention, the server includes means for collecting operation information of the base stations, means for acquiring weather data, means for analyzing the collected operation information and the acquired weather data and determining an optimal power consumption pattern and timing for switching between energy sources, means for controlling the power supply of the base stations based on the determined power consumption pattern and timing for switching between energy sources, means for monitoring the amount of power generated and consumed by the renewable energy device and optimizing efficient energy use, means for providing the system status through a user interface and manually adjusting settings, and means for recognizing the emotions of the driver or passengers and adjusting the driving environment and infotainment system. This makes it possible to provide a comfortable driving environment that corresponds to the emotional state of the user while optimizing the energy management of the autonomous vehicle.

[0884] The "means for collecting base station operation information" refers to a means for acquiring information such as power consumption, the number of connected terminals, and data traffic volume from the base station.

[0885] "Means for acquiring weather data" refers to means for acquiring weather information such as temperature, humidity, precipitation, wind speed, and sunshine hours in real time.

[0886] The "means for analyzing the collected operation information and the acquired weather data and determining the optimal power consumption pattern and timing for switching energy sources" refers to a means for determining the optimal timing for power consumption and energy source usage based on the collected data using machine learning algorithms, etc.

[0887] The "means for controlling the power supply of the base station based on the determined power consumption pattern and energy source switching timing" refers to means for changing the power supply or switching the energy source based on the analysis results.

[0888] "Means for monitoring the amount of power generated and consumed by renewable energy devices and optimizing efficient energy use" refers to means for monitoring the amount of power generated and the usage status of renewable energy devices such as solar power generation devices and wind power generation devices, and for optimally managing energy use.

[0889] "Means for providing system status through a user interface and for manually adjusting settings" refers to means for providing a user with an interface for displaying the operating status of the system and manually changing settings as necessary.

[0890] "Means for recognizing the emotions of the driver or passenger and adjusting the driving environment or infotainment system" refers to means for analyzing the emotions of the driver or passenger from voice, facial expressions, text input, etc., and adjusting the driving environment or in-car entertainment system based on the results.

[0891] This invention provides an energy management system for autonomous vehicles that optimizes battery management and renewable energy utilization. The system collects vehicle operation information and weather data, and analyzes this data to determine optimal energy consumption patterns and timing for switching energy sources. It also includes a function that recognizes the emotions of passengers and drivers and adjusts the driving environment and infotainment system accordingly.

[0892] The server first collects information from the autonomous vehicle, including battery information, speed, mileage, interior and exterior temperatures, and the location of charging stations. It also obtains real-time weather information using a weather data API. The collected data is preprocessed to complete missing data and normalize it.

[0893] The server then uses machine learning algorithms to determine optimal power consumption patterns and timing for switching between energy sources, taking into account historical data and current conditions. Based on the analysis, the server generates control commands for the energy management system for battery management and energy source switching, which are then sent to the vehicle.

[0894] The server constantly monitors the amount of power generated and consumed by renewable energy devices, such as solar power generation devices installed in vehicles, and controls them to maximize the efficiency of energy use.

[0895] Users can monitor the system status and manually adjust settings as needed through a user interface that displays analysis results and current operating status of the system.

[0896] Additionally, the server is equipped with an emotion engine that analyzes the driver's or passenger's emotions by analyzing voice, facial expressions, text input, etc., and adjusts the driving environment and infotainment system accordingly.

[0897] For example, if a user feels "stressed," the server's emotion engine will analyze that emotion and enable the infotainment system to operate more easily while also playing appropriate music. This system will enable the creation of a comfortable driving environment while optimizing the energy management of autonomous vehicles.

[0898] Example prompts for generative AI models

[0899] "Collect battery information and speed data from vehicle ID: example_vehicle_id, and obtain weather information through API. Then, preprocess and normalize the acquired data to generate optimal battery management and energy switching control commands. Also, implement a system that simplifies system operation and plays relaxing music based on the user's input that "I'm stressed."

[0900] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0901] Step 1:

[0902] The server collects information about the autonomous vehicle's battery, speed, mileage, interior and exterior temperatures, and the location of charging stations. This data is obtained through the autonomous vehicle's API. It receives various sensor information from the vehicle as input data and stores it in an internal database.

[0903] The result is a data set that represents the current operating state of the vehicle.

[0904] Step 2:

[0905] The server uses a weather data API to obtain real-time weather information, taking weather data such as temperature, humidity, precipitation, wind speed, and sunshine hours as input, and stores it in an internal database.

[0906] The result is a real-time weather dataset representing weather conditions.

[0907] Step 3:

[0908] The server preprocesses the collected operational information and weather data. Specifically, it complements missing data, corrects outliers, normalizes data, etc. It receives operational information and weather data as input data and outputs a clean, preprocessed data set.

[0909] This will provide a dataset suitable for analysis.

[0910] Step 4:

[0911] The server uses machine learning algorithms to analyze the preprocessed data. Specifically, it utilizes predictive models to determine optimal power consumption patterns and timing for switching energy sources. It receives preprocessed operational information and weather data as input, analyzes them, and generates optimal control commands.

[0912] As a result, optimal timing for power management and energy source switching is output.

[0913] Step 5:

[0914] The server generates control commands for each autonomous vehicle based on the analysis results. Specifically, it generates commands to instruct battery management and the timing of renewable energy usage. It receives the analysis results of the machine learning algorithm as input data and outputs them as control commands.

[0915] As a result, specific control actions are sent to the vehicle as commands.

[0916] Step 6:

[0917] The server constantly monitors the amount of power generated and consumed by renewable energy devices (e.g., solar power generation devices). As input data, it receives real-time data on power generation and consumption from the renewable energy devices and makes adjustments based on that data to maximize power generation efficiency.

[0918] This allows for efficient energy utilization.

[0919] Step 7:

[0920] The server provides the current status of the system through a user interface. As input data, it receives overall operational data of the system (operation information, weather information, energy consumption patterns, etc.) and outputs it as content to be displayed on the user interface.

[0921] As a result, users can monitor the current status of their system in real time and manually adjust settings as needed.

[0922] Step 8:

[0923] The server uses an emotion engine to analyze the driver's or passenger's emotions. It receives input data such as voice, facial expressions, and text input, and the emotion engine analyzes them to output an emotional state. Based on the analysis results, it automatically adjusts the driving environment and infotainment system.

[0924] This provides a comfortable driving environment that is tailored to the user's emotional state.

[0925] 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.

[0926] 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.

[0927] 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.

[0928] [Third embodiment]

[0929] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0930] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0931] 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).

[0932] 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.

[0933] 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.

[0934] 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).

[0935] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for 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.

[0936] 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.

[0937] 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.

[0938] 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.

[0939] 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.

[0940] 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."

[0941] The present invention relates to a system for optimizing energy conservation and renewable energy utilization in base stations. The system collects base station operation information, acquires weather data, analyzes the data, and determines optimal power consumption patterns and energy source switching timing. It also controls the base station's power supply and monitors the power generation and consumption of renewable energy devices to optimize efficient energy utilization. Furthermore, the system status is provided through a user interface, and settings can be manually adjusted.

[0942] Explanation of program processing

[0943] Data collection

[0944] The server collects base station operation information (power consumption, number of connected devices, data traffic volume, etc.). This includes periodic data transmission from each base station and data acquisition via API. The server also uses weather data API to acquire real-time weather information (temperature, humidity, precipitation, wind speed, sunshine hours, etc.).

[0945] Examples:

[0946] The server receives information on power consumption and the number of connected devices from base station A, and also obtains the current weather conditions through a weather data API.

[0947] Data preprocessing and analysis

[0948] The server preprocesses the collected data, fills in incomplete data, and converts data types. It then analyzes the preprocessed data to determine optimal power consumption patterns and the timing for switching energy sources. Machine learning algorithms are used for the analysis, and predictions are made based on historical data and current conditions.

[0949] Examples:

[0950] The server fills in missing data from base station A and weather data, normalizes the data, and then uses machine learning algorithms to analyze optimal power consumption patterns and determine when to switch between solar power generation and the power grid.

[0951] Control command generation and execution

[0952] Based on the analysis results, the server generates control commands for each base station to change its power consumption pattern and energy source, and sends these control commands to each base station to change its power supply.

[0953] Examples:

[0954] Based on the analysis results, the server generates a control command to switch power supply to solar power generation for base station A and sends it to base station A. Base station A receives this control command and switches the power supply source.

[0955] Renewable Energy Management

[0956] The server constantly monitors the power output of renewable energy devices (solar panels and wind turbines) and the power consumption of base stations. To ensure efficient power generation and consumption, it optimizes the use of renewable energy to maximize power generation efficiency.

[0957] Examples:

[0958] The server constantly monitors the amount of power generated by the solar panels and the power consumption of base station A, and adjusts the angle of the panels to maximize power generation efficiency.

[0959] Providing a user interface

[0960] The user can monitor the system status in real time and make necessary configuration changes using the provided user interface, which displays the analysis results and current operating status of the system.

[0961] Examples:

[0962] Users can check the power consumption and power generation status of base station A through the management panel, and if they want to manually fine-tune the settings, they can do so on the interface.

[0963] The processing flow will be explained below.

[0964] Step 1: Data collection

[0965] The server collects operational information from base stations. Specifically, it obtains information from each base station, such as power consumption, number of connected devices, and data traffic volume. This data is collected periodically using an API.

[0966] Examples:

[0967] The server obtains the power consumption of base station A through an API request.

[0968] Step 2: Obtaining weather data

[0969] The server obtains weather data in real time via an API, specifically information such as temperature, humidity, precipitation, wind speed, and sunshine hours.

[0970] Examples:

[0971] The server uses the weather data API to obtain weather data around base station A.

[0972] Step 3: Preprocessing the data

[0973] The server preprocesses the collected operational information and weather data, complementing incomplete data and converting and normalizing data types as necessary.

[0974] Examples:

[0975] If the power consumption data of base station A is missing, the server complements it based on past data.

[0976] Step 4: Analysis

[0977] The server then analyzes the preprocessed data using machine learning algorithms. Specifically, it determines the optimal power consumption pattern and timing for switching energy sources based on the collected data.

[0978] Examples:

[0979] The server provides input data to a machine learning model to infer when to switch from solar power generation to the power grid.

[0980] Step 5: Generate control commands

[0981] Based on the analysis results, the server generates power consumption patterns and control commands for switching energy sources for each base station.

[0982] Examples:

[0983] The server generates a control command including an instruction for base station A to switch from solar power generation to the power grid.

[0984] Step 6: Send and execute commands

[0985] The server transmits the generated control commands to each base station, which then executes the power supply changes accordingly.

[0986] Examples:

[0987] The server sends a control command to base station A, and based on this command, base station A switches its power source from solar power generation to the power grid.

[0988] Step 7: Renewable energy monitoring

[0989] The server constantly monitors the power generation and consumption of renewable energy devices (solar panels and wind turbines).

[0990] Examples:

[0991] The server periodically retrieves power generation data from the solar panels and compares it with consumption patterns.

[0992] Step 8: Optimize energy use

[0993] The server optimizes the settings to maximize the efficiency of renewable energy use, adjusting the angle of the panels and changing the proportion of wind power used based on weather and power generation data.

[0994] Examples:

[0995] The server sends adjustment instructions to optimize the angle of the solar panels on sunny days.

[0996] Step 9: Update the User Interface

[0997] The server updates the user interface with the analysis results and current system status accordingly.

[0998] Examples:

[0999] The server reflects the analysis results on the user interface, allowing users to view the information in real time.

[1000] Step 10: Accept manual adjustments

[1001] Users can manually adjust system settings using the provided user interface, which allows for immediate effect of setting changes under specific conditions.

[1002] Examples:

[1003] Users operate an administration panel and manually optimize the settings for a particular base station.

[1004] Example 1

[1005] 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."

[1006] In modern society, base stations consume a huge amount of power, making its optimization a critical issue. While utilizing renewable energy sources in particular contributes to the preservation of the global environment, their efficient use is difficult. Furthermore, due to fluctuations in weather conditions and daily changes in base station loads, it is necessary to optimize real-time power consumption patterns and the timing of switching between energy sources. Furthermore, systems must be able to provide users with an easy-to-understand overview of the system's status, with the ability to manually adjust it as needed. Because conventional methods are unable to adequately resolve these issues, new technologies are needed.

[1007] 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.

[1008] In this invention, the server includes means for collecting operation information of the base station, means for acquiring weather data, means for preprocessing the collected operation information and the acquired weather data to convert data types and complement incomplete data, means for analyzing the preprocessed data using a machine learning algorithm and determining an optimal power consumption pattern and timing for switching between energy sources, means for controlling the power supply of the base station based on the determined power consumption pattern and timing for switching between energy sources, means for constantly monitoring the amount of power generated and consumed by the renewable energy device and optimizing efficient energy use, and means for providing the system status through a user interface and manually adjusting settings. This makes it possible to optimize the power consumption pattern and energy source switching of the base station and achieve efficient energy use in real time.

[1009] A "base station" is a relay device for wireless communication, and is responsible for managing and controlling terminals and communications within a specific area.

[1010] "Operation information" refers to data relating to the operational status of a base station, and includes information such as power consumption, the number of connected terminals, and data traffic volume.

[1011] "Weather data" refers to information about the weather, including temperature, humidity, precipitation, wind speed, and sunshine hours.

[1012] "Preprocessing" refers to the process of completing incomplete data or converting data types before analyzing collected data.

[1013] A "machine learning algorithm" refers to a computational method that uses large amounts of data to learn patterns and rules and make predictions and classifications.

[1014] "Power consumption pattern" refers to the trend or tendency of the power consumed by a base station over a certain period of time.

[1015] "Energy source switching timing" refers to the optimal point in time to switch the power source to a different energy source, such as renewable energy or the commercial power grid.

[1016] "Control" refers to the process of giving instructions or commands to regulate and manage the behavior of a system.

[1017] A "renewable energy device" is a device that generates electricity using natural energy, including solar panels and wind turbines.

[1018] "Yield" refers to the amount of electricity generated by a renewable energy device.

[1019] "Consumption" refers to the amount of power used by the base station.

[1020] "User interface" refers to the mechanism that provides the means and environment for a user to interact with a system.

[1021] "Data analysis" refers to a series of processes for deriving new insights and patterns based on collected data.

[1022] The present invention relates to a system for optimizing energy conservation and renewable energy utilization in base stations. This system uses a server to collect base station operation information and weather data, analyzes this data, and determines optimal power consumption patterns and energy source switching timing. The server also controls the base station's power supply and monitors the power generation and consumption of renewable energy devices, optimizing efficient energy utilization. Furthermore, the system status is provided through a user interface, allowing users to manually adjust settings.

[1023] Hardware and software used

[1024] Hardware

[1025] Server: Data collection, pre-processing, analysis, and control (e.g., Dell PowerEdge R640)

[1026] Base station equipment: Provides operational status data

[1027] Renewable energy devices: solar panels, wind turbines, etc.

[1028] software

[1029] Data collection: Using APIs to obtain weather data and operational information (e.g., weather data API)

[1030] Data analysis: using machine learning algorithms (e.g., TensorFlow, scikit-learn)

[1031] User Interface: HTML5, CSS, JavaScript

[1032] Data collection

[1033] The server collects operational information from base stations, including power consumption, number of connected devices, and data traffic volume. This includes periodic data transmission from each base station and data acquisition via API. The server also obtains real-time weather information using a weather data API. This allows for optimal operation taking into account power consumption patterns and weather conditions.

[1034] Specific working example:

[1035] The server receives information on power consumption and the number of connected devices from base station A, and also obtains the current weather conditions through a weather data API.

[1036] Data preprocessing and analysis

[1037] The collected data is preprocessed, completing incomplete data and converting data types to make it suitable for analysis. The server analyzes the preprocessed data to determine optimal power consumption patterns and timing for switching energy sources. This analysis uses machine learning algorithms to make predictions based on historical data and current conditions.

[1038] Specific working example:

[1039] The server fills in missing data from base station A and weather data, normalizes the data, and then uses machine learning algorithms to analyze optimal power consumption patterns and determine when to switch between solar power generation and the power grid.

[1040] Control command generation and execution

[1041] Based on the analysis results, the server generates control commands for each base station to change the power consumption pattern and energy source, and sends the generated control commands to each base station to change the power supply.

[1042] Specific working example:

[1043] Based on the analysis results, the server generates a control command to switch power supply to solar power generation for base station A and sends it to base station A. Base station A receives this control command and switches the power supply source.

[1044] Renewable Energy Management

[1045] The server constantly monitors the amount of power generated by renewable energy devices (solar panels and wind turbines) and the power consumption of base stations, optimizing the use of renewable energy to maximize power generation efficiency.

[1046] Specific working example:

[1047] The server constantly monitors the amount of power generated by the solar panels and the power consumption of base station A, and adjusts the angle of the panels to maximize power generation efficiency.

[1048] Providing a user interface

[1049] The user can monitor the system status in real time and make necessary configuration changes using the provided user interface, which displays the system analysis results and current operating status.

[1050] Specific working example:

[1051] Users can check the power consumption and power generation status of base station A through the management panel, and if they want to manually fine-tune the settings, they can do so on the interface.

[1052] Examples of prompt statements

[1053] "Predict the optimal switching timing for base station power consumption patterns and renewable energy optimization using current weather data and past operational data."

[1054] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1055] Step 1: Data collection

[1056] Input: Operation information from base stations and weather data from weather data API

[1057] Output: Collected operational information and weather data

[1058] The server collects operational information from the base stations. This information includes power consumption, number of connected devices, data traffic volume, etc. The server periodically receives data from each base station or obtains data using an API. In addition, the server uses a weather data API to obtain real-time weather information (temperature, humidity, precipitation, wind speed, sunshine hours, etc.).

[1059] Specific working example:

[1060] The server automatically receives 24 hours' worth of power consumption data from base station A every day at midnight.

[1061] The server calls the API to obtain data on the number of connected terminals of base station B.

[1062] The server calls the weather data API at 6:00 AM to obtain the temperature and humidity data for the day in the base area.

[1063] Step 2: Preprocessing the data

[1064] Input: Collected operational information and weather data

[1065] Output: Preprocessed data (inserted data imputed and data type converted)

[1066] The server preprocesses the collected data, including imputing incomplete data and converting data types, such as by imputing missing values ​​and correcting outliers, to prepare the data in a format suitable for analysis.

[1067] Specific working example:

[1068] If there are missing values ​​in the power consumption data of base station A, the server complements them by referring to past data.

[1069] The server standardizes the units of the collected weather data and converts it into a standard format.

[1070] Step 3: Analyze the data

[1071] Input: Preprocessed data

[1072] Output: Optimal power consumption patterns and timing for switching between energy sources

[1073] The server analyzes the pre-processed data using machine learning algorithms to determine optimal power consumption patterns and timing for switching between energy sources based on historical data and current conditions.

[1074] Specific working example:

[1075] The server uses preprocessed data from base station A to predict historically most efficient power consumption patterns using machine learning algorithms.

[1076] Step 4: Generate control commands

[1077] Input: Analysis results (optimal power consumption pattern and timing for switching energy sources)

[1078] Output: Generated control commands

[1079] Based on the analysis results, the server generates power consumption patterns and control commands for switching energy sources for each base station.

[1080] Specific working example:

[1081] The server generates a control command to base station A to switch to solar power generation from 10:00 AM to 4:00 PM the next day.

[1082] Step 5: Execute control commands

[1083] Input: Generated control command

[1084] Power: Change the power supply of the base station

[1085] The base station executes the control command received from the server to change the power supply.

[1086] Specific working example:

[1087] Base station A switches to power supply from solar power generation in accordance with the control command received from the server.

[1088] Step 6: Renewable energy management

[1089] Input: Power generation amount of renewable energy device and power consumption status of base station

[1090] Output: Optimized power generation efficiency and power consumption

[1091] The server constantly monitors the amount of power generated by renewable energy devices (solar panels and wind turbines) and the power consumption of base stations, optimizing the use of renewable energy to maximize power generation efficiency.

[1092] Specific working example:

[1093] The server adjusts the angle of the solar panels to the optimal position to maximize power generation.

[1094] Step 7: Providing a User Interface

[1095] Input: System analysis results and current operating status

[1096] Output: System status and manual setting adjustments provided through the user interface

[1097] The user can monitor the system status in real time and make necessary configuration changes using the provided user interface, which displays the system analysis results and current operating status.

[1098] Specific working example:

[1099] Users can open the management panel to check base station A's power consumption data and renewable energy generation in real time, and if they want to manually fine-tune the settings, they can do so on the interface.

[1100] (Application example 1)

[1101] 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."

[1102] Conventional energy management systems for automated robots in factories face challenges in optimizing energy consumption and efficiently utilizing renewable energy. In particular, there is a need for technology that can collect robot operation information and analyze it in conjunction with weather data to determine optimal energy consumption patterns and the timing of switching between energy sources. Furthermore, there is a lack of a method for monitoring the amount of power generated and consumed by renewable energy devices in real time and optimizing efficient energy use. Additionally, a system that can be easily managed through a user interface is also needed.

[1103] 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.

[1104] In this invention, the server includes means for collecting operation information of automatic robots in a factory, means for acquiring weather data, means for analyzing the collected operation information and the acquired weather data and determining an optimal energy consumption pattern and timing for switching between energy sources, means for controlling the energy supply of the robots based on the determined energy consumption pattern and timing for switching between energy sources, means for monitoring the amount of power generated and consumed by a renewable energy device and optimizing efficient energy use, and means for providing the system status through a user interface and manually adjusting settings, thereby enabling the optimization of energy consumption of automatic robots in a factory and the efficient use of renewable energy.

[1105] An "automated factory robot" is a mechanical device that is used in the manufacturing and processing processes in a factory and can autonomously perform programmed actions.

[1106] "Operation information" is data related to the operation of the robot, and specifically includes the amount of power consumed, the operation time, and the type of task being executed.

[1107] "Weather data" refers to information about weather conditions obtained in real time, including temperature, humidity, and hours of sunshine.

[1108] "Energy consumption pattern" refers to data that indicates the state of energy consumption over a certain period of time.

[1109] "Energy source switching timing" refers to the optimal time to switch between renewable energy and conventional energy sources.

[1110] "Renewable energy devices" refer to devices that generate electricity using natural forces, such as solar power generation devices and wind power generation devices.

[1111] A "user interface" refers to the screens and input devices that allow a user to operate a system or monitor its status.

[1112] A "machine learning algorithm" is a calculation method that automatically derives optimal energy consumption patterns and timing for switching energy sources based on collected data.

[1113] This invention relates to an energy management system for automated robots in factories. This system collects and analyzes robot operation information and weather data to determine optimal energy consumption patterns and timing for switching between energy sources, thereby efficiently utilizing renewable energy.

[1114] Explanation of program processing

[1115] Data collection

[1116] The server collects operational information from the automated robots installed in the factory. This operational information includes power consumption, operating time, task type, etc. It also uses a weather data API to obtain real-time weather data, including temperature, humidity, and sunshine hours.

[1117] Data preprocessing and analysis

[1118] The server preprocesses the collected operational information and weather data, complements incomplete data, and converts data types. It then analyzes the preprocessed data and uses machine learning algorithms to make predictions to determine optimal energy consumption patterns and the timing for switching energy sources. The software used is Python and TensorFlow.

[1119] Control command generation and execution

[1120] Based on the analysis results, the server generates control commands for the robot to change its energy consumption pattern and energy source, and sends these control commands to the robot to change its energy supply.

[1121] Renewable energy equipment management

[1122] The server constantly monitors the amount of power generated and consumed by renewable energy devices (e.g., solar power generation devices, wind power generation devices) and optimizes their use to maximize power generation efficiency, thereby enabling efficient management of the robot's energy supply.

[1123] Providing a user interface

[1124] The user can monitor the system status in real time and make necessary configuration changes using the provided user interface, which displays the system analysis results and current operating status. The software used is Flask.

[1125] Specific examples

[1126] For example, you might use the following prompts to input to a generative AI model:

[1127] Prompt statement:

[1128] "Generate and send control commands to switch Robot A's power supply to solar power during sunny hours. Also provide optimal energy consumption patterns based on real-time weather data."

[1129] As described above, by building an energy management system for automated robots in factories, it is possible to optimize energy consumption and efficiently use renewable energy.

[1130] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1131] Step 1:

[1132] The server collects operational information from the automated robots installed in the factory. This operational information includes power consumption, operating time, task type, etc. The server periodically receives data from each robot and collects it via API and sensors.

[1133] Input: Operation information from the robot

[1134] Output: Collected operational information

[1135] Step 2:

[1136] The server uses a weather data API to obtain real-time weather data, including temperature, humidity, and sunshine hours. The server periodically sends requests to the API to obtain the latest weather information.

[1137] Input: API request

[1138] Output: Captured real-time weather data

[1139] Step 3:

[1140] The server preprocesses the collected operational information and acquired weather data. It performs data cleaning to fill in incomplete data and convert data types. Specifically, this includes filling in missing values, normalizing data, and correcting outliers.

[1141] Input: Collected operating information, acquired weather data

[1142] Output: Preprocessed data

[1143] Step 4:

[1144] The server analyzes the preprocessed data to determine optimal energy consumption patterns and timing for switching between energy sources. It uses machine learning algorithms to make predictions based on historical and current data. It runs the model using Python and TensorFlow.

[1145] Input: Preprocessed data

[1146] Output: Optimal energy consumption pattern, timing for switching energy sources

[1147] Step 5:

[1148] Based on the analysis results, the server generates control commands for the robot to determine its energy consumption pattern and switch its energy source. The server then sends the control commands to the robot to change its energy supply. The control commands specifically instruct the robot's behavior, including switching the power supply source.

[1149] Input: Optimal energy consumption pattern, timing to switch energy sources

[1150] Output: Control commands to the robot

[1151] Step 6:

[1152] The server constantly monitors the amount of power generated and consumed by renewable energy devices (solar power generation devices, wind power generation devices). Specifically, it periodically acquires the parameters of the power generation devices using a sensor system and sends the data to the monitoring system. The angle of the panels and turbines is automatically adjusted to maximize power generation efficiency.

[1153] Input: Sensor information of the power generation equipment

[1154] Output: Maximum efficiency power generation

[1155] Step 7:

[1156] The user uses the provided user interface to monitor the system status in real time and change the settings as needed. The interface displays the analysis results obtained from the system and the current operating status. The user can also manually adjust the settings. The interface is built using Flask.

[1157] Input: Analysis results and operational status from the system

[1158] Output: Operational status reflecting the user's configuration changes

[1159] 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.

[1160] This invention combines an emotion engine with a system for optimizing base station energy conservation and renewable energy utilization. The system collects base station operation information, acquires weather data, analyzes the data, and determines optimal power consumption patterns and energy source switching timing. It also controls the base station's power supply and monitors the power generation and consumption of renewable energy devices to optimize efficient energy utilization. Furthermore, the system status is displayed through a user interface, allowing for manual adjustment of settings. An emotion engine that recognizes user emotions has also been added to improve the user experience.

[1161] Explanation of program processing

[1162] Data collection

[1163] The server collects operational information from base stations. Specifically, it obtains information such as power consumption, number of connected devices, and data traffic volume from each base station. This data is collected periodically using an API. The server also uses a weather data API to obtain real-time weather information (temperature, humidity, precipitation, wind speed, sunshine hours, etc.).

[1164] Examples:

[1165] The server receives information on power consumption and the number of connected devices from base station A, and also obtains the current weather conditions through a weather data API.

[1166] Data preprocessing and analysis

[1167] The server preprocesses the collected data, complements incomplete data, and converts and normalizes data types as needed. The preprocessed data is then analyzed to determine optimal power consumption patterns and the timing of switching energy sources. Machine learning algorithms are used in the analysis to make predictions based on historical data and current conditions.

[1168] Examples:

[1169] The server fills in missing data from base station A and weather data, normalizes the data, and then uses machine learning algorithms to analyze optimal power consumption patterns and determine when to switch between solar power generation and the power grid.

[1170] Control command generation and execution

[1171] Based on the analysis results, the server generates control commands for each base station to change its power consumption pattern and energy source, and sends these control commands to each base station to change its power supply.

[1172] Examples:

[1173] Based on the analysis results, the server generates a control command including an instruction for base station A to switch from solar power generation to the power grid, and sends it to base station A. Base station A receives this control command and switches the power supply source.

[1174] Renewable Energy Management

[1175] The server constantly monitors the power output of renewable energy devices (solar panels and wind turbines) and the power consumption of base stations. To ensure efficient power generation and consumption, it optimizes the use of renewable energy to maximize power generation efficiency.

[1176] Examples:

[1177] The server constantly monitors the amount of power generated by the solar panels and the power consumption of base station A, and adjusts the angle of the panels to maximize power generation efficiency.

[1178] Providing a user interface

[1179] The user can monitor the system status in real time and make necessary configuration changes using the provided user interface, which displays the analysis results and current operating status of the system.

[1180] Examples:

[1181] Users can check the power consumption and power generation status of base station A through the management panel, and if they want to manually fine-tune the settings, they can do so on the interface.

[1182] Combining Emotion Engines

[1183] The server is equipped with an emotion engine that recognizes the user's emotions by analyzing the user's voice, facial expressions, text input, etc., and automatically adjusts the system settings and interface based on the analysis results.

[1184] Examples:

[1185] The server uses an emotion engine to analyze the voice tone and facial expressions of the user when operating the interface, and if it determines that the user is under high stress, it activates an assist function to make operating the interface easier.

[1186] This allows the system to maximize energy efficiency while providing a user-friendly operating environment.

[1187] The processing flow will be explained below.

[1188] Step 1: Data collection

[1189] The server collects operational information from base stations. Specifically, it obtains information from each base station, such as power consumption, number of connected devices, and data traffic volume. This data is collected periodically using an API.

[1190] Examples:

[1191] The server obtains the power consumption and number of connected terminals of base station A through an API request.

[1192] Step 2: Obtaining weather data

[1193] The server obtains weather data in real time via an API, specifically information such as temperature, humidity, precipitation, wind speed, and sunshine hours.

[1194] Examples:

[1195] The server uses the weather data API to obtain weather data around base station A.

[1196] Step 3: Preprocessing the data

[1197] The server preprocesses the collected operational information and weather data, complementing incomplete data and converting data types as necessary to normalize it.

[1198] Examples:

[1199] If there is a gap in the power consumption data of base station A, the server will supplement it based on past data.

[1200] Step 4: Analysis

[1201] The server analyzes the preprocessed data using machine learning algorithms. Specifically, it determines the optimal power consumption pattern and timing for switching energy sources based on the collected data.

[1202] Examples:

[1203] The server inputs the data into a machine learning model to infer when to switch from solar power generation to the power grid.

[1204] Step 5: Generate control commands

[1205] The server generates a power consumption pattern and a control command for switching the energy source for each base station based on the analysis result.

[1206] Examples:

[1207] The server generates a control command for base station A, which includes an instruction to switch from solar power generation to the power grid.

[1208] Step 6: Send and execute commands

[1209] The server transmits the generated control command to each base station to change the power supply.

[1210] Examples:

[1211] The server sends a control command to base station A, and base station A switches the power supply source from solar power generation to the power grid based on this command.

[1212] Step 7: Renewable energy monitoring

[1213] The server constantly monitors the power generation and consumption of renewable energy devices (solar panels and wind turbines).

[1214] Examples:

[1215] The server periodically retrieves power generation data from the solar panels and compares it with consumption patterns.

[1216] Step 8: Optimize energy use

[1217] The server optimizes the settings to maximize the efficiency of renewable energy use, adjusting the angle of the panels and changing the proportion of wind power used based on weather and power generation data.

[1218] Examples:

[1219] The server provides instructions to adjust the solar panels to the optimal angle for sunny days.

[1220] Step 9: Update the User Interface

[1221] The server updates the user interface with the analysis results and current system status accordingly.

[1222] Examples:

[1223] The server reflects the analysis results on the user interface, allowing users to view the information in real time.

[1224] Step 10: Accept manual adjustments

[1225] Users can manually adjust system settings using the provided user interface, which allows for immediate effect of setting changes under specific conditions.

[1226] Examples:

[1227] Users operate an administration panel and manually optimize the settings for a particular base station.

[1228] Step 11: Collect sentiment data

[1229] The server collects the user's emotional data, which can be obtained from the user's voice, facial expressions, text input, etc.

[1230] Examples:

[1231] The server collects voice tones and facial expressions via a camera and microphone when a user operates the management panel.

[1232] Step 12: Analyze the sentiment data

[1233] The server analyzes the collected emotion data and uses an emotion engine to determine the user's stress level and satisfaction.

[1234] Examples:

[1235] The server analyzes the user's voice data and determines that the user is feeling stressed.

[1236] Step 13: Emotion-Based System Adjustment

[1237] The server automatically adjusts the system settings based on the analysis of the emotional data, instantly changing the settings to reflect the user's emotional state.

[1238] Examples:

[1239] If the server determines that the user is feeling stressed, it activates an assist function that simplifies operation of the interface.

[1240] Through the above process, the present invention efficiently manages base stations and renewable energy, and further provides an interface that takes user emotions into consideration, thereby improving energy efficiency and maximizing user experience.

[1241] Example 2

[1242] 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."

[1243] Conventional base station energy management systems have room for improvement in optimizing power consumption and efficiently utilizing renewable energy. Furthermore, they lack effective methods for improving user experience. Therefore, there is a need to provide a more efficient and user-friendly means for base station energy management.

[1244] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1245] In this invention, the server includes means for collecting operation information of the base station, means for acquiring weather data, means for preprocessing and analyzing the collected operation information and the acquired weather data to determine an optimal power consumption pattern and timing for switching between energy sources, means for controlling the power supply of the base station based on the determined power consumption pattern and timing for switching between energy sources, means for monitoring the amount of power generated by the renewable energy device and the amount of power consumed by the base station in real time to optimize efficient energy use, means for providing the system status through a user interface and manually adjusting settings, and means for recognizing user emotions using an emotion engine and automatically adjusting the system status and interface, thereby enabling high energy efficiency and improved user satisfaction.

[1246] A "base station" is a fixed facility for conducting wireless communications that forms part of a communications network.

[1247] "Operation information" is data indicating the operational status of the base station, the amount of power consumed there, the number of connected terminals, the amount of data traffic, and so on.

[1248] "Weather data" refers to data that includes weather-related information such as temperature, humidity, precipitation, wind speed, and sunshine hours.

[1249] "Preprocessing" refers to a series of operations such as data interpolation, filtering, conversion, and normalization that are performed to prepare the collected raw data in an analyzable form.

[1250] "Analysis" is the act of using preprocessed data to extract and interpret information suitable for a specific purpose.

[1251] A "power consumption pattern" is a model that shows the tendency of how power is used and consumed under specific times and conditions.

[1252] "Energy source switching timing" refers to determining the timing of switching between different energy sources (e.g., renewable energy and the power grid) to make the most effective use of them.

[1253] A "renewable energy device" is a device that generates electricity using natural energy, such as solar panels or wind turbines.

[1254] "User interface" means the visual or operational means by which a user interacts with a system and inputs and obtains information.

[1255] The "emotion engine" is a function that analyzes the user's voice, facial expressions, text input, etc. to recognize emotions, and automatically adjusts the system settings and interface based on the results.

[1256] MODE FOR CARRYING OUT THE INVENTION

[1257] The present invention combines an emotion engine with a system for optimizing energy conservation and renewable energy utilization in base stations. The system mainly consists of the following components: a base station, a server, a renewable energy device, a user interface, and an emotion engine.

[1258] Hardware and Software Configuration

[1259] 1. Base Station: A fixed communication tower and its associated equipment that forms part of a communication network. Each base station is equipped with sensors to measure power consumption, the number of connected devices, and data traffic volume.

[1260] 2. Server: This is the central computer system that collects and analyzes data. The server has the ability to retrieve data from base stations and meteorological data services through APIs. The main software used includes a database management system, scripts for data preprocessing, and machine learning algorithms.

[1261] 3. Renewable energy devices: These are devices that generate electricity using natural energy sources such as solar panels and wind turbines.

[1262] 4. User interface: An interface that provides users with the operational status of the system in the form of an administration panel or dashboard and allows them to change settings.

[1263] 5. Emotion Engine: A software component that analyzes the user's voice, facial expressions, text input, etc. to recognize the user's emotional state, allowing the system to adaptively adjust its settings and interface.

[1264] Data collection and preprocessing

[1265] The server collects operational information from each base station, such as power consumption, number of connected devices, and data traffic volume. The server also uses a weather data API to obtain real-time weather data such as temperature, humidity, precipitation, wind speed, and sunshine hours.

[1266] Examples:

[1267] The server obtains information on power consumption and the number of connected devices from base station A via an API every five minutes.

[1268] The server uses the weather service API to obtain weather information every hour of the day.

[1269] Data preprocessing and analysis

[1270] The server preprocesses the collected data, including filling in missing values, filtering outliers, converting data types, and normalizing the data. It then performs analysis based on the preprocessed data to determine optimal power consumption patterns and the timing for switching between energy sources. Machine learning algorithms (e.g., regression analysis and time-series forecasting models) are used for the analysis.

[1271] Examples:

[1272] The server trains a machine learning model based on data from the past year and uses current data as input to predict optimal power consumption patterns.

[1273] The server takes into account the weather forecast and plans how to utilize renewable energy for the next day.

[1274] Control command generation and execution

[1275] Based on the analysis results, the server generates control commands for each base station, including details of power consumption patterns and timing for switching between energy sources. The base station then switches between power suppliers accordingly.

[1276] Examples:

[1277] The server generates a control command for base station A to prioritize power supply from the power grid during the night and solar power generation during the day.

[1278] Renewable Energy Management

[1279] The server monitors the power generation of renewable energy devices (solar panels, wind turbines, etc.) and the power consumption of base stations in real time, and adjusts consumption patterns accordingly to achieve optimal energy utilization.

[1280] Examples:

[1281] The server constantly monitors the amount of electricity generated by the solar panels and switches the power supply to renewable energy when it is more efficient.

[1282] User interface and emotion engine

[1283] The user can monitor the system status in real time through the provided user interface and make necessary setting changes. Furthermore, the server can recognize the user's emotions using an emotion engine and automatically adjust the interface and system settings based on the user's stress level and emotional state.

[1284] Examples:

[1285] Users can use the management panel to check the power consumption and power generation status of base station A in real time and fine-tune the settings as needed.

[1286] The server analyzes the user's voice tone and facial expressions using an emotion engine, and if it determines that the user is under high stress, it activates an assist function that simplifies operation.

[1287] Prompt Sentence Examples

[1288] Below are some examples of prompts that can be input to a generative AI model using this system:

[1289] "Please explain the program flow in which the server uses data collected from base station A to analyze the optimal power consumption pattern and determine the timing to switch from solar power generation to the power grid. Also, please provide a concrete example of monitoring the system status through the user interface and manually changing the settings."

[1290] In this way, the present invention can maximize the energy efficiency of the base station and provide a user-friendly operating environment.

[1291] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1292] Step 1:

[1293] The server collects operational information from each base station. This operational information includes power consumption, number of connected devices, data traffic volume, etc. The operational information received as input is obtained via API. The server also obtains temperature, humidity, precipitation, wind speed, and sunshine hours in real time from the weather data API. This information is stored in a database so that it can be used in subsequent analysis steps.

[1294] Specific behavior:

[1295] The server collects information on power consumption, number of connected terminals, and data traffic volume from base station A every five minutes.

[1296] The server retrieves weather information every hour through the weather data API.

[1297] Step 2:

[1298] The server preprocesses the collected operational information and weather data. Since the input data may contain missing or outliers, it complements the incomplete data and filters out the outliers. It also performs data type conversion and normalization to prepare the data in a format suitable for analysis. The preprocessed data is used in the next analysis step.

[1299] Specific behavior:

[1300] If there is a missing value in the data of base station A, the server complements the value based on the data before and after it.

[1301] The server filters out data points that record abnormally high power consumption.

[1302] Step 3:

[1303] The server analyzes the preprocessed data. It takes the preprocessed data as input and uses machine learning algorithms to predict optimal power consumption patterns and timing for switching between energy sources. This analysis uses regression analysis and time series prediction models. The prediction results are used in the control command generation step.

[1304] Specific behavior:

[1305] The server trains the machine learning model using operational information and weather data from the past year.

[1306] The server inputs the current data and predicts the best time to utilize solar power generation during the day the next day.

[1307] Step 4:

[1308] The server generates control commands for the base stations based on the analysis results. Using the results of the analysis step as input, the server creates control commands that include the power consumption patterns and energy source switching instructions for each base station. These commands are sent to each base station and executed.

[1309] Specific behavior:

[1310] The server generates a control command for base station A to prioritize power supply from the power grid during the night and solar power generation during the day.

[1311] The server generates commands including timing to utilize the power generation of the wind turbines when wind speeds are high.

[1312] Step 5:

[1313] The terminal (base station) executes the control command received from the server. It receives the control command from the server as input, switches the power supply source based on the command, and feeds back the execution result to the server.

[1314] Specific behavior:

[1315] Base station A switches to power supply from the power grid at night.

[1316] Base station A switches its power supply source to solar power when solar power is available during the day.

[1317] Step 6:

[1318] The server monitors the power generation of renewable energy devices and the power consumption of base stations in real time. It receives power generation data from renewable energy devices and consumption data from base stations as input, analyzes these data to ensure optimal energy usage, and makes necessary adjustments based on the results.

[1319] Specific behavior:

[1320] The server monitors the amount of electricity generated by the solar panels in real time and issues instructions to switch the power supply to renewable energy when it is more efficient.

[1321] Step 7:

[1322] The user monitors the system status in real time using the provided user interface and makes necessary configuration changes. The system receives operational information as input and adjusts the configuration based on that information.

[1323] Specific behavior:

[1324] The user can access the management panel using a tablet device and check the power consumption and power generation status of base station A.

[1325] Users can manually change settings to reduce power consumption during specific time periods.

[1326] Step 8:

[1327] The server uses an emotion engine to recognize the user's emotions, detecting and analyzing inputs such as the user's voice, facial expressions, and text input, and adjusting the system settings and interface based on the results.

[1328] Specific behavior:

[1329] The server analyzes the user's tone of voice and facial expressions, and if it determines that the user is under high stress, it activates an assist function to simplify operation.

[1330] (Application example 2)

[1331] 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."

[1332] While battery management and energy usage optimization are essential for current autonomous vehicles, there is a lack of efficient and real-time methods to do so. Furthermore, there is a lack of technology to adjust the driving environment and infotainment system according to the emotional state of the driver and passengers, creating a need for technologies to enhance user comfort.

[1333] 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.

[1334] In this invention, the server includes means for collecting operation information of the base stations, means for acquiring weather data, means for analyzing the collected operation information and the acquired weather data and determining an optimal power consumption pattern and timing for switching between energy sources, means for controlling the power supply of the base stations based on the determined power consumption pattern and timing for switching between energy sources, means for monitoring the amount of power generated and consumed by the renewable energy device and optimizing efficient energy use, means for providing the system status through a user interface and manually adjusting settings, and means for recognizing the emotions of the driver or passengers and adjusting the driving environment and infotainment system. This makes it possible to provide a comfortable driving environment that corresponds to the emotional state of the user while optimizing the energy management of the autonomous vehicle.

[1335] The "means for collecting base station operation information" refers to a means for acquiring information such as power consumption, the number of connected terminals, and data traffic volume from the base station.

[1336] "Means for acquiring weather data" refers to means for acquiring weather information such as temperature, humidity, precipitation, wind speed, and sunshine hours in real time.

[1337] The "means for analyzing the collected operation information and the acquired weather data and determining the optimal power consumption pattern and timing for switching energy sources" refers to a means for determining the optimal timing for power consumption and energy source usage based on the collected data using machine learning algorithms, etc.

[1338] The "means for controlling the power supply of the base station based on the determined power consumption pattern and energy source switching timing" refers to means for changing the power supply or switching the energy source based on the analysis results.

[1339] "Means for monitoring the amount of power generated and consumed by renewable energy devices and optimizing efficient energy use" refers to means for monitoring the amount of power generated and the usage status of renewable energy devices such as solar power generation devices and wind power generation devices, and for optimally managing energy use.

[1340] "Means for providing system status through a user interface and for manually adjusting settings" refers to means for providing a user with an interface for displaying the operating status of the system and manually changing settings as necessary.

[1341] "Means for recognizing the emotions of the driver or passenger and adjusting the driving environment or infotainment system" refers to means for analyzing the emotions of the driver or passenger from voice, facial expressions, text input, etc., and adjusting the driving environment or in-car entertainment system based on the results.

[1342] This invention provides an energy management system for autonomous vehicles that optimizes battery management and renewable energy utilization. The system collects vehicle operation information and weather data, and analyzes this data to determine optimal energy consumption patterns and timing for switching energy sources. It also includes a function that recognizes the emotions of passengers and drivers and adjusts the driving environment and infotainment system accordingly.

[1343] The server first collects information from the autonomous vehicle, including battery information, speed, mileage, interior and exterior temperatures, and the location of charging stations. It also obtains real-time weather information using a weather data API. The collected data is preprocessed to complete missing data and normalize it.

[1344] The server then uses machine learning algorithms to determine optimal power consumption patterns and timing for switching between energy sources, taking into account historical data and current conditions. Based on the analysis, the server generates control commands for the energy management system for battery management and energy source switching, which are then sent to the vehicle.

[1345] The server constantly monitors the amount of power generated and consumed by renewable energy devices, such as solar power generation devices installed in vehicles, and controls them to maximize the efficiency of energy use.

[1346] Users can monitor the system status and manually adjust settings as needed through a user interface that displays analysis results and current operating status of the system.

[1347] Additionally, the server is equipped with an emotion engine that analyzes the driver's or passenger's emotions by analyzing voice, facial expressions, text input, etc., and adjusts the driving environment and infotainment system accordingly.

[1348] For example, if a user feels "stressed," the server's emotion engine will analyze that emotion and enable the infotainment system to operate more easily while also playing appropriate music. This system will enable the creation of a comfortable driving environment while optimizing the energy management of autonomous vehicles.

[1349] Example prompts for generative AI models

[1350] "Collect battery information and speed data from vehicle ID: example_vehicle_id, and obtain weather information through API. Then, preprocess and normalize the acquired data to generate optimal battery management and energy switching control commands. Also, implement a system that simplifies system operation and plays relaxing music based on the user's input that "I'm stressed."

[1351] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1352] Step 1:

[1353] The server collects information about the autonomous vehicle's battery, speed, mileage, interior and exterior temperatures, and the location of charging stations. This data is obtained through the autonomous vehicle's API. It receives various sensor information from the vehicle as input data and stores it in an internal database.

[1354] The result is a data set that represents the current operating state of the vehicle.

[1355] Step 2:

[1356] The server uses a weather data API to obtain real-time weather information, taking weather data such as temperature, humidity, precipitation, wind speed, and sunshine hours as input, and stores it in an internal database.

[1357] The result is a real-time weather dataset representing weather conditions.

[1358] Step 3:

[1359] The server preprocesses the collected operational information and weather data. Specifically, it complements missing data, corrects outliers, normalizes data, etc. It receives operational information and weather data as input data and outputs a clean, preprocessed data set.

[1360] This will provide a dataset suitable for analysis.

[1361] Step 4:

[1362] The server uses machine learning algorithms to analyze the preprocessed data. Specifically, it utilizes predictive models to determine optimal power consumption patterns and timing for switching energy sources. It receives preprocessed operational information and weather data as input, analyzes them, and generates optimal control commands.

[1363] As a result, optimal timing for power management and energy source switching is output.

[1364] Step 5:

[1365] The server generates control commands for each autonomous vehicle based on the analysis results. Specifically, it generates commands to instruct battery management and the timing of renewable energy usage. It receives the analysis results of the machine learning algorithm as input data and outputs them as control commands.

[1366] As a result, specific control actions are sent to the vehicle as commands.

[1367] Step 6:

[1368] The server constantly monitors the amount of power generated and consumed by renewable energy devices (e.g., solar power generation devices). As input data, it receives real-time data on power generation and consumption from the renewable energy devices and makes adjustments based on that data to maximize power generation efficiency.

[1369] This allows for efficient energy utilization.

[1370] Step 7:

[1371] The server provides the current status of the system through a user interface. As input data, it receives overall operational data of the system (operation information, weather information, energy consumption patterns, etc.) and outputs it as content to be displayed on the user interface.

[1372] As a result, users can monitor the current status of their system in real time and manually adjust settings as needed.

[1373] Step 8:

[1374] The server uses an emotion engine to analyze the driver's or passenger's emotions. It receives input data such as voice, facial expressions, and text input, and the emotion engine analyzes them to output an emotional state. Based on the analysis results, it automatically adjusts the driving environment and infotainment system.

[1375] This provides a comfortable driving environment that is tailored to the user's emotional state.

[1376] 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.

[1377] 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.

[1378] 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.

[1379] [Fourth embodiment]

[1380] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1381] 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.

[1382] 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).

[1383] 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.

[1384] 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.

[1385] 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).

[1386] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for 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.

[1387] 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.

[1388] 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.

[1389] 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.

[1390] 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.

[1391] 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.

[1392] 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."

[1393] The present invention relates to a system for optimizing energy conservation and renewable energy utilization in base stations. The system collects base station operation information, acquires weather data, analyzes the data, and determines optimal power consumption patterns and energy source switching timing. It also controls the base station's power supply and monitors the power generation and consumption of renewable energy devices to optimize efficient energy utilization. Furthermore, the system status is provided through a user interface, and settings can be manually adjusted.

[1394] Explanation of program processing

[1395] Data collection

[1396] The server collects base station operation information (power consumption, number of connected devices, data traffic volume, etc.). This includes periodic data transmission from each base station and data acquisition via API. The server also uses weather data API to acquire real-time weather information (temperature, humidity, precipitation, wind speed, sunshine hours, etc.).

[1397] Examples:

[1398] The server receives information on power consumption and the number of connected devices from base station A, and also obtains the current weather conditions through a weather data API.

[1399] Data preprocessing and analysis

[1400] The server preprocesses the collected data, fills in incomplete data, and converts data types. It then analyzes the preprocessed data to determine optimal power consumption patterns and the timing for switching energy sources. Machine learning algorithms are used for the analysis, and predictions are made based on historical data and current conditions.

[1401] Examples:

[1402] The server fills in missing data from base station A and weather data, normalizes the data, and then uses machine learning algorithms to analyze optimal power consumption patterns and determine when to switch between solar power generation and the power grid.

[1403] Control command generation and execution

[1404] Based on the analysis results, the server generates control commands for each base station to change its power consumption pattern and energy source, and sends these control commands to each base station to change its power supply.

[1405] Examples:

[1406] Based on the analysis results, the server generates a control command to switch power supply to solar power generation for base station A and sends it to base station A. Base station A receives this control command and switches the power supply source.

[1407] Renewable Energy Management

[1408] The server constantly monitors the power output of renewable energy devices (solar panels and wind turbines) and the power consumption of base stations. To ensure efficient power generation and consumption, it optimizes the use of renewable energy to maximize power generation efficiency.

[1409] Examples:

[1410] The server constantly monitors the amount of power generated by the solar panels and the power consumption of base station A, and adjusts the angle of the panels to maximize power generation efficiency.

[1411] Providing a user interface

[1412] The user can monitor the system status in real time and make necessary configuration changes using the provided user interface, which displays the analysis results and current operating status of the system.

[1413] Examples:

[1414] Users can check the power consumption and power generation status of base station A through the management panel, and if they want to manually fine-tune the settings, they can do so on the interface.

[1415] The processing flow will be explained below.

[1416] Step 1: Data collection

[1417] The server collects operational information from base stations. Specifically, it obtains information from each base station, such as power consumption, number of connected devices, and data traffic volume. This data is collected periodically using an API.

[1418] Examples:

[1419] The server obtains the power consumption of base station A through an API request.

[1420] Step 2: Obtaining weather data

[1421] The server obtains weather data in real time via an API, specifically information such as temperature, humidity, precipitation, wind speed, and sunshine hours.

[1422] Examples:

[1423] The server uses the weather data API to obtain weather data around base station A.

[1424] Step 3: Preprocessing the data

[1425] The server preprocesses the collected operational information and weather data, complementing incomplete data and converting and normalizing data types as necessary.

[1426] Examples:

[1427] If the power consumption data of base station A is missing, the server complements it based on past data.

[1428] Step 4: Analysis

[1429] The server then analyzes the preprocessed data using machine learning algorithms. Specifically, it determines the optimal power consumption pattern and timing for switching energy sources based on the collected data.

[1430] Examples:

[1431] The server provides input data to a machine learning model to infer when to switch from solar power generation to the power grid.

[1432] Step 5: Generate control commands

[1433] Based on the analysis results, the server generates power consumption patterns and control commands for switching energy sources for each base station.

[1434] Examples:

[1435] The server generates a control command including an instruction for base station A to switch from solar power generation to the power grid.

[1436] Step 6: Send and execute commands

[1437] The server transmits the generated control commands to each base station, which then executes the power supply changes accordingly.

[1438] Examples:

[1439] The server sends a control command to base station A, and based on this command, base station A switches its power source from solar power generation to the power grid.

[1440] Step 7: Renewable energy monitoring

[1441] The server constantly monitors the power generation and consumption of renewable energy devices (solar panels and wind turbines).

[1442] Examples:

[1443] The server periodically retrieves power generation data from the solar panels and compares it with consumption patterns.

[1444] Step 8: Optimize energy use

[1445] The server optimizes the settings to maximize the efficiency of renewable energy use, adjusting the angle of the panels and changing the proportion of wind power used based on weather and power generation data.

[1446] Examples:

[1447] The server sends adjustment instructions to optimize the angle of the solar panels on sunny days.

[1448] Step 9: Update the User Interface

[1449] The server updates the user interface with the analysis results and current system status accordingly.

[1450] Examples:

[1451] The server reflects the analysis results on the user interface, allowing users to view the information in real time.

[1452] Step 10: Accept manual adjustments

[1453] Users can manually adjust system settings using the provided user interface, which allows for immediate effect of setting changes under specific conditions.

[1454] Examples:

[1455] Users operate an administration panel and manually optimize the settings for a particular base station.

[1456] Example 1

[1457] 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."

[1458] In modern society, base stations consume a huge amount of power, making its optimization a critical issue. While utilizing renewable energy sources in particular contributes to the preservation of the global environment, their efficient use is difficult. Furthermore, due to fluctuations in weather conditions and daily changes in base station loads, it is necessary to optimize real-time power consumption patterns and the timing of switching between energy sources. Furthermore, systems must be able to provide users with an easy-to-understand overview of the system's status, with the ability to manually adjust it as needed. Because conventional methods are unable to adequately resolve these issues, new technologies are needed.

[1459] 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.

[1460] In this invention, the server includes means for collecting operation information of the base station, means for acquiring weather data, means for preprocessing the collected operation information and the acquired weather data to convert data types and complement incomplete data, means for analyzing the preprocessed data using a machine learning algorithm and determining an optimal power consumption pattern and timing for switching between energy sources, means for controlling the power supply of the base station based on the determined power consumption pattern and timing for switching between energy sources, means for constantly monitoring the amount of power generated and consumed by the renewable energy device and optimizing efficient energy use, and means for providing the system status through a user interface and manually adjusting settings. This makes it possible to optimize the power consumption pattern and energy source switching of the base station and achieve efficient energy use in real time.

[1461] A "base station" is a relay device for wireless communication, and is responsible for managing and controlling terminals and communications within a specific area.

[1462] "Operation information" refers to data relating to the operational status of a base station, and includes information such as power consumption, the number of connected terminals, and data traffic volume.

[1463] "Weather data" refers to information about the weather, including temperature, humidity, precipitation, wind speed, and sunshine hours.

[1464] "Preprocessing" refers to the process of completing incomplete data or converting data types before analyzing collected data.

[1465] A "machine learning algorithm" refers to a computational method that uses large amounts of data to learn patterns and rules and make predictions and classifications.

[1466] "Power consumption pattern" refers to the trend or tendency of the power consumed by a base station over a certain period of time.

[1467] "Energy source switching timing" refers to the optimal point in time to switch the power source to a different energy source, such as renewable energy or the commercial power grid.

[1468] "Control" refers to the process of giving instructions or commands to regulate and manage the behavior of a system.

[1469] A "renewable energy device" is a device that generates electricity using natural energy, including solar panels and wind turbines.

[1470] "Yield" refers to the amount of electricity generated by a renewable energy device.

[1471] "Consumption" refers to the amount of power used by the base station.

[1472] "User interface" refers to the mechanism that provides the means and environment for a user to interact with a system.

[1473] "Data analysis" refers to a series of processes for deriving new insights and patterns based on collected data.

[1474] The present invention relates to a system for optimizing energy conservation and renewable energy utilization in base stations. This system uses a server to collect base station operation information and weather data, analyzes this data, and determines optimal power consumption patterns and energy source switching timing. The server also controls the base station's power supply and monitors the power generation and consumption of renewable energy devices, optimizing efficient energy utilization. Furthermore, the system status is provided through a user interface, allowing users to manually adjust settings.

[1475] Hardware and software used

[1476] Hardware

[1477] Server: Data collection, pre-processing, analysis, and control (e.g., Dell PowerEdge R640)

[1478] Base station equipment: Provides operational status data

[1479] Renewable energy devices: solar panels, wind turbines, etc.

[1480] software

[1481] Data collection: Using APIs to obtain weather data and operational information (e.g., weather data API)

[1482] Data analysis: using machine learning algorithms (e.g., TensorFlow, scikit-learn)

[1483] User Interface: HTML5, CSS, JavaScript

[1484] Data collection

[1485] The server collects operational information from base stations, including power consumption, number of connected devices, and data traffic volume. This includes periodic data transmission from each base station and data acquisition via API. The server also obtains real-time weather information using a weather data API. This allows for optimal operation taking into account power consumption patterns and weather conditions.

[1486] Specific working example:

[1487] The server receives information on power consumption and the number of connected devices from base station A, and also obtains the current weather conditions through a weather data API.

[1488] Data preprocessing and analysis

[1489] The collected data is preprocessed, completing incomplete data and converting data types to make it suitable for analysis. The server analyzes the preprocessed data to determine optimal power consumption patterns and timing for switching energy sources. This analysis uses machine learning algorithms to make predictions based on historical data and current conditions.

[1490] Specific working example:

[1491] The server fills in missing data from base station A and weather data, normalizes the data, and then uses machine learning algorithms to analyze optimal power consumption patterns and determine when to switch between solar power generation and the power grid.

[1492] Control command generation and execution

[1493] Based on the analysis results, the server generates control commands for each base station to change the power consumption pattern and energy source, and sends the generated control commands to each base station to change the power supply.

[1494] Specific working example:

[1495] Based on the analysis results, the server generates a control command to switch power supply to solar power generation for base station A and sends it to base station A. Base station A receives this control command and switches the power supply source.

[1496] Renewable Energy Management

[1497] The server constantly monitors the amount of power generated by renewable energy devices (solar panels and wind turbines) and the power consumption of base stations, optimizing the use of renewable energy to maximize power generation efficiency.

[1498] Specific working example:

[1499] The server constantly monitors the amount of power generated by the solar panels and the power consumption of base station A, and adjusts the angle of the panels to maximize power generation efficiency.

[1500] Providing a user interface

[1501] The user can monitor the system status in real time and make necessary configuration changes using the provided user interface, which displays the system analysis results and current operating status.

[1502] Specific working example:

[1503] Users can check the power consumption and power generation status of base station A through the management panel, and if they want to manually fine-tune the settings, they can do so on the interface.

[1504] Examples of prompt statements

[1505] "Predict the optimal switching timing for base station power consumption patterns and renewable energy optimization using current weather data and past operational data."

[1506] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1507] Step 1: Data collection

[1508] Input: Operation information from base stations and weather data from weather data API

[1509] Output: Collected operational information and weather data

[1510] The server collects operational information from the base stations. This information includes power consumption, number of connected devices, data traffic volume, etc. The server periodically receives data from each base station or obtains data using an API. In addition, the server uses a weather data API to obtain real-time weather information (temperature, humidity, precipitation, wind speed, sunshine hours, etc.).

[1511] Specific working example:

[1512] The server automatically receives 24 hours' worth of power consumption data from base station A every day at midnight.

[1513] The server calls the API to obtain data on the number of connected terminals of base station B.

[1514] The server calls the weather data API at 6:00 AM to obtain the temperature and humidity data for the day in the base area.

[1515] Step 2: Preprocessing the data

[1516] Input: Collected operational information and weather data

[1517] Output: Preprocessed data (inserted data imputed and data type converted)

[1518] The server preprocesses the collected data, including imputing incomplete data and converting data types, such as by imputing missing values ​​and correcting outliers, to prepare the data in a format suitable for analysis.

[1519] Specific working example:

[1520] If there are missing values ​​in the power consumption data of base station A, the server complements them by referring to past data.

[1521] The server standardizes the units of the collected weather data and converts it into a standard format.

[1522] Step 3: Analyze the data

[1523] Input: Preprocessed data

[1524] Output: Optimal power consumption patterns and timing for switching between energy sources

[1525] The server analyzes the pre-processed data using machine learning algorithms to determine optimal power consumption patterns and timing for switching between energy sources based on historical data and current conditions.

[1526] Specific working example:

[1527] The server uses preprocessed data from base station A to predict historically most efficient power consumption patterns using machine learning algorithms.

[1528] Step 4: Generate control commands

[1529] Input: Analysis results (optimal power consumption pattern and timing for switching energy sources)

[1530] Output: Generated control commands

[1531] Based on the analysis results, the server generates power consumption patterns and control commands for switching energy sources for each base station.

[1532] Specific working example:

[1533] The server generates a control command to base station A to switch to solar power generation from 10:00 AM to 4:00 PM the next day.

[1534] Step 5: Execute control commands

[1535] Input: Generated control command

[1536] Power: Change the power supply of the base station

[1537] The base station executes the control command received from the server to change the power supply.

[1538] Specific working example:

[1539] Base station A switches to power supply from solar power generation in accordance with the control command received from the server.

[1540] Step 6: Renewable energy management

[1541] Input: Power generation amount of renewable energy device and power consumption status of base station

[1542] Output: Optimized power generation efficiency and power consumption

[1543] The server constantly monitors the amount of power generated by renewable energy devices (solar panels and wind turbines) and the power consumption of base stations, optimizing the use of renewable energy to maximize power generation efficiency.

[1544] Specific working example:

[1545] The server adjusts the angle of the solar panels to the optimal position to maximize power generation.

[1546] Step 7: Providing a User Interface

[1547] Input: System analysis results and current operating status

[1548] Output: System status and manual setting adjustments provided through the user interface

[1549] The user can monitor the system status in real time and make necessary configuration changes using the provided user interface, which displays the system analysis results and current operating status.

[1550] Specific working example:

[1551] Users can open the management panel to check base station A's power consumption data and renewable energy generation in real time, and if they want to manually fine-tune the settings, they can do so on the interface.

[1552] (Application example 1)

[1553] 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."

[1554] Conventional energy management systems for automated robots in factories face challenges in optimizing energy consumption and efficiently utilizing renewable energy. In particular, there is a need for technology that can collect robot operation information and analyze it in conjunction with weather data to determine optimal energy consumption patterns and the timing of switching between energy sources. Furthermore, there is a lack of a method for monitoring the amount of power generated and consumed by renewable energy devices in real time and optimizing efficient energy use. Additionally, a system that can be easily managed through a user interface is also needed.

[1555] 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.

[1556] In this invention, the server includes means for collecting operation information of automatic robots in a factory, means for acquiring weather data, means for analyzing the collected operation information and the acquired weather data and determining an optimal energy consumption pattern and timing for switching between energy sources, means for controlling the energy supply of the robots based on the determined energy consumption pattern and timing for switching between energy sources, means for monitoring the amount of power generated and consumed by a renewable energy device and optimizing efficient energy use, and means for providing the system status through a user interface and manually adjusting settings, thereby enabling the optimization of energy consumption of automatic robots in a factory and the efficient use of renewable energy.

[1557] An "automated factory robot" is a mechanical device that is used in the manufacturing and processing processes in a factory and can autonomously perform programmed actions.

[1558] "Operation information" is data related to the operation of the robot, and specifically includes the amount of power consumed, the operation time, and the type of task being executed.

[1559] "Weather data" refers to information about weather conditions obtained in real time, including temperature, humidity, and hours of sunshine.

[1560] "Energy consumption pattern" refers to data that indicates the state of energy consumption over a certain period of time.

[1561] "Energy source switching timing" refers to the optimal time to switch between renewable energy and conventional energy sources.

[1562] "Renewable energy devices" refer to devices that generate electricity using natural forces, such as solar power generation devices and wind power generation devices.

[1563] A "user interface" refers to the screens and input devices that allow a user to operate a system or monitor its status.

[1564] A "machine learning algorithm" is a calculation method that automatically derives optimal energy consumption patterns and timing for switching energy sources based on collected data.

[1565] This invention relates to an energy management system for automated robots in factories. This system collects and analyzes robot operation information and weather data to determine optimal energy consumption patterns and timing for switching between energy sources, thereby efficiently utilizing renewable energy.

[1566] Explanation of program processing

[1567] Data collection

[1568] The server collects operational information from the automated robots installed in the factory. This operational information includes power consumption, operating time, task type, etc. It also uses a weather data API to obtain real-time weather data, including temperature, humidity, and sunshine hours.

[1569] Data preprocessing and analysis

[1570] The server preprocesses the collected operational information and weather data, complements incomplete data, and converts data types. It then analyzes the preprocessed data and uses machine learning algorithms to make predictions to determine optimal energy consumption patterns and the timing for switching energy sources. The software used is Python and TensorFlow.

[1571] Control command generation and execution

[1572] Based on the analysis results, the server generates control commands for the robot to change its energy consumption pattern and energy source, and sends these control commands to the robot to change its energy supply.

[1573] Renewable energy equipment management

[1574] The server constantly monitors the amount of power generated and consumed by renewable energy devices (e.g., solar power generation devices, wind power generation devices) and optimizes their use to maximize power generation efficiency, thereby enabling efficient management of the robot's energy supply.

[1575] Providing a user interface

[1576] The user can monitor the system status in real time and make necessary configuration changes using the provided user interface, which displays the system analysis results and current operating status. The software used is Flask.

[1577] Specific examples

[1578] For example, you might use the following prompts to input to a generative AI model:

[1579] Prompt statement:

[1580] "Generate and send control commands to switch Robot A's power supply to solar power during sunny hours. Also provide optimal energy consumption patterns based on real-time weather data."

[1581] As described above, by building an energy management system for automated robots in factories, it is possible to optimize energy consumption and efficiently use renewable energy.

[1582] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1583] Step 1:

[1584] The server collects operational information from the automated robots installed in the factory. This operational information includes power consumption, operating time, task type, etc. The server periodically receives data from each robot and collects it via API and sensors.

[1585] Input: Operation information from the robot

[1586] Output: Collected operational information

[1587] Step 2:

[1588] The server uses a weather data API to obtain real-time weather data, including temperature, humidity, and sunshine hours. The server periodically sends requests to the API to obtain the latest weather information.

[1589] Input: API request

[1590] Output: Captured real-time weather data

[1591] Step 3:

[1592] The server preprocesses the collected operational information and acquired weather data. It performs data cleaning to fill in incomplete data and convert data types. Specifically, this includes filling in missing values, normalizing data, and correcting outliers.

[1593] Input: Collected operating information, acquired weather data

[1594] Output: Preprocessed data

[1595] Step 4:

[1596] The server analyzes the preprocessed data to determine optimal energy consumption patterns and timing for switching between energy sources. It uses machine learning algorithms to make predictions based on historical and current data. It runs the model using Python and TensorFlow.

[1597] Input: Preprocessed data

[1598] Output: Optimal energy consumption pattern, timing for switching energy sources

[1599] Step 5:

[1600] Based on the analysis results, the server generates control commands for the robot to determine its energy consumption pattern and switch its energy source. The server then sends the control commands to the robot to change its energy supply. The control commands specifically instruct the robot's behavior, including switching the power supply source.

[1601] Input: Optimal energy consumption pattern, timing to switch energy sources

[1602] Output: Control commands to the robot

[1603] Step 6:

[1604] The server constantly monitors the amount of power generated and consumed by renewable energy devices (solar power generation devices, wind power generation devices). Specifically, it periodically acquires the parameters of the power generation devices using a sensor system and sends the data to the monitoring system. The angle of the panels and turbines is automatically adjusted to maximize power generation efficiency.

[1605] Input: Sensor information of the power generation equipment

[1606] Output: Maximum efficiency power generation

[1607] Step 7:

[1608] The user uses the provided user interface to monitor the system status in real time and change the settings as needed. The interface displays the analysis results obtained from the system and the current operating status. The user can also manually adjust the settings. The interface is built using Flask.

[1609] Input: Analysis results and operational status from the system

[1610] Output: Operational status reflecting the user's configuration changes

[1611] 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.

[1612] This invention combines an emotion engine with a system for optimizing base station energy conservation and renewable energy utilization. The system collects base station operation information, acquires weather data, analyzes the data, and determines optimal power consumption patterns and energy source switching timing. It also controls the base station's power supply and monitors the power generation and consumption of renewable energy devices to optimize efficient energy utilization. Furthermore, the system status is displayed through a user interface, allowing for manual adjustment of settings. An emotion engine that recognizes user emotions has also been added to improve the user experience.

[1613] Explanation of program processing

[1614] Data collection

[1615] The server collects operational information from base stations. Specifically, it obtains information such as power consumption, number of connected devices, and data traffic volume from each base station. This data is collected periodically using an API. The server also uses a weather data API to obtain real-time weather information (temperature, humidity, precipitation, wind speed, sunshine hours, etc.).

[1616] Examples:

[1617] The server receives information on power consumption and the number of connected devices from base station A, and also obtains the current weather conditions through a weather data API.

[1618] Data preprocessing and analysis

[1619] The server preprocesses the collected data, complements incomplete data, and converts and normalizes data types as needed. The preprocessed data is then analyzed to determine optimal power consumption patterns and the timing of switching energy sources. Machine learning algorithms are used in the analysis to make predictions based on historical data and current conditions.

[1620] Examples:

[1621] The server fills in missing data from base station A and weather data, normalizes the data, and then uses machine learning algorithms to analyze optimal power consumption patterns and determine when to switch between solar power generation and the power grid.

[1622] Control command generation and execution

[1623] Based on the analysis results, the server generates control commands for each base station to change its power consumption pattern and energy source, and sends these control commands to each base station to change its power supply.

[1624] Examples:

[1625] Based on the analysis results, the server generates a control command including an instruction for base station A to switch from solar power generation to the power grid, and sends it to base station A. Base station A receives this control command and switches the power supply source.

[1626] Renewable Energy Management

[1627] The server constantly monitors the power output of renewable energy devices (solar panels and wind turbines) and the power consumption of base stations. To ensure efficient power generation and consumption, it optimizes the use of renewable energy to maximize power generation efficiency.

[1628] Examples:

[1629] The server constantly monitors the amount of power generated by the solar panels and the power consumption of base station A, and adjusts the angle of the panels to maximize power generation efficiency.

[1630] Providing a user interface

[1631] The user can monitor the system status in real time and make necessary configuration changes using the provided user interface, which displays the analysis results and current operating status of the system.

[1632] Examples:

[1633] Users can check the power consumption and power generation status of base station A through the management panel, and if they want to manually fine-tune the settings, they can do so on the interface.

[1634] Combining Emotion Engines

[1635] The server is equipped with an emotion engine that recognizes the user's emotions by analyzing the user's voice, facial expressions, text input, etc., and automatically adjusts the system settings and interface based on the analysis results.

[1636] Examples:

[1637] The server uses an emotion engine to analyze the voice tone and facial expressions of the user when operating the interface, and if it determines that the user is under high stress, it activates an assist function to make operating the interface easier.

[1638] This allows the system to maximize energy efficiency while providing a user-friendly operating environment.

[1639] The processing flow will be explained below.

[1640] Step 1: Data collection

[1641] The server collects operational information from base stations. Specifically, it obtains information from each base station, such as power consumption, number of connected devices, and data traffic volume. This data is collected periodically using an API.

[1642] Examples:

[1643] The server obtains the power consumption and number of connected terminals of base station A through an API request.

[1644] Step 2: Obtaining weather data

[1645] The server obtains weather data in real time via an API, specifically information such as temperature, humidity, precipitation, wind speed, and sunshine hours.

[1646] Examples:

[1647] The server uses the weather data API to obtain weather data around base station A.

[1648] Step 3: Preprocessing the data

[1649] The server preprocesses the collected operational information and weather data, complementing incomplete data and converting data types as necessary to normalize it.

[1650] Examples:

[1651] If there is a gap in the power consumption data of base station A, the server will supplement it based on past data.

[1652] Step 4: Analysis

[1653] The server analyzes the preprocessed data using machine learning algorithms. Specifically, it determines the optimal power consumption pattern and timing for switching energy sources based on the collected data.

[1654] Examples:

[1655] The server inputs the data into a machine learning model to infer when to switch from solar power generation to the power grid.

[1656] Step 5: Generate control commands

[1657] The server generates a power consumption pattern and a control command for switching the energy source for each base station based on the analysis result.

[1658] Examples:

[1659] The server generates a control command for base station A, which includes an instruction to switch from solar power generation to the power grid.

[1660] Step 6: Send and execute commands

[1661] The server transmits the generated control command to each base station to change the power supply.

[1662] Examples:

[1663] The server sends a control command to base station A, and base station A switches the power supply source from solar power generation to the power grid based on this command.

[1664] Step 7: Renewable energy monitoring

[1665] The server constantly monitors the power generation and consumption of renewable energy devices (solar panels and wind turbines).

[1666] Examples:

[1667] The server periodically retrieves power generation data from the solar panels and compares it with consumption patterns.

[1668] Step 8: Optimize energy use

[1669] The server optimizes the settings to maximize the efficiency of renewable energy use, adjusting the angle of the panels and changing the proportion of wind power used based on weather and power generation data.

[1670] Examples:

[1671] The server provides instructions to adjust the solar panels to the optimal angle for sunny days.

[1672] Step 9: Update the User Interface

[1673] The server updates the user interface with the analysis results and current system status accordingly.

[1674] Examples:

[1675] The server reflects the analysis results on the user interface, allowing users to view the information in real time.

[1676] Step 10: Accept manual adjustments

[1677] Users can manually adjust system settings using the provided user interface, which allows for immediate effect of setting changes under specific conditions.

[1678] Examples:

[1679] Users operate an administration panel and manually optimize the settings for a particular base station.

[1680] Step 11: Collect sentiment data

[1681] The server collects the user's emotional data, which can be obtained from the user's voice, facial expressions, text input, etc.

[1682] Examples:

[1683] The server collects voice tones and facial expressions via a camera and microphone when a user operates the management panel.

[1684] Step 12: Analyze the sentiment data

[1685] The server analyzes the collected emotion data and uses an emotion engine to determine the user's stress level and satisfaction.

[1686] Examples:

[1687] The server analyzes the user's voice data and determines that the user is feeling stressed.

[1688] Step 13: Emotion-Based System Adjustment

[1689] The server automatically adjusts the system settings based on the analysis of the emotional data, instantly changing the settings to reflect the user's emotional state.

[1690] Examples:

[1691] If the server determines that the user is feeling stressed, it activates an assist function that simplifies operation of the interface.

[1692] Through the above process, the present invention efficiently manages base stations and renewable energy, and further provides an interface that takes user emotions into consideration, thereby improving energy efficiency and maximizing user experience.

[1693] Example 2

[1694] 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."

[1695] Conventional base station energy management systems have room for improvement in optimizing power consumption and efficiently utilizing renewable energy. Furthermore, they lack effective methods for improving user experience. Therefore, there is a need to provide a more efficient and user-friendly means for base station energy management.

[1696] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1697] In this invention, the server includes means for collecting operation information of the base station, means for acquiring weather data, means for preprocessing and analyzing the collected operation information and the acquired weather data to determine an optimal power consumption pattern and timing for switching between energy sources, means for controlling the power supply of the base station based on the determined power consumption pattern and timing for switching between energy sources, means for monitoring the amount of power generated by the renewable energy device and the amount of power consumed by the base station in real time to optimize efficient energy use, means for providing the system status through a user interface and manually adjusting settings, and means for recognizing user emotions using an emotion engine and automatically adjusting the system status and interface, thereby enabling high energy efficiency and improved user satisfaction.

[1698] A "base station" is a fixed facility for conducting wireless communications that forms part of a communications network.

[1699] "Operation information" is data indicating the operational status of the base station, the amount of power consumed there, the number of connected terminals, the amount of data traffic, and so on.

[1700] "Weather data" refers to data that includes weather-related information such as temperature, humidity, precipitation, wind speed, and sunshine hours.

[1701] "Preprocessing" refers to a series of operations such as data interpolation, filtering, conversion, and normalization that are performed to prepare the collected raw data in an analyzable form.

[1702] "Analysis" is the act of using preprocessed data to extract and interpret information suitable for a specific purpose.

[1703] A "power consumption pattern" is a model that shows the tendency of how power is used and consumed under specific times and conditions.

[1704] "Energy source switching timing" refers to determining the timing of switching between different energy sources (e.g., renewable energy and the power grid) to make the most effective use of them.

[1705] A "renewable energy device" is a device that generates electricity using natural energy, such as solar panels or wind turbines.

[1706] "User interface" means the visual or operational means by which a user interacts with a system and inputs and obtains information.

[1707] The "emotion engine" is a function that analyzes the user's voice, facial expressions, text input, etc. to recognize emotions, and automatically adjusts the system settings and interface based on the results.

[1708] MODE FOR CARRYING OUT THE INVENTION

[1709] The present invention combines an emotion engine with a system for optimizing energy conservation and renewable energy utilization in base stations. The system mainly consists of the following components: a base station, a server, a renewable energy device, a user interface, and an emotion engine.

[1710] Hardware and Software Configuration

[1711] 1. Base Station: A fixed communication tower and its associated equipment that forms part of a communication network. Each base station is equipped with sensors to measure power consumption, the number of connected devices, and data traffic volume.

[1712] 2. Server: This is the central computer system that collects and analyzes data. The server has the ability to retrieve data from base stations and meteorological data services through APIs. The main software used includes a database management system, scripts for data preprocessing, and machine learning algorithms.

[1713] 3. Renewable energy devices: These are devices that generate electricity using natural energy sources such as solar panels and wind turbines.

[1714] 4. User interface: An interface that provides users with the operational status of the system in the form of an administration panel or dashboard and allows them to change settings.

[1715] 5. Emotion Engine: A software component that analyzes the user's voice, facial expressions, text input, etc. to recognize the user's emotional state, allowing the system to adaptively adjust its settings and interface.

[1716] Data collection and preprocessing

[1717] The server collects operational information from each base station, such as power consumption, number of connected devices, and data traffic volume. The server also uses a weather data API to obtain real-time weather data such as temperature, humidity, precipitation, wind speed, and sunshine hours.

[1718] Examples:

[1719] The server obtains information on power consumption and the number of connected devices from base station A via an API every five minutes.

[1720] The server uses the weather service API to obtain weather information every hour of the day.

[1721] Data preprocessing and analysis

[1722] The server preprocesses the collected data, including filling in missing values, filtering outliers, converting data types, and normalizing the data. It then performs analysis based on the preprocessed data to determine optimal power consumption patterns and the timing for switching between energy sources. Machine learning algorithms (e.g., regression analysis and time-series forecasting models) are used for the analysis.

[1723] Examples:

[1724] The server trains a machine learning model based on data from the past year and uses current data as input to predict optimal power consumption patterns.

[1725] The server takes into account the weather forecast and plans how to utilize renewable energy for the next day.

[1726] Control command generation and execution

[1727] Based on the analysis results, the server generates control commands for each base station, including details of power consumption patterns and timing for switching between energy sources. The base station then switches between power suppliers accordingly.

[1728] Examples:

[1729] The server generates a control command for base station A to prioritize power supply from the power grid during the night and solar power generation during the day.

[1730] Renewable Energy Management

[1731] The server monitors the power generation of renewable energy devices (solar panels, wind turbines, etc.) and the power consumption of base stations in real time, and adjusts consumption patterns accordingly to achieve optimal energy utilization.

[1732] Examples:

[1733] The server constantly monitors the amount of electricity generated by the solar panels and switches the power supply to renewable energy when it is more efficient.

[1734] User interface and emotion engine

[1735] The user can monitor the system status in real time through the provided user interface and make necessary setting changes. Furthermore, the server can recognize the user's emotions using an emotion engine and automatically adjust the interface and system settings based on the user's stress level and emotional state.

[1736] Examples:

[1737] Users can use the management panel to check the power consumption and power generation status of base station A in real time and fine-tune the settings as needed.

[1738] The server analyzes the user's voice tone and facial expressions using an emotion engine, and if it determines that the user is under high stress, it activates an assist function that simplifies operation.

[1739] Prompt Sentence Examples

[1740] Below are some examples of prompts that can be input to a generative AI model using this system:

[1741] "Please explain the program flow in which the server uses data collected from base station A to analyze the optimal power consumption pattern and determine the timing to switch from solar power generation to the power grid. Also, please provide a concrete example of monitoring the system status through the user interface and manually changing the settings."

[1742] In this way, the present invention can maximize the energy efficiency of the base station and provide a user-friendly operating environment.

[1743] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1744] Step 1:

[1745] The server collects operational information from each base station. This operational information includes power consumption, number of connected devices, data traffic volume, etc. The operational information received as input is obtained via API. The server also obtains temperature, humidity, precipitation, wind speed, and sunshine hours in real time from the weather data API. This information is stored in a database so that it can be used in subsequent analysis steps.

[1746] Specific behavior:

[1747] The server collects information on power consumption, number of connected terminals, and data traffic volume from base station A every five minutes.

[1748] The server retrieves weather information every hour through the weather data API.

[1749] Step 2:

[1750] The server preprocesses the collected operational information and weather data. Since the input data may contain missing or outliers, it complements the incomplete data and filters out the outliers. It also performs data type conversion and normalization to prepare the data in a format suitable for analysis. The preprocessed data is used in the next analysis step.

[1751] Specific behavior:

[1752] If there is a missing value in the data of base station A, the server complements the value based on the data before and after it.

[1753] The server filters out data points that record abnormally high power consumption.

[1754] Step 3:

[1755] The server analyzes the preprocessed data. It takes the preprocessed data as input and uses machine learning algorithms to predict optimal power consumption patterns and timing for switching between energy sources. This analysis uses regression analysis and time series prediction models. The prediction results are used in the control command generation step.

[1756] Specific behavior:

[1757] The server trains the machine learning model using operational information and weather data from the past year.

[1758] The server inputs the current data and predicts the best time to utilize solar power generation during the day the next day.

[1759] Step 4:

[1760] The server generates control commands for the base stations based on the analysis results. Using the results of the analysis step as input, the server creates control commands that include the power consumption patterns and energy source switching instructions for each base station. These commands are sent to each base station and executed.

[1761] Specific behavior:

[1762] The server generates a control command for base station A to prioritize power supply from the power grid during the night and solar power generation during the day.

[1763] The server generates commands including timing to utilize the power generation of the wind turbines when wind speeds are high.

[1764] Step 5:

[1765] The terminal (base station) executes the control command received from the server. It receives the control command from the server as input, switches the power supply source based on the command, and feeds back the execution result to the server.

[1766] Specific behavior:

[1767] Base station A switches to power supply from the power grid at night.

[1768] Base station A switches its power supply source to solar power when solar power is available during the day.

[1769] Step 6:

[1770] The server monitors the power generation of renewable energy devices and the power consumption of base stations in real time. It receives power generation data from renewable energy devices and consumption data from base stations as input, analyzes these data to ensure optimal energy usage, and makes necessary adjustments based on the results.

[1771] Specific behavior:

[1772] The server monitors the amount of electricity generated by the solar panels in real time and issues instructions to switch the power supply to renewable energy when it is more efficient.

[1773] Step 7:

[1774] The user monitors the system status in real time using the provided user interface and makes necessary configuration changes. The system receives operational information as input and adjusts the configuration based on that information.

[1775] Specific behavior:

[1776] The user can access the management panel using a tablet device and check the power consumption and power generation status of base station A.

[1777] Users can manually change settings to reduce power consumption during specific time periods.

[1778] Step 8:

[1779] The server uses an emotion engine to recognize the user's emotions, detecting and analyzing inputs such as the user's voice, facial expressions, and text input, and adjusting the system settings and interface based on the results.

[1780] Specific behavior:

[1781] The server analyzes the user's tone of voice and facial expressions, and if it determines that the user is under high stress, it activates an assist function to simplify operation.

[1782] (Application example 2)

[1783] 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."

[1784] While battery management and energy usage optimization are essential for current autonomous vehicles, there is a lack of efficient and real-time methods to do so. Furthermore, there is a lack of technology to adjust the driving environment and infotainment system according to the emotional state of the driver and passengers, creating a need for technologies to enhance user comfort.

[1785] 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.

[1786] In this invention, the server includes means for collecting operation information of the base stations, means for acquiring weather data, means for analyzing the collected operation information and the acquired weather data and determining an optimal power consumption pattern and timing for switching between energy sources, means for controlling the power supply of the base stations based on the determined power consumption pattern and timing for switching between energy sources, means for monitoring the amount of power generated and consumed by the renewable energy device and optimizing efficient energy use, means for providing the system status through a user interface and manually adjusting settings, and means for recognizing the emotions of the driver or passengers and adjusting the driving environment and infotainment system. This makes it possible to provide a comfortable driving environment that corresponds to the emotional state of the user while optimizing the energy management of the autonomous vehicle.

[1787] The "means for collecting base station operation information" refers to a means for acquiring information such as power consumption, the number of connected terminals, and data traffic volume from the base station.

[1788] "Means for acquiring weather data" refers to means for acquiring weather information such as temperature, humidity, precipitation, wind speed, and sunshine hours in real time.

[1789] The "means for analyzing the collected operation information and the acquired weather data and determining the optimal power consumption pattern and timing for switching energy sources" refers to a means for determining the optimal timing for power consumption and energy source usage based on the collected data using machine learning algorithms, etc.

[1790] The "means for controlling the power supply of the base station based on the determined power consumption pattern and energy source switching timing" refers to means for changing the power supply or switching the energy source based on the analysis results.

[1791] "Means for monitoring the amount of power generated and consumed by renewable energy devices and optimizing efficient energy use" refers to means for monitoring the amount of power generated and the usage status of renewable energy devices such as solar power generation devices and wind power generation devices, and for optimally managing energy use.

[1792] "Means for providing system status through a user interface and for manually adjusting settings" refers to means for providing a user with an interface for displaying the operating status of the system and manually changing settings as necessary.

[1793] "Means for recognizing the emotions of the driver or passenger and adjusting the driving environment or infotainment system" refers to means for analyzing the emotions of the driver or passenger from voice, facial expressions, text input, etc., and adjusting the driving environment or in-car entertainment system based on the results.

[1794] This invention provides an energy management system for autonomous vehicles that optimizes battery management and renewable energy utilization. The system collects vehicle operation information and weather data, and analyzes this data to determine optimal energy consumption patterns and timing for switching energy sources. It also includes a function that recognizes the emotions of passengers and drivers and adjusts the driving environment and infotainment system accordingly.

[1795] The server first collects information from the autonomous vehicle, including battery information, speed, mileage, interior and exterior temperatures, and the location of charging stations. It also obtains real-time weather information using a weather data API. The collected data is preprocessed to complete missing data and normalize it.

[1796] The server then uses machine learning algorithms to determine optimal power consumption patterns and timing for switching between energy sources, taking into account historical data and current conditions. Based on the analysis, the server generates control commands for the energy management system for battery management and energy source switching, which are then sent to the vehicle.

[1797] The server constantly monitors the amount of power generated and consumed by renewable energy devices, such as solar power generation devices installed in vehicles, and controls them to maximize the efficiency of energy use.

[1798] Users can monitor the system status and manually adjust settings as needed through a user interface that displays analysis results and current operating status of the system.

[1799] Additionally, the server is equipped with an emotion engine that analyzes the driver's or passenger's emotions by analyzing voice, facial expressions, text input, etc., and adjusts the driving environment and infotainment system accordingly.

[1800] For example, if a user feels "stressed," the server's emotion engine will analyze that emotion and enable the infotainment system to operate more easily while also playing appropriate music. This system will enable the creation of a comfortable driving environment while optimizing the energy management of autonomous vehicles.

[1801] Example prompts for generative AI models

[1802] "Collect battery information and speed data from vehicle ID: example_vehicle_id, and obtain weather information through API. Then, preprocess and normalize the acquired data to generate optimal battery management and energy switching control commands. Also, implement a system that simplifies system operation and plays relaxing music based on the user's input that "I'm stressed."

[1803] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1804] Step 1:

[1805] The server collects information about the autonomous vehicle's battery, speed, mileage, interior and exterior temperatures, and the location of charging stations. This data is obtained through the autonomous vehicle's API. It receives various sensor information from the vehicle as input data and stores it in an internal database.

[1806] The result is a data set that represents the current operating state of the vehicle.

[1807] Step 2:

[1808] The server uses a weather data API to obtain real-time weather information, taking weather data such as temperature, humidity, precipitation, wind speed, and sunshine hours as input, and stores it in an internal database.

[1809] The result is a real-time weather dataset representing weather conditions.

[1810] Step 3:

[1811] The server preprocesses the collected operational information and weather data. Specifically, it complements missing data, corrects outliers, normalizes data, etc. It receives operational information and weather data as input data and outputs a clean, preprocessed data set.

[1812] This will provide a dataset suitable for analysis.

[1813] Step 4:

[1814] The server uses machine learning algorithms to analyze the preprocessed data. Specifically, it utilizes predictive models to determine optimal power consumption patterns and timing for switching energy sources. It receives preprocessed operational information and weather data as input, analyzes them, and generates optimal control commands.

[1815] As a result, optimal timing for power management and energy source switching is output.

[1816] Step 5:

[1817] The server generates control commands for each autonomous vehicle based on the analysis results. Specifically, it generates commands to instruct battery management and the timing of renewable energy usage. It receives the analysis results of the machine learning algorithm as input data and outputs them as control commands.

[1818] As a result, specific control actions are sent to the vehicle as commands.

[1819] Step 6:

[1820] The server constantly monitors the amount of power generated and consumed by renewable energy devices (e.g., solar power generation devices). As input data, it receives real-time data on power generation and consumption from the renewable energy devices and makes adjustments based on that data to maximize power generation efficiency.

[1821] This allows for efficient energy utilization.

[1822] Step 7:

[1823] The server provides the current status of the system through a user interface. As input data, it receives overall operational data of the system (operation information, weather information, energy consumption patterns, etc.) and outputs it as content to be displayed on the user interface.

[1824] As a result, users can monitor the current status of their system in real time and manually adjust settings as needed.

[1825] Step 8:

[1826] The server uses an emotion engine to analyze the driver's or passenger's emotions. It receives input data such as voice, facial expressions, and text input, and the emotion engine analyzes them to output an emotional state. Based on the analysis results, it automatically adjusts the driving environment and infotainment system.

[1827] This provides a comfortable driving environment that is tailored to the user's emotional state.

[1828] 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.

[1829] 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.

[1830] 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.

[1831] 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.

[1832] 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.

[1833] 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.

[1834] 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).

[1835] 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.

[1836] 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."

[1837] 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.

[1838] 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).

[1839] 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.

[1840] In the above embodiment, ...

Claims

1. means for collecting operation information of base stations; a means for acquiring weather data; a means for analyzing the collected operation information and the acquired weather data to determine an optimal power consumption pattern and timing for switching between energy sources; means for controlling power supply to the base station based on the determined power consumption pattern and energy source switching timing; a means for monitoring the generation and consumption of renewable energy devices and optimizing efficient energy utilization; providing system status through a user interface and a means to manually adjust settings; A system including:

2. The system of claim 1 , wherein the renewable energy devices include solar power generation devices and wind power generation devices.

3. The system of claim 1 , wherein the analysis means uses a machine learning algorithm to infer optimal power consumption patterns and timing for switching between energy sources.

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